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github
|
jacksky64/imageProcessing-master
|
lse_bfe.m
|
.m
|
imageProcessing-master/segmentation/levelset_segmentation_biasCorrection_v1/levelset_segmentation_biasCorrection_v1/lse_bfe.m
| 3,246 |
utf_8
|
f5fa95fc0201c04b6a285f465145b25a
|
function [u, b, C]= lse_bfe(u0,Img, b, Ksigma,KONE, nu,timestep,mu,epsilon, iter_lse)
% This code implements the level set evolution (LSE) and bias field estimation
% proposed in the following paper:
% C. Li, R. Huang, Z. Ding, C. Gatenby, D. N. Metaxas, and J. C. Gore,
% "A Level Set Method for Image Segmentation in the Presence of Intensity
% Inhomogeneities with Application to MRI", IEEE Trans. Image Processing, 2011
%
% Note:
% This code implements the two-phase formulation of the model in the above paper.
% The two-phase formulation uses the signs of a level set function to represent
% two disjoint regions, and therefore can be used to segment an image into two regions,
% which are represented by (u>0) and (u<0), where u is the level set function.
%
% All rights researved by Chunming Li, who formulated the model, designed and
% implemented the algorithm in the above paper.
%
% E-mail: [email protected]
% URL: http://www.engr.uconn.edu/~cmli/
% Copyright (c) by Chunming Li
% Author: Chunming Li
u=u0;
KB1 = conv2(b,Ksigma,'same');
KB2 = conv2(b.^2,Ksigma,'same');
C =updateC(Img, u, KB1, KB2, epsilon);
KONE_Img = Img.^2.*KONE;
u = updateLSF(Img,u, C, KONE_Img, KB1, KB2, mu, nu, timestep, epsilon, iter_lse);
Hu=Heaviside(u,epsilon);
M(:,:,1)=Hu;
M(:,:,2)=1-Hu;
b =updateB(Img, C, M, Ksigma);
% update level set function
function u = updateLSF(Img, u0, C, KONE_Img, KB1, KB2, mu, nu, timestep, epsilon, iter_lse)
u=u0;
Hu=Heaviside(u,epsilon);
M(:,:,1)=Hu;
M(:,:,2)=1-Hu;
N_class=size(M,3);
e=zeros(size(M));
u=u0;
for kk=1:N_class
e(:,:,kk) = KONE_Img - 2*Img.*C(kk).*KB1 + C(kk)^2*KB2;
end
for kk=1:iter_lse
u=NeumannBoundCond(u);
K=curvature_central(u); % div()
DiracU=Dirac(u,epsilon);
ImageTerm=-DiracU.*(e(:,:,1)-e(:,:,2));
penalizeTerm=mu*(4*del2(u)-K);
lengthTerm=nu.*DiracU.*K;
u=u+timestep*(lengthTerm+penalizeTerm+ImageTerm);
end
% update b
function b =updateB(Img, C, M, Ksigma)
PC1=zeros(size(Img));
PC2=PC1;
N_class=size(M,3);
for kk=1:N_class
PC1=PC1+C(kk)*M(:,:,kk);
PC2=PC2+C(kk)^2*M(:,:,kk);
end
KNm1 = conv2(PC1.*Img,Ksigma,'same');
KDn1 = conv2(PC2,Ksigma,'same');
b = KNm1./KDn1;
% Update C
function C_new =updateC(Img, u, Kb1, Kb2, epsilon)
Hu=Heaviside(u,epsilon);
M(:,:,1)=Hu;
M(:,:,2)=1-Hu;
N_class=size(M,3);
for kk=1:N_class
Nm2 = Kb1.*Img.*M(:,:,kk);
Dn2 = Kb2.*M(:,:,kk);
C_new(kk) = sum(Nm2(:))/sum(Dn2(:));
end
% Make a function satisfy Neumann boundary condition
function g = NeumannBoundCond(f)
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
function k = curvature_central(u)
% compute curvature for u with central difference scheme
[ux,uy] = gradient(u);
normDu = sqrt(ux.^2+uy.^2+1e-10);
Nx = ux./normDu;
Ny = uy./normDu;
[nxx,junk] = gradient(Nx);
[junk,nyy] = gradient(Ny);
k = nxx+nyy;
function h = Heaviside(x,epsilon)
h=0.5*(1+(2/pi)*atan(x./epsilon));
function f = Dirac(x, epsilon)
f=(epsilon/pi)./(epsilon^2.+x.^2);
|
github
|
jacksky64/imageProcessing-master
|
drlse_edge.m
|
.m
|
imageProcessing-master/segmentation/DRLSE_v0/drlse_edge.m
| 3,599 |
utf_8
|
ee52d75877b9ca4db832b038f27fd28e
|
function phi = drlse_edge(phi_0, g, lambda,mu, alfa, epsilon, timestep, iter, potentialFunction)
% This Matlab code implements an edge-based active contour model as an
% application of the Distance Regularized Level Set Evolution (DRLSE) formulation in Li et al's paper:
%
% C. Li, C. Xu, C. Gui, M. D. Fox, "Distance Regularized Level Set Evolution and Its Application to Image Segmentation",
% IEEE Trans. Image Processing, vol. 19 (12), pp.3243-3254, 2010.
%
% Input:
% phi_0: level set function to be updated by level set evolution
% g: edge indicator function
% mu: weight of distance regularization term
% timestep: time step
% lambda: weight of the weighted length term
% alfa: weight of the weighted area term
% epsilon: width of Dirac Delta function
% iter: number of iterations
% potentialFunction: choice of potential function in distance regularization term.
% As mentioned in the above paper, two choices are provided: potentialFunction='single-well' or
% potentialFunction='double-well', which correspond to the potential functions p1 (single-well)
% and p2 (double-well), respectively.%
% Output:
% phi: updated level set function after level set evolution
%
% Author: Chunming Li, all rights reserved
% E-mail: [email protected]
% [email protected]
% URL: http://www.imagecomputing.org/~cmli/
phi=phi_0;
[vx, vy]=gradient(g);
for k=1:iter
phi=NeumannBoundCond(phi);
[phi_x,phi_y]=gradient(phi);
s=sqrt(phi_x.^2 + phi_y.^2);
smallNumber=1e-10;
Nx=phi_x./(s+smallNumber); % add a small positive number to avoid division by zero
Ny=phi_y./(s+smallNumber);
curvature=div(Nx,Ny);
if strcmp(potentialFunction,'single-well')
distRegTerm = 4*del2(phi)-curvature; % compute distance regularization term in equation (13) with the single-well potential p1.
elseif strcmp(potentialFunction,'double-well');
distRegTerm=distReg_p2(phi); % compute the distance regularization term in eqaution (13) with the double-well potential p2.
else
disp('Error: Wrong choice of potential function. Please input the string "single-well" or "double-well" in the drlse_edge function.');
end
diracPhi=Dirac(phi,epsilon);
areaTerm=diracPhi.*g; % balloon/pressure force
edgeTerm=diracPhi.*(vx.*Nx+vy.*Ny) + diracPhi.*g.*curvature;
phi=phi + timestep*(mu*distRegTerm + lambda*edgeTerm + alfa*areaTerm);
end
function f = distReg_p2(phi)
% compute the distance regularization term with the double-well potential p2 in eqaution (16)
[phi_x,phi_y]=gradient(phi);
s=sqrt(phi_x.^2 + phi_y.^2);
a=(s>=0) & (s<=1);
b=(s>1);
ps=a.*sin(2*pi*s)/(2*pi)+b.*(s-1); % compute first order derivative of the double-well potential p2 in eqaution (16)
dps=((ps~=0).*ps+(ps==0))./((s~=0).*s+(s==0)); % compute d_p(s)=p'(s)/s in equation (10). As s-->0, we have d_p(s)-->1 according to equation (18)
f = div(dps.*phi_x - phi_x, dps.*phi_y - phi_y) + 4*del2(phi);
function f = div(nx,ny)
[nxx,junk]=gradient(nx);
[junk,nyy]=gradient(ny);
f=nxx+nyy;
function f = Dirac(x, sigma)
f=(1/2/sigma)*(1+cos(pi*x/sigma));
b = (x<=sigma) & (x>=-sigma);
f = f.*b;
function g = NeumannBoundCond(f)
% Make a function satisfy Neumann boundary condition
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
|
github
|
jacksky64/imageProcessing-master
|
SegmentRefine.m
|
.m
|
imageProcessing-master/segmentation/SegTool/SegmentRefine.m
| 7,789 |
utf_8
|
efd3606b9a14570a8983aa2dbfc38097
|
function [] = SegmentRefine(ImName)
% SEGMENTREFINE - Main graph cuts segmentation function
% SEGMENTREFINE(IMNAME) - ImName - Image to segment
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
% Global Variables
global sopt ih_img fgpixels bgpixels fgflag segImageHandle;
% GUI specific flag
fgflag = 2;
% Data from GC(AutoCut) or prev GC+LZ(AutoCutRefine)
load('iter_data');
% Stop unnecessary warnings
warning('off','all');
% Read input image
I = imread(ImName);
I1 = I(:,:,1);
I2 = I(:,:,2);
I3 = I(:,:,3);
%%%% Final Segmented Image
SegImage = zeros(size(I));
% LZ
% Get Foreground and Background Pixels for Smart Refine
if(~isempty(fgpixels))
FY = fgpixels(:,1);
FX = fgpixels(:,2);
end
if(~isempty(bgpixels))
BY = bgpixels(:,1);
BX = bgpixels(:,2);
end
% GC - fp bounding rectangle
load rectpts;
%% Width of Strip
StripWidth = sopt.StripWidth_AC_ACR;
[fxi,fyi] = meshgrid(min(fp(:,1)):max(fp(:,1)),min(fp(:,2)):max(fp(:,2)));
InsideFindices = sub2ind(size(I1),fyi, fxi);
InsideFindices = InsideFindices(:);
%Form the Outside rectangular bounding box -- add StripWidth on all 4 sides
[fxo,fyo] = meshgrid( max((min(fp(:,1)) - StripWidth),1) : min((max(fp(:,1)) + StripWidth), size(I1,2)) , max((min(fp(:,2)) - StripWidth),1) : min((max(fp(:,2)) + StripWidth), size(I1,1)) );
OutsideFindices = sub2ind(size(I1),fyo, fxo);
OutsideFindices = OutsideFindices(:);
Bindices = setdiff(OutsideFindices, InsideFindices);
Allindices = [1:size(I1,1)*size(I1,2)]';
Findices = InsideFindices;
numLabels = max(L(:));
PI = regionprops(L,'PixelIdxList');
%%%% BLabels remain fixed -- no change
%%%% ULabels -- Uncertain labels -- on which optimization done...
ULabels = FLabels;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% Finding Foreground and Background Labels from pixel seeds%%
try
if(~isempty(fgpixels))
FindicesStr = sub2ind(size(I1),FX,FY);
FLabelsStr = L(int32(FindicesStr));
FLabelsStr = union(FLabelsStr,[]); %% Set-ifying the set of labels (Sorting?)
if(FLabelsStr(1)==0) %% Removing Boundary Labels
FLabelsStr = FLabelsStr(2:end);
end
% Get Common label indices to index into fdist and bdist
[FLabelsCom FIndCom] = intersect(ULabels, FLabelsStr);
end
if(~isempty(bgpixels))
BindicesStr = sub2ind(size(I1),BX,BY);
BLabelsStr = L(int32(BindicesStr));
BLabelsStr = union(BLabelsStr,[]); %% Set-ifying the set of labels
if(BLabelsStr(1)==0) %% Removing Boundary Labels
BLabelsStr = BLabelsStr(2:end);
end
[BLabelsCom BIndCom] = intersect(ULabels, BLabelsStr);
end
catch
beep
disp('Error: Seeds are outside Image limits ...Please restart');
return;
end
FColors = MeanColors(FLabels,:);
BColors = MeanColors(BLabels,:);
%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lambda = sopt.lambda_ACR;
numIter = sopt.numIter_ACR;
for i = 1:numIter
disp(['Iteration - ', num2str(i)]);
% Foreground and Background edge weights
[FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);
[BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);
% Hard Seeds
if(~isempty(fgpixels))
FDist(FIndCom) = 0;
BDist(FIndCom) = 1000;
end
if(~isempty(bgpixels))
FDist(BIndCom) = 1000;
BDist(BIndCom) = 0;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Segments labeled from 0 now -- -1 is for boundary pixels
L = L-1;
FLabels = FLabels-1;
BLabels = BLabels-1;
ULabels = ULabels-1;
%%%%%%%%%%%%% The Mex Function for GraphCutSegment %%%%%%%%%%%%
%%% SegImage is the segmented image, %%% LLabels is the binary label
%%% for each watershed label
[SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels, FDist, BDist, lambda); %%% SegImage is the segmented image, %%% LLabels is the binary label for each watershed label
%% Again Labeled from 1...
L = L+1;
FLabels = FLabels+1;
BLabels = BLabels+1;
ULabels = ULabels+1;
if(i < numIter)
%%%%%% Do NOT do this if final iteration -- just display the segmented image
%%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%
newFLabels = ULabels(find(LLabels==1.0));
newBLabels = ULabels(find(LLabels==0.0));
%%%%%% Whether new background labels will contain the old ones?
% newBLabels = union(newBLabels,BLabels);
FColors = MeanColors(newFLabels,:);
BColors = MeanColors(newBLabels,:);
%%%%%%%% Calculating FG and BG distances based on new segmentation
%%%%%%%% %%%%%%%%%%%%%%%%%%
[newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);
[newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);
for k=1:NumFClusters
relColors = FColors(find(newFInd==k),:); %% Colors belonging to cluster k
FCClusters(:,k) = mean(relColors,1)';
FCovs(:,:,k) = cov(relColors);
FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);
end
for k=1:NumBClusters
relColors = BColors(find(newBInd==k),:); %% Colors belonging to cluster k
BCClusters(:,k) = mean(relColors,1)';
BCovs(:,:,k) = cov(relColors);
BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);
end
end
end
%%%%%%%%%%%%%%%%%%%%% Display the segmented image %%%%%%%%%%%%%%%%%%%
edge_img = edge(SegImage,'canny');
% Put image on black background
SegImage = repmat(SegImage,[1,1,3]);
SegNewImage = uint8(SegImage) .* uint8(I);
% Mark a segmentation boundary on original image
% Set the image
[IInd,JInd] = ind2sub(size(I1),find(edge_img));
boundImage1 = I(:,:,2);
boundImage1(find(edge_img)) = 255;
boundImage = I;
boundImage(:,:,2) = boundImage1;
set(ih_img, 'Cdata', uint8(boundImage));
axis('image');axis('ij');axis('off');
drawnow;
figure(segImageHandle);
imshow(uint8(SegNewImage));
SegMask = SegImage;
SegResult = SegNewImage;
% Save Segmentation Result
save('SegResult', 'SegMask', 'SegResult');
% Required for AutoRefine
save('iter_data','L', 'MeanColors', 'FLabels', 'BLabels', 'FCClusters',...
'FCovs', 'FWeights', 'BCClusters', 'BCovs', 'BWeights');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)
% CLUSTDISTMEMBERSHIP - Calcuates FG and BG Distances
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
NumFClusters = size(FCClusters,2);
numULabels = size(MeanColors,1);
FDist = zeros(numULabels,1);
FInd = zeros(numULabels,1);
Ftmp = zeros(numULabels, NumFClusters);
for k=1:NumFClusters
M = FCClusters(:,k);
CovM = FCovs(:,:,k);
W = FWeights(1,k);
V = MeanColors - repmat(M',numULabels,1);
Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));
end
[FDist, FInd] = min(Ftmp,[],2);
|
github
|
jacksky64/imageProcessing-master
|
SegmentBoth.m
|
.m
|
imageProcessing-master/segmentation/SegTool/SegmentBoth.m
| 9,046 |
utf_8
|
7075544eb046a7ff90fa6c51d76c43da
|
function [] = SegmentGC(ImName);
%%%Main Grab Cuts segmentation function
global ih_img fgpixels bgpixels;
load segimage;
BiImage = SegImage;
figure;
imagesc(BiImage);
SegImage = [];
I = imread(ImName);
I1 = I(:,:,1);
I2 = I(:,:,2);
I3 = I(:,:,3);
%%%% Final Segmented Image
SegImage = zeros(size(I));
Findices = find(BiImage);
Bindices = find(BiImage == 0);
% Get Foreground and Background Pixels for Smart Refine
FY = fgpixels(:,1);
FX = fgpixels(:,2);
BY = bgpixels(:,1);
BX = bgpixels(:,2);
% LZ
FindicesStr = sub2ind(size(I1),FX,FY);
BindicesStr = sub2ind(size(I1),BX,BY);
size(FindicesStr)
size(BindicesStr)
'indices'
Findices = union(Findices,FindicesStr);
[WrongB WrIndB] = intersect(Bindices,FindicesStr);
size(WrIndB)
'Wrindb'
Bindices(WrIndB) = [];
Bindices = union(Bindices,BindicesStr);
[WrongF WrIndF] = intersect(Findices,BindicesStr);
size(WrIndF)
'Wrinf'
Findices(WrIndF) = [];
L = watershed(I(:,:,1)); %%%% Doing watershed on the red channel -- SEE if you can do it on the color image
%%%%%%%%% Finding Mean colors of the regions %%%%%%%%%%%%%%%%%%
%%%%% SEE if this can be vectorised %%%%%%%%%%%%%%%%%%%%%%%%%%%
numLabels = max(L(:));
PI = regionprops(L,'PixelIdxList');
MeanColors = zeros(numLabels,3);
for i=1:numLabels
MeanColors(i,1) = mean(I1(PI(i).PixelIdxList));
MeanColors(i,2) = mean(I2(PI(i).PixelIdxList));
MeanColors(i,3) = mean(I3(PI(i).PixelIdxList));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% Finding Foreground and Background Labels from pixels%%
FLabels = L(int32(Findices));
FLabels = union(FLabels,[]); %% Set-ifying the set of labels (Sorting?)
if(FLabels(1)==0) %% Removing Boundary Labels
FLabels = FLabels(2:end);
end
BLabels = L(int32(Bindices));
BLabels = union(BLabels,[]); %% Set-ifying the set of labels
if(BLabels(1)==0) %% Removing Boundary Labels
BLabels = BLabels(2:end);
end
% Common labels and indices among GC and LZ
[FIndCom] = intersect(Findices, FindicesStr);
[BIndCom] = intersect(Bindices, BindicesStr);
size(FIndCom)
size(BIndCom)
%%%% BLabels remain fixed -- no change
%%%% ULabels -- Uncertain labels -- on which optimization done...
ULabels = FLabels;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Finding Initial Estimate of foregound and background color clusters %%%%%%%%%%%%
NumFClusters = 5;
NumBClusters = 5;
FColors = MeanColors(FLabels,:);
BColors = MeanColors(BLabels,:);
%%%%%%%%%%% Initializing the GMM's %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%% Using EM_GM %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% [FWeights,FCClusters,FCovs,FLikelihood] = EM_GM(FColors,NumFClusters);
% [BWeights,BCClusters,BCovs,BLikelihood] = EM_GM(BColors,NumBClusters);
%%%%%%%%%%% Using Just kmeans %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[FId FCClusters] = vgg_kmeans(FColors, NumFClusters);
Fdim = size(FColors,2);
FCClusters = zeros(Fdim, NumFClusters);
FWeights = zeros(1,NumFClusters);
FCovs = zeros(Fdim, Fdim, NumFClusters);
for k=1:NumFClusters
relColors = FColors(find(FId==k),:); %% Colors belonging to cluster k
FCClusters(:,k) = mean(relColors,1)';
FCovs(:,:,k) = cov(relColors);
FWeights(1,k) = length(find(FId==k)) / length(FId);
end
[BId BCClusters] = vgg_kmeans(BColors, NumBClusters);
Bdim = size(BColors,2);
BCClusters = zeros(Bdim, NumBClusters);
BWeights = zeros(1,NumBClusters);
BCovs = zeros(Bdim, Bdim, NumBClusters);
for k=1:NumBClusters
relColors = BColors(find(BId==k),:); %% Colors belonging to cluster k
BCClusters(:,k) = mean(relColors,1)';
BCovs(:,:,k) = cov(relColors);
BWeights(1,k) = length(find(BId==k)) / length(BId);
end
%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % Initialize FDist and BDist with LZ values
% FDist = zeros(length(FLabels),1);
% BDist = zeros(length(BLabels),1);
numIter = 6;
for i=1:numIter
i
[FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);
[BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);
% Lets hope somebody doesnt choose the same thing as FG and BG, BG will
% prevail
FDist(FIndCom) = 0;
BDist(FIndCom) = 10000;
FDist(BIndCom) = 10000;
BDist(BIndCom) = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%% The Mex Function for GraphCutSegment %%%%%%%%%%%%
L = L-1; %% Segments labeled from 0 now -- -1 is for boundary pixels
FLabels = FLabels-1;
BLabels = BLabels-1;
ULabels = ULabels-1;
[SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels, FDist, BDist); %%% SegImage is the segmented image, %%% LLabels is the binary label for each watershed label
L = L+1; %% Again Labeled from 1...
FLabels = FLabels+1;
BLabels = BLabels+1;
ULabels = ULabels+1;
%%%%%%%%%% Showing the intermediate segmentation %%%%%%%%%%%%%%%%%%%%%
% edge_img = edge(SegImage,'canny');
%
%
% figure;
% imshow(I);
% [IInd,JInd] = ind2sub(size(I1),find(edge_img));
% hold on;plot(JInd,IInd,'b.');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if(i<numIter) %%%%%% Do NOT do this if final iteration -- just display the segmented image
%%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%
newFLabels = ULabels(find(LLabels==1.0));
newBLabels = ULabels(find(LLabels==0.0));
% newBLabels = union(newBLabels,BLabels); %%%%%% Whether new background labels will contain the old ones?
FColors = MeanColors(newFLabels,:);
BColors = MeanColors(newBLabels,:);
%%%%%%%% Making new GMM's based on new segmentation %%%%%%%%%%%%%%%%%%
%%%%%%%% Used for defining distances in the next iterative step %%%%%%
% [FWeights,FCClusters,FCovs,FLikelihood] = EM_GM(FColors,NumFClusters);
% [BWeights,BCClusters,BCovs,BLikelihood] = EM_GM(BColors,NumBClusters);
[newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);
[newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);
for k=1:NumFClusters
relColors = FColors(find(newFInd==k),:); %% Colors belonging to cluster k
FCClusters(:,k) = mean(relColors,1)';
FCovs(:,:,k) = cov(relColors);
FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);
end
for k=1:NumBClusters
relColors = BColors(find(newBInd==k),:); %% Colors belonging to cluster k
BCClusters(:,k) = mean(relColors,1)';
BCovs(:,:,k) = cov(relColors);
BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);
end
end
end
%%%%%%%%%%%%%%%%%%%%% Display the segmented image %%%%%%%%%%%%%%%%%%%
%set position
% data.ui.ah_img = axes('Position',[0.5 0.2 .603 .604]);%,'drawmode','fast');
% data.ui.ih_img = imagesc;
% %set image data
% set(data.ui.ih_img, 'Cdata', SegImage);
% axis('image');axis('ij');axis('off');
% drawnow;
edge_img = edge(SegImage,'canny');
% Put image on black background
SegNewImage1 = zeros(size(SegImage));
SegNewImage2 = zeros(size(SegImage));
SegNewImage3 = zeros(size(SegImage));
idx = find(SegImage);
length(idx)
for(i = 1:length(idx))
SegNewImage1(int32(idx(i))) = I1(int32(idx(i)));
SegNewImage2(int32(idx(i))) = I2(int32(idx(i)));
SegNewImage3(int32(idx(i))) = I3(int32(idx(i)));
end
SegNewImage(:,:,1) = SegNewImage1;
SegNewImage(:,:,2) = SegNewImage2;
SegNewImage(:,:,3) = SegNewImage3;
set(ih_img, 'Cdata', uint8(SegNewImage));
axis('image');axis('ij');axis('off');
drawnow;
figure;
imshow(uint8(SegNewImage));
save segnewimage SegNewImage;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)
NumFClusters = size(FCClusters,2);
numULabels = size(MeanColors,1);
FDist = zeros(numULabels,1);
FInd = zeros(numULabels,1);
Ftmp = zeros(numULabels, NumFClusters);
for k=1:NumFClusters
M = FCClusters(:,k);
CovM = FCovs(:,:,k);
W = FWeights(1,k);
V = MeanColors - repmat(M',numULabels,1);
Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));
% keyboard
end
[FDist, FInd] = min(Ftmp,[],2);
|
github
|
jacksky64/imageProcessing-master
|
SmartSelectSeg.m
|
.m
|
imageProcessing-master/segmentation/SegTool/SmartSelectSeg.m
| 2,669 |
utf_8
|
b0d3edf879d53194cac94ed20fd13b29
|
function SmartSelectSeg
% SMARTSELECTSEG - This function displays chosen image and creates buttons
% for marking seeds and calling Segment function
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
% Global Variables
% Req by radio button RadioButtonFn
global hfig longfilename;
% Get handle to current figure;
hfig = gcf;
% Call built-in file dialog to select image file
[filename,pathname] = uigetfile('images/*.*','Select image file');
if ~ischar(filename); return; end
%%%%%%%%%%%%%%%%%%%Radio Buttons%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
h = uibuttongroup('visible','off','Position',[0.7 0.7 0.15 0.18]);
u0 = uicontrol('Style','Radio','String','SmartRectangle',...
'pos',[10 20 120 30],'parent',h,'HandleVisibility','off');
u1 = uicontrol('Style','Radio','String','SmartRefine',...
'pos',[10 60 120 30],'parent',h,'HandleVisibility','off');
set(h,'SelectionChangeFcn',@RadioButtonFn);
set(h,'SelectedObject',[]);
set(h,'Visible','on');
%%%%%%%%%%%%%%%%%%%Draw Image%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load Image file
longfilename = strcat(pathname,filename);
Im = imread(longfilename);
% Get the position of the image
data.ui.ah_img = axes('Position',[0.01 0.2 .603 .604]);
data.ui.ih_img = image;
% Set the image
set(data.ui.ih_img, 'Cdata', Im);
axis('image');axis('ij');axis('off');
drawnow;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function RadioButtonFn(source, eventdata)
% RADIOBUTTONFUNCTION - This function is called whenever there is change in
% choice of radio button
% Global Variables
global hfig longfilename;
% Pass string value to seed selection function
strg = get(eventdata.NewValue,'String');
%%%%%%%%%%%%%%%%Seed Selection Buttons%%%%%%%%%%%%%%%%%%%
% Calls fginput - gets foreground pixels from the user
data.ui.push_fg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .6 .1 .05], ...
'String','Foreground','Callback', ['fginput ',strg]);
% Calls bginput - gets background pixels from the user
data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .5 .1 .05], ...
'String','Background','Callback', ['bginput ',strg]);
% Calls Segment - graph-cuts on the image
data.ui.push_bg = uicontrol(hfig, 'Style','pushbutton', 'Units', 'Normalized','Position',[.7 .4 .1 .05], ...
'String','Graph Cuts','Callback', ['Segment ',longfilename]);
drawnow;
|
github
|
jacksky64/imageProcessing-master
|
bginput.m
|
.m
|
imageProcessing-master/segmentation/SegTool/bginput.m
| 1,327 |
utf_8
|
40555c86d5e0244acebf33c28803bb1f
|
%--------------------------------------------------------------------------
function bginput(strg)
% BGINPUT - This function gets the background pixels from user input
% BGINPUT(STRG) - strg - Smart Refine or Smart Rectangle
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
% Global variables referenced in this funciton
global fgflag fgbc bgbc fgpixels bgpixels;
if(strcmp(strg,'SmartRectangle'))
% Gui related flag
fgflag = 2;
% Get two points from the user
bp = ginput(2);
bp = round(bp);
% Form the rectangular bounding box from the two points
[bx,by] = meshgrid(min(bp(:,1)):max(bp(:,1)),min(bp(:,2)):max(bp(:,2)));
bpixelsx =[];
bpixelsy =[];
for(i = 1:size(bx,2))
bpixelsx = [bpixelsx bx(:,i)'];
bpixelsy = [bpixelsy by(:,i)'];
end
bpixels = [bpixelsx' bpixelsy'];
% Concatenate
bgpixels = vertcat(bgpixels,bpixels);
% Plot the Rectangle
hfig = gcf;
axis('image');axis('ij');axis('off');
hold on;
plot(bgpixels(:,1),bgpixels(:,2),'b.');
else
hfig = gcf;
hold on;
% Gui related flag
fgflag = 0;
% Call track function on button press
set(hfig,'windowbuttondownfcn',{@track});
end
|
github
|
jacksky64/imageProcessing-master
|
SegmentGC.m
|
.m
|
imageProcessing-master/segmentation/SegTool/SegmentGC.m
| 8,578 |
utf_8
|
f39aab15bbf3c9ece14572e50f90061e
|
function [] = SegmentGC(ImName, ih, alg)
% SEGMENTGC - Main GrabCut segmentation function
% SEGMENTGC(IMNAME, IH, ALG) - ImName - Image to segment
% IH - Image Handle
% ALG - AutoCut or AutoRefine (0 or 1)
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
% Global Variables
global sopt segImageHandle;
% Read input image
I = imread(ImName);
I1 = I(:,:,1);
I2 = I(:,:,2);
I3 = I(:,:,3);
% Final Segmented Image
SegImage = zeros(size(I));
% Stop unnecessary warnings
warning('off','all');
%%%%%%% Marking the Inner Rectangle around the foreground object
%%%%%%% Get two points from the user
% Set the image
set(ih, 'Cdata', I);
axis('image');axis('ij');axis('off');
drawnow;
fp = ginput(2);
fp = round(fp);
% Save the points for future use
save rectpts fp;
% Width of Strip
StripWidth = sopt.StripWidth_AC_ACR;
% Get inner indices
[fxi,fyi] = meshgrid(min(fp(:,1)):max(fp(:,1)),min(fp(:,2)):max(fp(:,2)));
InsideFindices = sub2ind(size(I1),fyi, fxi);
InsideFindices = InsideFindices(:);
% Form the Outside rectangular bounding box -- add StripWidth on all 4 sides
[fxo,fyo] = meshgrid( max((min(fp(:,1)) - StripWidth),1) : min((max(fp(:,1)) + StripWidth), size(I1,2)) , max((min(fp(:,2)) - StripWidth),1) : min((max(fp(:,2)) + StripWidth), size(I1,1)) );
OutsideFindices = sub2ind(size(I1),fyo, fxo);
OutsideFindices = OutsideFindices(:);
Bindices = setdiff(OutsideFindices, InsideFindices);
Allindices = [1:size(I1,1)*size(I1,2)]';
Findices = InsideFindices;
% Water shed pixels
L = watershed(I(:,:,1));
%%%%%%%%% Finding Mean colors of the regions %%%%%%%%%%%%%%%%%%
numLabels = max(L(:));
PI = regionprops(L,'PixelIdxList');
MeanColors = zeros(numLabels,3);
for i=1:numLabels
MeanColors(i,1) = mean(I1(PI(i).PixelIdxList));
MeanColors(i,2) = mean(I2(PI(i).PixelIdxList));
MeanColors(i,3) = mean(I3(PI(i).PixelIdxList));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% Finding Foreground and Background Labels from pixels%%
FLabels = L(int32(Findices));
FLabels = union(FLabels,[]); %% Set-ifying the set of labels (Sorting)
if(FLabels(1)==0) %% Removing Boundary Labels
FLabels = FLabels(2:end);
end
BLabels = L(int32(Bindices));
BLabels = union(BLabels,[]); %% Set-ifying the set of labels
if(BLabels(1)==0) %% Removing Boundary Labels
BLabels = BLabels(2:end);
end
%%%% BLabels remain fixed -- no change
%%%% ULabels -- Uncertain labels -- on which optimization done...
ULabels = FLabels;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Finding Initial Estimate of foregound and background color clusters %%%%%%%%%%%%
NumFClusters = sopt.NumFClusters_AC;
NumBClusters = sopt.NumBClusters_AC;
FColors = MeanColors(FLabels,:);
BColors = MeanColors(BLabels,:);
%%%%%%%%%%% Initializing the GMM's %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%% Using Just kmeans %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[FId FCClusters] = kmeans(FColors, NumFClusters);
Fdim = size(FColors,2);
FCClusters = zeros(Fdim, NumFClusters);
FWeights = zeros(1,NumFClusters);
FCovs = zeros(Fdim, Fdim, NumFClusters);
for k=1:NumFClusters
relColors = FColors(find(FId==k),:); %% Colors belonging to cluster k
FCClusters(:,k) = mean(relColors,1)';
FCovs(:,:,k) = cov(relColors);
FWeights(1,k) = length(find(FId==k)) / length(FId);
end
[BId BCClusters] = kmeans(BColors, NumBClusters);
Bdim = size(BColors,2);
BCClusters = zeros(Bdim, NumBClusters);
BWeights = zeros(1,NumBClusters);
BCovs = zeros(Bdim, Bdim, NumBClusters);
for k=1:NumBClusters
relColors = BColors(find(BId==k),:); %% Colors belonging to cluster k
BCClusters(:,k) = mean(relColors,1)';
BCovs(:,:,k) = cov(relColors);
BWeights(1,k) = length(find(BId==k)) / length(BId);
end
%%%%%%%%%%%% GMM's Initialized %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% The iterative Loop %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lambda = sopt.lambda_AC;
numIter = sopt.numIter_AC;
for i = 1:numIter
disp(['Iteration - ', num2str(i)]);
% Foreground and Background edge weights
[FDist, FInd] = ClustDistMembership(MeanColors(ULabels,:), FCClusters, FCovs, FWeights);
[BDist, BInd] = ClustDistMembership(MeanColors(ULabels,:), BCClusters, BCovs, BWeights);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Segments labeled from 0 now -- -1 is for boundary pixels
L = L-1;
FLabels = FLabels-1;
BLabels = BLabels-1;
ULabels = ULabels-1;
%%%%%%%%%%%%% The Mex Function for GraphCutSegment %%%%%%%%%%%%
%%% SegImage is the segmented image, %%% LLabels is the binary label
%%% for each watershed label
[SegImage LLabels] = GraphCutSegment(L, MeanColors, ULabels, BLabels,...
FDist, BDist, lambda);
%% Again Labeled from 1...
L = L+1;
FLabels = FLabels+1;
BLabels = BLabels+1;
ULabels = ULabels+1;
if(i < numIter)
%%%%%% Do NOT do this if final iteration -- just display the segmented image
%%%%%% Making new FLabels and BLabels based on the segmentation %%%%%%
newFLabels = ULabels(find(LLabels==1.0));
newBLabels = ULabels(find(LLabels==0.0));
%%%%%% Whether new background labels will contain the old ones?
% newBLabels = union(newBLabels,BLabels);
FColors = MeanColors(newFLabels,:);
BColors = MeanColors(newBLabels,:);
%%%%%%%% Calculating FG and BG distances based on new segmentation %%%%%%%%%%%%%%%%%%
[newFDists newFInd] = ClustDistMembership(FColors, FCClusters, FCovs, FWeights);
[newBDists newBInd] = ClustDistMembership(BColors, BCClusters, BCovs, BWeights);
for k = 1:NumFClusters
relColors = FColors(find(newFInd==k),:); %% Colors belonging to cluster k
FCClusters(:,k) = mean(relColors,1)';
FCovs(:,:,k) = cov(relColors);
FWeights(1,k) = length(find(newFInd==k)) / length(newFInd);
end
for k=1:NumBClusters
relColors = BColors(find(newBInd==k),:); %% Colors belonging to cluster k
BCClusters(:,k) = mean(relColors,1)';
BCovs(:,:,k) = cov(relColors);
BWeights(1,k) = length(find(newBInd==k)) / length(newBInd);
end
end
end
%%%%%%%%%%%%%%%%%%%%% Display the segmented image %%%%%%%%%%%%%%%%%%%
edge_img = edge(SegImage,'canny');
% Put image on black background
SegImage = repmat(SegImage,[1,1,3]);
SegNewImage = uint8(SegImage) .* uint8(I);
% If AutoCut, just show the image
if(~alg)
figure;
imshow(uint8(SegNewImage));
else
% If AutoRefine mark a segmentation boundary on original image
[IInd,JInd] = ind2sub(size(I1),find(edge_img));
boundImage1 = I(:,:,2);
boundImage1(find(edge_img)) = 255;
boundImage = I;
boundImage(:,:,2) = boundImage1;
% Set the image
set(ih, 'Cdata', uint8(boundImage));
axis('image');axis('ij');axis('off');
drawnow;
segImageHandle = figure;
imshow(uint8(SegNewImage));
end
SegMask = SegImage;
SegResult = SegNewImage;
% Save Segmentation Result
save('SegResult', 'SegMask', 'SegResult');
% Required for AutoCutRefine
save('iter_data','L', 'MeanColors', 'FLabels', 'BLabels', 'FCClusters',...
'FCovs', 'FWeights', 'BCClusters', 'BCovs', 'BWeights');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%% Helper Functions declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [FDist, FInd] = ClustDistMembership(MeanColors, FCClusters, FCovs, FWeights)
% CLUSTDISTMEMBERSHIP - Calcuates FG and BG Distances
% Authors - Mohit Gupta, Krishnan Ramnath
% Affiliation - Robotics Institute, CMU, Pittsburgh
% 2006-05-15
NumFClusters = size(FCClusters,2);
numULabels = size(MeanColors,1);
FDist = zeros(numULabels,1);
FInd = zeros(numULabels,1);
Ftmp = zeros(numULabels, NumFClusters);
for k = 1:NumFClusters
M = FCClusters(:,k);
CovM = FCovs(:,:,k);
W = FWeights(1,k);
V = MeanColors - repmat(M',numULabels,1);
Ftmp(:,k) = -log((W / sqrt(det(CovM))) * exp(-( sum( ((V * inv(CovM)) .* V),2) /2)));
end
[FDist, FInd] = min(Ftmp,[],2);
|
github
|
jacksky64/imageProcessing-master
|
creaseg.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/creaseg.m
| 4,537 |
utf_8
|
c7fe3231ee32b19b499802aa425d316a
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg()
clear all;
close all hidden;
clc;
addpath src;
warning('off');
%-- create interface gui
creaseg_gui();
|
github
|
jacksky64/imageProcessing-master
|
creaseg_createreference.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_createreference.m
| 6,343 |
utf_8
|
8b8ed702092f714ebf41dce7f98e013b
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Honk Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_createreference()
%-- parameters
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- do nothing if no image is loaded
if ( isempty(fd.data) )
return;
end
%-- clean up overlay display
keepLS = 1;
creaseg_cleanOverlays(keepLS);
fd = get(ud.imageId,'userdata');
%-- flush previous reference if any
if ( ~isempty(fd.reference) )
fd.reference = zeros(size(fd.data));
end
%-- Display information messages
set(ud.txtInfo1,'string','Left click to add a point','color','y');
set(ud.txtInfo2,'string','Right click stop drawing contour','color','y');
set(ud.txtInfo3,'string','Middle click to save reference contour','color','y');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
%-- "Unchecking" the figure buttons
for i=1:1:3
set(ud.buttonAction(i),'BackgroundColor',[240/255 173/255 105/255]);
end
for i=6:size(ud.buttonAction)
set(ud.buttonAction(i),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- Put the "Create" button in darker
set(ud.handleAlgoComparison(17),'BackgroundColor',[160/255 130/255 95/255]);
%-- put see result to disable, just in case
set(ud.handleAlgoComparison(24),'enable','off');
%-- cancel initialization drawing mode
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
pan off;
%-- keep in minf reference mode
ud.LastPlot = 'reference';
fd.method = 'Reference';
%-- Set the reference drawing Callbacks
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawMultiReferenceContours});
set(ud.gcf,'WindowButtonUpFcn','');
%-- UPDATE FD AND UD STRUCTURES ATTACHED TO IMAGEID AND FIG HANDLES
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_spline.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_spline.m
| 5,098 |
utf_8
|
fbbdc22b0913e532c31a5e2f2cff495f
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function [xs, ys] = creaseg_spline(x, y)
%-- "Looping" the points to have a nice closed contour
i = 7; %-- Number of extra points added
if length(x) > i %-- Check if the number of point is sufficient
x = [x x(1:i)]; y = [y y(1:i)];
else
i2 = i-length(x);
x = [x x]; y = [y y];
x = [x x(1:i2)]; y = [y y(1:i2)];
end
s_div = 1/10;
t = 1:length(x);
ts = 1:s_div:length(x);
xs = spline(t,x,ts);
ys = spline(t,y,ts);
%-- Deleting the extra segment (at the begining and at the end)
xs = xs(ceil(i/2)/s_div:(length(x)-floor(i/2))/s_div);
ys = ys(ceil(i/2)/s_div:(length(x)-floor(i/2))/s_div);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_lankton.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_lankton.m
| 15,575 |
utf_8
|
9d8d19987c200b1ba9e64a4ee5adc544
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Localized Region Based Active Contour Segmentation:
%
% seg = localized_seg(I,init_mask,max_its,rad,alpha,method)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% rad (optional) Localization Radius (in pixels)
% smaller = more local, bigger = more global
% alpha (optional) Weight of smoothing term
% higer = smoother
% method (optional) selects localized energy
% 1 = Yezzi Energy (usually works better)
% 2 = Chan-Vese Energy
%
% Outputs: seg Final segmentation mask (1=fg, 0=bg)
%
% Example:
% img = imread('tire.tif'); %-- load the image
% m = false(size(img)); %-- create initial mask
% m(28:157,37:176) = true;
% seg = localized_seg(img,m,150);
%
% Description: This code implements the paper: "Localizing Region Based
% Active Contours" By Lankton and Tannenbaum. In this work, typical
% region-based active contour energies are localized in order to handle
% images with non-homogeneous foregrounds and backgrounds.
%
% Coded by: Shawn Lankton (www.shawnlankton.com)
%------------------------------------------------------------------------
function [seg,phi,its] = creaseg_lankton(I,init_mask,max_its,rad,alpha,thresh,method,neigh,color,display)
%-- default value for parameter alpha is .1
if(~exist('alpha','var'))
alpha = .2;
end
if(~exist('thresh','var'))
thresh = 0;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default value for parameter method is 2
if(~exist('method','var'))
method = 1;
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
%-- Ensures image is 2D double matrix
I = im2graydouble(I);
%-- Default localization radius is 1/10 of average length
[dimy dimx] = size(I);
if(~exist('rad','var'))
rad = round((dimy+dimx)/(2*8));
end
% init_mask = init_mask<=0;
%-- Create a signed distance map (SDF) from mask
phi = mask2phi(init_mask);
%-- Create disk
disk = getnhood(strel('disk', rad));
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%--main loop
its = 0; stop = 0;
prev_mask = init_mask; c = 0;
while ((its < max_its) && ~stop)
%-- get the curve's narrow band
idx = find(phi <= 1.2 & phi >= -1.2)';
[y x] = ind2sub(size(phi),idx);
if ~isempty(idx)
switch neigh
case 2 % Square NHood
%-- get windows for localized statistics
xneg = x-rad; xpos = x+rad; %get subscripts for local regions
yneg = y-rad; ypos = y+rad;
xneg(xneg<1)=1; yneg(yneg<1)=1; %check bounds
xpos(xpos>dimx)=dimx; ypos(ypos>dimy)=dimy;
%-- re-initialize u,v,Ain,Aout
u=zeros(size(idx)); v=zeros(size(idx));
Ain=zeros(size(idx)); Aout=zeros(size(idx));
for i = 1:numel(idx) % for every point in the narrow band
img = I(yneg(i):ypos(i),xneg(i):xpos(i)); %sub image
P = phi(yneg(i):ypos(i),xneg(i):xpos(i)); %sub phi
upts = find(P<=0); %local interior
Ain(i) = length(upts)+eps;
u(i) = sum(img(upts))/Ain(i);
vpts = find(P>0); %local exterior
Aout(i) = length(vpts)+eps;
v(i) = sum(img(vpts))/Aout(i);
end
%-- get image-based forces
switch method %-choose which energy is localized
case 1, %-- YEZZI
F = -((u-v).*((I(idx)-u)./Ain+(I(idx)-v)./Aout));
otherwise, %-- CHAN VESE
F = -(u-v).*(2.*I(idx)-u-v);
end
case 1 % Circle NHood
%-- compute local stats and get image-based forces
F = zeros(1,length(idx));
for i = 1:numel(idx) % for every point in the narrow band
F(1,i) = local_nhood(I,phi,y(i),x(i),disk,method);
end
end
%-- get forces from curvature penalty
curvature = get_curvature(phi,idx,x,y);
%-- gradient descent to minimize energy
dphidt = F./max(abs(F)) + alpha*curvature;
%-- maintain the CFL condition
dt = .45/(max(abs(dphidt))+eps);
%-- evolve the curve
phi(idx) = phi(idx) + dt.*dphidt;
%-- Keep SDF smooth
phi = sussman(phi, .5);
new_mask = phi<=0;
c = convergence(prev_mask,new_mask,thresh,c);
if c <= 5
its = its + 1;
prev_mask = new_mask;
else stop = 1;
end
%-- intermediate output
if (display>0)
if ( mod(its,50)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(its,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
drawnow;
end
end
else
break;
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%-- converts a mask to a SDF
function phi = mask2phi(init_a)
phi = bwdist(init_a)-bwdist(1-init_a)+im2double(init_a)-.5;
%-- compute curvature along SDF
function curvature = get_curvature(phi,idx,x,y)
[dimy, dimx] = size(phi);
%-- get subscripts of neighbors
ym1 = y-1; xm1 = x-1; yp1 = y+1; xp1 = x+1;
%-- bounds checking
ym1(ym1<1) = 1; xm1(xm1<1) = 1;
yp1(yp1>dimy)=dimy; xp1(xp1>dimx) = dimx;
%-- get indexes for 8 neighbors
idup = sub2ind(size(phi),yp1,x);
iddn = sub2ind(size(phi),ym1,x);
idlt = sub2ind(size(phi),y,xm1);
idrt = sub2ind(size(phi),y,xp1);
idul = sub2ind(size(phi),yp1,xm1);
idur = sub2ind(size(phi),yp1,xp1);
iddl = sub2ind(size(phi),ym1,xm1);
iddr = sub2ind(size(phi),ym1,xp1);
%-- get central derivatives of SDF at x,y
phi_x = -phi(idlt)+phi(idrt);
phi_y = -phi(iddn)+phi(idup);
phi_xx = phi(idlt)-2*phi(idx)+phi(idrt);
phi_yy = phi(iddn)-2*phi(idx)+phi(idup);
phi_xy = -0.25*phi(iddl)-0.25*phi(idur)...
+0.25*phi(iddr)+0.25*phi(idul);
phi_x2 = phi_x.^2;
phi_y2 = phi_y.^2;
%-- compute curvature (Kappa)
curvature = ((phi_x2.*phi_yy + phi_y2.*phi_xx - 2*phi_x.*phi_y.*phi_xy)./...
(phi_x2 + phi_y2 +eps).^(3/2)).*(phi_x2 + phi_y2).^(1/2);
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if(isfloat(img)) % image is a double
if(c==3)
img = rgb2gray(uint8(img));
end
else % image is a int
if(c==3)
img = rgb2gray(img);
end
img = double(img);
end
%-- level set re-initialization by the sussman method
function D = sussman(D, dt)
% forward/backward differences
a = D - shiftR(D); % backward
b = shiftL(D) - D; % forward
c = D - shiftD(D); % backward
d = shiftU(D) - D; % forward
a_p = a; a_n = a; % a+ and a-
b_p = b; b_n = b;
c_p = c; c_n = c;
d_p = d; d_n = d;
a_p(a < 0) = 0;
a_n(a > 0) = 0;
b_p(b < 0) = 0;
b_n(b > 0) = 0;
c_p(c < 0) = 0;
c_n(c > 0) = 0;
d_p(d < 0) = 0;
d_n(d > 0) = 0;
dD = zeros(size(D));
D_neg_ind = find(D < 0);
D_pos_ind = find(D > 0);
dD(D_pos_ind) = sqrt(max(a_p(D_pos_ind).^2, b_n(D_pos_ind).^2) ...
+ max(c_p(D_pos_ind).^2, d_n(D_pos_ind).^2)) - 1;
dD(D_neg_ind) = sqrt(max(a_n(D_neg_ind).^2, b_p(D_neg_ind).^2) ...
+ max(c_n(D_neg_ind).^2, d_p(D_neg_ind).^2)) - 1;
D = D - dt .* sussman_sign(D) .* dD;
%-- whole matrix derivatives
function shift = shiftD(M)
shift = shiftR(M')';
function shift = shiftL(M)
shift = [ M(:,2:size(M,2)) M(:,size(M,2)) ];
function shift = shiftR(M)
shift = [ M(:,1) M(:,1:size(M,2)-1) ];
function shift = shiftU(M)
shift = shiftL(M')';
function S = sussman_sign(D)
S = D ./ sqrt(D.^2 + 1);
% Convergence Test
function c = convergence(p_mask,n_mask,thresh,c)
diff = p_mask - n_mask;
n_diff = sum(abs(diff(:)));
if n_diff < thresh
c = c + 1;
else c = 0;
end
function feature = local_nhood(img, phi, xref, yref, disk, m)
[dimx, dimy] = size(img);
rad = (size(disk,1)+1)/2;
u = 0; v = 0;
Au = 0; Av = 0;
[X, Y] = find(disk == 1); % X(i) E [1; 2rad-1]
X = xref + X - rad; Y = yref + Y - rad;
X(X < 1) = 1; X(X > dimx) = dimx; % check bounds
Y(Y < 1) = 1; Y(Y > dimy) = dimy;
for i = 1:1:length(X)
if phi(X(i), Y(i)) <= 0
u = u + img(X(i), Y(i));
Au = Au + 1;
else
v = v + img(X(i), Y(i));
Av = Av + 1;
end
end
u = u/(Au+eps); v = v/(Av+eps);
switch m %-choose which energy is localized
case 1, %-- YEZZI
feature = -(u-v)*((img(xref, yref)-u)/Au+(img(xref, yref)-v)/Av); % YEZZI
otherwise, %-- CHAN VESE
feature = -(u-v)*(2*img(xref, yref)-u-v); % CHAN VESE
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_drawManualContour.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_drawManualContour.m
| 8,753 |
utf_8
|
4eb1e62aae8e74976a72ba4b072f54a0
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_drawManualContour(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
%-- Clic outside the image => do nothing
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 )
return;
end
if ( strcmp(get(ud.gcf,'SelectionType'),'normal') )
%-- Set drawingManualFlag flag to 1
fd.drawingManualFlag = 1;
%-- delete any overlay lines
if ( size(fd.handleManual,2)>0 )
for k=1:size(fd.handleManual{1},1)
delete(fd.handleManual{1}(k));
end
fd.handleManual(1)=[];
end
%-- Get point coordinates
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
if (isempty(fd.points))
fd.points = [pt(1),pt(2)];
hold on; h = plot(pt(1), pt(2), 'oy', 'linewidth', 2);
fd.handleManual{1} = h;
else
fd.points(end+1,:) = [pt(1),pt(2)];
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if length(fd.points(:,1)) < 3 % If there's only 2 points, display a line instead of spline
hold on; h1 = plot(fd.points(:,1),fd.points(:,2),'--','color',color{1},'Linewidth',2);
tmp = fd.points(1,:); tmp(end+1,:) = fd.points(end,:);
h2 = plot(tmp(:,1), tmp(:,2), 'y--', 'linewidth', 2);
else
[xs, ys] = creaseg_spline(fd.points(:,1)',fd.points(:,2)');
% Find the position of the last (fin) and first (deb) points of fd.points in xs and ys
fin = find((xs == fd.points(end,1)) & (ys == fd.points(end,2)) );
deb = find((xs == fd.points(1,1)) & (ys == fd.points(1,2)) );
% Change the point order to have deb->fin->deb
xs = xs([deb:end, 1:deb]); ys = ys([deb:end, 1:deb]);
if deb > fin % And compute the new position of the last point
idx = length(xs) + fin - deb;
else
idx = fin - deb;
end
clear deb fin;
hold on; h1 = plot(xs(1:idx),ys(1:idx),'--','color',color{1},'Linewidth',2);
hold on; h2 = plot(xs(idx:end),ys(idx:end),'y--','Linewidth',2);
end
h3 = plot(fd.points(:,1), fd.points(:,2), 'oy', 'linewidth', 2);
hold off;
fd.handleManual{1} = [h1;h2;h3];
end
else %-- create final contour
%-- Set drawingManualFlag flag to 0
fd.drawingManualFlag = 0;
%-- display final contour
if ( size(fd.points,1)>2 )
%-- delete any overlay lines
if ( size(fd.handleManual,2)>0 )
for k=1:size(fd.handleManual{1},1)
delete(fd.handleManual{1}(k));
end
fd.handleManual(1)=[];
end
%--
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if length(fd.points(:,1)) < 3
tmp = fd.points;
tmp(end+1,:) = tmp(1,:);
hold on; h = plot(tmp(:,1),tmp(:,2),'--','color',color{1},'Linewidth',2);
else
[xs, ys] = creaseg_spline(fd.points(:,1)',fd.points(:,2)');
hold on; h = plot(xs,ys,'--','color',color{1},'Linewidth',2);
end
fd.handleManual{1} = h;
%-- create manual mask
X = get(fd.handleManual{1},'X');
Y = get(fd.handleManual{1},'Y');
fd.levelset = roipoly(fd.data,X,Y);
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- enable run and pointer buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
end
fd.points = [];
end
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_shi.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_shi.m
| 17,807 |
utf_8
|
6f8e7899e227bafa2afc8bbfe60644a9
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Description: This code implements the paper: "A Real-Time Algorithm for
% the Approximation of Level-Set-Based Curve Evolution." By Yonggang Shi.
%
% Coded by: Olivier Bernard (www.creatis.insa-lyon.fr/~bernard)
%------------------------------------------------------------------------
function [seg,phi,n] = creaseg_shi(img,init_mask,max_its,Na,Ns,Sigma,Ng,color,display)
%-- default value for parameter max_its is 100
if(~exist('max_its','var'))
max_its = 100;
end
%-- default value for parameter na is 30
if(~exist('Na','var'))
Na = 30;
end
%-- default value for parameter ns is 3
if(~exist('Ns','var'))
Ns = 3;
end
%-- default value for parameter sigma is 9
if(~exist('Sigma','var'))
Sigma = 3;
end
%-- default value for parameter ng is 7
if(~exist('Ng','var'))
Ng = 1;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
% init_mask = init_mask<=0;
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%-- Create the initial level-set
[phi,Lin,Lout,size_in,size_out] = createInitialLevelSet(init_mask);
%-- Create feature image
[feature,u,v,Ain,Aout] = createFeatureImage(phi,img);
%-- main looop
stop_cond = 0; % Stopping condition
n = 1;
while ( (n<=max_its) && (stop_cond==0) )
% Data dependent evolution
na = 0;
while ( (na<Na) && (n<=max_its) && (stop_cond==0) )
[Lin,Lout,phi,u,v,Ain,Aout,size_in,size_out] = ...
shi_evolution_subCV(img,Lin,Lout,phi,feature,u,v,Ain,Aout,size_in,size_out);
%-- intermediate output
if (display>0)
if ( mod(na,50)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',n),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(na,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',n),'color',[1 1 0]);
drawnow;
end
end
stop_cond = stopping_condition(feature,Lin,Lout,size_in,size_out);
if ( stop_cond==0 ) % Mise à jour de la feature image
feature = zeros(size(phi));
[x,y] = find(abs(phi) < 2);
feature(x,y) = -(img(x,y) - u).^2 + (img(x,y) - v).^2;
feature = feature./max(abs(feature(:)));
end
na = na+1;
n = n+1;
end
% smoothing evolution
for ns=1:1:Ns
Fint = smoothing(phi,Lin,Lout,size_in,size_out,Ng,Sigma);
[Lin,Lout,phi,u,v,Ain,Aout,size_in,size_out] = ...
shi_evolution_subCV(img,Lin,Lout,phi,Fint,u,v,Ain,Aout,size_in,size_out);
n = n+1;
end
if (stop_cond==0)
feature = zeros(size(phi));
[x,y] = find(abs(phi) < 2);
feature(x,y) = -(img(x,y) - u).^2 + (img(x,y) - v).^2; % Chan & Vese
feature = feature./max(abs(feature(:)));
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%-- Create the initial discrete level-set from mask
function [u,Lin,Lout,size_in,size_out] = createInitialLevelSet(mask)
tmp = ones(size(mask))*3;
tmp(mask>0) = -3;
u = tmp;
[nrow,ncol] = size(u);
[x,y] = find(tmp==-3);
for j=1:size(x,1)
neigh = voisinage([x(j);y(j)],nrow,ncol);
k = 1;
stop = 0;
while ( ( k<5 ) && ( stop==0 ) )
if ( tmp(neigh(1,k),neigh(2,k)) > -3 )
u(x(j),y(j)) = 1;
stop = 1;
end
k = k + 1;
end
end
tmp(u>-3) = 3;
[x,y] = find(tmp==-3);
for j=1:size(x,1)
neigh = voisinage([x(j);y(j)],nrow,ncol);
k = 1;
stop = 0;
while ( ( k<5 ) && ( stop==0 ) )
if ( tmp(neigh(1,k),neigh(2,k)) > -3 )
u(x(j),y(j)) = -1;
stop = 1;
end
k = k + 1;
end
end
Lin = zeros(2,round(size(u,1)*size(u,2)/6));
Lout = zeros(2,round(size(u,1)*size(u,2)/6));
size_in = 0;
size_out = 0;
for i=1:1:size(u,1)
for j=1:1:size(u,2)
if ( u(i,j) == 1 )
size_out = size_out + 1;
Lout(:,size_out) = [i;j];
end
if ( u(i,j) == -1 )
size_in = size_in + 1;
Lin(:,size_in) = [i;j];
end
end
end
%-- Find the neighborhood of one pixel
function N = voisinage(x,nrow,ncol)
i = x(1);
j = x(2);
I1 = i+1;
if (I1 > nrow)
I1 = nrow;
end
I2 = i-1;
if (I2 < 1)
I2 = 1;
end
J1 = j+1;
if (J1 > ncol)
J1 = ncol;
end
J2 = j-1;
if (J2 < 1)
J2 = 1;
end
N = [I1, I2, i, i; j, j, J1, J2];
%-- Create feature image for data dependent cycle
function [feature, u, v, Ain, Aout] = createFeatureImage(phi, im)
upts = find(phi<=0); % interior points
vpts = find(phi>0); % exterior points
Ain = length(upts); % interior area
Aout = length(vpts); % exterior area
u = sum(im(upts))/(Ain+eps); % interior mean
v = sum(im(vpts))/(Aout+eps); % exterior mean
feature = zeros(size(phi));
[x,y] = find(abs(phi) < 2);
feature(x,y) = -(im(x,y) - u).^2 + (im(x,y) - v).^2;
feature = feature./max(abs(feature(:)));
%-- Testing convergence
function sc = stopping_condition(F, Li, Lo,size_in,size_out)
sc = 1; i = 1;
while( i<size_out && sc )
x = Lo(1,i);
y = Lo(2,i);
if F(x,y)>0
sc = 0;
end
i = i+1;
end
i = 1;
while( i<size_in && sc )
x = Li(1,i);
y = Li(2,i);
if F(x,y)<0
sc = 0;
end
i = i + 1;
end
%-- Create feature image for smoothing cycle
function Fi = smoothing(phi, Li, Lo,size_in,size_out, sg, sigma)
[nr, nc] = size(phi);
Fi = zeros(nr, nc);
Gaussian = fspecial('gaussian', [sg, sg], sigma);
H = zeros(size(phi));
H(phi<0) = 1;
HG = imfilter(H, Gaussian);
for i = 1:1:size_out
x = Lo(1,i);
y = Lo(2,i);
if ( HG(x,y)>1/2 )
Fi(x,y) = 1;
end
end
for i = 1:1:size_in
x = Li(1,i);
y = Li(2,i);
if ( HG(x,y)<1/2 )
Fi(x,y) = -1;
end
end
%-- shi_evolution_subCV
function [Linmod, Loutmod, Phimod, umod, vmod, Ai, Ao,s_i,s_o] = ...
shi_evolution_subCV(img, Lin, Lout, phi, feature, u, v, Ain, Aout,size_in,size_out)
[nrow,ncol] = size(phi);
Linmod = Lin;
Loutmod = Lout;
Phimod = phi;
s_i = size_in;
s_o = size_out;
umod = u;
Ai = Ain;
vmod = v;
Ao = Aout;
% Step 1: Outward evolution
c = 1;
N = s_o;
while ( c <= N )
i = Loutmod(1, c); j = Loutmod(2, c);
if ( feature(i, j) > 0 )
[Linmod, Loutmod, Phimod,s_i,s_o] = ...
switch_in(c, Linmod, Loutmod, Phimod, s_i, s_o, nrow, ncol);
umod = (umod*Ai + img(i, j))/(Ai + 1);
vmod = (vmod*Ao - img(i, j))/(Ao - 1);
Ai = Ai + 1; Ao = Ao - 1;
c = c-1;
N = N-1;
end
c = c+1;
end
% Step 2: Eliminate redundant point in Lin
[Linmod, Phimod, s_i] = suppr_Lin(Linmod, Phimod, s_i, nrow, ncol);
% Step 3: Inward evolution
c = 1;
N = s_i;
while (c <= N)
i = Linmod(1, c); j = Linmod(2, c);
if ( feature(i, j) < 0 )
[Linmod, Loutmod, Phimod,s_i,s_o] = ...
switch_out(c, Linmod, Loutmod, Phimod,s_i,s_o, nrow, ncol);
umod = (umod*Ai - img(i, j))/(Ai - 1);
vmod = (vmod*Ao + img(i, j))/(Ao + 1);
Ai = Ai - 1; Ao = Ao + 1;
c = c-1;
N = N-1;
end
c = c+1;
end
% Step 4: Eliminate redundant point in Lout
[Loutmod, Phimod,s_o] = suppr_Lout(Loutmod, Phimod,s_o,nrow, ncol);
function [Linmod, Loutmod, Phimod,s_i,s_o] = ...
switch_in(c, Lin, Lout, phi,size_in,size_out, nrow, ncol)
x = [Lout(1, c); Lout(2, c)];
Phimod = phi;
Linmod = Lin;
Loutmod = Lout;
% on ajoute x a Lin
Linmod(:,size_in+1) = x;
Phimod(x(1, 1), x(2, 1)) = -1;
s_i = size_in + 1;
% Suppression de x de Lout
if (c == 1)
Loutmod(:,1:size_out-1) = Lout(:, 2:size_out);
elseif (c == size_out)
Loutmod(:,1:size_out-1) = Lout(:, 1:size_out-1);
else
Loutmod(:,1:size_out-1) = [Lout(:, 1:c-1),Lout(:, c+1:size_out)];
end
s_o = size_out - 1;
% Mise a jour du voisinage
N = voisinage(x, nrow, ncol);
for k=1:1:4
y = [N(1, k); N(2, k)];
i = N(1, k);
j = N(2, k);
if phi(i, j) == 3 % y est un point exterieur
Phimod(i, j) = 1; % Mise a jour de phi(j)
Loutmod(:,s_o+1) = y; % Mise a jour de Lout
s_o = s_o + 1;
end
end
function [Linmod, Loutmod, Phimod,s_i,s_o] = ...
switch_out(c, Lin, Lout, phi,size_in,size_out, nrow, ncol)
x = [Lin(1, c); Lin(2, c)];
Phimod = phi;
Linmod = Lin;
Loutmod = Lout;
% on ajoute x a Lout
Loutmod(:,size_out+1) = x;
Phimod(x(1, 1), x(2, 1)) = 1;
s_o = size_out + 1;
% Suppression de x de Lin
if (c == 1)
Linmod(:,1:size_in-1) = Lin(:, 2:size_in);
elseif (c == size_in)
Linmod(:,1:size_in-1) = Lin(:, 1:size_in-1);
else
Linmod(:,1:size_in-1) = [Lin(:, 1:c-1), Lin(:, c+1:size_in)];
end
s_i = size_in-1;
N = voisinage(x, nrow, ncol);
for k=1:1:4
y = [N(1, k); N(2, k)];
i = N(1, k);
j = N(2, k);
if (phi(i, j) == -3) % y est un point interieur
Phimod(i, j) = -1; % Mise a jour de phi(j)
Linmod(:,s_i+1) = y; % Mise a jour de Lin
s_i = s_i + 1;
end
end
function [Linmod, Phimod,s_i] = suppr_Lin(Lin, phi,size_in, nrow, ncol)
Linmod = Lin;
Phimod = phi;
s_i = size_in;
k=1;
while (k <= s_i)
x = [Lin(1, k); Lin(2, k)];
N = voisinage(x, nrow, ncol);
i = x(1, 1);
j = x(2, 1);
b = 0;
for c=1:1:4
if (phi(N(1, c), N(2, c)) < 0)
b = b+1;
end
end
if (b == 4)
% Suppression de x de Lin
if (k == 1)
Linmod(:,1:s_i-1) = Lin(:,2:s_i);
elseif (k == s_i)
Linmod(:,1:s_i-1) = Lin(:,1:s_i-1);
else
Linmod(:,1:s_i-1) = [Lin(:,1:k-1),Lin(:,k+1:s_i)];
end
k = k-1;
s_i = s_i-1;
Phimod(i,j) = -3;
Lin = Linmod;
end
k = k+1;
end
function [Loutmod, Phimod,s_o] = suppr_Lout(Lout, phi,size_out, nrow, ncol)
Loutmod = Lout;
Phimod = phi;
s_o = size_out;
k = 1;
while (k <= s_o)
x = [Lout(1, k); Lout(2, k)];
N = voisinage(x, nrow, ncol);
i = x(1, 1);
j = x(2, 1);
b = 0;
for c = 1:1:4
if (phi(N(1, c), N(2, c)) > 0)
b = b+1;
end
end
if (b == 4)
% Suppression de x de Lout
if (k == 1)
Loutmod(:,1:s_o-1) = Lout(:, 2:s_o);
elseif (k == s_o)
Loutmod(:,1:s_o-1) = Lout(:, 1:s_o-1);
else
Loutmod(:,1:s_o-1) = [Lout(:, 1:k-1),Lout(:, k+1:s_o)];
end
k = k-1;
s_o = s_o-1;
Phimod(i, j) = 3;
Lout = Loutmod;
end
k = k+1;
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_chunmingli.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_chunmingli.m
| 12,415 |
utf_8
|
e5611a1a5cab20cb2692380b71eef4a4
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Description: This code implements the paper: "Minimization of
% Region-Scalable Fitting Energy for Image Segmentation." By Chunming Li.
%
% Coded by: Chunming Li
% E-mail: [email protected]
% URL: http://www.engr.uconn.edu/~cmli/
%------------------------------------------------------------------------
function [seg,phi,its] = creaseg_chunmingli(img,init_mask,max_its,length,regularization,scale,thresh,color,display)
%-- default value for parameter max_its is 100
if(~exist('max_its','var'))
max_its = 100;
end
%-- default value for parameter length is 1
if(~exist('length','var'))
length = 1;
end
%-- default value for parameter penalizing is 1
if(~exist('regularization','var'))
regularization = 1;
end
%-- default value for parameter scale is 1
if(~exist('scale','var'))
scale = 1;
end
%-- default value for parameter thresh is 0
if(~exist('thresh','var'))
thresh = 0;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
% init_mask = init_mask<=0;
%--
lambda1 = 1.0;
lambda2 = 1.0;
nu = length*255*255; % coefficient of the length term
%--
initialLSF = -init_mask.*4 + (1 - init_mask).*4;
phi = initialLSF;
%--
timestep = .1; % time step
mu = regularization; % coefficient of the level set (distance) regularization term P(\phi)
epsilon = 1.0; % the paramater in the definition of smoothed Dirac function
sigma = scale; % scale parameter in Gaussian kernel
% Note: A larger scale parameter sigma, such as sigma=10, would make the LBF algorithm more robust
% to initialization, but the segmentation result may not be as accurate as using
% a small sigma when there is severe intensity inhomogeneity in the image. If the intensity
% inhomogeneity is not severe, a relatively larger sigma can be used to increase the robustness of the LBF
% algorithm.
K = fspecial('gaussian',round(2*sigma)*2+1,sigma); % the Gaussian kernel
KI = conv2(img,K,'same'); % compute the convolution of the image with the Gaussian kernel outside the iteration
% See Section IV-A in the above IEEE TIP paper for implementation.
KONE = conv2(ones(size(img)),K,'same'); % compute the convolution of Gaussian kernel and constant 1 outside the iteration
% See Section IV-A in the above IEEE TIP paper for implementation.
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%--main loop
its = 0; stop = 0;
prev_mask = init_mask; c = 0;
while ((its < max_its) && ~stop)
%--
phi = LSE_LBF(phi,img,K,KI,KONE,nu,timestep,mu,lambda1,lambda2,epsilon,1);
new_mask = phi<=0;
c = convergence(prev_mask,new_mask,thresh,c);
if c <= 5
its = its + 1;
prev_mask = new_mask;
else stop = 1;
end
%-- intermediate output
if (display>0)
if ( mod(its,15)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(its,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
drawnow;
end
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
% LSE_LBF implements the level set evolution (LSE) for the method in Chunming Li et al's paper:
% "Minimization of Region-Scalable Fitting Energy for Image Segmentation",
% IEEE Trans. Image Processing(TIP), vol. 17 (10), pp.1940-1949, 2008.
%
% Author: Chunming Li, all rights reserved
% E-mail: [email protected]
% URL: http://www.engr.uconn.edu/~cmli/
% For easy understanding of my code, please read the comments in the code that refer
% to the corresponding equations in the above IEEE TIP paper.
% (Comments added by Ren Zhao at Univ. of Waterloo)
function phi = LSE_LBF(phi0,img,Ksigma,KI,KONE,nu,timestep,mu,lambda1,lambda2,epsilon,numIter)
phi = phi0;
for k1=1:numIter
phi = NeumannBoundCond(phi);
K = curvature_central(phi);
DrcU = (epsilon/pi)./(epsilon^2.+phi.^2); % eq.(9)
[f1,f2] = localBinaryFit(img,phi,KI,KONE,Ksigma,epsilon);
%-- compute lambda1*e1-lambda2*e2
s1 = lambda1.*f1.^2-lambda2.*f2.^2; % compute lambda1*e1-lambda2*e2 in the 1st term in eq. (15) in IEEE TIP 08
s2 = lambda1.*f1-lambda2.*f2;
dataForce = (lambda1-lambda2)*KONE.*img.*img+conv2(s1,Ksigma,'same')-2.*img.*conv2(s2,Ksigma,'same'); % eq.(15)
A = -DrcU.*dataForce; % 1st term in eq. (15)
P = mu*(4*del2(phi)-K); % 3rd term in eq. (15), where 4*del2(u) computes the laplacian (d^2u/dx^2 + d^2u/dy^2)
L = nu.*DrcU.*K; % 2nd term in eq. (15)
phi = phi+timestep*(L+P+A); % eq.(15)
end
%-- compute f1 and f2
function [f1,f2] = localBinaryFit(img,u,KI,KONE,Ksigma,epsilon)
Hu = 0.5*(1+(2/pi)*atan(u./epsilon)); % eq.(8)
I = img.*Hu;
c1 = conv2(Hu,Ksigma,'same');
c2 = conv2(I,Ksigma,'same'); % the numerator of eq.(14) for i = 1
f1 = c2./(c1); % compute f1 according to eq.(14) for i = 1
f2 = (KI-c2)./(KONE-c1); % compute f2 according to the formula in Section IV-A,
% which is an equivalent expression of eq.(14) for i = 2.
%-- Neumann boundary condition
function g = NeumannBoundCond(f)
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
%-- compute curvature
function k = curvature_central(u)
[ux,uy] = gradient(u);
normDu = sqrt(ux.^2+uy.^2+1e-10); % the norm of the gradient plus a small possitive number
% to avoid division by zero in the following computation.
Nx = ux./normDu;
Ny = uy./normDu;
nxx = gradient(Nx);
[junk,nyy] = gradient(Ny);
k = nxx+nyy; % compute divergence
% Convergence Test
function c = convergence(p_mask,n_mask,thresh,c)
diff = p_mask - n_mask;
n_diff = sum(abs(diff(:)));
if n_diff < thresh
c = c + 1;
else c = 0;
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_mouseMove.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_mouseMove.m
| 9,921 |
utf_8
|
dd456413178c29ef801a415ebf0b4a12
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_mouseMove(src,evt)
current_object = hittest;
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
if ( ~isempty(ud) )
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca,'CurrentPoint'));
if ( isempty(ud.imageId) )
if ( strcmp(get(current_object,'Type'), 'image') && ( ...
strcmp(get(current_object,'Tag'), 'mainImg') ) && ...
(pos(1,1)>0) && pos(1,2)>0 )
set(ud.txtPositionIntensity,'string',sprintf('x:%02d y:%02d i:NaN',pos(1,1),pos(1,2)),'foregroundcolor',[1 1 1]);
else
set(ud.txtPositionIntensity,'string','');
end
else
if ( ~isempty(fd.data) )
if ( ~strcmp(get(current_object,'Tag'),'pan') && strcmp(get(current_object,'Tag'),'mainImg') && ...
(pos(1,1)>0) && (pos(1,2)>0) && (pos(1,1)<=size(fd.data,2)) && ...
(pos(1,2)<=size(fd.data,1)) )
%-- check whether the pan button is pressed or not
if (get(ud.buttonAction(6),'background')~=[160/255 130/255 95/255])
%-- set mouse pointer to pointer if pointer mode is selected
if (get(ud.buttonAction(3),'background')==[160/255 130/255 95/255])
set(fig,'pointer','arrow');
%-- set mouse pointer to crosshair if drawing mode is selected
elseif (get(ud.buttonAction(1),'background')==[160/255 130/255 95/255])
set(fig,'pointer','crosshair');
%-- set mouse pointer to crosshair if drawing mode is selected
elseif (get(ud.handleAlgoComparison(17),'background')==[160/255 130/255 95/255])
set(fig,'pointer','crosshair');
else
set(fig,'pointer','arrow');
end
drawnow;
end
set(ud.txtPositionIntensity,'string',sprintf('x:%02d y:%02d i:%03.1f',pos(1,1),pos(1,2),fd.data(pos(1,2),pos(1,1))),'foregroundcolor',[1 1 1]);
else
%-- set mouse pointer to watch
set(fig,'pointer','arrow');
drawnow;
set(ud.txtPositionIntensity,'string','');
end
else
if ( strcmp(get(current_object,'Type'), 'image') && ( ...
strcmp(get(current_object,'Tag'), 'mainImg') ) && ...
(pos(1,1)>0) && pos(1,2)>0 )
set(ud.txtPositionIntensity,'string',sprintf('x:%02d y:%02d i:NaN',pos(1,1),pos(1,2)),'foregroundcolor',[1 1 1]);
else
set(ud.txtPositionIntensity,'string','');
end
end
end
end
% % % %-- Checking if a Reference contour is being drawn and if a point is currently being modified
% % % if ( strcmp(fd.method, 'Reference') && isfield(fd,'PtsRef') && ~isempty(fd.PtsRef) )
% % % % axes(get(ud.imageId,'parent'));
% % %
% % % ctr = fd.PtsRef;
% % % % ctr is a 3xn matrix containing the x and y coordinates and a flag to
% % % % know if the point is selected (position will be modified) or not
% % %
% % % m = find(fd.PtsRef(3,:) == 0,1); %-- Find the index of the first point whose flag is 0 (ie that is modified)
% % %
% % % if ~isempty(m)
% % % %--
% % % pos = floor(get(ud.gca(1),'CurrentPoint'));
% % % x = pos(1,1); y = pos(1,2);
% % %
% % % %-- Updating the point position (+ check bounds)
% % % ctr(1,m) = min( max( y, 1), size(fd.data,1) );
% % % ctr(2,m) = min( max( x, 1), size(fd.data,2) );
% % %
% % % method = (get(ud.handleAlgoComparison(19),'Value') - 1) * ud.Spline;
% % %
% % % %-- Deleting all the lines outside the function so that ud has not to be a parameter of the display function
% % % delete(findobj(get(ud.imageId(1),'parent'),'type','line'));
% % % show_contour(ctr, method, m);
% % % end
% % % end
% % %
% % % end
% % %
% % %
% % % %----- Display Function -----%
% % % function show_contour(ctr, method, m)
% % % if ~isempty(ctr)
% % % switch method
% % % case 0
% % % hold on;
% % % plot(ctr(2,:),ctr(1,:),'y--','Linewidth',3);
% % % tmp = ctr(:,1); tmp(:,end+1) = ctr(:,end);
% % % plot(tmp(2,:), tmp(1,:), 'y--', 'linewidth', 3);
% % % plot(ctr(2,:), ctr(1,:), 'or', 'linewidth', 2);
% % % hold off;
% % % case 1
% % % spline = cscvn([[ctr(2,:) ctr(2,1)]; [ctr(1,:) ctr(1,1)]]);
% % % hold on; fnplt(spline, 3, 'y--');
% % % plot(ctr(2,:), ctr(1,:), 'or', 'linewidth', 2);
% % % hold off;
% % % end
% % % %-- Plot in green the point that is selected
% % % hold on; plot(ctr(2,m),ctr(1,m),'og','Linewidth',2); hold off;
% % % end
% % %
|
github
|
jacksky64/imageProcessing-master
|
creaseg_cleanOverlays.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_cleanOverlays.m
| 6,644 |
utf_8
|
239fbd07c65e3a4d1dba76563a072b6c
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Description: This code implements the paper: "Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution." By Olivier Bernard.
%
% Coded by: Olivier Bernard (www.creatis.insa-lyon.fr/~bernard)
%------------------------------------------------------------------------
%------------------------------------------------------------------
function creaseg_cleanOverlays(keepLS)
%-- default value for parameter max_its is 100
if(~exist('keepLS','var'))
keepLS = 0;
end
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- refresh flag in case
fd.drawingManualFlag = 0;
fd.drawingMultiManualFlag = 0;
fd.drawingReferenceFlag = 0;
%-- clean all
if ( strcmp(fd.method,'Caselles') || strcmp(fd.method,'Chan & Vese') || ...
strcmp(fd.method,'Chunming Li') || strcmp(fd.method,'Lankton') || ...
strcmp(fd.method,'Bernard') || strcmp(fd.method,'Shi') || ...
strcmp(fd.method,'Personal') || strcmp(fd.method,'Reference') || ...
strcmp(fd.method,'Comparison') )
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
fd.method = [];
end
if ( size(fd.handleRect,2) > 0 )
for k=size(fd.handleRect,2):-1:1
delete(fd.handleRect{k});
fd.handleRect(k)=[];
end
end
if ( size(fd.handleElliRect,2) > 0 )
for k=size(fd.handleElliRect,2):-1:1
delete(fd.handleElliRect{k}(2));
fd.handleElliRect(k)=[];
end
end
if ( size(fd.handleManual,2) > 0 )
for k=size(fd.handleManual,2):-1:1
for l=1:size(fd.handleManual{k},1)
if ( fd.handleManual{k}(l) ~= 0 )
delete(fd.handleManual{k}(l));
end
end
fd.handleManual(k)=[];
fd.points = [];
end
end
if ( keepLS == 0 )
fd.levelset = zeros(size(fd.data));
end
set(ud.imageId,'userdata',fd);
%-- end clean all
|
github
|
jacksky64/imageProcessing-master
|
creaseg_drawMultiManualContours.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_drawMultiManualContours.m
| 8,867 |
utf_8
|
6efb32e8df6bfe08fa1b3acd6f26d2eb
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_drawMultiManualContours(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 )
return; %Clic outside the image => do nothing
end
if ( strcmp(get(ud.gcf,'SelectionType'),'normal') )
%-- Set drawingMultiManualFlag flag to 1
fd.drawingMultiManualFlag = 1;
%-- disable run and pointer buttons
set(ud.buttonAction(2),'enable','off');
set(ud.buttonAction(3),'enable','off');
%-- delete any overlay lines
if ( size(fd.handleManual,2)>0 )
for k=1:size(fd.handleManual{end},1)
if ( fd.handleManual{end}(k) ~= 0 )
delete(fd.handleManual{end}(k));
end
end
else
fd.handleManual{1} = 0;
end
%-- Get point coordinates
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
if (isempty(fd.points))
fd.points = [pt(1),pt(2)];
hold on; h = plot(pt(1), pt(2), 'oy', 'linewidth', 2);
fd.handleManual{end} = h;
else
fd.points(end+1,:) = [pt(1),pt(2)];
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if length(fd.points(:,1)) < 3
hold on; h1 = plot(fd.points(:,1),fd.points(:,2),'--','color',color{1},'Linewidth',2);
tmp = fd.points(1,:); tmp(end+1,:) = fd.points(end,:);
h2 = plot(tmp(:,1), tmp(:,2), 'y--', 'linewidth', 2);
else
[xs, ys] = creaseg_spline(fd.points(:,1)',fd.points(:,2)');
fin = find((xs == fd.points(end,1)) & (ys == fd.points(end,2)) );
deb = find((xs == fd.points(1,1)) & (ys == fd.points(1,2)) );
xs = xs([deb:end, 1:deb]); ys = ys([deb:end, 1:deb]);
if deb > fin
idx = length(xs) + fin - deb;
else
idx = fin - deb;
end
clear deb fin;
hold on; h1 = plot(xs(1:idx),ys(1:idx),'--','color',color{1},'Linewidth',2);
hold on; h2 = plot(xs(idx:end),ys(idx:end),'y--','Linewidth',2);
end
h3 = plot(fd.points(:,1), fd.points(:,2), 'oy', 'linewidth', 2);
hold off;
fd.handleManual{end} = [h1;h2;h3];
end
else %-- create final contour
%-- Set drawingMultiManualFlag flag to 1
fd.drawingMultiManualFlag = 1;
%-- enable run and pointer buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
set(ud.buttonAction(6),'enable','on');
%-- display final contour
if ( size(fd.points,1)>2 )
%-- delete any overlay lines
if ( size(fd.handleManual,2)>0 )
for k=1:size(fd.handleManual{end},1)
delete(fd.handleManual{end}(k));
end
end
%--
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if length(fd.points(:,1)) < 3
tmp = fd.points;
tmp(end+1,:) = tmp(1,:);
hold on; h = plot(tmp(:,1),tmp(:,2),'--','color',color{1},'Linewidth',2);
else
[xs, ys] = creaseg_spline(fd.points(:,1)',fd.points(:,2)');
hold on; h = plot(xs,ys,'--','color',color{1},'Linewidth',2);
end
fd.handleManual{end} = h;
%-- create manual mask
X = get(fd.handleManual{end},'X');
Y = get(fd.handleManual{end},'Y');
fd.levelset = xor(roipoly(fd.data,X,Y),fd.levelset);
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- prepare next contour
fd.handleManual{end+1} = 0;
end
fd.points = [];
end
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_show.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_show.m
| 4,785 |
utf_8
|
4342d9bf2b06bf50633bc90d41206b4b
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_show()
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- parameters
img = fd.visu;
imageId = ud.imageId;
s = size(img);
%-- show image
figure(fig);
set(imageId,'cdata',double(img),'xdata',1:s(2),'ydata',1:s(1));
set(get(imageId,'parent'),'xlim',[1 s(2)],'ylim',[1 s(1)]);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_loadimage.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_loadimage.m
| 8,808 |
utf_8
|
d10078f1fec9727042491dd7e1cc550e
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_loadimage(varargin)
if nargin == 1
fig = varargin{1};
else
fig = gcbf;
end
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%--
[fname,pname] = uigetfile('*.png;*.jpg;*.pgm;*.bmp;*.gif;*.tif;*.dcm;','Pick a file','multiselect','off','data/Image');
input_file = fullfile(pname,fname);
if ~exist(input_file,'file')
warning(['File: ' input_file ' does not exist']);
return;
else
[pathstr, name, ext] = fileparts(input_file);
end
try
if (ext=='.dcm')
img = dicomread(input_file);
info = dicominfo(input_file);
else
img = imread(input_file);
info = imfinfo(input_file);
end
catch
warning(['Could not load: ' input_file]);
return;
end
img = im2graydouble(img);
fd.data = img;
fd.visu = img;
fd.tagImage = 1;
fd.dimX = size(img,2);
fd.dimY = size(img,1);
fd.info = info;
if ( isfield(fd.info,'Width') )
set(ud.txtInfo1,'string',sprintf('width:%d pixels',fd.info.Width), 'color', [1 1 0]);
end
if ( isfield(fd.info,'Height') )
set(ud.txtInfo2,'string',sprintf('height:%d pixels',fd.info.Height));
end
if ( isfield(fd.info,'BitDepth') )
set(ud.txtInfo3,'string',sprintf('bit depth:%d',fd.info.BitDepth));
end
if ( isfield(fd.info,'XResolution') && (~isempty(fd.info.XResolution)) )
if ( isfield(fd.info,'ResolutionUnit') )
if ( strcmp(fd.info.ResolutionUnit,'meter') )
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f mm',fd.info.XResolution/1000));
elseif ( strcmp(fd.info.ResolutionUnit,'millimeter') )
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f mm',fd.info.XResolution));
else
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f',fd.info.XResolution));
end
else
set(ud.txtInfo4,'string',sprintf('XResolution:%f',fd.info.XResolution));
end
else
set(ud.txtInfo4,'string','');
end
if ( isfield(fd.info,'YResolution') && (~isempty(fd.info.YResolution)) )
if ( isfield(fd.info,'ResolutionUnit') )
if ( strcmp(fd.info.ResolutionUnit,'meter') )
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f mm',fd.info.YResolution/1000));
elseif ( strcmp(fd.info.ResolutionUnit,'millimeter') )
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f mm',fd.info.YResolution));
else
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f',fd.info.YResolution));
end
else
set(ud.txtInfo5,'string',sprintf('YResolution:%f',fd.info.XResolution));
end
else
set(ud.txtInfo5,'string','');
end
%-- reset drawing buttons selection
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- clean overlays before udating fd structure
creaseg_cleanOverlays();
%--
fd.levelset = zeros(size(fd.data));
fd.reference = zeros(size(fd.data));
fd.method = '';
set(ud.buttonAction,'background',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'background',[160/255 130/255 95/255]);
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
end
set(ud.handleAlgoConfig(end),'Visible','on');
set(ud.buttonAction(1),'background',[160/255 130/255 95/255]);
set(ud.handleAlgoComparison(16),'Enable','on');
set(ud.handleAlgoComparison(17),'Enable','on');
set(ud.handleAlgoComparison(24),'Enable','off');
%-- ATTACH FD AND UD STRUCTURE TO IMAGEID AND FIG HANDLES
set(ud.imageId,'userdata',fd);
%--
creaseg_show();
%------------------------------------------------------
%------------------------------------------------------
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if(isfloat(img)) % image is a double
if(c==3)
img = rgb2gray(uint8(img));
end
else % image is a int
if(c==3)
img = rgb2gray(img);
end
img = double(img);
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_chanvese.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_chanvese.m
| 12,585 |
utf_8
|
96f30e3263b583f69c472864aa955ad6
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Region Based Active Contour Segmentation
%
% seg = region_seg(I,init_mask,max_its,alpha,display)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% alpha (optional) Weight of smoothing term
% higer = smoother. default = 0.2
% display (optional) displays intermediate outputs
% default = true
%
% Outputs: seg Final segmentation mask (1=fg, 0=bg)
%
% Description: This code implements the paper: "Active Contours Without
% Edges" By Chan Vese. This is a nice way to segment images whose
% foregrounds and backgrounds are statistically different and homogeneous.
%
% Example:
% img = imread('tire.tif');
% m = zeros(size(img));
% m(33:33+117,44:44+128) = 1;
% seg = region_seg(img,m,500);
%
% Coded by: Shawn Lankton (www.shawnlankton.com)
%------------------------------------------------------------------------
function [seg,phi,its] = creaseg_chanvese(I,init_mask,max_its,alpha,thresh,color,display)
%-- default value for parameter alpha is .1
if(~exist('alpha','var'))
alpha = .2;
end
%-- default value for parameter thresh is 0
if(~exist('thresh','var'))
thresh = 0;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
%-- ensures image is 2D double matrix
I = im2graydouble(I);
% init_mask = init_mask<=0;
%-- Create a signed distance map (SDF) from mask
phi = mask2phi(init_mask);
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%--main loop
its = 0; stop = 0;
prev_mask = init_mask; c = 0;
while ((its < max_its) && ~stop)
idx = find(phi <= 1.2 & phi >= -1.2); % get the curve's narrow band
if ~isempty(idx)
%-- intermediate output
if (display>0)
if ( mod(its,50)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(its,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
drawnow;
end
end
%-- find interior and exterior mean
upts = find(phi<=0); % interior points
vpts = find(phi>0); % exterior points
u = sum(I(upts))/(length(upts)+eps); % interior mean
v = sum(I(vpts))/(length(vpts)+eps); % exterior mean
F = (I(idx)-u).^2-(I(idx)-v).^2; % force from image information
curvature = get_curvature(phi,idx); % force from curvature penalty
dphidt = F./max(abs(F)) + alpha*curvature; % gradient descent to minimize energy
%-- maintain the CFL condition
dt = .45/(max(abs(dphidt))+eps);
%-- evolve the curve
phi(idx) = phi(idx) + dt.*dphidt;
%-- Keep SDF smooth
phi = sussman(phi, .5);
new_mask = phi<=0;
c = convergence(prev_mask,new_mask,thresh,c);
if c <= 5
its = its + 1;
prev_mask = new_mask;
else stop = 1;
end
else
break;
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%-- converts a mask to a SDF
function phi = mask2phi(init_a)
phi=bwdist(init_a)-bwdist(1-init_a)+im2double(init_a)-.5;
%-- compute curvature along SDF
function curvature = get_curvature(phi,idx)
[dimy, dimx] = size(phi);
[y x] = ind2sub([dimy,dimx],idx); % get subscripts
%-- get subscripts of neighbors
ym1 = y-1; xm1 = x-1; yp1 = y+1; xp1 = x+1;
%-- bounds checking
ym1(ym1<1) = 1; xm1(xm1<1) = 1;
yp1(yp1>dimy)=dimy; xp1(xp1>dimx) = dimx;
%-- get indexes for 8 neighbors
idup = sub2ind(size(phi),yp1,x);
iddn = sub2ind(size(phi),ym1,x);
idlt = sub2ind(size(phi),y,xm1);
idrt = sub2ind(size(phi),y,xp1);
idul = sub2ind(size(phi),yp1,xm1);
idur = sub2ind(size(phi),yp1,xp1);
iddl = sub2ind(size(phi),ym1,xm1);
iddr = sub2ind(size(phi),ym1,xp1);
%-- get central derivatives of SDF at x,y
phi_x = -phi(idlt)+phi(idrt);
phi_y = -phi(iddn)+phi(idup);
phi_xx = phi(idlt)-2*phi(idx)+phi(idrt);
phi_yy = phi(iddn)-2*phi(idx)+phi(idup);
phi_xy = -0.25*phi(iddl)-0.25*phi(idur)...
+0.25*phi(iddr)+0.25*phi(idul);
phi_x2 = phi_x.^2;
phi_y2 = phi_y.^2;
%-- compute curvature (Kappa)
curvature = ((phi_x2.*phi_yy + phi_y2.*phi_xx - 2*phi_x.*phi_y.*phi_xy)./...
(phi_x2 + phi_y2 +eps).^(3/2)).*(phi_x2 + phi_y2).^(1/2);
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if(isfloat(img)) % image is a double
if(c==3)
img = rgb2gray(uint8(img));
end
else % image is a int
if(c==3)
img = rgb2gray(img);
end
img = double(img);
end
%-- level set re-initialization by the sussman method
function D = sussman(D, dt)
% forward/backward differences
a = D - shiftR(D); % backward
b = shiftL(D) - D; % forward
c = D - shiftD(D); % backward
d = shiftU(D) - D; % forward
a_p = a; a_n = a; % a+ and a-
b_p = b; b_n = b;
c_p = c; c_n = c;
d_p = d; d_n = d;
a_p(a < 0) = 0;
a_n(a > 0) = 0;
b_p(b < 0) = 0;
b_n(b > 0) = 0;
c_p(c < 0) = 0;
c_n(c > 0) = 0;
d_p(d < 0) = 0;
d_n(d > 0) = 0;
dD = zeros(size(D));
D_neg_ind = find(D < 0);
D_pos_ind = find(D > 0);
dD(D_pos_ind) = sqrt(max(a_p(D_pos_ind).^2, b_n(D_pos_ind).^2) ...
+ max(c_p(D_pos_ind).^2, d_n(D_pos_ind).^2)) - 1;
dD(D_neg_ind) = sqrt(max(a_n(D_neg_ind).^2, b_p(D_neg_ind).^2) ...
+ max(c_n(D_neg_ind).^2, d_p(D_neg_ind).^2)) - 1;
D = D - dt .* sussman_sign(D) .* dD;
%-- whole matrix derivatives
function shift = shiftD(M)
shift = shiftR(M')';
function shift = shiftL(M)
shift = [ M(:,2:size(M,2)) M(:,size(M,2)) ];
function shift = shiftR(M)
shift = [ M(:,1) M(:,1:size(M,2)-1) ];
function shift = shiftU(M)
shift = shiftL(M')';
function S = sussman_sign(D)
S = D ./ sqrt(D.^2 + 1);
% Convergence Test
function c = convergence(p_mask,n_mask,thresh,c)
diff = p_mask - n_mask;
n_diff = sum(abs(diff(:)));
if n_diff < thresh
c = c + 1;
else
c = 0;
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_bernard.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_bernard.m
| 21,003 |
utf_8
|
c1f942411317c2e73100a8dde1950761
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Description: This code implements the paper: "Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution." By Olivier Bernard.
%
% Coded by: Olivier Bernard (www.creatis.insa-lyon.fr/~bernard)
%------------------------------------------------------------------------
function [seg,phi,its] = creaseg_bernard(img,init_mask,max_its,scale,thresh,color,display)
%-- default value for parameter max_its is 1
if(~exist('max_its','var'))
max_its = 100;
end
%-- default value for parameter scale is 1
if(~exist('scale','var'))
scale = 1;
end
%-- default value for parameter thresh is 0
if(~exist('thresh','var'))
thresh = 0;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
%-- Ensures image is 2D double matrix
img = im2graydouble(img);
% init_mask = init_mask<=0;
%-- Take care that the scale is an integer value strictly lower that 5
scale = round(scale);
if ( scale > 4 )
scale = 4;
end
%-- Make sure that image is in correct dimension (multiple of scale)
[dimI,dimJ] = size(img);
dimIN = dimI; dimJN = dimJ;
val = power(2,scale);
diff = dimIN / val - fix( dimIN / val );
while ( diff ~= 0 )
dimIN = dimIN + 1;
diff = dimIN / val - fix( dimIN / val );
end
diff = dimJN / val - fix( dimJN / val );
while ( diff ~= 0 )
dimJN = dimJN + 1;
diff = dimJN / val - fix( dimJN / val );
end
imgN = repmat(0,[dimIN dimJN]);
imgN(1:size(img,1),1:size(img,2)) = img;
for i=(dimI+1):1:dimIN
imgN(i,1:dimJ) = img(end,:);
end
for j=(dimJ+1):1:dimJN
imgN(1:dimI,j) = img(:,end);
end
img = imgN;
clear imgN;
%-- Same for mask
init_maskN = repmat(0,[dimIN dimJN]);
init_maskN(1:size(init_mask,1),1:size(init_mask,2)) = init_mask;
init_mask = init_maskN;
clear init_maskN;
%-- Compute the corresponding bspline filter used for the comutation of
%-- the energy gradient from the Bslpine coefficients
if ( scale == 0 )
filter = [ 0.1667 0.6667 0.1667 ];
elseif ( scale == 1 )
filter = [ 0.0208 0.1667 0.4792 0.6667 0.4792 0.1667 0.0208 ];
elseif ( scale == 2 )
filter = [ 0.0026 0.0208 0.0703 0.1667 0.3151 0.4792 0.6120 ...
0.6667 0.6120 0.4792 0.3151 0.1667 0.0703 0.0208 0.0026 ];
elseif ( scale == 3 )
filter = [ 3.2552e-004 0.0026 0.0088 0.0208 0.0407 0.0703 0.1117 ...
0.1667 0.2360 0.3151 0.3981 0.4792 0.5524 0.6120 0.6520 ...
0.6667 0.6520 0.6120 0.5524 0.4792 0.3981 0.3151 0.2360 ...
0.1667 0.1117 0.0703 0.0407 0.0208 0.0088 0.0026 3.2552e-004 ];
elseif ( scale == 4 )
filter = [ 4.0690e-005 3.2552e-004 0.0011 0.0026 0.0051 0.0088 ...
0.0140 0.0208 0.0297 0.0407 0.0542 0.0703 0.0894 0.1117 ...
0.1373 0.1667 0.1997 0.2360 0.2747 0.3151 0.3565 0.3981 ...
0.4392 0.4792 0.5171 0.5524 0.5843 0.6120 0.6348 0.6520 ...
0.6629 0.6667 0.6629 0.6520 0.6348 0.6120 0.5843 0.5524 ...
0.5171 0.4792 0.4392 0.3981 0.3565 0.3151 0.2747 0.2360 ...
0.1997 0.1667 0.1373 0.1117 0.0894 0.0703 0.0542 0.0407 ...
0.0297 0.0208 0.0140 0.0088 0.0051 0.0026 0.0011 ...
3.2552e-004 4.0690e-005 ];
else
filter = 0;
end
%-- Create a signed distance map (SDF) from mask
phi = mask2phi(init_mask);
%-- Create BSpline coefficient image from phi
[bspline,phi] = Initialization(phi,scale);
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%--main loop
its = 0; stop = 0;
prev_mask = init_mask; c = 0;
[u,v,NRJ] = MinimizedFromFeatureParameters(0,0,phi,img,bitmax); % Initializing u, v, NRJ
while ((its < max_its) && ~stop)
%-- Minimized energy from the BSpline coefficients
[u,v,phi,bspline,img,NRJ] = ...
MinimizedFromBSplineCoefficients(u,v,phi,bspline,img,NRJ,filter,scale);
new_mask = phi<=0;
c = convergence(prev_mask,new_mask,thresh,c);
if c <= 5
its = its + 1;
prev_mask = new_mask;
else stop = 1;
end
%-- intermediate output
if (display>0)
if ( mod(its,1)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(its,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
drawnow;
end
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
phi = phi(1:dimI,1:dimJ);
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%-- converts a mask to a SDF
function phi = mask2phi(init_a)
phi=bwdist(init_a)-bwdist(1-init_a)+im2double(init_a)-.5;
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if(isfloat(img)) % image is a double
if(c==3)
img = rgb2gray(uint8(img));
end
else % image is a int
if(c==3)
img = rgb2gray(img);
end
img = double(img);
end
%-- Converts image to BSpline coeffcients image (double)
function BSpline = ConvertImageToBSpline(BSpline)
for i=1:1:size(BSpline,1)
BSpline(i,:) = ConvertSignalToBSpline(BSpline(i,:));
end
for j=1:1:size(BSpline,2)
BSpline(:,j) = ConvertSignalToBSpline(BSpline(:,j));
end
%-- Converts Signal to BSpline coefficients signal(double)
function BSpline = ConvertSignalToBSpline(BSpline)
z = sqrt(3)-2;
lambda = (1-z)*(1-1/z);
BSpline = lambda*BSpline;
BSpline(1) = GetInitialCausalCoefficient(BSpline,z);
for n=2:1:length(BSpline)
BSpline(n) = BSpline(n) + z * BSpline(n-1);
end
BSpline(end) = (z * BSpline(end-1) + BSpline(end)) * z / (z * z - 1);
for n=(length(BSpline)-1):-1:1
BSpline(n) = z * ( BSpline(n+1) - BSpline(n) );
end
%-- Compute first BSpline coefficients signal (double)
function val = GetInitialCausalCoefficient(BSpline,z)
len = length(BSpline);
tolerance = 1e-6;
z1 = z;
zn = power(z,len-1);
sum = BSpline(1) + zn * BSpline(end);
horizon = 2 + round( log(tolerance) / log(abs(z)));
if ( horizon > len )
horizon = len;
end
zn = zn * zn;
for n=2:1:horizon
zn = zn / z;
sum = sum + (z1 + zn) * BSpline(n);
z1 = z1 * z;
end
val = sum / (1-power(z,2*len-2));
%-- Converts BSpline coeffcients image to image (double)
function Image = ConvertBSplineToImage(Image)
for i=1:1:size(Image,1)
Image(i,:) = ConvertBSplineToSignal(Image(i,:));
end
for j=1:1:size(Image,2)
Image(:,j) = ConvertBSplineToSignal(Image(:,j));
end
%-- Converts BSpline coeffcients signal to signal (double)
function Signal = ConvertBSplineToSignal(BSpline)
len = length(BSpline);
Signal = zeros(size(BSpline));
kernelFilter = [4/6 1/6];
Signal(1) = BSpline(1) * kernelFilter(1) + 2 * BSpline(2) * kernelFilter(2);
for n=2:1:(len-1)
Signal(n) = BSpline(n) * kernelFilter(1) + ...
BSpline(n-1) * kernelFilter(2) + BSpline(n+1) * kernelFilter(2);
end
Signal(end) = BSpline(end) * kernelFilter(1) + 2 * BSpline(end-1) * kernelFilter(2);
%-- Create initial BSpline coefficients from phi with normalization procedure
function [bspline,phi] = Initialization(phi,scale)
phiDown = imresize(phi,1/power(2,scale));
bspline = ConvertImageToBSpline(phiDown);
Linf = max(abs(bspline(:)));
bspline = 3 * bspline / Linf;
phiDown = ConvertBSplineToImage(bspline);
phi = imresize(phiDown,power(2,scale));
%-- Minimized energy from the feature parameters
function [u,v,NRJ] = MinimizedFromFeatureParameters(u,v,phi,img,NRJ)
%-- Compute new feature parameters
un = sum( img(:) .* heavyside(phi(:)) ) / sum( heavyside(phi(:)) );
vn = sum( img(:) .* ( 1 - heavyside(phi(:)) ) ) / sum( 1 - heavyside(phi(:)) );
NewNRJ = sum( (img(:)-un).^2 .* heavyside(phi(:)) + (img(:)-vn).^2 .* (1-heavyside(phi(:))) );
%-- Update feature parameters
if ( NewNRJ < NRJ )
u = un;
v = vn;
NRJ = NewNRJ;
end
%-- Compute the regularized heaviside function
function y = heavyside(x)
epsilon = 0.5;
y = 0.5 * ( 1 + (2/pi) * atan(x/epsilon) );
%-- Compute the regularized dirac function
function y = dirac(x)
epsilon = 0.5;
y = (1/(pi*epsilon)) ./ ( 1 + (x/epsilon).^2 );
%-- Minimized energy from the BSpline coefficients
function [u,v,phi,bspline,img,NRJ] = ...
MinimizedFromBSplineCoefficients(u,v,phi,bspline,img,NRJ,filter,scale)
%-- Compute energy gradient image
feature = ( (img-u).^2 - (img-v).^2 ) .* dirac(phi);
valMax = max(abs(feature(:)));
feature = feature / valMax;
grad = ComputeGradientEnergyFromBSpline(feature,filter,scale);
%-- Compute Gradient descent with feedback adjustement
nbItMax = 5;
diffNRJ = 1;
it = 0;
mu = 1.5;
while ( ( diffNRJ > 0 ) && ( it < nbItMax ) )
%-- Update mu and it
it = it + 1;
mu = mu / 1.5;
%-- Compute new BSpline values
bspline_new = bspline - mu*grad;
Linf = max(abs(bspline_new(:)));
bspline_new = 3 * bspline_new / Linf;
%-- Compute the corresponding Levelset
phi_new = MultiscaleUpSampling(bspline_new,scale);
%-- Compute the corresponding energy value
[u_new,v_new,NRJ_new] = MinimizedFromFeatureParameters(u,v,phi_new,img,NRJ);
% Update diffNRJ value
diffNRJ = NRJ_new - NRJ;
end
if ( diffNRJ < 0 )
bspline = bspline_new;
phi = phi_new;
NRJ = NRJ_new;
u = u_new;
v = v_new;
end
%-- Compute the energy gradient form the Bspline taking into account the
%-- scaling factor
function grad = ComputeGradientEnergyFromBSpline(feature,filter,scale)
nI = size(feature,1);
nJ = size(feature,2);
nIScale = nI / power(2,scale);
nJScale = nJ / power(2,scale);
tmp = zeros(nIScale,nJ);
grad = zeros(nIScale,nJScale);
for j=1:1:nJ
tmp(:,j) = GetMultiscaleConvolution(feature(:,j),filter,scale);
end
for i=1:1:nIScale
vec = GetMultiscaleConvolution(tmp(i,:),filter,scale);
grad(i,:) = vec;
end
%-- Compute the energy gradient form the Bspline taking into account the
%-- scaling factor for a signal
function out = GetMultiscaleConvolution(in,filter,scale)
%-- parameters
scaleN = power(2,scale);
width = length(in);
widthScale = width / scaleN;
nx2 = 2 * width - 2;
size = scaleN * 4 - 1;
index = zeros(1,size);
out = zeros(1,widthScale);
%-- main loop
for n=0:1:(widthScale-1)
%-- Compute indexes
x = n * scaleN;
i = round(floor(x)) - floor(size/2);
for k=0:1:(size-1)
index(k+1) = i;
i = i + 1;
end
%-- Apply the anti-mirror boundary conditions
subImage = zeros(1,size);
for k=0:1:(size-1)
m = index(k+1);
if ( (m>=0) && (m<width) )
subImage(k+1) = in(m+1);
elseif (m>=width)
subImage(k+1) = 2*in(width)-in(nx2-m+2);
elseif (m<0)
subImage(k+1) = 2*in(1)-in(-m+1);
end
end
%-- Compute value
w = 0;
for k=0:1:(size-1)
w = w + filter(k+1) * subImage(k+1);
end
out(n+1) = w;
end
%-- Upsample by a factor of power(2,h)
function output = MultiscaleUpSampling(input,h)
dimI = size(input,1);
dimJ = size(input,2);
scaleDimI = dimI * power(2,h);
scaleDimJ = dimJ * power(2,h);
output = zeros(scaleDimI,scaleDimJ);
%-- Initialization
nx2 = 2 * dimI - 2;
ny2 = 2 * dimJ - 2;
scale = power(2,h);
xIndex = zeros(1,4);
yIndex = zeros(1,4);
xWeight = zeros(1,4);
yWeight = zeros(1,4);
subImage = zeros(4,4);
%-- Compute the sampled image
for u=0:1:(scaleDimI-1)
for v=0:1:(scaleDimJ-1)
%-- Initialization
x = u / scale;
y = v / scale;
%-- Compute the interpolation indexes
i = floor(x) - 1;
j = floor(y) - 1;
for k=0:1:3
xIndex(k+1) = i;
yIndex(k+1) = j;
i = i + 1;
j = j + 1;
end
%-- Compute the interpolation weights
%-- x --%
w = x - xIndex(2);
xWeight(4) = (1.0 / 6.0) * w * w * w;
xWeight(1) = (1.0 / 6.0) + (1.0 / 2.0) * w * (w - 1.0) - xWeight(4);
xWeight(3) = w + xWeight(1) - 2.0 * xWeight(4);
xWeight(2) = 1.0 - xWeight(1) - xWeight(3) - xWeight(4);
%-- y --%
w = y - yIndex(2);
yWeight(4) = (1.0 / 6.0) * w * w * w;
yWeight(1) = (1.0 / 6.0) + (1.0 / 2.0) * w * (w - 1.0) - yWeight(4);
yWeight(3) = w + yWeight(1) - 2.0 * yWeight(4);
yWeight(2) = 1.0 - yWeight(1) - yWeight(3) - yWeight(4);
%-- Apply the anti-mirror boundary conditions
for k=0:1:3
m = xIndex(k+1);
for l=0:1:3
n = yIndex(l+1);
if ( (m>=0) && (m<dimI) )
if ( (n>=0) && (n<dimJ) )
subImage(k+1,l+1) = input(m+1,n+1);
elseif (n>=dimJ)
subImage(k+1,l+1) = 2*(input(m+1,dimJ))-input(m+1,ny2-n+2);
elseif (n<0)
subImage(k+1,l+1) = 2*(input(m+1,n+2))-input(m+1,n+3);
end
elseif (m>=dimI)
if ( (n>=0) && (n<dimJ) )
subImage(k+1,l+1) = 2*(input(dimI,n+1))-input(nx2-m+2,n+1);
elseif (n>=dimJ)
subImage(k+1,l+1) = 2*(input(dimI,dimJ))-input(nx2-m+2,ny2-n+2);
elseif (n<0)
subImage(k+1,l+1) = 2*(input(dimI,n+2))-input(nx2-m+2,n+3);
end
elseif (m<0)
if ( (n>=0) && (n<dimJ) )
subImage(k+1,l+1) = 2*(input(m+2,n+1))-input(m+3,n+1);
elseif (n>=dimJ)
subImage(k+1,l+1) = 2*(input(m+2,dimJ))-input(m+3,ny2-n+2);
elseif (n<0)
subImage(k+1,l+1) = 2*(input(m+2,n+2))-input(m+3,n+3);
end
end
end
end
%-- perform interpolation
val = 0;
for k=0:1:3
w = 0;
for l=0:1:3
w = w + xWeight(l+1) * subImage(l+1,k+1);
end
val = val + yWeight(k+1) * w;
end
output(u+1,v+1) = val;
end
end
% Convergence Test
function c = convergence(p_mask,n_mask,thresh,c)
diff = p_mask - n_mask;
n_diff = sum(abs(diff(:)));
if n_diff < thresh
c = c + 1;
else c = 0;
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_caselles.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_caselles.m
| 13,230 |
utf_8
|
6cfc1c6a7f5ac2f69a422609ce2fec7e
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
%------------------------------------------------------------------------
% Description: This code implements the paper: "Geodesic active contours.
% International Journal of Computer Vision." By Vincent Caselles.
%
% Coded by: Olivier Bernard (www.creatis.insa-lyon.fr/~bernard)
%------------------------------------------------------------------------
function [seg,phi,its] = creaseg_caselles(img,init_mask,max_its,propag,thresh,color,display)
%-- default value for parameter img and init_mask
if(~exist('img','var'))
img = imread('data/Image/simu1.bmp');
init_mask = repmat(0,[size(img,1) size(img,2)]);
init_mask(round(size(img,1)/3):size(img,1)-round(size(img,1)/3),...
round(size(img,2)/3):size(img,2)-round(size(img,2)/3)) = 1;
end
%-- default value for parameter max_its is 100
if(~exist('max_its','var'))
max_its = 100;
end
%-- default value for parameter propag is 1
if(~exist('propag','var'))
propag = 1;
end
%-- default value for parameter max_its is 1
if(~exist('thresh','var'))
thresh = 0;
end
%-- default value for parameter color is 'r'
if(~exist('color','var'))
color = 'r';
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
%-- ensures image is 2D double matrix
img = im2graydouble(img);
% init_mask = init_mask<=0;
%-- Create a signed distance map (SDF) from mask
phi = mask2phi(init_mask);
%-- Compute feature image from gradient information
h = fspecial('gaussian',[5 5],1);
feature = imfilter(img,h,'same');
[FX,FY] = gradient(feature);
feature = sqrt(FX.^2+FY.^2+eps);
feature = 1 ./ ( 1 + feature.^2 );
%--
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
%--main loop
its = 0; stop = 0;
prev_mask = init_mask; c = 0;
while ((its < max_its) && ~stop)
idx = find(phi <= 1.2 & phi >= -1.2); %-- get the curve's narrow band
if ~isempty(idx)
%-- intermediate output
if (display>0)
if ( mod(its,50)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
showCurveAndPhi(phi,ud,color);
drawnow;
end
else
if ( mod(its,10)==0 )
set(ud.txtInfo1,'string',sprintf('iteration: %d',its),'color',[1 1 0]);
drawnow;
end
end
%-- force from image information
F = feature(idx);
[curvature,normGrad,FdotGrad] = ...
get_evolution_functions(phi,feature,idx); % force from curvature penalty
%-- gradient descent to minimize energy
dphidt1 = F.*curvature.*normGrad;
dphidt1 = dphidt1./max(abs(dphidt1(:)));
dphidt2 = FdotGrad;
dphidt2 = dphidt2./max(abs(dphidt2(:)));
dphidt3 = F.*normGrad;
dphidt3 = dphidt3./max(abs(dphidt3(:)));
dphidt = dphidt1 + dphidt2 - propag*dphidt3;
%-- maintain the CFL condition
dt = .45/(max(abs(dphidt))+eps);
%-- evolve the curve
phi(idx) = phi(idx) + dt.*dphidt;
%-- Keep SDF smooth
phi = sussman(phi, .5);
new_mask = phi<=0;
c = convergence(prev_mask,new_mask,thresh,c);
if c <= 5
its = its + 1;
prev_mask = new_mask;
else stop = 1;
end
else
break;
end
end
%-- final output
showCurveAndPhi(phi,ud,color);
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(phi,ud,cl)
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(phi,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%-- converts a mask to a SDF
function phi = mask2phi(init_a)
phi=bwdist(init_a)-bwdist(1-init_a)+im2double(init_a)-.5;
%-- compute curvature along SDF
function [curvature,normGrad,FdotGrad] = get_evolution_functions(phi,feature,idx)
[dimy, dimx] = size(phi);
[y x] = ind2sub([dimy,dimx],idx); % get subscripts
%-- get subscripts of neighbors
ym1 = y-1; xm1 = x-1; yp1 = y+1; xp1 = x+1;
%-- bounds checking
ym1(ym1<1) = 1; xm1(xm1<1) = 1;
yp1(yp1>dimy)=dimy; xp1(xp1>dimx) = dimx;
%-- get indexes for 8 neighbors
idup = sub2ind(size(phi),yp1,x);
iddn = sub2ind(size(phi),ym1,x);
idlt = sub2ind(size(phi),y,xm1);
idrt = sub2ind(size(phi),y,xp1);
idul = sub2ind(size(phi),yp1,xm1);
idur = sub2ind(size(phi),yp1,xp1);
iddl = sub2ind(size(phi),ym1,xm1);
iddr = sub2ind(size(phi),ym1,xp1);
%-- get central derivatives of SDF at x,y
phi_x = (-phi(idlt)+phi(idrt))/2;
phi_y = (-phi(iddn)+phi(idup))/2;
phi_xx = phi(idlt)-2*phi(idx)+phi(idrt);
phi_yy = phi(iddn)-2*phi(idx)+phi(idup);
phi_xy = 0.25*phi(iddl)+0.25*phi(idur)...
-0.25*phi(iddr)-0.25*phi(idul);
phi_x2 = phi_x.^2;
phi_y2 = phi_y.^2;
%-- compute curvature (Kappa)
curvature = ((phi_x2.*phi_yy + phi_y2.*phi_xx - 2*phi_x.*phi_y.*phi_xy)./...
(phi_x2 + phi_y2 +eps).^(3/2));
%-- compute norm of gradient
phi_xm = phi(idx)-phi(idlt);
phi_xp = phi(idrt)-phi(idx);
phi_ym = phi(idx)-phi(iddn);
phi_yp = phi(idup)-phi(idx);
normGrad = sqrt( (max(phi_xm,0)).^2 + (min(phi_xp,0)).^2 + ...
(max(phi_ym,0)).^2 + (min(phi_yp,0)).^2 );
%-- compute scalar product between the feature image and the gradient of phi
F_x = 0.5*feature(idrt)-0.5*feature(idlt);
F_y = 0.5*feature(idup)-0.5*feature(iddn);
FdotGrad = (max(F_x,0)).*(phi_xp) + (min(F_x,0)).*(phi_xm) + ...
(max(F_y,0)).*(phi_yp) + (min(F_y,0)).*(phi_ym);
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if (isfloat(img))
if (c==3)
img = rgb2gray(uint8(img));
end
else
if (c==3)
img = rgb2gray(img);
end
img = double(img);
end
%-- level set re-initialization by the sussman method
function D = sussman(D, dt)
% forward/backward differences
a = D - shiftR(D); % backward
b = shiftL(D) - D; % forward
c = D - shiftD(D); % backward
d = shiftU(D) - D; % forward
a_p = a; a_n = a; % a+ and a-
b_p = b; b_n = b;
c_p = c; c_n = c;
d_p = d; d_n = d;
a_p(a < 0) = 0;
a_n(a > 0) = 0;
b_p(b < 0) = 0;
b_n(b > 0) = 0;
c_p(c < 0) = 0;
c_n(c > 0) = 0;
d_p(d < 0) = 0;
d_n(d > 0) = 0;
dD = zeros(size(D));
D_neg_ind = find(D < 0);
D_pos_ind = find(D > 0);
dD(D_pos_ind) = sqrt(max(a_p(D_pos_ind).^2, b_n(D_pos_ind).^2) ...
+ max(c_p(D_pos_ind).^2, d_n(D_pos_ind).^2)) - 1;
dD(D_neg_ind) = sqrt(max(a_n(D_neg_ind).^2, b_p(D_neg_ind).^2) ...
+ max(c_n(D_neg_ind).^2, d_p(D_neg_ind).^2)) - 1;
D = D - dt .* sussman_sign(D) .* dD;
%-- whole matrix derivatives
function shift = shiftD(M)
shift = shiftR(M')';
function shift = shiftL(M)
shift = [ M(:,2:size(M,2)) M(:,size(M,2)) ];
function shift = shiftR(M)
shift = [ M(:,1) M(:,1:size(M,2)-1) ];
function shift = shiftU(M)
shift = shiftL(M')';
function S = sussman_sign(D)
S = D ./ sqrt(D.^2 + 1);
% Convergence Test
function c = convergence(p_mask,n_mask,thresh,c)
diff = p_mask - n_mask;
n_diff = sum(abs(diff(:)));
if n_diff < thresh
c = c + 1;
else c = 0;
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_drawMultiReferenceContours.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_drawMultiReferenceContours.m
| 11,499 |
utf_8
|
5403388b94e5f8f3962a6ca277f2844c
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_drawMultiReferenceContours(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 )
return; %Clic outside the image => do nothing
end
if ( strcmp(get(ud.gcf,'SelectionType'),'normal') )
%-- Set drawingReferenceFlag flag to 1
fd.drawingReferenceFlag = 1;
%-- disable drawing, run and pointer buttons
set(ud.buttonAction(1),'enable','off');
set(ud.buttonAction(2),'enable','off');
set(ud.buttonAction(3),'enable','off');
%-- delete any overlay lines
if ( size(fd.handleReference,2)>0 )
for k=1:size(fd.handleReference{1},1)
if ( fd.handleReference{1}(k) ~= 0 )
delete(fd.handleReference{1}(k));
end
end
else
fd.handleReference{1} = 0;
end
%-- Get point coordinates
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
if (isempty(fd.pointsRef))
fd.pointsRef = [pt(1),pt(2)];
hold on; h = plot(pt(1), pt(2), 'oy', 'linewidth', 2);
fd.handleReference{1} = h;
else
fd.pointsRef(end+1,:) = [pt(1),pt(2)];
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
switch (get(ud.handleAlgoComparison(19),'value')-1)
case 0
hold on; h1 = plot(fd.pointsRef(:,1),fd.pointsRef(:,2),'--','color',color{1},'Linewidth',2);
tmp = fd.pointsRef(1,:); tmp(end+1,:) = fd.pointsRef(end,:);
h2 = plot(tmp(:,1), tmp(:,2), 'y--', 'linewidth', 2);
case 1
if length(fd.pointsRef(:,1)) < 3
hold on; h1 = plot(fd.pointsRef(:,1),fd.pointsRef(:,2),'--','color',color{1},'Linewidth',2);
tmp = fd.pointsRef(1,:); tmp(end+1,:) = fd.pointsRef(end,:);
h2 = plot(tmp(:,1), tmp(:,2), 'y--', 'linewidth', 2);
else
[xs, ys] = creaseg_spline(fd.pointsRef(:,1)',fd.pointsRef(:,2)');
fin = find((xs == fd.pointsRef(end,1)) & (ys == fd.pointsRef(end,2)) );
deb = find((xs == fd.pointsRef(1,1)) & (ys == fd.pointsRef(1,2)) );
xs = xs([deb:end, 1:deb]); ys = ys([deb:end, 1:deb]);
if deb > fin
idx = length(xs) + fin - deb;
else
idx = fin - deb;
end
clear deb fin;
hold on; h1 = plot(xs(1:idx),ys(1:idx),'--','color',color{1},'Linewidth',2);
hold on; h2 = plot(xs(idx:end),ys(idx:end),'y--','Linewidth',2);
end
end
h3 = plot(fd.pointsRef(:,1), fd.pointsRef(:,2), 'oy', 'linewidth', 2);
hold off;
fd.handleReference{1} = [h1;h2;h3];
end
elseif ( strcmp(get(ud.gcf,'SelectionType'),'alt') ) %-- create final contour
%-- Set drawingReferenceFlag flag to 1
fd.drawingReferenceFlag = 1;
%-- enable drawing, run and pointer buttons
set(ud.buttonAction(1),'enable','on');
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
%-- display final contour
if ( size(fd.pointsRef,1)>2 )
%-- delete any overlay lines
if ( size(fd.handleReference,2)>0 )
for k=1:size(fd.handleReference{1},1)
delete(fd.handleReference{1}(k));
end
end
%--
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
switch (get(ud.handleAlgoComparison(19),'value') - 1)
case 0
tmp = fd.pointsRef;
tmp(end+1,:) = tmp(1,:);
hold on; h = plot(tmp(:,1),tmp(:,2),'color',color{1},'Linewidth',2); hold off;
case 1
if length(fd.pointsRef(:,1)) < 3
tmp = fd.pointsRef;
tmp(end+1,:) = tmp(1,:);
hold on; h = plot(tmp(:,1),tmp(:,2),'--','color',color{1},'Linewidth',2);
else
[xs, ys] = creaseg_spline(fd.pointsRef(:,1)',fd.pointsRef(:,2)');
hold on; h = plot(xs,ys,'--','color',color{1},'Linewidth',2);
end
end
fd.handleReference{1} = h;
%-- create manual mask
X = get(fd.handleReference{1},'X');
Y = get(fd.handleReference{1},'Y');
fd.reference = xor(roipoly(fd.data,X,Y),fd.reference);
%-- prepare next contour
fd.handleReference{1} = 0;
end
fd.pointsRef = [];
else %-- save reference
%-- Set drawingReferenceFlag flag to 1
fd.drawingReferenceFlag = 0;
[filename, pathname] = uiputfile({'*.mat', 'All .mat Files'; ...
'*.*', 'All Files (*.*)'}, 'Save as', 'data/Reference');
%-- if ok, then save reference
if not(isequal(filename,0) || isequal(pathname,0))
%-- save reference as a mat file
save_file = fullfile(pathname, filename);
refLSF = fd.reference;
save(save_file,'refLSF');
%-- Put the "Create" button in bright color
set(ud.handleAlgoComparison(17),'BackgroundColor',[240/255 173/255 105/255]);
%-- Dislpay confirmation message
set(ud.txtInfo1,'string','Reference has been succesfully saved','color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
else
%-- otherwise clean up everything
set(ud.txtInfo1,'string','Reference has not been saved','color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
%-- Put the "Create" button in bright color
set(ud.handleAlgoComparison(17),'BackgroundColor',[240/255 173/255 105/255]);
%-- reinitialize fd.reference
fd.reference = zeros(size(fd.data));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
end
end
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_managedrawing.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_managedrawing.m
| 28,116 |
utf_8
|
aceff7a2d216a6673f1d67b174d1c4d7
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_managedrawing(src,evt,type)
%-- parameters
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(3),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(6),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(1),'background',[160/255 130/255 95/255]);
pan off;
%-- switch case
if ( type == 1 ) %-- first button: draw rectangle
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+1),'BackgroundColor',[160/255 130/255 95/255]);
%-- enable run, pointer and pan buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
set(ud.buttonAction(6),'enable','on');
%--
displayDrawingInfo(ud,isempty(fd.data),1);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@startdragrectangle});
set(ud.gcf,'WindowButtonUpFcn',{@stopdragrectangle});
elseif ( type == 2 ) %-- second button draw multi-rectangles
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+2),'BackgroundColor',[160/255 130/255 95/255]);
%-- enable run, pointer and pan buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
set(ud.buttonAction(6),'enable','on');
%--
displayDrawingInfo(ud,isempty(fd.data),4);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@startdragmultirectangle});
set(ud.gcf,'WindowButtonUpFcn',{@stopdragmultirectangle});
elseif ( type == 3 ) %-- thrid button: draw ellipse
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+3),'BackgroundColor',[160/255 130/255 95/255]);
%-- enable run, pointer and pan buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
set(ud.buttonAction(6),'enable','on');
%--
displayDrawingInfo(ud,isempty(fd.data),2);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@startdragellipse});
set(ud.gcf,'WindowButtonUpFcn',{@stopdragellipse});
elseif ( type == 4 ) %-- forth button: draw multi-ellipse
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+4),'BackgroundColor',[160/255 130/255 95/255]);
%-- enable run, pointer and pan buttons
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
set(ud.buttonAction(6),'enable','on');
%--
displayDrawingInfo(ud,isempty(fd.data),5);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@startdragmultiellipse});
set(ud.gcf,'WindowButtonUpFcn',{@stopdragmultiellipse});
elseif ( type == 5 ) %-- fith button: draw manual contour
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+5),'BackgroundColor',[160/255 130/255 95/255]);
%-- disable run and pointer buttons
set(ud.buttonAction(2),'enable','off');
set(ud.buttonAction(3),'enable','off');
%--
displayDrawingInfo(ud,isempty(fd.data),3);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawManualContour});
set(ud.gcf,'WindowButtonUpFcn','');
elseif ( type == 6 ) %-- sixth button: draw multi-manuals
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
set(ud.handleInit(2+6),'BackgroundColor',[160/255 130/255 95/255]);
%-- disable run and pointer buttons
set(ud.buttonAction(2),'enable','off');
set(ud.buttonAction(3),'enable','off');
%--
displayDrawingInfo(ud,isempty(fd.data),6);
%--
creaseg_cleanOverlays();
%--
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawMultiManualContours});
set(ud.gcf,'WindowButtonUpFcn','');
end
%------------------------------------------------------------------
function startdragrectangle(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 ) % Clic outside the image
return;
end
%-- clean rectangle overlay
if ( size(fd.handleRect,2)>0 )
delete(fd.handleRect{1});
fd.handleRect(1)=[];
end
%-- initialize rectangle display
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
h = rectangle('Position',[pt(1),pt(2),1,1],'Linewidth',1,'EdgeColor','y','LineStyle','--');
%-- save rectangle position
fd.handleRect{1} = h;
set(ud.imageId,'userdata',fd);
%-- initialize the interactive rectangle display
set(ud.gcf,'WindowButtonMotionFcn',{@dragrectangle,pt,h});
%------------------------------------------------------------------
function startdragmultirectangle(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 ) % Clic outside the image
return;
end
%-- initialize rectangle display
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
h = rectangle('Position',[pt(1),pt(2),1,1],'Linewidth',1,'EdgeColor','y','LineStyle','--');
%-- save rectangle position
fd.rect = [pt(1),pt(2),1,1];
fd.handleRect{end+1} = h;
set(ud.imageId,'userdata',fd);
%-- initialize the interactive rectangle display
set(ud.gcf,'WindowButtonMotionFcn',{@dragrectangle,pt,h});
%------------------------------------------------------------------
function dragrectangle(varargin)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pt1 = varargin{3}; h = varargin{4};
pt2 = get(ud.gca,'CurrentPoint');
pt2 = pt2(1,1:2);
%-- Check bounds
pt2(1) = min( max( pt2(1), 1), size(fd.data,2));
pt2(2) = min( max( pt2(2), 1), size(fd.data,1));
wp = abs(pt1(1)-pt2(1));
if ( wp == 0 )
wp = eps;
end
hp = abs(pt1(2)-pt2(2));
if ( hp == 0 )
hp = eps;
end
if ( (pt1(1)>pt2(1)) && (pt1(2)>pt2(2)) )
xp = pt2(1); yp = pt2(2);
elseif ( (pt1(1)>=pt2(1)) && (pt1(2)<=pt2(2)) )
xp = pt2(1); yp = pt1(2);
elseif ( (pt1(1)<=pt2(1)) && (pt1(2)>=pt2(2)) )
xp = pt1(1); yp = pt2(2);
else
xp = pt1(1); yp = pt1(2);
end
set(h,'Position',[xp,yp,wp,hp]);
%-- save rectangle position
fd.handleRect{end} = h;
set(ud.imageId,'userdata',fd);
%------------------------------------------------------------------
function stopdragrectangle(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if ~isempty(fd.handleRect)
set(fd.handleRect{1},'Linewidth',2,'EdgeColor',color{1},'LineStyle','-');
end
%-- create rectangle mask
if ( size(fd.handleRect,2) > 0 )
rect = get(fd.handleRect{1},'Position');
fd.levelset = roipoly(fd.data,[rect(1),rect(1),min(rect(1)+rect(3),size(fd.data,1)),min(rect(1)+rect(3),size(fd.data,1))]...
,[rect(2),min(rect(2)+rect(4),size(fd.data,2)),min(rect(2)+rect(4),size(fd.data,2)),rect(2)]);
end
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
%-- switch off the interactive rectangle mode
set(ud.gcf,'WindowButtonMotionFcn',{@creaseg_mouseMove});
%------------------------------------------------------------------
function stopdragmultirectangle(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- dislpay last final rectangle in "color"
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
if ~isempty(fd.handleRect)
set(fd.handleRect{end},'Linewidth',2,'EdgeColor',color{1},'LineStyle','-');
end
%-- create multirectangle mask
if ( size(fd.handleRect,2) > 0 )
rect = get(fd.handleRect{end},'Position');
fd.levelset = xor(roipoly(fd.data,[rect(1),rect(1),rect(1)+rect(3),rect(1)+rect(3)]...
,[rect(2),rect(2)+rect(4),rect(2)+rect(4),rect(2)]),fd.levelset);
end
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
%-- switch off the interactive rectangle mode
set(ud.gcf,'WindowButtonMotionFcn',{@creaseg_mouseMove});
%------------------------------------------------------------------
function startdragellipse(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 ) % Clic outside the image
if ( size(fd.handleElliRect,2)>0 )
delete(fd.handleElliRect{1}(2));
fd.handleElliRect(1)=[];
end
set(ud.imageId,'userdata',fd);
return;
end
%-- clean image overlay
if ( size(fd.handleElliRect,2)>0 )
delete(fd.handleElliRect{1}(2));
fd.handleElliRect(1)=[];
end
%-- initialize enclosing rectangle display
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
h1 = rectangle('Position',[pt(1),pt(2),1,1],'Linewidth',1,'EdgeColor','y','LineStyle','--');
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
hold on; h2 = plot(pt(1),pt(2),'-','color',color{1},'linewidth',2);
%-- save rectangle position
fd.handleElliRect{1} = [h1;h2];
set(ud.imageId,'userdata',fd);
%-- initialize the interactive rectangle display
set(ud.gcf,'WindowButtonMotionFcn',{@dragellipse,pt});
%------------------------------------------------------------------
function startdragmultiellipse(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = floor(get(ud.gca(1),'CurrentPoint'));
if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 ) % Clic outside the image
if ( size(fd.handleElliRect,2)>0 )
delete(fd.handleElliRect{1}(2));
fd.handleElliRect(1)=[];
end
set(ud.imageId,'userdata',fd);
return;
end
%-- initialize enclosing rectangle display
pt = get(ud.gca,'CurrentPoint');
pt = pt(1,1:2);
h1 = rectangle('Position',[pt(1),pt(2),1,1],'Linewidth',1,'EdgeColor','y','LineStyle','--');
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
hold on; h2 = plot(pt(1),pt(2),'-','color',color{1},'linewidth',2);
%-- save rectangle position
fd.handleElliRect{end+1} = [h1;h2];
set(ud.imageId,'userdata',fd);
%-- initialize the interactive rectangle display
set(ud.gcf,'WindowButtonMotionFcn',{@dragellipse,pt});
%------------------------------------------------------------------
function dragellipse(varargin)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%--
pt1 = varargin{3};
pt2 = get(ud.gca,'CurrentPoint');
pt2 = pt2(1,1:2);
wp = abs(pt1(1)-pt2(1));
if ( wp == 0 )
wp = eps;
end
hp = abs(pt1(2)-pt2(2));
if ( hp == 0 )
hp = eps;
end
if ( (pt1(1)>pt2(1)) && (pt1(2)>pt2(2)) )
xp = pt2(1); yp = pt2(2);
elseif ( (pt1(1)>=pt2(1)) && (pt1(2)<=pt2(2)) )
xp = pt2(1); yp = pt1(2);
elseif ( (pt1(1)<=pt2(1)) && (pt1(2)>=pt2(2)) )
xp = pt1(1); yp = pt2(2);
else
xp = pt1(1); yp = pt1(2);
end
set(fd.handleElliRect{end}(1),'Position',[xp,yp,wp,hp]);
%-- save rectangle position and draw embedded ellipse
[X,Y] = computeEllipse(xp,yp,wp,hp);
set(fd.handleElliRect{end}(2),'X',X,'Y',Y);
set(ud.imageId,'userdata',fd);
%------------------------------------------------------------------
function stopdragellipse(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- delete enclosing rectangle
if ( size(fd.handleElliRect,2)> 0 )
delete(fd.handleElliRect{end}(1));
end
%-- create ellipse mask
if ( size(fd.handleElliRect,2) > 0 )
X = get(fd.handleElliRect{1}(2),'X');
Y = get(fd.handleElliRect{1}(2),'Y');
fd.levelset = roipoly(fd.data,X,Y);
end
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
%-- switch off the interactive rectangle mode
set(ud.gcf,'WindowButtonMotionFcn',{@creaseg_mouseMove});
%------------------------------------------------------------------
function stopdragmultiellipse(src,evt)
%-- get structures
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- delete enclosing rectangle
if ( size(fd.handleElliRect,2)> 0 )
delete(fd.handleElliRect{end}(1));
end
%-- create multi-ellipse mask
if ( size(fd.handleElliRect,2) > 0 )
X = get(fd.handleElliRect{end}(2),'X');
Y = get(fd.handleElliRect{end}(2),'Y');
fd.levelset = xor(roipoly(fd.data,X,Y),fd.levelset);
end
%-- save initialization info
ud.LastPlot = 'levelset';
fd.method = 'Initial region';
%-- save structure
set(ud.imageId,'userdata',fd);
set(ud.gcf,'userdata',ud);
%-- switch off the interactive rectangle mode
set(ud.gcf,'WindowButtonMotionFcn',{@creaseg_mouseMove});
%------------------------------------------------------------------
%-- Draw Ellipse from rectangle info
function [X,Y] = computeEllipse(x,y,w,h)
%-- determine major axis
xa = [x+w/2,x+w/2];
ya = [y,y+h];
%-- determine minor axis
xi = [x,x+w];
yi = [y+h/2,y+h/2];
%-- determine centroid based on major axis selection
x0 = mean(xa);
y0 = mean(ya);
%-- determine a and b from user input
a = sqrt(diff(xa)^2 + diff(ya)^2)/2;
b = sqrt(diff(xi)^2 + diff(yi)^2)/2;
%-- determine rho based on major axis selection
rho = atan(diff(xa)/diff(ya));
%-- prepare display
theta = [-0.03:0.01:2*pi];
%-- Parametric equation of the ellipse
%----------------------------------------
x = a*cos(theta);
y = b*sin(theta);
%-- Coordinate transform
%----------------------------------------
Y = cos(rho)*x - sin(rho)*y;
X = sin(rho)*x + cos(rho)*y;
X = X + x0;
Y = Y + y0;
% %------------------------------------------------------------------
% function drawMultiManualContours(src,evt)
%
% %-- get structures
% fig = findobj(0,'tag','creaseg');
% ud = get(fig,'userdata');
% fd = get(ud.imageId,'userdata');
%
% pos = floor(get(ud.gca(1),'CurrentPoint'));
% if ~( pos(1,1) < size(fd.data,2) && pos(1,1) > 1 && pos(1,2) < size(fd.data,1) && pos(1,2) >1 )
% return; %Clic outside the image => do nothing
% end
%
% if ( strcmp(get(ud.gcf,'SelectionType'),'normal') )
%
% %-- disable run, pointer and pan button
% set(ud.buttonAction(2),'enable','off');
% set(ud.buttonAction(3),'enable','off');
% set(ud.buttonAction(6),'enable','off');
%
% %-- delete any overlay lines
% if ( size(fd.handleManual,2)>0 )
% for k=1:size(fd.handleManual{end},1)
% if ( fd.handleManual{end}(k) ~= 0 )
% delete(fd.handleManual{end}(k));
% end
% end
% else
% fd.handleManual{1} = 0;
% end
%
% %-- Get point coordinates
% pt = get(ud.gca,'CurrentPoint');
% pt = pt(1,1:2);
% if (isempty(fd.points))
% fd.points = [pt(1),pt(2)];
% hold on; h = plot(pt(1), pt(2), 'oy', 'linewidth', 2);
% fd.handleManual{end} = h;
% else
% fd.points(end+1,:) = [pt(1),pt(2)];
% color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
%
% switch ud.Spline
% case 0
% hold on; h1 = plot(fd.points(:,1),fd.points(:,2),'--','color',color{1},'Linewidth',2);
% tmp = fd.points(1,:); tmp(end+1,:) = fd.points(end,:);
% h2 = plot(tmp(:,1), tmp(:,2), 'y--', 'linewidth', 2);
% case 1
% if size(fd.points,1) == 2
% spline = cscvn([[fd.points(:,2); fd.points(1,2)]'; [fd.points(:,1); fd.points(1,1)]']);
% A = fnplt(spline,'.');
% hold on; h2 = plot(A(2,:),A(1,:),'y--','Linewidth',2);
% h1 = [];
% else
% spline = cscvn([[fd.points(:,2); fd.points(1,2)]'; [fd.points(:,1); fd.points(1,1)]']);
% A = fnplt(spline,'.');
% a = find(A(2,:) == fd.points(end,1),1);
% hold on; h1 = plot(A(2,1:a),A(1,1:a),'--','color',color{1},'Linewidth',2);
% h2 = plot(A(2,a:end),A(1,a:end),'y--','Linewidth',2);
% end
% end
%
% h3 = plot(fd.points(:,1), fd.points(:,2), 'oy', 'linewidth', 2);
% hold off;
% fd.handleManual{end} = [h1;h2;h3];
% end
%
%
% else %-- create final contour
%
% %-- enable run, pointer and pan button
% set(ud.buttonAction(2),'enable','on');
% set(ud.buttonAction(3),'enable','on');
% set(ud.buttonAction(6),'enable','on');
%
% %-- display final contour
% if ( size(fd.points,1)>2 )
% %-- delete any overlay lines
% if ( size(fd.handleManual,2)>0 )
% for k=1:size(fd.handleManual{end},1)
% delete(fd.handleManual{end}(k));
% end
% end
% %--
% tmp = fd.points;
% tmp(end+1,:) = tmp(1,:);
% color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
%
% switch ud.Spline
% case 0
% hold on; h = plot(tmp(:,1),tmp(:,2),'color',color{1},'Linewidth',2); hold off;
% case 1
% spline = cscvn([tmp(:,2)'; tmp(:,1)']);
% A = fnplt(spline,'.');
% hold on; h = plot(A(2,:),A(1,:),'color',color{1},'Linewidth',2); hold off;
% end
% fd.handleManual{end} = h;
% %-- create manual mask
% X = get(fd.handleManual{end},'X');
% Y = get(fd.handleManual{end},'Y');
% fd.levelset = xor(roipoly(fd.data,X,Y),fd.levelset);
% %-- save initialization info
% ud.LastPlot = 'levelset';
% fd.method = 'Initial region';
% %-- prepare next contour
% fd.handleManual{end+1} = 0;
% end
% fd.points = [];
%
% end
%
% %-- save structure
% set(ud.imageId,'userdata',fd);
% set(ud.gcf,'userdata',ud);
%------------------------------------------------------------------
function displayDrawingInfo(ud,flag,var)
if (~flag)
switch var
case 1 %-- draw rectangle
set(ud.txtInfo1,'string',sprintf('Click and drag \nto draw a rectangle'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
case 2 %-- draw ellipse
set(ud.txtInfo1,'string',sprintf('Click and drag \nto draw an ellipse'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
case 3 %-- draw manual points
set(ud.txtInfo1,'string',sprintf('Left click to add a point\nRight click to end'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
case 4 %-- draw multi-rectangle
set(ud.txtInfo1,'string',sprintf('Click and drag \nto draw multi-rectangles'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
case 5 %-- draw multi-ellipse
set(ud.txtInfo1,'string',sprintf('Click and drag \nto draw multi-ellipses'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
case 6 %-- draw multi-manual points
set(ud.txtInfo1,'string',sprintf('Left click to add a point\nRight click to end'),'color','y');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
end
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_run.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_run.m
| 20,056 |
utf_8
|
e09b5d345491ea24c5125c3d878c9766
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_run(src,evt)
%-- parameters
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
if ( isempty(fd.data) )
return;
end
if ( sum(fd.levelset(:)) == 0 )
set(ud.txtInfo1,'string',sprintf('Error: No initial contour has been given'),'color',[1 0 0]);
return;
end
%-- deal with initialization mode
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%--
fd.seg = [];
img = fd.visu;
levelset = fd.levelset;
display = 1;
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
ud.LastPlot = 'levelset';
%-- set mouse pointer to watch
set(fig,'pointer','watch');
drawnow;
%-- invoke function that corresponds to the selected method
numIts = 0;
numMethod = find(strcmp(get(ud.handleIconAlgo,'State'),'on'));
if isempty(numMethod)
creaseg_cleanOverlays();
set(ud.txtInfo1,'string',sprintf('Error: No algorithm has been selected'),'color',[1 0 0]);
set(fig,'pointer','arrow');
return
else % Displaying the corresponding panel
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
if k<size(ud.handleAlgoConfig,1)-1
set(ud.handleIconAlgo(k),'State','off');
end
end
set(ud.handleAlgoConfig(numMethod),'Visible','on');
set(ud.handleIconAlgo(numMethod),'State','on');
end
%-- clean overlays and update fd structure
creaseg_cleanOverlays();
fd = get(ud.imageId,'userdata');
switch numMethod(1)
case 1 %-- Caselles
[seg,levelset,numIts] = run_caselles(ud,img,levelset,color,display);
fd.method = 'Caselles';
case 2 %-- Chan & Vese
[seg,levelset,numIts] = run_chanvese(ud,img,levelset,color,display);
fd.method = 'Chan & Vese';
case 3 %-- Chunming Li
[seg,levelset,numIts] = run_chunmingli(ud,img,levelset,color,display);
fd.method = 'Chunming Li';
case 4 %-- Lankton
[seg,levelset,numIts] = run_lankton(ud,img,levelset,color,display);
fd.method = 'Lankton';
case 5 %-- Bernard
[seg,levelset,numIts] = run_bernard(ud,img,levelset,color,display);
fd.method = 'Bernard';
case 6 %-- Shi
[seg,levelset,numIts] = run_shi(ud,img,levelset,color,display);
fd.method = 'Shi';
case 7 %-- Personal
% [seg,levelset,numIts] = run_personal(ud,img,levelset,color,display);
% fd.method = 'Personal';
case {8, 9} %-- Comparison
test = 0;
for i=1:1:7
test = test + get(ud.handleAlgoComparison(6+i),'Value');
end
if test ~= 0 % Checking if at least one algorithm has been selected
if max(fd.reference(:)) ~= 0 % Checking if a reference has been given
% Displaying the Results panel
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
if k<size(ud.handleAlgoConfig,1)-1
set(ud.handleIconAlgo(k),'State','off');
end
end
set(ud.handleAlgoConfig(9),'Visible','on');
set(ud.handleIconAlgo(8),'State','on');
% Enabling the visual check boxes and reseting them
set(ud.handleAlgoResults(5),'Value',1);
for i=1:1:7
if get(ud.handleAlgoComparison(6+i),'Value')
set(ud.handleAlgoResults(5+i),'Enable','on');
set(ud.handleAlgoResults(5+i),'Value',1);
else
set(ud.handleAlgoResults(5+i),'Enable','off');
set(ud.handleAlgoResults(5+i),'Value',0);
end
end
% Running the comparison
fd.seg = run_comp(ud,img,levelset,color);
fd.method = 'Comparison';
%-- UPDATE FD AND UD STRUCTURES ATTACHED TO IMAGEID AND FIG HANDLES
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
% Showing all contours
creaseg_plotresults(src,evt)
set(ud.handleAlgoComparison(24),'Enable','on');
set(ud.txtInfo1,'string','','color',[1 1 0]);
ud.LastPlot = 'results';
else
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
set(ud.txtInfo1,'string',sprintf('Error: No reference has been given'),'color',[1 0 0]);
end
else
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
set(ud.txtInfo1,'string',sprintf('Error: No algorithm has been selected'),'color',[1 0 0]);
end
end
%-- UPDATE FD AND UD STRUCTURES ATTACHED TO IMAGEID AND FIG HANDLES
fd.levelset = levelset;
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
%-- set mouse pointer to arrow
set(fig,'pointer','arrow');
%-- put pointer button to select
set(ud.buttonAction(3),'BackgroundColor',[160/255 130/255 95/255]);
%--
if numMethod<8
set(ud.txtInfo1,'string',sprintf('End of run in %2d its',numIts),'color',[1 1 0]);
end
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
function [seg,levelset,numIts] = run_caselles(ud,img,levelset,color,display) %-- Caselles
max_its = str2double(get(ud.handleAlgoCaselles(4),'string'));
thresh = str2double(get(ud.handleAlgoCaselles(6),'string'));
propag = str2double(get(ud.handleAlgoCaselles(10),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Caselles'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_caselles(img,levelset,max_its,propag,thresh,color,display);
function [seg,levelset,numIts] = run_chanvese(ud,img,levelset,color,display) %-- Chan & Vese
max_its = str2double(get(ud.handleAlgoChanVese(4),'string'));
thresh = str2double(get(ud.handleAlgoChanVese(6),'string'));
curvature = str2double(get(ud.handleAlgoChanVese(10),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Chan & Vese'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_chanvese(img,levelset,max_its,curvature,thresh,color,display);
function [seg,levelset,numIts] = run_chunmingli(ud,img,levelset,color,display) %-- Chunming Li
max_its = str2double(get(ud.handleAlgoLi(4),'string'));
thresh = str2double(get(ud.handleAlgoLi(6),'string'));
length = str2double(get(ud.handleAlgoLi(10),'string'));
regularization = str2double(get(ud.handleAlgoLi(12),'string'));
scale = str2double(get(ud.handleAlgoLi(14),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Li'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_chunmingli(img,levelset,max_its,length,regularization,scale,thresh,color,display);
function [seg,levelset,numIts] = run_lankton(ud,img,levelset,color,display) %-- Lankton
max_its = str2double(get(ud.handleAlgoLankton(4),'string'));
thresh = str2double(get(ud.handleAlgoLankton(6),'string'));
method = get(ud.handleAlgoLankton(10),'value');
neigh = get(ud.handleAlgoLankton(12),'value');
curvature = str2double(get(ud.handleAlgoLankton(14),'string'));
radius = str2double(get(ud.handleAlgoLankton(16),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Lankton'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_lankton(img,levelset,max_its,radius,curvature,thresh,method,neigh,color,display);
function [seg,levelset,numIts] = run_bernard(ud,img,levelset,color,display) %-- Bernard
max_its = str2double(get(ud.handleAlgoBernard(4),'string'));
thresh = str2double(get(ud.handleAlgoBernard(6),'string'));
scale = str2double(get(ud.handleAlgoBernard(10),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Bernard'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_bernard(img,levelset,max_its,scale,thresh,color,display);
function [seg,levelset,numIts] = run_shi(ud,img,levelset,color,display) %-- Shi
max_its = str2double(get(ud.handleAlgoShi(4),'string'));
na = str2double(get(ud.handleAlgoShi(8),'string'));
ns = str2double(get(ud.handleAlgoShi(10),'string'));
sigma = str2double(get(ud.handleAlgoShi(12),'string'));
ng = str2double(get(ud.handleAlgoShi(14),'string'));
%--
set(ud.txtInfo2,'string',sprintf('Shi'),'color',[1 1 0]);
[seg,levelset,numIts] = creaseg_shi(img,levelset,max_its,na,ns,sigma,ng,color,display);
% function [seg,levelset,numIts] = run_personal(ud,img,levelset,color,display)
% max_its = str2double(get(ud.handleAlgoPersonal(4),'string'));
% thresh = str2double(get(ud.handleAlgoPersonal(6),'string'));
% parameter1 = str2double(get(ud.handleAlgoPersonal(10),'string'));
% parameter2 = str2double(get(ud.handleAlgoPersonal(12),'string'));
% %-- Run your personal method here
% set(ud.txtInfo2,'string',sprintf('Personal Algorithm'),'color',[1 1 0]);
% set(ud.txtInfo2,'string',sprintf(''),'color',[1 1 0]);
function seg = run_comp(ud,img,levelset,color) %-- Comparison
fd = get(ud.imageId,'userdata');
seg = zeros(size(img,1),size(img,2),7);
res = zeros(7,2);
d = get(ud.handleAlgoComparison(22),'Value');
if ud.Version
set(ud.handleAlgoResults(2), 'Data', res);
switch d % switching depending on the selected criteria
case 1 % displaying its name in the table
name = get(ud.handleAlgoResults(2), 'ColumnName');
name(2) = {'Dice'};
set(ud.handleAlgoResults(2), 'ColumnName', name);
case 2
name = get(ud.handleAlgoResults(2), 'ColumnName');
name(2) = {'PSNR'};
set(ud.handleAlgoResults(2), 'ColumnName', name);
case 3
name = get(ud.handleAlgoResults(2), 'ColumnName');
name(2) = {'Hausdorff'};
set(ud.handleAlgoResults(2), 'ColumnName', name);
case 4
name = get(ud.handleAlgoResults(2), 'ColumnName');
name(2) = {'MSSD'};
set(ud.handleAlgoResults(2), 'ColumnName', name);
end
end
display = get(ud.handleAlgoComparison(23),'Value');
if get(ud.handleAlgoComparison(7),'Value') %-- Caselles
tic;
[junk,seg(:,:,1)] = run_caselles(ud,img,levelset,color,display);
res(1,1) = toc;
res(1,2) = distance(fd.reference, seg(:,:,1), d);
end
if get(ud.handleAlgoComparison(8),'Value') %-- Chan & Vese
tic;
[junk,seg(:,:,2)] = run_chanvese(ud,img,levelset,color,display);
res(2,1) = toc;
res(2,2) = distance(fd.reference, seg(:,:,2), d);
end
if get(ud.handleAlgoComparison(9),'Value') %-- Chunming Li
tic;
[junk,seg(:,:,3)] = run_chunmingli(ud,img,levelset,color,display);
res(3,1) = toc;
res(3,2) = distance(fd.reference, seg(:,:,3), d);
end
if get(ud.handleAlgoComparison(10),'Value') %-- Lankton
tic;
[junk,seg(:,:,4)] = run_lankton(ud,img,levelset,color,display);
res(4,1) = toc;
res(4,2) = distance(fd.reference, seg(:,:,4), d);
end
if get(ud.handleAlgoComparison(11),'Value') %-- Bernard
tic;
[junk,seg(:,:,5)] = run_bernard(ud,img,levelset,color,display);
res(5,1) = toc;
res(5,2) = distance(fd.reference, seg(:,:,5), d);
end
if get(ud.handleAlgoComparison(12),'Value') %-- Shi
tic;
[junk,seg(:,:,6)] = run_shi(ud,img,levelset,color,display);
res(6,1) = toc;
res(6,2) = distance(fd.reference, seg(:,:,6), d);
end
% if get(ud.handleAlgoComparison(13),'Value') %-- Personal
% tic;
% [junk,seg(:,:,7)] = run_personal(ud,img,levelset,color,display);
% res(7,1) = toc;
% res(7,2) = distance(fd.reference, seg(:,:,7), d);
% end
set(ud.txtInfo2,'string','','color',[1 1 0]);
if ud.Version
set(ud.handleAlgoResults(2),'Data',res);
end
switch d
case 1, dstr = 'Dice';
case 2, dstr = 'PSNR';
case 3, dstr = 'Hausdorff';
case 4, dstr = 'MSSD';
end
save_results(res,dstr);
function dist = distance(ref,img,d)
% Function that call the correct distance function
img = img <= 0; % Creating a mask from the LS function
switch d
case 1, dist = dist_Dice(ref,img);
case 2, dist = dist_PSNR(ref,img);
case 3, dist = dist_Hausdorff(ref,img);
case 4, dist = dist_MSSD(ref,img);
end
function dist = dist_Dice(ref,img)
% Calculation of the Dice Coefficient
idx_img = find(img == 1);
idx_ref = find(ref == 1);
idx_inter = find((img == 1) & (ref == 1));
dist = 2*length(idx_inter)/(length(idx_ref)+length(idx_img));
function dist = dist_PSNR(ref,img)
% Calculation of the PSNR
[nrow, ncol] = size(ref);
idx1 = find((ref == 1)&(img == 0));
idx2 = find((ref == 0)&(img == 1));
dist = (length(idx1)+length(idx2))/(nrow*ncol);
dist = -10*log10(dist);
function dist = dist_Hausdorff(ref,img)
% Calculation of the Hausdorff distance
% Create a distance function for the reference and result
phi_ref = bwdist(ref)+bwdist(1-ref);
phi_img = bwdist(img)+bwdist(1-img);
% Get the reference and image contour
se = strel('diamond',1);
contour_ref = ref - imerode(ref,se);
contour_img = img - imerode(img,se);
dist = max(max(phi_ref(contour_img == 1)), max(phi_img(contour_ref == 1)));
function dist = dist_MSSD(ref,img)
% Calculation of the Mean Sum of Square Distance
% Create a distance function for the reference and result
phi_ref = bwdist(ref)+bwdist(1-ref);
phi_img = bwdist(img)+bwdist(1-img);
% Get the reference and image contour
se = strel('diamond',1);
contour_ref = ref - imerode(ref,se);
contour_img = img - imerode(img,se);
dist1 = sum(phi_ref(contour_img == 1).^2)/(sum(contour_img(:)) + eps);
dist2 = sum(phi_img(contour_ref == 1).^2)/(sum(contour_ref(:)) + eps);
dist = max(dist1,dist2);
function save_results(res,dstr)
% Open or create the .txt File
fid = fopen('results/results.txt','w');
% Write the results
fprintf(fid,'%s\t %s\t %s\n','Algorithm','Calculation Time', dstr);
fprintf(fid,'%s\t \t %3.3f\t \t %1.3f\n','Caselles',res(1,1),res(1,2));
fprintf(fid,'%s\t \t %3.3f\t \t %1.3f\n','Chan & Vese',res(2,1),res(2,2));
fprintf(fid,'%s\t \t %3.3f\t \t %1.3f\n','Chunming Li',res(3,1),res(3,2));
fprintf(fid,'%s\t \t \t %3.3f\t \t %1.3f\n','Lankton',res(4,1),res(4,2));
fprintf(fid,'%s\t \t \t %3.3f\t \t %1.3f\n','Bernard',res(5,1),res(5,2));
fprintf(fid,'%s\t \t \t %3.3f\t \t %1.3f\n','Shi',res(6,1),res(6,2));
fprintf(fid,'%s\t \t %3.3\t \t %1.3f\n','Personal',res(7,1),res(7,2));
% Close the file
fclose(fid);
|
github
|
jacksky64/imageProcessing-master
|
creaseg_gui.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_gui.m
| 80,541 |
utf_8
|
4da6ba874bf11f79dbd19da0e148024e
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_gui(ud)
%-- Checking the Matlab version and if the Spline Toolbox is available
a = ver;
ud.Spline = 0;
i = 1;
while ( (i <= size(a,2)) && (~strcmp(a(i).Name,'MATLAB')) )
i = i+ 1;
end
idx = find(a(i).Version == '.');
v1 = str2double(a(i).Version(1:idx-1)); v2 = str2double(a(i).Version(idx+1:end));
if ( (v1 >= 7) && (v2 >= 6) )
ud.Version = 1;
else
ud.Version = 0;
end
ud.LastPlot = '';
%-- create main figure
ss = get(0,'ScreenSize');
ud.gcf = figure('position',[300 200 ss(3)*2/3 ss(4)*2/3],'menubar','none','tag','creaseg','color',[87/255 86/255 84/255],'name','CREASEG','NumberTitle','off');
%-- create main menu
h1 = uimenu('parent',ud.gcf,'label','File');
uimenu('parent',h1,'label','Open','callback','creaseg_loadimage');
uimenu('parent',h1,'label','Close','callback',{@closeInterface});
uimenu('parent',h1,'label','Save screen','callback',{@saveResult,1});
uimenu('parent',h1,'label','Save result','callback',{@saveResult,3},'separator','on');
%-- create Algorithm item submenu
h1 = uimenu('parent',ud.gcf,'label','Algorithms');
h2 = uimenu('parent',h1,'label','1-Caselles','callback',{@manageAlgoItem,1},'ForegroundColor',[255/255, 0/255, 0/255],'Checked','on');
h3 = uimenu('parent',h1,'label','2-Chan & Vese','callback',{@manageAlgoItem,2});
h4 = uimenu('parent',h1,'label','3-Chunming Li','callback',{@manageAlgoItem,3});
h5 = uimenu('parent',h1,'label','4-Lankton','callback',{@manageAlgoItem,4});
h6 = uimenu('parent',h1,'label','5-Bernard','callback',{@manageAlgoItem,5});
h7 = uimenu('parent',h1,'label','6-Shi','callback',{@manageAlgoItem,6});
h8 = uimenu('parent',h1,'label','7-Personal Algorithm','callback',{@manageAlgoItem,7});
h9 = uimenu('parent',h1,'label','C-Comparison Mode','callback',{@manageCompItem,1},'separator','on');
ud.handleMenuAlgorithms = [h2;h3;h4;h5;h6;h7;h8;h9];
S = ['1-Caselles ';'2-Chan & Vese';'3-Chunming Li'; ...
'4-Lankton ';'5-Bernard ';'6-Shi '; ...
'7-Personal ';'C-Comparison '];
ud.handleMenuAlgorithmsName = cellstr(S);
%-- create Tool item submenu
h1 = uimenu('parent',ud.gcf,'label','Tool');
uimenu('parent',h1,'label','Draw Initial Region','callback',{@manageAction,1});
uimenu('parent',h1,'label','Run','callback','creaseg_run');
%-- create Help item submenu
h1 = uimenu('parent',ud.gcf,'label','Help');
uimenu('parent',h1,'label','About Creaseg','callback',{@open_help});
uimenu('parent',h1,'label','About the author','callback',{@open_author},'separator','on');
%-- create Image Area
ud.imagePanel = uipanel('units','normalized','position',[0.35 0.05 0.62 0.80],'BorderType','line','Backgroundcolor',[37/255 37/255 37/255],'HighlightColor',[0/255 0/255 0/255],'tag','imgPanel');
ud.gca = axes('parent',ud.imagePanel,'Tag','mainAxes','DataAspectRatio',[1 1 1],'units','normalized','position',[0.05 0.05 0.9 0.9],'visible','off','tag','mainAxes');
ud.img = image([1 256],[1 256],repmat(37/255,[256,256,3]),'parent',ud.gca,'Tag','mainImg');
axis(ud.gca,'equal');
set(ud.gca,'visible','off');
pos = get(ud.gca,'position');
colormap(gray(256));
ud.imageId = ud.img;
ud.imageMask(1,:) = pos;
ud.panelIcons = uipanel('parent',ud.gcf,'units','normalized','position',[0.35 0.87 0.42 0.08],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255],'tag','panelIcons','userdata',0);
ud.panelText = uipanel('parent',ud.gcf,'units','normalized','position',[0.80 0.87 0.17 0.08],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
%--
load('misc/icons/drawInitialization.mat');
ha1 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.10 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[160/255 130/255 95/255],'tooltip','draw initial region','Callback',{@manageAction,1});
load('misc/icons/run2.mat');
ha2 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.22 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','Backgroundcolor',[240/255 173/255 105/255],'tooltip','Run','Callback',{@manageAction,2});
load('misc/icons/arrow.mat');
ha3 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.34 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'tooltip','Pointer','Callback',{@manageAction,3});
load('misc/icons/zoomIn.mat');
ha4 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.46 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'tooltip','Zoom In','Callback',{@manageAction,4});
load('misc/icons/zoomOut.mat');
ha5 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.58 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'tooltip','Zoom Out','Callback',{@manageAction,5});
load('misc/icons/pan.mat');
ha6 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.70 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'tooltip','Pan','Callback',{@manageAction,6});
load('misc/icons/info.mat');
ha7 = uicontrol('parent',ud.panelIcons,'units','normalized','position',[0.82 0.25 0.08 0.5],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'tooltip','Image Info','Callback',{@manageAction,7});
ud.buttonAction = [ha1;ha2;ha3;ha4;ha5;ha6;ha7];
%--
ud.txtPositionIntensity = uicontrol('parent',ud.panelText,'style','text','enable','inactive','fontsize',8,...
'backgroundcolor',[113/255 113/255 113/255],'foregroundcolor',[.9 .9 .9],'horizontalalignment','left');
SetTextIntensityPosition(ud);
ud.txtInfo1 = text('Parent',ud.gca,'units','normalized','position',[0.05,0.95], ...
'string','','color',[255/255 255/255 0/255],'FontSize',8);
ud.txtInfo2 = text('Parent',ud.gca,'units','normalized','position',[0.05,0.90], ...
'string','','color',[255/255 255/255 0/255],'FontSize',8);
ud.txtInfo3 = text('Parent',ud.gca,'units','normalized','position',[0.05,0.85], ...
'string','','color',[255/255 255/255 0/255],'FontSize',8);
ud.txtInfo4 = text('Parent',ud.gca,'units','normalized','position',[0.05,0.80], ...
'string','','color',[255/255 255/255 0/255],'FontSize',8);
ud.txtInfo5 = text('Parent',ud.gca,'units','normalized','position',[0.05,0.75], ...
'string','','color',[255/255 255/255 0/255],'FontSize',8);
%-- create Toolbar
hToolbar = uitoolbar('Parent',ud.gcf,'HandleVisibility','callback');
load('misc/icons/open.mat');
uipushtool('Parent',hToolbar,'TooltipString','Open File','CData',cdata,'HandleVisibility','callback','ClickedCallback','creaseg_loadimage');
load('misc/icons/screenshot.mat');
uipushtool('Parent',hToolbar,'TooltipString','Save Screen','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@saveResult,1});
load('misc/icons/save.mat');
uipushtool('Parent',hToolbar,'TooltipString','Save data','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@saveResult,3});
load('misc/icons/one.mat');
ud.AlgoIcon(:,:,:,1) = cdata;
hp1 = uitoggletool('Parent',hToolbar,'Separator','on','TooltipString','Caselles','State','On','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,1});
load('misc/icons/two.mat');
ud.AlgoIcon(:,:,:,2) = cdata;
hp2 = uitoggletool('Parent',hToolbar,'TooltipString','ChanVese','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,2});
load('misc/icons/three.mat');
ud.AlgoIcon(:,:,:,3) = cdata;
hp3 = uitoggletool('Parent',hToolbar,'TooltipString','Chunming Li','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,3});
load('misc/icons/four.mat');
ud.AlgoIcon(:,:,:,4) = cdata;
hp4 = uitoggletool('Parent',hToolbar,'TooltipString','Lankton','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,4});
load('misc/icons/five.mat');
ud.AlgoIcon(:,:,:,5) = cdata;
hp5 = uitoggletool('Parent',hToolbar,'TooltipString','Bernard','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,5});
load('misc/icons/six.mat');
ud.AlgoIcon(:,:,:,6) = cdata;
hp6 = uitoggletool('Parent',hToolbar,'TooltipString','Shi','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,6});
load('misc/icons/seven.mat');
ud.AlgoIcon(:,:,:,7) = cdata;
hp7 = uitoggletool('Parent',hToolbar,'TooltipString','Personal Algorithm','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageAlgoItem,7});
load('misc/icons/comp.mat');
ud.AlgoIcon(:,:,:,8) = cdata;
hp8 = uitoggletool('Parent',hToolbar,'Separator','on','TooltipString','Comparison Mode','CData',cdata,'HandleVisibility','callback','ClickedCallback',{@manageCompItem,1});
load('misc/icons/brushR.mat');
hp9 = uipushtool('Parent',hToolbar,'Separator','on','TooltipString','Change contour color','CData',cdata,'HandleVisibility','callback','userdata',1,'ClickedCallback',{@changeContourColor});
ud.handleContourColor = hp9;
load('misc/icons/help.mat');
uipushtool('Parent',hToolbar,'Separator','on','TooltipString','Help','CData',cdata,'HandleVisibility','callback','userdata',1,'ClickedCallback',{@open_help});
ud.handleIconAlgo = [hp1;hp2;hp3;hp4;hp5;hp6;hp7;hp8];
ud.colorSpec = {'r','g','b','y','w','k'};
%-- Create Icon for selected algorithm (in Comparison Mode)
load('misc/icons/oneSel.mat');
ud.AlgoIconSel(:,:,:,1) = cdata;
load('misc/icons/twoSel.mat');
ud.AlgoIconSel(:,:,:,2) = cdata;
load('misc/icons/threeSel.mat');
ud.AlgoIconSel(:,:,:,3) = cdata;
load('misc/icons/fourSel.mat');
ud.AlgoIconSel(:,:,:,4) = cdata;
load('misc/icons/fiveSel.mat');
ud.AlgoIconSel(:,:,:,5) = cdata;
load('misc/icons/sixSel.mat');
ud.AlgoIconSel(:,:,:,6) = cdata;
load('misc/icons/sevenSel.mat');
ud.AlgoIconSel(:,:,:,7) = cdata;
%-- INTERFACE -> SEGMENTATION CONTROL
h1 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h2 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h3 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h4 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h5 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h6 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h7 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h8 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
h9 = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'Visible','off','BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
hi = uipanel('parent',ud.gcf,'position',[0.03 0.05 0.30 0.9],'BorderType','line','Backgroundcolor',[87/255 86/255 84/255],'HighlightColor',[0/255 0/255 0/255]);
ud.handleAlgoConfig = [h1;h2;h3;h4;h5;h6;h7;h8;h9;hi];
%-- INTERFACE -> INITIALIZATION CONTROL
hi1 = uicontrol('parent',hi,'units','normalized','position',[0 0.92 1 0.05],'Style','text','String','Initialization','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
hi2 = uipanel('parent',hi,'units','normalized','position',[0.07 0.05 0.85 0.85],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
load('misc/icons/rectangle.mat');
hi3 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.80 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,1});
load('misc/icons/multirectangles.mat');
hi4 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.70 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,2});
load('misc/icons/ellipse.mat');
hi5 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.60 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,3});
load('misc/icons/multiellipses.mat');
hi6 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.50 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,4});
load('misc/icons/manual.mat');
hi7 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.40 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,5});
load('misc/icons/multimanuals.mat');
hi8 = uicontrol('parent',hi2,'units','normalized','position',[0.35 0.30 0.30 0.05],'Style','pushbutton','CData',cdata,'Enable','On','BackgroundColor',[240/255 173/255 105/255],'Callback',{@creaseg_managedrawing,6});
ud.handleInit = [hi1;hi2;hi3;hi4;hi5;hi6;hi7;hi8];
%-- INTERFACE -> CASELLES CONTROL
h11 = uicontrol('parent',h1,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Caselles','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h12 = uipanel('parent',h1,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h13 = uicontrol('parent',h12,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h14 = uicontrol('parent',h12,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h15 = uicontrol('parent',h12,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h16 = uicontrol('parent',h12,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','BackgroundColor',[240/255 173/255 105/255]);
h17 = uicontrol('parent',h1,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h18 = uipanel('parent',h1,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h181 = uicontrol('parent',h18,'units','normalized','position',[0.07 0.88 0.5 0.05],'Style','text','String','Propagation term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h182 = uicontrol('parent',h18,'units','normalized','position',[0.55 0.88 0.3 0.055],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoCaselles = [h11;h12;h13;h14;h15;h16;h17;h18;h181;h182];
%-- INTERFACE -> CHAN VESE CONTROL
h21 = uicontrol('parent',h2,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Chan & Vese','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h22 = uipanel('parent',h2,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h23 = uicontrol('parent',h22,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h24 = uicontrol('parent',h22,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h25 = uicontrol('parent',h22,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h26 = uicontrol('parent',h22,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','units','normalized','BackgroundColor',[240/255 173/255 105/255]);
h27 = uicontrol('parent',h2,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h28 = uipanel('parent',h2,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h281 = uicontrol('parent',h28,'units','normalized','position',[0.07 0.88 0.5 0.05],'Style','text','String','Curvature term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h282 = uicontrol('parent',h28,'units','normalized','position',[0.55 0.88 0.3 0.055],'Style','edit','String','0.2','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoChanVese = [h21;h22;h23;h24;h25;h26;h27;h28;h281;h282];
%-- INTERFACE -> LI CONTROL
h31 = uicontrol('parent',h3,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Chunming Li','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h32 = uipanel('parent',h3,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h33 = uicontrol('parent',h32,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h34 = uicontrol('parent',h32,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h35 = uicontrol('parent',h32,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h36 = uicontrol('parent',h32,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','units','normalized','BackgroundColor',[240/255 173/255 105/255]);
h37 = uicontrol('parent',h3,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h38 = uipanel('parent',h3,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h39 = uicontrol('parent',h38,'units','normalized','position',[0.07 0.88 0.5 0.05],'Style','text','String','Length term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h391 = uicontrol('parent',h38,'units','normalized','position',[0.55 0.88 0.3 0.055],'Style','edit','String','0.003','BackgroundColor',[240/255 173/255 105/255]);
h392 = uicontrol('parent',h38,'units','normalized','position',[0.07 0.78 0.5 0.05],'Style','text','String','Regularization term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h393 = uicontrol('parent',h38,'units','normalized','position',[0.55 0.78 0.3 0.055],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
h394 = uicontrol('parent',h38,'units','normalized','position',[0.07 0.68 0.5 0.05],'Style','text','String','Scale term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h395 = uicontrol('parent',h38,'units','normalized','position',[0.55 0.68 0.3 0.055],'Style','edit','String','7','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoLi = [h31;h32;h33;h34;h35;h36;h37;h38;h39;h391;h392;h393;h394;h395];
%-- INTERFACE -> LANKTON CONTROL
h41 = uicontrol('parent',h4,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Lankton','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h42 = uipanel('parent',h4,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h43 = uicontrol('parent',h42,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h44 = uicontrol('parent',h42,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h45 = uicontrol('parent',h42,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h46 = uicontrol('parent',h42,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','units','normalized','BackgroundColor',[240/255 173/255 105/255]);
h47 = uicontrol('parent',h4,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h48 = uipanel('parent',h4,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h49 = uicontrol('parent',h48,'units','normalized','position',[0.07 0.88 0.5 0.05],'Style','text','String','Feature type','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h491 = uicontrol('parent',h48,'units','normalized','position',[0.55 0.90 0.39 0.04],'Style','popupmenu','String',{'Yezzi','Chan Vese'},'FontSize',9,'BackgroundColor',[240/255, 173/255, 105/255]);
h492 = uicontrol('parent',h48,'units','normalized','position',[0.07 0.78 0.5 0.05],'Style','text','String','Neighborhood','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h493 = uicontrol('parent',h48,'units','normalized','position',[0.55 0.80 0.39 0.04],'Style','popupmenu','String',{'Circle','Square'},'FontSize',9,'BackgroundColor',[240/255, 173/255, 105/255]);
h494 = uicontrol('parent',h48,'units','normalized','position',[0.07 0.68 0.5 0.05],'Style','text','String','Curvature term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h495 = uicontrol('parent',h48,'units','normalized','position',[0.55 0.68 0.3 0.06],'Style','edit','String','0.2','BackgroundColor',[240/255 173/255 105/255]);
h496 = uicontrol('parent',h48,'units','normalized','position',[0.07 0.58 0.5 0.05],'Style','text','String','Radius term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h497 = uicontrol('parent',h48,'units','normalized','position',[0.55 0.58 0.3 0.06],'Style','edit','String','9','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoLankton = [h41;h42;h43;h44;h45;h46;h47;h48;h49;h491;h492;h493;h494;h495;h496;h497];
%-- INTERFACE -> BERNARD CONTROL
h51 = uicontrol('parent',h5,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Bernard','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h52 = uipanel('parent',h5,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h53 = uicontrol('parent',h52,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h54 = uicontrol('parent',h52,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h55 = uicontrol('parent',h52,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h56 = uicontrol('parent',h52,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','units','normalized','BackgroundColor',[240/255 173/255 105/255]);
h57 = uicontrol('parent',h5,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h58 = uipanel('parent',h5,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h59 = uicontrol('parent',h58,'units','normalized','position',[0.07 0.88 0.5 0.05],'Style','text','String','Scale term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h591 = uicontrol('parent',h58,'units','normalized','position',[0.55 0.88 0.3 0.055],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoBernard = [h51;h52;h53;h54;h55;h56;h57;h58;h59;h591];
%-- INTERFACE -> SHI CONTROL
h61 = uicontrol('parent',h6,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Shi','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h62 = uipanel('parent',h6,'units','normalized','position',[0.07 0.83 0.85 0.07],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h621 = uicontrol('parent',h62,'units','normalized','position',[0.07 0.2 0.5 0.5],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h622 = uicontrol('parent',h62,'units','normalized','position',[0.60 0.25 0.3 0.5],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h63 = uicontrol('parent',h6,'units','normalized','position',[0.1 0.76 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h64 = uipanel('parent',h6,'units','normalized','position',[0.07 0.05 0.85 0.71],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h641 = uicontrol('parent',h64,'units','normalized','position',[0.07 0.89 0.5 0.05],'Style','text','String','Na term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h642 = uicontrol('parent',h64,'units','normalized','position',[0.55 0.90 0.3 0.047],'Style','edit','String','30','BackgroundColor',[240/255 173/255 105/255]);
h643 = uicontrol('parent',h64,'units','normalized','position',[0.07 0.81 0.5 0.05],'Style','text','String','Ns term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h644 = uicontrol('parent',h64,'units','normalized','position',[0.55 0.82 0.3 0.047],'Style','edit','String','3','BackgroundColor',[240/255 173/255 105/255]);
h645 = uicontrol('parent',h64,'units','normalized','position',[0.07 0.73 0.5 0.05],'Style','text','String','Sigma term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h646 = uicontrol('parent',h64,'units','normalized','position',[0.55 0.74 0.3 0.047],'Style','edit','String','3','BackgroundColor',[240/255 173/255 105/255]);
h647 = uicontrol('parent',h64,'units','normalized','position',[0.07 0.65 0.5 0.05],'Style','text','String','Ng term','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h648 = uicontrol('parent',h64,'units','normalized','position',[0.55 0.66 0.3 0.047],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoShi = [h61;h62;h621;h622;h63;h64;h641;h642;h643;h644;h645;h646;h647;h648];
%-- INTERFACE -> PERSONAL ALGO CONTROL
h71 = uicontrol('parent',h7,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Personal Algorithm','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h72 = uipanel('parent',h7,'units','normalized','position',[0.07 0.75 0.85 0.15],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h73 = uicontrol('parent',h72,'units','normalized','position',[0.07 0.58 0.5 0.23],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h74 = uicontrol('parent',h72,'units','normalized','position',[0.60 0.61 0.3 0.23],'Style','edit','String','200','BackgroundColor',[240/255 173/255 105/255]);
h75 = uicontrol('parent',h72,'units','normalized','position',[0.07 0.13 0.5 0.23],'Style','text','String','Convergence thres.','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h76 = uicontrol('parent',h72,'units','normalized','position',[0.60 0.16 0.3 0.23],'Style','edit','String','2','BackgroundColor',[240/255 173/255 105/255]);
h77 = uicontrol('parent',h7,'units','normalized','position',[0.1 0.66 0.85 0.04],'Style','text','String','Specific parameters','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h78 = uipanel('parent',h7,'units','normalized','position',[0.07 0.05 0.85 0.61],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h781 = uicontrol('parent',h78,'units','normalized','position',[0.07 0.89 0.5 0.05],'Style','text','String','Parameter 1','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h782 = uicontrol('parent',h78,'units','normalized','position',[0.55 0.90 0.3 0.047],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
h783 = uicontrol('parent',h78,'units','normalized','position',[0.07 0.81 0.5 0.05],'Style','text','String','Parameter 2','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h784 = uicontrol('parent',h78,'units','normalized','position',[0.55 0.82 0.3 0.047],'Style','edit','String','1','BackgroundColor',[240/255 173/255 105/255]);
ud.handleAlgoPersonal = [h71;h72;h73;h74;h75;h76;h77;h78;h781;h782;h783;h784];
%-- INTERFACE -> COMPARISON CONTROL
h81 = uicontrol('parent',h8,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Comparison Mode','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h82 = uipanel('parent',h8,'units','normalized','position',[0.07 0.83 0.85 0.07],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h821 = uicontrol('parent',h82,'units','normalized','position',[0.07 0.2 0.5 0.5],'Style','text','String','Number of iterations','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h822 = uicontrol('parent',h82,'units','normalized','position',[0.60 0.25 0.3 0.5],'Style','edit','String','200','callback',{@setAllNbIt},'BackgroundColor',[240/255 173/255 105/255]);
h83 = uicontrol('parent',h8,'units','normalized','position',[0.1 0.77 0.85 0.03],'Style','text','String','Algorithms','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h84 = uipanel('parent',h8,'units','normalized','position',[0.07 0.52 0.85 0.25],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h841 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.83 0.85 0.12],'Style','checkbox','String','1- Caselles','FontSize',9,'callback',{@SetIconSelected,1},'BackgroundColor',[113/255 113/255 113/255]);
h842 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.70 0.85 0.12],'Style','checkbox','String','2- Chan & Vese','FontSize',9,'callback',{@SetIconSelected,2},'BackgroundColor',[113/255 113/255 113/255]);
h843 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.57 0.85 0.12],'Style','checkbox','String','3- Chunming Li','FontSize',9,'callback',{@SetIconSelected,3},'BackgroundColor',[113/255 113/255 113/255]);
h844 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.44 0.85 0.12],'Style','checkbox','String','4- Lankton','FontSize',9,'callback',{@SetIconSelected,4},'BackgroundColor',[113/255 113/255 113/255]);
h845 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.31 0.85 0.12],'Style','checkbox','String','5- Bernard','FontSize',9,'callback',{@SetIconSelected,5},'BackgroundColor',[113/255 113/255 113/255]);
h846 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.18 0.85 0.12],'Style','checkbox','String','6- Shi','FontSize',9,'callback',{@SetIconSelected,6},'BackgroundColor',[113/255 113/255 113/255]);
h847 = uicontrol('parent',h84,'units','normalized','position',[0.07 0.05 0.85 0.12],'Style','checkbox','String','7- Personal Algorithm','FontSize',9,'callback',{@SetIconSelected,7},'BackgroundColor',[113/255 113/255 113/255]);
h85 = uicontrol('parent',h8,'units','normalized','position',[0.1 0.46 0.85 0.03],'Style','text','String','Reference','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h86 = uipanel('parent',h8,'units','normalized','position',[0.07 0.34 0.85 0.12],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h861 = uicontrol('parent',h86,'units','normalized','position',[0.15 0.58 0.30 0.31],'Style','pushbutton','String','Load','Backgroundcolor',[240/255 173/255 105/255],'callback','creaseg_loadreference','Enable','off');
h862 = uicontrol('parent',h86,'units','normalized','position',[0.53 0.58 0.30 0.31],'Style','pushbutton','String','Create','Backgroundcolor',[240/255 173/255 105/255],'callback','creaseg_createreference','Enable','off');
h863 = uicontrol('parent',h86,'units','normalized','position',[0.07 0.18 0.50 0.25],'Style','text','String','Interpolation Type','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h864 = uicontrol('parent',h86,'units','normalized','position',[0.63 0.20 0.30 0.25],'Style','popupmenu','String',{'Polygon','Spline'},'FontSize',9,'BackgroundColor',[240/255, 173/255, 105/255]);
h87 = uipanel('parent',h8,'units','normalized','position',[0.07 0.16 0.85 0.12],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h871 = uicontrol('parent',h87,'units','normalized','position',[0.07 0.55 0.40 0.29],'Style','text','String','Similarity Criteria','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[113/255 113/255 113/255]);
h872 = uicontrol('parent',h87,'units','normalized','position',[0.53 0.58 0.40 0.32],'Style','popupmenu','String',{'Dice','PSNR','Hausdorff','MSSD'},'FontSize',9,'BackgroundColor',[240/255, 173/255, 105/255]);
h873 = uicontrol('parent',h87,'units','normalized','position',[0.07 0.18 0.85 0.32],'Style','checkbox','String','Intermediate Output','BackgroundColor',[113/255 113/255 113/255],'Value',1);
h88 = uicontrol('parent',h8,'units','normalized','position',[0.3 0.086 0.40 0.035],'Style','pushbutton','String','See Results','callback',{@manageCompItem,2},'Enable','off','Backgroundcolor',[240/255 173/255 105/255]);
ud.handleAlgoComparison = [h81;h82;h821;h822;h83;h84;h841;h842;h843;h844;h845;h846;h847;h85;h86;h861;h862;h863;h864;h87;h871;h872;h873;h88];
%-- INTERFACE -> RESULTS CONTROL
h91 = uicontrol('parent',h9,'units','normalized','position',[0.0 0.92 1.0 0.05],'Style','text','String','Results','FontSize',10,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
if ud.Version
h92 = uitable('parent',h9,'units','normalized','position',[0.07 0.62 0.85 0.3],'ColumnName',{'Calculation Time', 'Dice'},'ColumnFormat',{'Bank', 'Bank'},'RowName',{'1','2','3','4','5','6','7'});
else
h92 = uicontrol('parent',h9,'units','normalized','position',[0.07 0.62 0.85 0.3],'Style','text','String','Results cannot be displayed here because your Matlab version do not support uitable. Therefore the results are saved in the file results.txt',...
'FontSize',12,'HorizontalAlignment','center','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 0 0]);
end
h93 = uicontrol('parent',h9,'units','normalized','position',[0.1 0.55 0.85 0.03],'Style','text','String','Visual Criteria','FontSize',9,'HorizontalAlignment','left','Backgroundcolor',[87/255 86/255 84/255],'Foregroundcolor',[255/255 255/255 255/255]);
h94 = uipanel('parent',h9,'units','normalized','position',[0.07 0.25 0.85 0.3],'BorderType','line','Backgroundcolor',[113/255 113/255 113/255],'HighlightColor',[0/255 0/255 0/255]);
h941 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.87 0.85 0.11],'Style','checkbox','String','Reference (White)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h942 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.75 0.85 0.11],'Style','checkbox','String','1- Caselles (Yellow)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h943 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.63 0.85 0.11],'Style','checkbox','String','2- Chan & Vese (Blue)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h944 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.51 0.85 0.11],'Style','checkbox','String','3- Chunming Li (Cyan)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h945 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.39 0.85 0.11],'Style','checkbox','String','4- Lankton (Red)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h946 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.27 0.85 0.11],'Style','checkbox','String','5- Bernard (Green)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h947 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.15 0.85 0.11],'Style','checkbox','String','6- Shi (Magenta)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h948 = uicontrol('parent',h94,'units','normalized','position',[0.07 0.03 0.85 0.11],'Style','checkbox','String','7- Personal Algorithm (Black)','FontSize',9,'callback',{@creaseg_plotresults},'BackgroundColor',[113/255 113/255 113/255]);
h95 = uicontrol('parent',h9,'units','normalized','position',[0.3 0.086 0.4 0.035],'Style','pushbutton','String','See Parameters','callback',{@manageCompItem,1},'Backgroundcolor',[240/255 173/255 105/255]);
ud.handleAlgoResults = [h91;h92;h93;h94;h941;h942;h943;h944;h945;h946;h947;h948;h95];
if ud.Version
SetTableColumnWidth(ud);
end
%-- create structure to image handle
fd = [];
fd.data = [];
fd.visu = [];
fd.tagImage = 0;
fd.levelset = [];
fd.visuTmp = [];
fd.levelsetTmp = [];
fd.translation = [0 0];
fd.info = [];
fd.reference = [];
fd.points = [];
fd.pointsRef = [];
fd.handleRect = {};
fd.handleElliRect = {};
fd.handleManual = {};
fd.handleReference = {};
fd.method = '';
fd.drawingManualFlag = 0;
fd.drawingMultiManualFlag = 0;
%-- Set function to special events
set(ud.imageId,'userdata',fd);
set(ud.gcf,'WindowButtonMotionFcn',{@creaseg_mouseMove},'visible','on','HandleVisibility','callback','interruptible','off');
set(ud.gcf,'CloseRequestFcn',{@closeInterface});
%-- ATTACH UD STRUCTURE TO FIG HANDLE
set(ud.gcf,'userdata',ud);
set(ud.gcf,'ResizeFcn',@figResize);
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%------------------------------------------------------------------
function manageAlgoItem(src,evt,num)
fig = gcbf;
ud = get(fig,'userdata');
%-- cancel drawing mode
set(ud.buttonAction(1),'background',[240/255 173/255 105/255]);
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- check whether the create button of comparison mode is unselect
if ( get(ud.handleAlgoComparison(17),'BackgroundColor')~=[160/255 130/255 95/255] )
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
%-- put pointer button to select
set(ud.buttonAction(3),'BackgroundColor',[160/255 130/255 95/255]);
end
%-- put run button to unselect
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
%--
if strcmp(get(ud.handleIconAlgo(8),'State'),'off')||(get(ud.handleAlgoComparison(6+num),'Value')==0)
if strcmp(get(ud.handleIconAlgo(8),'State'),'on')
EnableDisableNbit(ud,'on');
end
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
if k<size(ud.handleAlgoConfig,1)-1
set(ud.handleIconAlgo(k),'State','off');
end
end
set(ud.handleAlgoConfig(num),'Visible','on');
set(ud.handleIconAlgo(num),'State','on');
%--
setAllIcon(ud);
%--
for k=1:size(ud.handleMenuAlgorithms,1)
set(ud.handleMenuAlgorithms(k),'label',ud.handleMenuAlgorithmsName{k},'ForegroundColor',[0/255, 0/255, 0/255],'Checked','off');
end
set(ud.handleMenuAlgorithms(num),'label',ud.handleMenuAlgorithmsName{num},'ForegroundColor',[255/255, 0/255, 0/255],'Checked','on');
else
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
if k<size(ud.handleAlgoConfig,1)-1
set(ud.handleIconAlgo(k),'State','off');
end
end
set(ud.handleAlgoConfig(num),'Visible','on');
set(ud.handleIconAlgo(8),'State','on');
end
%--
function manageCompItem(src, evt, num)
fig = gcbf;
ud = get(fig,'userdata');
EnableDisableNbit(ud,'off');
%-- cancel drawing mode
set(ud.buttonAction(1),'background',[240/255 173/255 105/255]);
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- check whether the create button of comparison mode is unselect
if ( get(ud.handleAlgoComparison(17),'BackgroundColor')~=[160/255 130/255 95/255] )
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
%-- put pointer button to select
set(ud.buttonAction(3),'BackgroundColor',[160/255 130/255 95/255]);
end
%-- put run button to unselect
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
%--
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
if k<size(ud.handleAlgoConfig,1)-1
set(ud.handleIconAlgo(k),'State','off');
end
end
if num == 1
set(ud.handleAlgoConfig(8),'Visible','on');
set(ud.handleIconAlgo(8),'State','on');
else
set(ud.handleAlgoConfig(9),'Visible','on');
set(ud.handleIconAlgo(8),'State','on')
creaseg_plotresults(src,evt);
end
for k=1:size(ud.handleMenuAlgorithms,1)-1
set(ud.handleMenuAlgorithms(k),'label',ud.handleMenuAlgorithmsName{k},'ForegroundColor',[0/255, 0/255, 0/255],'Checked','off');
end
set(ud.handleMenuAlgorithms(8),'label',ud.handleMenuAlgorithmsName{8},'ForegroundColor',[255/255, 0/255, 0/255],'Checked','on');
setAllIcon(ud);
setAllNbIt(src,evt);
%--
function manageInit(src,evt)
fig = gcbf;
ud = get(fig,'userdata');
for k=1:size(ud.handleAlgoConfig,1)
set(ud.handleAlgoConfig(k),'Visible','off');
end
set(ud.handleAlgoConfig(end),'Visible','on');
%--
function closeInterface(src,evt)
delete(gcbf);
%--
function figResize(src,evt)
fig = gcbf;
ud = get(fig,'userdata');
SetTextIntensityPosition(ud);
if ud.Version
SetTableColumnWidth(ud);
end
%--
function SetTableColumnWidth(ud)
ss = get(ud.gcf,'position');
posPanel = get(ud.handleAlgoConfig(9),'position');
posTable = get(ud.handleAlgoResults(2),'position');
w = ss(3)*posPanel(3)*posTable(3)-35;
set(ud.handleAlgoResults(2),'ColumnWidth',{w/2,w/2});
%--
function SetTextIntensityPosition(ud)
ss = get(ud.gcf,'position');
pos = get(ud.panelText,'position');
w = ss(3)*pos(3);
h = ss(4)*pos(4);
a = w/5;
c = w-2*a;
b = 3*h/8;
d = h-2*b;
set(ud.txtPositionIntensity,'units','pixels','position',[a b c d]);
%-- save images
function saveResult(src,evt,num)
fig = gcbf;
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
img = fd.visu;
method = fd.method;
cl = ud.colorSpec(get(ud.handleContourColor,'userdata'));
S = method;
switch method
case 'Reference'
levelset = fd.reference;
case 'Comparison'
if num == 1
num = 2;
end
levelset = zeros(size(img,1),size(img,2),8);
levelset(:,:,1) = fd.reference;
levelset(:,:,2:end) = fd.seg;
cl = {'w','y','b','c','r','g','m','k'};
S = ['Reference ';'Caselles ';'Chan & Vese';'Chunming Li'; ...
'Lankton ';'Bernard ';'Shi ';'Personal '];
otherwise
levelset = fd.levelset;
end
method = S;
switch num
case 1 %-- save screen (one contour)
if ( ~isempty(img) )
img = img - min(img(:));
img = uint8(255*img/max(img(:)));
imgrgb = repmat(img,[1 1 3]);
if ( (~isempty(levelset)) && (size(img,1)==size(levelset,1)) && (size(img,2)==size(levelset,2)))
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(levelset,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
tt = round(c);
%--
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
%--
tt = tt(:, (tt(1,:)~=0) & (tt(1,:)>=1) & (tt(1,:)<=size(img,2)) ...
& (tt(2,:)>=1) & (tt(2,:)<=size(img,1)));
imgContour = repmat(0,size(img));
for k=1:size(tt,2)
imgContour(tt(2,k),tt(1,k),1) = 1;
end
if ( min(size(img)) <= 225 )
se = strel('arbitrary',[1 1; 1 1]);
elseif ( min(size(img)) <= 450 )
se = strel('disk',1);
elseif ( min(size(img)) <= 775 )
se = strel('disk',2);
else
se = strel('disk',3);
end
imgContour = imdilate(imgContour,se);
[y,x] = find(imgContour~=0);
switch cl{1}
case 'r'
val = [255,0,0];
case 'g'
val = [0,255,0];
case 'b'
val = [0,0,255];
case 'y'
val = [255,255,0];
case 'w'
val = [255,255,255];
case 'k'
val = [0,0,0];
end
for k=1:size(x,1)
imgrgb(y(k),x(k),1) = val(1);
imgrgb(y(k),x(k),2) = val(2);
imgrgb(y(k),x(k),3) = val(3);
end
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
end
[filename, pathname] = uiputfile({'*.png','Png (*.png)';...
'*.bmp','Bmp (*.bmp)';'*.tif','Tif (*.tif)';...
'*.gif','Gif (*.gif)';'*.jpg','Jpg (*.jpg)'},'Save as');
if ( ~isempty(pathname) && ~isempty(filename) )
imwrite(imgrgb,[pathname filename]);
end
end
case 2 %-- save screen (Multiple contours)
if ( ~isempty(img) )
img = img - min(img(:));
img = uint8(255*img/max(img(:)));
imgrgb = repmat(img,[1 1 3]);
if ( (~isempty(levelset)) && (size(img,1)==size(levelset,1)) && (size(img,2)==size(levelset,2)))
for i = 1:1:size(levelset,3)
if (max(max(levelset(:,:,i)))~=0) && (get(ud.handleAlgoResults(4+i),'Value'))
axes(get(ud.imageId,'parent'));
hold on; [c,h] = contour(levelset(:,:,i),[0 0],cl{i},'Linewidth',3); hold off;
delete(h);
tt = round(c);
%--
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{i},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{i},'Linewidth',3);
c = c(:,s+2:end);
end
end
%--
tt = tt(:, (tt(1,:)~=0) & (tt(1,:)>=1) & (tt(1,:)<=size(img,2)) ...
& (tt(2,:)>=1) & (tt(2,:)<=size(img,1)));
imgContour = repmat(0,size(img));
for k=1:size(tt,2)
imgContour(tt(2,k),tt(1,k),1) = 1;
end
if ( min(size(img)) <= 225 )
se = strel('arbitrary',[1 1; 1 1]);
elseif ( min(size(img)) <= 450 )
se = strel('disk',1);
elseif ( min(size(img)) <= 775 )
se = strel('disk',2);
else
se = strel('disk',3);
end
imgContour = imdilate(imgContour,se);
[y,x] = find(imgContour~=0);
switch cl{i}
case 'r'
val = [255,0,0];
case 'g'
val = [0,255,0];
case 'b'
val = [0,0,255];
case 'y'
val = [255,255,0];
case 'w'
val = [255,255,255];
case 'k'
val = [0,0,0];
case 'm'
val = [255,0,255];
case 'c'
val = [0,255,255];
end
for k=1:size(x,1)
imgrgb(y(k),x(k),1) = val(1);
imgrgb(y(k),x(k),2) = val(2);
imgrgb(y(k),x(k),3) = val(3);
end
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
end
end
end
[filename, pathname] = uiputfile({'*.png','Png (*.png)';...
'*.bmp','Bmp (*.bmp)';'*.tif','Tif (*.tif)';...
'*.gif','Gif (*.gif)';'*.jpg','Jpg (*.jpg)'},'Save as');
if ( ~isempty(pathname) && ~isempty(filename) )
imwrite(imgrgb,[pathname filename]);
end
end
case 3 %-- save data
if ( ~isempty(img) )
if ( (~isempty(levelset)) )
result = struct('img',img,'levelset',levelset, 'Method', method);
else
result = img;
end
[filename, pathname] = uiputfile({'*.mat','MAT-files (*.mat)'},'Save as');
if ( ~isempty(pathname) && ~isempty(filename) )
save([pathname filename],'result');
end
end
end
%-- change color of contour display on the image
function changeContourColor(src,evt)
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
pos = get(src,'userdata');
if (pos==6)
pos = 1;
else
pos = pos+1;
end
filename = {'brushR' 'brushG' 'brushB' 'brushY' 'brushW' 'brushK'};
load(['misc/icons/' filename{pos} '.mat']);
set(src,'cdata',cdata,'userdata',pos);
%--
if ( ~isempty(fd.data) )
switch ud.LastPlot
case 'levelset'
if ( (~isempty(fd.levelset)) && (size(fd.data,1)==size(fd.levelset,1)) ...
&& (size(fd.data,2)==size(fd.levelset,2)))
cl = ud.colorSpec(pos);
if ( size(fd.handleRect,2) > 0 )
for k=size(fd.handleRect,2):-1:1
set(fd.handleRect{k},'EdgeColor',cl{1});
end
elseif ( size(fd.handleElliRect,2) > 0 )
for k=size(fd.handleElliRect,2):-1:1
set(fd.handleElliRect{k}(2),'color',cl{1});
end
elseif ( size(fd.handleManual,2) > 0 )
for k=size(fd.handleManual,2):-1:1
if ( (size(fd.handleManual{k},1)==1) && (fd.handleManual{k}>0) )
set(fd.handleManual{k},'color',cl{1});
end
end
else
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(fd.levelset,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
end
end
case 'reference'
if ( (~isempty(fd.reference)) && (size(fd.data,1)==size(fd.reference,1)) ...
&& (size(fd.data,2)==size(fd.reference,2)))
cl = ud.colorSpec(pos);
axes(get(ud.imageId,'parent'));
delete(findobj(get(ud.imageId,'parent'),'type','line'));
hold on; [c,h] = contour(fd.reference,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
end
end
end
%--
function SetIconSelected(src,evt,num)
fig = gcbf;
ud = get(fig,'userdata');
if get(ud.handleAlgoComparison(6+num),'Value')
set(ud.handleIconAlgo(num),'cdata',ud.AlgoIconSel(:,:,:,num))
set(ud.handleMenuAlgorithms(num),'label',ud.handleMenuAlgorithmsName{num},'ForegroundColor',[0/255, 153/255, 51/255],'Checked','on');
else
set(ud.handleIconAlgo(num),'cdata',ud.AlgoIcon(:,:,:,num))
set(ud.handleMenuAlgorithms(num),'label',ud.handleMenuAlgorithmsName{num},'ForegroundColor',[0/255, 0/255, 0/255],'Checked','off');
end
%--
function setAllIcon(ud)
for i=1:1:7
if (get(ud.handleAlgoComparison(6+i),'Value')) && strcmp(get(ud.handleIconAlgo(8),'State'),'on')
set(ud.handleIconAlgo(i),'cdata',ud.AlgoIconSel(:,:,:,i));
set(ud.handleMenuAlgorithms(i),'label',ud.handleMenuAlgorithmsName{i},'ForegroundColor',[0/255, 153/255, 51/255],'Checked','on');
else
set(ud.handleIconAlgo(i),'cdata',ud.AlgoIcon(:,:,:,i));
set(ud.handleMenuAlgorithms(i),'label',ud.handleMenuAlgorithmsName{i},'ForegroundColor',[0/255, 0/255, 0/255],'Checked','off');
end
end
%--
function EnableDisableNbit(ud,s)
set(ud.handleAlgoCaselles(4),'Enable',s);
set(ud.handleAlgoChanVese(4),'Enable',s);
set(ud.handleAlgoLi(4),'Enable',s);
set(ud.handleAlgoLankton(4),'Enable',s);
set(ud.handleAlgoBernard(4),'Enable',s);
set(ud.handleAlgoShi(4),'Enable',s);
set(ud.handleAlgoPersonal(4),'Enable',s);
%--
function setAllNbIt(src,evt)
fig = gcbf;
ud = get(fig,'userdata');
NbIt = get(ud.handleAlgoComparison(4),'String');
set(ud.handleAlgoCaselles(4),'String',NbIt);
set(ud.handleAlgoChanVese(4),'String',NbIt);
set(ud.handleAlgoLi(4),'String',NbIt);
set(ud.handleAlgoLankton(4),'String',NbIt);
set(ud.handleAlgoBernard(4),'String',NbIt);
set(ud.handleAlgoShi(4),'String',NbIt);
set(ud.handleAlgoPersonal(4),'String',NbIt);
%--
function creaseg_inittype(src, evt)
fig = gcbf;
ud = get(fig,'userdata');
switch get(ud.handleInit(4),'Value')
case {1,2}
InitText_OnOff(ud,0);
case 3
InitText_OnOff(ud,1);
set(ud.handleInit(5),'String','Center');
set(ud.handleInit(7),'String','Xc');
set(ud.handleInit(9),'String','Yc');
set(ud.handleInit(13),'String','X Axis');
set(ud.handleInit(15),'String','Y Axis');
init_param(ud,get(ud.handleInit(4),'Value'));
case 4
InitText_OnOff(ud,1);
set(ud.handleInit(5),'String','Center');
set(ud.handleInit(7),'String','Xc');
set(ud.handleInit(9),'String','Yc');
set(ud.handleInit(13),'String','Length');
set(ud.handleInit(15),'String','Width');
init_param(ud,get(ud.handleInit(4),'Value'));
case 5
InitText_OnOff(ud,1);
set(ud.handleInit(5),'String','Space');
set(ud.handleInit(7),'String','X');
set(ud.handleInit(9),'String','Y');
set(ud.handleInit(13),'String','Radius');
set(ud.handleInit(15),'Enable','Off');
set(ud.handleInit(16),'Enable','Off');
init_param(ud,get(ud.handleInit(4),'Value'));
case 6
InitText_OnOff(ud,1);
set(ud.handleInit(5),'String','Space');
set(ud.handleInit(7),'String','X');
set(ud.handleInit(9),'String','Y');
set(ud.handleInit(13),'String','Length');
set(ud.handleInit(15),'String','Width');
init_param(ud,get(ud.handleInit(4),'Value'));
end
%--
function InitText_OnOff(ud,type)
if type
set(ud.handleInit(7),'Enable','On');
set(ud.handleInit(8),'Enable','On');
set(ud.handleInit(9),'Enable','On');
set(ud.handleInit(10),'Enable','On');
set(ud.handleInit(13),'Enable','On');
set(ud.handleInit(14),'Enable','On');
set(ud.handleInit(15),'Enable','On');
set(ud.handleInit(16),'Enable','On');
else
set(ud.handleInit(7),'Enable','Off');
set(ud.handleInit(8),'Enable','Off');
set(ud.handleInit(9),'Enable','Off');
set(ud.handleInit(10),'Enable','Off');
set(ud.handleInit(13),'Enable','Off');
set(ud.handleInit(14),'Enable','Off');
set(ud.handleInit(15),'Enable','Off');
set(ud.handleInit(16),'Enable','Off');
end
%--
function init_param(ud, method)
fd = get(ud.imageId,'userdata');
if ( isempty(fd.data) )
return;
end
switch method
case {3, 4}
set(ud.handleInit(8),'string',num2str(size(fd.data,2)/2));
set(ud.handleInit(10),'string',num2str(size(fd.data,1)/2));
set(ud.handleInit(14),'string',num2str(size(fd.data,2)/4));
set(ud.handleInit(16),'string',num2str(size(fd.data,1)/4));
case {5, 6}
set(ud.handleInit(8),'string',num2str(size(fd.data,2)/40));
set(ud.handleInit(10),'string',num2str(size(fd.data,1)/40));
set(ud.handleInit(14),'string',num2str(size(fd.data,2)/20));
set(ud.handleInit(16),'string',num2str(size(fd.data,1)/20));
end
%--
function open_author(src,evt)
web('http://www.creatis.insa-lyon.fr/~bernard');
%--
function open_help(src,evt)
web('http://www.creatis.insa-lyon.fr/~bernard/creaseg');
%--
function manageAction(src,evt,nbBut)
%-- parameters
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
%-- deal with pan option
if ( (nbBut==2) || (nbBut==3) )
pan off;
end
%-- clean up all messages
if ( nbBut<7)
set(ud.txtInfo1,'string','');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
end
if (nbBut==6)
if (get(ud.buttonAction(6),'background')==[160/255 130/255 95/255])
set(ud.buttonAction(6),'background',[240/255 173/255 105/255]);
else
set(ud.buttonAction(6),'background',[160/255 130/255 95/255]);
end
end
if (nbBut==7)
if (get(ud.buttonAction(7),'background')==[160/255 130/255 95/255])
set(ud.buttonAction(7),'background',[240/255 173/255 105/255]);
set(ud.txtInfo1,'string','');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
else
set(ud.buttonAction(7),'background',[160/255 130/255 95/255]);
end
end
%-- ACTION
if ( (fd.tagImage == 1 ) || (nbBut == 1) )
switch nbBut
case 1 %-- Draw initial region
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(3),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(1),'BackgroundColor',[160/255 130/255 95/255]);
%-- switch off create button of comparison mode
set(ud.handleAlgoComparison(17),'BackgroundColor',[240/255 173/255 105/255]);
%-- do corresponding action
manageInit(src,evt);
case 2 %-- Run method
set(ud.buttonAction(1),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(2),'BackgroundColor',[160/255 130/255 95/255]);
set(ud.buttonAction(6),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
%-- switch off create button of comparison mode
set(ud.handleAlgoComparison(17),'BackgroundColor',[240/255 173/255 105/255]);
%-- do corresponding action
creaseg_run(src,evt);
case 3 %-- Set mouse pointer to own (disable current figure option icon properties)
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(6),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(3),'BackgroundColor',[160/255 130/255 95/255]);
%-- put drawing to unclick
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- do corresponding action
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
case 4 %-- Zoom in by a factor of 2
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(5),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(4),'BackgroundColor',[160/255 130/255 95/255]);
%-- do corresponding action
axes(get(ud.imageId,'parent'));
zoom(ud.gca,2);
case 5 %-- Zoom out by a factor of 2
set(ud.buttonAction(2),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(4),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
set(ud.buttonAction(5),'BackgroundColor',[160/255 130/255 95/255]);
%-- do corresponding action
axes(get(ud.imageId,'parent'));
zoom(ud.gca,0.5);
case 6 %-- Pan into main figure
%--
set(ud.buttonAction(7),'BackgroundColor',[240/255 173/255 105/255]);
%--
if (get(ud.buttonAction(6),'background')==[240/255 173/255 105/255])
%-- first pan off
pan off;
if ( get(ud.buttonAction(1),'background')==[240/255 173/255 105/255] )
if ( get(ud.handleAlgoComparison(17),'background')==[240/255 173/255 105/255] )
%-- then put pointer button to selected
set(ud.buttonAction(3),'BackgroundColor',[160/255 130/255 95/255]);
end
end
%-- then go back to manual drawing mode if any
if ( fd.drawingManualFlag == 1 )
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawManualContour});
set(ud.gcf,'WindowButtonUpFcn','');
end
%-- then go back to multi manual drawing mode if any
if ( fd.drawingMultiManualFlag == 1 )
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawMultiManualContours});
set(ud.gcf,'WindowButtonUpFcn','');
end
%-- then go back to reference manual drawing mode if any
if ( fd.drawingReferenceFlag == 1 )
set(ud.gcf,'WindowButtonDownFcn',{@creaseg_drawMultiReferenceContours});
set(ud.gcf,'WindowButtonUpFcn','');
end
else
%-- put pointer button to unselected
set(ud.buttonAction(3),'BackgroundColor',[240/255 173/255 105/255]);
%-- do corresponding action
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
axes(get(ud.imageId,'parent'));
pan(ud.gca);
end
case 7 %-- Display or not current image properties
if (get(ud.buttonAction(7),'background')==[160/255 130/255 95/255])
fd = get(ud.imageId,'userdata');
if ( ~isempty(fd.info) )
if ( isfield(fd.info,'Width') )
set(ud.txtInfo1,'string',sprintf('width:%d pixels',fd.info.Width),'color',[1 1 0]);
end
if ( isfield(fd.info,'Height') )
set(ud.txtInfo2,'string',sprintf('height:%d pixels',fd.info.Height),'color',[1 1 0]);
end
if ( isfield(fd.info,'BitDepth') )
set(ud.txtInfo3,'string',sprintf('bit depth:%d',fd.info.BitDepth),'color',[1 1 0]);
end
if ( isfield(fd.info,'XResolution') && (~isempty(fd.info.XResolution)) )
if ( isfield(fd.info,'ResolutionUnit') )
if ( strcmp(fd.info.ResolutionUnit,'meter') )
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f mm',fd.info.XResolution/1000),'color',[1 1 0]);
elseif ( strcmp(fd.info.ResolutionUnit,'millimeter') )
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f mm',fd.info.XResolution),'color',[1 1 0]);
else
set(ud.txtInfo4,'string',sprintf('XResolution:%0.3f',fd.info.XResolution),'color',[1 1 0]);
end
else
set(ud.txtInfo4,'string',sprintf('XResolution:%f',fd.info.XResolution),'color',[1 1 0]);
end
end
if ( isfield(fd.info,'YResolution') && (~isempty(fd.info.YResolution)) )
if ( isfield(fd.info,'ResolutionUnit') )
if ( strcmp(fd.info.ResolutionUnit,'meter') )
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f mm',fd.info.YResolution/1000),'color',[1 1 0]);
elseif ( strcmp(fd.info.ResolutionUnit,'millimeter') )
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f mm',fd.info.YResolution),'color',[1 1 0]);
else
set(ud.txtInfo5,'string',sprintf('YResolution:%0.3f',fd.info.YResolution),'color',[1 1 0]);
end
else
set(ud.txtInfo5,'string',sprintf('YResolution:%f',fd.info.XResolution),'color',[1 1 0]);
end
end
end
end
end
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_loadreference.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_loadreference.m
| 7,423 |
utf_8
|
037a1b94310e10c56c297b383370be4c
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_loadreference(varargin)
if nargin == 1
fig = varargin{1};
else
fig = gcbf;
end
ud = get(fig,'userdata');
%-- clean figure screen
set(ud.txtInfo1,'string','');
set(ud.txtInfo2,'string','');
set(ud.txtInfo3,'string','');
set(ud.txtInfo4,'string','');
set(ud.txtInfo5,'string','');
%-- Put the "Create" button in brighter
set(ud.handleAlgoComparison(17),'BackgroundColor',[240/255 173/255 105/255]);
%-- in case, enable drawing, run and pointer buttons
set(ud.buttonAction(1),'enable','on');
set(ud.buttonAction(2),'enable','on');
set(ud.buttonAction(3),'enable','on');
%-- clean overlays and update fd structure
keepLS = 1;
creaseg_cleanOverlays(keepLS);
fd = get(ud.imageId,'userdata');
%-- flush reference strucuture if any
fd.handleReference{1} = 0;
%-- Set drawingReferenceFlag flag to 0
fd.drawingReferenceFlag = 0;
%-- Set pointsRef to empty
fd.pointsRef = [];
%-- cancel drawing mode
for k=3:size(ud.handleInit,1)
set(ud.handleInit(k),'BackgroundColor',[240/255 173/255 105/255]);
end
%-- put run button to nonselected
set(ud.buttonAction(2),'background',[240/255 173/255 105/255]);
%--
set(ud.gcf,'WindowButtonDownFcn','');
set(ud.gcf,'WindowButtonUpFcn','');
%--
[fname,pname] = uigetfile('*.mat','Pick a file','multiselect','off','data/Reference');
input_file = fullfile(pname,fname);
if ~exist(input_file,'file')
warning(['File: ' input_file ' does not exist']);
return;
end
try
junk = load(input_file);
reference = junk.refLSF;
clear junk;
catch
warning(['Could not load: ' input_file]);
return;
end
%-- Check if the reference size is correct
if (size(fd.data,1)~=size(reference,1)) || (size(fd.data,2)~=size(reference,2))
fd.reference = [];
set(ud.txtInfo1,'string','Error:Image and Reference must be of the same size','color', [1 0 0]);
else
fd.reference = reference;
ud.LastPlot = 'reference';
fd.method = 'Reference';
%--
color = ud.colorSpec(get(ud.handleContourColor,'userdata'));
show_ref(fd.reference,ud,color);
set(ud.handleAlgoComparison(24),'Enable','off');
end
%-- UPDATE FD AND UD STRUCTURES ATTACHED TO IMAGEID AND FIG HANDLES
set(ud.imageId,'userdata',fd);
set(fig,'userdata',ud);
function show_ref(ref,ud,cl)
hold on; [c,h] = contour(ref,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while (test==false)
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'Linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'Linewidth',3);
c = c(:,s+2:end);
end
end
|
github
|
jacksky64/imageProcessing-master
|
creaseg_plotresults.m
|
.m
|
imageProcessing-master/segmentation/creaseg/creaseg/creaseg/src/creaseg_plotresults.m
| 5,561 |
utf_8
|
315135765ad2dc382968b0561b6d4a98
|
% Copyright or © or Copr. CREATIS laboratory, Lyon, France.
%
% Contributor: Olivier Bernard, Associate Professor at the french
% engineering university INSA (Institut National des Sciences Appliquees)
% and a member of the CREATIS-LRMN laboratory (CNRS 5220, INSERM U630,
% INSA, Claude Bernard Lyon 1 University) in France (Lyon).
%
% Date of creation: 8th of October 2009
%
% E-mail of the author: [email protected]
%
% This software is a computer program whose purpose is to evaluate the
% performance of different level-set based segmentation algorithms in the
% context of image processing (and more particularly on biomedical
% images).
%
% The software has been designed for two main purposes.
% - firstly, CREASEG allows you to use six different level-set methods.
% These methods have been chosen in order to work with a wide range of
% level-sets. You can select for instance classical methods such as
% Caselles or Chan & Vese level-set, or more recent approaches such as the
% one developped by Lankton or Bernard.
% - finally, the software allows you to compare the performance of the six
% level-set methods on different images. The performance can be evaluated
% either visually, or from measurements (either using the Dice coefficient
% or the PSNR value) between a reference and the results of the
% segmentation.
%
% The level-set segmentation platform is citationware. If you are
% publishing any work, where this program has been used, or which used one
% of the proposed level-set algorithms, please remember that it was
% obtained free of charge. You must reference the papers shown below and
% the name of the CREASEG software must be mentioned in the publication.
%
% CREASEG software
% "T. Dietenbeck, M. Alessandrini, D. Friboulet, O. Bernard. CREASEG: a
% free software for the evaluation of image segmentation algorithms based
% on level-set. In IEEE International Conference On Image Processing.
% Hong Kong, China, 2010."
%
% Bernard method
% "O. Bernard, D. Friboulet, P. Thevenaz, M. Unser. Variational B-Spline
% Level-Set: A Linear Filtering Approach for Fast Deformable Model
% Evolution. In IEEE Transactions on Image Processing. volume 18, no. 06,
% pp. 1179-1191, 2009."
%
% Caselles method
% "V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours.
% International Journal of Computer Vision, volume 22, pp. 61-79, 1997."
%
% Chan & Vese method
% "T. Chan and L. Vese. Active contours without edges. IEEE Transactions on
% Image Processing. volume10, pp. 266-277, February 2001."
%
% Lankton method
% "S. Lankton, A. Tannenbaum. Localizing Region-Based Active Contours. In
% IEEE Transactions on Image Processing. volume 17, no. 11, pp. 2029-2039,
% 2008."
%
% Li method
% "C. Li, C.Y. Kao, J.C. Gore, Z. Ding. Minimization of Region-Scalable
% Fitting Energy for Image Segmentation. In IEEE Transactions on Image
% Processing. volume 17, no. 10, pp. 1940-1949, 2008."
%
% Shi method
% "Yonggang Shi, William Clem Karl. A Real-Time Algorithm for the
% Approximation of Level-Set-Based Curve Evolution. In IEEE Transactions
% on Image Processing. volume 17, no. 05, pp. 645-656, 2008."
%
% This software is governed by the BSD license and
% abiding by the rules of distribution of free software.
%
% As a counterpart to the access to the source code and rights to copy,
% modify and redistribute granted by the license, users are provided only
% with a limited warranty and the software's author, the holder of the
% economic rights, and the successive licensors have only limited
% liability.
%
% In this respect, the user's attention is drawn to the risks associated
% with loading, using, modifying and/or developing or reproducing the
% software by the user in light of its specific status of free software,
% that may mean that it is complicated to manipulate, and that also
% therefore means that it is reserved for developers and experienced
% professionals having in-depth computer knowledge. Users are therefore
% encouraged to load and test the software's suitability as regards their
% requirements in conditions enabling the security of their systems and/or
% data to be ensured and, more generally, to use and operate it in the
% same conditions as regards security.
%
%------------------------------------------------------------------------
function creaseg_plotresults(src,evt)
%-- parameters
fig = findobj(0,'tag','creaseg');
ud = get(fig,'userdata');
fd = get(ud.imageId,'userdata');
if ( isempty(fd.data) )
return;
end
delete(findobj(get(ud.imageId,'parent'),'type','line'));
color = {'w','y','b','c','r','g','m','k'};
if get(ud.handleAlgoResults(5),'Value')
show_contour(fd.reference,ud,color(1));
end
for i=1:1:7
if ( get(ud.handleAlgoResults(5+i),'Value') )
show_contour(fd.seg(:,:,i),ud,color(i+1));
end
end
function show_contour(mask,ud,cl)
axes(get(ud.imageId,'parent'));
hold on; [c,h] = contour(mask,[0 0],cl{1},'Linewidth',3); hold off;
delete(h);
test = isequal(size(c,2),0);
while ( (test==false) && (size(c,1) ~= 0) && (size(c,2) ~= 0) )
s = c(2,1);
if ( s == (size(c,2)-1) )
t = c;
hold on; plot(t(1,2:end)',t(2,2:end)',cl{1},'linewidth',3);
test = true;
else
t = c(:,2:s+1);
hold on; plot(t(1,1:end)',t(2,1:end)',cl{1},'linewidth',3);
c = c(:,s+2:end);
end
end
|
github
|
jacksky64/imageProcessing-master
|
localized_seg.m
|
.m
|
imageProcessing-master/segmentation/ActiveContours/Activeontours/localized_seg.m
| 7,761 |
utf_8
|
a248792540c81523bb28994de267cee4
|
% Localized Region Based Active Contour Segmentation:
%
% seg = localized_seg(I,init_mask,max_its,rad,alpha,method)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% rad (optional) Localization Radius (in pixels)
% smaller = more local, bigger = more global
% alpha (optional) Weight of smoothing term
% higer = smoother
% method (optional) selects localized energy
% 1 = Chan-Vese Energy
% 2 = Yezzi Energy (usually works better)
%
% Outputs: seg Final segmentation mask (1=fg, 0=bg)
%
% Example:
% img = imread('tire.tif'); %-- load the image
% m = false(size(img)); %-- create initial mask
% m(28:157,37:176) = true;
% seg = localized_seg(img,m,150);
%
% Description: This code implements the paper: "Localizing Region Based
% Active Contours" By Lankton and Tannenbaum. In this work, typical
% region-based active contour energies are localized in order to handle
% images with non-homogeneous foregrounds and backgrounds.
%
% Coded by: Shawn Lankton (www.shawnlankton.com)
%------------------------------------------------------------------------
function seg = localized_seg(I,init_mask,max_its,rad,alpha,method,FigRefreshRate,display)
%-- default value for parameter alpha is .1
if(~exist('alpha','var'))
alpha = .2;
end
%-- default value for parameter method is 2
if(~exist('method','var'))
method = 2;
end
%-- default behavior is to display intermediate outputs
if(~exist('display','var'))
display = true;
end
if(~exist('FigRefreshRate','var'))
FigRefreshRate =20;
end
%-- Ensures image is 2D double matrix
I = im2graydouble(I);
%-- Default localization radius is 1/10 of average length
[dimy dimx] = size(I);
if(~exist('rad','var'))
rad = round((dimy+dimx)/(2*8));
if(display>0)
disp(['localiztion radius is: ' num2str(rad) ' pixels']);
end
end
%-- Create a signed distance map (SDF) from mask
phi = mask2phi(init_mask);
%--main loop
for its = 1:max_its % Note: no automatic convergence test
%-- get the curve's narrow band
idx = find(phi <= 1.2 & phi >= -1.2)';
[y x] = ind2sub(size(phi),idx);
%-- get windows for localized statistics
xneg = x-rad; xpos = x+rad; %get subscripts for local regions
yneg = y-rad; ypos = y+rad;
xneg(xneg<1)=1; yneg(yneg<1)=1; %check bounds
xpos(xpos>dimx)=dimx; ypos(ypos>dimy)=dimy;
%-- re-initialize u,v,Ain,Aout
u=zeros(size(idx)); v=zeros(size(idx));
Ain=zeros(size(idx)); Aout=zeros(size(idx));
%-- compute local stats
for i = 1:numel(idx) % for every point in the narrow band
img = I(yneg(i):ypos(i),xneg(i):xpos(i)); %sub image
P = phi(yneg(i):ypos(i),xneg(i):xpos(i)); %sub phi
upts = find(P<=0); %local interior
Ain(i) = length(upts)+eps;
u(i) = sum(img(upts))/Ain(i);
vpts = find(P>0); %local exterior
Aout(i) = length(vpts)+eps;
v(i) = sum(img(vpts))/Aout(i);
end
%-- get image-based forces
switch method %-choose which energy is localized
case 1, %-- CHAN VESE
F = -(u-v).*(2.*I(idx)-u-v);
otherwise, %-- YEZZI
F = -((u-v).*((I(idx)-u)./Ain+(I(idx)-v)./Aout));
end
%-- get forces from curvature penalty
curvature = get_curvature(phi,idx,x,y);
%-- gradient descent to minimize energy
dphidt = F./max(abs(F)) + alpha*curvature;
%-- maintain the CFL condition
dt = .45/(max(dphidt)+eps);
%-- evolve the curve
phi(idx) = phi(idx) + dt.*dphidt;
%-- Keep SDF smooth
phi = sussman(phi, .5);
%-- intermediate output
if((display>0)&&(mod(its,FigRefreshRate) == 0))
showCurveAndPhi(I,phi,its);
end
end
%-- final output
if(display)
showCurveAndPhi(I,phi,its);
end
%-- make mask from SDF
seg = phi<=0; %-- Get mask from levelset
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- AUXILIARY FUNCTIONS ----------------------------------------------
%---------------------------------------------------------------------
%---------------------------------------------------------------------
%-- Displays the image with curve superimposed
function showCurveAndPhi(I, phi, i)
imshow(I,[]); hold on;
contour(phi, [0 0], 'g','LineWidth',4);
contour(phi, [0 0], 'k','LineWidth',2);
title(['Localized Region Based Active Contour Segmentation ',num2str(i) ' Iterations']); hold off;drawnow;
%-- converts a mask to a SDF
function phi = mask2phi(init_a)
phi=bwdist(init_a)-bwdist(1-init_a)+im2double(init_a)-.5;
%-- compute curvature along SDF
function curvature = get_curvature(phi,idx,x,y)
[dimy, dimx] = size(phi);
%-- get subscripts of neighbors
ym1 = y-1; xm1 = x-1; yp1 = y+1; xp1 = x+1;
%-- bounds checking
ym1(ym1<1) = 1; xm1(xm1<1) = 1;
yp1(yp1>dimy)=dimy; xp1(xp1>dimx) = dimx;
%-- get indexes for 8 neighbors
idup = sub2ind(size(phi),yp1,x);
iddn = sub2ind(size(phi),ym1,x);
idlt = sub2ind(size(phi),y,xm1);
idrt = sub2ind(size(phi),y,xp1);
idul = sub2ind(size(phi),yp1,xm1);
idur = sub2ind(size(phi),yp1,xp1);
iddl = sub2ind(size(phi),ym1,xm1);
iddr = sub2ind(size(phi),ym1,xp1);
%-- get central derivatives of SDF at x,y
phi_x = -phi(idlt)+phi(idrt);
phi_y = -phi(iddn)+phi(idup);
phi_xx = phi(idlt)-2*phi(idx)+phi(idrt);
phi_yy = phi(iddn)-2*phi(idx)+phi(idup);
phi_xy = -0.25*phi(iddl)-0.25*phi(idur)...
+0.25*phi(iddr)+0.25*phi(idul);
phi_x2 = phi_x.^2;
phi_y2 = phi_y.^2;
%-- compute curvature (Kappa)
curvature = ((phi_x2.*phi_yy + phi_y2.*phi_xx - 2*phi_x.*phi_y.*phi_xy)./...
(phi_x2 + phi_y2 +eps).^(3/2)).*(phi_x2 + phi_y2).^(1/2);
%-- Converts image to one channel (grayscale) double
function img = im2graydouble(img)
[dimy, dimx, c] = size(img);
if(isfloat(img)) % image is a double
if(c==3)
img = rgb2gray(uint8(img));
end
else % image is a int
if(c==3)
img = rgb2gray(img);
end
img = double(img);
end
%-- level set re-initialization by the sussman method
function D = sussman(D, dt)
% forward/backward differences
a = D - shiftR(D); % backward
b = shiftL(D) - D; % forward
c = D - shiftD(D); % backward
d = shiftU(D) - D; % forward
a_p = a; a_n = a; % a+ and a-
b_p = b; b_n = b;
c_p = c; c_n = c;
d_p = d; d_n = d;
a_p(a < 0) = 0;
a_n(a > 0) = 0;
b_p(b < 0) = 0;
b_n(b > 0) = 0;
c_p(c < 0) = 0;
c_n(c > 0) = 0;
d_p(d < 0) = 0;
d_n(d > 0) = 0;
dD = zeros(size(D));
D_neg_ind = find(D < 0);
D_pos_ind = find(D > 0);
dD(D_pos_ind) = sqrt(max(a_p(D_pos_ind).^2, b_n(D_pos_ind).^2) ...
+ max(c_p(D_pos_ind).^2, d_n(D_pos_ind).^2)) - 1;
dD(D_neg_ind) = sqrt(max(a_n(D_neg_ind).^2, b_p(D_neg_ind).^2) ...
+ max(c_n(D_neg_ind).^2, d_p(D_neg_ind).^2)) - 1;
D = D - dt .* sussman_sign(D) .* dD;
%-- whole matrix derivatives
function shift = shiftD(M)
shift = shiftR(M')';
function shift = shiftL(M)
shift = [ M(:,2:size(M,2)) M(:,size(M,2)) ];
function shift = shiftR(M)
shift = [ M(:,1) M(:,1:size(M,2)-1) ];
function shift = shiftU(M)
shift = shiftL(M')';
function S = sussman_sign(D)
S = D ./ sqrt(D.^2 + 1);
|
github
|
jacksky64/imageProcessing-master
|
ActiveContoursWihoutEdges.m
|
.m
|
imageProcessing-master/segmentation/ActiveContours/Activeontours/ActiveContoursWihoutEdges.m
| 6,749 |
utf_8
|
2556c180e19cbdcde6de9d1172c0064d
|
function ActiveContoursWihoutEdges(hObject,mask)
%This function implements the paper "Active Contours without Edges" by
%Tony Chan and Luminita Vese. It also present results accourding to user
%wish (from ActiveCountorsGUI). Coding- Nikolay S. & Alex B.
%Input argument- a Handle to an object of ActiveCountorsGUI
handles=guidata(hObject);
%% get Alg parametrs from GUI
N=get(handles.NAlgEdit,'Value'); % number of iterations
Lambda_1=get(handles.Lambda1AlgEdit,'Value');
Lambda_2=get(handles.Lambda2AlgEdit,'Value');
miu=get(handles.MiuAlgEdit,'Value'); % varies from 255*1e-1 to 255*1e-4
v=get(handles.NuAlgEdit,'Value');
delta_t=get(handles.DeltaTAlgEdit,'Value');
HTflag=get(handles.HTBasedAlg,'Value');
%% Get visual/plotting parameters
FigRefresRate=get(handles.RefreshRateEdit,'Value');
SaveRefresRate=get(handles.SaveRateEdit,'Value');
SegDispaly=get(handles.SegmentOn,'Value');
EnergyDispaly=get(handles.EnergyOn,'Value');
EnergyImageType=get(handles.EnergyPlotTypeMenu,'Value');
no_of_plots=SegDispaly+EnergyDispaly;
uipael_handle=handles.Axis_uipanel;
if no_of_plots==1
%set(handles.GUIfig,'CurrentAxes',handles.Axes)
subplot(no_of_plots,1,1,'Units','Normalized','Parent',uipael_handle)
end
%% get I/O parameters
out_dir=handles.OutDirPath;
% get file name from path
file_str=handles.ImageFileAddr;
% [pathstr, name, ext, versn] = fileparts(filename)
[~, in_file_name, ~] = fileparts(file_str);
text_line_length=60;
%% divide name too long to cell array to allow compact presentation in text command
length_file_str=length(file_str);
cell_array_length=ceil(length_file_str/text_line_length);
file_str4text=cell(1,cell_array_length);
for ind=1:cell_array_length-1
file_str4text{ind}=[file_str((1+(ind-1)*text_line_length):...
ind*text_line_length),'...'];
end
file_str4text{ind+1}=file_str((1+ind*text_line_length):end);
%% load image
img=handles.ImageData;
[img_x,img_y]=size(img);
%% Init Phi
phi=bwdist(1-mask)-bwdist(mask);
% phi=phi.^3;
phi=phi/(max(max(phi))-min(min(phi))); %normilize to amplitude=1
% K=zeros(img_x,img_y); %init K matrix
if ~strcmpi(class(phi),'double')
phi=double(phi);
end
%define HT
if (HTflag)
% x=-i*sign(linspace(0,1,150)-.5);
N_U=10;N_V=10;
[U,V]=meshgrid(linspace(-fix(N_U/2),fix(N_U/2),N_U),linspace(-fix(N_V/2),fix(N_V/2),N_U));
H_UV_HT=sign(U).*sign(V);
h_ht=fftshift(ifft(H_UV_HT));
end
for n=1:N % main loop
%% Active contours iterations
c_1=sum(img(phi>=0))/max(1,length(img(phi>0))); % prevent division by zero
c_2=sum(img(phi<0))/max(1,length(img(phi<0))); % prevent division by zero
if (HTflag)
dx=filter2(h_ht,phi,'same');
dy=dx.';
else
[dx,dy]=gradient(phi);
end
grad_norm=max(eps,sqrt(dx.^2+dy.^2));%we want to prevent division by zero
% [dxx,dxy]=gradient(dx);
% [dyx,dyy]=gradient(dy);
% K=(dxx.*dy.^2-2*dx.*dy.*dxy+dyy.*dx.^2)./(grad_norm).^3; % another way to define div(grad/|grad|)
K=divergence(dx./grad_norm,dy./grad_norm); %this one is a bit faster
speed = Delta_eps(phi).*(miu*K-v-Lambda_1*(img-c_1).^2+Lambda_2*(img-c_2).^2);
speed =speed/ sqrt(sum(sum(speed.^2)));%norm by square root of sum of square elements
phi=phi+delta_t*speed;
%% Presenting relevant graphs
if (~mod(n,FigRefresRate)) % it's time to draw current image
pause(0.001);
if (SegDispaly)
if (EnergyDispaly) % two axis on display
subplot(no_of_plots,1,1,'Units','Normalized',...
'Parent',uipael_handle);
end
% imshow(uint8(repmat(img,[1,1,3])));hold on;
strechedImg=img-min(img(:)); % now values are between 0:Max
strechedImg=255*strechedImg/max(strechedImg(:)); % now values between 0:255
imshow(repmat(uint8(strechedImg),[1,1,3]),[]); hold on;
contour(sign(phi),[0 0],'g','LineWidth',2);
iterNum=['Segmentation with: Active Contours wihtout Edges, ',num2str(n),' iterations'];
title(iterNum,'FontSize',14);
axis off;axis equal;hold off;
end
if (EnergyDispaly)
if (SegDispaly) % two axis on display
subplot(no_of_plots,1,2,'Units','Normalized',...
'Parent',uipael_handle)
end
switch(EnergyImageType)
case(1) %surf(phi)
surf(phi);
case(2) %mesh(phi)
mesh(phi);
case(3) %imagesc(phi)
imagesc(phi);
axis equal;
axis off;
case(4) %surf(sign|phi|)
surf(sign(phi));
case(5) %mesh(sign|phi|)
mesh(sign(phi));
case(6) %imagesc(sign|phi|);
imagesc(sign(phi));
axis equal;axis off;
end
colormap('Jet');
title(['\phi_{',num2str(n),'}'],'FontSize',18);
end
if (SegDispaly)&&(EnergyDispaly)
text_pos=[0.5,1.35];
else
text_pos=[0.5,-0.02];
end
text(text_pos(1),text_pos(2),{'Applied to file:',file_str4text{:}},...
'HorizontalAlignment','center','Units','Normalized',...
'FontUnits','Normalized');
drawnow;
%% it's time to save current image
if (~mod(n,SaveRefresRate))
tmp=zeros(size(img,1),size(img,2));
tmp(phi>0)=255;
temp_img=repmat(img(:,:,1),[1 1 3]);
temp_img(:,:,2)=temp_img(:,:,2)+tmp;
imwrite(uint8(temp_img),[out_dir,filesep,in_file_name,'Segment_n_',num2str(n),'.jpg'],'jpg');
max_phi=max(max(phi));min_phi=min(min(phi));
tmp=phi; tmp=255*(tmp-min_phi)/(max_phi-min_phi);
imwrite(uint8(tmp),[out_dir,filesep,in_file_name,'Phi_n_',num2str(n),'.jpg'],'jpg');
% saveas(gca,[out_dir,filesep,num2str(n)],'fig') ;%save figure
end % if (~mod(n,SaveRefresRate))
end % if (~mod(n,FigRefresRate))
end % for n=1:N % main loop
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Servise sub function %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function out=Delta_eps(z,epsilon)
if nargin==1
epsilon=1;
end
out=epsilon/pi./(epsilon^2+z.^2);
% out=(1/(2*epsilon))*(1+cos(pi*z/epsilon)).*(abs(z)<=epsilon);
|
github
|
jacksky64/imageProcessing-master
|
LevelSetEvolutionWithoutReinitialization.m
|
.m
|
imageProcessing-master/segmentation/ActiveContours/Activeontours/LevelSetEvolutionWithoutReinitialization.m
| 5,441 |
utf_8
|
e95cf463293db58a1afa6f202ea6596d
|
function LevelSetEvolutionWithoutReinitialization(Img,sigma,epsilon,mu,lambda,alf,c0,N,PlotRate,mask)
% This Matlab file demomstrates the level set method in Li et al's paper
% "Level Set Evolution Without Re-initialization: A New Variational Formulation"
% in Proceedings of CVPR'05, vol. 1, pp. 430-436.
% Author: Chunming Li, all rights reserved.
% E-mail: [email protected]
% URL: http://www.engr.uconn.edu/~cmli/
if(~exist('PlotRate','var'))
PlotRate = 20;
end
% Img = imread('twoObj.bmp'); % The same cell image in the paper is used here
Img=double(Img(:,:,1));
% sigma=1.5; % scale parameter in Gaussian kernel for smoothing.
G=fspecial('gaussian',15,sigma);
Img_smooth=conv2(Img,G,'same'); % smooth image by Gaussiin convolution
[Ix,Iy]=gradient(Img_smooth);
f=Ix.^2+Iy.^2;
g=1./(1+f); % edge indicator function.
% epsilon=1.5; % the papramater in the definition of smoothed Dirac function
% mu=0.04;
timestep=0.2/mu;
% timestep=5; % time step, try timestep=10, 20, ..., 50, ...
% mu=0.2/timestep; % coefficient of the internal (penalizing) energy term P(\phi)
% Note: the product timestep*mu must be less than 0.25 for stability!
% lambda=5; % coefficient of the weighted length term Lg(\phi)
% alf=1.5; % coefficient of the weighted area term Ag(\phi);
% Note: Choose a positive(negative) alf if the initial contour is outside(inside) the object.
[nrow, ncol]=size(Img);
% figure(1);
% imagesc(Img, [0, 255]);colormap(gray);hold on;
% text(10,10,{'1.Left click to get points, right click to get end point','2.Drag the shape to desired posiiton',...
% '3.Double click to run the algorithm'},'FontSize',[12],'Color', 'r');
%
% % Click mouse to specify initial contour/region
% BW = roipoly; % get a region R inside a polygon, BW is a binary image with 1 and 0 inside or outside the polygon;
% % c0=4; % the constant value used to define binary level set function;
initialLSF= c0*2*(0.5-mask); % initial level set function: -c0 inside R, c0 outside R;
u=initialLSF;
% [nrow, ncol]=size(Img);
% initialLSF=c0*ones(nrow,ncol);
% w=round((nrow+ncol)/20);
% initialLSF(w+1:end-w, w+1:end-w)=0; % zero level set is on the boundary of R.
% % Note: this can be commented out. The intial LSF does NOT necessarily need a zero level set.
% initialLSF(w+2:end-w-1, w+2: end-w-1)=-c0; % negative constant -c0 inside of R, postive constant c0 outside of R.
% u=initialLSF;
imshow(Img, []); hold on; axis off;axis equal;
contour(u,[0 0],'r','LineWidth',2);
title('Initial contour');
% start level set evolution
for n=1:N
u=EVOLUTION(u, g ,lambda, mu, alf, epsilon, timestep, 1);
if mod(n,PlotRate)==0
pause(0.001);
imshow(Img, []); hold on;axis off;axis equal;
contour(u,[0 0],'r','LineWidth',2);
iterNum=['Level Set Evolution Without Re-initialization: A New Variational Formulation ',num2str(n),' iterations'];
title(iterNum);
hold off;
end
end
imshow(Img, []);hold on;
contour(u,[0 0],'r','LineWidth',2);
axis off;axis equal;
iterNum=['Level Set Evolution Without Re-initialization: A New Variational Formulation ',num2str(n),' iterations'];
title(iterNum);
function u = EVOLUTION(u0, g, lambda, mu, alf, epsilon, delt, numIter)
% EVOLUTION(u0, g, lambda, mu, alf, epsilon, delt, numIter) updates the level set function
% according to the level set evolution equation in Chunming Li et al's paper:
% "Level Set Evolution Without Reinitialization: A New Variational Formulation"
% in Proceedings CVPR'2005,
% Usage:
% u0: level set function to be updated
% g: edge indicator function
% lambda: coefficient of the weighted length term L(\phi)
% mu: coefficient of the internal (penalizing) energy term P(\phi)
% alf: coefficient of the weighted area term A(\phi), choose smaller alf
% epsilon: the papramater in the definition of smooth Dirac function, default value 1.5
% delt: time step of iteration, see the paper for the selection of time step and mu
% numIter: number of iterations.
%
% Author: Chunming Li, all rights reserved.
% e-mail: [email protected]
% http://vuiis.vanderbilt.edu/~licm/
u=u0;
[vx,vy]=gradient(g);
for k=1:numIter
u=NeumannBoundCond(u);
[ux,uy]=gradient(u);
normDu=sqrt(ux.^2 + uy.^2 + 1e-10);
Nx=ux./normDu;
Ny=uy./normDu;
diracU=Dirac(u,epsilon);
K=curvature_central(Nx,Ny);
weightedLengthTerm=lambda*diracU.*(vx.*Nx + vy.*Ny + g.*K);
penalizingTerm=mu*(4*del2(u)-K);
weightedAreaTerm=alf.*diracU.*g;
u=u+delt*(weightedLengthTerm + weightedAreaTerm + penalizingTerm); % update the level set function
end
% the following functions are called by the main function EVOLUTION
function f = Dirac(x, sigma)
f=(1/2/sigma)*(1+cos(pi*x/sigma));
b = (x<=sigma) & (x>=-sigma);
f = f.*b;
function K = curvature_central(nx,ny)
[nxx,junk]=gradient(nx);
[junk,nyy]=gradient(ny);
K=nxx+nyy;
function g = NeumannBoundCond(f)
% Make a function satisfy Neumann boundary condition
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
|
github
|
jacksky64/imageProcessing-master
|
maskcircle2.m
|
.m
|
imageProcessing-master/segmentation/Chan-Vese/maskcircle2.m
| 2,101 |
utf_8
|
5172c5d8c496225ecc2354991153d114
|
function m = maskcircle2(I,type)
% auto pick a circular mask for image I
% built-in mask creation function
% Input: I : input image
% type: mask shape keywords
% Output: m : mask image
% Copyright (c) 2009,
% Yue Wu @ ECE Department, Tufts University
% All Rights Reserved
if size(I,3)~=3
temp = double(I(:,:,1));
else
temp = double(rgb2gray(I));
end
h = [0 1 0; 1 -4 1; 0 1 0];
T = conv2(temp,h);
T(1,:) = 0;
T(end,:) = 0;
T(:,1) = 0;
T(:,end) = 0;
thre = max(max(abs(T)))*.5;
idx = find(abs(T) > thre);
[cx,cy] = ind2sub(size(T),idx);
cx = round(mean(cx));
cy = round(mean(cy));
[x,y] = meshgrid(1:min(size(temp,1),size(temp,2)));
m = zeros(size(temp));
[p,q] = size(temp);
switch lower (type)
case 'small'
r = 10;
n = zeros(size(x));
n((x-cx).^2+(y-cy).^2<r.^2) = 1;
m(1:size(n,1),1:size(n,2)) = n;
%m((x-cx).^2+(y-cy).^2<r.^2) = 1;
case 'medium'
r = min(min(cx,p-cx),min(cy,q-cy));
r = max(2/3*r,25);
n = zeros(size(x));
n((x-cx).^2+(y-cy).^2<r.^2) = 1;
m(1:size(n,1),1:size(n,2)) = n;
%m((x-cx).^2+(y-cy).^2<r.^2) = 1;
case 'large'
r = min(min(cx,p-cx),min(cy,q-cy));
r = max(2/3*r,60);
n = zeros(size(x));
n((x-cx).^2+(y-cy).^2<r.^2) = 1;
m(1:size(n,1),1:size(n,2)) = n;
%m((x-cx).^2+(y-cy).^2<r.^2) = 1;
case 'whole'
r = 9;
m = zeros(round(ceil(max(p,q)/2/(r+1))*3*(r+1)));
siz = size(m,1);
sx = round(siz/2);
i = 1:round(siz/2/(r+1));
j = 1:round(0.9*siz/2/(r+1));
j = j-round(median(j));
m(sx+2*j*(r+1),(2*i-1)*(r+1)) = 1;
se = strel('disk',r);
m = imdilate(m,se);
m = m(round(siz/2-p/2-6):round(siz/2-p/2-6)+p-1,round(siz/2-q/2-6):round(siz/2-q/2-6)+q-1);
end
tem(:,:,1) = m;
M = padarray(m,[floor(2/3*r),floor(2/3*r)],0,'post');
tem(:,:,2) = M(floor(2/3*r)+1:end,floor(2/3*r)+1:end);
m = tem;
return
|
github
|
jacksky64/imageProcessing-master
|
chenvese.m
|
.m
|
imageProcessing-master/segmentation/Chan-Vese/chenvese.m
| 14,373 |
utf_8
|
03a43bed54d85efa5e620ce6bc002161
|
%==========================================================================
%
% Active contour with Chen-Vese Method
% for image segementation
%
% Implemented by Yue Wu ([email protected])
% Tufts University
% Feb 2009
% http://sites.google.com/site/rexstribeofimageprocessing/
%
% all rights reserved
% Last update 02/26/2009
%--------------------------------------------------------------------------
% Usage of varibles:
% input:
% I = any gray/double/RGB input image
% mask = initial mask, either customerlized or built-in
% num_iter = total number of iterations
% mu = weight of length term
% method = submethods pick from ('chen','vector','multiphase')
%
% Types of built-in mask functions
% 'small' = create a small circular mask
% 'medium' = create a medium circular mask
% 'large' = create a large circular mask
% 'whole' = create a mask with holes around
% 'whole+small' = create a two layer mask with one layer small
% circular mask and the other layer with holes around
% (only work for method 'multiphase')
% Types of methods
% 'chen' = general CV method
% 'vector' = CV method for vector image
% 'multiphase'= CV method for multiphase (2 phases applied here)
%
% output:
% phi0 = updated level set function
%
%--------------------------------------------------------------------------
%
% Description: This code implements the paper: "Active Contours Without
% Edges" by Chan and Vese for method 'chen', the paper:"Active Contours Without
% Edges for vector image" by Chan and Vese for method 'vector', and the paper
% "A Multiphase Level Set Framework for Image Segmentation Using the
% Mumford and Shah Model" by Chan and Vese.
%
%--------------------------------------------------------------------------
% Deomo: Please see HELP file for details
%==========================================================================
function seg = chenvese(I,mask,num_iter,mu,method)
%%
%-- Default settings
% length term mu = 0.2 and default method = 'chan'
if(~exist('mu','var'))
mu=0.2;
end
if(~exist('method','var'))
method = 'chan';
end
%-- End default settings
%%
%-- Initializations on input image I and mask
% resize original image
s = 200./min(size(I,1),size(I,2)); % resize scale
if s<1
I = imresize(I,s);
end
% auto mask settings
if ischar(mask)
switch lower (mask)
case 'small'
mask = maskcircle2(I,'small');
case 'medium'
mask = maskcircle2(I,'medium');
case 'large'
mask = maskcircle2(I,'large');
case 'whole'
mask = maskcircle2(I,'whole');
%mask = init_mask(I,30);
case 'whole+small'
m1 = maskcircle2(I,'whole');
m2 = maskcircle2(I,'small');
mask = zeros(size(I,1),size(I,2),2);
mask(:,:,1) = m1(:,:,1);
mask(:,:,2) = m2(:,:,2);
otherwise
error('unrecognized mask shape name (MASK).');
end
else
if s<1
mask = imresize(mask,s);
end
if size(mask,1)>size(I,1) || size(mask,2)>size(I,2)
error('dimensions of mask unmathch those of the image.')
end
switch lower(method)
case 'multiphase'
if (size(mask,3) == 1)
error('multiphase requires two masks but only gets one.')
end
end
end
switch lower(method)
case 'chan'
if size(I,3)== 3
P = rgb2gray(uint8(I));
P = double(P);
elseif size(I,3) == 2
P = 0.5.*(double(I(:,:,1))+double(I(:,:,2)));
else
P = double(I);
end
layer = 1;
case 'vector'
s = 200./min(size(I,1),size(I,2)); % resize scale
I = imresize(I,s);
mask = imresize(mask,s);
layer = size(I,3);
if layer == 1
display('only one image component for vector image')
end
P = double(I);
case 'multiphase'
layer = size(I,3);
if size(I,1)*size(I,2)>200^2
s = 200./min(size(I,1),size(I,2)); % resize scale
I = imresize(I,s);
mask = imresize(mask,s);
end
P = double(I); %P store the original image
otherwise
error('!invalid method')
end
%-- End Initializations on input image I and mask
%%
%-- Core function
switch lower(method)
case {'chan','vector'}
%-- SDF
% Get the distance map of the initial mask
mask = mask(:,:,1);
phi0 = bwdist(mask)-bwdist(1-mask)+im2double(mask)-.5;
% initial force, set to eps to avoid division by zeros
force = eps;
%-- End Initialization
%-- Display settings
figure();
subplot(2,2,1); imshow(I); title('Input Image');
subplot(2,2,2); contour(flipud(phi0), [0 0], 'r','LineWidth',1); title('initial contour');
subplot(2,2,3); title('Segmentation');
%-- End Display original image and mask
%-- Main loop
for n=1:num_iter
inidx = find(phi0>=0); % frontground index
outidx = find(phi0<0); % background index
force_image = 0; % initial image force for each layer
for i=1:layer
L = im2double(P(:,:,i)); % get one image component
c1 = sum(sum(L.*Heaviside(phi0)))/(length(inidx)+eps); % average inside of Phi0
c2 = sum(sum(L.*(1-Heaviside(phi0))))/(length(outidx)+eps); % verage outside of Phi0
force_image=-(L-c1).^2+(L-c2).^2+force_image;
% sum Image Force on all components (used for vector image)
% if 'chan' is applied, this loop become one sigle code as a
% result of layer = 1
end
% calculate the external force of the image
force = mu*kappa(phi0)./max(max(abs(kappa(phi0))))+1/layer.*force_image;
% normalized the force
force = force./(max(max(abs(force))));
% get stepsize dt
dt=0.5;
% get parameters for checking whether to stop
old = phi0;
phi0 = phi0+dt.*force;
new = phi0;
indicator = checkstop(old,new,dt);
% intermediate output
if(mod(n,20) == 0)
showphi(I,phi0,n);
end;
if indicator % decide to stop or continue
showphi(I,phi0,n);
%make mask from SDF
seg = phi0<=0; %-- Get mask from levelset
subplot(2,2,4); imshow(seg); title('Global Region-Based Segmentation');
return;
end
end;
showphi(I,phi0,n);
%make mask from SDF
seg = phi0<=0; %-- Get mask from levelset
subplot(2,2,4); imshow(seg); title('Global Region-Based Segmentation');
case 'multiphase'
%-- Initializations
% Get the distance map of the initial masks
mask1 = mask(:,:,1);
mask2 = mask(:,:,2);
phi1=bwdist(mask1)-bwdist(1-mask1)+im2double(mask1)-.5;%Get phi1 from the initial mask 1
phi2=bwdist(mask2)-bwdist(1-mask2)+im2double(mask2)-.5;%Get phi1 from the initial mask 2
%-- Display settings
figure();
subplot(2,2,1);
if layer ~= 1
imshow(I); title('Input Image');
else
imagesc(P); axis image; colormap(gray);title('Input Image');
end
subplot(2,2,2);
hold on
contour(flipud(mask1),[0,0],'r','LineWidth',2.5);
contour(flipud(mask1),[0,0],'x','LineWidth',1);
contour(flipud(mask2),[0,0],'g','LineWidth',2.5);
contour(flipud(mask2),[0,0],'x','LineWidth',1);
title('initial contour');
hold off
subplot(2,2,3); title('Segmentation');
%-- End display settings
%Main loop
for n=1:num_iter
%-- Narrow band for each phase
nb1 = find(phi1<1.2 & phi1>=-1.2); %narrow band of phi1
inidx1 = find(phi1>=0); %phi1 frontground index
outidx1 = find(phi1<0); %phi1 background index
nb2 = find(phi2<1.2 & phi2>=-1.2); %narrow band of phi2
inidx2 = find(phi2>=0); %phi2 frontground index
outidx2 = find(phi2<0); %phi2 background index
%-- End initiliazaions on narrow band
%-- Mean calculations for different partitions
%c11 = mean (phi1>0 & phi2>0)
%c12 = mean (phi1>0 & phi2<0)
%c21 = mean (phi1<0 & phi2>0)
%c22 = mean (phi1<0 & phi2<0)
cc11 = intersect(inidx1,inidx2); %index belong to (phi1>0 & phi2>0)
cc12 = intersect(inidx1,outidx2); %index belong to (phi1>0 & phi2<0)
cc21 = intersect(outidx1,inidx2); %index belong to (phi1<0 & phi2>0)
cc22 = intersect(outidx1,outidx2); %index belong to (phi1<0 & phi2<0)
f_image11 = 0;
f_image12 = 0;
f_image21 = 0;
f_image22 = 0; % initial image force for each layer
for i=1:layer
L = im2double(P(:,:,i)); % get one image component
if isempty(cc11)
c11 = eps;
else
c11 = mean(L(cc11));
end
if isempty(cc12)
c12 = eps;
else
c12 = mean(L(cc12));
end
if isempty(cc21)
c21 = eps;
else
c21 = mean(L(cc21));
end
if isempty(cc22)
c22 = eps;
else
c22 = mean(L(cc22));
end
%-- End mean calculation
%-- Force calculation and normalization
% force on each partition
f_image11=(L-c11).^2.*Heaviside(phi1).*Heaviside(phi2)+f_image11;
f_image12=(L-c12).^2.*Heaviside(phi1).*(1-Heaviside(phi2))+f_image12;
f_image21=(L-c21).^2.*(1-Heaviside(phi1)).*Heaviside(phi2)+f_image21;
f_image22=(L-c22).^2.*(1-Heaviside(phi1)).*(1-Heaviside(phi2))+f_image22;
end
% sum Image Force on all components (used for vector image)
% if 'chan' is applied, this loop become one sigle code as a
% result of layer = 1
% calculate the external force of the image
% curvature on phi1
curvature1 = mu*kappa(phi1);
curvature1 = curvature1(nb1);
% image force on phi1
fim1 = 1/layer.*(-f_image11(nb1)+f_image21(nb1)-f_image12(nb1)+f_image22(nb1));
fim1 = fim1./max(abs(fim1)+eps);
% curvature on phi2
curvature2 = mu*kappa(phi2);
curvature2 = curvature2(nb2);
% image force on phi2
fim2 = 1/layer.*(-f_image11(nb2)+f_image12(nb2)-f_image21(nb2)+f_image22(nb2));
fim2 = fim2./max(abs(fim2)+eps);
% force on phi1 and phi2
force1 = curvature1+fim1;
force2 = curvature2+fim2;
%-- End force calculation
% detal t
dt = 1.5;
old(:,:,1) = phi1;
old(:,:,2) = phi2;
%update of phi1 and phi2
phi1(nb1) = phi1(nb1)+dt.*force1;
phi2(nb2) = phi2(nb2)+dt.*force2;
new(:,:,1) = phi1;
new(:,:,2) = phi2;
indicator = checkstop(old,new,dt);
if indicator
showphi(I, new, n);
%make mask from SDF
seg11 = (phi1>0 & phi2>0); %-- Get mask from levelset
seg12 = (phi1>0 & phi2<0);
seg21 = (phi1<0 & phi2>0);
seg22 = (phi1<0 & phi2<0);
se = strel('disk',1);
aa1 = imerode(seg11,se);
aa2 = imerode(seg12,se);
aa3 = imerode(seg21,se);
aa4 = imerode(seg22,se);
seg = aa1+2*aa2+3*aa3+4*aa4;
subplot(2,2,4); imagesc(seg);axis image;title('Global Region-Based Segmentation');
return
end
% re-initializations
phi1 = reinitialization(phi1, 0.6);%sussman(phi1, 0.6);%
phi2 = reinitialization(phi2, 0.6);%sussman(phi2,0.6);
%intermediate output
if(mod(n,20) == 0)
phi(:,:,1) = phi1;
phi(:,:,2) = phi2;
showphi(I, phi, n);
end;
end;
phi(:,:,1) = phi1;
phi(:,:,2) = phi2;
showphi(I, phi, n);
%make mask from SDF
seg11 = (phi1>0 & phi2>0); %-- Get mask from levelset
seg12 = (phi1>0 & phi2<0);
seg21 = (phi1<0 & phi2>0);
seg22 = (phi1<0 & phi2<0);
se = strel('disk',1);
aa1 = imerode(seg11,se);
aa2 = imerode(seg12,se);
aa3 = imerode(seg21,se);
aa4 = imerode(seg22,se);
seg = aa1+2*aa2+3*aa3+4*aa4;
%seg = bwlabel(seg);
subplot(2,2,4); imagesc(seg);axis image;title('Global Region-Based Segmentation');
end
|
github
|
jacksky64/imageProcessing-master
|
thresholdLocally.m
|
.m
|
imageProcessing-master/segmentation/segmenttool/thresholdLocally.m
| 5,276 |
utf_8
|
c3ad5f3e07a48056bf2ec67e275a56d9
|
function B = thresholdLocally(A, blksz, varargin)
% Performs LOCAL Otsu thresholding on an image; user can specify blocksize
%
% SYNTAX: B = thresholdLocally(A,PV_Pairs)
%
% THRESHOLDLOCALLY processes an image, calling graythresh on LOCAL
% blocks in an image. This facilitates easy thresholding of images with
% uneven background illumination, for which global thresholding is
% inadequate. Uses the Image Processing Toolbox function BLOCKPROC
% (R2009b).
%
% INPUTS:
% A: Any image (or path/name of an image) suitable for processing
% with im2bw()
%
% BLKSZ: Blocksize ([M,N]) with which to process the image.
% DEFAULT: [32 32]
%
% (OPTIONAL):
% PV_Pairs: Any valid parameter-value pairs accepted by blockproc.
% DEFAULTS:
% 'BorderSize': [6 6]
% 'PadPartialBlocks': true
% 'PadMethod': 'replicate'
% 'TrimBorder': true
% 'Destination': [NOT SPECIFIED] (See BLOCKPROC for usage)
% FudgeFactor: As an additional PV_Pair, one may enter:
% 'FudgeFactor', value (DEFAULT = 1),
% You may provide a scalar multiplier for the local value
% returned by graythresh.
%
% OUTPUT:
% B: Output image (Unless 'DESTINATION' output is specified.)
%
% USAGE NOTE: To specify any PV_Pairs, BLKSZ must be provided as the second
% input. If the default value of blksz is desired, an empty bracket ([]) may be
% provided. (See Example 3.)
%
% EXAMPLES:
%
% % NOTE: All examples use image 'rice.png', which ships with the Image
% % Processing Toolbox.
%
% img = imread('rice.png');
%
% % EXAMPLE 1) Default usage:
% thresholded = thresholdLocally(img);
% imshow(thresholded)
%
% % EXAMPLE 2) Specifying non-default blocksize:
% thresholded = thresholdLocally(img,[16 16]);
%
% % EXAMPLE 3) Specifying non-default padmethod (using default blocksize):
% thresholded = thresholdLocally(img,[],'PadMethod','symmetric');
%
% % EXAMPLE 4) Comparing and local thresholding, and thresholding after
% % background normalization using tophat filtering:
% figure
% subplot(2,2,1);imshow(img);title('Original Image');
% tmp = im2bw(img,graythresh(img));
% subplot(2,2,2);imshow(tmp);title('Globally Thresholded');
% tmp = imtophat(img,strel('disk',15));
% tmp = im2bw(tmp,graythresh(tmp));
% subplot(2,2,3);imshow(tmp);title('Globally Thresholded after Tophat')
% tmp = thresholdLocally(img);
% subplot(2,2,4);imshow(tmp);title('Locally Thresholded');
%
% Written by Brett Shoelson, PhD.
% 12/17/2010
% Modifications:
% * 12/20/2010 Modified significantly to accept as optional inputs all
% parameter-value pairs accepted by BLOCKPROC, as well as an additional
% "fudge factor" parameter that allows one to scale the local graythresh
% value by a scalar multiple.
% * 02/08/2011 Modified default blocksize to that calculated by bestblk
%
% Copyright 2010 The MathWorks
%
% See also: blockproc, graythresh, im2bw
if ~nargin
error('THRESHOLDLOCALLY: Requires at least one input argument.')
end
if ischar(A)
A = imread(A);
end
% DEFAULTS
% M = 32;
% N = 32;
[M, N] = bestblk(size(A));
opts.BorderSize = [6 6];
opts.PadPartialBlocks = true;
opts.PadMethod = 'replicate';
opts.TrimBorder = true;
opts.Destination = [];
opts.FudgeFactor = 1;
if nargin > 1 && ~isempty(blksz)
if numel(blksz) == 1
M = blksz; N = M;
elseif numel(blksz) == 2
M = blksz(1);N = blksz(2);
else
error('THRESHOLDLOCALLY: Improper specification of blocksize parameter');
end
end
if nargin > 2
opts = parsePV_Pairs(opts,varargin);
end
fun = @(block_struct) im2bw(block_struct.data,...
min(max(opts.FudgeFactor*graythresh(block_struct.data),0),1));
if isempty(opts.Destination)
B = blockproc(A,[M N],fun,...
'BorderSize',opts.BorderSize,...
'PadPartialBlocks',opts.PadPartialBlocks,...
'PadMethod',opts.PadMethod,...
'TrimBorder',opts.TrimBorder);
else
B = [];
blockproc(A,[M N],fun,...
'BorderSize',opts.BorderSize,...
'PadPartialBlocks',opts.PadPartialBlocks,...
'PadMethod',opts.PadMethod,...
'TrimBorder',opts.TrimBorder,...
'Destination',opts.Destination);
end
end
function opts = parsePV_Pairs(opts,UserInputs)
ind = find(strcmpi(UserInputs,'BorderSize'));
if ~isempty(ind)
opts.BorderSize = UserInputs{ind + 1};
end
ind = find(strcmpi(UserInputs,'PadPartialBlocks'));
if ~isempty(ind)
opts.PadPartialBlocks = UserInputs{ind + 1};
end
ind = find(strcmpi(UserInputs,'PadMethod'));
if ~isempty(ind)
opts.PadMethod = UserInputs{ind + 1};
end
ind = find(strcmpi(UserInputs,'TrimBorder'));
if ~isempty(ind)
opts.TrimBorder = UserInputs{ind + 1};
end
ind = find(strcmpi(UserInputs,'Destination'));
if ~isempty(ind)
opts.Destination = UserInputs{ind + 1};
end
ind = find(strcmpi(UserInputs,'FudgeFactor'));
if ~isempty(ind)
opts.FudgeFactor = UserInputs{ind + 1};
end
end
|
github
|
jacksky64/imageProcessing-master
|
uigetvariables.m
|
.m
|
imageProcessing-master/segmentation/segmenttool/uigetvariables.m
| 16,902 |
utf_8
|
f4620a99811d119011fab23a47c295ed
|
function varout = uigetvariables(prompts,intro,types,ndimensions)
%%
% uigetvariables Open variable selection dialog box
%
% VARS = uigetvariables(PROMPTS) creates a dialog box that returns
% variables selected from the base workspace. PROMPTS is a cell array of
% strings, with one entry for each variable you would like the user to
% select. VARS is a cell array containing the selected variables. Each
% element of VARS corresponds with the selection for the same element of
% PROMPTS.
%
% If the user hits CANCEL or dismisses the dialog, VARS is an empty cell
% array. If the user does not select a variable for a given prompt, the
% value in VARS for that prompt is an empty array.
%
% VARS = uigetvariables(PROMPTS,INTRO) includes introductory text to guide the user
% of the dialog. INTRO is a string. If INTRO is not specified, no
% introduction is included. The text in INTRO is wrapped automatically to
% fit in the dialog.
%
% VARS = uigetvariables(PROMPTS,INTRO,TYPES) restricts the types of the variables
% which can be selected for each prompt. TYPES is a cell array of strings
% of the same length as PROMPTS, each entry specifies the allowable type of
% variables for each prompt. The elements of TYPES may be any of the
% following:
% any Any type. Use this if you don't care.
% numeric Any numeric type, as determined by isnumeric
% logical Logical
% string String or cell array of strings
%
% vars = uigetvariables(PROMPTS,INTRO,TYPES,NDIMENSIONS) also specifies required
% dimensionality of variables. NDIMENSIONS is a numeric array of length PROMPTS,
% with each element specifying the required dimensionality of the variables
% for the corresponding element of PROMPTS. NDIMENSIONS works a little
% different from ndims, in that it allows you to distinguish among scalars,
% vectors, and matrices.
% Allowable values are:
%
% Value Meaning
% ------------ ----------
% Inf Any size. Use this if you don't care, or want more than one allowable size
% 0 Scalar (1x1)
% 1 Vector (1xN or Nx1)
% 2 Matrix (NxM)
% 3 or higher Specified number of dimensions
%
% vars = uigetvariables(PROMPTS,INTRO,VALFCN) applies an arbitrary
% validation function to determine which variables are valid for each
% prompt. VALFCN is either a single function handle, or a cell array of
% function handles of same length as PROMPTS. If VALFCN is a single
% function handle, it is applied to every prompt. Use a cell array of
% function handles to specify a unique validation function for each prompt.
% VALFCN must return true if a variable passes the validation or false if
% the variable does not. Syntax of VALFCN must be
% TF = VALFCN(variable)
%
% Examples
%
% % Put some sample data in your base workspace:
% scalar1 = 1;
% str1 = 'a string';
% cellstr1 = {'a string';'in a cell'};cellstr2 = {'another','string','in','a','cell'};
% cellstr3 = {'1','2';,'3','4'}
% vector1 = rand(1,10); vector2 = rand(5,1);
% array1 = rand(5,5); array2 = rand(5,5); array3 = rand(10,10);
% threed1 = rand(3,4,5);
% fourd1 = rand(1,2,3,4);
%
% % Select any two variables from entire workspace
% tvar = uigetvariables({'Please select any variable','And another'});
%
% % Include introductory text
% tvar = uigetvariables({'Please select any variable','And another'},'Here are some very detailed directions about how you should use this dialog. Pick one variable, then pick another variable.');
%
% % Control type of variables
% tvar = uigetvariables({'Pick a number:','Pick a string:','Pick another number:'},[],{'numeric','string','numeric'});
%
% % Control size of variables.
% tvar = uigetvariables({'Pick a scalar:','Pick a vector:','Pick a matrix:'},[],[],[0 1 2]);
%
% % Control type and size of variables
% tvar = uigetvariables({'Pick a scalar:','Pick a string','Pick a 4D array'},[],{'numeric','string','numeric'},[0 Inf 4]);
% tvar = uigetvariables({'Pick a scalar:','Pick a string vector','Pick a 3D array'},[],{'numeric','string','numeric'},[0 1 3]);
%
% % Advanced - use custom validation functions
%
% % Custom validation functions
% tvar = uigetvariables({'Pick a number:','Any number:','One more, please:'},'Use a custom validation function to require every input to be numeric',@isnumeric);
% tvar = uigetvariables({'Pick a number:','Pick a cell string:','Pick a 3D array:'},[],{@isnumeric,@iscellstr,@(x) ndims(x)==3});
%
% % No variable found
% tvar = uigetvariables('Pick a 6D numeric array:','What if there is no valid data?',@(x) isnumeric(x)&&ndims(x)==6);
% Scott Hirsch
% [email protected]
% Copyright 2010-2013 The Mathworks Inc
% Allow for single prompt as string
if ~iscell(prompts)
prompts = {prompts};
end
if nargin<2 || isempty(intro)
intro = '';
end
nPrompts = length(prompts);
% Assume the user didn't specify validation function
specifiedValidationFcn = false;
%%
if nargin==3 && ~isempty(types) % See if I've got function handle
% Grab the first value from cell to test type
if iscell(types)
test = types{1};
else
test = types;
end
switch class(test)
case 'function_handle'
% Put "types" input variable into valfcn
% Replicate a single function handle into a cell array if necessary
if ~iscell(types)
valfcn = cell(nPrompts,1);
valfcn = cellfun(@(f) types,valfcn,'UniformOutput',false);
else
valfcn = types;
end
specifiedValidationFcn = true;
end
end
%%
% If the user didn't specify the validation function, we will build it for
% them.
if ~specifiedValidationFcn
if nargin<3 || isempty(types)
types = cellstr(repmat('any',nPrompts,1));
elseif length(types)==1 % allow for single prompt with single type
types = {types};
end
if nargin<4 || isempty(ndimensions)
ndimensions = inf(1,nPrompts);
end
% Base validation functions to choose from:
isscalarfcn = @(var) numel(var)==1;
isvectorfcn = @(var) length(size(var))==2&&any(size(var)==1)&&~isscalarfcn(var);
isndfcn = @(var,dim) ndims(var)==dim && ~isscalar(var) && ~isvectorfcn(var);
isanyfcn = @(var) true; % What an optimistic function! :)
isnumericfcn = @(var) isnumeric(var);
islogicalfcn = @(var) islogical(var);
isstringfcn = @(var) ischar(var) | iscellstr(var);
valfcn = cell(1,nPrompts);
for ii=1:nPrompts
switch types{ii}
case 'any'
valfcn{ii} = isanyfcn;
case 'numeric'
valfcn{ii} = isnumericfcn;
case 'logical'
valfcn{ii} = islogicalfcn;
case 'string'
valfcn{ii} = isstringfcn;
otherwise
valfcn{ii} = isanyfcn;
end
switch ndimensions(ii)
case 0 % 0 - scalar
valfcn{ii} = @(var) isscalarfcn(var) & valfcn{ii}(var);
case 1 % 1 - vector
valfcn{ii} = @(var) isvectorfcn(var) & valfcn{ii}(var);
case Inf % Inf - Any shape
valfcn{ii} = @(var) isanyfcn(var) & valfcn{ii}(var);
otherwise % ND
valfcn{ii} = @(var) isndfcn(var,ndimensions(ii)) & valfcn{ii}(var);
end
end
end
%% Get list of variables in base workspace
allvars = evalin('base','whos');
nVars = length(allvars);
varnames = {allvars.name};
vartypes = {allvars.class};
varsizes = {allvars.size};
% Convert variable sizes from numbers:
% [N M], [N M P], ... etc
% to text:
% NxM, NxMxP
varsizes = cellfun(@mat2str,varsizes,'UniformOutput',false);
%too lazy for regexp. Strip off brackets
varsizes = cellfun(@(s) s(2:end-1),varsizes,'UniformOutput',false);
% replace blank with x
varsizes = strrep(varsizes,' ','x');
vardisplay = strcat(varnames,' (',varsizes,{' '},vartypes,')');
%% Build list of variables for each prompt
% Also include one that's prettied up a bit for display, which has an extra
% first entry saying '(select one)'. This allows for no selection, for
% optional input arguments.
validVariables = cell(nPrompts,1);
validVariablesDisplay = cell(nPrompts,1);
for ii=1:nPrompts
% turn this into cellfun once I understand what I'm doing.
assignin('base','validationfunction_',valfcn{ii})
validVariables{ii} = cell(nVars,1);
validVariablesDisplay{ii} = cell(nVars+1,1);
t = false(nVars,1);
for jj = 1:nVars
t(jj) = evalin('base',['validationfunction_(' varnames{jj} ');']);
end
if any(t) % Found at least one variable
validVariables{ii} = varnames(t);
validVariablesDisplay{ii} = vardisplay(t);
validVariablesDisplay{ii}(2:end+1) = validVariablesDisplay{ii};
validVariablesDisplay{ii}{1} = '(select one)';
else
validVariables{ii} = '(no valid variables)';
validVariablesDisplay{ii} = '(no valid variables)';
end
evalin('base','clear validationfunction_')
end
%% Compute layout
offset = 1;
maxStringLength = max(cellfun(@(s) length(s),prompts));
componentWidth = max([maxStringLength, 50]);
componentHeight = 1;
% Buttons
buttonHeight = 1.77;
buttonWidth = 10.6;
% Wrap intro string. Need to do this now to include height in dialog.
% Could use textwrap, which comes with MATLAB, instead of linewrap. This would just take a
% bit more shuffling around with the order I create and size things.
if ~isempty(intro)
intro = linewrap(intro,componentWidth);
introHeight = length(intro); % Intro is now an Nx1 cell string
else
introHeight = 0;
end
dialogWidth = componentWidth + 2*offset;
dialogHeight = 2*nPrompts*(componentHeight+offset) + buttonHeight + offset + introHeight;
% Component positions, starting from bottom of figure
popuppos = [offset 2*offset+buttonHeight componentWidth componentHeight];
textpos = popuppos; textpos(2) = popuppos(2)+componentHeight;
%% Build figure
% hFig = dialog('Units','Characters','WindowStyle','modal','Name','Select variable(s)','CloseRequestFcn',@nestedCloseReq);
hFig = dialog('Units','Characters','WindowStyle','normal','Name','Select variable(s)','CloseRequestFcn',@nestedCloseReq);
% set(hFig,'DefaultUicontrolFontSize',10)
% set(hFig,'DefaultUicontrolFontName','Arial')
pos = get(hFig,'Position');
set(hFig,'Position',[pos(1:2) dialogWidth dialogHeight])
uicontrol('Parent',hFig,'style','Pushbutton','Callback',@nestedCloseReq,'String','OK', 'Units','characters','Position',[dialogWidth-2*offset-2*buttonWidth .5*offset buttonWidth buttonHeight]);
uicontrol('Parent',hFig,'style','Pushbutton','Callback',@nestedCloseReq,'String','Cancel','Units','characters','Position',[dialogWidth-offset-buttonWidth .5*offset buttonWidth buttonHeight]);
for ii=nPrompts:-1:1
uicontrol('Parent',hFig,'Style','text', 'Units','char','Position',textpos, 'String',prompts{ii},'HorizontalAlignment','left');
hPopup(ii) = uicontrol('Parent',hFig,'Style','popupmenu','Units','char','Position',popuppos,'String',validVariablesDisplay{ii},'UserData',validVariables{ii});
% Set up positions for next go round
popuppos(2) = popuppos(2) + 1.5*offset + 2*componentHeight;
textpos(2) = textpos(2) + 1.5*offset + 2*componentHeight;
end
if ~isempty(intro)
intropos = [offset dialogHeight-introHeight-1 componentWidth introHeight+.5];
uicontrol('Parent',hFig,'Style','text','Units','Characters','Position',intropos, 'String',intro,'HorizontalAlignment','left');
end
uiwait(hFig)
function nestedCloseReq(obj,~)
% How did I get here?
% If pressed OK, get variables. Otherwise, don't.
if strcmp(get(obj,'type'),'uicontrol') && strcmp(get(obj,'String'),'OK')
for ind=1:nPrompts
str = get(hPopup(ind),'UserData'); % Store real variable name here
val = get(hPopup(ind),'Value')-1; % Remove offset to account for '(select one)' as initial entry
if val==0 % User didn't select anything
varout{ind} = [];
elseif strcmp(str,'(no valid variables)')
varout{ind} = [];
else
varout{ind} = evalin('base',str{val});
end
end
else % Cancel - return empty
varout = {};
end
delete(hFig)
end
end
function c = linewrap(s, maxchars)
%LINEWRAP Separate a single string into multiple strings
% C = LINEWRAP(S, MAXCHARS) separates a single string into multiple
% strings by separating the input string, S, on word breaks. S must be a
% single-row char array. MAXCHARS is a nonnegative integer scalar
% specifying the maximum length of the broken string. C is a cell array
% of strings.
%
% C = LINEWRAP(S) is the same as C = LINEWRAP(S, 80).
%
% Note: Words longer than MAXCHARS are not broken into separate lines.
% This means that C may contain strings longer than MAXCHARS.
%
% This implementation was inspired a blog posting about a Java line
% wrapping function:
% http://joust.kano.net/weblog/archives/000060.html
% In particular, the regular expression used here is the one mentioned in
% Jeremy Stein's comment.
%
% Example
% s = 'Image courtesy of Joe and Frank Hardy, MIT, 1993.'
% c = linewrap(s, 40)
%
% See also TEXTWRAP.
% Steven L. Eddins
% $Revision: 1.7 $ $Date: 2006/02/08 16:54:51 $
% http://www.mathworks.com/matlabcentral/fileexchange/9909-line-wrap-a-string
% Copyright (c) 2009, The MathWorks, Inc.
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
% * Neither the name of the The MathWorks, Inc. nor the names
% of its contributors may be used to endorse or promote products derived
% from this software without specific prior written permission.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
narginchk(1, 2);
bad_s = ~ischar(s) || (ndims(s) > 2) || (size(s, 1) ~= 1); %#ok<ISMAT>
if bad_s
error('S must be a single-row char array.');
end
if nargin < 2
% Default value for second input argument.
maxchars = 80;
end
% Trim leading and trailing whitespace.
s = strtrim(s);
% Form the desired regular expression from maxchars.
exp = sprintf('(\\S\\S{%d,}|.{1,%d})(?:\\s+|$)', maxchars, maxchars);
% Interpretation of regular expression (for maxchars = 80):
% '(\\S\\S{80,}|.{1,80})(?:\\s+|$)'
%
% Match either a non-whitespace character followed by 80 or more
% non-whitespace characters, OR any sequence of between 1 and 80
% characters; all followed by either one or more whitespace characters OR
% end-of-line.
tokens = regexp(s, exp, 'tokens').';
% Each element if the cell array tokens is single-element cell array
% containing a string. Convert this to a cell array of strings.
get_contents = @(f) f{1};
c = cellfun(get_contents, tokens, 'UniformOutput', false);
% Remove trailing whitespace characters from strings in c. This can happen
% if multiple whitespace characters separated the last word on a line from
% the first word on the following line.
c = deblank(c);
end
|
github
|
jacksky64/imageProcessing-master
|
regiongrowing.m
|
.m
|
imageProcessing-master/segmentation/RegionGrowing/regiongrowing.m
| 8,119 |
utf_8
|
1f10aec0bc9c96dd3b669f6b0c1ac29f
|
function lMask = RegionGrowing(dImg, dMaxDif, iSeed)
%REGIONGROWING A MEXed 2D/3D region growing algorithm.
%
% lMASK = REGIONGROWING(dIMG, dMAXDIF, iSEED) Returns a binary mask
% lMASK, the result of growing a region from the seed point iSEED.
% The stoping critereon is fulfilled if no voxels in the region's
% 4-neighbourhood have an intensity difference smaller than dMAXDIF to
% the region's mean intensity.
%
% If the seed point is not supplied, a GUI lets you select it. If no
% output is requested, the result of the region growing is visualized
%
% IMPORTANT NOTE: This Matlab function is a front-end for a fast mex
% function. Compile it by making the directiory containing this file your
% current Matlab working directory and typing
%
% >> mex RegionGrowing_mex.cpp
%
% in the Matlab console.
%
%
% Example (requires image processing toolbox):
%
% load mri; % Gives variable D;
% RegionGrowing(squeeze(D), 10, [1 1 1]); % <- segments the background
%
% Copyright 2013 Christian Wuerslin, University of Tuebingen and
% University of Stuttgart, Germany.
% Contact: [email protected]
% =========================================================================
% *** FUNCTION regiongrowing
% ***
% *** See above for description.
% ***
% =========================================================================
dOPACITY = 0.6;
% -------------------------------------------------------------------------
% Parse the input arguments
if nargin < 2, error('At least two input arguments required!'); end
if ndims(dImg) > 3, error('Input image must be either 2D or 3D!'); end
dImg = double(dImg);
if ~isscalar(dMaxDif), error('Second input argument (MaxDif) must be a scalar!'); end
if nargin < 3
iSeed = uint16(fGetSeed(dImg));
if isempty(iSeed), return; end
else
if numel(iSeed) ~= ndims(dImg), error('Invalid seed point! Must have ndims(dImg) elements!'); end
iSeed = uint16(iSeed(:));
end
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% If the mex file has not been compiled yet, try to do so.
if exist('RegionGrowing_mex', 'file') ~= 3
fprintf(1, 'Trying to compile mex file...');
sCurrentPath = cd;
sPath = fileparts(mfilename('fullpath'));
cd(sPath)
try
mex([sPath, filesep, 'RegionGrowing_mex.cpp']);
fprintf(1, 'done\n');
catch
error('Could not compile the mex file :(. Please try to do so manually!');
end
cd(sCurrentPath);
end
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% Start the region growing process by calling the mex function
if max(dImg(:)) == min(dImg(:))
lMask = true(size(dImg));
warning('All image elements have the same value!');
else
lMask = RegionGrowing_mex(dImg, iSeed, dMaxDif);
end
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% If no output requested, visualize the result
if ~nargout
dImg = dImg - min(dImg(:)); % Normalize the image
dImg = dImg./max(dImg(:));
dImg = permute(dImg, [1 2 4 3]); % Change to RGB-mode
dImg = repmat(dImg, [1 1 3 1]);
dMask = double(permute(lMask, [1 2 4 3]));
dMask = cat(3, dMask, zeros(size(dMask)), zeros(size(dMask))); % Make mask the red channel -> red overlay
dImg = 1 - (1 - dImg).*(1 - dOPACITY.*dMask); % The 'screen' overlay mode
% reduce montage size by selecting the interesting slices, only
lSlices = squeeze(sum(sum(dMask(:,:,1,:), 1), 2) > 0 );
figure, montage(dImg(:,:,:,lSlices));
end
% -------------------------------------------------------------------------
end
% =========================================================================
% *** END FUNCTION regiongrowing
% =========================================================================
% =========================================================================
% *** FUNCTION fGetSeed
% ***
% *** A little GUI to select a seed
% ***
% =========================================================================
function iSeed = fGetSeed(dImg)
iSlice = 1;
dImg = dImg./max(dImg(:));
dImg = dImg - min(dImg(:));
iImg = uint8(dImg.*255);
try
hF = figure(...
'Position' , [0 0 size(dImg, 2) size(dImg, 1)], ...
'Units' , 'pixels', ...
'Color' , 'k', ...
'DockControls' , 'off', ...
'MenuBar' , 'none', ...
'Name' , 'Select Seed', ...
'NumberTitle' , 'off', ...
'BusyAction' , 'cancel', ...
'Pointer' , 'crosshair', ...
'CloseRequestFcn' , 'delete(gcbf)', ...
'WindowButtonDownFcn' , 'uiresume(gcbf)', ...
'KeyPressFcn' , @fKeyPressFcn, ...
'WindowScrollWheelFcn' , @fWindowScrollWheelFcn);
catch
hF = figure(...
'Position' , [0 0 size(dImg, 2) size(dImg, 1)], ...
'Units' , 'pixels', ...
'Color' , 'k', ...
'DockControls' , 'off', ...
'MenuBar' , 'none', ...
'Name' , 'Select Seed', ...
'NumberTitle' , 'off', ...
'BusyAction' , 'cancel', ...
'Pointer' , 'crosshair', ...
'CloseRequestFcn' , 'delete(gcbf)', ...
'WindowButtonDownFcn' , 'uiresume(gcbf)', ...
'KeyPressFcn' , @fKeyPressFcn);
end
hA = axes(...
'Parent' , hF, ...
'Position' , [0 0 1 1]);
hI = image(iImg(:,:,1), ...
'Parent' , hA, ...
'CDataMapping' , 'scaled');
colormap(gray(256));
movegui('center');
uiwait;
if ~ishandle(hF)
iSeed = [];
else
iPos = uint16(get(hA, 'CurrentPoint'));
iSeed = [iPos(1, 2) iPos(1, 1) iSlice];
delete(hF);
end
% ---------------------------------------------------------------------
% * * NESTED FUNCTION fKeyPressFcn (nested in fGetSeed)
% * *
% * * Changes the active slice
% ---------------------------------------------------------------------
function fKeyPressFcn(hObject, eventdata)
switch(eventdata.Key)
case 'uparrow'
iSlice = min([size(iImg, 3), iSlice + 1]);
set(hI, 'CData', iImg(:,:,iSlice));
case 'downarrow'
iSlice = max([1, iSlice - 1]);
set(hI, 'CData', iImg(:,:,iSlice));
end
end
% ---------------------------------------------------------------------
% * * END OF NESTED FUNCTION fKeyPressFcn (nested in fGetSeed)
% ---------------------------------------------------------------------
% ---------------------------------------------------------------------
% * * NESTED FUNCTION fWindowScrollWheelFcn (nested in fGetSeed)
% * *
% * * Changes the active slice
% ---------------------------------------------------------------------
function fWindowScrollWheelFcn(hObject, eventdata)
iSlice = min([size(iImg, 3), iSlice + eventdata.VerticalScrollCount]);
iSlice = max([1, iSlice]);
set(hI, 'CData', iImg(:,:,iSlice));
end
% ---------------------------------------------------------------------
% * * END OF NESTED FUNCTION fWindowScrollWheelFcn (nested in fGetSeed)
% ---------------------------------------------------------------------
end
% =========================================================================
% *** END FUNCTION fGetSeed (and its nested function)
% =========================================================================
|
github
|
jacksky64/imageProcessing-master
|
fGetSeed.m
|
.m
|
imageProcessing-master/segmentation/RegionGrowing/fGetSeed.m
| 3,953 |
utf_8
|
9abe0cd8d5d9544d06406345cc7363ab
|
% =========================================================================
% *** FUNCTION fGetSeed
% ***
% *** A little GUI to select a seed
% ***
% =========================================================================
function iSeed = fGetSeed(dImg)
iSlice = 1;
dImg = dImg./max(dImg(:));
dImg = dImg - min(dImg(:));
iImg = uint8(dImg.*255);
try
hF = figure(...
'Position' , [0 0 size(dImg, 2) size(dImg, 1)], ...
'Units' , 'pixels', ...
'Color' , 'k', ...
'DockControls' , 'off', ...
'MenuBar' , 'none', ...
'Name' , 'Select Seed', ...
'NumberTitle' , 'off', ...
'BusyAction' , 'cancel', ...
'Pointer' , 'crosshair', ...
'CloseRequestFcn' , 'delete(gcbf)', ...
'WindowButtonDownFcn' , 'uiresume(gcbf)', ...
'KeyPressFcn' , @fKeyPressFcn, ...
'WindowScrollWheelFcn' , @fWindowScrollWheelFcn);
catch
hF = figure(...
'Position' , [0 0 size(dImg, 2) size(dImg, 1)], ...
'Units' , 'pixels', ...
'Color' , 'k', ...
'DockControls' , 'off', ...
'MenuBar' , 'none', ...
'Name' , 'Select Seed', ...
'NumberTitle' , 'off', ...
'BusyAction' , 'cancel', ...
'Pointer' , 'crosshair', ...
'CloseRequestFcn' , 'delete(gcbf)', ...
'WindowButtonDownFcn' , 'uiresume(gcbf)', ...
'KeyPressFcn' , @fKeyPressFcn);
end
hA = axes(...
'Parent' , hF, ...
'Position' , [0 0 1 1]);
hI = image(iImg(:,:,1), ...
'Parent' , hA, ...
'CDataMapping' , 'scaled');
colormap(gray(256));
movegui('center');
uiwait;
if ~ishandle(hF)
iSeed = [];
else
iPos = uint16(get(hA, 'CurrentPoint'));
iSeed = [iPos(1, 2) iPos(1, 1) iSlice];
delete(hF);
end
% ---------------------------------------------------------------------
% * * NESTED FUNCTION fKeyPressFcn (nested in fGetSeed)
% * *
% * * Changes the active slice
% ---------------------------------------------------------------------
function fKeyPressFcn(hObject, eventdata)
switch(eventdata.Key)
case 'uparrow'
iSlice = min([size(iImg, 3), iSlice + 1]);
set(hI, 'CData', iImg(:,:,iSlice));
case 'downarrow'
iSlice = max([1, iSlice - 1]);
set(hI, 'CData', iImg(:,:,iSlice));
end
end
% ---------------------------------------------------------------------
% * * END OF NESTED FUNCTION fKeyPressFcn (nested in fGetSeed)
% ---------------------------------------------------------------------
% ---------------------------------------------------------------------
% * * NESTED FUNCTION fWindowScrollWheelFcn (nested in fGetSeed)
% * *
% * * Changes the active slice
% ---------------------------------------------------------------------
function fWindowScrollWheelFcn(hObject, eventdata)
iSlice = min([size(iImg, 3), iSlice + eventdata.VerticalScrollCount]);
iSlice = max([1, iSlice]);
set(hI, 'CData', iImg(:,:,iSlice));
end
% ---------------------------------------------------------------------
% * * END OF NESTED FUNCTION fWindowScrollWheelFcn (nested in fGetSeed)
% ---------------------------------------------------------------------
end
% =========================================================================
% *** END FUNCTION fGetSeed (and its nested function)
% =========================================================================
|
github
|
jacksky64/imageProcessing-master
|
Main.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/Main.m
| 10,629 |
utf_8
|
8fef5db166a4f95c56ece910e8b6367d
|
function varargout = Main(varargin)
% MAIN MATLAB code for Main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in MAIN.M with the given input arguments.
%
% MAIN('Property','Value',...) creates a new MAIN or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before Main_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to Main_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help Main
% Last Modified by GUIDE v2.5 31-Aug-2015 12:04:25
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Main_OpeningFcn, ...
'gui_OutputFcn', @Main_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before Main is made visible.
function Main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to Main (see VARARGIN)
% Choose default command line output for Main
handles.output = hObject;
handles.image = 0;
handles.resultImage = 0;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Main wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Main_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
addpath('regionGrowing');
addpath('PSO');
addpath('GraphSeg');
addpath('adaptcluster_kmeans');
addpath('FCMLSM');
addpath('ISODATA');
addpath('html');
% --- Executes on button press in Browse.
function Browse_Callback(hObject, eventdata, handles)
% hObject handle to Browse (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[filename, filepath] = uigetfile({'*.jpg;*.png;*.bmp;*.jpeg;*.tiff','Image Files (*.jpg,*.png,*.bmp,*.jpeg,*.tiff)'});
handles.image = imread(strcat(filepath,filename));
guidata(hObject, handles);
imshow(handles.image, 'Parent', handles.axes1);
% --- Executes on selection change in chooseMethod.
function chooseMethod_Callback(hObject, eventdata, handles)
% hObject handle to chooseMethod (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns chooseMethod contents as cell array
% contents{get(hObject,'Value')} returns selected item from chooseMethod
persistent t;
persistent k;
if (isfield(handles, 't'))
handles = rmfield (handles, 't');
delete(t);
end
if (isfield(handles, 'k'))
handles = rmfield (handles, 'k');
delete(k);
end
option = get(handles.chooseMethod, 'Value');
switch option
case 3
f = handles.figure1;
t = uicontrol(f, 'style', 'text', 'string', 'K : levels of segmentation = 2', 'position', [710 540 140 17.8]);
k = uicontrol(f, 'style', 'slider', 'min', 2, 'max', 20, 'value', 2, 'position', [870 540 140 17.8], 'sliderstep' , [1/18 1/18]);
handles.t = t;
handles.k = k;
%k = uicontrol(f, 'Style', 'slider',
case 13
f = handles.figure1;
t = uicontrol(f, 'style', 'text', 'string', 'K : levels of segmentation = 2', 'position', [710 540 140 17.8]);
k = uicontrol(f, 'style', 'slider', 'min', 2, 'max', 20, 'value', 2, 'position', [870 540 140 17.8], 'sliderstep' , [1/18 1/18]);
handles.t = t;
handles.k = k;
case 14
f = handles.figure1;
t = uicontrol(f, 'style', 'text', 'string', 'K : levels of segmentation = 2', 'position', [710 540 140 17.8]);
k = uicontrol(f, 'style', 'slider', 'min', 2, 'max', 20, 'value', 2, 'position', [870 540 140 17.8], 'sliderstep' , [1/18 1/18]);
handles.t = t;
handles.k = k;
case 15
f = handles.figure1;
t = uicontrol(f, 'style', 'text', 'string', 'K : levels of segmentation = 2', 'position', [710 540 140 17.8]);
k = uicontrol(f, 'style', 'slider', 'min', 2, 'max', 20, 'value', 2, 'position', [850 540 140 17.8], 'sliderstep' , [1/18 1/18]);
handles.t = t;
handles.k = k;
case 16
f = handles.figure1;
t = uicontrol(f, 'style', 'text', 'string', 'K : levels of segmentation = 2', 'position', [710 540 140 17.8]);
k = uicontrol(f, 'style', 'slider', 'min', 2, 'max', 20, 'value', 2, 'position', [850 540 140 17.8], 'sliderstep' , [1/18 1/18]);
handles.t = t;
handles.k = k;
end
guidata(hObject, handles);
% --- Executes during object creation, after setting all properties.
function chooseMethod_CreateFcn(hObject, eventdata, handles)
% hObject handle to chooseMethod (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in do.
function do_Callback(hObject, eventdata, handles)
% hObject handle to do (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
image = imread('please-wait.png');
imshow(image, 'Parent', handles.axes2);
pause(1);
option = get(handles.chooseMethod, 'Value');
switch option
case 2
level = graythresh(handles.image);
image = im2bw(handles.image,level);
handles.resultImage = image;
case 3
k = get(handles.k,'Value');
image = (handles.image);
level = multithresh(handles.image,k);
image = imquantize(image,level);
image = label2rgb(image);
handles.resultImage = image;
case 4
labledImage = adaptcluster_kmeans(handles.image);
image = label2rgb(labledImage);
handles.resultImage = image;
case 5
image = (handles.image);
image = edge(image,'Canny');
handles.resultImage = image;
case 6
image = (handles.image);
image = edge(image,'log');
handles.resultImage = image;
case 7
image = (handles.image);
image = edge(image,'Prewitt');
handles.resultImage = image;
case 8
image = (handles.image);
image = edge(image,'Roberts');
handles.resultImage = image;
case 9
image = (handles.image);
image = edge(image,'Sobel');
handles.resultImage = image;
case 10
image = (handles.image);
image = edge(image,'zerocross');
handles.resultImage = image;
case 11
image = (handles.image);
[~, threshold] = edge (image, 'Sobel');
fudgeFactor = 0.5;
BWs = edge(image,'sobel', threshold * fudgeFactor);
se90 = strel('line', 3, 90);
se0 = strel('line', 3, 0);
BWsdil = imdilate(BWs, [se90 se0]);
BWdfill = imfill(BWsdil, 'holes');
seD = strel('diamond',1);
BWfinal = imerode(BWdfill,seD);
BWfinal = imerode(BWfinal,seD);
BWoutline = bwperim(BWfinal);
Segout = handles.image;
Segout(repmat(BWoutline,[1,1,size(handles.image,3)])) = 255;
handles.resultImage = Segout;
case 12
image = (handles.image);
[x, y] = size(image);
[~, mask] = regionGrowing(image,[floor(rand()*x), floor(rand()*y)]);
image = double(handles.image).*repmat(mask,[1,1,size(handles.image,3)]);
handles.resultImage = image;
case 13
k = get(handles.k,'Value');
image = segmentation(handles.image,k,'PSO');
handles.resultImage = image;
case 14
k = get(handles.k,'Value');
image = segmentation(handles.image,k,'DPSO');
handles.resultImage = image;
case 15
k = get(handles.k,'Value');
image = segmentation(handles.image,k,'FODPSO');
handles.resultImage = image;
case 16
k = get(handles.k,'Value');
image = (handles.image);
image = SFCM2D(image,k);
for i= 1:k
resultImage(:,:,i) = reshape(image(i,:,:),size(handles.image,1),size(handles.image,2));
end
% h = vision.AlphaBlender;
% image = resultImage(:,:,1);
% for i=2:k
%image = step(h,image, resultImage(:,:,i));
% image = imfuse(image, resultImage(:,:,i),'blend');
% end
%image = reshape(image(1,:,:),size(handles.image,1),size(handles.image,2));
image = ones(size(resultImage,1),size(resultImage,2),1,size(resultImage,3));
image(:,:,1,:) = resultImage;
montage(image,'Size',[NaN 3]);
case 17
[~, image] = isodata(( handles.image));
handles.resultImage = image;
end
guidata(hObject, handles);
if (option ~= 16)
imshow(handles.resultImage, 'Parent', handles.axes2);
end
function k_Callback(handles)
set(handles.t, 'string', strcat('K : levels of segmentation = ', handles.get(k,'value')));
|
github
|
jacksky64/imageProcessing-master
|
FCLSM.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/FCMLSM/FCLSM.m
| 3,102 |
utf_8
|
525c470209f7e282a36c61b3edde9eae
|
function [imgls,opts,sls]=FCLSM(img,imgfcm,beta)
img=double(img);
se=5; %template radius for spatial filtering
sigma=2; %spatial filter weight
d0=.5; %fuzzy thresholding
epsilon=1.5; %Dirac regulator
%adaptive definition of penalizing item mu
u=d0<=imgfcm;
bwa=bwarea(u); %area of initial contour
bw2=bwperim(u);
bwp=sum(sum(bw2)); %peripherium of initial contour
mu=bwp/bwa; %Coefficient of the internal (penalizing) energy term P(\phi);
opts.mu=mu;
timestep=0.2/mu; %The product timestep*mu must be less than 0.25 for stability
opts.timestep=timestep;
%end
% fs=fspecial('disk',5);
% imgs=imfilter(img,fs,'replicate');
fs=fspecial('gaussian',se,sigma);
img_smooth=conv2(double(img),double(fs),'same');
[Ix,Iy]=gradient(img_smooth);
f=Ix.^2+Iy.^2;
g=1./(1+f); % edge indicator function.
% define initial level set function as -c0, c0
% at points outside and inside of a region R, respectively.
u=u-0.5;
u=4*epsilon*u;
sls(1,:,:)=double(u);
lambda=1/mu;
opts.lambda=lambda;
% nu=2*imgfcm-1; % Coefficient of the weighted area term Ag(\phi);
nu=-2*(2*beta*imgfcm-(1-beta));
opts.alf=nu;
%Note: Choose a positive(negative) alf if the initial contour is
% outside(inside) the object.
% start level set evolution
bGo=1;
nTi=0;
while bGo
u=EVOLUTION(u, g, lambda, mu, nu, epsilon, timestep, 100);
nTi=nTi+1;
sls(nTi+1,:,:)=u;
pause(0.001);
imshow(img,[]);hold on;
[c,h] = contour(u,[0 0],'m');
title(sprintf('Time Step: %d',nTi*100));
hold off
if ~strcmp(questdlg('Continue or not?'),'Yes'),bGo=0;end
pause(0.1);
end
imgls=u;
imshow(img,[]);
hold on
imgt(:,:)=sls(1,:,:);
[c,h] = contour(imgt,[0 0],'m');
[c,h] = contour(u,[0 0],'g','linewidth',2);
totalIterNum=[num2str(nTi*100), ' iterations'];
title(['Magenta: Initial; Green: Final after ', totalIterNum]);
hold off
%% core functions of level set methods
function u = EVOLUTION(u0, g, lambda, mu, nu, epsilon, delt, numIter)
u=u0;
[vx,vy]=gradient(g);
for k=1:numIter
u=NeumannBoundCond(u);
[ux,uy]=gradient(u);
normDu=sqrt(ux.^2 + uy.^2 + 1e-10);
Nx=ux./normDu;
Ny=uy./normDu;
diracU=Dirac(u,epsilon);
K=curvature_central(Nx,Ny);
weightedLengthTerm=lambda*diracU.*(vx.*Nx + vy.*Ny + g.*K);
penalizingTerm=mu*(4*del2(u)-K);
weightedAreaTerm=nu.*diracU.*g;
u=u+delt*(weightedLengthTerm + weightedAreaTerm + penalizingTerm);
% update the level set function
end
% the following functions are called by the main function EVOLUTION
function f = Dirac(x, sigma)
f=(1/2/sigma)*(1+cos(pi*x/sigma));
b = (x<=sigma) & (x>=-sigma);
f = f.*b;
function K = curvature_central(nx,ny)
[nxx,junk]=gradient(nx);
[junk,nyy]=gradient(ny);
K=nxx+nyy;
function g = NeumannBoundCond(f)
% Make a function satisfy Neumann boundary condition
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
|
github
|
jacksky64/imageProcessing-master
|
fuzzyLSM.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/FCMLSM/fuzzyLSM.m
| 3,564 |
utf_8
|
86b8ad22aa23a0e3a9951328c398a912
|
function [imgls,sls]=fuzzyLSM(img,imgfcm,beta)
% Enhancing level set segmentation by spatial fuzzy clustering
% [imgls,sls]=fuzzyLSM(img,imgfcm,fcmind,beta)
% img: input grayscale image
% imgfcm: the result of spatial fuzzy clustering
% beta: modulating argument
% imgls: the result level set function
% sls: historic records of level set evolution
% LI Bing Nan @ NUS, Feb 2009
% If you think it is helpful, please cite:
% B.N. Li, C.K. Chui, S. Chang, S.H. Ong (2011) Integrating spatial fuzzy
% clustering with level set methods for automated medical image
% segmentation. Computers in Biology and Medicine 41(1) 1-10.
%--------------------------------------------------------------------------
img=double(img);
se=5; %template radius for spatial filtering
sigma=2; %gaussian filter weight
d0=.5; %fuzzy thresholding
epsilon=1.5; %Dirac regulator
%adaptive definition of penalizing item mu
u=(d0<=imgfcm);
bwa=bwarea(u); %area of initial contour
bw2=bwperim(u);
bwp=sum(sum(bw2)); %peripherium of initial contour
mu=bwp/bwa; %Coefficient of the internal (penalizing) energy term P(\phi);
timestep=0.2/mu; %The product timestep*mu must be less than 0.25 for stability
%end
fs=fspecial('gaussian',se,sigma);
img_smooth=conv2(double(img),double(fs),'same');
[Ix,Iy]=gradient(img_smooth);
f=Ix.^2+Iy.^2;
g=1./(1+f); % edge indicator function.
% define initial level set function as -c0, c0
% at points outside and inside of a region R, respectively.
u=u-0.5;
u=4*epsilon*u;
sls(1,:,:)=double(u);
lambda=1/mu;
nu=-2*(2*beta*imgfcm-(1-beta));
%Note: Choose a positive(negative) alf if the initial contour is
% outside(inside) the object.
% start level set evolution
bGo=1;
nTi=0;
while bGo
u=EVOLUTION(u, g, lambda, mu, nu, epsilon, timestep, 100);
nTi=nTi+1;
sls(nTi+1,:,:)=u;
imshow(img,[]);hold on;
[c,h] = contour(u,[0 0],'m');
title(sprintf('Time Step: %d',nTi*100));
hold off
pause(0.05);
if ~strcmp(questdlg('Continue or not?'),'Yes'),bGo=0;end
end
imgls=u;
imshow(img,[]);
hold on
imgt(:,:)=sls(1,:,:);
contour(imgt,[0 0],'m');
contour(u,[0 0],'g','linewidth',2);
totalIterNum=[num2str(nTi*100), ' iterations'];
title(['Magenta: Initial; Green: Final after ', totalIterNum]);
hold off
%% core functions of level set methods
function u = EVOLUTION(u0, g, lambda, mu, nu, epsilon, delt, numIter)
u=u0;
[vx,vy]=gradient(g);
for k=1:numIter
u=NeumannBoundCond(u);
[ux,uy]=gradient(u);
normDu=sqrt(ux.^2 + uy.^2 + 1e-10);
Nx=ux./normDu;
Ny=uy./normDu;
diracU=Dirac(u,epsilon);
K=curvature_central(Nx,Ny);
weightedLengthTerm=lambda*diracU.*(vx.*Nx + vy.*Ny + g.*K);
penalizingTerm=mu*(4*del2(u)-K);
weightedAreaTerm=nu.*diracU.*g;
u=u+delt*(weightedLengthTerm + weightedAreaTerm + penalizingTerm);
% update the level set function
end
% the following functions are called by the main function EVOLUTION
function f = Dirac(x, sigma)
f=(1/2/sigma)*(1+cos(pi*x/sigma));
b = (x<=sigma) & (x>=-sigma);
f = f.*b;
function K = curvature_central(nx,ny)
[nxx,junk]=gradient(nx);
[junk,nyy]=gradient(ny);
K=nxx+nyy;
function g = NeumannBoundCond(f)
% Make a function satisfy Neumann boundary condition
[nrow,ncol] = size(f);
g = f;
g([1 nrow],[1 ncol]) = g([3 nrow-2],[3 ncol-2]);
g([1 nrow],2:end-1) = g([3 nrow-2],2:end-1);
g(2:end-1,[1 ncol]) = g(2:end-1,[3 ncol-2]);
|
github
|
jacksky64/imageProcessing-master
|
srm_getborders.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/srm/srm_getborders.m
| 226 |
utf_8
|
9f24b148c7e587960b326e5d9bfd11e3
|
function borders = srm_getborders(map)
dx = conv2(map, [-1 1], 'same');
dy = conv2(map, [-1 1]', 'same');
dy(end,:) = 0; % ignore the last row of dy
dx(:,end) = 0; % and the last col of dx
borders = find(dx ~= 0 | dy ~= 0);
|
github
|
jacksky64/imageProcessing-master
|
srm.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/srm/srm.m
| 4,175 |
utf_8
|
34efb3176d6b172ea78cf7ab221e3df9
|
% Statistical Region Merging
%
% Nock, Richard and Nielsen, Frank 2004. Statistical Region Merging. IEEE Trans. Pattern Anal. Mach. Intell. 26, 11 (Nov. 2004), 1452-1458.
% DOI= http://dx.doi.org/10.1109/TPAMI.2004.110
%Segmentation parameter Q; Q small few segments, Q large may segments
function [maps,images]=srm(image,Qlevels)
% Smoothing the image, comment this line if you work on clean or synthetic images
h=fspecial('gaussian',[3 3],1);
image=imfilter(image,h,'symmetric');
smallest_region_allowed=10;
size_image=size(image);
n_pixels=size_image(1)*size_image(2);
% Compute image gradient
[Ix,Iy]=srm_imgGrad(image(:,:,:));
Ix=max(abs(Ix),[],3);
Iy=max(abs(Iy),[],3);
normgradient=sqrt(Ix.^2+Iy.^2);
Ix(:,end)=[];
Iy(end,:)=[];
[~,index]=sort(abs([Iy(:);Ix(:)]));
n_levels=numel(Qlevels);
maps=cell(n_levels,1);
images=cell(n_levels,1);
im_final=zeros(size_image);
map=reshape(1:n_pixels,size_image(1:2));
% gaps=zeros(size(map)); % For future release
treerank=zeros(size_image(1:2));
size_segments=ones(size_image(1:2));
image_seg=image;
%Building pairs
n_pairs=numel(index);
idx2=reshape(map(:,1:end-1),[],1);
idx1=reshape(map(1:end-1,:),[],1);
pairs1=[ idx1;idx2 ];
pairs2=[ idx1+1;idx2+size_image(1) ];
for Q=Qlevels
iter=find(Q==Qlevels);
for i=1:n_pairs
C1=pairs1(index(i));
C2=pairs2(index(i));
%Union-Find structure, here are the finds, average complexity O(1)
while (map(C1)~=C1 ); C1=map(C1); end
while (map(C2)~=C2 ); C2=map(C2); end
% Compute the predicate, region merging test
g=256;
logdelta=2*log(6*n_pixels);
dR=(image_seg(C1)-image_seg(C2))^2;
dG=(image_seg(C1+n_pixels)-image_seg(C2+n_pixels))^2;
dB=(image_seg(C1+2*n_pixels)-image_seg(C2+2*n_pixels))^2;
logreg1 = min(g,size_segments(C1))*log(1.0+size_segments(C1));
logreg2 = min(g,size_segments(C2))*log(1.0+size_segments(C2));
dev1=((g*g)/(2.0*Q*size_segments(C1)))*(logreg1 + logdelta);
dev2=((g*g)/(2.0*Q*size_segments(C2)))*(logreg2 + logdelta);
dev=dev1+dev2;
predicat=( (dR<dev) && (dG<dev) && (dB<dev) );
if (((C1~=C2)&&predicat) || xor(size_segments(C1)<=smallest_region_allowed, size_segments(C2)<=smallest_region_allowed))
% Find the new root for both regions
if treerank(C1) > treerank(C2)
map(C2) = C1; reg=C1;
elseif treerank(C1) < treerank(C2)
map(C1) = C2; reg=C2;
elseif C1 ~= C2
map(C2) = C1; reg=C1;
treerank(C1) = treerank(C1) + 1;
end
if C1~=C2
% Merge regions
nreg=size_segments(C1)+size_segments(C2);
image_seg(reg)=(size_segments(C1)*image_seg(C1)+size_segments(C2)*image_seg(C2))/nreg;
image_seg(reg+n_pixels)=(size_segments(C1)*image_seg(C1+n_pixels)+size_segments(C2)*image_seg(C2+n_pixels))/nreg;
image_seg(reg+2*n_pixels)=(size_segments(C1)*image_seg(C1+2*n_pixels)+size_segments(C2)*image_seg(C2+2*n_pixels))/nreg;
size_segments(reg)=nreg;
end
end
end
% Done, building two result figures, figure 1 is the segmentation map,
% figure 2 is the segmentation map with the average color in each segment
while 1
map_ = map(map) ;
if isequal(map_,map) ; break ; end
map = map_ ;
end
for i=1:3
im_final(:,:,i)=image_seg(map+(i-1)*n_pixels);
end
images{iter}=im_final;
[clusterlist,~,labels] = unique(map) ;
labels=reshape(labels,size(map));
nlabels=numel(clusterlist);
maps{iter}=map;
bgradient = sparse(srm_boundarygradient(labels, nlabels, normgradient));
bgradient = bgradient - tril(bgradient);
idx=find(bgradient>0);
[~,index]=sort(bgradient(idx));
n_pairs=numel(idx);
[xlabels,ylabels]=ind2sub([nlabels,nlabels],idx);
pairs1=clusterlist(xlabels);
pairs2=clusterlist(ylabels);
end
|
github
|
jacksky64/imageProcessing-master
|
adaptcluster_kmeans.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/adaptcluster_kmeans/adaptcluster_kmeans.m
| 4,790 |
utf_8
|
710b9eaae8fa6dcb28751eecea74f382
|
function [lb,center] = adaptcluster_kmeans(im)
% This code is written to implement kmeans clustering for segmenting any
% Gray or Color image. There is no requirement to mention the number of cluster for
% clustering.
% IM - is input image to be clustered.
% LB - is labeled image (Clustered Image).
% CENTER - is array of cluster centers.
% Execution of this code is very fast.
% It generates consistent output for same image.
% Written by Ankit Dixit.
% January-2014.
if size(im,3)>1
[lb,center] = ColorClustering(im); % Check Image is Gray or not.
else
[lb,center] = GrayClustering(im);
end
function [lb,center] = GrayClustering(gray)
gray = double(gray);
array = gray(:); % Copy value into an array.
% distth = 25;
i = 0;j=0; % Intialize iteration Counters.
tic
while(true)
seed = mean(array); % Initialize seed Point.
i = i+1; %Increment Counter for each iteration.
while(true)
j = j+1; % Initialize Counter for each iteration.
dist = (sqrt((array-seed).^2)); % Find distance between Seed and Gray Value.
distth = (sqrt(sum((array-seed).^2)/numel(array)));% Find bandwidth for Cluster Center.
% distth = max(dist(:))/5;
qualified = dist<distth;% Check values are in selected Bandwidth or not.
newseed = mean(array(qualified));% Update mean.
if isnan(newseed) % Check mean is not a NaN value.
break;
end
if seed == newseed || j>10 % Condition for convergence and maximum iteration.
j=0;
array(qualified) = [];% Remove values which have assigned to a cluster.
center(i) = newseed; % Store center of cluster.
break;
end
seed = newseed;% Update seed.
end
if isempty(array) || i>10 % Check maximum number of clusters.
i = 0; % Reset Counter.
break;
end
end
toc
center = sort(center); % Sort Centers.
newcenter = diff(center);% Find out Difference between two consecutive Centers.
intercluster = (max(gray(:)/10));% Findout Minimum distance between two cluster Centers.
center(newcenter<=intercluster)=[];% Discard Cluster centers less than distance.
% Make a clustered image using these centers.
vector = repmat(gray(:),[1,numel(center)]); % Replicate vector for parallel operation.
centers = repmat(center,[numel(gray),1]);
distance = ((vector-centers).^2);% Find distance between center and pixel value.
[~,lb] = min(distance,[],2);% Choose cluster index of minimum distance.
lb = reshape(lb,size(gray));% Reshape the labelled index vector.
function [lb,center] = ColorClustering(im)
im = double(im);
red = im(:,:,1); green = im(:,:,2); blue = im(:,:,3);
array = [red(:),green(:),blue(:)];
% distth = 25;
i = 0;j=0;
tic
while(true)
seed(1) = mean(array(:,1));
seed(2) = mean(array(:,2));
seed(3) = mean(array(:,3));
i = i+1;
while(true)
j = j+1;
seedvec = repmat(seed,[size(array,1),1]);
dist = sum((sqrt((array-seedvec).^2)),2);
distth = 0.25*max(dist);
qualified = dist<distth;
newred = array(:,1);
newgreen = array(:,2);
newblue = array(:,3);
newseed(1) = mean(newred(qualified));
newseed(2) = mean(newgreen(qualified));
newseed(3) = mean(newblue(qualified));
if isnan(newseed)
break;
end
if (seed == newseed) | j>10
j=0;
array(qualified,:) = [];
center(i,:) = newseed;
% center(2,i) = nnz(qualified);
break;
end
seed = newseed;
end
if isempty(array) || i>10
i = 0;
break;
end
end
toc
centers = sqrt(sum((center.^2),2));
[centers,idx]= sort(centers);
while(true)
newcenter = diff(centers);
intercluster =25; %(max(gray(:)/10));
a = (newcenter<=intercluster);
% center(a,:)=[];
% centers = sqrt(sum((center.^2),2));
centers(a,:) = [];
idx(a,:)=[];
% center(a,:)=0;
if nnz(a)==0
break;
end
end
center1 = center;
center =center1(idx,:);
% [~,idxsort] = sort(centers) ;
vecred = repmat(red(:),[1,size(center,1)]);
vecgreen = repmat(green(:),[1,size(center,1)]);
vecblue = repmat(blue(:),[1,size(center,1)]);
distred = (vecred - repmat(center(:,1)',[numel(red),1])).^2;
distgreen = (vecgreen - repmat(center(:,2)',[numel(red),1])).^2;
distblue = (vecblue - repmat(center(:,3)',[numel(red),1])).^2;
distance = sqrt(distred+distgreen+distblue);
[~,label_vector] = min(distance,[],2);
lb = reshape(label_vector,size(red));
%
|
github
|
jacksky64/imageProcessing-master
|
CoherenceFilter.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/GraphSeg/coherenceFilter/CoherenceFilter.m
| 9,898 |
utf_8
|
a3aca1656b08e401f9082f5159c4d930
|
function u = CoherenceFilter(u,Options)
% This function COHERENCEFILTER will perform Anisotropic Diffusion of a
% 2D gray/color image or 3D image volume, Which will reduce the noise in
% an image while preserving the region edges, and will smooth along
% the image edges removing gaps due to noise.
%
% Don't forget to compile the c-code by executing compile_c_files.m
%
% Iout = CoherenceFilter(Iin, Options)
%
% inputs,
% Iin : 2D gray/color image or 3D image volume. Use double datatype in 2D
% and single data type in 3D. Range of image data must be
% approximately [0 1], for default constants.
% Options : Struct with filtering options
%
% outputs,
% Iout : The anisotropic diffusion filtered image
%
% Options,
% Options.Scheme : The numerical diffusion scheme used
% 'R', Rotation Invariant, Standard Discretization
% (implicit) 5x5 kernel (Default)
% 'I', Implicit Discretization (only works in 2D)
% 'S', Standard Discretization
% 'N', Non-negativity Discretization
% Options.T : The total diffusion time (default 5)
% Options.dt : Diffusion time stepsize, in case of scheme R or I
% defaults to 1, in case of scheme S or N defaults to
% 0.1.
% Options.sigma : Sigma of gaussian smoothing before calculation of the
% image Hessian, default 1.
% Options.rho : Rho gives the sigma of the Gaussian smoothing of the
% Hessian, default 1.
% Options.verbose : Show information about the filtering, values :
% 'none', 'iter' (default) , 'full'
%
% Constants which determine the amplitude of the diffusion smoothing in
% Weickert equation
% Options.C : Default 1e-10
% Options.m : Default 1
% Options.alpha : Default 0.001
%
% The basis of the method used is the one introduced by Weickert:
% 1, Calculate Hessian from every pixel of the gaussian smoothed input image
% 2, Gaussian Smooth the Hessian, and calculate its eigenvectors and values
% (Image edges give large eigenvalues, and the eigenvectors corresponding
% to those large eigenvalues describe the direction of the edge)
% 3, The eigenvectors are used as diffusion tensor directions. The
% amplitude of the diffusion in those 3 directions is determined
% by equations below.
% 4, An Finite Difference scheme is used to do the diffusion
% 5, Back to step 1, till a certain diffusion time is reached.
%
% Weickert equation 2D:
% lambda1 = alpha + (1 - alpha)*exp(-C/(mu1-mu2).^(2*m));
% lambda2 = alpha;
% Weickert extended to 3D:
% lambda1 = alpha + (1 - alpha)*exp(-C/(mu1-mu3).^(2*m));
% lambda2 = alpha;
% lambda3 = alpha;
% (with mu1 the largest eigenvalue and mu3 the smallest)
%
% Notes:
% - If the time step is choosing to large the scheme becomes unstable, this
% can be seen by setting verbose to 'full'. The image variance has to
% decrease every itteration if the scheme is stable.
% - Weickert's equation can be found in several code files. But this is
% only one of the possible diffusion equations, you can for instance
% do plane smoothing instead of line smoothing by edditing "lambda2 = alpha"
% to "lambda2 = alpha + (1 - alpha)*exp(-C/(mu2-mu3).^(2*m)); ", or
% use the equation of Siham Tabik.
%
% Literature used:
% - Weickert : "A Scheme for Coherence-Enhancing Diffusion Filtering
% with Optimized Rotation Invariance"
% - Weickert : "Anisotropic Diffusion in Image Processing", Thesis 1996
% - Laura Fritz : "Diffusion-Based Applications for Interactive Medical
% Image Segmentation"
% - Siham Tabik, et al. : "Multiprocessing of Anisotropic Nonlinear
% Diffusion for filtering 3D image"
%
% example 2d,
% I = im2double(imread('images/sync_noise.png'));
% JR = CoherenceFilter(I,struct('T',4,'rho',10,'Scheme','R'));
% JI = CoherenceFilter(I,struct('T',4,'rho',10,'Scheme','I'));
% JS = CoherenceFilter(I,struct('T',4,'rho',10,'Scheme','N'));
% figure,
% subplot(2,2,1), imshow(I), title('Before Filtering');
% subplot(2,2,2), imshow(JR), title('Rotation Invariant Scheme');
% subplot(2,2,3), imshow(JI), title('Implicit Scheme');
% subplot(2,2,4), imshow(JI), title('Non Negative Scheme');
%
% example 3d,
% % First compile the c-code by executing compile_c_files.m
% load('images/sphere');
% showcs3(V);
% JR = CoherenceFilter(V,struct('T',50,'dt',2,'Scheme','R'));
% showcs3(JR);
%
% Written by D.Kroon University of Twente (September 2009)
% add all needed function paths
try
functionname='CoherenceFilter.m';
functiondir=which(functionname);
functiondir=functiondir(1:end-length(functionname));
addpath([functiondir '/functions2D'])
addpath([functiondir '/functions3D'])
addpath([functiondir '/functions'])
catch me
disp(me.message);
end
% Default parameters
defaultoptions=struct('T',2,'dt',[],'sigma', 1, 'rho', 1, 'TensorType', 1, 'C', 1e-10, 'm',1,'alpha',0.001,'C2',0.3,'m2',8,'alpha2',1,'RealDerivatives',false,'Scheme','R','verbose','iter');
if(~exist('Options','var')),
Options=defaultoptions;
else
tags = fieldnames(defaultoptions);
for i=1:length(tags)
if(~isfield(Options,tags{i})), Options.(tags{i})=defaultoptions.(tags{i}); end
end
if(length(tags)~=length(fieldnames(Options))),
warning('CoherenceFilter:unknownoption','unknown options found');
end
end
if(isempty(Options.dt))
switch lower(Options.Scheme)
case 'r', Options.dt=1;
case 'i', Options.dt=1;
case 's', Options.dt=0.1;
case 'n', Options.dt=0.1;
otherwise
error('CoherenceFilter:unknownoption','unknown scheme');
end
end
% Initialization
dt_max = Options.dt; t = 0;
% In case of 3D use single precision to save memory
if(size(u,3)<4), u=double(u); else u=single(u); end
% Process time
%process_time=tic;
tic;
% Show information
switch lower(Options.verbose(1))
case 'i'
disp('Diffusion time Sec. Elapsed');
case 'f'
disp('Diffusion time Sec. Elapsed Image mean Image variance');
end
% Anisotropic diffusion main loop
while (t < (Options.T-0.001))
% Update time, adjust last time step to exactly finish at the wanted
% diffusion time
Options.dt = min(dt_max,Options.T-t); t = t + Options.dt;
tn=toc;
switch lower(Options.verbose(1))
case 'n'
case 'i'
s=sprintf(' %5.0f %5.0f ',t,round(tn)); disp(s);
case 'f'
s=sprintf(' %5.0f %5.0f %13.6g %13.6g ',t,round(tn), mean(u(:)), var(u(:))); disp(s);
end
if(size(u,3)<4) % Check if 2D or 3D
% Do a diffusion step
if(strcmpi(Options.Scheme,'R@'))
u=CoherenceFilterStep2D(u,Options);
else
u=Anisotropic_step2D(u,Options);
end
else
% Do a diffusion step
if(strcmpi(Options.Scheme,'R'))
u=CoherenceFilterStep3D(u,Options);
else
u=Anisotropic_step3D(u,Options);
end
end
end
function u=Anisotropic_step2D(u,Options)
% Perform tensor-driven diffusion filtering update
% Gaussian smooth the image, for better gradients
usigma=imgaussian(u,Options.sigma,4*Options.sigma);
% Calculate the gradients
switch lower(Options.Scheme)
case 'r'
ux=derivatives(usigma,'x'); uy=derivatives(usigma,'y');
case 'i'
ux=derivatives(usigma,'x'); uy=derivatives(usigma,'y');
case 's'
[uy,ux]=gradient(usigma);
case 'n'
[uy,ux]=gradient(usigma);
otherwise
error('CoherenceFilter:unknownoption','unknown scheme');
end
% Compute the 2D structure tensors J of the image
[Jxx, Jxy, Jyy] = StructureTensor2D(ux,uy,Options.rho);
% Compute the eigenvectors and values of the strucure tensors, v1 and v2, mu1 and mu2
[mu1,mu2,v1x,v1y,v2x,v2y]=EigenVectors2D(Jxx,Jxy,Jyy);
% Construct the edge preserving diffusion tensors D = [Dxx,Dxy;Dxy,Dyy]
[Dxx,Dxy,Dyy]=ConstructDiffusionTensor2D(mu1,mu2,v1x,v1y,v2x,v2y,Options);
% Do the image diffusion
switch lower(Options.Scheme)
case 'r'
u=diffusion_scheme_2D_rotation_invariant(u,Dxx,Dxy,Dyy,Options.dt);
case 'i'
u=diffusion_scheme_2D_implicit(u,Dxx,Dxy,Dyy,Options.dt);
case 's'
u=diffusion_scheme_2D_standard(u,Dxx,Dxy,Dyy,Options.dt);
case 'n'
u=diffusion_scheme_2D_non_negativity(u,Dxx,Dxy,Dyy,Options.dt);
otherwise
error('CoherenceFilter:unknownoption','unknown scheme');
end
function u=Anisotropic_step3D(u,Options)
% Perform tensor-driven diffusion filtering update
% Gaussian smooth the image, for better gradients
usigma=imgaussian(u,Options.sigma,4*Options.sigma);
% Calculate the gradients
ux=derivatives(usigma,'x');
uy=derivatives(usigma,'y');
uz=derivatives(usigma,'z');
% Compute the 3D structure tensors J of the image
[Jxx, Jxy, Jxz, Jyy, Jyz, Jzz] = StructureTensor3D(ux,uy,uz, Options.rho);
% Free memory
clear ux; clear uy; clear uz;
% Compute the eigenvectors and eigenvalues of the hessian and directly
% use the equation of Weickert to convert them to diffusion tensors
[Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=StructureTensor2DiffusionTensor3DWeickert(Jxx,Jxy,Jxz,Jyy,Jyz,Jzz,Options);
% Free memory
clear J*;
% Do the image diffusion
switch lower(Options.Scheme)
case 'r'
u=diffusion_scheme_3D_rotation_invariant(u,Dxx,Dxy,Dxz,Dyy,Dyz,Dzz,Options.dt);
case 'i'
u=diffusion_scheme_3D_implicit(u,Dxx,Dxy,Dxz,Dyy,Dyz,Dzz,Options.dt);
case 's'
u=diffusion_scheme_3D_standard(u,Dxx,Dxy,Dxz,Dyy,Dyz,Dzz,Options.dt);
case 'n'
u=diffusion_scheme_3D_non_negativity(u,Dxx,Dxy,Dxz,Dyy,Dyz,Dzz,Options.dt);
otherwise
error('CoherenceFilter:unknownoption','unknown scheme');
end
|
github
|
jacksky64/imageProcessing-master
|
showcs3.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/GraphSeg/coherenceFilter/functions/showcs3.m
| 8,626 |
utf_8
|
34593633a0794db73b412179ff1ca339
|
function varargout = showcs3(varargin)
% SHOWCS3 M-file for showcs3.fig
% SHOWCS3, by itself, creates a new SHOWCS3 or raises the existing
% singleton*.
%
% H = SHOWCS3 returns the handle to a new SHOWCS3 or the handle to
% the existing singleton*.
%
% SHOWCS3('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in SHOWCS3.M with the given input arguments.
%
% SHOWCS3('Property','Value',...) creates a new SHOWCS3 or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before showcs3_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to showcs3_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help showcs3
% Last Modified by GUIDE v2.5 02-Apr-2008 10:10:49
% Begin initialization code - DO NOT EDIT
gui_Singleton = 0;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @showcs3_OpeningFcn, ...
'gui_OutputFcn', @showcs3_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before showcs3 is made visible.
function showcs3_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to showcs3 (see VARARGIN)
% Choose default command line output for showcs3
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
data.HandleWindow=gcf;
set(data.HandleWindow, 'Renderer', 'opengl')
hold on; axis equal; axis xy;
if((~isempty(varargin))&&(numel(varargin{1})>8))
data.voxelvolume=varargin{1};
if((length(varargin)>=2)&&(length(varargin{2})==3))
data.scales=varargin{2};
else
data.scales=[1 1 1];
end
data.sizes=size(data.voxelvolume);
data.posx=0.5; data.posy=0.5; data.posz=0.5;
set(handles.slider_x,'value',data.posx);
set(handles.slider_y,'value',data.posy);
set(handles.slider_z,'value',data.posz);
daspect(data.scales)
data=showslices(data);
setMyData(data);
rotate3d on;
view(3)
else
disp('Voxel volume not found');
end
% UIWAIT makes showcs3 wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = showcs3_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on slider movement.
function slider_x_Callback(hObject, eventdata, handles)
% hObject handle to slider_x (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
data=getMyData();
data.posx=get(handles.slider_x,'value');
data=showslices(data);
setMyData(data);
% --- Executes during object creation, after setting all properties.
function slider_x_CreateFcn(hObject, eventdata, handles)
% hObject handle to slider_x (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
% --- Executes on slider movement.
function slider_y_Callback(hObject, eventdata, handles)
% hObject handle to slider_y (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
data=getMyData();
data.posy=get(handles.slider_y,'value');
data=showslices(data);
setMyData(data);
% --- Executes during object creation, after setting all properties.
function slider_y_CreateFcn(hObject, eventdata, handles)
% hObject handle to slider_y (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
% --- Executes on slider movement.
function slider_z_Callback(hObject, eventdata, handles)
% hObject handle to slider_z (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
data=getMyData();
data.posz=get(handles.slider_z,'value');
data=showslices(data);
setMyData(data);
% --- Executes during object creation, after setting all properties.
function slider_z_CreateFcn(hObject, eventdata, handles)
% hObject handle to slider_z (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function data=showslices(data)
try delete(data.h1); delete(data.h2); delete(data.h3); catch end
posx=max(min(round(data.sizes(1)*data.posx),data.sizes(1)),1);
posy=max(min(round(data.sizes(2)*data.posy),data.sizes(2)),1);
posz=max(min(round(data.sizes(3)*data.posz),data.sizes(3)),1);
if(ndims(data.voxelvolume)==3)
slicexg = im2uint8(squeeze(data.voxelvolume(posx,:,:)))';
sliceyg = im2uint8(squeeze(data.voxelvolume(:,posy,:)))';
slicezg = im2uint8(squeeze(data.voxelvolume(:,:,posz)))';
slicex=ind2rgb(slicexg,gray(256));
slicey=ind2rgb(sliceyg,gray(256));
slicez=ind2rgb(slicezg,gray(256));
else
slicexs = im2uint8(squeeze(data.voxelvolume(posx,:,:,:)));
sliceys = im2uint8(squeeze(data.voxelvolume(:,posy,:,:)));
slicezs = im2uint8(squeeze(data.voxelvolume(:,:,posz,:)));
slicex=uint8(zeros([size(slicexs,2) size(slicexs,1) 3]));
slicey=uint8(zeros([size(sliceys,2) size(sliceys,1) 3]));
slicez=uint8(zeros([size(slicezs,2) size(slicezs,1) 3]));
for i=1:3,
slicex(:,:,i)=slicexs(:,:,i)'; slicey(:,:,i)=sliceys(:,:,i)'; slicez(:,:,i)=slicezs(:,:,i)';
end
end
slicex_x=[posx posx;posx posx]; slicex_y=[0 (data.sizes(2)-1);0 (data.sizes(2)-1)]; slicex_z=[0 0;(data.sizes(3)-1) (data.sizes(3)-1)];
slicey_x=[0 (data.sizes(1)-1);0 (data.sizes(1)-1)]; slicey_y=[posy posy;posy posy]; slicey_z=[0 0;(data.sizes(3)-1) (data.sizes(3)-1)];
slicez_x=[0 (data.sizes(1)-1);0 (data.sizes(1)-1)]; slicez_y=[0 0;(data.sizes(2)-1) (data.sizes(2)-1)]; slicez_z=[posz posz;posz posz];
data.h1=surface(slicex_x,slicex_y,slicex_z, slicex,'FaceColor','texturemap', 'EdgeColor','none', 'CDataMapping','direct','FaceAlpha',1);
data.h2=surface(slicey_x,slicey_y,slicey_z, slicey,'FaceColor','texturemap', 'EdgeColor','none', 'CDataMapping','direct','FaceAlpha',1);
data.h3=surface(slicez_x,slicez_y,slicez_z, slicez,'FaceColor','texturemap', 'EdgeColor','none', 'CDataMapping','direct','FaceAlpha',1);
function data=getMyData()
data=getappdata(gcf,'showcsdata');
function setMyData(data)
setappdata(data.HandleWindow,'showcsdata',data);
|
github
|
jacksky64/imageProcessing-master
|
StructureTensor2DiffusionTensor3DWeickert.m
|
.m
|
imageProcessing-master/segmentation/Image Segmentation & Edge Detection Toolbox/Bc project/GraphSeg/coherenceFilter/functions3D/StructureTensor2DiffusionTensor3DWeickert.m
| 1,567 |
utf_8
|
1e02eb447c16d2e5c366e997f1bca437
|
function [Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=StructureTensor2DiffusionTensor3DWeickert(Jxx,Jxy,Jxz,Jyy,Jyz,Jzz,Options)
% From Structure Tensor to Diffusion Tensor, a 3D implementation of the 2D
% equations by Weickert
%
% [Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=StructureTensor2DiffusionTensor3DWeickert(Jxx,Jxy,Jxz,Jyy,Jyz,Jzz,Options)
%
% Function is written by D.Kroon University of Twente (September 2009)
% Compute the eigenvectors and values of the structure tensors, v1, v2
% and v3, mu1, mu2 and mu3
[mu1,mu2,mu3,v3x,v3y,v3z,v2x,v2y,v2z,v1x,v1y,v1z]=EigenVectors3D(Jxx, Jxy, Jxz, Jyy, Jyz, Jzz);
[Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=ConstructDiffusionTensor3D(v1x,v1y,v1z,v2x,v2y,v2z,v3x,v3y,v3z,mu1,mu2,mu3,Options);
function [Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=ConstructDiffusionTensor3D(v1x,v1y,v1z,v2x,v2y,v2z,v3x,v3y,v3z,mu1,mu2,mu3,Options)
% Construct the edge preserving diffusion tensors D = [Dxx,Dxy,Dxz;Dxy,Dyy,Dyz;Dxz,Dyz,Dzz]
% Scaling of diffusion tensors
di=(mu1-mu3);
di((di<1e-15)&(di>-1e-15))=1e-15;
lambda1 = Options.alpha + (1 - Options.alpha)*exp(-Options.C./di.^(2*Options.m));
lambda2 = Options.alpha;
lambda3 = Options.alpha;
% Construct the tensors
Dxx = lambda1.*v1x.^2 + lambda2.*v2x.^2 + lambda3.*v3x.^2;
Dyy = lambda1.*v1y.^2 + lambda2.*v2y.^2 + lambda3.*v3y.^2;
Dzz = lambda1.*v1z.^2 + lambda2.*v2z.^2 + lambda3.*v3z.^2;
Dxy = lambda1.*v1x.*v1y + lambda2.*v2x.*v2y + lambda3.*v3x.*v3y;
Dxz = lambda1.*v1x.*v1z + lambda2.*v2x.*v2z + lambda3.*v3x.*v3z;
Dyz = lambda1.*v1y.*v1z + lambda2.*v2y.*v2z + lambda3.*v3y.*v3z;
|
github
|
jacksky64/imageProcessing-master
|
drawEdge.m
|
.m
|
imageProcessing-master/computationalGeometry/geom2d/geom2d/drawEdge.m
| 3,439 |
utf_8
|
6bd3883bf488326d6364143f61f8ae42
|
function varargout = drawEdge(varargin)
%DRAWEDGE Draw an edge given by 2 points
%
% drawEdge(x1, y1, x2, y2);
% draw an edge between the points (x1 y1) and (x2 y2).
%
% drawEdge([x1 y1 x2 y2]) ;
% drawEdge([x1 y1], [x2 y2]);
% specify data either as bundled edge, or as 2 points
%
% The function supports 3D edges:
% drawEdge([x1 y1 z1 x2 y2 z2]);
% drawEdge([x1 y1 z1], [x2 y2 z2]);
% drawEdge(x1, y1, z1, x2, y2, z2);
%
% Arguments can be single values or array of size [N*1]. In this case,
% the function draws multiple edges.
%
% H = drawEdge(..., OPT), with OPT being a set of pairwise options, can
% specify color, line width and so on...
%
% H = drawEdge(...) return handle(s) to created edges(s)
%
% See also:
% edges2d, drawCenteredEdge, drawLine
%
% ---------
% author : David Legland
% INRA - TPV URPOI - BIA IMASTE
% created the 31/10/2003.
%
% HISTORY
% 19/02/2004 add support for arrays of edges.
% 31/03/2004 change format of edges to [P1 P2] and variants.
% 28/11/2004 add support for 3D edges
% 01/08/2005 add support for drawing options
% 31/05/2007 update doc, and code makeup
% 03/08/2010 re-organize code
% separate edge and optional arguments
[ax edge options] = parseInputArguments(varargin{:});
% draw the edges
if size(edge, 2) == 4
h = drawEdge_2d(ax, edge, options);
else
h = drawEdge_3d(ax, edge, options);
end
% eventually return handle to created edges
if nargout > 0
varargout = {h};
end
function h = drawEdge_2d(ax, edge, options)
h = -1 * ones(size(edge, 1), 1);
for i = 1:size(edge, 1)
if isnan(edge(i,1))
continue;
end
h(i) = plot(ax, edge(i, [1 3]), edge(i, [2 4]), options{:});
end
function h = drawEdge_3d(ax, edge, options)
h = -1 * ones(size(edge, 1), 1);
for i = 1:size(edge, 1)
if isnan(edge(i,1))
continue;
end
h(i) = plot3(ax, edge(i, [1 4]), edge(i, [2 5]), edge(i, [3 6]), options{:});
end
function [ax edge options] = parseInputArguments(varargin)
% extract handle of axis to draw on
if isAxisHandle(varargin{1})
ax = varargin{1};
varargin(1) = [];
else
ax = gca;
end
% find the number of arguments defining edges
nbVal = 0;
for i = 1:length(varargin)
if isnumeric(varargin{i})
nbVal = nbVal+1;
else
% stop at the first non-numeric value
break;
end
end
% extract drawing options
options = varargin(nbVal+1:end);
% ensure drawing options have correct format
if length(options) == 1
options = [{'color'}, options];
end
% extract edges characteristics
switch nbVal
case 1
% all parameters in a single array
edge = varargin{1};
case 2
% parameters are two points, or two arrays of points, of size N*2.
p1 = varargin{1};
p2 = varargin{2};
edge = [p1 p2];
case 4
% parameters are 4 parameters of the edge : x1 y1 x2 and y2
edge = [varargin{1} varargin{2} varargin{3} varargin{4}];
case 6
% parameters are 6 parameters of the edge : x1 y1 z1 x2 y2 and z2
edge = [varargin{1} varargin{2} varargin{3} varargin{4} varargin{5} varargin{6}];
otherwise
error('drawEdge:WrongNumberOfParameters', 'Wrong number of parameters');
end
|
github
|
jacksky64/imageProcessing-master
|
enclosingCircle.m
|
.m
|
imageProcessing-master/computationalGeometry/geom2d/geom2d/enclosingCircle.m
| 1,890 |
utf_8
|
1ee6df61c95316d1cf6c7fac10c49dc4
|
function circle = enclosingCircle(pts)
%ENCLOSINGCIRCLE Find the minimum circle enclosing a set of points.
%
% CIRCLE = enclosingCircle(POINTS);
% compute cirlce CIRCLE=[xc yc r] which enclose all points POINTS given
% as an [Nx2] array.
%
%
% Rewritten from a file from
% Yazan Ahed ([email protected])
%
% which was rewritten from a Java applet by Shripad Thite:
% http://heyoka.cs.uiuc.edu/~thite/mincircle/
%
% See also:
% circles2d, points2d, boxes2d, circumCircle
%
% ---------
% author : David Legland
% INRA - TPV URPOI - BIA IMASTE
% created the 07/07/2005.
%
% works on convex hull : it is faster
pts = pts(convhull(pts(:,1), pts(:,2)), :);
circle = recurseCircle(size(pts, 1), pts, 1, zeros(3, 2));
function circ = recurseCircle(n, p, m, b)
% n: number of points given
% m: an argument used by the function. Always use 1 for m.
% bnry: an argument (3x2 array) used by the function to set the points that
% determines the circle boundry. You have to be careful when choosing this
% array's values. I think the values should be somewhere outside your points
% boundary. For my case, for example, I know the (x,y) I have will be something
% in between (-5,-5) and (5,5), so I use bnry as:
% [-10 -10
% -10 -10
% -10 -10]
if m==4
circ = createCircle(b(1,:), b(2,:), b(3,:));
return;
end
circ = [Inf Inf 0];
if m == 2
circ = [b(1,1:2) 0];
elseif m == 3
c = (b(1,:) + b(2,:))/2;
circ = [c distancePoints(b(1,:), c)];
end
for i = 1:n
if distancePoints(p(i,:), circ(1:2)) > circ(3)
if sum(b(:,1)==p(i,1) & b(:,2)==p(i,2)) == 0
b(m,:) = p(i,:);
circ = recurseCircle(i, p, m+1, b);
end
end
end
|
github
|
jacksky64/imageProcessing-master
|
polynomialCurveSetFit.m
|
.m
|
imageProcessing-master/computationalGeometry/geom2d/polynomialCurves2d/polynomialCurveSetFit.m
| 6,539 |
utf_8
|
7dca1c7b8d6bdf724a0298ab6004b13f
|
function varargout = polynomialCurveSetFit(seg, varargin)
%POLYNOMIALCURVESETFIT Fit a set of polynomial curves to a segmented image
%
% COEFS = polynomialCurveSetFit(IMG);
% COEFS = polynomialCurveSetFit(IMG, DEG);
% Result is a cell array of matrices. Each matrix is DEG+1-by-2, and
% contains coefficients of polynomial curve for each coordinate.
% IMG is first binarised, then skeletonized. Each cure
%
% [COEFS LBL] = polynomialCurveSetFit(...);
% also returns an image of labels for the segmented curves. The max label
% is the number of curves, and the length of COEFS.
%
% Requires the toolboxes:
% - Optimization
% - Image Processing
%
% Example
% % Fit a set of curves to a binary skeleton
% img = imread('circles.png');
% % compute skeleton, and ensure one-pixel thickness
% skel = bwmorph(img, 'skel', 'Inf');
% skel = bwmorph(skel, 'shrink');
% figure; imshow(skel==0)
% coeffs = polynomialCurveSetFit(skel, 2);
% % Display segmented image with curves
% figure; imshow(~img); hold on;
% for i = 1:length(coeffs)
% hc = drawPolynomialCurve([0 1], coeffs{i});
% set(hc, 'linewidth', 2, 'color', 'g');
% end
%
% See also
% polynomialCurves2d, polynomialCurveFit
%
% ------
% Author: David Legland
% e-mail: [email protected]
% Created: 2007-03-21
% Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.
%% Initialisations
% default degree for curves
deg = 2;
if ~isempty(varargin)
deg = varargin{1};
end
% ensure image is binary
seg = seg > 0;
%% Extract branching points and terminating points
% compute image of end points
imgEndPoints = imfilter(double(seg), ones([3 3])) .* seg == 2;
% compute centroids of end points
lblEndPoints = bwlabel(imgEndPoints, 4);
regEndPoints = bwconncomp(imgEndPoints, 4);
struct = regionprops(regEndPoints, 'Centroid');
endPoints = cat(1, struct.Centroid);
% compute image of multiple points (intersections between curves)
imgBranching = imfilter(double(seg), ones([3 3])) .* seg > 3;
% compute coordinate of nodes, as centroids of the multiple points
lblBranching = bwlabel(imgBranching, 4);
regBranching = bwconncomp(imgBranching, 4);
struct = regionprops(regBranching, 'Centroid');
branchPoints = cat(1, struct.Centroid);
% list of nodes (all categories)
nodes = [branchPoints; endPoints];
% image of node labels
lblNodes = lblBranching;
lblNodes(lblEndPoints > 0) = lblEndPoints(lblEndPoints > 0) + size(branchPoints, 1);
% isolate branches
imgBranches = seg & ~imgBranching & ~imgEndPoints;
lblBranches = bwlabel(imgBranches, 8);
% number of curves
nBranches = max(lblBranches(:));
% allocate memory
coefs = cell(nBranches, 1);
% For each curve, find interpolated polynomial curve
for i = 1:nBranches
%disp(i);
% extract points corresponding to current curve
imgBranch = lblBranches == i;
points = chainPixels(imgBranch);
% if number of points is not sufficient, simply create a line segment
if size(points, 1) < max(deg+1-2, 2)
% find labels of nodes
inds = unique(lblNodes(imdilate(imgBranch, ones(3,3))));
inds = inds(inds~=0);
if length(inds)<2
disp(['Could not find extremities of branch number ' num2str(i)]);
coefs{i} = [0 0;0 0];
continue;
end
% consider extremity nodes
node0 = nodes(inds(1),:);
node1 = nodes(inds(2),:);
% use only a linear approximation
xc = zeros(1, deg+1);
yc = zeros(1, deg+1);
xc(1) = node0(1);
yc(1) = node0(2);
xc(2) = node1(1)-node0(1);
yc(2) = node1(2)-node0(2);
% assigne au tableau de courbes
coefs{i} = [xc;yc];
% next branch
continue;
end
% find nodes closest to first and last points of the current curve
[dist, ind0] = minDistancePoints(points(1, :), nodes); %#ok<*ASGLU>
[dist, ind1] = minDistancePoints(points(end, :), nodes);
% add nodes to the curve.
points = [nodes(ind0,:); points; nodes(ind1,:)]; %#ok<AGROW>
% parametrization of the polyline
t = parametrize(points);
t = t/max(t);
% fit a polynomial curve to the set of points
[xc yc] = polynomialCurveFit(...
t, points, deg, ...
0, {points(1,1), points(1,2)},...
1, {points(end,1), points(end,2)});
% stores result
coefs{i} = [xc ; yc];
end
%% Post-processing
% manage outputs
if nargout == 1
varargout = {coefs};
elseif nargout == 2
varargout = {coefs, lblBranches};
end
function points = chainPixels(img, varargin)
%CHAINPIXELS return the list of points which constitute a curve on image
% output = chainPixels(input)
%
% Example
% chainPixels
%
% See also
%
% ------
% Author: David Legland
% e-mail: [email protected]
% Created: 2007-03-21
% Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.
conn = 8;
if ~isempty(varargin)
conn = varargin{1};
end
% matrice de voisinage
if conn==4
f = [0 1 0;1 1 1;0 1 0];
elseif conn==8
f = ones([3 3]);
end
% find extremity points
nb = imfilter(double(img), f).*img;
imgEnding = nb==2 | nb==1;
[yi xi] = find(imgEnding);
% extract coordinates of points
[y x] = find(img);
% index of first point
if isempty(xi)
% take arbitrary point
ind = 1;
else
ind = find(x==xi(1) & y==yi(1));
end
% allocate memory
points = zeros(length(x), 2);
if conn==8
for i=1:size(points, 1)
% avoid multiple neighbors (can happen in loops)
ind = ind(1);
% add current point to chained curve
points(i,:) = [x(ind) y(ind)];
% remove processed coordinate
x(ind) = []; y(ind) = [];
% find next candidate
ind = find(abs(x-points(i,1))<=1 & abs(y-points(i,2))<=1);
end
else
for i=1:size(points, 1)
% avoid multiple neighbors (can happen in loops)
ind = ind(1);
% add current point to chained curve
points(i,:) = [x(ind) y(ind)];
% remove processed coordinate
x(ind) = []; y(ind) = [];
% find next candidate
ind = find(abs(x-points(i,1)) + abs(y-points(i,2)) <=1 );
end
end
|
github
|
jacksky64/imageProcessing-master
|
polynomialCurvePosition.m
|
.m
|
imageProcessing-master/computationalGeometry/geom2d/polynomialCurves2d/polynomialCurvePosition.m
| 2,965 |
utf_8
|
e8933d9b90e0b043fc8392746e0be496
|
function pos = polynomialCurvePosition(tBounds, varargin)
%POLYNOMIALCURVEPOSITION Compute position on a curve for a given length
%
% POS = polynomialCurvePosition(T, XCOEF, YCOEF, L)
% XCOEF and YCOEF are row vectors of coefficients, in the form:
% [a0 a1 a2 ... an]
% T is a 1x2 row vector, containing the bounds of the parametrization
% variable: T = [T0 T1], with T taking all values between T0 and T1.
% L is the geodesic length corresponding to the searched position.
% POS is a scalar, verifying relation:
% L = polynomialCurveLength([T(1) POS], XCOEF, YCOEF);
%
% POS = polynomialCurvePosition(T, COEFS, L)
% COEFS is either a 2xN matrix (one row for the coefficients of each
% coordinate), or a cell array.
%
% POS = polynomialCurvePosition(..., TOL)
% TOL is the tolerance fo computation (absolute).
%
% See also
% polynomialCurves2d
%
% ------
% Author: David Legland
% e-mail: [email protected]
% Created: 2007-02-26
% Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.
% parametrization bounds
t0 = tBounds(1);
t1 = tBounds(end);
% polynomial coefficients for each coordinate
var = varargin{1};
if iscell(var)
xCoef = var{1};
yCoef = var{2};
varargin(1) = [];
elseif size(var, 1)==1
xCoef = varargin{1};
yCoef = varargin{2};
varargin(1:2)=[];
else
xCoef = var(1,:);
yCoef = var(2,:);
varargin(1)=[];
end
% geodesic length corresponding to searched position
L = varargin{1};
varargin(1)=[];
% tolerance
tol = 1e-6;
if ~isempty(varargin)
tol = varargin{1};
end
% compute derivative of the polynomial
dx = polynomialDerivate(xCoef);
dy = polynomialDerivate(yCoef);
% convert to format of polyval
dx = dx(end:-1:1);
dy = dy(end:-1:1);
% avoid warning for t=0
warning off 'MATLAB:quad:MinStepSize'
% set up precision for t
options = optimset('TolX', tol);
% starting point, located in the middle of the paramtrization domain
ts = (t0+t1)/2;
% compute parameter corresponding to geodesic position by solving g(t)-tg=0
pos = fzero(@(t)funCurveLength(t0, t, dx, dy, tol)- L, ts, options);
function res = funCurveLength(t0, t1, c1, c2, varargin)
%FUNCURVELENGTH return the length of polynomial curve arc
% output = funCurveLength(t0, t1, c1, c2)
% t0 and t1 are the limits of the integral
% c1 and c2 are derivative polynoms of each coordinate parametrization,
% given from greater order to lower order (polyval convention).
% c1 = [an a_n-1 ... a2 a1 a0].
%
% Example
% funCurveLength(0, 1, C2, C2);
% funCurveLength(0, 1, C2, C2, RES);
% RES is the resolution (ex: 1e-6).
%
% See also
%
%
% ------
% Author: David Legland
% e-mail: [email protected]
% Created: 2007-02-14
% Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.
res = quad(@(t)sqrt(polyval(c1, t).^2+polyval(c2, t).^2), t0, t1, varargin{:});
|
github
|
jacksky64/imageProcessing-master
|
ShowLRImages.m
|
.m
|
imageProcessing-master/SuperResolution/Visualization/ShowLRImages.m
| 1,991 |
utf_8
|
0ddc97784ff38d0ac6eaf73615d4e62a
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
% This function displays a set of low-resolution images, provided in a cell
% array.
function ShowLRImages(images)
numCols = ceil(sqrt(numel(images)));
numRows = numCols;
for i = 1 : numRows
for j = 1 : numCols
imageInd = (i - 1) * numCols + j;
subplot(numCols, numRows, imageInd);
imshow(images{imageInd});
hold on;
title(sprintf('Low-Res Image No.%d', imageInd));
end
end
end
|
github
|
jacksky64/imageProcessing-master
|
SynthDataset.m
|
.m
|
imageProcessing-master/SuperResolution/TestUtils/SynthDataset.m
| 2,989 |
utf_8
|
9bf1d7906f16583abaee6e20996e96e9
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
% This function creates a synthetic dataset for test the SR algorithm.
% It's inputs are a reference image, a blur sigma, and a number of low-res
% images to be generated.
% The outputs are a set of randomly translated low-res images, their
% translation offsets, and the original image cropped to the frame that can
% be restored from the low-res image set (their common area).
function [ images offsets croppedOriginal ] = SynthDataset(im, numImages, blurSigma)
padRatio = 0.2;
workingRowSub = round(0.5 * padRatio * size(im, 1)) : round((1 - 0.5 * padRatio) * size(im, 1));
workingColSub = round(0.5 * padRatio * size(im, 2)) : round((1 - 0.5 * padRatio) * size(im, 2));
croppedOriginal = im(workingRowSub, workingColSub);
offsets(1, :) = [ 0 0 ];
images{1} = im(workingRowSub, workingColSub);
for i = 2 : numImages
offsets(i, :) = 2 * rand - 1;
offsetRowSub = workingRowSub - offsets(i, 2);
offsetColSub = workingColSub - offsets(i, 1);
[ x y ] = meshgrid(1 : size(im, 2), 1 : size(im, 1));
[ x2 y2 ] = meshgrid(offsetColSub, offsetRowSub);
images{i} = interp2(x, y, im, x2, y2);
end
blurKernel = fspecial('gaussian', 3, blurSigma);
for i = 1 : numImages
images{i} = conv2(images{i}, blurKernel, 'same');
curIm = images{i};
images{i} = curIm(2 : 2 : end - 1, 2 : 2 : end - 1);
end
end
|
github
|
jacksky64/imageProcessing-master
|
SREquations.m
|
.m
|
imageProcessing-master/SuperResolution/Algorithm/SREquations.m
| 4,605 |
utf_8
|
46a4c282949824aa944ff5b3431b2778
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
% Creates the Super-Resolution linear equations for the given data.
% The imaging model implemented here is spatial translation->blur->decimation.
% The boundary conditions are circular.
% The output arguments lhs and rhs are the left-hand side and right-hand
% sides of a linear system whose solution is the super-resolution image.
function [ lhs rhs ] = SREquations(images, offsets, blurSigma)
lhs = [];
rhs = [];
superSize = 2 * size(images{1}) + [ 1 1 ];
for i = 1 : numel(images)
transMat = TransMat(superSize, offsets(i, :));
blurMat = BlurMat(superSize, blurSigma);
decMat = DecMat(superSize);
curLhs = decMat * blurMat * transMat;
curRhs = images{i};
lhs = [ lhs ; curLhs ];
rhs = [ rhs ; curRhs(:) ];
end
end
% Creates a translation operator.
function transMat = TransMat(superSize, offsets)
transposeMat = TransposeMat(superSize);
transMat = ...
transposeMat * TransMatY(superSize, offsets(1)) ...
* transposeMat * TransMatY(superSize, offsets(2));
end
% Creates a translation operator for the image Y axis.
function transMatX = TransMatY(superSize, offset)
row1 = zeros(1, prod(superSize));
nzInd = floor(1 - offset) : ceil(1 - offset);
filterValues = LinearKernel(1 - offset - nzInd);
nzInd(nzInd < 1) = prod(superSize) - nzInd(nzInd < 1);
row1(nzInd) = filterValues;
col1 = zeros(1, prod(superSize));
col1(1) = row1(1);
col1(2) = row1(end);
transMatX = sptoeplitz(col1, row1);
end
% Creates a matrix transposition operator.
function transposeMat = TransposeMat(superSize)
[ row col ] = meshgrid(1 : superSize(1), 1 : superSize(2));
inputPixInd = sub2ind(superSize, row, col);
outputPixInd = sub2ind(superSize, col, row);
transposeMat = sparse(outputPixInd, inputPixInd, ones(size(outputPixInd)));
end
% Creates a blurring operator.
function blurMat = BlurMat(superSize, blurSigma)
transposeMat = TransposeMat(superSize);
blurMat = ...
transposeMat * BlurMatY(superSize, blurSigma) ...
* transposeMat * BlurMatY(superSize, blurSigma);
end
% Creates a blurring operator for the Y axis.
function blurMatY = BlurMatY(superSize, blurSigma)
blurKernel = GaussianKernel(-1 : 1, blurSigma);
blurKernel = blurKernel ./ sum(blurKernel(:));
row1 = zeros(1, prod(superSize));
row1([ end 1 2 ] ) = blurKernel;
col1 = zeros(1, prod(superSize));
col1(1) = row1(1);
col1(2) = row1(2);
blurMatY = sptoeplitz(col1, row1);
end
% Creates a decimation operator.
function decMat = DecMat(superSize)
sampledSize = 0.5 * (superSize - 1);
[ outputRow outputCol ] = meshgrid(1 : sampledSize(1), 1 : sampledSize(2));
inputRow = 2 * outputRow;
inputCol = 2 * outputCol;
inputInd = sub2ind(superSize, inputRow, inputCol);
outputInd = sub2ind(sampledSize, outputRow, outputCol);
decMat = sparse(outputInd, inputInd, ones(numel(outputInd), 1), prod(sampledSize), prod(superSize));
end
|
github
|
jacksky64/imageProcessing-master
|
GaussianKernel.m
|
.m
|
imageProcessing-master/SuperResolution/Algorithm/GaussianKernel.m
| 1,580 |
utf_8
|
4008eefe6d6a055b98ddb93a345ea20c
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
function y = GaussianKernel(x, sigma)
y = exp(-(x.^2) ./ (2 * sigma^2));
end
|
github
|
jacksky64/imageProcessing-master
|
LinearKernel.m
|
.m
|
imageProcessing-master/SuperResolution/Algorithm/LinearKernel.m
| 1,651 |
utf_8
|
faa46fcf18cc9d76810196cb3030e3fb
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
function y = LinearKernel(x)
aboveThresh = abs(x) >= 1;
y(aboveThresh) = 0;
y(~aboveThresh) = 1 - abs(x(~aboveThresh));
end
|
github
|
jacksky64/imageProcessing-master
|
GradientDescent.m
|
.m
|
imageProcessing-master/SuperResolution/Algorithm/GradientDescent.m
| 2,025 |
utf_8
|
aa5a1f6cd526eb796b6ee62be58549d1
|
% Author: Victor May
% Contact: mayvic(at)gmail(dot)com
% $Date: 2011-11-19 $
% $Revision: $
%
% Copyright 2011, Victor May
%
% All Rights Reserved
%
% All commercial use of this software, whether direct or indirect, is
% strictly prohibited including, without limitation, incorporation into in
% a commercial product, use in a commercial service, or production of other
% artifacts for commercial purposes.
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for research purposes is hereby granted without fee,
% provided that the above copyright notice appears in all copies and that
% both that copyright notice and this permission notice appear in
% supporting documentation, and that the name of the author
% not be used in advertising or publicity pertaining to
% distribution of the software without specific, written prior permission.
%
% For commercial uses contact the author.
%
% THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
% THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
% FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE
% LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
% DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
% PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
% ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
% THIS SOFTWARE.
% A simple implementation of a gradient descent optimization.
function x = GradientDescent(lhs, rhs, initialGuess)
maxIter = 100;
iter = 0;
eps = 0.01;
x = initialGuess;
res = lhs' * (rhs - lhs * x);
mse = res' * res;
mse0 = mse;
while (iter < maxIter && mse > eps^2 * mse0)
res = lhs' * (rhs - lhs * x);
x = x + res;
mse = res' * res;
fprintf(1, 'Gradient Descent Iteration %d mean-square error %3.3f\n', iter, mse);
iter = iter + 1;
end
end
|
github
|
jacksky64/imageProcessing-master
|
xml_write.m
|
.m
|
imageProcessing-master/xmlIO/xml_write.m
| 18,772 |
utf_8
|
4f952d9ca0351040dbffeb946e877bb0
|
function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'filename' using Matlab's xmlwrite
% function. Optionally one can also use alternative version of xmlwrite
% function which directly calls JAVA functions for XML writing without
% MATLAB middleware. This function is provided as a patch to existing
% bugs in xmlwrite (in R2006b).
%
% xml_write(filename, tree, RootName, Pref) allows you to specify
% additional preferences about file format
%
% DOMnode = xml_write([], tree) same as above except that DOM node is
% not saved to the file but returned.
%
% INPUT
% filename file name
% tree Matlab structure tree to store in xml file.
% RootName String with XML tag name used for root (top level) node
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% Pref Other preferences:
% Pref.ItemName - default 'item' - name of a special tag used to
% itemize cell or struct arrays
% Pref.XmlEngine - let you choose the XML engine. Currently default is
% 'Xerces', which is using directly the apache xerces java file.
% Other option is 'Matlab' which uses MATLAB's xmlwrite and its
% XMLUtils java file. Both options create identical results except in
% case of CDATA sections where xmlwrite fails.
% Pref.CellItem - default 'true' - allow cell arrays to use 'item'
% notation. See below.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.StructItem - default 'true' - allow arrays of structs to use
% 'item' notation. For example "Pref.StructItem = true" gives:
% <a>
% <b>
% <item> ... <\item>
% <item> ... <\item>
% <\b>
% <\a>
% while "Pref.StructItem = false" gives:
% <a>
% <b> ... <\b>
% <b> ... <\b>
% <\a>
%
%
% Several special xml node types can be created if special tags are used
% for field names of 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields
% (usually ATTRIBUTE are present. Usually data section is stored
% directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - create comment child node from the string. For global
% comments see "RootName" input variable.
% - node.PROCESSING_INSTRUCTIONS - create "processing instruction" child
% node from the string. For global "processing instructions" see
% "RootName" input variable.
% - node.CDATA_SECTION - stores node's CDATA section (string). Only works
% if Pref.XmlEngine='Xerces'. For more info, see comments of F_xmlwrite.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes and notation nodes are not being handled by
% 'xml_write' at the moment.
%
% OUTPUT
% DOMnode Document Object Model (DOM) node tree in the format
% required as input to xmlwrite. (optional)
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% type('test.xml')
% %See also xml_tutorial.m
%
% See also
% xml_read, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
%% Check Matlab Version
v = ver('MATLAB');
v = str2double(regexp(v.Version, '\d.\d','match','once'));
if (v<7)
error('Your MATLAB version is too old. You need version 7.0 or newer.');
end
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.StructItem = true; % allow arrays of structs to use 'item' notation
DPref.CellItem = true; % allow cell arrays to use 'item' notation
DPref.StructTable= 'Html';
DPref.CellTable = 'Html';
DPref.XmlEngine = 'Matlab'; % use matlab provided XMLUtils
%DPref.XmlEngine = 'Xerces'; % use Xerces xml generator directly
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % Input is root node only
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
%% read user preferences
if (nargin>3)
if (isfield(Pref, 'TableName' )), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'StructItem')), DPref.StructItem = Pref.StructItem; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'CellTable')), DPref.CellTable = Pref.CellTable; end
if (isfield(Pref, 'StructTable')), DPref.StructTable= Pref.StructTable; end
if (isfield(Pref, 'XmlEngine' )), DPref.XmlEngine = Pref.XmlEngine; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if (nargin<3 || isempty(RootName)), RootName=inputname(2); end
if (isempty(RootName)), RootName='ROOT'; end
if (iscell(RootName)) % RootName also stores global text node data
rName = RootName;
RootName = char(rName{1});
if (length(rName)>1), GlobalProcInst = char(rName{2}); end
if (length(rName)>2), GlobalComment = char(rName{3}); end
if (length(rName)>3), GlobalDocType = char(rName{4}); end
end
if(~RootOnly && isstruct(tree)) % if struct than deal with each field separatly
fields = fieldnames(tree);
for i=1:length(fields)
field = fields{i};
x = tree(1).(field);
if (strcmp(field, 'COMMENT'))
GlobalComment = x;
elseif (strcmp(field, 'PROCESSING_INSTRUCTION'))
GlobalProcInst = x;
elseif (strcmp(field, 'DOCUMENT_TYPE'))
GlobalDocType = x;
else
RootName = field;
t = x;
end
end
tree = t;
end
%% Initialize jave object that will store xml data structure
RootName = varName2str(RootName);
if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = com.mathworks.xml.XMLUtils.createDocumentType(GlobalDocType);
% end
% DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName, dtype);
warning('xml_io_tools:write:docType', ...
'DOCUMENT_TYPE node was encountered which is not supported yet. Ignoring.');
end
DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName);
%% Use recursive function to convert matlab data structure to XML
root = DOMnode.getDocumentElement;
struct2DOMnode(DOMnode, root, tree, DPref.ItemName, DPref);
%% Remove the only child of the root node
root = DOMnode.getDocumentElement;
Child = root.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
if (nChild==1)
node = root.removeChild(root.getFirstChild);
while(node.hasChildNodes)
root.appendChild(node.removeChild(node.getFirstChild));
end
while(node.hasAttributes) % copy all attributes
root.setAttributeNode(node.removeAttributeNode(node.getAttributes.item(0)));
end
end
%% Save exotic Global nodes
if (~isempty(GlobalComment))
DOMnode.insertBefore(DOMnode.createComment(GlobalComment), DOMnode.getFirstChild());
end
if (~isempty(GlobalProcInst))
n = strfind(GlobalProcInst, ' ');
if (~isempty(n))
proc = DOMnode.createProcessingInstruction(GlobalProcInst(1:(n(1)-1)),...
GlobalProcInst((n(1)+1):end));
DOMnode.insertBefore(proc, DOMnode.getFirstChild());
end
end
% Not supported yet as the code below does not work
% if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = DOMnode.createDocumentType(GlobalDocType);
% DOMnode.insertBefore(dtype, DOMnode.getFirstChild());
% end
% end
%% save java DOM tree to XML file
if (~isempty(filename))
if (strcmpi(DPref.XmlEngine, 'Xerces'))
xmlwrite_xerces(filename, DOMnode);
else
xmlwrite(filename, DOMnode);
end
end
%% =======================================================================
% === struct2DOMnode Function ===========================================
% =======================================================================
function [] = struct2DOMnode(xml, parent, s, TagName, Pref)
% struct2DOMnode is a recursive function that converts matlab's structs to
% DOM nodes.
% INPUTS:
% xml - jave object that will store xml data structure
% parent - parent DOM Element
% s - Matlab data structure to save
% TagName - name to be used in xml tags describing 's'
% Pref - preferenced
% OUTPUT:
% parent - modified 'parent'
% perform some conversions
if (ischar(s) && min(size(s))>1) % if 2D array of characters
s=cellstr(s); % than convert to cell array
end
% if (strcmp(TagName, 'CONTENT'))
% while (iscell(s) && length(s)==1), s = s{1}; end % unwrap cell arrays of length 1
% end
TagName = varName2str(TagName);
%% == node is a 2D cell array ==
% convert to some other format prior to further processing
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
if (iscell(s) && nDim==2 && strcmpi(Pref.CellTable, 'Matlab'))
s = var2str(s, Pref.PreserveSpace);
end
if (nDim==2 && (iscell (s) && strcmpi(Pref.CellTable, 'Vector')) || ...
(isstruct(s) && strcmpi(Pref.StructTable, 'Vector')))
s = s(:);
end
if (nDim>2), s = s(:); end % can not handle this case well
nItem = numel(s);
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
%% == node is a cell ==
if (iscell(s)) % if this is a cell or cell array
if ((nDim==2 && strcmpi(Pref.CellTable,'Html')) || (nDim< 2 && Pref.CellItem))
% if 2D array of cells than can use HTML-like notation or if 1D array
% than can use item notation
if (strcmp(TagName, 'CONTENT')) % CONTENT nodes already have <TagName> ... </TagName>
array2DOMnode(xml, parent, s, Pref.ItemName, Pref ); % recursive call
else
node = xml.createElement(TagName); % <TagName> ... </TagName>
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
end
else % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
end
%% == node is a struct ==
elseif (isstruct(s)) % if struct than deal with each field separatly
if ((nDim==2 && strcmpi(Pref.StructTable,'Html')) || (nItem>1 && Pref.StructItem))
% if 2D array of structs than can use HTML-like notation or
% if 1D array of structs than can use 'items' notation
node = xml.createElement(TagName);
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
elseif (nItem>1) % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
else % otherwise save each struct separatelly
fields = fieldnames(s);
node = xml.createElement(TagName);
for i=1:length(fields) % add field by field to the node
field = fields{i};
x = s.(field);
switch field
case {'COMMENT', 'CDATA_SECTION', 'PROCESSING_INSTRUCTION'}
if iscellstr(x) % cell array of strings -> add them one by one
array2DOMnode(xml, node, x(:), field, Pref ); % recursive call will modify 'node'
elseif ischar(x) % single string -> add it
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
else % not a string - Ignore
warning('xml_io_tools:write:badSpecialNode', ...
['Struct field named ',field,' encountered which was not a string. Ignoring.']);
end
case 'ATTRIBUTE' % set attributes of the node
if (isempty(x)), continue; end
if (isstruct(x))
attName = fieldnames(x); % get names of all the attributes
for k=1:length(attName) % attach them to the node
att = xml.createAttribute(varName2str(attName(k)));
att.setValue(var2str(x.(attName{k}),Pref.PreserveSpace));
node.setAttributeNode(att);
end
else
warning('xml_io_tools:write:badAttribute', ...
'Struct field named ATTRIBUTE encountered which was not a struct. Ignoring.');
end
otherwise % set children of the node
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
end
end % end for i=1:nFields
parent.appendChild(node);
end
%% == node is a leaf node ==
else % if not a struct and not a cell than it is a leaf node
switch TagName % different processing depending on desired type of the node
case 'COMMENT' % create comment node
com = xml.createComment(s);
parent.appendChild(com);
case 'CDATA_SECTION' % create CDATA Section
cdt = xml.createCDATASection(s);
parent.appendChild(cdt);
case 'PROCESSING_INSTRUCTION' % set attributes of the node
OK = false;
if (ischar(s))
n = strfind(s, ' ');
if (~isempty(n))
proc = xml.createProcessingInstruction(s(1:(n(1)-1)),s((n(1)+1):end));
parent.insertBefore(proc, parent.getFirstChild());
OK = true;
end
end
if (~OK)
warning('xml_io_tools:write:badProcInst', ...
['Struct field named PROCESSING_INSTRUCTION need to be',...
' a string, for example: xml-stylesheet type="text/css" ', ...
'href="myStyleSheet.css". Ignoring.']);
end
case 'CONTENT' % this is text part of already existing node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace)); % convert to text
parent.appendChild(txt);
otherwise % I guess it is a regular text leaf node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace));
node = xml.createElement(TagName);
node.appendChild(txt);
parent.appendChild(node);
end
end % of struct2DOMnode function
%% =======================================================================
% === array2DOMnode Function ============================================
% =======================================================================
function [] = array2DOMnode(xml, parent, s, TagName, Pref)
% Deal with 1D and 2D arrays of cell or struct. Will modify 'parent'.
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
switch nDim
case 2 % 2D array
for r=1:size(s,1)
subnode = xml.createElement(Pref.TableName{1});
for c=1:size(s,2)
v = s(r,c);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, subnode, v, Pref.TableName{2}, Pref ); % recursive call
end
parent.appendChild(subnode);
end
case 1 %1D array
for iItem=1:numel(s)
v = s(iItem);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, parent, v, TagName, Pref ); % recursive call
end
case 0 % scalar -> this case should never be called
if ~isempty(s)
if iscell(s), s = s{1}; end
struct2DOMnode(xml, parent, s, TagName, Pref );
end
end
%% =======================================================================
% === var2str Function ==================================================
% =======================================================================
function str = var2str(object, PreserveSpace)
% convert matlab variables to a string
switch (1)
case isempty(object)
str = '';
case (isnumeric(object) || islogical(object))
if ndims(object)>2, object=object(:); end % can't handle arrays with dimention > 2
str=mat2str(object); % convert matrix to a string
% mark logical scalars with [] (logical arrays already have them) so the xml_read
% recognizes them as MATLAB objects instead of strings. Same with sparse
% matrices
if ((islogical(object) && isscalar(object)) || issparse(object)),
str = ['[' str ']'];
end
if (isinteger(object)),
str = ['[', class(object), '(', str ')]'];
end
case iscell(object)
if ndims(object)>2, object=object(:); end % can't handle cell arrays with dimention > 2
[nr nc] = size(object);
obj2 = object;
for i=1:length(object(:))
str = var2str(object{i}, PreserveSpace);
if (ischar(object{i})), object{i} = ['''' object{i} '''']; else object{i}=str; end
obj2{i} = [object{i} ','];
end
for r = 1:nr, obj2{r,nc} = [object{r,nc} ';']; end
obj2 = obj2.';
str = ['{' obj2{:} '}'];
case isstruct(object)
str='';
warning('xml_io_tools:write:var2str', ...
'Struct was encountered where string was expected. Ignoring.');
case isa(object, 'function_handle')
str = ['[@' char(object) ']'];
case ischar(object)
str = object;
otherwise
str = char(object);
end
%% string clean-up
str=str(:); str=str.'; % make sure this is a row vector of char's
if (~isempty(str))
str(str<32|str==127)=' '; % convert no-printable characters to spaces
if (~PreserveSpace)
str = strtrim(str); % remove spaces from begining and the end
str = regexprep(str,'\s+',' '); % remove multiple spaces
end
end
%% =======================================================================
% === var2Namestr Function ==============================================
% =======================================================================
function str = varName2str(str)
% convert matlab variable names to a sting
str = char(str);
p = strfind(str,'0x');
if (~isempty(p))
for i=1:length(p)
before = str( p(i)+(0:3) ); % string to replace
after = char(hex2dec(before(3:4))); % string to replace with
str = regexprep(str,before,after, 'once', 'ignorecase');
p=p-3; % since 4 characters were replaced with one - compensate
end
end
str = regexprep(str,'_COLON_',':', 'once', 'ignorecase');
str = regexprep(str,'_DASH_' ,'-', 'once', 'ignorecase');
|
github
|
jacksky64/imageProcessing-master
|
xml_read.m
|
.m
|
imageProcessing-master/xmlIO/xml_read.m
| 24,408 |
utf_8
|
4931c3d512db336d744ec43f7fa0b368
|
function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your preferences
%
% [tree, RootName, DOMnode] = xml_read(xmlfile) get additional information
% about XML file
%
% INPUT:
% xmlfile URL or filename of xml file to read
% Pref Preferences:
% Pref.ItemName - default 'item' - name of a special tag used to itemize
% cell arrays
% Pref.ReadAttr - default true - allow reading attributes
% Pref.ReadSpec - default true - allow reading special nodes
% Pref.Str2Num - default 'smart' - convert strings that look like numbers
% to numbers. Options: "always", "never", and "smart"
% Pref.KeepNS - default true - keep or strip namespace info
% Pref.NoCells - default true - force output to have no cell arrays
% Pref.Debug - default false - show mode specific error messages
% Pref.NumLevels- default infinity - how many recursive levels are
% allowed. Can be used to speed up the function by prunning the tree.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.CellItem - default 'true' - leave 'item' nodes in cell notation.
% OUTPUT:
% tree tree of structs and/or cell arrays corresponding to xml file
% RootName XML tag name used for root (top level) node.
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% DOMnode output of xmlread
%
% DETAILS:
% Function xml_read first calls MATLAB's xmlread function and than
% converts its output ('Document Object Model' tree of Java objects)
% to tree of MATLAB struct's. The output is in format of nested structs
% and cells. In the output data structure field names are based on
% XML tags, except in cases when tags produce illegal variable names.
%
% Several special xml node types result in special tags for fields of
% 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields are
% present. Usually data section is stored directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - stores node's comment section (string). For global
% comments see "RootName" output variable.
% - node.CDATA_SECTION - stores node's CDATA section (string).
% - node.PROCESSING_INSTRUCTIONS - stores "processing instruction" child
% node. For global "processing instructions" see "RootName" output variable.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes, notation nodes and processing instruction nodes
% will be treated like regular nodes
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% [tree treeName] = xml_read ('test.xml');
% disp(treeName)
% gen_object_display()
% % See also xml_examples.m
%
% See also:
% xml_write, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
% References:
% - Function inspired by Example 3 found in xmlread function.
% - Output data structures inspired by xml_toolbox structures.
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.CellItem = false; % leave 'item' nodes in cell notation
DPref.ReadAttr = true; % allow reading attributes
DPref.ReadSpec = true; % allow reading special nodes: comments, CData, etc.
DPref.KeepNS = true; % Keep or strip namespace info
DPref.Str2Num = 'smart';% convert strings that look like numbers to numbers
DPref.NoCells = true; % force output to have no cell arrays
DPref.NumLevels = 1e10; % number of recurence levels
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % return root node with no top level special nodes
Debug = false; % show specific errors (true) or general (false)?
tree = [];
RootName = [];
%% Check Matlab Version
v = ver('MATLAB');
version = str2double(regexp(v.Version, '\d.\d','match','once'));
if (version<7.1)
error('Your MATLAB version is too old. You need version 7.1 or newer.');
end
%% read user preferences
if (nargin>1)
if (isfield(Pref, 'TableName')), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'Str2Num' )), DPref.Str2Num = Pref.Str2Num ; end
if (isfield(Pref, 'NoCells' )), DPref.NoCells = Pref.NoCells ; end
if (isfield(Pref, 'NumLevels')), DPref.NumLevels = Pref.NumLevels; end
if (isfield(Pref, 'ReadAttr' )), DPref.ReadAttr = Pref.ReadAttr; end
if (isfield(Pref, 'ReadSpec' )), DPref.ReadSpec = Pref.ReadSpec; end
if (isfield(Pref, 'KeepNS' )), DPref.KeepNS = Pref.KeepNS; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'Debug' )), Debug = Pref.Debug ; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if ischar(DPref.Str2Num), % convert from character description to numbers
DPref.Str2Num = find(strcmpi(DPref.Str2Num, {'never', 'smart', 'always'}))-1;
if isempty(DPref.Str2Num), DPref.Str2Num=1; end % 1-smart by default
end
%% read xml file using Matlab function
if isa(xmlfile, 'org.apache.xerces.dom.DeferredDocumentImpl');
% if xmlfile is a DOMnode than skip the call to xmlread
try
try
DOMnode = xmlfile;
catch ME
error('Invalid DOM node: \n%s.', getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Invalid DOM node. \n');
end
else % we assume xmlfile is a filename
if (Debug) % in debuging mode crashes are allowed
DOMnode = xmlread(xmlfile);
else % in normal mode crashes are not allowed
try
try
DOMnode = xmlread(xmlfile);
catch ME
error('Failed to read XML file %s: \n%s',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Failed to read XML file %s\n',xmlfile);
end
end
end
Node = DOMnode.getFirstChild;
%% Find the Root node. Also store data from Global Comment and Processing
% Instruction nodes, if any.
GlobalTextNodes = cell(1,3);
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
while (~isempty(Node))
if (Node.getNodeType==Node.ELEMENT_NODE)
RootNode=Node;
elseif (Node.getNodeType==Node.PROCESSING_INSTRUCTION_NODE)
data = strtrim(char(Node.getData));
target = strtrim(char(Node.getTarget));
GlobalProcInst = [target, ' ', data];
GlobalTextNodes{2} = GlobalProcInst;
elseif (Node.getNodeType==Node.COMMENT_NODE)
GlobalComment = strtrim(char(Node.getData));
GlobalTextNodes{3} = GlobalComment;
% elseif (Node.getNodeType==Node.DOCUMENT_TYPE_NODE)
% GlobalTextNodes{4} = GlobalDocType;
end
Node = Node.getNextSibling;
end
%% parse xml file through calls to recursive DOMnode2struct function
if (Debug) % in debuging mode crashes are allowed
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
else % in normal mode crashes are not allowed
try
try
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
catch ME
error('Unable to parse XML file %s: \n %s.',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Unable to parse XML file %s.',xmlfile);
end
end
%% If there were any Global Text nodes than return them
if (~RootOnly)
if (~isempty(GlobalProcInst) && DPref.ReadSpec)
t.PROCESSING_INSTRUCTION = GlobalProcInst;
end
if (~isempty(GlobalComment) && DPref.ReadSpec)
t.COMMENT = GlobalComment;
end
if (~isempty(GlobalDocType) && DPref.ReadSpec)
t.DOCUMENT_TYPE = GlobalDocType;
end
t.(RootName) = tree;
tree=t;
end
if (~isempty(GlobalTextNodes))
GlobalTextNodes{1} = RootName;
RootName = GlobalTextNodes;
end
%% =======================================================================
% === DOMnode2struct Function ===========================================
% =======================================================================
function [s TagName LeafNode] = DOMnode2struct(node, Pref, level)
%% === Step 1: Get node name and check if it is a leaf node ==============
[TagName LeafNode] = NodeName(node, Pref.KeepNS);
s = []; % initialize output structure
%% === Step 2: Process Leaf Nodes (nodes with no children) ===============
if (LeafNode)
if (LeafNode>1 && ~Pref.ReadSpec), LeafNode=-1; end % tags only so ignore special nodes
if (LeafNode>0) % supported leaf node types
try
try % use try-catch: errors here are often due to VERY large fields (like images) that overflow java memory
s = char(node.getData);
if (isempty(s)), s = ' '; end % make it a string
% for some reason current xmlread 'creates' a lot of empty text
% fields with first chatacter=10 - those will be deleted.
if (~Pref.PreserveSpace || s(1)==10)
if (isspace(s(1)) || isspace(s(end))), s = strtrim(s); end % trim speces is any
end
if (LeafNode==1), s=str2var(s, Pref.Str2Num, 0); end % convert to number(s) if needed
catch ME % catch for mablab versions 7.5 and higher
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
getReport(ME)
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
end
end
if (LeafNode==3) % ProcessingInstructions need special treatment
target = strtrim(char(node.getTarget));
s = [target, ' ', s];
end
return % We are done the rest of the function deals with nodes with children
end
if (level>Pref.NumLevels+1), return; end % if Pref.NumLevels is reached than we are done
%% === Step 3: Process nodes with children ===============================
if (node.hasChildNodes) % children present
Child = node.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
% --- pass 1: how many children with each name -----------------------
f = [];
for iChild = 1:nChild % read in each child
[cname cLeaf] = NodeName(Child.item(iChild-1), Pref.KeepNS);
if (cLeaf<0), continue; end % unsupported leaf node types
if (~isfield(f,cname)),
f.(cname)=0; % initialize first time I see this name
end
f.(cname) = f.(cname)+1; % add to the counter
end % end for iChild
% text_nodes become CONTENT & for some reason current xmlread 'creates' a
% lot of empty text fields so f.CONTENT value should not be trusted
if (isfield(f,'CONTENT') && f.CONTENT>2), f.CONTENT=2; end
% --- pass 2: store all the children as struct of cell arrays ----------
for iChild = 1:nChild % read in each child
[c cname cLeaf] = DOMnode2struct(Child.item(iChild-1), Pref, level+1);
if (cLeaf && isempty(c)) % if empty leaf node than skip
continue; % usually empty text node or one of unhandled node types
elseif (nChild==1 && cLeaf==1)
s=c; % shortcut for a common case
else % if normal node
if (level>Pref.NumLevels), continue; end
n = f.(cname); % how many of them in the array so far?
if (~isfield(s,cname)) % encountered this name for the first time
if (n==1) % if there will be only one of them ...
s.(cname) = c; % than save it in format it came in
else % if there will be many of them ...
s.(cname) = cell(1,n);
s.(cname){1} = c; % than save as cell array
end
f.(cname) = 1; % initialize the counter
else % already have seen this name
s.(cname){n+1} = c; % add to the array
f.(cname) = n+1; % add to the array counter
end
end
end % for iChild
end % end if (node.hasChildNodes)
%% === Step 4: Post-process struct's created for nodes with children =====
if (isstruct(s))
fields = fieldnames(s);
nField = length(fields);
% Detect structure that looks like Html table and store it in cell Matrix
if (nField==1 && strcmpi(fields{1},Pref.TableName{1}))
tr = s.(Pref.TableName{1});
fields2 = fieldnames(tr{1});
if (length(fields2)==1 && strcmpi(fields2{1},Pref.TableName{2}))
% This seems to be a special structure such that for
% Pref.TableName = {'tr','td'} 's' corresponds to
% <tr> <td>M11</td> <td>M12</td> </tr>
% <tr> <td>M12</td> <td>M22</td> </tr>
% Recognize it as encoding for 2D struct
nr = length(tr);
for r = 1:nr
row = tr{r}.(Pref.TableName{2});
Table(r,1:length(row)) = row; %#ok<AGROW>
end
s = Table;
end
end
% --- Post-processing: convert 'struct of cell-arrays' to 'array of structs'
% Example: let say s has 3 fields s.a, s.b & s.c and each field is an
% cell-array with more than one cell-element and all 3 have the same length.
% Then change it to array of structs, each with single cell.
% This way element s.a{1} will be now accessed through s(1).a
vec = zeros(size(fields));
for i=1:nField, vec(i) = f.(fields{i}); end
if (numel(vec)>1 && vec(1)>1 && var(vec)==0) % convert from struct of
s = cell2struct(struct2cell(s), fields, 1); % arrays to array of struct
end % if anyone knows better way to do above conversion please let me know.
end
%% === Step 5: Process nodes with attributes =============================
if (node.hasAttributes && Pref.ReadAttr)
if (~isstruct(s)), % make into struct if is not already
ss.CONTENT=s;
s=ss;
end
Attr = node.getAttributes; % list of all attributes
for iAttr = 1:Attr.getLength % for each attribute
name = char(Attr.item(iAttr-1).getName); % attribute name
name = str2varName(name, Pref.KeepNS); % fix name if needed
value = char(Attr.item(iAttr-1).getValue); % attribute value
value = str2var(value, Pref.Str2Num, 1); % convert to number if possible
s.ATTRIBUTE.(name) = value; % save again
end % end iAttr loop
end % done with attributes
if (~isstruct(s)), return; end %The rest of the code deals with struct's
%% === Post-processing: fields of "s"
% convert 'cell-array of structs' to 'arrays of structs'
fields = fieldnames(s); % get field names
nField = length(fields);
for iItem=1:length(s) % for each struct in the array - usually one
for iField=1:length(fields)
field = fields{iField}; % get field name
% if this is an 'item' field and user want to leave those as cells
% than skip this one
if (strcmpi(field, Pref.ItemName) && Pref.CellItem), continue; end
x = s(iItem).(field);
if (iscell(x) && all(cellfun(@isstruct,x(:))) && numel(x)>1) % it's cell-array of structs
% numel(x)>1 check is to keep 1 cell-arrays created when Pref.CellItem=1
try % this operation fails sometimes
% example: change s(1).a{1}.b='jack'; s(1).a{2}.b='john'; to
% more convinient s(1).a(1).b='jack'; s(1).a(2).b='john';
s(iItem).(field) = [x{:}]'; %#ok<AGROW> % converted to arrays of structs
catch %#ok<CTCH>
% above operation will fail if s(1).a{1} and s(1).a{2} have
% different fields. If desired, function forceCell2Struct can force
% them to the same field structure by adding empty fields.
if (Pref.NoCells)
s(iItem).(field) = forceCell2Struct(x); %#ok<AGROW>
end
end % end catch
end
end
end
%% === Step 4: Post-process struct's created for nodes with children =====
% --- Post-processing: remove special 'item' tags ---------------------
% many xml writes (including xml_write) use a special keyword to mark
% arrays of nodes (see xml_write for examples). The code below converts
% s.item to s.CONTENT
ItemContent = false;
if (isfield(s,Pref.ItemName))
s.CONTENT = s.(Pref.ItemName);
s = rmfield(s,Pref.ItemName);
ItemContent = Pref.CellItem; % if CellItem than keep s.CONTENT as cells
end
% --- Post-processing: clean up CONTENT tags ---------------------
% if s.CONTENT is a cell-array with empty elements at the end than trim
% the length of this cell-array. Also if s.CONTENT is the only field than
% remove .CONTENT part and store it as s.
if (isfield(s,'CONTENT'))
if (iscell(s.CONTENT) && isvector(s.CONTENT))
x = s.CONTENT;
for i=numel(x):-1:1, if ~isempty(x{i}), break; end; end
if (i==1 && ~ItemContent)
s.CONTENT = x{1}; % delete cell structure
else
s.CONTENT = x(1:i); % delete empty cells
end
end
if (nField==1)
if (ItemContent)
ss = s.CONTENT; % only child: remove a level but ensure output is a cell-array
s=[]; s{1}=ss;
else
s = s.CONTENT; % only child: remove a level
end
end
end
%% =======================================================================
% === forceCell2Struct Function =========================================
% =======================================================================
function s = forceCell2Struct(x)
% Convert cell-array of structs, where not all of structs have the same
% fields, to a single array of structs
%% Convert 1D cell array of structs to 2D cell array, where each row
% represents item in original array and each column corresponds to a unique
% field name. Array "AllFields" store fieldnames for each column
AllFields = fieldnames(x{1}); % get field names of the first struct
CellMat = cell(length(x), length(AllFields));
for iItem=1:length(x)
fields = fieldnames(x{iItem}); % get field names of the next struct
for iField=1:length(fields) % inspect all fieldnames and find those
field = fields{iField}; % get field name
col = find(strcmp(field,AllFields),1);
if isempty(col) % no column for such fieldname yet
AllFields = [AllFields; field]; %#ok<AGROW>
col = length(AllFields); % create a new column for it
end
CellMat{iItem,col} = x{iItem}.(field); % store rearanged data
end
end
%% Convert 2D cell array to array of structs
s = cell2struct(CellMat, AllFields, 2);
%% =======================================================================
% === str2var Function ==================================================
% =======================================================================
function val=str2var(str, option, attribute)
% Can this string 'str' be converted to a number? if so than do it.
val = str;
len = numel(str);
if (len==0 || option==0), return; end % Str2Num="never" of empty string -> do not do enything
if (len>10000 && option==1), return; end % Str2Num="smart" and string is very long -> probably base64 encoded binary
digits = '(Inf)|(NaN)|(pi)|[\t\n\d\+\-\*\.ei EI\[\]\;\,]';
s = regexprep(str, digits, ''); % remove all the digits and other allowed characters
if (~all(~isempty(s))) % if nothing left than this is probably a number
if (~isempty(strfind(str, ' '))), option=2; end %if str has white-spaces assume by default that it is not a date string
if (~isempty(strfind(str, '['))), option=2; end % same with brackets
str(strfind(str, '\n')) = ';';% parse data tables into 2D arrays, if any
if (option==1) % the 'smart' option
try % try to convert to a date, like 2007-12-05
datenum(str); % if successful than leave it as string
catch %#ok<CTCH> % if this is not a date than ...
option=2; % ... try converting to a number
end
end
if (option==2)
if (attribute)
num = str2double(str); % try converting to a single number using sscanf function
if isnan(num), return; end % So, it wasn't really a number after all
else
num = str2num(str); %#ok<ST2NM> % try converting to a single number or array using eval function
end
if(isnumeric(num) && numel(num)>0), val=num; end % if convertion to a single was succesful than save
end
elseif ((str(1)=='[' && str(end)==']') || (str(1)=='{' && str(end)=='}')) % this looks like a (cell) array encoded as a string
try
val = eval(str);
catch %#ok<CTCH>
val = str;
end
elseif (~attribute) % see if it is a boolean array with no [] brackets
str1 = lower(str);
str1 = strrep(str1, 'false', '0');
str1 = strrep(str1, 'true' , '1');
s = regexprep(str1, '[01 \;\,]', ''); % remove all 0/1, spaces, commas and semicolons
if (~all(~isempty(s))) % if nothing left than this is probably a boolean array
num = str2num(str1); %#ok<ST2NM>
if(isnumeric(num) && numel(num)>0), val = (num>0); end % if convertion was succesful than save as logical
end
end
%% =======================================================================
% === str2varName Function ==============================================
% =======================================================================
function str = str2varName(str, KeepNS)
% convert a sting to a valid matlab variable name
if(KeepNS)
str = regexprep(str,':','_COLON_', 'once', 'ignorecase');
else
k = strfind(str,':');
if (~isempty(k))
str = str(k+1:end);
end
end
str = regexprep(str,'-','_DASH_' ,'once', 'ignorecase');
if (~isvarname(str)) && (~iskeyword(str))
str = genvarname(str);
end
%% =======================================================================
% === NodeName Function =================================================
% =======================================================================
function [Name LeafNode] = NodeName(node, KeepNS)
% get node name and make sure it is a valid variable name in Matlab.
% also get node type:
% LeafNode=0 - normal element node,
% LeafNode=1 - text node
% LeafNode=2 - supported non-text leaf node,
% LeafNode=3 - supported processing instructions leaf node,
% LeafNode=-1 - unsupported non-text leaf node
switch (node.getNodeType)
case node.ELEMENT_NODE
Name = char(node.getNodeName);% capture name of the node
Name = str2varName(Name, KeepNS); % if Name is not a good variable name - fix it
LeafNode = 0;
case node.TEXT_NODE
Name = 'CONTENT';
LeafNode = 1;
case node.COMMENT_NODE
Name = 'COMMENT';
LeafNode = 2;
case node.CDATA_SECTION_NODE
Name = 'CDATA_SECTION';
LeafNode = 2;
case node.DOCUMENT_TYPE_NODE
Name = 'DOCUMENT_TYPE';
LeafNode = 2;
case node.PROCESSING_INSTRUCTION_NODE
Name = 'PROCESSING_INSTRUCTION';
LeafNode = 3;
otherwise
NodeType = {'ELEMENT','ATTRIBUTE','TEXT','CDATA_SECTION', ...
'ENTITY_REFERENCE', 'ENTITY', 'PROCESSING_INSTRUCTION', 'COMMENT',...
'DOCUMENT', 'DOCUMENT_TYPE', 'DOCUMENT_FRAGMENT', 'NOTATION'};
Name = char(node.getNodeName);% capture name of the node
warning('xml_io_tools:read:unkNode', ...
'Unknown node type encountered: %s_NODE (%s)', NodeType{node.getNodeType}, Name);
LeafNode = -1;
end
|
github
|
jacksky64/imageProcessing-master
|
imwritesc.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/imwritesc.m
| 1,398 |
utf_8
|
68650e74e308c991970251d3bed6b85f
|
% IMWRITESC - Writes an image to file, rescaling if necessary.
%
% Usage: imwritesc(im,name)
%
% Floating point image values are rescaled to the range 0-1 so that no
% overflow occurs when writing 8-bit intensity values. The image format to
% use is determined by MATLAB from the file ending.
% If the image type is of uint8 no rescaling is performed.
% Copyright (c) 1999-2005 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% October 1999 - Original version
% March 2004 - Modified to allow colour images of class 'double'
% August 2005 - Octave compatibility
% January 2013 - Separate Octave code path no longer needed
function imwritesc(im,name)
if strcmp(class(im), 'double')
im = im - min(im(:)); % Offset so that min value is 0.
im = im./max(im(:)); % Rescale so that max is 1.
end
imwrite(im,name);
|
github
|
jacksky64/imageProcessing-master
|
circlesineramp.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/circlesineramp.m
| 5,528 |
utf_8
|
07bd0de1d4131398674bfe94cce19c1d
|
% CIRCLESINERAMP Generates test image for evaluating cyclic colour maps
%
% Usage: [im, alpha] = circlesineramp(sze, amp, wavelen, p, hole);
% [im, alpha] = circlesineramp;
%
% Arguments: sze - Size of test image. Defaults to 512x512.
% amp - Amplitude of sine wave. Defaults to pi/10
% wavelen - Wavelength of sine wave at half radius of the
% circular test image. Defaults to 8 pixels.
% p - Power to which the linear attenuation of amplitude,
% from outside edge to centre, is raised. For no
% attenuation use p = 0. For linear attenuation use a
% value of 1. The default value is 2, quadratic
% attenuation.
% hole - Flag 0/1 indicating whether the test image should have
% a 'hole' in its centre. The default is 1, to have a
% hole, this removes the distraction of the orientation
% singularlity at the centre.
% Returns:
% im - The test image.
% alpha - Alpha mask matching the regions outside of of the
% circular test image that are set to NaN. Used if you
% want to write an image with these regions transparent.
%
% The test image is a circular pattern consistsing of a sine wave superimposed
% on a spiral ramp function. The spiral ramp starts at a value of 0 pointing
% right, increasing anti-clockwise to a value of 2*pi as it completes the full
% circle. This gives a 2*pi discontinuity on the right side of the image. The
% amplitude of the superimposed sine wave is modulated from its full value at
% the outside of the circular pattern to 0 at the centre. The default sine wave
% amplitude of pi/10 means that the overall size of the sine wave from peak to
% trough represents 2*(pi/10)/(2*pi) = 10% of the total spiral ramp of 2*pi. If
% you are testing your colour map over a cycle of pi you should use amp = pi/20
% to obtain an equivalent ratio of sine wave to circular ramp.
%
% The image is designed for evaluating the effectiveness of cyclic colour maps.
% It is the cyclic companion to SINERAMP. Ideally the sine wave pattern should
% be equally discernible over all angles around the test image. In practice
% many colourmaps have uneven perceptual contrast over their range and often
% include 'flat spots' of no perceptual contrast that can hide significant
% features. Try MATLAB's hsv colour map.
%
% Ideally the test image should be rendered with a cyclic colour map using
% SHOWANGULARIM though, in this case, rendering the image with SHOW or IMAGESC
% will also be fine because all image values lie within, and use the full range
% of, 0-2*pi. However, in general, default display methods typically do not
% respect data values directly and can perform inappropriate offsetting and
% normalisation of the angular data before display and rendering with a colour
% map.
%
% For angular data to be rendered correctly it is important that the data values
% are respected so that data values are correctly assigned to specific entries
% in a cyclic colour map. The assignment of values to colours also depends on
% whether the data is cyclic over pi, or 2*pi. SHOWANGULARIM supports this.
%
% See also: SHOWANGULARIM, SINERAMP, CHIRPLIN, CHIRPEXP, EQUALISECOLOURMAP, CMAP
% Copyright (c) 2014 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% September 2014 Original version.
% October 2014 Number of cycles calculated from wave length rather than
% being specified directly.
function [im, alpha] = circlesineramp(sze, amp, wavelen, p, hole)
if ~exist('sze','var'), sze = 512; end
if ~exist('amp','var'), amp = pi/10; end
if ~exist('wavelen','var'), wavelen = 8; end
if ~exist('p','var'), p = 2; end
if ~exist('hole','var'), hole = 1; end
% Set values for inner and outer radii of test pattern
maxr = sze/2 * 0.9;
if hole
minr = 0.15*sze;
else
minr = 0;
end
% Determine number of cycles to achieve desired wavelength at half radius
meanr = (maxr + minr)/2;
circum = 2*pi*meanr;
cycles = round(circum/wavelen);
% Angles are +ve anticlockwise and mod 2*pi
[x,y] = meshgrid([0:sze-1]-sze/2);
theta = mod(atan2(-y,x), 2*pi);
rad = sqrt(x.^2 + y.^2);
% Normalise radius so that it varies 0-1 over minr to maxr
rad = (rad-minr)/(maxr-minr);
% Form the image
im = amp*rad.^p .* sin(cycles*theta) + theta;
% Ensure all values are within 0-2*pi so that a simple default display
% with a cyclic colour map will render the image correctly.
im = mod(im, 2*pi);
% 'Nanify' values outside normalised radius values of 0-1
alpha = ones(size(im));
im(rad > 1) = NaN;
alpha(rad > 1) = 0;
if hole
im(rad < 0) = NaN;
alpha(rad < 0 ) = 0;
end
|
github
|
jacksky64/imageProcessing-master
|
showsurf.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/showsurf.m
| 3,887 |
utf_8
|
1f50f8abbac642a925b579a58b8e724d
|
% SHOWSURF - shows parametric surface in a convenient way
%
% This function wraps up the commands I usually use to display a surface.
%
% The surface is displayed using SURFL with interpolated shading, in my
% favourite colormap of 'copper', with rotate3d on, and axis vis3d set.
%
% Usage can be any of the following
% showsurf(Z)
% showsurf(Z, figNo)
% showsurf(Z, title)
% showsurf(Z, figNo, title)
% showsurf(X, Y, Z)
% showsurf(X, Y, Z, figNo)
% showsurf(X, Y, Z, title)
% showsurf(X, Y, Z, figNo, title)
%
% If no figure number is specified a new figure is created. If you want the
% current figure or subplot to be used specify 0 as the figure number.
%
% See also: SHOW
% Copyright (c) 2009 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk at csse uwa edu au
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% PK May 2009
function showsurf(varargin)
[X,Y,Z,figNo,titleString] = checkargs(varargin(:));
if figNo == -1
figure
elseif figNo > 0
figure(figNo), clf
end
surfl(X,Y,Z), shading interp, colormap(copper)
rotate3d on, axis vis3d, title(titleString);
%------------------------------------------------
function [X,Y,Z,figNo,title] = checkargs(args)
nArgs = length(args);
sze = cell(nArgs,1);
for n = 1:nArgs
sze{n} = size(args{n});
end
% default values
figNo = -1; % Value to indicate create new window
title = '';
if nArgs == 1 % Assume we user has only supplied Z
[X,Y] = meshgrid(1:sze{1}(2),1:sze{1}(1));
Z = args{1};
elseif nArgs == 2 % We have Z,figNo or Z,title
if strcmp(class(args{2}),'char')
title = args{2};
else
figNo = args{2};
end
[X,Y] = meshgrid(1:sze{1}(2),1:sze{1}(1));
Z = args{1};
elseif nArgs == 3 % We have Z,figNo,title or X,Y,Z
if strcmp(class(args{3}),'char')
[X,Y] = meshgrid(1:sze{1}(2),1:sze{1}(1));
Z = args{1};
figNo = args{2};
title = args{3};
else
X = args{1};
Y = args{2};
Z = args{3};
end
elseif nArgs == 4 % We have X,Y,Z,figNo or X,Y,Z,title
if strcmp(class(args{4}),'char')
title = args{4};
else
figNo = args{4};
end
X = args{1};
Y = args{2};
Z = args{3};
elseif nArgs == 5 % We have X,Y,Z,figNo,title
X = args{1};
Y = args{2};
Z = args{3};
figNo = args{4};
title = args{5};
else
error('Wrong number of arguments');
end
% Final sanity check because the code above made quite a few assumptions
% about the validity of the supplied arguments
if ~all(size(X)==size(Y)) || ~all(size(X)==size(Z))
error('X,Y,Z must have the same dimensions');
end
if ~strcmp(class(title),'char')
error('Expecting a string for the figure title');
end
if length(figNo) ~= 1 || ~isnumeric(figNo)
error('Figure number should be a single numeric value');
end
|
github
|
jacksky64/imageProcessing-master
|
randmap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/randmap.m
| 731 |
utf_8
|
620b6965c1e7db6de65c332753df18f4
|
% RANDMAP Generates a colourmap of random colours
%
% Useful for displaying a labeled segmented image
%
% map = randmap(N)
%
% Argument: N - Number of elements in the colourmap. Default = 1024.
% This ensures images that have been segmented up to 1024
% regions will (well, are more likely to) have a unique
% colour for each region.
%
% See also: HSVMAP, LABMAP, GRAYMAP, BILATERALMAP, HSV, GRAY
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% February 2013
function map = randmap(N)
if ~exist('N', 'var'), N = 1024; end
map = rand(N, 3);
map(1,:) = [0 0 0]; % Make first entry black
|
github
|
jacksky64/imageProcessing-master
|
viewlabspace2.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/viewlabspace2.m
| 5,039 |
utf_8
|
27e0e43cd469627a1e9eefb0e5ad2119
|
% VIEWLABSPACE2 Visualisation of L*a*b* space
%
% Usage: viewlabspace2(dtheta)
%
% Argument: dtheta - Optional specification of increment in angle of plane
% through L*a*b* space. Defaults to pi/30
%
% Function allows interactive viewing of a sequence of images corresponding to
% different vertical slices in L*a*b* space.
% Initially a vertical slice in the a* direction is displayed.
% Pressing arrow up/right will rotate the plane +dtheta
% Pressing arrow down/left will rotate the plane by -dtheta
% Press 'x' to exit.
%
% See also: VIEWLABSPACE, CMAP
% To Do: Should be integrated with VIEWLABSPACE so that we get both views
% Copyright (c) 2013 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% March 2013
function viewlabspace2(dtheta)
if ~exist('dtheta', 'var'), dtheta = pi/30; end
% Define some reference colours in rgb
rgb = [1 0 0
0 1 0
0 0 1
1 1 0
0 1 1
1 0 1];
colours = {'red '
'green '
'blue '
'yellow '
'cyan '
'magenta'};
% ... and convert them to lab
labv = applycform(rgb, makecform('srgb2lab'));
% Obtain cylindrical coordinates in lab space
labradius = sqrt(labv(:,2).^2+labv(:,3).^2);
labtheta = atan2(labv(:,3), labv(:,2));
% Define lightness - radius grid for image
scale = 2;
[rad, L] = meshgrid([-140:1/scale:140], [0:1/scale:100]);
[rows,cols] = size(rad);
% Scale and offset lab coords to fit image coords
labc = zeros(size(labv));
labc(:,1) = round(labv(:,1));
labc(:,2) = round(scale*labv(:,2) + cols/2);
labc(:,3) = round(scale*labv(:,3) + rows/2);
% Print out lab values
labv = round(labv);
fprintf('\nCoordinates of standard colours in L*a*b* space\n\n');
for n = 1:length(labv)
fprintf('%s L%3d a %4d b %4d angle %4.1f radius %4d\n',...
colours{n}, ...
labv(n,1), labv(n,2), ...
labv(n,3), labtheta(n), round(labradius(n)));
end
fprintf('\n\n')
% Generate axis tick values
tickval = [-100 -50 0 50 100];
tickcoords = scale*tickval + cols/2;
ticklabels = {'-100'; '-50'; '0'; '50'; '100'};
ytickval = [0 20 40 60 80 100];
ytickcoords = scale*ytickval;
yticklabels = {'0'; '20'; '40'; '60'; '80'; '100'};
fprintf('Place cursor within figure\n');
fprintf('Use arrow keys to rotate the plane through L*a*b* space\n');
fprintf('''x'' to exit\n');
ch = 'l';
theta = 0;
while ch ~= 'x'
% Build image in lab space
lab = zeros(rows,cols,3);
lab(:,:,1) = L;
lab(:,:,2) = rad.*cos(theta);
lab(:,:,3) = rad.*sin(theta);
% Generate rgb values from lab
rgb = applycform(lab, makecform('lab2srgb'));
% Invert to reconstruct the lab values
lab2 = applycform(rgb, makecform('srgb2lab'));
% Where the reconstructed lab values differ from the specified values is
% an indication that we have gone outside of the rgb gamut. Apply a
% mask to the rgb values accordingly
mask = max(abs(lab-lab2),[],3);
for n = 1:3
rgb(:,:,n) = rgb(:,:,n).*(mask<2); % tolerance of 1
end
figure(2), image(rgb), title(sprintf('Angle %d', round(theta/pi*180)));
axis square, axis xy
set(gca, 'xtick', tickcoords);
set(gca, 'ytick', ytickcoords);
set(gca, 'xticklabel', ticklabels);
set(gca, 'yticklabel', yticklabels);
xlabel('a*b* radius'); ylabel('L*');
impixelinfo
hold on,
plot(cols/2, rows/2, 'r+'); % Centre point for reference
%{
% Plot reference colour positions
for n = 1:length(labc)
plot(labc(n,2), labc(n,3), 'w+')
text(labc(n,2), labc(n,3), ...
sprintf(' %s\n %d %d %d ',colours{n},...
labv(n,1), labv(n,2), labv(n,3)),...
'color', [1 1 1])
end
%}
hold off
% Handle keypresses within the figure
pause
ch = lower(get(gcf,'CurrentCharacter'));
if ch == 29 || ch == 30
theta = mod(theta + dtheta, 2*pi);
elseif ch == 28 || ch == 31
theta = mod(theta - dtheta, 2*pi);
end
end
|
github
|
jacksky64/imageProcessing-master
|
chirplin.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/chirplin.m
| 2,163 |
utf_8
|
c6a50bf097f1b12fc8896b68dc94130b
|
% CHIRPLIN Generates linear chirp test image
%
% The test image consists of a linear chirp signal in the horizontal direction
% with the amplitude of the chirp being modulated from 1 at the top of the image
% to 0 at the bottom.
%
% Usage: im = chirplin(sze, w0, w1, p)
%
% Arguments: sze - [rows cols] specifying size of test image. If a
% single value is supplied the image is square.
% w0, w1 - Initial and final wavelengths of the chirp pattern.
% p - Power to which the linear attenuation of amplitude,
% from top to bottom, is raised. For no attenuation use
% p = 0. For contrast sensitivity experiments use larger
% values of p. The default value is 4.
%
% Example: im = chirplin(500, 40, 2, 4)
%
% I have used this test image to evaluate the effectiveness of different
% colourmaps, and sections of colourmaps, over varying spatial frequencies and
% contrast.
%
% See also: CHIRPEXP, SINERAMP
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% March 2012
% February 2015 Changed the arguments so that the chirp is specifeied in
% terms of the initial and final wavelengths.
function im = chirplin(sze, w0, w1, p)
if length(sze) == 1
rows = sze; cols = sze;
elseif length(sze) == 2
rows = sze(1); cols = sze(2);
else
error('size must be a 1 or 2 element vector');
end
if ~exist('p', 'var'), p = 4; end
if w1 > w0
tmp = w1;
w1 = w0;
w0 = tmp;
flip = 1;
else
flip = 0;
end
x = 0:cols-1;
% Spatial frequency varies from f0 = 1/w0 to f1 = 1/w1 over the width of the
% image following the expression f(x) = f0*(k*x+1)
% We need to compute k given w0, w1 and width of the image.
f0 = 1/w0;
f1 = 1/w1;
k = (f1/f0 - 1)/(cols-1);
fx = sin(f0*(k.*x+1).*x);
A = ([(rows-1):-1:0]/(rows-1)).^p;
if flip
im = fliplr(A'*fx);
else
im = A'*fx;
end
|
github
|
jacksky64/imageProcessing-master
|
supertorus.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/supertorus.m
| 2,444 |
utf_8
|
91c1e7e27c0219d233582240dcb7c17e
|
% SUPERTORUS - generates a 'supertorus' surface
%
% Usage:
% [x,y,z] = supertorus(xscale, yscale, zscale, rad, e1, e2, n)
%
% Arguments:
% xscale, yscale, zscale - Scaling in the x, y and z directions.
% e1, e2 - Exponents of the x and y coords.
% rad - Mean radius of torus.
% n - Number of subdivisions of logitude and latitude on
% the surface.
%
% Returns: x,y,z - matrices defining paramteric surface of superquadratic
%
% If the result is not assigned to any output arguments the function
% plots the surface for you, otherwise the x, y and z parametric
% coordinates are returned for subsequent display using, say, SURFL.
%
% If rad is set to 0 the surface becomes a superquadratic
%
% Examples:
% supertorus(1, 1, 1, 2, 1, 1, 100) - classical torus 100 subdivisions
% supertorus(1, 1, 1, .8, 1, 1, 100) - an 'orange'
% supertorus(1, 1, 1, 2, .1, 1, 100) - a round 'washer'
% supertorus(1, 1, 1, 2, .1, 2, 100) - a square 'washer'
%
% See also: SUPERQUAD
% Copyright (c) 2000 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk at csse uwa edu au
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% September 2000
function [x,y,z] = supertorus(xscale,yscale,zscale,rad,e1,e2, n)
long = ones(n,1)*[-pi:2*pi/(n-1):pi];
lat = [-pi:2*pi/(n-1): pi]'*ones(1,n);
x = xscale * (rad + pow(cos(lat),e1)) .* pow(cos(long),e2);
y = yscale * (rad + pow(cos(lat),e1)) .* pow(sin(long),e2);
z = zscale * pow(sin(lat),e1);
if nargout == 0
surfl(x,y,z), shading interp, colormap(copper), axis equal
clear x y z % suppress output
end
%--------------------------------------------------------------------
% Internal function providing a modified definition of power whereby the
% sign of the result always matches the sign of the input value.
function r = pow(a,p)
r = sign(a).* abs(a.^p);
|
github
|
jacksky64/imageProcessing-master
|
cmyk2rgb.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/cmyk2rgb.m
| 890 |
utf_8
|
6d12e2501c39dec555e670c4190cf6e7
|
% CMYK2RGB Basic conversion of CMYK colour table to RGB
%
% Usage: map = cmyk2rgb(cmyk)
%
% Argument: cmyk - N x 4 table of cmyk values (assumed 0 - Returns)
% 1: map - N x 3 table of RGB values
%
% Note that you can use MATLAB's functions MAKECFORM and APPLYCFORM to
% perform the conversion. However I find that either the gamut mapping, or
% my incorrect use of these functions does not result in a reversable
% CMYK->RGB->CMYK conversion. Hence this simple function and its companion
% RGB2CMYK
%
% See also: RGB2CMYK, MAP2GEOSOFTTBL, GEOSOFTTBL2MAP
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% PK July 2013
function map = cmyk2rgb(cmyk)
c = cmyk(:,1); m = cmyk(:,2); y = cmyk(:,3); k = cmyk(:,4);
r = (1-c).*(1-k);
g = (1-m).*(1-k);
b = (1-y).*(1-k);
map = [r g b];
|
github
|
jacksky64/imageProcessing-master
|
graymap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/graymap.m
| 865 |
utf_8
|
b4f07ef527be1d1937f6ade25c5c67ac
|
% GRAYMAP Generates a gray colourmap over a specified range
%
% Usage: map = graymap(gmin, gmax, N)
%
% Arguments: gmin, gmax - Minimum and maximum gray values desired in
% colourmap. Defaults are 0 and 1
% N - Number of elements in the colourmap. Default = 256.
%
% See also: HSVMAP, LABMAP, RANDMAP, BILATERALMAP, HSV, GRAY
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% March 2012
function map = graymap(gmin, gmax, N)
if ~exist('gmin', 'var'), gmin = 0; end
if ~exist('gmax', 'var'), gmax = 1; end
if ~exist('N', 'var'), N = 256; end
assert(gmin < gmax & gmin >= 0 & gmax <= 1, ...
'gmin and gmax must be between 0 and 1');
g = (0:N-1)'/(N-1) * (gmax-gmin) + gmin;
map = [g g g];
|
github
|
jacksky64/imageProcessing-master
|
findimages.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/findimages.m
| 801 |
utf_8
|
20b68f3c818e0bc36b576390784e740c
|
% FINDIMAGES - invokes image dialog box for multiple image loading
%
% Usage: [im, filename] = findimages
%
% Returns:
% im - Cell array of images
% filename - Cell arrauy of filenames of images
%
% See Also: FINDIMAGE
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% March 2013
function [im, filename] = findimages
[filename, pathname] = uigetfile({'*.*'}, ...
'Select images' ,'multiselect','on');
if ~iscell(filename) % Assume canceled
im = {};
filename = {};
return;
end
for n = 1:length(filename)
filename{n} = [pathname filename{n}];
im{n} = imread(filename{n});
end
|
github
|
jacksky64/imageProcessing-master
|
showlogfft.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/showlogfft.m
| 1,718 |
utf_8
|
8bf452f34bee6609519ee61053657aa2
|
% SHOWLOGFFT - Displays log amplitude spectrum of an fft.
%
% Usage: showlogfft(ft, figNo)
%
% Arguments: ft - Fourier transform to be displayed
% figNo - Optional figure number to display image in.
%
% The fft is quadrant shifted to place zero frequency at the centre and the
% log of the amplitude displayed (to compress grey values). An offset of 1
% is added to avoid log(0)
%
% If figNo is omitted a new figure window is created. If figNo is supplied,
% and the figure exists, the existing window is reused to display the image,
% otherwise a new window is created.
% Copyright (c) 1999 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% October 1999
% September 2008 Octave compatible
function showlogfft(im, figNo)
Octave = exist('OCTAVE_VERSION', 'builtin') == 5; % Are we running under Octave
Title = inputname(1); % Get variable name of image data
if nargin == 2
figure(figNo); % Reuse or create a figure window with this number
else
figNo = figure; % Create new figure window
end
imagesc(log(fftshift(abs(im))+1));
colormap(gray); title(Title), axis('image')
if ~Octave; truesize(figNo), end
|
github
|
jacksky64/imageProcessing-master
|
polyfit2d.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/polyfit2d.m
| 2,438 |
utf_8
|
a05847f0b684d1549e8bf19c81e011eb
|
% POLYFIT2D Fits 2D polynomial surface to data
%
% Usage: c = polyfit2d(x, y, z, degree)
%
% Arguments: x, y, z - coordinates of data points
% degree - degree of polynomial surface
%
% Returns: c - The coefficients of polynomial surface.
% There will be (degree+1)*(degree+2)/2
% coefficients.
%
% For a degree 3 surface the coefficients progress in the form
% 00 01 02 03 10 11 12 20 21 30
% where the first digit is the y exponent and the 2nd the x exponent
%
% 0 0 0 1 0 2 0 3 1 0 1 1 1 2
% c1 x y + c2 x y + c3 x y + c4 x y + c5 x y + c6 x y + c7 x y + ...
%
% To reduce numerical problems this function rescales the values of x and y to a
% maximum magnitude of 1. The calculated coefficients are then rescaled to
% account for this. Ideally the values of x and y would also be centred to have
% zero mean. However, the correction that would then have to be applied to the
% coefficients is not so simply done. If you do find that you have numerical
% problems you could try centering x and y prior to calling this function.
%
% See also: POLYVAL2D
% Peter Kovesi 2014
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% PK July 2014
function c = polyfit2d(x, y, z, degree)
% Ensure input are column vectors
x = x(:);
y = y(:);
z = z(:);
% To reduce numerical problems we perform normalisation of the data to keep
% the maximum magnitude of x and y to 1.
scale = max(abs([x; y]));
x = x/scale;
y = y/scale;
nData = length(x);
ncoeff = (degree+1)*(degree+2)/2;
% Build Vandermonde matrix. p1 is the x exponent and p2 is the y exponent
V = zeros(nData, ncoeff);
col = 1;
for p2 = 0:degree
for p1 = 0:(degree-p2)
V(:,col) = x.^p1 .* y.^p2;
col = col+1;
end
end
[Q,R] = qr(V,0); % Solution via QR decomposition
c = R\(Q'*z);
if condest(R) > 1e10
warning('Solution is ill conditioned. Coefficient values will be suspect')
end
% Scale coefficients to account for the earlier normalisation of x and y.
col = 1;
for p2 = 0:degree
for p1 = 0:(degree-p2)
c(col) = c(col)/scale^(p1+p2);
col = col+1;
end
end
|
github
|
jacksky64/imageProcessing-master
|
rgb2lab.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/rgb2lab.m
| 1,063 |
utf_8
|
29f5354eef11a490d28c05d752252407
|
% RGB2LAB - RGB to L*a*b* colour space
%
% Usage: Lab = rgb2lab(im, wp)
%
% Arguments: im - RGB image or Nx3 colourmap for conversion
% wp - Optional string specifying the adapted white point.
% This defaults to 'D65'.
%
% Returns: Lab - The converted image or colourmap.
%
% This function wraps up calls to MAKECFORM and APPLYCFORM in a convenient
% form. Note that if the image is of type uint8 this function casts it to
% double and divides by 255 so that RGB values are in the range 0-1 and the
% transformed image can have the proper negative values for a and b.
%
% See also: LAB2RGB, RGB2NRGB, RGB2CMYK
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% PK May 2009
function Lab = rgb2lab(im, wp)
if ~exist('wp', 'var'), wp = 'D65'; end
cform = makecform('srgb2lab',...
'adaptedwhitepoint', whitepoint(wp));
if strcmp(class(im),'uint8')
im = double(im)/255;
end
Lab = applycform(im, cform);
|
github
|
jacksky64/imageProcessing-master
|
pathlist.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/pathlist.m
| 1,202 |
utf_8
|
2551671f5f3c9a17293d82ed6aee8167
|
% PATHLIST Produces a cell array of directories along a directory path
%
% Usage: plist = pathlist(fullpath)
%
% Example: If fullpath = '/Users/pk/Matlab/Spatial'
% plist =
% '/' '/Users/' '/Users/pk/' '/Users/pk/Matlab/' '/Users/pk/Matlab/Spatial'
%
% plist{end} is always fullpath
% plist{end-1} is the parent directory
% etc
%
% Not sure if this works appropriately under Windows
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% September 2010
function plist = pathlist(fullpath)
% Find locations of a forward or back slash in the full file name
ind = find(fullpath == '/' | fullpath =='\');
% If there were no / or \ in the full path just return fullpath
if isempty(ind)
plist{1} = fullpath;
else % Step along the path and extract each incremental part
for n = 1:length(ind)
plist{n} = fullpath(1:ind(n));
end
% If there is no / or \ at the end of the full path make fullpath the
% final entry in the list
if ind(end) ~= length(fullpath)
plist{n+1} = fullpath;
end
end
|
github
|
jacksky64/imageProcessing-master
|
superquad.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/superquad.m
| 2,798 |
utf_8
|
2dfc95eb41c2bf03bb78f5f0bb8b7c07
|
% SUPERQUAD - generates a superquadratic surface
%
% Usage: [x,y,z] = superquad(xscale, yscale, zscale, e1, e2, n)
%
% Arguments:
% xscale, yscale, zscale - Scaling in the x, y and z directions.
% e1, e2 - Exponents of the x and y coords.
% n - Number of subdivisions of logitude and latitude on
% the surface.
%
% Returns: x,y,z - matrices defining paramteric surface of superquadratic
%
% If the result is not assigned to any output arguments the function
% plots the surface for you, otherwise the x, y and z parametric
% coordinates are returned for subsequent display using, say, SURFL.
%
% Examples:
% superquad(1, 1, 1, 1, 1, 100) - sphere of radius 1 with 100 subdivisions
% superquad(1, 1, 1, 2, 2, 100) - octahedron of radius 1
% superquad(1, 1, 1, 3, 3, 100) - 'pointy' octahedron
% superquad(1, 1, 1, .1, .1, 100) - cube (with rounded edges)
% superquad(1, 1, .2, 1, .1, 100) - 'square cushion'
% superquad(1, 1, .2, .1, 1, 100) - cylinder
%
% See also: SUPERTORUS
% Copyright (c) 2000 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk at csse uwa edu au
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% September 2000
function [x,y,z] = superquad(xscale, yscale, zscale, e1, e2, n)
% Set up parameters of the parametric surface, in this case matrices
% corresponding to longitude and latitude on our superquadratic sphere.
long = ones(n,1)*[-pi:2*pi/(n-1):pi];
lat = [-pi/2:pi/(n-1): pi/2]'*ones(1,n);
x = xscale * pow(cos(lat),e1) .* pow(cos(long),e2);
y = yscale * pow(cos(lat),e1) .* pow(sin(long),e2);
z = zscale * pow(sin(lat),e1);
% Ensure top and bottom ends are closed. If we do not do this you find
% that due to numerical errors the ends may not be perfectly closed.
x(1,:) = 0; y(1,:) = 0;
x(end,:) = 0; y(end,:) = 0;
if nargout == 0
surfl(x,y,z), shading interp, colormap(copper), axis equal
clear x y z % suppress output
end
%--------------------------------------------------------------------
% Internal function providing a modified definition of power whereby the
% sign of the result always matches the sign of the input value.
function r = pow(a, p)
r = sign(a).* abs(a).^p;
|
github
|
jacksky64/imageProcessing-master
|
hsvmap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/hsvmap.m
| 1,842 |
utf_8
|
ec6d79772fe1bba81deabf0312847a24
|
% HSVMAP Generates an HSV colourmap over a specified range of hues
%
% The function generates colours over a specified range of hues from the HSV
% colourtmap
%
% map = hsvmap(hmin, hmax, N)
%
% Arguments: hmin - Minimum hue value 0 - 1. Default = 0
% hmax - Maximum hue value 0 - 2. Default = 1
% N - Number of elements in the colourmap. Default = 256
%
% Note that hue values range from 0 to 1 in a cyclic manner. hmax can be set to
% a value greater than one to allow one to specify a hue range that straddles
% the 0 point. The resulting map is modulus 1. For example using
% hmin = 0.9;
% hmax = 1.1;
% Will generate hues ranging from 0.9 up to 1.0, followed by hues 0.0 to 0.1
%
% hsvmap(0, 1, 256) will generate a colourmap that is identical to MATLAB's hsv
% colourmap.
%
% See also: LABMAP, GRAYMAP, RANDMAP, BILATERALMAP, HSV, GRAY
% Copyright (c) 2012 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% March 2012
function map = hsvmap(hmin, hmax, N)
if ~exist('N', 'var'), N = 256; end
if ~exist('hmin', 'var'), hmin = 0; end
if ~exist('hmax', 'var'), hmax = 1; end
assert(hmax<2, 'hmax must be less than 2');
h = [0:(N-1)]'/(N-0)*(hmax-hmin)+hmin;
h(h>1) = h(h>1)-1; % Enforce hue wraparound 0-1
map = hsv2rgb([h ones(N,2)]);
|
github
|
jacksky64/imageProcessing-master
|
rgb2nrgb.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/rgb2nrgb.m
| 1,125 |
utf_8
|
743c25dcc6996a893f06243bfc96fa8e
|
% RGB2NRGB - RGB to normalised RGB
%
% Usage: nrgb = rgb2nrgb(im, offset)
%
% Arguments: im - Colour image to be normalised
% offset - Optional value added to (R+G+B) to discount low
% intensity colour values. Defaults to 1
%
% r = R / (R + G + B) etc
% Copyright (c) 2009 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% http://www.csse.uwa.edu.au/~pk/research/matlabfns/
% May 2009
function nrgb = rgb2nrgb(im, offset)
if ndims(im) ~= 3;
error('Image must be a colour image');
end
% Convert to double if needed and define an offset = 1/255 max value to
% be used in the normalization to avoid division by zero
if ~strcmp(class(im), 'double')
im = double(im);
if ~exist('offset', 'var'), offset = 1; end
else % Assume we have doubles in range 0..1
if ~exist('offset', 'var'), offset = 1/255; end
end
nrgb = zeros(size(im));
gim = sum(im,3) + offset;
nrgb(:,:,1) = im(:,:,1)./gim;
nrgb(:,:,2) = im(:,:,2)./gim;
nrgb(:,:,3) = im(:,:,3)./gim;
|
github
|
jacksky64/imageProcessing-master
|
bbspline.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/bbspline.m
| 2,976 |
utf_8
|
104e9e1224ddb3f6d34b0cca8a1f5bfa
|
% BBSPLINE - Basic B-spline
%
% Usage: S = bbspline(P, k, N)
%
% Arguments: P - [dim x Npts] array of control points
% k - order of spline (>= 2).
% k = 2: Linear
% k = 3: Quadratic, etc
% N - Optional number of points to evaluate along
% spline. Defaults to 100.
%
% Returns: S - spline curve [dim x N] spline points
%
% See also: PBSPLINE
% PK Jan 2014
% Nov 2015 Made basis calculation slightly less wasteful
function S = bbspline(P, k, N)
if ~exist('N', 'var'), N = 100; end
[dim, np1] = size(P);
n = np1-1;
assert(k >= 2, 'Spline order must be 2 or greater');
assert(np1 >= k, 'No of control points must be >= k');
assert(N >= 2, 'Spline must be evaluated at 2 or more points');
% Set up open uniform knot vector from 0 - 1.
% There are k repeated knots at each end.
ti = 0:(k+n - 2*(k-1));
ti = ti/ti(end);
ti = [repmat(ti(1), 1, k-1), ti, repmat(ti(end), 1, k-1)];
nK = length(ti);
% Generate values of t that the spline will be evaluated at
dt = (ti(end)-ti(1))/(N-1);
t = ti(1):dt:ti(end);
% Build complete array of basis functions. We maintain two
% arrays, one storing the basis functions at the current level of
% recursion, and one storing the basis functions from the previous
% level of recursion
B = cell(1,nK-1);
Blast = cell(1,nK-1);
% 1st level of recursive construction
for i = 1:nK-1
Blast{i} = t >= ti(i) & t < ti(i+1) & ti(i) < ti(i+1);
end
% Subsequent levels of recursive basis construction. Note the logic to
% handle repeated knot values where ti(i) == ti(i+1)
for ki = 2:k
for i = 1:nK-ki
if (ti(i+ki-1) - ti(i)) < eps
V1 = 0;
else
V1 = (t - ti(i))/(ti(i+ki-1) - ti(i)) .* Blast{i};
end
if (ti(i+ki) - ti(i+1)) < eps
V2 = 0;
else
V2 = (ti(i+ki) - t)/(ti(i+ki) - ti(i+1)) .* Blast{i+1};
end
B{i} = V1 + V2;
% This is the ideal equation that the code above implements
% B{i,ki} = (t - ti(i))/(ti(i+ki-1) - ti(i)) .* B{i,ki-1} + ...
% (ti(i+ki) - t)/(ti(i+ki) - ti(i+1)) .* B{i+1,ki-1};
end
% Swap B and Blast, but only if this is not the last iteration
if ki < k
tmp = Blast;
Blast = B;
B = tmp;
end
end
% Apply basis functions to the control points
S = zeros(dim, length(t));
for d = 1:dim
for i = 1:np1
S(d,:) = S(d,:) + P(d,i)*B{i};
end
end
% Set the last point of the spline. This is not evaluated by the code above
% because the basis functions are defined from ti(i) <= t < ti(i+1)
S(:,end) = P(:,end);
|
github
|
jacksky64/imageProcessing-master
|
weightedhistc.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/weightedhistc.m
| 1,498 |
utf_8
|
b474157c0a9fa58fc7eb358c7c7ccc37
|
% WEIGHTEDHISTC Weighted histogram count
%
% This function provides a basic equivalent to MATLAB's HISTC function for
% weighted data.
%
% Usage: h = weightedhistc(vals, weights, edges)
%
% Arguments:
% vals - vector of values.
% weights - vector of weights associated with each element in vals. vals
% and weights must be vectors of the same length.
% edges - vector of bin boundaries to be used in the weighted histogram.
%
% Returns:
% h - The weighted histogram
% h(k) will count the weighted value vals(i)
% if edges(k) <= vals(i) < edges(k+1).
% The last bin will count any values of vals that match
% edges(end). Values outside the values in edges are not counted.
%
% Use bar(edges,h) to display histogram
%
% See also: HISTC
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% November 2010
function h = weightedhistc(vals, weights, edges)
if ~isvector(vals) || ~isvector(weights) || length(vals)~=length(weights)
error('vals and weights must be vectors of the same size');
end
Nedge = length(edges);
h = zeros(size(edges));
for n = 1:Nedge-1
ind = find(vals >= edges(n) & vals < edges(n+1));
if ~isempty(ind)
h(n) = sum(weights(ind));
end
end
ind = find(vals == edges(end));
if ~isempty(ind)
h(Nedge) = sum(weights(ind));
end
|
github
|
jacksky64/imageProcessing-master
|
bilateralmap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/bilateralmap.m
| 2,549 |
utf_8
|
c9a16e3cdc46d415b9bb829195bb212e
|
% BILATERALMAP Generates a bilateral colourmap
%
% This function generate a colourmap where the first half has one hue and the
% second half has another hue. Saturation varies linearly from 0 in the middle
% to 1 at ech end. This gives a colourmap which varies from white at the middle
% to an increasing saturation of the different hues as one moves to the ends.
% This colourmap is useful where your data has a clear origin. The hue
% indicates the polarity of your data, and saturation indicate amplitude.
%
% Usage: map = bilateralmap(H1, H2, V, N)
%
% Arguments:
% H1 - Hue value for 1st half of map. This must be a value between
% 0 and 1, defaults to 0.65 (blue).
% H2 - Hue value for 2nd half of map, defaults to 1.0 (red).
% V - Value as in 'V' in 'HSV', defaults to 1. Reduce this if you
% want a darker map.
% N - Number of elements in colourmap, defaults to 256.
%
% Returns:
% map - N x 3 colourmap of RGB values.
%
% Some nominal hue values:
% 0 - red
% 0.07 - orange
% 0.17 - yellow
% 0.3 - green
% 0.5 - cyan
% 0.65 - blue
% 0.85 - magenta
% 1.0 - red
%
% See also: LABMAP, HSVMAP, GRAYMAP, RANDMAP
% Copyright (c) 2012 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% October 2012
function map = bilateralmap(H1, H2, V, N)
% Default colourmap values
if ~exist('H1','var'), H1 = 0.65; end % blue
if ~exist('H2','var'), H2 = 1.00; end % red
if ~exist('V' ,'var'), V = 1; end % 'value' of 1
if ~exist('N', 'var'), N = 256; end
% Construct map in HSV then convert to RGB at end
Non2 = round(N/2);
map = zeros(N,3);
% First half of map has hue H1 and 2nd half H2
map(1:Non2, 1) = H1;
map(1+Non2 : end, 1) = H2;
% Saturation varies linearly from 0 in the middle to 1 at each end
map(1:Non2, 2) = (Non2-1:-1:0)'/Non2;
map(1+Non2 : end, 2) = (1:N-Non2)'/(N-Non2);
% Value is constant throughout
map(:,3) = V;
map = hsv2rgb(map);
|
github
|
jacksky64/imageProcessing-master
|
clouds.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/clouds.m
| 2,219 |
utf_8
|
55889e6ed17ca0979b08f7fb102f76e6
|
% CLOUDS
%
% Function to create a movie of noise images having 1/f amplitude spectum properties
%
% Usage: clouds(size, factor, meandev, stddev, lowvel, velfactor, nframes)
%
% size - size of image to produce
% factor - controls spectrum = 1/(f^factor)
% meandev - mean change in phaseangle per frame
% stddev - stddev of change in phaseangle per frame
% lowvel - phase velocity at 0 frequency
% velfactor - phase velocity = freq^velfactor
% nframes - no of frames in movie
%
% factor = 0 - raw Gaussian noise image
% = 1 - gives the 1/f `standard' drop-off for `natural' images
% = 1.5 - seems to give the most intersting `cloud patterns'
% = 2 or greater - produces `blobby' images
% PK 18-4-00
%
function clouds(size, factor, meandev, stddev, lowvel, velfactor, nframes)
rows = size;
cols = size;
phase = 2*pi*rand(size,size); % Random uniform distribution 0 - 2pi
% Create two matrices, x and y. All elements of x have a value equal to its
% x coordinate relative to the centre, elements of y have values equal to
% their y coordinate relative to the centre. From these two matrices produce
% a radius matrix that gives distances from the middle
x = ones(rows,1) * (-cols/2 : (cols/2 - 1));
y = (-rows/2 : (rows/2 - 1))' * ones(1,cols);
x = x/(cols/2);
y = y/(rows/2);
radius = sqrt(x.^2 + y.^2); % Matrix values contain radius from centre.
radius(rows/2+1,cols/2+1) = 1; % .. avoid division by zero.
filter = 1./(radius.^factor); % Construct the filter.
filter = fftshift(filter);
phasemod = fftshift(radius.^velfactor + lowvel);
% Construct fft of noise image with the specified amplitude spectrum
for n = 1:nframes
if ~mod(n,10), fprintf('\r %d', n); end
dphase = meandev + stddev*randn(size,size);
dphase = dphase.*phasemod;
phase = phase + dphase;
newfft = filter .* exp(i*phase);
im = real(ifft2(newfft)); % Invert to obtain final noise image
show(im,1), colormap(gray); axis('equal'), axis('off');
if n==1
CloudMovie = moviein(nframes);
end
CloudMovie(:,n) = getframe;
end
movie(CloudMovie,-4,12);
save('CloudMovie','CloudMovie');
|
github
|
jacksky64/imageProcessing-master
|
matprint.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/matprint.m
| 1,453 |
utf_8
|
bb847e0506584e9e043482b7a69bcbb4
|
% MATPRINT - prints a matrix with specified format string
%
% Usage: matprint(a, fmt, fid)
%
% a - Matrix to be printed.
% fmt - C style format string to use for each value.
% fid - Optional file id.
%
% Eg. matprint(a,'%3.1f') will print each entry to 1 decimal place
% Copyright (c) 2002 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% March 2002
function matprint(a, fmt, fid)
if nargin < 3
fid = 1;
end
[rows,cols] = size(a);
% Construct a format string for each row of the matrix consisting of
% 'cols' copies of the number formating specification
fmtstr = [];
for c = 1:cols
fmtstr = [fmtstr, ' ', fmt];
end
fmtstr = [fmtstr '\n']; % Add a line feed
fprintf(fid, fmtstr, a'); % Print the transpose of the matrix because
% fprintf runs down the columns of a matrix.
|
github
|
jacksky64/imageProcessing-master
|
noiseonf.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/noiseonf.m
| 2,320 |
utf_8
|
fad84fc70d1aff602b459ef9097309a4
|
% NOISEONF - Creates 1/f spectrum noise images.
%
% Function to create noise images having 1/f amplitude spectum properties.
% When displayed as a surface these images also generate great landscape
% terrain.
%
% Usage: im = noiseonf(size, factor)
%
% size - A 1 or 2-vector specifying size of image to produce [rows cols]
% factor - controls spectrum = 1/(f^factor)
%
% factor = 0 - raw Gaussian noise image
% = 1 - gives the 1/f 'standard' drop-off for 'natural' images
% = 1.5 - seems to give the most interesting 'cloud patterns'
% = 2 or greater - produces 'blobby' images
% Copyright (c) 1996-2014 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
% The Software is provided "as is", without warranty of any kind.
% December 1996
% March 2009 Arbitrary image size
% September 2011 Code tidy up
% April 2014 Fixed to work with odd dimensioned images
function im = noiseonf(sze, factor)
if length(sze) == 2
rows = sze(1); cols = sze(2);
elseif length(sze) == 1
rows = sze; cols = sze;
else
error('size must be a 1 or 2-vector');
end
% Generate an image of random Gaussian noise, mean 0, std dev 1.
im = randn(rows,cols);
imfft = fft2(im);
mag = abs(imfft); % Get magnitude
phase = imfft./mag; % and phase
% Construct the amplitude spectrum filter
% Add 1 to avoid divide by 0 problems later
radius = filtergrid(rows,cols)*max(rows,cols) + 1;
filter = 1./(radius.^factor);
% Reconstruct fft of noise image, but now with the specified amplitude
% spectrum
newfft = filter .* phase;
im = real(ifft2(newfft));
%caption = sprintf('noise with 1/(f^%2.1f) amplitude spectrum',factor);
%imagesc(im), axis('equal'), axis('off'), title(caption);
|
github
|
jacksky64/imageProcessing-master
|
fillnan.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/fillnan.m
| 1,968 |
utf_8
|
d3651b29d884e867d1804ea9c4dc1fd7
|
% FILLNAN - fills NaN values in an image with closest non Nan value
%
% NaN values in an image are replaced with the value in the closest pixel that
% is not a NaN. This can be used as a crude (but quick) 'inpainting' function
% to allow a FFT to be computed on an image containing NaN values. While the
% 'inpainting' is very crude it is typically good enough to remove most of the
% edge effects one might get at the boundaries of the NaN regions. The NaN
% regions should then be remasked out of the final processed image.
%
% Usage: [newim, mask] = fillnan(im);
%
% Argument: im - Image to be 'filled'
% Returns: newim - Filled image
% mask - Binary image indicating NaN regions in the original
% image.
%
% See Also: REMOVENAN
% Copyright (c) 2007 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk at csse uwa edu au
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
function [newim, mask] = fillnan(im);
% Generate distance transform from non NaN regions of the image.
% L will contain indices of closest non NaN points in the image
mask = ~isnan(im);
if all(isnan(im(:)))
newim = im;
warning('All elements are NaN, no filling possible\n');
return
end
[~,L] = bwdist(mask);
ind = find(isnan(im)); % Indices of points that are NaN
% Fill NaN locations with value of closest non NaN pixel
newim = im;
newim(ind) = im(L(ind));
|
github
|
jacksky64/imageProcessing-master
|
testdbscan.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/testdbscan.m
| 2,247 |
utf_8
|
aa690910417795cfda803d2e25a271c2
|
% TESTDBSCAN Program to test/demonstrate the DBSCAN clustering algorithm
%
% Simple usage: testdbscan;
%
% Full usage: [C, ptsC] = testdbscan(E, minPts)
%
%
% Arguments:
% E - Distance threshold for clustering. Defaults to 0.3
% minPts - Minimum number of points required to form a cluster.
% Defaults to 3
%
% Returns:
% C - Cell array of length Nc listing indices of points associated with
% each cluster.
% ptsC - Array of length Npts listing the cluster number associated with
% each point. If a point is denoted as noise (not enough nearby
% elements to form a cluster) its cluster number is 0.
%
% See also: DBSCAN
% Copyright (c) 2013 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Jan 2013
function [C, ptsC, centres] = testdbscan(E, minPts)
if ~exist('E', 'var'), E = 0.3; end;
if ~exist('minPts', 'var'), minPts = 3; end;
figure(1), clf, axis([-1 1 -1 1]);
fprintf('Digitise a series of points that form some clusters. Right-click to finish\n');
[x,y] = digipts;
hold on
% Perform clustering
P = [x'; y'];
[C, ptsC, centres] = dbscan(P, E, minPts);
for n = 1:length(x)
text(x(n),y(n)+.04, sprintf('%d',ptsC(n)), 'color', [0 0 1]);
end
title('Points annotated by cluster number')
hold off
%--------------------------------------------------------------------------
% DIGIPTS - digitise points in an image
%
% Function to digitise points in an image. Points are digitised by clicking
% with the left mouse button. Clicking any other button terminates the
% function. Each location digitised is marked with a red '+'.
%
% Usage: [u,v] = digipts
%
% where u and v are nx1 arrays of x and y coordinate values digitised in
% the image.
%
% This function uses the cross-hair cursor provided by GINPUT. This is
% much more useable than IMPIXEL
function [u,v] = digipts
hold on
u = []; v = [];
but = 1;
while but == 1
[x y but] = ginput(1);
if but == 1
u = [u;x];
v = [v;y];
plot(u,v,'r+');
end
end
hold off
|
github
|
jacksky64/imageProcessing-master
|
polartrans.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/polartrans.m
| 3,367 |
utf_8
|
9aed63869d7ac444d0e2198fcd91e306
|
% POLARTRANS - Transforms image to polar coordinates
%
% Usage: pim = polartrans(im, nrad, ntheta, cx, cy, linlog, shape)
%
% Arguments:
% im - image to be transformed.
% nrad - number of radius values.
% ntheta - number of theta values.
% cx, cy - optional specification of origin. If this is not
% specified it defaults to the centre of the image.
% linlog - optional string 'linear' or 'log' to obtain a
% transformation with linear or logarithmic radius
% values. linear is the default.
% shape - optional string 'full' or 'valid'
% 'full' results in the full polar transform being
% returned (the circle that fully encloses the original
% image). This is the default.
% 'valid' returns the polar transform of the largest
% circle that can fit within the image.
%
% Returns pim - image in polar coordinates with radius increasing
% down the rows and theta along the columns. The size
% of the image is nrad x ntheta. Note that theta is
% +ve clockwise as x is considered +ve along the
% columns and y +ve down the rows.
%
% When specifying the origin it is assumed that the top left pixel has
% coordinates (1,1).
% Copyright (c) 2002 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% December 2002
% November 2006 Correction to calculation of maxlogr (thanks to Chang Lei)
function pim = polartrans(im, nrad, ntheta, cx, cy, linlog, shape)
[rows, cols] = size(im);
if nargin==3 % Set origin to centre.
cx = cols/2+.5; % Add 0.5 because indexing starts at 1
cy = rows/2+.5;
end
if nargin < 7, shape = 'full'; end
if nargin < 6, linlog = 'linear'; end
if strcmp(shape,'full') % Find maximum radius value
dx = max([cx-1, cols-cx]);
dy = max([cy-1, rows-cy]);
rmax = sqrt(dx^2+dy^2);
elseif strcmp(shape,'valid') % Find minimum radius value
rmax = min([cx-1, cols-cx, cy-1, rows-cy]);
else
error('Invalid shape specification');
end
% Increments in radius and theta
deltatheta = 2*pi/ntheta;
if strcmp(linlog,'linear')
deltarad = rmax/(nrad-1);
[theta, radius] = meshgrid([0:ntheta-1]*deltatheta, [0:nrad-1]*deltarad);
elseif strcmp(linlog,'log')
maxlogr = log(rmax);
deltalogr = maxlogr/(nrad-1);
[theta, radius] = meshgrid([0:ntheta-1]*deltatheta, exp([0:nrad-1]*deltalogr));
else
error('Invalid radial transformtion (must be linear or log)');
end
xi = radius.*cos(theta) + cx; % Locations in image to interpolate data
yi = radius.*sin(theta) + cy; % from.
[x,y] = meshgrid([1:cols],[1:rows]);
pim = interp2(x, y, double(im), xi, yi);
|
github
|
jacksky64/imageProcessing-master
|
labmap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/labmap.m
| 8,483 |
utf_8
|
e7cda593bf59d9e52b88e7ef4e13bb57
|
% LABMAP - Generates a colourmap based on L*a*b* space
%
% This function can generate a wide range of colourmaps but it can be a bit
% difficult to drive...
%
% The idea: Create a spiral path within in L*a*b* space to use as a
% colourmap. L*a*b* space is designed to be perceptually uniform so, in
% principle, it should be a good space in which to design colourmaps.
%
% L*a*b* space is treated (a bit inappropriately) as a cylindrical colourspace.
% The spiral path is created by specifying linear ramps in: the angular value
% around the origin of the a*b* plane; the Lightness variation; and the
% saturation (the radius out from the centre of the a*b* plane).
%
% As an alternative an option is available to use a simple straight line
% interpolation from the first colour, through the colourspace, to the final
% colour. One is much less likely to go outside of the rgb gamut but the
% variety of colourmaps will be limited
%
% Usage: map = labmap(theta, L, saturation, N, linear, debug)
%
% Arguments:
% theta - 2-vector specifyinhg start and end angles in the a*b*
% plane over which to define the colourmap (radians).
% These angles specify a +ve or -ve ramp of values over
% which opponent colour values vary. If you want
% values to straddle the origin use theta values > 2pi
% or < 0 as needed.
% L - 2-vector specifying the lightness variation from
% start to end over the colourmap. Values are in the
% range 0-100. (You normally want a lightness
% variation over the colourmap)
% saturation - 2-vector specifying the saturation variation from
% start to end over the colourmap. This specifies the
% radius out from the centre of the a*b* plane where
% the colours are defined. Values are in the range 0-127.
% N - Number of elements in the colourmap. Default = 256.
% linear - Flag 0/1. If this flag is set a simple straight line
% interpolation, from the first to last colour, through
% the colourspace is used. Default value is 0.
% debug - Optional flag 0/1. If debug is set a plot of the
% colourmap is displayed along with a diagnostic plot
% of the specified L*a*b* values is generated along
% with the values actually achieved. These are usually
% quite different due to gamut limitations. However,
% as long as the lightness varies in a near linear
% fashion the colourmap is probably ok.
%
% theta, L and saturation can be specified as single values in which case
% they are assumed to be specifying a 'ramp' of constant value.
%
% The colourmap is generated from a* and b* values that form a spiral about
% the point (0, 0). a* = saturation*cos(theta) and b* = saturation*sin(theta)
% while Lightness varies along the specified ramp.
% a* +ve indicates magenta, a* -ve indicates cyan
% b* +ve indicates yellow, b* -ve indicates blue
%
% Changing lightness values can change things considerably and you will need
% some experimentation to get the colours you want. It is often useful to try
% reversing the lightness ramp on your coloumap to see what it does. Note also
% it is quite possible (quite common) to end up specifying L*a*b* values that
% are outside the gamut of RGB space. Run the function with the debug flag
% set to monitor this.
%
% A weakness of this simple cylindrical coordinate approach used here is that it
% generates colours that are out of gamut far too readily. To do: A better approach
% might be to show the allowable colours for a set of lightness values, allow
% the user to specify 3 colours, and then fit some kind of spline through these
% colours in lab space.
%
% L*a*b* space is more perceptually uniform than, say, HSV space. HSV and other
% rainbow-like colourmaps can be quite problematic, especially around yellow
% because lightness varies in a non-linear way along the colourmap. Personally I
% find you can generate some rather nice colourmaps with LABMAP.
%
%
% Coordinates of standard colours in L*a*b* space and in cylindrical coordinates
%
% red L 54 a 81 b 70 theta 0.7 radius 107
% green L 88 a -79 b 81 theta 2.3 radius 113
% blue L 30 a 68 b -112 theta -1.0 radius 131
% yellow L 98 a -16 b 93 theta 1.7 radius 95
% cyan L 91 a -51 b -15 theta -2.9 radius 53
% magenta L 60 a 94 b -61 theta -0.6 radius 111
%
% Example colourmaps:
%
% labmap([pi pi/2], [20 100], [60 127]); % Dark green to yellow colourmap
% labmap([3*pi/2 pi/3], [10 100], [60 127]); % Blue to yellow
% labmap([3*pi/2 9*pi/4], [10 60], [60 127]); % Blue to red
% lapmap( 0, [0 100], 0); % Lightness greyscale
%
% See also: VIEWLABSPACE, HSVMAP, GRAYMAP, HSV, GRAY, RANDMAP, BILATERALMAP
% Copyright (c) 2012-2013 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% March 2012
% March 2013 Allow theta, lightness and saturation to vary as a ramps. Allow
% cylindrical and linear interpolation across the colour space
function map = labmap(theta, L, sat, N, linear, debug)
if ~exist('theta', 'var'), theta = [0 2*pi]; end
if ~exist('L', 'var'), L = 60; end
if ~exist('sat', 'var'), sat = 127; end
if ~exist('N', 'var'), N = 256; end
if ~exist('linear', 'var'), linear = 0; end
if ~exist('debug', 'var'), debug = 0; end
if length(theta) == 1, theta = [theta theta]; end
if length(L) == 1, L = [L L]; end
if length(sat) == 1, sat = [sat sat]; end
if ~linear % Use cylindrical interpolation
% Generate linear ramps in theta, lightness and saturation
thetar = [0:N-1]'/(N-1) * (theta(2)-theta(1)) + theta(1);
Lr = [0:N-1]'/(N-1) * (L(2)-L(1)) + L(1);
satr = [0:N-1]'/(N-1) * (sat(2)-sat(1)) + sat(1);
lab = [Lr satr.*cos(thetar) satr.*sin(thetar)];
map = applycform(lab, makecform('lab2srgb'));
else % Interpolate a straight line between start and end colours
c1 = [L(1) sat(1)*cos(theta(1)) sat(1)*sin(theta(1))];
c2 = [L(2) sat(2)*cos(theta(2)) sat(2)*sin(theta(2))];
dc = c2-c1;
lab = [[0:N-1]'/(N-1).*dc(:,1)+c1(:,1) [0:N-1]'/(N-1).*dc(:,2)+c1(:,2)...
[0:N-1]'/(N-1).*dc(:,3)+c1(:,3)];
map = applycform(lab, makecform('lab2srgb'));
end
if debug
% Display colourmap
ramp = repmat(0:0.5:255, 100, 1);
show(ramp,1), colormap(map);
% Test 'integrity' of colourmap. Convert rgb back to lab. If this is
% significantly different from the original lab map then we have gone
% outside the rgb gamut
labmap = applycform(map, makecform('srgb2lab'));
diff = lab-labmap;
R = 1:N;
figure(2), plot(R,lab(:,1),'k-',R,lab(:,2),'r-',R,lab(:,3),'b-',...
R,labmap(:,1),'k--',R,labmap(:,2),'r--',R,labmap(:,3),'b--')
legend('Specified L', 'Specified a*', 'Specified b*',...
'Achieved L', 'Achieved a*', 'Achieved b*')
title('Specified and achieved L*a*b* values in colourmap')
% Crude threshold on adherance to specified lab values
if max(diff(:)) > 10
warning(sprintf(['Colormap is probably out of gamut. \nMaximum difference' ...
' between desired and achieved lab values is %d'], ...
round(max(diff(:)))));
end
end
|
github
|
jacksky64/imageProcessing-master
|
basename.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/basename.m
| 549 |
utf_8
|
619048021d1d621f2344798ad8fec477
|
% BASENAME Trims off the .ending of a filename
%
% Usage: bname = basename(name)
%
% Argument: name - Name of a file with a .ending
% Returns: bname - Name with the suffix trimmed
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% August 2010
function bname = basename(name)
% Find last instance of a '.' in the file name
ind = find(name == '.', 1, 'last');
if isempty(ind)
bname = name;
else
bname = name(1:ind(end)-1);
end
|
github
|
jacksky64/imageProcessing-master
|
showfft.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/showfft.m
| 1,588 |
utf_8
|
a10b28024b7bb20f06be15f948efa5b2
|
% SHOWFFT - Displays amplitude spectrum of an fft.
%
% Usage: showfft(ft, figNo)
%
% Arguments: ft - Fourier transform to be displayed
% figNo - Optional figure number to display image in.
%
% The fft is quadrant shifted to place zero frequency at the centre.
%
% If figNo is omitted a new figure window is created. If figNo is supplied,
% and the figure exists, the existing window is reused to display the image,
% otherwise a new window is created.
% Copyright (c) 1999 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% October 1999
% September 2008 Octave compatible
function showfft(im, figNo)
Octave = exist('OCTAVE_VERSION', 'builtin') == 5; % Are we running under Octave
Title = inputname(1); % Get variable name of image data
if nargin == 2
figure(figNo); % Reuse or create a figure window with this number
else
figNo = figure; % Create new figure window
end
imagesc(fftshift(abs(im)));
colormap(gray); title(Title), axis('image')
if ~Octave; truesize(figNo) end
|
github
|
jacksky64/imageProcessing-master
|
deres.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/deres.m
| 525 |
utf_8
|
013e5ac5596abe1811cec7954e1abca0
|
% DERES - Deresolves an image.
%
% Usage: im2 = deres(im, s)
%
% Arguments: im - image to be deresolved
% s = deresolution factor
%
% Returns the deresolved image
% PK October 2000
function im2 = deres(im, s)
if ndims(im) == 3 % Assume colour image
im2 = zeros(size(im));
im2(:,:,1) = blkproc(im(:,:,1),[s s], 'mean2(x)');
im2(:,:,2) = blkproc(im(:,:,2),[s s], 'mean2(x)');
im2(:,:,3) = blkproc(im(:,:,3),[s s], 'mean2(x)');
else
im2 = blkproc(im,[s s], 'mean2(x)');
end
|
github
|
jacksky64/imageProcessing-master
|
showangularim.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/showangularim.m
| 5,543 |
utf_8
|
75afab7bd1c9c526602370d0b685a282
|
% SHOWANGULARIM - Displays image of angular data
%
% For angular data to be rendered correctly it is important that the data values
% are respected so that data values are correctly assigned to specific entries
% in a cyclic colour map. The assignment of values to colours also depends on
% whether the data is cyclic over pi, or 2*pi.
%
% In contrast, default display methods typically do not respect data values
% directly and can perform inappropriate offsetting and normalisation of the
% angular data before display and rendering with a colour map.
%
% The rendering of the angular data with a specified colour map can be modulated
% as a function of an associated image amplitude. This allows the colour map
% encoding of the angular information to be modulated to represent the
% amplitude/reliability/coherence of the angular data.
%
% Usage: rgbim = showangularim(ang, map);
% rgbim = showangularim(ang, map, param_name, value, ...);
%
% Arguments:
% ang - Image of angular data to be displayed.
% map - Colour map to render the angular data with, ideally a
% cyclic colour map.
%
% Possible param_name - value options
%
% 'amp' - Amplitude image used to modulate the mapped colours of the
% angular data. If not supplied no modulation of colours is
% performed.
% 'bw' - Flag 0/1 indicating whether the amplitude image is used to
% modulate the colour mapped image values towards black, 0
% or white, 1. The default is 0, towards black.
% 'cycle' - The cycle length of the angular data. Use a value of pi
% if the data represents orientations, or 2*pi if the data
% represents phase values. If the input data is in degrees
% simply set cycle in degrees and the data will be
% rendered appropriately. Default is 2*pi.
% 'fig' - Optional figure number to use. If not specified a new
% figure is created. If set to 0 the function runs
% 'silently' returning rgbim without displaying the image.
%
% Returns: rgbim - The rendered image.
%
% Parameter name strings can be abbreviated to their first letter except for
% 'amp' which can only be abbreviated to 'am'
%
% For a list of all cyclic colour maps that can be generated by LABMAPLIB use:
% >> labmaplib('cyclic')
%
% See also: SCALOGRAM, RIDGEORIENT, LABMAPLIB, APPLYCOLOURMAP
% Copyright (c) 2014 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% September 2014
% October 2014 Changed optional argument handling to param_name - value pairs
%function rgbim = showangularim(ang, amp, map, cycle, bw, fig)
function rgbim = showangularim(varargin)
[ang, amp, map, cycle, bw, fig] = parseinputs(varargin{:});
% Apply colour map to angular data. Some care is needed with this. Unlike
% normal 'linear' data one cannot apply shifts and/or rescale values to
% normalise them. The raw angular data values have to be respected.
ang = mod(ang, cycle); % Ensure data values are within range 0 - cycle
rgbim = applycolourmap(ang, map, [0 cycle]);
if ~isempty(amp) % Display image with rgb values modulated by amplitude
amp = normalise(amp); % Enforce amplitude 0 - 1
if ~bw % Modulate rgb values by amplitude fading to black
for n = 1:3
rgbim(:,:,n) = rgbim(:,:,n).*amp;
end
else % Modulate rgb values by amplitude fading to white
for n = 1:3
rgbim(:,:,n) = 1 - (1 - rgbim(:,:,n)).*amp;
end
end
end
if fig
show(rgbim,fig)
end
% If function was not called with any output arguments clear rgbim so that
% it is not printed on the screen.
if ~nargout
clear('rgbim')
end
%-----------------------------------------------------------------------
% Function to parse the input arguments and set defaults
function [ang, amp, map, cycle, bw, fig] = parseinputs(varargin)
p = inputParser;
numericORlogical = @(x) isnumeric(x) || islogical(x);
% The first arguments are the image of angular data and the colour map.
addRequired(p, 'ang', @isnumeric);
addRequired(p, 'map', @isnumeric);
% Optional parameter-value pairs and their defaults
addParameter(p, 'amp', [], @isnumeric);
addParameter(p, 'cycle', 2*pi, @isnumeric);
addParameter(p, 'bw', 0, numericORlogical);
addParameter(p, 'fig', -1, @isnumeric);
parse(p, varargin{:});
ang = p.Results.ang;
map = p.Results.map;
amp = p.Results.amp;
cycle = p.Results.cycle;
bw = p.Results.bw;
fig = p.Results.fig;
if fig < 0, fig = figure; end
if ~isempty(amp) && ~all(size(amp)==size(ang))
error('Amplitude data must be same size as angular data');
end
|
github
|
jacksky64/imageProcessing-master
|
sineramp.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/sineramp.m
| 6,455 |
utf_8
|
05497c013c694f1734bc5d8f1a51fe4a
|
% SINERAMP - Generates sine on a ramp colour map test image
%
% The test image consists of a sine wave superimposed on a ramp function The
% amplitude of the sine wave is modulated from its full value at the top of the
% image to 0 at the bottom.
%
% The image is useful for evaluating the effectiveness of different colour maps.
% Ideally the sine wave pattern should be equally discernible over the full
% range of the colour map. In addition, across the bottom of the image, one
% should not see any identifiable features as the underlying signal is a smooth
% ramp. In practice many colour maps have uneven perceptual contrast over their
% range and often include 'flat spots' of no perceptual contrast that can hide
% significant features.
%
% Usage: im = sineramp(sze, amp, wavelen, p)
% im = sineramp;
%
% Arguments: sze - [rows cols] specifying size of test image. If a
% single value is supplied the image is square.
% Defaults to [256 512]; Note the number of columns is
% nominal and will be ajusted so that there are an
% integer number of sine wave cycles across the image.
% amp - Amplitude of sine wave. Defaults to 12.5
% wavelen - Wavelength of sine wave in pixels. Defaults to 8.
% p - Power to which the linear attenuation of amplitude,
% from top to bottom, is raised. For no attenuation use
% p = 0. For linear attenuation use a value of 1. For
% contrast sensitivity experiments use larger values of
% p. The default value is 2.
%
% The ramp function that the sine wave is superimposed on is adjusted slightly
% for each row so that each row of the image spans the full data range of 0 to
% 255. Thus using a large sine wave amplitude will result in the ramp at the
% top of the test image being reduced relative to the slope of the ramp at
% the bottom of the image.
%
% To start with try
% >> im = sineramp;
%
% This is equivalent to
% >> im = sineramp([256 512], 12.5, 8, 2);
%
% View it under 'gray' then try the 'jet', 'hsv', 'hot' etc colour maps. The
% results may cause you some concern!
%
% If you are wishing to evaluate a cyclic colour map, say hsv, it is suggested
% that you use the test image generated CIRCLESINERAMP. However you can use
% this function to perform a basic evaluation of a cyclic colour map by
% displaying two copies of the SINERAMP test image concatenated side-by-side.
%
% >> show([sineramp sineramp]), colour map(map_to_be_tested)
%
% However, note that despite there being an integer number of sine wave cycles
% across the image and that each row has been adjusted to span the full data
% range there will be a slight cyclic discontinuity at the top of the image,
% though this is progressively removed as you move down the test image.
%
% See source code comments for more details on the default wavelength and amplitude.
%
% See also: CIRCLESINERAMP, CHIRPLIN, CHIRPEXP, EQUALISECOLOURMAP, CMAP
% The Default Wavelength:
% The default wavelength is 8 pixels. On a computer monitor with a nominal
% pixel pitch of 0.25mm this corresponds to a wavelength of 2mm. With a monitor
% viewing distance of 600mm this corresponds to 0.19 degrees of viewing angle or
% approximately 5.2 cycles per degree. This falls within the range of spatial
% frequencies (3-7 cycles per degree ) at which most people have maximal
% contrast sensitivity to a sine wave grating (this varies with mean luminance).
% A wavelength of 8 pixels is also sufficient to provide a reasonable discrete
% representation of a sine wave. The aim is to present a stimulus that is well
% matched to the performance of the human visual system so that what we are
% primarily evaluating is the colour map's perceptual contrast and not the visual
% performance of the viewer.
%
% The Default Amplitude:
% This is set at 12.5 so that from peak to trough we have a local feature of
% magnitude 25. This is approximately 10% of the 256 levels in a standard
% colour map. It is not uncommon for colour maps to have perceptual flat spots
% that can hide features of this magnitude.
% Copyright (c) 2013-2014 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% July 2013 Original version.
% March 2014 Adjustments to make it better for evaluating cyclic colour maps.
% June 2014 Default wavelength changed from 10 to 8.
function im = sineramp2(sze, amp, wavelen, p)
if ~exist('sze','var'), sze = [256 512]; end
if ~exist('amp','var'), amp = 12.5; end
if ~exist('wavelen','var'), wavelen = 8; end
if ~exist('p','var'), p = 2; end
if length(sze) == 1
rows = sze; cols = sze;
elseif length(sze) == 2
rows = sze(1); cols = sze(2);
else
error('size must be a 1 or 2 element vector');
end
% Adjust width of image so that we have an integer number of cycles of
% the sinewave. This is helps should one be using the test image to
% evaluate a cyclic colour map. However you will still see a slight
% cyclic discontinuity at the top of the image, though this will
% disappear at the bottom of the test image
cycles = round(cols/wavelen);
cols = cycles*wavelen;
% Sine wave
x = 0:cols-1;
fx = amp*sin( 1/wavelen * 2*pi*x);
% Vertical modulating function
A = ([(rows-1):-1:0]/(rows-1)).^p;
im = A'*fx;
% Add ramp
ramp = meshgrid(0:(cols-1), 1:rows)/(cols-1);
im = im + ramp*(255 - 2*amp);
% Now normalise each row so that it spans the full data range from 0 to 255.
% Again, this is important for evaluation of cyclic colour maps though a
% small cyclic discontinuity will remain at the top of the test image.
for r = 1:rows
im(r,:) = normalise(im(r,:));
end
im = im * 255;
|
github
|
jacksky64/imageProcessing-master
|
pbspline.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/pbspline.m
| 3,825 |
utf_8
|
dc440d359c84c00b130be8c1b75884a8
|
% PBSPLINE - Basic Periodic B-spline
%
% Usage: S = pbspline(P, k, N)
%
% Arguments: P - [dim x Npts] array of control points
% k - order of spline (>= 2).
% k = 2: Linear
% k = 3: Quadratic, etc
% N - Optional number of points to evaluate along
% spline. Defaults to 100.
%
% Returns: S - spline curve [dim x N] spline points
%
% See also: BBSPLINE
% PK March 2014
% Nov 2015 Made basis calculation slightly less wasteful
% Needs a bit of tidying up and checking on domain of curve. Should be
% merged with BBSPLINE
function S = pbspline(P, k, N)
if ~exist('N', 'var'), N = 100; end
% For a closed spline check if 1st and last control points match. If not
% add another control point so that they do match
if norm(P(:,1) - P(:,end)) > 0.01
P = [P P(:,1)];
end
% Now add k - 1 control points that wrap over the first control points
P = [P P(:,2:2+k-1)];
[dim, np1] = size(P);
n = np1-1;
assert(k >= 2, 'Spline order must be 2 or greater');
assert(np1 >= k, 'No of control points must be >= k');
assert(N >= 2, 'Spline must be evaluated at 2 or more points');
% Form a uniform sequence. Number of knot points is m + 1 where m = n + k + 1
ti = [0:(n+k+1)]/(n+k+1);
nK = length(ti);
% Domain of curve is [ti_k to ti_n] or [ti_(k+1) to ti_(n+1)] ???
tstart = ti(k);
tend = ti(n);
dt = (tend-tstart)/(N-1);
t = tstart:dt:tend;
% Build complete array of basis functions. We maintain two
% arrays, one storing the basis functions at the current level of
% recursion, and one storing the basis functions from the previous
% level of recursion
B = cell(1,nK-1);
Blast = cell(1,nK-1);
% 1st level of recursive construction
for i = 1:nK-1
Blast{i} = t >= ti(i) & t < ti(i+1) & ti(i) < ti(i+1);
end
% Subsequent levels of recursive basis construction. Note the logic to
% handle repeated knot values where ti(i) == ti(i+1)
for ki = 2:k
for i = 1:nK-ki
if (ti(i+ki-1) - ti(i)) < eps
V1 = 0;
else
V1 = (t - ti(i))/(ti(i+ki-1) - ti(i)) .* Blast{i};
end
if (ti(i+ki) - ti(i+1)) < eps
V2 = 0;
else
V2 = (ti(i+ki) - t)/(ti(i+ki) - ti(i+1)) .* Blast{i+1};
end
B{i} = V1 + V2;
% This is the ideal equation that the code above implements
% N{i,ki} = (t - ti(i))/(ti(i+ki-1) - ti(i)) .* N{i,ki-1} + ...
% (ti(i+ki) - t)/(ti(i+ki) - ti(i+1)) .* N{i+1,ki-1};
end
% Swap B and Blast, but only if this is not the last iteration
if ki < k
tmp = Blast;
Blast = B;
B = tmp;
end
end
% Apply basis functions to the control points
S = zeros(dim, length(t));
for d = 1:dim
for i = 1:np1
S(d,:) = S(d,:) + P(d,i)*B{i};
end
end
% Finally, because of the knot arrangements, the start of the spline may not
% be close to the first control point if the spline order is 3 or greater.
% Normally for a closed spline this is irrelevant. However for our purpose
% of using closed bplines to form paths in a colourspace this is important to
% us. The simple brute force solution used here is to search through the
% spline points for the point that is closest to the 1st control point and
% then rotate the spline points accordingly
distsqrd = 0;
for d = 1:dim
distsqrd = distsqrd + (S(d,:) - P(d,1)).^2;
end
[~,ind] = min(distsqrd);
S = circshift(S, [0, -ind+1]);
|
github
|
jacksky64/imageProcessing-master
|
cloud9.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/cloud9.m
| 3,590 |
utf_8
|
3ce5e64cce1547247f3b914e7a6fc5c8
|
% CLOUD9 - Cloud movie of 1/f noise.
%
% Function to create a movie of noise images having 1/f amplitude spectum
% properties.
%
% Usage: CloudMovie = cloud9(size, factor, nturns, velfactor, nframes)
%
% size - [rows cols] size of image to produce
% factor - controls spectrum = 1/(f^factor)
% nturns - No of 2pi cycles phase can change over the whole sequence
% lowvel - phase velocity at 0 frequency
% velfactor - phase velocity = freq^velfactor
% nframes - no of frames in movie
%
% factor = 0 - raw Gaussian noise image
% = 1 - gives the 1/f `standard' drop-off for `natural' images
% = 1.5 - seems to give the most intersting `cloud patterns'
% = 2 or greater - produces `blobby' images
%
% Favourite parameters:
% m = cloud9([480 640], 1.5, 4, 1, .1, 100);
%
% Copyright (c) 2000 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% April 2000
function CloudMovie = cloud9(sze, factor, nturns, lowvel, velfactor, nframes)
rows = sze(1);
cols = sze(2);
phase = i*random('Uniform',0,2*pi,rows,cols); % Random uniform distribution 0 - 2pi
% Create two matrices, x and y. All elements of x have a value equal to its
% x coordinate relative to the centre, elements of y have values equal to
% their y coordinate relative to the centre. From these two matrices produce
% a radius matrix that gives distances from the middle
x = ones(rows,1) * (-cols/2 : (cols/2 - 1)); % x = x/(cols/2);
y = (-rows/2 : (rows/2 - 1))' * ones(1,cols);% y = y/(rows/2);
radius = sqrt(x.^2 + y.^2); % Matrix values contain radius from centre.
radius(rows/2+1,cols/2+1) = 1; % .. avoid division by zero.
amp = 1./(radius.^factor); % Construct the amplitude spectrum
amp = fftshift(amp);
phasemod = round(fftshift(radius.^velfactor + lowvel));
phasechange = 2*pi*((random('unid',nturns+1,rows,cols) -1 - nturns/2) .* phasemod );
maxturns = max(max(phasechange/(2*pi)))
maxturns = min(min(phasechange/(2*pi)))
minturns = min(min(abs(phasechange)/(2*pi)))
dphase = i*phasechange/(nframes-1); % premultiply by i to save time in th eloop
% Construct fft of noise image with the specified amplitude spectrum
fig = figure(1), warning off, imagesc(zeros(rows,cols)), axis('off'), truesize(1)
set(fig,'DoubleBuffer','on');
%set(gca,'xlim',[-80 80],'ylim',[-80 80],...
% 'NextPlot','replace','Visible','off')
mov = avifile('cloud')
a = 0.7; % Set up colormap
map = a*bone + (1-a)*gray;
for n = 1:nframes
fprintf('frame %d/%d \r',n, nframes);
phase = phase + dphase;
newfft = amp .* exp(phase);
im = real(ifft2(newfft)); % Invert to obtain final noise image
imagesc(im), colormap(bone),axis('equal'), axis('off'), truesize(1)
%if n==1
% CloudMovie = moviein(nframes); % initialise movie storage
%end
F = getframe(gca);
mov = addframe(mov,F);
%CloudMovie(:,n) = getframe;
end
fprintf('\n');
warning on
mov = close(mov);
%movie(CloudMovie,5,30);
%save('CloudMovie','CloudMovie');
|
github
|
jacksky64/imageProcessing-master
|
circle.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/circle.m
| 1,226 |
utf_8
|
3ce939f9b481cd974576f40a598a69b6
|
% CIRCLE - Draws a circle.
%
% Usage: circle(c, r, n, col)
%
% Arguments: c - A 2-vector [x y] specifying the centre.
% r - The radius.
% n - Optional number of sides in the polygonal approximation.
% (defualt is 16 sides)
% col - optional colour, defaults to blue.
% Copyright (c) 1996-2005 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
function h = circle(c, r, nsides, col)
if nargin == 2
nsides = 16;
end
if nargin < 4
col = [0 0 1];
end
nsides = max(round(nsides),3); % Make sure it is an integer >= 3
a = [0:2*pi/nsides:2*pi];
h = line(r*cos(a)+c(1), r*sin(a)+c(2), 'color', col);
|
github
|
jacksky64/imageProcessing-master
|
showdivim.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/showdivim.m
| 2,519 |
utf_8
|
9205933da4b7d7051f7944c5724dc03b
|
% SHOWDIVIM - Displays image with diverging colour map
%
% For data to be displayed correctly with a diverging colour map it is
% important that the data values are respected so that the reference value in
% the data is correctly associated with the centre entry of a diverging
% colour map.
%
% In contrast, default display methods typically do not respect data values
% directly and can perform inappropriate offsetting and normalisation of the
% angular data before display and rendering with a colour map.
%
% Usage: rgbim = showdivim(im, map, refval, fig)
%
% Arguments:
% im - Image to be displayed.
% map - Colour map to render the data with.
% refval - Reference value to be associated with centre point of
% diverging colour map. Defaults to 0.
% fig - Optional figure number to use. If not specified a new
% figure is created. If set to 0 the function runs
% 'silently' returning rgbim without displaying the image.
% Returns:
% rgbim - The rendered image.
%
% For a list of all diverging colour maps that can be generated by LABMAPLIB
% use: >> labmaplib('div')
%
% See also: SHOW, SHOWANGULARIM, APPLYCOLOURMAP
% Copyright (c) 2014 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% PK October 2014
function rgbim = showdivim(im, map, refval, fig)
if ~exist('refval', 'var'), refval = 0; end
if ~exist('fig', 'var'), fig = figure; end
minv = min(im(:));
maxv = max(im(:));
if refval < minv || refval > maxv
fprintf('Warning: reference value is outside the range of image values\n');
end
dr = max([maxv - refval, refval - minv]);
range = [-dr dr] + refval;
rgbim = applycolourmap(im, map, range);
if fig
show(rgbim,fig)
end
% If function was not called with any output arguments clear rgbim so that
% it is not printed on the screen.
if ~nargout
clear('rgbim')
end
|
github
|
jacksky64/imageProcessing-master
|
applycolourmap.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/applycolourmap.m
| 2,248 |
utf_8
|
cdab830abbc0e87f8934481ecf26f5c4
|
% APPLYCOLOURMAP Applies colourmap to a single channel image to obtain an RGB result
%
% Usage: rgbim = applycolourmap(im, map, range)
%
% Arguments: im - Single channel image to apply colourmap to.
% map - RGB colourmap of size ncolours x 3. RGB values are
% floating point values in the range 0-1.
% range - Optional 2-vector specifying the min and max values in
% the image to be mapped across the colour map. Values
% outside this range are mapped to the end points of the
% colour map. If range is omitted, or empty, the full range
% of image values are used.
%
% Returns: rgbim - RGB image of floating point values in the range 0-1.
% NaN values in the input image are rendered as black.
%
% This function is used by RELIEF as a base image upon which to apply relief
% shading. Is is also used by SHOWANGULARIM.
%
% See also: IRELIEF, RELIEF, SHOWANGULARIM
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% November 2013
% September 2014 - Optional range specification added and speeded up by removing loops!
function rgbim = applycolourmap(im, map, range)
[ncolours,chan] = size(map);
assert(chan == 3, 'Colourmap must have 3 columns');
assert(ndims(im) == 2, 'Image must be single channel');
if ~isa(im,'double'), im = double(im); end
if ~exist('range', 'var') || isempty(range)
range = [min(im(:)) max(im(:))];
end
assert(range(1) < range(2), 'range(1) must be less than range(2)');
[rows,cols] = size(im);
% Convert image values to integers that can be used to index into colourmap
im = round( (im-range(1))/(range(2)-range(1)) * (ncolours-1) ) + 1;
mask = isnan(im);
im(mask) = 1; % Set any Nan entries to 1 and
im(im < 1) = 1; % clamp out of range entries.
im(im > ncolours) = ncolours;
rgbim = zeros(rows,cols,3);
rgbim(:,:,1) = ~mask.*reshape(map(im,1), rows, cols);
rgbim(:,:,2) = ~mask.*reshape(map(im,2), rows, cols);
rgbim(:,:,3) = ~mask.*reshape(map(im,3), rows, cols);
|
github
|
jacksky64/imageProcessing-master
|
derespolar.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/derespolar.m
| 2,543 |
utf_8
|
16675236c863fa3ce1c748e6f5264835
|
% DERESPOLAR - Desresolves image in polar coordinates.
%
% Performs a deresolution operation on an image using Polar Coordinates
%
% Usage: deres = derespolar(im, nr, na, xc, yc)
% where: nr = resolution in the radial direction
% na = resolution in the angular direction
% xc = column of polar origin (optional)
% yc = row of polar origin (optional)
%
% If xc and yc are omitted the polar origin defaults to the centre of the image
% Copyright (c) 1999 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% May 1999
% This code is horribly inefficient and needs a rewrite...
function deres = derespolar(im,nr,na,xc,yc)
[rows,cols] = size(im);
if nargin == 3
xc = round(cols/2);
yc = round(rows/2);
end
if ndims(im) == 3 % Assume colour image
deres = uint8(zeros(size(im)));
deres(:,:,1) = iderespolar(im(:,:,1),nr,na,xc,yc);
deres(:,:,2) = iderespolar(im(:,:,2),nr,na,xc,yc);
deres(:,:,3) = iderespolar(im(:,:,3),nr,na,xc,yc);
else
deres = iderespolar(im,nr,na,xc,yc);
end
% Internal function that does the work
function deres = iderespolar(im,nr,na,xc,yc)
[rows,cols] = size(im);
%x = ones(rows,1) * (-cols/2 : (cols/2 - 1));
%y = (-rows/2 : (rows/2 - 1))' * ones(1,cols);
[x,y] = meshgrid(-xc:cols-xc-1, -yc:rows-yc-1);
radius = sqrt(x.^2 + y.^2); % Matrix values contain radius from centre.
theta = atan2(y,x); % Matrix values contain polar angle.
dr = max(max(radius))/nr;
da = max(max(theta+pi))/na;
rp = round(radius/dr)*dr;
ra = round(theta/da)*da;
rowp = yc + rp.*sin(ra);
colp = xc + rp.*cos(ra);
rowp = round(rowp);
colp = round(colp);
rowp = max(rowp,1); rowp = min(rowp,rows);
colp = max(colp,1); colp = min(colp,cols);
for row = 1:rows
for col = 1:cols
deres(row,col) = im(rowp(row,col), colp(row,col));
end
end
|
github
|
jacksky64/imageProcessing-master
|
map2geosofttbl.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/map2geosofttbl.m
| 1,642 |
utf_8
|
8886e0a749224765428fd3f92e8ac2c4
|
% MAP2GEOSOFTTBL Converts MATLAB colourmap to Geosoft .tbl file
%
% Usage: map2geosofttbl(map, filename, cmyk)
%
% Arguments: map - N x 3 rgb colourmap
% filename - Output filename
% cmyk - Optional flag 0/1 indicating whether CMYK values should
% be written. Defaults to 0 whereby RGB values are
% written
%
% This function writes a RGB colourmap out to a .tbl file that can be loaded
% into Geosoft Oasis Montaj or QGIS
%
% See also: RGB2CMYK
% Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% PK October 2012
% June 2014 - RGB or CMYK option
function map2geosofttbl(map, filename, cmyk)
if ~exist('cmyk', 'var'), cmyk = 0; end
[N, cols] = size(map);
if cols ~= 3
error('Colourmap must be N x 3 in size')
end
% Ensure filename ends with .tbl
if ~strendswith(filename, '.tbl')
filename = [filename '.tbl'];
end
fid = fopen(filename, 'wt');
if cmyk % Convert RGB values in map to CMYK and scale 0 - 255
cmyk = round(rgb2cmyk(map)*255);
kcmy = circshift(cmyk, [0 1]); % Shift K to 1st column
fprintf(fid, '{ blk cyn mag yel }\n');
for n = 1:N
fprintf(fid, ' %03d %03d %03d %03d \n', kcmy(n,:));
end
else % Write RGB values
map = round(map*255);
fprintf(fid, '{ red grn blu }\n');
for n = 1:N
fprintf(fid, ' %3d %3d %3d\n', map(n,:));
end
end
fclose(fid);
|
github
|
jacksky64/imageProcessing-master
|
digipts.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/digipts.m
| 876 |
utf_8
|
95a93e018771e295649e1cc41496df4e
|
% DIGIPTS - digitise points in an image
%
% Function to digitise points in an image. Points are digitised by clicking
% with the left mouse button. Clicking any other button terminates the
% function. Each location digitised is marked with a red '+'.
%
% Usage: [u,v] = digipts
%
% where u and v are nx1 arrays of x and y coordinate values digitised in
% the image.
%
% This function uses the cross-hair cursor provided by GINPUT. This is
% much more useable than IMPIXEL
% Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk @ csse uwa edu au
% http://www.csse.uwa.edu.au/~pk
%
% May 2002
function [u,v] = digipts
hold on
u = []; v = [];
but = 1;
while but == 1
[x y but] = ginput(1);
if but == 1
u = [u;x];
v = [v;y];
plot(u,v,'r+');
end
end
hold off
|
github
|
jacksky64/imageProcessing-master
|
polyval2d.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/polyval2d.m
| 1,403 |
utf_8
|
699c1158bb9d569730ba4b087241556c
|
% POLYVAL2D Evaluates 2D polynomial surface generated by POLYFIT2D
%
% Usage: z = polyval2d(x, y, c)
%
% Arguments: x, y - Locations where polynomial is to be evaluated
% c - Coefficients of polynomial as generated by polyfit2d
%
% Returns z - The surface values evalated at x, y
%
% For a degree 3 surface the coefficients are expected to progress in the
% form
% 00 01 02 03 10 11 12 20 21 30
% where the first digit is the y exponent and the 2nd the x exponent
%
% 0 0 0 1 0 2 0 3 1 0 1 1 1 2
% c1 x y + c2 x y + c3 x y + c4 x y + c5 x y + c6 x y + c7 x y + ...
%
% See also: POLYFIT2D
% Peter Kovesi 2014
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
% PK July 2014
function z = polyval2d(x, y, c)
% Solve quadratic to determin degree of polynomial
% ncoeff = (degree+1)*(degree+2)/2
ncoeff = length(c);
degree = -1.5 + sqrt(2.25 - 2*(1-ncoeff));
if round(degree) ~= degree
error('Cannot determine polynomial degree from number of coefficients');
end
% p1 is the x exponent and p2 is the y exponent
z = zeros(size(x));
ind = 1;
for p2 = 0:degree
for p1 = 0:(degree-p2)
z = z + c(ind) * x.^p1 .* y.^p2;
ind = ind+1;
end
end
|
github
|
jacksky64/imageProcessing-master
|
implace.m
|
.m
|
imageProcessing-master/Matlab Code for Computer Vision/Misc/implace.m
| 3,014 |
utf_8
|
e28ca1e9784b3b7297c12b70c7278015
|
% IMPLACE - place image at specified location within larger image
%
% Usage: newim = implace(im1, im2, roff, coff)
%
% Arguments:
%
% im1 - Image that im2 is to be placed in.
% im2 - Image to be placed.
% roff - Row and column offset of placement of im2 relative
% coff to im1, (0,0) aligns top left corners.
%
% Warning: The class of final image matches the class of im1. If im1 is of
% type double and im2 is a uint8 colour image you will obtain a double image
% having 3 colour channels with values ranging between 0-255. This will
% need to be cast to uint8, or divided by 255, for display via imshow or
% show.
% Copyright (c) 2004-2008 Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% http://www.csse.uwa.edu.au/
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% September 2004 Original version
% July 2008 Bug fixes for colour images
function newim = implace(im1, im2, roff, coff)
[rows1, cols1, d] = size(im1);
[rows2, cols2, d] = size(im2);
% Find min row and column of im2 that will appear in im1
rmin2 = max(1,1-roff);
cmin2 = max(1,1-coff);
% Find min row and column within im1 that im2 covers
rmin1 = max(1,1+roff);
cmin1 = max(1,1+coff);
% Find max row and column of im2 that will appear in im1
rmax2 = min(rows1-roff, rows2);
cmax2 = min(cols1-coff, cols2);
% Find max row and column within im1 that im2 covers
rmax1 = min(rows2+roff, rows1);
cmax1 = min(cols2+coff, cols1);
% Check for the case where there is no overlap of the images
if rmax1 < 1 | cmax1 < 1 | rmax2 < 1 | cmax2 < 1 | ...
rmin1 > rows1 | cmin1 > cols1 | rmin2 > rows2 | cmin2 > cols2
newim = im1; % Simply copy im1 to newim
else % Place im2 into im1
% Check if either image is colour and if one needs promoting to colour
ndim1 = ndims(im1);
ndim2 = ndims(im2);
if ndim1 == 2 & ndim2 == 3
fprintf('promoting im1 \n');
im1 = uint8(repmat(im1,[1,1,3])); % 'Promote' im1 to 3 channels
ndim1 = 3;
elseif ndim2 == 2 & ndim1 == 3
fprintf('promoting im2 \n');
im2 = uint8(repmat(im2,[1,1,3])); % 'Promote' im2 to 3 channels
ndim2 = 3;
end
newim = im1;
if ndim1 ==2 % Greyscale
newim(rmin1:rmax1, cmin1:cmax1) = ...
im2(rmin2:rmax2, cmin2:cmax2);
else % Assume colour
newim(rmin1:rmax1, cmin1:cmax1,:) = ...
im2(rmin2:rmax2, cmin2:cmax2,:);
end
end
|
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