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stringclasses 1
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stringlengths 13
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stringlengths 3
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stringclasses 1
value | path
stringlengths 12
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stringclasses 9
values | md5
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|---|---|---|---|---|---|---|---|---|
github
|
jjjjfrench/UW-UIOPS-master
|
read_binary_SEA.m
|
.m
|
UW-UIOPS-master/read_binary/read_binary_SEA.m
| 12,340 |
utf_8
|
3d7e92cc325973ca7580749e35bbeb78
|
function read_binary_SEA(infilename,outfilename)
%% Function to decompress SEA raw files
% Need to double check the file format and code for each probes
% This only works for MC3E filed campaign
% * July 11, 2016, Created this new interface function, Wei Wu
starpos = find(infilename == '*',1,'last');
nWierdTotal = 0;
if ~isempty(starpos)
files = dir(infilename);
filenums = length(files);
filedir = infilename(1:starpos-1);
else
filenums = 1;
end
for i = 1:filenums
if filenums > 1
infilename = [filedir,files(i).name];
end
if outfilename == '1'
slashpos = find(infilename == '/',1,'last');
outfilename = ['DIMG.',infilename(slashpos+1:end),'.cdf'];
end
fid=fopen(infilename,'r','l');
infilename
%%% Updated for new MATLAB NETCDF interface
f = netcdf.create([outfilename, '.CIP.cdf'], 'clobber');
dimid0 = netcdf.defDim(f,'time',netcdf.getConstant('NC_UNLIMITED'));
dimid1 = netcdf.defDim(f,'ImgRowlen',8);
dimid2 = netcdf.defDim(f,'ImgBlocklen',1700);
varid0 = netcdf.defVar(f,'year','double',dimid0);
varid1 = netcdf.defVar(f,'month','double',dimid0);
varid2 = netcdf.defVar(f,'day','double',dimid0);
varid3 = netcdf.defVar(f,'hour','double',dimid0);
varid4 = netcdf.defVar(f,'minute','double',dimid0);
varid5 = netcdf.defVar(f,'second','double',dimid0);
varid6 = netcdf.defVar(f,'millisec','double',dimid0);
varid7 = netcdf.defVar(f,'wkday','double',dimid0);
varid8 = netcdf.defVar(f,'data','double',[dimid1 dimid2 dimid0]);
netcdf.endDef(f)
f1 = netcdf.create([outfilename, '.2DC.cdf'], 'clobber');
dimid01 = netcdf.defDim(f1,'time',netcdf.getConstant('NC_UNLIMITED'));
dimid11 = netcdf.defDim(f1,'ImgRowlen',8);
dimid21 = netcdf.defDim(f1,'ImgBlocklen',1700);
varid01 = netcdf.defVar(f1,'year','double',dimid01);
varid11 = netcdf.defVar(f1,'month','double',dimid01);
varid21 = netcdf.defVar(f1,'day','double',dimid01);
varid31 = netcdf.defVar(f1,'hour','double',dimid01);
varid41 = netcdf.defVar(f1,'minute','double',dimid01);
varid51 = netcdf.defVar(f1,'second','double',dimid01);
varid61 = netcdf.defVar(f1,'millisec','double',dimid01);
varid71 = netcdf.defVar(f1,'wkday','double',dimid01);
varid81 = netcdf.defVar(f1,'data','double',[dimid11 dimid21 dimid01]);
netcdf.endDef(f)
kk=1;
wkday = 1;
datatemp = [0 0 0 0 0 0 0 0 0 0];
numFilename = 0;
nread =0;
endfile = 0;
% while feof(fid)==0 & kk <= 3000
while feof(fid)==0 & endfile == 0
[datalast,datatemp]=readDir(fid,datatemp);
doffset1 = -datatemp(2);
if datatemp(1)==999
nTagNext=1;
else
nTagNext=0;
end
while nTagNext==0
[datalast,datatemp]=readDir(fid,datatemp);
if datatemp(1)==33000 & datatemp(3)==4098
%datalast
datatemp
[datalast,datatemp]=readDir(fid,datatemp);
%datatemp
ttt = readTime(fid);
year =ttt(1);
month=ttt(2);
day =ttt(3);
hour =ttt(4);
minute=ttt(5);
second=ttt(6);
millisec=ttt(7);
wkday=1;
data1 = fread(fid,4098,'uchar');
fseek(fid,-4150,0);
[datalast,datatemp]=readDir(fid,datatemp);
data = data1(1:4096);
% data=reshape(fread(fid,4096*8,'ubit1'),4096,8);
% b1 = [num2str(data(:,1)),num2str(data(:,2)),num2str(data(:,3)),num2str(data(:,4)),num2str(data(:,5)),...
% num2str(data(:,6)),num2str(data(:,7)),num2str(data(:,8))];
bytes=dec2hex(data,2);
kk;
i=1;
ii=1;
b1full=dec2bin(hex2dec(bytes(:,:)),8);
b2 = bin2dec(b1full(:,4:8));
while i<4096
b1 = b1full(i,:);
curi = i;
i=i+1;
if b1(3) == '1'
% i=i+1;
elseif b1(1) == '0' & b1(2) == '0'
% b2=bin2dec(b1(4:8));
for k=1:b2(curi)+1;
if i < length(bytes)
decomp(ii,:)=bytes(i,:);
else break
end
ii=ii+1;
i=i+1;
end
elseif b1(1) == '1' & b1(2) == '0'
% b2=bin2dec(b1(4:8));
for k=1:b2(curi)+1;
decomp(ii,:)='00';
ii=ii+1;
end
elseif b1(2) == '1' & b1(1) == '0'
% b2=bin2dec(b1(4:8));
for k=1:b2(curi)+1;
decomp(ii,:)='FF';
ii=ii+1;
end
else
kk;
end
end
found = 0;
i=1;
count=0;
while found == 0
if decomp(i)=='AA'
count=count+1;
else
count=0;
end
if count == 8
found=1;
dd=i+1:8:length(decomp)-7;
nWierd=0;
end
if i==length(decomp) % Add to avoid no 'AA' even though wierd to have no 'AA'...
found =1;
nWierd=1;
nWierdTotal =nWierdTotal +1;
end
i=i+1;
end
if nWierd ==0
%
% decomp_convert=[hex2dec(decomp(dd,:)),hex2dec(decomp(dd+1,:)),hex2dec(decomp(dd+2,:)),hex2dec(decomp(dd+3,:)),...
% hex2dec(decomp(dd+4,:)),hex2dec(decomp(dd+5,:)),hex2dec(decomp(dd+6,:)),hex2dec(decomp(dd+7,:))];
decomp_convert=[hex2dec(decomp(dd+7,:)),hex2dec(decomp(dd+6,:)),hex2dec(decomp(dd+5,:)),hex2dec(decomp(dd+4,:)),...
hex2dec(decomp(dd+3,:)),hex2dec(decomp(dd+2,:)),hex2dec(decomp(dd+1,:)),hex2dec(decomp(dd,:))];
k2=[decomp(dd,:),decomp(dd+1,:),decomp(dd+2,:),decomp(dd+3,:),decomp(dd+4,:),decomp(dd+5,:),decomp(dd+6,:),decomp(dd+7,:)];
% length_diff=length(decomp_convert) - length(handles.matrix(kk-1,:,:));
% matrix_size(kk)=length(decomp_convert);
% if length_diff > 0
% handles.matrix(1:kk-1,length(handles.matrix(kk-1,:,:)):length(decomp_convert),:)=-1;
% elseif length_diff < 0
% decomp_convert(length(decomp_convert):length(handles.matrix(kk-1,:,:)),:)=-1;
% end
if length(decomp_convert) < 1700
decomp_convert(length(decomp_convert):1700,:)=-1;
end
netcdf.putVar ( f, varid0, kk-1, 1, year )
netcdf.putVar ( f, varid1, kk-1, 1, month );
netcdf.putVar ( f, varid2, kk-1, 1, day );
netcdf.putVar ( f, varid3, kk-1, 1, hour );
netcdf.putVar ( f, varid4, kk-1, 1, minute );
netcdf.putVar ( f, varid5, kk-1, 1, second );
netcdf.putVar ( f, varid6, kk-1, 1, millisec );
netcdf.putVar ( f, varid7, kk-1, 1, wkday );
netcdf.putVar ( f, varid8, [0, 0, kk-1], [8,1700,1], decomp_convert' );
kk=kk+1;
if mod(kk,100) == 0
kk
datestr(now)
end
end
elseif datatemp(1)==5000 && datatemp(3)==4096
[datalast,datatemp]=readDir(fid,datatemp);
[datalast,datatemp]=readDir(fid,datatemp);
[datalast,datatemp]=readDir(fid,datatemp);
[datalast,datatemp]=readDir(fid,datatemp);
[datalast,datatemp]=readDir(fid,datatemp);
ttt = readTime(fid);
year =ttt(1);
month=ttt(2);
day =ttt(3);
hour =ttt(4);
minute=ttt(5);
second=ttt(6);
millisec=ttt(7);
wkday=1;
%dataother = fread(fid,14,'uchar');
temp0011 = fread(fid,1,'int16');
temp0012 = fread(fid,1,'int16');
fread(fid,10,'char');
% temp003 = fread(fid,1,'char');
% temp004 = fread(fid,1,'short');
% temp003/temp0011*temp0012*2*0.001
% temp002*25*0.000001
data1 = fread(fid,4096,'uchar');
fseek(fid,-4162,0);
[datalast,datatemp]=readDir(fid,datatemp);
tas=temp0011/temp0012*50*1000*25*0.000001;
datafinal = reshape(data1,4,1024);
temp1234 = datafinal(1,:);
datafinal(1,:)=datafinal(4,:);
datafinal(4,:)=temp1234;
temp1234 = datafinal(2,:);
datafinal(2,:)=datafinal(3,:);
datafinal(3,:)=temp1234;
netcdf.putVar ( f1, varid01, kk-1, 1, year )
netcdf.putVar ( f1, varid11, kk-1, 1, month );
netcdf.putVar ( f1, varid21, kk-1, 1, day );
netcdf.putVar ( f1, varid31, kk-1, 1, hour );
netcdf.putVar ( f1, varid41, kk-1, 1, minute );
netcdf.putVar ( f1, varid51, kk-1, 1, second );
netcdf.putVar ( f1, varid61, kk-1, 1, millisec );
netcdf.putVar ( f1, varid71, kk-1, 1, wkday );
netcdf.putVar ( f1, varid91, kk-1, 1, tas );
netcdf.putVar ( f1, varid81, [0, 0, kk-1], [4,1024,1], datafinal );
kk=kk+1;
if mod(kk,100) == 0
kk
datestr(now)
end
end
clear decomp dd k2 b1 b2
if datatemp(1)==999
doffset2 = datatemp(2);
nTagNext = 1;
fseek(fid,doffset2+doffset1,0);
end
end
for j=1:16
bb=fread(fid,1,'int8');
if feof(fid) == 1
endfile=1;
break
end
end
fseek(fid,-16,'cof');
end
end
fclose(fid);
% close(f);
netcdf.close(f); % New interface by Will
netcdf.close(f1); % New interface by Will
nWierdTotal
end
function [ldata,tdata]=readDir(fid,tdata)
tagNumber=fread(fid,1,'uint16');
dataOffset=fread(fid,1,'uint16');
numberBytes=fread(fid,1,'uint16');
samples=fread(fid,1,'uint16');
bytesPerSample=fread(fid,1,'uint16');
type=fread(fid,1,'uint8');
param1=fread(fid,1,'uint8');
param2=fread(fid,1,'uint8');
param3=fread(fid,1,'uint8');
address=fread(fid,1,'uint16');
ldata = tdata;
tdata = [tagNumber dataOffset numberBytes samples bytesPerSample type param1 param2 param3 address];
end
function time=readTime(fid)
for i=1:2
year=fread(fid,1,'uint16');
month=fread(fid,1,'uint16');
day=fread(fid,1,'uint16');
hour=fread(fid,1,'uint16');
minute=fread(fid,1,'uint16');
second=fread(fid,1,'uint16');
fracsec=fread(fid,1,'uint16');
maxfreq=fread(fid,1,'uint16');
bls=fread(fid,1,'uint16');
time=[year month day hour minute second fracsec maxfreq bls];
end
end
function readTable(fid)
filename = fread(fid,8,'uint8');
filename = char(filename);
filename=filename';
tfiles = fread(fid,datalast(3),'uint8');
abc = char(tfiles);
abc'
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
write2d.m
|
.m
|
UW-UIOPS-master/read_binary/@cip/write2d.m
| 6,333 |
utf_8
|
5a49ac1e4c6920239f612e1cb8ee14a1
|
function write2d(obj,filebase)
% WRITE2D - Convert an unpacked CIP file to RAF/OAP format
%
% write2d(obj,filebase)
% obj - CIP class object
% filebase - the base of the file name
% if not specified, use the first eight characters of cipfile
% (YYYYMMMDD)
if nargin < 2
fbase = obj.cipfile{1};
fbase = fbase(1:8);
else
fbase = filebase;
end
% Read the CIP csv data
[csvsod,csvtas,dt] = obj.ciptas(obj.csvfile);
% Open the output file
outfile = [fbase '_cip.2d'];
fprintf('Writing %s\n',outfile);
f2d = fopen(outfile,'w','ieee-be');
% The probe ID for the CIP is set to C5 because xpms2d has a particular
% format for the timing word that is constructed here
% 0xAAAAAAxxxxxxxxxx, where the time part is the number of 12 microsec
% clicks since UTC midnight.
probeid = 'C5';
id = uint16(double(probeid)*[256;1]);
dstr = datestr(dt,'mm/dd/yyyy');
xmlstart = '<OAP version="1">';
% Read in the PMS (1D-C and 1D-P) file
pmsfile = [ fbase '_pms.2d' ];
converttas = false;
if exist(pmsfile,'file')
fid = fopen(pmsfile,'r','ieee-be');
fprintf('Opened %s\n', pmsfile);
while (1)
line = fgetl(fid);
% Check for the old style <PMS2D> files instead of <OAP> where
% the true air speed is scaled by 255/125
if ~isempty(strfind(line,'<PMS2D>'))
converttas = true;
fprintf(f2d,'%s\n',xmlstart);
continue;
end
% Don't include the <Source> attribute
if ~isempty(strfind(line,'<Source>')); continue; end
% Found the end of the xml header
if ~isempty(strfind(line,'</OAP>')); break; end
if ~isempty(strfind(line,'</PMS2D>')); break; end
fprintf(f2d,'%s\n',line);
end
% Read the PMS data
pms = fread(fid,'*uint16');
npms = length(pms)/2058;
pms = reshape(pms,2058,npms);
% Calculate the record times
hd = double(pms(1:9,:));
pmstm = datenum(hd(5,:),hd(6,:),hd(7,:),...
hd(2,:),hd(3,:),hd(4,:)+hd(9,:)/1000);
% Convert the tas if needed
if converttas; pms(8,:) = uint16(hd(8,:) * 125 / 255); end
clear hd;
else
% Write the header if there is no PMS 2D file to copy it from
fprintf('The PMS 2D file: %s was not found.\n', pmsfile);
fprintf(f2d,'<?xml version="1.0" encoding="ISO08858-1"?>\n');
fprintf(f2d,'%s\n',xmlstart);
fprintf(f2d, ...
' <Institution>University of Wyoming Atmospheric Science</Institution>\n');
fprintf(f2d, ...
' <FormatURL>http://www.eol.ucar.edu/raf/Software/OAPfiles.html</FormatURL>\n');
% If there is no PMS 2d file to merge the data with, try reading the
% Project and FlightNumber from a raw file
rawfile = [fbase '_raw.nc'];
if exist(rawfile,'file')
nc = netcdf.open(rawfile,0);
fprintf('Reading attributes from %s\n',rawfile);
proj = netcdf.getAtt(nc,netcdf.getConstant('NC_GLOBAL'),'ProjectName');
fprintf(f2d,' <Project>%s</Project>\n',proj);
flt = netcdf.getAtt(nc,netcdf.getConstant('NC_GLOBAL'),'FlightNumber');
fprintf(f2d,' <FlightNumber>%s</FlightNumber>\n',flt);
netcdf.close(nc);
end
fprintf(f2d,' <FlightDate>%s</FlightDate>\n',dstr);
fprintf(f2d,' <Platform>N2UW</Platform>\n');
npms = 0;
end
% Write the XML <probe> attributes for the CIP
fprintf(f2d,[' <probe id="%s" type="Fast2DC" resolution="25" ' ...
'nDiodes="64" suffix="_IBR"/>\n'],...
probeid);
fprintf(f2d,'</OAP>\n');
% Initialize some values
rec = zeros(1024,1,'uint32');
twos = power(2,31:-1:0)';
tmask = uint32(hex2dec('AAAAAA00'));
ipms = 1;
% Open and read each file of unpacked cip images
for ii = 1:length(obj.cipfile)
fprintf('Reading %s\n',obj.cipfile{ii})
fin = fopen(obj.cipfile{ii},'r','ieee-be');
data = fread(fin,'ubit2=>uint8');
fclose(fin);
% Find the start index, time, and number of slices for each particle
disp('Calling cipindex')
[idx,sod,ns]=obj.cipindex(data);
disp('Writing to the 2D file');
% Convert two bit values to one bit
data = data > 1;
% CIP image data are on a 12 hour clock
delt = csvsod(1) - sod(1);
if delt > 7*3600 && delt < 17*3600; sod = sod + 12*3600; end
% Convert the second of the day to matlab time
dnum = dt + sod/86400;
% Construct the timing words
tword = timing(sod',tmask);
% Interpolate the true airspeeds from the CSV files for each particle
tas = interp1(csvsod,csvtas,sod);
% Loop through each CIP particle
jj = 1;
for i=1:length(idx)-1
% Pad by 64 values to insert timer word later
img = double(data(idx(i):idx(i)+(ns(i)+1)*64-1));
nw = ns(i)*2 + 2;
% Convert the 1 bit values to 32 bit values (two 32 bit values per slice)
img = reshape(img,32,nw)' * twos;
% Append with timer word
img(nw-1) = tword(1,i);
img(nw) = tword(2,i);
% Check to see if the full particle fits in the record
nrem = 1025-jj;
if nw > nrem
% Write out what fits
rec(jj:1024) = img(1:nrem);
% Write out PMS records that are before this CIP record
while ipms <= npms && pmstm(ipms) < dnum(i)
fwrite(f2d,pms(:,ipms),'uint16');
ipms = ipms + 1;
end
recout(f2d,id,dnum(i),tas(i),rec);
% Save the rest of the particle
rec(1:nw-nrem) = img(nrem+1:end);
jj = nw-nrem+1;
else
% It all fits, stuff it in
rec(jj:jj+nw-1) = img;
jj = jj + nw;
end
end
end
% Write out the rest of the PMS records
if ipms < npms; fwrite(f2d,pms(:,ipms:npms),'uint16'); end
fclose(f2d);
function recout(fout,id,daten,tas,rec)
% RECOUT - writes a particle record to file
%
% recout(fout,id,daten,tas,rec)
% fout - output file id
% id - record identifier (C5)
% daten - matlab time stamp of the last particle in the record
% tas - true air speed
% rec - 4096 byte image record
%
% Convert the timestamp to a vector
dv = datevec(daten);
msec = mod(dv(6),1) * 1000;
ovld = 0;
% Write out the record header
fwrite(fout, ...
[id,dv(4),dv(5),floor(dv(6)),dv(1),dv(2),dv(3),...
tas,msec,ovld], 'uint16');
% Write out the image data
fwrite(fout,rec,'uint32');
function tword = timing(dsec,tmask)
% TIMING - Create the timing word
%
% timing(sod,tmask)
% sod - second of the day for this particle
% tmask - timing work pattern that indicates that it is a timing word
% 0xAAAAAA0000000000
dsec = dsec * 12E6;
% 4294967296 is 2^32
tword = [bitor(uint32(mod(dsec/4294967296,16)),tmask); ...
uint32(mod(dsec,4294967296))];
|
github
|
jjjjfrench/UW-UIOPS-master
|
cip_obj_to_netcdf.m
|
.m
|
UW-UIOPS-master/read_binary/@cip/cip_obj_to_netcdf.m
| 3,981 |
utf_8
|
14c826715464ee56bafdb86f9aafde8e
|
function cip_obj_to_netcdf(obj, outfile)
% Read the CIP csv data
[timestamp,csvtas, dt] = obj.ciptas(obj.cipdir, obj.csvfile);
timestamp = timestamp - datenum(dt); %Get just datenum format of corresponding sod referenced from first day
csvsod = timestamp*86400.; %Convert to seconds from date number format
% The probe ID for the CIP is set to C5 because xpms2d has a particular
% format for the timing word that is constructed here
% 0xAAAAAAxxxxxxxxxx, where the time part is the number of 12 microsec
% clicks since UTC midnight.
tmask = uint32(hex2dec('AAAAAA00'));
ipms = 1;
% Create netCDF file
f = netcdf.create(outfile, 'clobber');
dimid0 = netcdf.defDim(f,'time',netcdf.getConstant('NC_UNLIMITED'));
dimid1 = netcdf.defDim(f,'ImgRowlen',64);
dimid2 = netcdf.defDim(f,'ImgBlocklen',512);
varid0 = netcdf.defVar(f,'year', 'double', dimid0);
varid1 = netcdf.defVar(f,'month','double',dimid0);
varid2 = netcdf.defVar(f,'day','double',dimid0);
varid3 = netcdf.defVar(f,'hour','double',dimid0);
varid4 = netcdf.defVar(f,'minute','double',dimid0);
varid5 = netcdf.defVar(f,'second','double',dimid0);
varid6 = netcdf.defVar(f,'millisec','double',dimid0);
varid8 = netcdf.defVar(f,'data','double',[dimid1 dimid2 dimid0]);
netcdf.endDef(f)
date_vec = datevec(dt);
year = date_vec(1);
month = date_vec(2);
day = date_vec(3);
% Open and read each file of unpacked cip images
index = 1;
for ii = 1:length(obj.cipfile)
fprintf('Reading %s\n',obj.cipfile{ii})
fin = fopen(obj.cipfile{ii},'r','ieee-be');
data = fread(fin,'ubit2=>uint8');
fclose(fin);
particle_index = 1;
% Find the start index, time, and number of slices for each particle
disp('Calling cipindex')
[idx,sod,ns]=obj.cipindex(data);
disp('Writing to the netCDF file');
% CIP image data are on a 12 hour clock
% CSV sod have been corrected already
delt = csvsod(1) - sod(1);
if delt > 7*3600 && delt < 17*3600
sod = sod + 12*3600.;
end
% Convert the second of the day to matlab date number format
dnum = dt + sod/86400;
% Interpolate the true airspeeds from the CSV files for each particle
tas = interp1(csvsod,csvtas,sod);
% Roll back HHMMSS if a flight crosses midnight
sod(sod>=240000) = sod(sod>=240000) - 240000;
% Construct 4096 byte buffer (512 pixels)
while(particle_index < length(ns))
len = 1;
img_array = -1*ones(512,64);
while(len+ns(particle_index)+1 < 512 && particle_index < length(ns))
img_array(len:len+ns(particle_index)+1, :) = reshape(data(idx(particle_index):((idx(particle_index))+(ns(particle_index)+2)*64-1)), 64, ns(particle_index)+2)';
len = len+ns(particle_index)+2;
particle_index = particle_index+1;
end
hour = floor(sod(particle_index)/3600);
minute = floor((sod(particle_index)-hour*3600)/60);
second = floor(sod(particle_index)-hour*3600-minute*60);
millisec = floor((sod(particle_index)-hour*3600-minute*60-second)*100);
disp(['Writing frame ' num2str(index)]);
% Write buffer
netcdf.putVar ( f, varid0, index, 1, year );
netcdf.putVar ( f, varid1, index, 1, month );
netcdf.putVar ( f, varid2, index, 1, day );
netcdf.putVar ( f, varid3, index, 1, hour );
netcdf.putVar ( f, varid4, index, 1, minute );
netcdf.putVar ( f, varid5, index, 1, second );
netcdf.putVar ( f, varid6, index, 1, millisec );
netcdf.putVar ( f, varid8, [0, 0, index], [64,512,1], img_array' );
index = index+1;
end
end
netcdf.close(f);
function tword = timing(dsec,tmask)
% TIMING - Create the timing word
%
% timing(sod,tmask)
% sod - second of the day for this particle
% tmask - timing work pattern that indicates that it is a timing word
% 0xAAAAAA0000000000
dsec = dsec * 12E6;
% 4294967296 is 2^32
tword = [bitor(uint32(mod(dsec/4294967296,16)),tmask); ...
uint32(mod(dsec,4294967296))];
|
github
|
jjjjfrench/UW-UIOPS-master
|
calc_sa_randombins.m
|
.m
|
UW-UIOPS-master/size_dist/calc_sa_randombins.m
| 1,543 |
utf_8
|
cb8c10040d091f0b4de96c6092c55177
|
% Calculate image sample area assuming Heymsfield and Parish (1978)
% bins_mid - mid-point of each bins in doide number
% res - photodiode resolution, bin width in microns
% armdst - distance between probe arms in millimeters
% num_diodes - number of photodiodes (does not need to equal number of bins)
% SAmethod - method to calculate SA. Could be 0: center in, 1: entire in and 2 with correction
%
% ** Created to replace calc_sa to include different bin setup, and three choices of SA
% calculation methods. Notice the parameter differences. Will, 2014/06/04
function sa = calc_sa_randombins(bins_mid,res,armdst,num_diodes, SAmethod, probetype)
% calculates OAP SA in mm^2 with res in um, armdist in mm
res = res * 1e-3; %mm
%max_diameter = max_diameter*.5e-3;
radius = bins_mid/2; %radius = bins_mid .* res/2; You can use this one if you provide midpoint in doide number
diameter = 2 * radius;
% Calculate the width
switch SAmethod
case 0
% Center in
EAWci = num_diodes*res;
EAWri = EAWci;
case 1
% Entire in
EAWci = (num_diodes-(bins_mid/res)+1)*res;
EAWci(EAWci<0)=0;
EAWri = EAWci;
case 2
% With Correction
EAWci = num_diodes*res;
EAWri = EAWci + 0.72*diameter;
end
% Calculate the DOF
lambda = 680 * 1e-6; % mm,laser wavelength
if probetype==2
DOF = 20.52*radius.^2*1000;
else
DOF = 6*radius.^2/lambda; % Using
end
DOF(DOF > armdst) = armdst;
%DOF = armdst;
sa = DOF .* EAWri;
|
github
|
jjjjfrench/UW-UIOPS-master
|
sizeDist.m
|
.m
|
UW-UIOPS-master/size_dist/sizeDist.m
| 94,194 |
utf_8
|
8a37ee7097c7ff455f57007b112dcf12
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Derive the area and size distribution for entire-in particles
% Include the IWC calculation
% Include the effective radius
% Created by Will Wu, 09/18/2013
%
% **************************
% *** Modification Notes ***
% **************************
% * Modified to use the new maximum size and derive both maximum size distribution
% and area-equivalent size distribution.
% Will Wu, 10/26/2013
% * Modified to calculate terminal velocity using Heymsfield and Westbrook (2010) method
% and precipitation rate.
% Will Wu, 01/15/2014
% * Modified to include mass size distribution with habit info.
% Will Wu, 02/09/2014
% * Modified to include particle area using A-D relations.
% Will Wu, 02/14/2014
% * Special Edition for Boston Cloud workshop.
% Wei Wu, 04/01/2014
% * Gneralized as a new sorting function for all probes.
% Wei Wu, 07/25/2014
% * Modified to allow the option to ingest/use interarrival time dynamic threshold
% Dan Stechman, 05/06/2016
% * Added project and date specific capabilities (including spiral-dependent interarrival
% thresholding). Also cleaned up code and improved efficiency in places.
% Dan Stechman, 06/03/2016
% * Added shatter removal using array of interarrival time thresholds (either constant or varrying [e.g., different threshold for
% each spiral in PECAN project]). Also added experimental shatter reacceptance option to allow for potential diffraction fringes
% originally flagged as shattered to be reaccepted.
% Dan Stechman, 06/09/2016
% * Expanded upon time-varying interarrival time thresholds and reacceptance of particles for GPM (GCPEx, OLYMPEX) campaigns.
% Also added option to save out information on interarrival times and sample volume.
% Bug fix for calculation of 'n' and 'count' to un-normalize by binwidth.
% Bug fix when syncing particle time with flight time.
% Joe Finlon, 03/03/2017
% * Resolved indexing issue in the event that tas array covers more time
% than the autoanalysis file when attempting to sync times
% Added typecasting when writing netcdf file with putVar to resolve
% errors where putVar would break because variables were of wrong type
% Adam Majewski, 05/26/2017
%
% Usage:
% infile: Input filename, string
% outfile: Output filename, string
% tas: True air speed, double array
% timehhmmss: Time in hhmmss format, double array
% probename: Should be one of 'HVPS', 'CIP', 'PIP', '2DC', '2DP', 'F2DC'
% d_choice: the definition of Dmax, should use 6 usually. [1-6]
% SAmethod: 0: Center in; 1: Entire in; 2: With Correction
% Pres: 1 second pressure data
% Temp: 1 second temperature data
% projectname: Project name, string
% ddate: Date to be analyzed, string (YYYYMMDD)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sizeDist(infile, outfile, tas, timehhmmss, probename, d_choice, SAmethod, Pres, Temp, projectname, ddate, varargin)
iCreateBad = 0; % Default not to output bad particles PSDs and other info
iCreateAspectRatio = 0; % Default not to process aspect ratio info
iSaveIntArrSV = 0; % Default not to save inter-arrival and sample volume information
%% Interarrival threshold file specification
% Can be implemented if a time-dependent threshold is required - add 'varargin' to arguments in function header above
if length(varargin) == 1
iaThreshFile = varargin{1};
elseif length(varargin)>1
display('You have added too many inputs!')
iaThreshFile = 'NONE';
end
%% Define input and output files and initialize time variable
f = netcdf.open(infile,'nowrite');
mainf = netcdf.create(outfile, 'clobber');
% Fix flight times if they span multiple days - Added by Joe Finlon -
% 03/03/17
timehhmmss(find(diff(timehhmmss)<0)+1:end)=...
timehhmmss(find(diff(timehhmmss)<0)+1:end) + 240000;
% tas_char = num2str(timehhmmss); %Unused
tas_time = floor(timehhmmss/10000)*3600+floor(mod(timehhmmss,10000)/100)*60+floor(mod(timehhmmss,100));
% averaging_time = 1;
timehhmmss = mod(timehhmmss, 240000);
%% Project-, probe-, and date-specific information
switch projectname
case 'PECAN'
switch probename
case 'CIP'
num_diodes =64;
diodesize = 0.025; % units of mm
armdst=100.;
% num_bins = 64;
% kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
num_bins=19;
kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]/1000; %Array in microns - converted to mm
probetype=1;
tasMax=200; % Max airspeed that can be sampled without under-sampling (images would appear skewed)
applyIntArrThresh = 1;
defaultIntArrThresh = 1e-5;
reaccptShatrs = 1;
reaccptD = 0.5; % Diammeter (in mm) to reaccept if initially flagged as shattered
reaccptMaxIA = 2.5e-7; % Max interarrival time in seconds a particle can have to be reaccepted if
% size criteria are met. Possible definition of this is the time of one slice, so in
% this case, with an airspeed of ~100 m/s and a slice of 25 um, this would be 2.5e-7.
% Get start and end times (in seconds) of spirals; interarrival time thresholds for each spiral
[startT, endT, ~, ~, intar_threshold_spirals] = getPECANparams(ddate, probename);
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
for ix = 1:length(tas_time)
for iz = 1:length(startT)
if (tas_time(ix) >= startT(iz) && tas_time(ix) < endT(iz))
intar_threshold(ix) = intar_threshold_spirals(iz);
end
end
end
case 'PIP'
num_diodes =64;
diodesize = 0.1; %units of mm
armdst=260.;
num_bins = 64;
% kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
kk=diodesize/2:diodesize:(num_bins+0.6)*diodesize;
% num_bins=19;
% kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
% 700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]*4/1000;
probetype=1;
tasMax=200;
applyIntArrThresh = 1;
defaultIntArrThresh = 1e-5;
reaccptShatrs = 1;
reaccptD = 0.5; % Diammeter (in mm) to reaccept if initially flagged as shattered
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
% Get start and end times (in seconds) of spirals; interarrival time thresholds for each spiral
[startT, endT, ~, ~, intar_threshold_spirals] = getPECANparams(ddate, probename);
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
for ix = 1:length(tas_time)
for iz = 1:length(startT)
if (tas_time(ix) >= startT(iz) && tas_time(ix) < endT(iz))
intar_threshold(ix) = intar_threshold_spirals(iz);
end
end
end
end
case 'GPM'
switch probename
case '2DS'
% For the 2DS
num_diodes =128;
diodesize = .010;
armdst=63.;
num_bins =22;
kk=[40.0 60.0 80.0 100.0 125.0 150.0 200.0 250.0 300.0 350.0 400.0 ...
475.0 550.0 625.0 700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]/1000;
probetype=2;
tasMax=170;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 1;
defaultIntArrThresh = 1e-6;
reaccptShatrs = 1;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
case 'HVPS'
% For the HVPS
num_diodes =128;
diodesize = .150;
armdst=161.;
num_bins = 28;
kk=[200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 1600.0 1800.0 2200.0 2600.0 ...
3000.0 3400.0 3800.0 4200.0 4600.0 5000.0 6000.0 7000.0 8000.0 9000.0 10000.0 ...
12000.0 14000.0 16000.0 18000.0 20000.0 25000.0 30000.0]/1000;
probetype=2;
tasMax=170;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 1e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
end
otherwise
switch probename
case 'HVPS'
% For the HVPS
num_diodes =128;
diodesize = .150;
armdst=161.;
num_bins = 28;
kk=[200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 1600.0 1800.0 2200.0 2600.0 ...
3000.0 3400.0 3800.0 4200.0 4600.0 5000.0 6000.0 7000.0 8000.0 9000.0 10000.0 ...
12000.0 14000.0 16000.0 18000.0 20000.0 25000.0 30000.0]/1000;
%num_bins =128;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
probetype=2;
tasMax=170;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 4e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case '2DS'
% For the HVPS
num_diodes =128;
diodesize = .010;
armdst=61.;
%num_bins = 28;
%kk=[200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 1600.0 1800.0 2200.0 2600.0 ...
% 3000.0 3400.0 3800.0 4200.0 4600.0 5000.0 6000.0 7000.0 8000.0 9000.0 10000.0 ...
% 12000.0 14000.0 16000.0 18000.0 20000.0 25000.0 30000.0]/1000/15;
num_bins =128;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
kk=diodesize/2:diodesize:(num_bins+0.6)*diodesize;
probetype=2;
tasMax=170;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 4e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case 'CIP'
% For the CIP
num_diodes =64;
diodesize = .025; %units of mm
armdst=61.;
%num_bins = 64;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
num_bins=19;
kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]/1000; %Array in microns - converted to mm
probetype=1;
tasMax=200; % Max airspeed that can be sampled without under-sampling (images would appear skewed)
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 1e-5;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case 'PIP'
num_diodes =64;
diodesize = .1; %units of mm
armdst=260.;
num_bins = 64;
% kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
kk=diodesize/2:diodesize:(num_bins+0.6)*diodesize;
% num_bins=19;
% kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
% 700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]*4/1000;
probetype=1;
tasMax=200;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 1e-5;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case '2DC'
% For the 2DC
num_diodes =32;
diodesize = .03; %.025;
armdst=61.;
%num_bins = 32;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
num_bins=19;
kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]/1000;
probetype=0;
tasMax=125;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 4e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case '2DP'
% For the 2DP
num_diodes =32;
diodesize = .200; %.025;
armdst=260.; %75.77; %61.;
%num_bins = 32;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
num_bins=19;
kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]*8/1000;
probetype=0;
tasMax = 125;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 4e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
case 'F2DC'
% For the 2DC
num_diodes =64;
diodesize = .025; %.025;
armdst=61.; %60; %
%num_bins = 32;
%kk=diodesize/2:diodesize:(num_bins+0.5)*diodesize;
num_bins=19;
kk=[50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 475.0 550.0 625.0 ...
700.0 800.0 900.0 1000.0 1200.0 1400.0 1600.0 1800.0 2000.0]/1000;
probetype=0;
% Interarrival threshold and reaccept max interarrival time are often flight-/instrument-specific
% **Values here may not be correct**
% The interarrival threshold can be modifided to change second-by-second if desired
applyIntArrThresh = 0;
defaultIntArrThresh = 4e-6;
reaccptShatrs = 0;
reaccptD = 0.5;
reaccptMaxIA = 1e-6; % (Slice size [m])/(avg. airspeed [m/s])
intar_threshold = ones(size(tas_time))*defaultIntArrThresh;
end
end
if applyIntArrThresh && ~reaccptShatrs
fprintf('Beginning sizeDist_Paris.m for %s %s - %s probe\n\t**Optional parameters active:\n\t- Shatter removal\n\n',projectname,ddate,probename);
elseif applyIntArrThresh && reaccptShatrs
fprintf('Beginning sizeDist_Paris.m for %s %s - %s probe\n\t**Optional parameters active:\n\t- Shatter removal\n\t- Shatter reacceptance\n\n',...
projectname,ddate,probename);
else
fprintf('Beginning sizeDist_Paris.m for %s %s - %s probe\n\n',projectname,ddate,probename);
end
res=diodesize*1000;
binwidth=diff(kk);
% SAmethod = 2;
% for i=1:num_bins+1
% kk(i)= (diodesize*i)^2*3.1415926/4;
% end
%% Define Variables
% Good particles (not rejected)
particle_dist_minR = zeros(length(tas),num_bins)*NaN;
particle_dist_AreaR = zeros(length(tas),num_bins)*NaN;
particle_aspectRatio = zeros(length(tas),num_bins)*NaN;
particle_aspectRatio1 = zeros(length(tas),num_bins)*NaN;
particle_areaRatio1 = zeros(length(tas),num_bins)*NaN;
particle_area = zeros(length(tas),num_bins)*NaN;
cip2_meanp = zeros(length(tas),num_bins)*NaN;
cip2_iwc = zeros(length(tas),num_bins)*NaN;
cip2_iwcbl = zeros(length(tas),num_bins)*NaN;
cip2_vt = zeros(length(tas),num_bins)*NaN;
cip2_pr = zeros(length(tas),num_bins)*NaN;
cip2_partarea = zeros(length(tas),num_bins)*NaN;
cip2_re = zeros(1,length(tas))*NaN;
good_partpercent=zeros(length(tas),1);
goodintpercent=zeros(length(tas),1);
numGoodparticles=zeros(length(tas),1);
one_sec_ar=zeros(length(tas),1);
cip2_habitsd = zeros(length(tas),num_bins,10)*NaN;
cip2_habitmsd = zeros(length(tas),num_bins,10)*NaN;
area_dist2 = zeros(length(tas),num_bins,10)*NaN;
rejectpercentbycriterion=zeros(length(tas),14);
% Bad particles (rejected)
bad_particle_dist_minR = zeros(length(tas),num_bins)*NaN;
bad_particle_dist_AreaR = zeros(length(tas),num_bins)*NaN;
bad_particle_aspectRatio = zeros(length(tas),num_bins)*NaN;
bad_particle_aspectRatio1 = zeros(length(tas),num_bins)*NaN;
bad_particle_areaRatio1 = zeros(length(tas),num_bins)*NaN;
bad_particle_area = zeros(length(tas),num_bins)*NaN;
bad_cip2_meanp = zeros(length(tas),num_bins)*NaN;
bad_cip2_iwc = zeros(length(tas),num_bins)*NaN;
bad_cip2_iwcbl = zeros(length(tas),num_bins)*NaN;
bad_cip2_vt = zeros(length(tas),num_bins)*NaN;
bad_cip2_pr = zeros(length(tas),num_bins)*NaN;
bad_cip2_partarea = zeros(length(tas),num_bins)*NaN;
bad_cip2_re = zeros(1,length(tas))*NaN;
badintpercent=zeros(length(tas),1);
numBadparticles=zeros(length(tas),1);
bad_one_sec_ar=zeros(length(tas),1);
bad_cip2_habitsd = zeros(length(tas),num_bins,10)*NaN;
bad_cip2_habitmsd = zeros(length(tas),num_bins,10)*NaN;
bad_area_dist2 = zeros(length(tas),num_bins,10)*NaN;
% particle_dist2 = zeros(length(tas),num_bins)*NaN; %Unused
% time_interval1 = zeros(length(tas), 1); %Unused
% cip2_ar = zeros(1,length(tas))*NaN; %Unused
% throwoutpercent=zeros(length(tas),1); %Used in legacy interarrival time analysis
% totalint=zeros(length(tas),1); %Used in legacy interarrival time analysis
% intsum=zeros(length(tas),1); %Used in legacy interarrival time analysis
area_bins = 0:.1:1.01;
one_sec_times = tas_time;
one_sec_dur = length(one_sec_times);
total_one_sec_locs(1:one_sec_dur) = 0;
start_time = floor(tas_time(1));
end_time = ceil(tas_time(end));
one_sec_tas(1:one_sec_dur) = 0;
one_sec_tas_entire(1:one_sec_dur) = 0;
deadtime(1:one_sec_dur) = 0;
warning off all
one_sec_times=[one_sec_times;one_sec_times(one_sec_dur)+1];
time_interval2 = zeros(one_sec_dur,1);
TotalPC1 = zeros(one_sec_dur,1)';
TotalPC2 = zeros(one_sec_dur,1)';
% Used for debugging of interarrival time analysis
shatrReject_times = [];
shatrReject_intArr = [];
shatrReject_diam = [];
rccptReject_times = [];
rccptReject_intArr = [];
rccptReject_diam = [];
loopedTimes = [];
loopedIntArr = [];
loopedDiam = [];
loopedAutoRej = [];
%% Load particles for each second, and then process them
% Only for large files cannot be processed at once
[~, NumofPart] = netcdf.inqDim(f,0); % Check the number of particles
dateall = netcdf.getVar(f,netcdf.inqVarID(f,'Date'));
if 1==probetype
image_time_hhmmssall = netcdf.getVar(f,netcdf.inqVarID(f,'particle_time'));
elseif 2==probetype
image_time_hhmmssallbuffer = netcdf.getVar(f,netcdf.inqVarID(f,'Time'));
% image_time_hhmmssallbuffer(image_time_hhmmssallbuffer<10000 & image_time_hhmmssallbuffer>=0)=image_time_hhmmssallbuffer(image_time_hhmmssallbuffer<10000 & image_time_hhmmssallbuffer>=0)+240000;
alltimeinseconds = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'));
time_msec_all = netcdf.getVar(f,netcdf.inqVarID(f,'msec'),0,1);
indexRollback=find(diff(alltimeinseconds)<-250)+1;
for i=1:length(indexRollback)
alltimeinseconds(indexRollback(i):end)=alltimeinseconds(indexRollback(i):end)+(2^32-1)*(res/10^6/tasMax);
end
% alltimeinsecondsstart=alltimeinseconds(indexBuffert);
% increaseAllinseconds= alltimeinseconds-alltimeinseconds(1);
% increaseAllinseconds(increaseAllinseconds<0)=increaseAllinseconds(increaseAllinseconds<0)+(2^32-1)*(res/10^6/170);
% image_time_hhmmssall = insec2hhmmss(floor(47069+time_msec_all(1)/1000.0+increaseAllinseconds*170/110));
image_time_hhmmssall = image_time_hhmmssallbuffer;
else
image_time_hhmmssall = netcdf.getVar(f,netcdf.inqVarID(f,'Time'));
end
% image_time_hhmmssall = netcdf.getVar(f,netcdf.inqVarID(f,'Time'));
% image_time_hhmmssall(image_time_hhmmssall<50000 & image_time_hhmmssall>=0)=image_time_hhmmssall(image_time_hhmmssall<50000 & image_time_hhmmssall>=0)+120000;
% Fix particle times if they span multiple days - Added by Joe Finlon -
% 03/03/17
%image_time_hhmmssall(find(diff(image_time_hhmmssall)<0)+1:end)=...
% image_time_hhmmssall(find(diff(image_time_hhmmssall)<0)+1:end) + 240000;
% Find all indices (true/1) with a unique time in hhmmss - in other words, we're getting the particle index where each new
% one-second period starts
startindex=[true;(diff(hhmmss2insec(image_time_hhmmssall))>0)]; % & diff(hhmmss2insec(image_time_hhmmssall))<5)]; % Simplified (tested/changed by DS)
startdate=dateall(startindex);
% startindex=int8(image_time_hhmmssall*0);
% for i=1:length(timehhmmss)
% indexofFirstTime = find(image_time_hhmmssall==timehhmmss(i),1);
% if ( ~isempty(indexofFirstTime) )
% startindex(indexofFirstTime)=1;
% end
% disp([i,length(timehhmmss)]);
% end
% Get the start time for each new one-second period
starttime=image_time_hhmmssall(startindex); % Simplified (tested/changed by DS)
% Find all instances where startindex is true (where image_time_hhmmssall changes by more than 0) and shift indices back by one to
% facilitate proper particle counts for each one-second period
start_all=find(startindex)-1; % Simplified (tested/changed by DS)
% Sort the particle one-second time array in the event it is out of order and redefine the start_all variable as needed
%[starttime,newindexofsort]=sort(starttime);
%start_all=start_all(newindexofsort);
%% Remove times when there is no tas data available
% nNoTAS=0;
% for i=1:length(starttime)
% if isempty(timehhmmss(timehhmmss == starttime(i)))
% starttime(i)=500000;
% nNoTAS=nNoTAS+1;
% end
%
% if i>5 & i<length(starttime)-5 & hhmmss2insec(starttime(i))>mean(hhmmss2insec(starttime(i-5:i+5)))+5
% starttime(i)=500000;
% end
% end
% nNoTAS
% start_all = start_all(starttime<500000);
% count_all = count_all(starttime<500000);
% starttime = starttime(starttime<500000);
%% Remove any duplicate times and determine how many particles are present in each one-second period
fprintf('Number of duplicate times = %d\n\n',(length(starttime)-length(unique(starttime))));
%[starttime, ia, ~] = unique(starttime,'first');
%start_all = start_all(ia);
count_all= [diff(start_all); NumofPart-start_all(end)];
count_all(count_all<0)=1;
%% Remove times when there are less than 10 particles in one second
% starttime = starttime(count_all>10);
% start_all = start_all(count_all>10);
% count_all = count_all(count_all>10);
%if (int32(timehhmmss(1))>int32(starttime(2)))
% error('Watch Out for less TAS time from begining!')
%end
%% Main loop over the length of the true air speed variable (1-sec resolution)
jjj=1;
sumIntArrGT1 = 0;
intArrGT1 = [];
% nThrow11=0; % Used in legacy interarrival time analysis
% maxRecNum=1; % Used in legacy interarrival time analysis
fprintf('Beginning size distribution calculations and sorting %s\n\n',datestr(now));
for i=1:length(tas)
if ( int32(mod(timehhmmss(end)-timehhmmss(i),240000))<=int32(mod(timehhmmss(end)-starttime(jjj),240000)) )
% Attempt to sync TAS file time (timehhmmss) with particle time
% if (int32(timehhmmss(i))>int32(starttime(jjj))) %% Deprecated
% (Joe Finlon - 03/03/17)
% Rearranged order to successfully break out of the loop at the
% right index in the event that the tas array covers more time than
% the autoanalysis files
while ( (int32(timehhmmss(i))>int32(starttime(jjj))) && (int32(starttime(jjj))>int32(timehhmmss(end)))) % Added by Joe Finlon - 03/03/17
if (jjj>=length(start_all))
break;
end
jjj=jjj+1;
end
start=start_all(jjj);
count=count_all(jjj);
jjj=min(jjj+1,length(start_all));
% Load autoanalysis parameters. Start at beginning (start) of some one-second period and load the values for every
% particle in that period (count)
cdate = netcdf.getVar(f,netcdf.inqVarID(f,'Date'),start,count);
msec = netcdf.getVar(f,netcdf.inqVarID(f,'particle_millisec'),start,count);
microsec = netcdf.getVar(f,netcdf.inqVarID(f,'particle_microsec'),start,count);
auto_reject = netcdf.getVar(f,netcdf.inqVarID(f,'image_auto_reject'),start,count);
im_width = netcdf.getVar(f,netcdf.inqVarID(f,'image_width'),start,count);
im_length = netcdf.getVar(f,netcdf.inqVarID(f,'image_length'),start,count);
area = netcdf.getVar(f,netcdf.inqVarID(f,'image_area'),start,count);
perimeter = netcdf.getVar(f,netcdf.inqVarID(f,'image_perimeter'),start,count);
if probetype == 0
rec_nums = netcdf.getVar(f,netcdf.inqVarID(f,'parent_rec_num'),start,count);
end
% top_edges = netcdf.getVar(f,netcdf.inqVarID(f,'image_max_top_edge_touching'),start,count); %Unused
% bot_edges = netcdf.getVar(f,netcdf.inqVarID(f,'image_max_bottom_edge_touching'),start,count); %Unused
% longest_y = netcdf.getVar(f,netcdf.inqVarID(f,'image_longest_y'),start,count); %Unused
size_factor = netcdf.getVar(f,netcdf.inqVarID(f,'size_factor'),start,count);
habit1 = netcdf.getVar(f,netcdf.inqVarID(f,'holroyd_habit'),start,count);
centerin = netcdf.getVar(f,netcdf.inqVarID(f,'image_center_in'),start,count);
entirein = netcdf.getVar(f,netcdf.inqVarID(f,'image_touching_edge'),start,count);
particle_diameter_AreaR = netcdf.getVar(f,netcdf.inqVarID(f,'image_diam_AreaR'),start,count);
particle_diameter_AreaR = particle_diameter_AreaR * diodesize;
Time_in_seconds = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'),start,count);
% SliceCount = netcdf.getVar(f,netcdf.inqVarID(f,'SliceCount'),start,count); %Unused
DMT_DOF_SPEC_OVERLOAD = netcdf.getVar(f,netcdf.inqVarID(f,'DMT_DOF_SPEC_OVERLOAD'),start,count);
Particle_count = netcdf.getVar(f,netcdf.inqVarID(f,'Particle_number_all'),start,count);
if 1==probetype
auto_reject(DMT_DOF_SPEC_OVERLOAD~=0)='D';
end
if iCreateAspectRatio == 1
aspectRatio = netcdf.getVar(f,netcdf.inqVarID(f,'image_RectangleW'),start,count)./netcdf.getVar(f,netcdf.inqVarID(f,'image_RectangleL'),start,count);
aspectRatio1 = netcdf.getVar(f,netcdf.inqVarID(f,'image_EllipseW'),start,count)./netcdf.getVar(f,netcdf.inqVarID(f,'image_EllipseL'),start,count);
end
TotalPC1(i)=length(Particle_count);
TotalPC2(i)=Particle_count(end)-Particle_count(1);
if 0==probetype
int_arr=Time_in_seconds;
else
if start-1 <= 0
int_arr = [0;diff(Time_in_seconds)];
int_arr2 = []; %Won't bother reaccepting particles at the beginning or end of dataset
else
Time_in_seconds2 = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'),start-1,count+1);
int_arr = diff(Time_in_seconds2);
if start ~= start_all(end)
%Time_in_seconds3 = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'),(start+count)-1,2);
%int_arr2 = diff(Time_in_seconds3); %Single value describing interarrival time of first particle of next 1-sec period
else
int_arr2 = [];
end
end
%int_arr2(int_arr2<0)=0;
if reaccptShatrs
if start ~= start_all(end)
Time_in_seconds4 = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'),start,count+1);
int_arr3 = diff(Time_in_seconds4);
else
Time_in_seconds4 = Time_in_seconds;
int_arr3 = diff(Time_in_seconds4);
int_arr3 = [int_arr3;int_arr3(end)];
end
int_arr3(int_arr3<0)=0;
end
end
% if 2==probetype
% int_arr=int_arr*(res/10^6/170);
% end
if 2==probetype
int_arr(int_arr<-10)=int_arr(int_arr<-10)+(2^32-1)*(res/10^6/tasMax);
elseif 0==probetype
int_arr(int_arr<0)=int_arr(int_arr<0)+(2^24-1)*(res/10^6/tasMax);
end
if sum(int_arr<0)>0
fprintf(2,'\nAt index %d number of int_arr < 0: %d\n',i,sum(int_arr<0));
disp([int_arr(int_arr<0),int_arr(int_arr<0)+(2^32-1)*(res/10^6/tasMax)]);
elseif sum(int_arr>1)>0
sumIntArrGT1 = sumIntArrGT1 + sum(int_arr > 1);
tempLocs = find(int_arr > 1);
intArrGT1 = vertcat(intArrGT1,int_arr(tempLocs));
% fprintf(2,'\nAt index %d number of int_arr > 1: %d\n',i,sum(int_arr>1));
% disp([int_arr(int_arr>1)-(2^32-1)*(res/10^6/tasMax), int_arr(int_arr>1), Time_in_seconds(int_arr>1)/(0.15/(10^3)/170), Time_in_seconds((int_arr>1))/(0.15/(10^3)/170)]);
end
% auto_reject(int_arr<0 | int_arr>1)='I';
auto_reject(int_arr<0)='I';
int_arr(int_arr<0)=0;
% int_arr(int_arr>1)=0;
% max_dimension = im_width;
% max_dimension(im_length>im_width)=im_length(im_length>im_width);
% Size definition chosen based on the d_choice given in the function call
if 1==d_choice
particle_diameter_minR = im_length * diodesize; %(im_length+
elseif 2==d_choice
particle_diameter_minR = im_width * diodesize; %(im_length+
elseif 3==d_choice
particle_diameter_minR = (im_length + im_width)/2 * diodesize; %(im_length+
elseif 4==d_choice
particle_diameter_minR = sqrt(im_width.^2+im_length.^2) * diodesize; %(im_length+
elseif 5==d_choice
particle_diameter_minR = max(im_width, im_length) * diodesize; %(im_length+
elseif 6==d_choice
particle_diameter_minR = netcdf.getVar(f,netcdf.inqVarID(f,'image_diam_minR'),start,count); % * diodesize
end
% if 1==strcmp('2DC',probename) % Adjust resolution from 25 to 30
% particle_diameter_minR=particle_diameter_minR*1.2;
% area = area*1.44;
% end
% Legacy: Added for Paris meeting, 08/25/2014
% Used in intercomparison with Environment Canada and University of Blaise Pascal
%{
diffPartCount=[1;diff(Particle_count)];
time_interval22(i) = (Time_in_seconds(end)-Time_in_seconds(1));
time_interval32(i) = sum(int_arr(diffPartCount==1));
time_interval42(i) = sum(int_arr);
time_interval52(i) = sum(int_arr(diffPartCount~=1));
time_interval62(i) = sum(int_arr(DMT_DOF_SPEC_OVERLOAD==0));
lengthForTemp = im_length * diodesize;
particle_diameter_minR(entirein~=0)=lengthForTemp(entirein~=0);
if time_interval22(i)<0
time_interval22(i)=time_interval22(i)+(2^32-1)*(res/10^6/tasMax); %#ok<*AGROW>
end
if RejectCriterier==1
particle_diameter_minR = particle_diameter_minR .* size_factor;
end
if 1==probetype
image_time_hhmmss = netcdf.getVar(f,netcdf.inqVarID(f,'particle_time'),start,count);
image_time_hhmmssnew = netcdf.getVar(f,netcdf.inqVarID(f,'particle_time'),start,count);
elseif 2==probetype
alltimeinseconds = netcdf.getVar(f,netcdf.inqVarID(f,'Time_in_seconds'),start,count);
increaseAllinseconds= alltimeinseconds-alltimeinseconds(1);
increaseAllinseconds(increaseAllinseconds<0)=increaseAllinseconds(increaseAllinseconds<0)+(2^32-1)*(res/10^6/170);
image_time_hhmmss = floor(hhmmss2insec(netcdf.getVar(f,netcdf.inqVarID(f,'Time'),start,count))+netcdf.getVar(f,netcdf.inqVarID(f,'msec'),start,count)/1000+increaseAllinseconds); % 'Time'?
image_time_hhmmss = insec2hhmmss(image_time_hhmmss);
image_time_hhmmssnew = image_time_hhmmss;
end
%}
time_interval72(i) = sum(int_arr(DMT_DOF_SPEC_OVERLOAD~=0));
%2DP overload handling - Adam Majewski 9/28/17
if probetype == 0
rec_start = diff(rec_nums)>0;
time_interval72(i) = sum(DMT_DOF_SPEC_OVERLOAD(rec_start))/1000.; %total overload time for the one second period in seconds
end
% Simplified by DS - Removed image_time_hhmmssnew as it was defined by and never changed from image_time_hhmmss
image_time_hhmmss = image_time_hhmmssall(start+1:start+count);
% If image time crosses midnight, add 240000 to all times past midnight
% image_time_hhmmss(image_time_hhmmss<10000)=image_time_hhmmss(image_time_hhmmss<10000)+240000;
% Save an intermediate output file every 8000 steps through the loop
if i==8000
save([outfile(1:end-3) 'tempComp.mat']);
end
%% Calculate area of particle according to image reconstruction and airspeed (if tasMax exceeded)
% Correct for airspeeds exceeding the max airspeed for the probe
if(tas(i) > tasMax) % Set to threshold as necessary - stretch area of particle
fprintf(2,'TAS at tas index %d exceeds tasMax (%.1f) of probe. Reconstructing area...\n\n',...
i,tasMax);
area = area*tas(i)/tasMax;
end
particle_mass = area*0;
calcd_area = area*0;
for iiii=1:length(area)
particle_mass(iiii)=single_mass(particle_diameter_minR(iiii)/10, habit1(iiii)); % in unit of gram
calcd_area(iiii)=single_area(particle_diameter_minR(iiii)/10, habit1(iiii)); % in unit of mm^2
end
particle_massbl=0.115/1000*area.^(1.218); % in unit of gram
%% Added by Robert Jackson -- old version did not have area ratio code
area_ratio = area./(pi/4.*particle_diameter_minR.^2);
auto_reject(area_ratio < .2) = 'z';
%% Added by Will to calculate terminal velocity and precipitation rate
particle_vt = area*0;
for iiii=1:length(area)
particle_vt(iiii)=single_vt(particle_diameter_minR(iiii)/1000, area_ratio(iiii), particle_mass(iiii)/1000,Pres(i),Temp(i)); % in unit of gram
end
particle_pr=particle_mass.*particle_vt;
%% Time-dependent threshold for interarrival time - Added by Dan Stechman - 5/10/16 & Modified by Joe Finlon - 03/03/17
% Enable this section to use a time-dependent threshold for interarrival time. Also need to enable section at top of
% script allowing for threshold file to be pulled in
% Ingest previously calculated interarrival time threshold and flag in auto_reject appropriately to remove particle
% flagged with short inter arrv time, and the one immediately before it
if applyIntArrThresh && length(varargin) == 1 && strcmp(iaThreshFile,'NONE') == 0
auto_reject_preIAT = auto_reject;
iaThresh_ncid = netcdf.open(iaThreshFile,'nowrite');
iaThresh = netcdf.getVar(iaThresh_ncid,netcdf.inqVarID(iaThresh_ncid,'threshold'),start,count);
netcdf.close(iaThresh_ncid);
if ((length(int_arr) == 1) && (int_arr(1) <= iaThresh(1)))
auto_reject(1) = 'S';
else
if int_arr(1) <= iaThresh(1)
auto_reject(1) = 'S';
end
for ix = 2:length(int_arr)
if int_arr(ix) <= iaThresh(ix)
auto_reject((ix-1):ix) = 'S';
end
end
end
% Experimental option to reaccept particles flagged as shattered which may in fact be the result of diffraction
% fringes
% Added by Dan Stechman - 6/8/2015 & Modified by Joe Finlon - 03/03/17 - with base code by Wei Wu
if reaccptShatrs
% Start by defining the indices for the beginning and end of individual shattering events
rBegin = ((int_arr > iaThresh & int_arr3 < iaThresh));
rEnd = ((int_arr < iaThresh & int_arr3 > iaThresh));
maxParticle = reaccptD;
eIndex = [];
% We search through each individual set of shattering events and check to see if any of the particles are both
% larger than the reacceptance diameter and have an interarrival time less than the reacceptance threshold as we'd
% expect diffraction fringes to be larger than shattered particles and to have a particularly small interarrival time
for iEvent = find(rBegin):find(rEnd)
if ((particle_diameter_minR(iEvent) > maxParticle) && (int_arr(iEvent) < reaccptMaxIA))
maxParticle = particle_diameter_minR(iEvent);
eIndex = iEvent;
end
end
auto_reject(eIndex) = 'R';
end
% Following vars used for verifying shatter removal and reacceptance in external script - can be commented out if desired
shatterLocs = find(auto_reject == 'S');
shatterIA = int_arr(shatterLocs);
shatterTimes = Time_in_seconds(shatterLocs);
shatterDiam = particle_diameter_minR(shatterLocs);
shatrReject_times = vertcat(shatrReject_times, shatterTimes);
shatrReject_intArr = vertcat(shatrReject_intArr, shatterIA);
shatrReject_diam = vertcat(shatrReject_diam, shatterDiam);
rccptLocs = find(auto_reject == 'R');
rccptIA = int_arr(rccptLocs);
rccptTimes = Time_in_seconds(rccptLocs);
rccptDiam = particle_diameter_minR(rccptLocs);
rccptReject_times = vertcat(rccptReject_times, rccptTimes);
rccptReject_intArr = vertcat(rccptReject_intArr, rccptIA);
rccptReject_diam = vertcat(rccptReject_diam, rccptDiam);
loopedTimes = vertcat(loopedTimes, Time_in_seconds);
loopedIntArr = vertcat(loopedIntArr, int_arr);
loopedDiam = vertcat(loopedDiam, particle_diameter_minR);
loopedAutoRej = vertcat(loopedAutoRej, auto_reject);
end
%% Legacy interarrival time integrity analysis
%{
% Time and interarrival calculation. Modified by Will Wu 11/12/2013
% Simplified (tested/changed by DS)
if strcmp(probename,'2DC')==1 || strcmp(probename,'2DP')==1 || strcmp(probename,'F2DC')==1
fracseccc= netcdf.getVar(f,netcdf.inqVarID(f,'msec'),start,count);
image_timeia = hhmmss2insec(image_time_hhmmss)+fracseccc*1e-2; % for 2DC
elseif strcmp(probename,'CIP')==1 || strcmp(probename,'PIP')==1
image_timeia = hhmmss2insec(image_time_hhmmss)+msec*1e-3+microsec; % for CIP
else
image_timeia = hhmmss2insec(image_time_hhmmss)+msec*1e-3+microsec/10^6; % for HVPS
end
disp('Checking Interarrival Times')
nThrow=0;
for(itemp=min(rec_nums):max(rec_nums))
rec_particles = find(rec_nums == itemp);
rej = auto_reject(rec_particles);
arr = int_arr(rec_particles);
sum_arr = sum(arr(2:end));
if(~isempty(rec_particles) && length(rec_particles) > 1)
int_arr(rec_particles(1)) = int_arr(rec_particles(2));
elseif(length(rec_particles) == 1)
int_arr(rec_particles(1)) = 0;
end
if (strcmp(probename,'CIP')==1 || strcmp(probename,'PIP')==1 || strcmp(probename,'HVPS')==1 ) % 2DC use the interarrival time for every particles, not absolute time
if(isempty(rec_particles))
sum_int_arr_good = 0;
else
sum_int_arr_good = image_timeia(rec_particles(end))-image_timeia(rec_particles(1));
end
if ~(sum_int_arr_good >= .6*sum_arr && sum_int_arr_good <= 1.4*sum_arr)
auto_reject(rec_particles) = 'I';
%disp(['Record ' num2str(itemp) ' thrown out: Accepted time = ' num2str(sum_int_arr_good) ' total time = ' num2str(sum_arr)]);
nThrow=nThrow+1;
nThrow11=nThrow11+1;
end
end
end
disp([num2str(100*nThrow/(max(rec_nums)-min(rec_nums)+1)),'% is thrown out']);
throwoutpercent(i)=100*nThrow/(max(rec_nums)-min(rec_nums)+1);
maxRecNum=max(max(rec_nums),maxRecNum);
totalint(i)=sum_int_arr_good;
intsum(i)=sum_arr;
save('intarrhvps.mat','int_arr')
%}
%% Shatter identification and removal - Added by Dan Stechman on 5/31/2016
% Currently this is spiral-dependent and uses a threshold defined in the header of this script
% Flag particles as shattered if their interarrival time is less than or equal to the threshold. Also flag the particle
% immediately before the target particle.
%{
if applyIntArrThresh
% If the first particle in the next 1-sec period has a small interarrival time, we flag the last particle of
% the current period as shattered as well
if ~isempty(int_arr2)
if int_arr2 <= intar_threshold(i)
auto_reject(end) = 'S';
end
end
if (length(int_arr) == 1 && int_arr(1) <= intar_threshold(i))
auto_reject(1) = 'S';
else
if int_arr(1) <= intar_threshold(i)
auto_reject(1) = 'S';
end
for ix = 2:length(int_arr)
if int_arr(ix) <= intar_threshold(i)
auto_reject(ix-1:ix) = 'S';
end
end
end
% Experimental option to reaccept particles flagged as shattered which may in fact be the result of diffraction
% fringes
% Added by Dan Stechman - 6/8/2015 - with base code by Wei Wu
if reaccptShatrs
% Start by defining the indices for the beginning and end of individual shattering events
rBegin = ((int_arr > intar_threshold(i) & int_arr3 < intar_threshold(i)));
rEnd = ((int_arr < intar_threshold(i) & int_arr3 > intar_threshold(i)));
maxParticle = reaccptD;
eIndex = [];
% We search through each individual set of shattering events and check to see if any of the particles are both
% larger than the reacceptance diameter and have an interarrival time less than the reacceptance threshold as we'd
% expect diffraction fringes to be larger than shattered particles and to have a particularly small interarrival time
for iEvent = find(rBegin):find(rEnd)
if ((particle_diameter_minR(iEvent) > maxParticle) && (int_arr(iEvent) < reaccptMaxIA))
maxParticle = particle_diameter_minR(iEvent);
eIndex = iEvent;
end
end
auto_reject(eIndex) = 'R';
end
% Following vars used for verifying shatter removal and reacceptance in external script - can be commented out if desired
shatterLocs = find(auto_reject == 'S');
shatterIA = int_arr(shatterLocs);
shatterTimes = Time_in_seconds(shatterLocs);
shatterDiam = particle_diameter_minR(shatterLocs);
shatrReject_times = vertcat(shatrReject_times, shatterTimes);
shatrReject_intArr = vertcat(shatrReject_intArr, shatterIA);
shatrReject_diam = vertcat(shatrReject_diam, shatterDiam);
rccptLocs = find(auto_reject == 'R');
rccptIA = int_arr(rccptLocs);
rccptTimes = Time_in_seconds(rccptLocs);
rccptDiam = particle_diameter_minR(rccptLocs);
rccptReject_times = vertcat(rccptReject_times, rccptTimes);
rccptReject_intArr = vertcat(rccptReject_intArr, rccptIA);
rccptReject_diam = vertcat(rccptReject_diam, rccptDiam);
loopedTimes = vertcat(loopedTimes, Time_in_seconds);
loopedIntArr = vertcat(loopedIntArr, int_arr);
loopedDiam = vertcat(loopedDiam, particle_diameter_minR);
loopedAutoRej = vertcat(loopedAutoRej, auto_reject);
end
%}
%% Apply rejection criteria and identify good and bad particles
% Modify the next line to include/exclude any particles you see fit.
good_particles = (auto_reject == '0' | auto_reject == 'H' | auto_reject == 'h' | auto_reject == 'u' | auto_reject == 'R');
bad_particles = ~(auto_reject == '0' | auto_reject == 'H' | auto_reject == 'h' | auto_reject == 'u' | auto_reject == 'R');
% bad_particles = (auto_reject == 'S');
% Legacy: Rejection criteria used in the past
%{
%if RejectCriterier==0
% good_particles = (auto_reject ~= 'c'); % & centerin==1; % & int_arr > 1e-5 int_arr > 1e-5 &
%else
% good_particles = (auto_reject == '0' | auto_reject == 'H' | auto_reject == 'h' | auto_reject == 'u' & int_arr > intar_threshold) % | auto_reject == 'u'); % & centerin==1; % & int_arr > 1e-5;
%end
%}
if SAmethod==0
good_particles = good_particles & centerin==1;
bad_particles = bad_particles & centerin==1;
elseif SAmethod==1
good_particles = good_particles & entirein==0;
bad_particles = bad_particles & centerin==0;
end
good_partpercent(i)=sum(good_particles)/length(good_particles);
rejectpercentbycriterion(i,1)=sum(centerin==1)/length(good_particles);
rejectpercentbycriterion(i,2)=sum(auto_reject == '0')/length(good_particles);
rejectpercentbycriterion(i,3)=sum(auto_reject == 'H')/length(good_particles);
rejectpercentbycriterion(i,4)=sum(auto_reject == 'h')/length(good_particles);
rejectpercentbycriterion(i,5)=sum(auto_reject == 'u')/length(good_particles);
rejectpercentbycriterion(i,6)=sum(auto_reject == 'a')/length(good_particles);
rejectpercentbycriterion(i,7)=sum(auto_reject == 't')/length(good_particles);
rejectpercentbycriterion(i,8)=sum(auto_reject == 'p')/length(good_particles);
rejectpercentbycriterion(i,9)=sum(auto_reject == 's')/length(good_particles);
rejectpercentbycriterion(i,10)=sum(auto_reject == 'z')/length(good_particles);
rejectpercentbycriterion(i,11)=sum(auto_reject == 'i')/length(good_particles);
rejectpercentbycriterion(i,12)=sum(auto_reject == 'A')/length(good_particles);
rejectpercentbycriterion(i,13)=sum(auto_reject == 'S')/length(good_particles); %Shattered - Added DS
rejectpercentbycriterion(i,14)=sum(auto_reject == 'R')/length(good_particles); %Reaccepted - Added DS
numGoodparticles(i)=length(good_particles);
numBadparticles(i)=length(bad_particles);
% disp([int32(timehhmmss(i)), sum(good_particles),length(good_particles),length(good_particles)-sum(good_particles)]);
% Edited to handle day rollovers - Adam Majewski 9/21/2017
image_time = hhmmss2insec(image_time_hhmmss) + ( datenum(num2str(cdate),'yyyymmdd') - datenum(ddate,'yyyymmdd') ).*86400;
% Good (accepted) particles
good_image_times = image_time(good_particles);
good_particle_diameter_minR = particle_diameter_minR(good_particles);
good_particle_diameter_AreaR = particle_diameter_AreaR(good_particles);
good_int_arr=int_arr(good_particles);
good_ar = area_ratio(good_particles);
good_area = area(good_particles);
good_perimeter = perimeter(good_particles);
if iCreateAspectRatio == 1
good_AspectRatio = aspectRatio(good_particles & entirein==0);
good_AspectRatio1 = aspectRatio1(good_particles & entirein==0);
end
good_ar1 = area_ratio(good_particles & entirein==0);
good_image_times1 = image_time(good_particles & entirein==0);
good_iwc=particle_mass(good_particles);
good_partarea=calcd_area(good_particles);
good_iwcbl=particle_massbl(good_particles);
good_vt=particle_vt(good_particles);
good_pr=particle_pr(good_particles);
good_habit=habit1(good_particles);
good_particle_diameter=good_particle_diameter_minR;
good_particle_diameter1 = particle_diameter_minR(good_particles & entirein==0);
if iCreateBad == 1
% Bad (rejected) particles
bad_image_times = image_time(bad_particles);
bad_particle_diameter_minR = particle_diameter_minR(bad_particles);
bad_particle_diameter_AreaR = particle_diameter_AreaR(bad_particles);
bad_int_arr=int_arr(bad_particles);
bad_ar = area_ratio(bad_particles);
bad_area = area(bad_particles);
bad_perimeter = perimeter(bad_particles);
if iCreateAspectRatio == 1 % added if statement if not creating aspect ratio - Joe Finlon - 03/03/17
bad_AspectRatio = aspectRatio(bad_particles & entirein==0);
bad_AspectRatio1 = aspectRatio1(bad_particles & entirein==0);
end
bad_ar1 = area_ratio(bad_particles & entirein==0);
bad_image_times1 = image_time(bad_particles & entirein==0);
bad_iwc=particle_mass(bad_particles);
bad_partarea=calcd_area(bad_particles);
bad_iwcbl=particle_massbl(bad_particles);
bad_vt=particle_vt(bad_particles);
bad_pr=particle_pr(bad_particles);
bad_habit=habit1(bad_particles);
bad_particle_diameter=bad_particle_diameter_minR;
bad_particle_diameter1 = particle_diameter_minR(bad_particles & entirein==0);
end
%% Perform various status and error checks
if mod(i,1000) == 0
fprintf('%d/%d | %s\n',i,one_sec_dur,datestr(now));
end
total_one_sec_locs(i) = length(find(image_time >= one_sec_times(i) & image_time < one_sec_times(i+1)));
time_interval2(i) = sum(int_arr(image_time >= one_sec_times(i) & image_time < one_sec_times(i+1)));
if sum(image_time >= one_sec_times(i) & image_time < one_sec_times(i+1)) ~= length(image_time)
fprintf(2,'%d / %d\tError on sizing at index %d\n',sum(image_time >= one_sec_times(i) & image_time < one_sec_times(i+1)),length(image_time),i);
end
if(total_one_sec_locs(i) == 0)
time_interval2(i) = 1;
end
%% Sort good (accepted) particles into size distributions
good_one_sec_locs = find(good_image_times >= one_sec_times(i) & good_image_times < one_sec_times(i+1));
good_one_sec_locs1 = find(good_image_times1 >= one_sec_times(i) & good_image_times1 < one_sec_times(i+1));
goodintpercent(i) = sum(good_int_arr(good_image_times >= one_sec_times(i) & good_image_times < one_sec_times(i+1)))/time_interval2(i);
one_sec_ar(i) = mean(good_ar1(good_one_sec_locs1));
if ~isempty(good_one_sec_locs)
for j = 1:num_bins
particle_dist_minR(i,j) = length(find(good_particle_diameter_minR(good_one_sec_locs) >= kk(j) &...
good_particle_diameter_minR(good_one_sec_locs) < kk(j+1)));
particle_dist_AreaR(i,j) = length(find(good_particle_diameter_AreaR(good_one_sec_locs) >= kk(j) &...
good_particle_diameter_AreaR(good_one_sec_locs) < kk(j+1)));
% Create Habit Number Size Distribution
cip2_habitsd(i,j,1) = length(find(good_habit(good_one_sec_locs)=='s' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,2) = length(find(good_habit(good_one_sec_locs)=='l' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,3) = length(find(good_habit(good_one_sec_locs)=='o' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,4) = length(find(good_habit(good_one_sec_locs)=='t' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,5) = length(find(good_habit(good_one_sec_locs)=='h' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,6) = length(find(good_habit(good_one_sec_locs)=='i' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,7) = length(find(good_habit(good_one_sec_locs)=='g' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,8) = length(find(good_habit(good_one_sec_locs)=='d' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,9) = length(find(good_habit(good_one_sec_locs)=='a' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitsd(i,j,10) = length(find(good_habit(good_one_sec_locs)=='I' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
% Create Habit Mass Size Distribution
cip2_habitmsd(i,j,1) = sum(good_iwc(good_habit(good_one_sec_locs)=='s' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,2) = sum(good_iwc(good_habit(good_one_sec_locs)=='l' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,3) = sum(good_iwc(good_habit(good_one_sec_locs)=='o' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,4) = sum(good_iwc(good_habit(good_one_sec_locs)=='t' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,5) = sum(good_iwc(good_habit(good_one_sec_locs)=='h' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,6) = sum(good_iwc(good_habit(good_one_sec_locs)=='i' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,7) = sum(good_iwc(good_habit(good_one_sec_locs)=='g' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,8) = sum(good_iwc(good_habit(good_one_sec_locs)=='d' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,9) = sum(good_iwc(good_habit(good_one_sec_locs)=='a' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
cip2_habitmsd(i,j,10) = sum(good_iwc(good_habit(good_one_sec_locs)=='I' & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
particle_area(i,j) = nansum(good_area(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
cip2_meanp(i,j) = nanmean(good_perimeter(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
if iCreateAspectRatio == 1
particle_aspectRatio(i,j) = nanmean(good_AspectRatio(good_one_sec_locs1(good_particle_diameter1(good_one_sec_locs1) >= kk(j) &...
good_particle_diameter1(good_one_sec_locs1) < kk(j+1))));
particle_aspectRatio1(i,j) = nanmean(good_AspectRatio1(good_one_sec_locs1(good_particle_diameter1(good_one_sec_locs1) >= kk(j) &...
good_particle_diameter1(good_one_sec_locs1) < kk(j+1))));
end
particle_areaRatio1(i,j) = nanmean(good_ar1(good_one_sec_locs1(good_particle_diameter1(good_one_sec_locs1) >= kk(j) &...
good_particle_diameter1(good_one_sec_locs1) < kk(j+1))));
cip2_iwc(i,j) = nansum(good_iwc(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
cip2_partarea(i,j) = nansum(good_partarea(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
cip2_iwcbl(i,j) = nansum(good_iwcbl(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
cip2_vt(i,j) = nansum(good_vt(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
cip2_pr(i,j) = nansum(good_pr(good_one_sec_locs(good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1))));
for k = 1:length(area_bins)-1
area_dist2(i,j,k) = length(find(good_ar(good_one_sec_locs) >= area_bins(k) & ...
good_ar(good_one_sec_locs) < area_bins(k+1) & good_particle_diameter(good_one_sec_locs) >= kk(j) &...
good_particle_diameter(good_one_sec_locs) < kk(j+1)));
end
end
% Normalize by binwidth and convert from mm to cm
particle_dist_minR(i,:)=particle_dist_minR(i,:)./binwidth*10;
particle_dist_AreaR(i,:)=particle_dist_AreaR(i,:)./binwidth*10;
cip2_iwc(i,:)=cip2_iwc(i,:)./binwidth*10; %g/cm
cip2_iwcbl(i,:)=cip2_iwcbl(i,:)./binwidth*10;
cip2_vt(i,:)=cip2_vt(i,:)./binwidth*10;
cip2_pr(i,:)=cip2_pr(i,:)./binwidth*10;
cip2_partarea(i,:)=cip2_partarea(i,:)./binwidth*10;
particle_area(i,:)=particle_area(i,:)./binwidth*10;
for mmmmmm=1:10
cip2_habitsd(i,:,mmmmmm)=cip2_habitsd(i,:,mmmmmm)./binwidth*10;
cip2_habitmsd(i,:,mmmmmm)=cip2_habitmsd(i,:,mmmmmm)./binwidth*10;
end
for mmmmmm = 1:length(area_bins)-1
area_dist2(i,:,mmmmmm) =area_dist2(i,:,mmmmmm)./binwidth*10 ;
end
% Generalized effective radius calculation from Fu (1996)
cip2_re(i) = (sqrt(3)/(3*0.91))*1000*(sum(cip2_iwc(i,:)./binwidth,2)/sum(particle_area(i,:)./binwidth,2))*1000; % in unit of um
else
particle_dist_minR(i,1:num_bins) = 0;
particle_dist_AreaR(i,1:num_bins) = 0;
area_dist2(i,1:num_bins,1:length(area_bins)-1) = 0;
cip2_partarea(i,:) = 0;
cip2_iwc(i,:) = 0;
cip2_iwcbl(i,:) = 0;
cip2_vt(i,:) = 0;
cip2_pr(i,:) = 0;
cip2_re(i) = 0;
cip2_habitsd(i,:,:) = 0;
cip2_habitmsd(i,:,:) = 0;
time_interval2(i) = 1;
% Legacy: used in Paris intercomparison
%{
time_interval22(i) = 1;
time_interval32(i) = 1;
time_interval42(i) = 1;
time_interval52(i) = 0;
time_interval62(i) = 1;
%}
time_interval72(i) = 0;
TotalPC1(i)=1;
TotalPC2(i)=1;
end
if iCreateBad == 1
%% Sort bad (rejected) particles into size distributions
bad_one_sec_locs = find(bad_image_times >= one_sec_times(i) & bad_image_times < one_sec_times(i+1));
bad_one_sec_locs1 = find(bad_image_times1 >= one_sec_times(i) & bad_image_times1 < one_sec_times(i+1));
badintpercent(i) = sum(bad_int_arr(bad_image_times >= one_sec_times(i) & bad_image_times < one_sec_times(i+1)))/time_interval2(i);
bad_one_sec_ar(i) = mean(bad_ar1(bad_one_sec_locs1));
if ~isempty(bad_one_sec_locs)
for j = 1:num_bins
bad_particle_dist_minR(i,j) = length(find(bad_particle_diameter_minR(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter_minR(bad_one_sec_locs) < kk(j+1)));
bad_particle_dist_AreaR(i,j) = length(find(bad_particle_diameter_AreaR(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter_AreaR(bad_one_sec_locs) < kk(j+1)));
% Create Habit Number Size Distribution
bad_cip2_habitsd(i,j,1) = length(find(bad_habit(bad_one_sec_locs)=='s' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,2) = length(find(bad_habit(bad_one_sec_locs)=='l' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,3) = length(find(bad_habit(bad_one_sec_locs)=='o' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,4) = length(find(bad_habit(bad_one_sec_locs)=='t' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,5) = length(find(bad_habit(bad_one_sec_locs)=='h' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,6) = length(find(bad_habit(bad_one_sec_locs)=='i' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,7) = length(find(bad_habit(bad_one_sec_locs)=='g' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,8) = length(find(bad_habit(bad_one_sec_locs)=='d' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,9) = length(find(bad_habit(bad_one_sec_locs)=='a' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitsd(i,j,10) = length(find(bad_habit(bad_one_sec_locs)=='I' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
% Create Habit Mass Size Distribution
bad_cip2_habitmsd(i,j,1) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='s' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,2) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='l' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,3) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='o' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,4) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='t' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,5) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='h' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,6) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='i' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,7) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='g' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,8) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='d' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,9) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='a' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_cip2_habitmsd(i,j,10) = sum(bad_iwc(bad_habit(bad_one_sec_locs)=='I' & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
bad_particle_area(i,j) = nansum(bad_area(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
bad_cip2_meanp(i,j) = nanmean(bad_perimeter(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
if iCreateAspectRatio == 1 % added if statement if not creating aspect ratio - Joe Finlon - 03/03/17
bad_particle_aspectRatio(i,j) = nanmean(bad_AspectRatio(bad_one_sec_locs1(bad_particle_diameter1(bad_one_sec_locs1) >= kk(j) &...
bad_particle_diameter1(bad_one_sec_locs1) < kk(j+1))));
bad_particle_aspectRatio1(i,j) = nanmean(bad_AspectRatio1(bad_one_sec_locs1(bad_particle_diameter1(bad_one_sec_locs1) >= kk(j) &...
bad_particle_diameter1(bad_one_sec_locs1) < kk(j+1))));
end
bad_particle_areaRatio1(i,j) = nanmean(bad_ar1(bad_one_sec_locs1(bad_particle_diameter1(bad_one_sec_locs1) >= kk(j) &...
bad_particle_diameter1(bad_one_sec_locs1) < kk(j+1))));
bad_cip2_iwc(i,j) = nansum(bad_iwc(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
bad_cip2_partarea(i,j) = nansum(bad_partarea(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
bad_cip2_iwcbl(i,j) = nansum(bad_iwcbl(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
bad_cip2_vt(i,j) = nansum(bad_vt(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
bad_cip2_pr(i,j) = nansum(bad_pr(bad_one_sec_locs(bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1))));
for k = 1:length(area_bins)-1
bad_area_dist2(i,j,k) = length(find(bad_ar(bad_one_sec_locs) >= area_bins(k) & ...
bad_ar(bad_one_sec_locs) < area_bins(k+1) & bad_particle_diameter(bad_one_sec_locs) >= kk(j) &...
bad_particle_diameter(bad_one_sec_locs) < kk(j+1)));
end
end
% Normalize by binwidth and convert from mm to cm
bad_particle_dist_minR(i,:)=bad_particle_dist_minR(i,:)./binwidth*10;
bad_particle_dist_AreaR(i,:)=bad_particle_dist_AreaR(i,:)./binwidth*10;
bad_cip2_iwc(i,:)=bad_cip2_iwc(i,:)./binwidth*10; %g/cm
bad_cip2_iwcbl(i,:)=bad_cip2_iwcbl(i,:)./binwidth*10;
bad_cip2_vt(i,:)=bad_cip2_vt(i,:)./binwidth*10;
bad_cip2_pr(i,:)=bad_cip2_pr(i,:)./binwidth*10;
bad_cip2_partarea(i,:)=bad_cip2_partarea(i,:)./binwidth*10;
bad_particle_area(i,:)=bad_particle_area(i,:)./binwidth*10;
for mmmmmm=1:10
bad_cip2_habitsd(i,:,mmmmmm)=bad_cip2_habitsd(i,:,mmmmmm)./binwidth*10;
bad_cip2_habitmsd(i,:,mmmmmm)=bad_cip2_habitmsd(i,:,mmmmmm)./binwidth*10;
end
for mmmmmm = 1:length(area_bins)-1
bad_area_dist2(i,:,mmmmmm)=bad_area_dist2(i,:,mmmmmm)./binwidth*10 ;
end
% Generalized effective radius calculation from Fu (1996)
bad_cip2_re(i) = (sqrt(3)/(3*0.91))*1000*(sum(bad_cip2_iwc(i,:)./binwidth,2)/sum(bad_particle_area(i,:)./binwidth,2))*1000; % in unit of um
else
bad_particle_dist_minR(i,1:num_bins) = 0;
bad_particle_dist_AreaR(i,1:num_bins) = 0;
bad_area_dist2(i,1:num_bins,1:length(area_bins)-1) = 0;
bad_cip2_partarea(i,:) = 0;
bad_cip2_iwc(i,:) = 0;
bad_cip2_iwcbl(i,:) = 0;
bad_cip2_vt(i,:) = 0;
bad_cip2_pr(i,:) = 0;
bad_cip2_re(i) = 0;
bad_cip2_habitsd(i,:,:) = 0;
bad_cip2_habitmsd(i,:,:) = 0;
end
end
warning on all
elseif (int32(timehhmmss(i))<int32(starttime(jjj)))
particle_dist_minR(i,1:num_bins) = NaN;
particle_dist_AreaR(i,1:num_bins) = NaN;
area_dist2(i,1:num_bins,1:length(area_bins)-1) = NaN;
cip2_partarea(i,:) = NaN;
cip2_iwc(i,:) = NaN;
cip2_iwcbl(i,:) = NaN;
cip2_vt(i,:) = NaN;
cip2_pr(i,:) = NaN;
cip2_re(i) = NaN;
cip2_habitsd(i,:,:) = NaN;
cip2_habitmsd(i,:,:) = NaN;
one_sec_ar(i) = NaN;
good_partpercent(i)=1;
rejectpercentbycriterion(i,:)=NaN;
numGoodparticles(i)=NaN;
time_interval2(i) = 1;
% Legacy: used in Paris intercomparison
%{
time_interval22(i) = 1;
time_interval32(i) = 1;
time_interval42(i) = 1;
time_interval52(i) = 0;
time_interval62(i) = 1;
%}
time_interval72(i) = 0;
TotalPC1(i)=1;
TotalPC2(i)=1;
end
end
% Finished Sorting and close input file.
netcdf.close(f);
fprintf('int_arr > 1 mean: %.4f, max: %.4f\nNumber of particles with int_arr > 1: %d\n\n',...
mean(intArrGT1),max(intArrGT1),sumIntArrGT1);
fprintf('Size distribution calculations and sorting completed %s\n\n', datestr(now));
%% Check TAS length, should be the same
% if (jjj~=length(start_all))
% disp([jjj, length(start_all)])
% %error('Watch Out for less TAS time at the end!')
% end
%disp([num2str(100*nThrow11/maxRecNum),'% is thrown out IN TOTAL']);
%% Combine - calculate sample volumes, and divide by sample volumes
% Modified by Will, Nov 27th, 2013. For flexible bins
cip2_binmin = kk(1:end-1);
cip2_binmax = kk(2:end);
cip2_binmid = (cip2_binmin+cip2_binmax)/2;
cip2_bindD = diff(kk);
% Legacy bin and surface area calculations
%{
% cip2_binmin = diodesize/2:diodesize:(num_bins-0.5)*diodesize; %(12.5:25:(num_bins-0.5)*25);
% cip2_binmax = 3*diodesize/2:diodesize:(num_bins+0.5)*diodesize; %(37.5:25:(num_bins+0.5)*25);
% cip2_binmid = diodesize:diodesize:num_bins*diodesize; %(25:25:num_bins*25);
% cip2_bindD = diodesize*ones(1,num_bins);
% sa2 = calc_sa(num_bins,res,armdst,num_bins); %mm2
% switch probename
% case 'PIP'
% sa2 = calc_sa_randombins_PIP(cip2_binmid,res,armdst,num_diodes, SAmethod); %(bins_mid,res,armdst,num_diodes)
% case '2DS'
% sa2 = calc_sa_randombins(cip2_binmid,res,armdst,num_diodes, SAmethod); %(bins_mid,res,armdst,num_diodes)
% end
%}
sa2 = calc_sa_randombins(cip2_binmid,res,armdst,num_diodes,SAmethod, probetype); %(bins_mid,res,armdst,num_diodes)
% Clocking problem correction
vol_scale_factor = tas/tasMax;
vol_scale_factor(vol_scale_factor < 1) = 1;
TotalPC2_pre = TotalPC2;
if probetype==2
time_interval200=1-time_interval72';
elseif probetype==1
% Correct offset in probe particle count (TotalPC2) when we have negative values
TotalPC2(TotalPC2<0)=TotalPC2(TotalPC2<0)+2^16;
% Derive a linear scale factor based on the difference between number of images (TotalPC1)
% and number of particles counted by the probe (TotalPC2).
time_interval199=(TotalPC1./TotalPC2)';
elseif 0==probetype
time_interval200=1-time_interval72';
end
% Experimental - Use with care!
% It was discovered that for data collected during the PECAN project, there were quite
% a few periods of time when the number of images we had for a 1-sec period of time was
% up to twice that of the number of particles the probe counted.
% This next if-statement contains code to find and change these instances to 1, resolving
% the far exaggerated concentrations that resulted otherwise.
if probetype==1
TotalPCerrIx = find(time_interval199 > 1);
time_interval200 = time_interval199;
time_interval200(TotalPCerrIx) = 1;
fprintf(['Total image count exceeded probe particle count %d times\ntime_interval200',...
' was set to 1 in these cases. See TotalPCerrIx variable for indices of occurence.\n\n'],...
length(TotalPCerrIx)); % moved inside if statement - Joe Finlon - 03/03/17
end
for j=1:num_bins
% Sample volume is in m-3
% svol_old(j,:)=dof/100.*sa/100.*tas;
svol2(j,:) = sa2(j)*(1e-3)^2*time_interval200.*tas; %m3 .*vol_scale_factor
end
svol2 = svol2*100^3; %cm3
for j = 1:10
svol2a(:,:,j) = svol2';
end
% Good (accepted) particles
cip2_conc_minR = particle_dist_minR./svol2';
cip2_conc_AreaR = particle_dist_AreaR./svol2';
cip2_area = particle_area./svol2';
cip2_partarea = cip2_partarea./svol2';
cip2_iwc = cip2_iwc./svol2';
cip2_iwcbl = cip2_iwcbl./svol2';
cip2_vt = cip2_vt./svol2';
cip2_pr = cip2_pr./svol2';
cip2_countP_no = particle_dist_minR.*repmat(binwidth,[length(tas) 1])/10; % un-normalized by binwitdh - Joe Finlon - 03/03/17
cip2_conc_areaDist = permute(double(area_dist2)./svol2a, [3 2 1]);
cip2_n = nansum(cip2_conc_minR.*repmat(binwidth,[length(tas) 1]),2)/10; % un-normalized by binwitdh & converted to cm^-3 - Joe Finlon - 03/03/17
cip2_lwc = lwc_calc(cip2_conc_minR,cip2_binmid);
% Bad (rejected) particles
bad_cip2_conc_minR = bad_particle_dist_minR./svol2';
bad_cip2_conc_AreaR = bad_particle_dist_AreaR./svol2';
bad_cip2_area = bad_particle_area./svol2';
bad_cip2_partarea = bad_cip2_partarea./svol2';
bad_cip2_iwc = bad_cip2_iwc./svol2';
bad_cip2_iwcbl = bad_cip2_iwcbl./svol2';
bad_cip2_vt = bad_cip2_vt./svol2';
bad_cip2_pr = bad_cip2_pr./svol2';
bad_cip2_countP_no = bad_particle_dist_minR.*repmat(binwidth,[length(tas) 1])/10; % un-normalized by binwitdh - Joe Finlon - 03/03/17
bad_cip2_conc_areaDist = permute(double(bad_area_dist2)./svol2a, [3 2 1]);
bad_cip2_n = nansum(bad_cip2_conc_minR.*repmat(binwidth,[length(tas) 1]),2)/10; % un-normalized by binwitdh & converted to cm^-3 - Joe Finlon - 03/03/17
bad_cip2_lwc = lwc_calc(bad_cip2_conc_minR,cip2_binmid);
%% Output results into NETCDF files (mainf)
fprintf('Now writing output files %s\n\n',datestr(now));
if applyIntArrThresh
save([outfile(1:end-3) 'noShatters.mat']);
else
save([outfile(1:end-3) 'withShatters.mat']);
end
% Define Dimensions
dimid0 = netcdf.defDim(mainf,'CIPcorrlen',num_bins);
dimid1 = netcdf.defDim(mainf,'CIParealen',10);
dimid2 = netcdf.defDim(mainf,'Time',length(timehhmmss));
dimid3 = netcdf.defDim(mainf,'Habit',10);
% Define Variables
varid0 = netcdf.defVar(mainf,'time','double',dimid2);
netcdf.putAtt(mainf, varid0,'units','HHMMSS');
netcdf.putAtt(mainf, varid0,'name','Time');
varid1 = netcdf.defVar(mainf,'bin_min','double',dimid0);
netcdf.putAtt(mainf, varid1,'units','millimeter');
netcdf.putAtt(mainf, varid1,'long_name','bin minimum size');
netcdf.putAtt(mainf, varid1,'short_name','bin min');
varid2 = netcdf.defVar(mainf,'bin_max','double',dimid0);
netcdf.putAtt(mainf, varid2,'units','millimeter');
netcdf.putAtt(mainf, varid2,'long_name','bin maximum size');
netcdf.putAtt(mainf, varid2,'short_name','bin max');
varid3 = netcdf.defVar(mainf,'bin_mid','double',dimid0);
netcdf.putAtt(mainf, varid3,'units','millimeter');
netcdf.putAtt(mainf, varid3,'long_name','bin midpoint size');
netcdf.putAtt(mainf, varid3,'short_name','bin mid');
varid4 = netcdf.defVar(mainf,'bin_dD','double',dimid0);
netcdf.putAtt(mainf, varid4,'units','millimeter');
netcdf.putAtt(mainf, varid4,'long_name','bin size');
netcdf.putAtt(mainf, varid4,'short_name','bin size');
% Good (accepted) particles
varid5 = netcdf.defVar(mainf,'conc_minR','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid5,'units','cm-4');
netcdf.putAtt(mainf, varid5,'long_name','Size distribution using Dmax');
netcdf.putAtt(mainf, varid5,'short_name','N(Dmax)');
varid6 = netcdf.defVar(mainf,'area','double',[dimid1 dimid0 dimid2]);
netcdf.putAtt(mainf, varid6,'units','cm-4');
netcdf.putAtt(mainf, varid6,'long_name','binned area ratio');
netcdf.putAtt(mainf, varid6,'short_name','binned area ratio');
varid7 = netcdf.defVar(mainf,'conc_AreaR','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid7,'units','cm-4');
netcdf.putAtt(mainf, varid7,'long_name','Size distribution using area-equivalent Diameter');
netcdf.putAtt(mainf, varid7,'short_name','N(Darea)');
varid8 = netcdf.defVar(mainf,'n','double',dimid2);
netcdf.putAtt(mainf, varid8,'units','cm-3');
netcdf.putAtt(mainf, varid8,'long_name','number concentration');
netcdf.putAtt(mainf, varid8,'short_name','N');
varid9 = netcdf.defVar(mainf,'total_area','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid9,'units','mm2/cm4');
netcdf.putAtt(mainf, varid9,'long_name','projected area (extinction)');
netcdf.putAtt(mainf, varid9,'short_name','Ac');
varid10 = netcdf.defVar(mainf,'mass','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid10,'units','g/cm4');
netcdf.putAtt(mainf, varid10,'long_name','mass using m-D relations');
netcdf.putAtt(mainf, varid10,'short_name','mass');
varid11 = netcdf.defVar(mainf,'habitsd','double',[dimid3 dimid0 dimid2]);
netcdf.putAtt(mainf, varid11,'units','cm-4');
netcdf.putAtt(mainf, varid11,'long_name','Size Distribution with Habit');
netcdf.putAtt(mainf, varid11,'short_name','habit SD');
varid12 = netcdf.defVar(mainf,'re','double',dimid2);
netcdf.putAtt(mainf, varid12,'units','mm');
netcdf.putAtt(mainf, varid12,'long_name','effective radius');
netcdf.putAtt(mainf, varid12,'short_name','Re');
varid13 = netcdf.defVar(mainf,'ar','double',dimid2);
netcdf.putAtt(mainf, varid13,'units','100/100');
netcdf.putAtt(mainf, varid13,'long_name','Area Ratio');
netcdf.putAtt(mainf, varid13,'short_name','AR');
varid14 = netcdf.defVar(mainf,'massBL','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid14,'units','g/cm4');
netcdf.putAtt(mainf, varid14,'long_name','mass using Baker and Lawson method');
netcdf.putAtt(mainf, varid14,'short_name','mass_BL');
varid15 = netcdf.defVar(mainf,'Reject_ratio','double',dimid2);
netcdf.putAtt(mainf, varid15,'units','100/100');
netcdf.putAtt(mainf, varid15,'long_name','Reject Ratio');
varid16 = netcdf.defVar(mainf,'vt','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid16,'units','g/cm4');
netcdf.putAtt(mainf, varid16,'long_name','Mass-weighted terminal velocity');
varid17 = netcdf.defVar(mainf,'Prec_rate','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid17,'units','mm/hr');
netcdf.putAtt(mainf, varid17,'long_name','Precipitation Rate');
varid18 = netcdf.defVar(mainf,'habitmsd','double',[dimid3 dimid0 dimid2]);
netcdf.putAtt(mainf, varid18,'units','g/cm-4');
netcdf.putAtt(mainf, varid18,'long_name','Mass Size Distribution with Habit');
netcdf.putAtt(mainf, varid18,'short_name','Habit Mass SD');
varid19 = netcdf.defVar(mainf,'Calcd_area','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid19,'units','mm^2/cm4');
netcdf.putAtt(mainf, varid19,'long_name','Particle Area Calculated using A-D realtions');
netcdf.putAtt(mainf, varid19,'short_name','Ac_calc');
varid20 = netcdf.defVar(mainf,'count','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid20,'units','1');
netcdf.putAtt(mainf, varid20,'long_name','number count for partial images without any correction');
if iCreateAspectRatio == 1
varid21 = netcdf.defVar(mainf,'mean_aspect_ratio_rectangle','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid21,'units','1');
netcdf.putAtt(mainf, varid21,'long_name','Aspect Ratio by Rectangle fit');
varid22 = netcdf.defVar(mainf,'mean_aspect_ratio_ellipse','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid22,'units','1');
netcdf.putAtt(mainf, varid22,'long_name','Aspect Ratio by Ellipse fit');
end
varid23 = netcdf.defVar(mainf,'mean_area_ratio','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid23,'units','1');
netcdf.putAtt(mainf, varid23,'long_name','Area Ratio');
varid24 = netcdf.defVar(mainf,'mean_perimeter','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid24,'units','um');
netcdf.putAtt(mainf, varid24,'long_name','mean perimeter');
if iCreateBad == 1
% Bad (rejected) particles
varid25 = netcdf.defVar(mainf,'REJ_conc_minR','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid25,'units','cm-4');
netcdf.putAtt(mainf, varid25,'long_name','Size distribution of rejected particles using Dmax');
netcdf.putAtt(mainf, varid25,'short_name','N(Dmax) rejected');
varid26 = netcdf.defVar(mainf,'REJ_area','double',[dimid1 dimid0 dimid2]);
netcdf.putAtt(mainf, varid26,'units','cm-4');
netcdf.putAtt(mainf, varid26,'long_name','binned area ratio of rejected particles');
netcdf.putAtt(mainf, varid26,'short_name','binned area ratio of rejected particles');
varid27 = netcdf.defVar(mainf,'REJ_conc_AreaR','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid27,'units','cm-4');
netcdf.putAtt(mainf, varid27,'long_name','Size distribution of rejected particles using area-equivalent Diameter');
netcdf.putAtt(mainf, varid27,'short_name','N(Darea) rejected');
varid28 = netcdf.defVar(mainf,'REJ_n','double',dimid2);
netcdf.putAtt(mainf, varid28,'units','cm-3');
netcdf.putAtt(mainf, varid28,'long_name','number concentration of rejected particles');
netcdf.putAtt(mainf, varid28,'short_name','N_rejected');
varid29 = netcdf.defVar(mainf,'REJ_total_area','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid29,'units','mm2/cm4');
netcdf.putAtt(mainf, varid29,'long_name','projected area (extinction) of rejected particles');
netcdf.putAtt(mainf, varid29,'short_name','Ac_rejected');
varid30 = netcdf.defVar(mainf,'REJ_mass','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid30,'units','g/cm4');
netcdf.putAtt(mainf, varid30,'long_name','mass of rejected particles using m-D relations');
netcdf.putAtt(mainf, varid30,'short_name','mass_rejected');
varid31 = netcdf.defVar(mainf,'REJ_habitsd','double',[dimid3 dimid0 dimid2]);
netcdf.putAtt(mainf, varid31,'units','cm-4');
netcdf.putAtt(mainf, varid31,'long_name','Size Distribution with Habit of rejected particles');
netcdf.putAtt(mainf, varid31,'short_name','habit SD rejected');
varid32 = netcdf.defVar(mainf,'REJ_re','double',dimid2);
netcdf.putAtt(mainf, varid32,'units','mm');
netcdf.putAtt(mainf, varid32,'long_name','effective radius of rejected particles');
netcdf.putAtt(mainf, varid32,'short_name','Re_rejected');
varid33 = netcdf.defVar(mainf,'REJ_ar','double',dimid2);
netcdf.putAtt(mainf, varid33,'units','100/100');
netcdf.putAtt(mainf, varid33,'long_name','Area Ratio of rejected particles');
netcdf.putAtt(mainf, varid33,'short_name','AR_rejected');
varid34 = netcdf.defVar(mainf,'REJ_massBL','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid34,'units','g/cm4');
netcdf.putAtt(mainf, varid34,'long_name','mass of rejected particles using Baker and Lawson method');
netcdf.putAtt(mainf, varid34,'short_name','mass_BL_rejected');
varid35 = netcdf.defVar(mainf,'REJ_vt','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid35,'units','g/cm4');
netcdf.putAtt(mainf, varid35,'long_name','Mass-weighted terminal velocity of rejected particles');
varid36 = netcdf.defVar(mainf,'REJ_Prec_rate','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid36,'units','mm/hr');
netcdf.putAtt(mainf, varid36,'long_name','Precipitation Rate of rejected particles');
varid37 = netcdf.defVar(mainf,'REJ_habitmsd','double',[dimid3 dimid0 dimid2]);
netcdf.putAtt(mainf, varid37,'units','g/cm-4');
netcdf.putAtt(mainf, varid37,'long_name','Mass Size Distribution with Habit of rejected particles');
netcdf.putAtt(mainf, varid37,'short_name','Habit Mass SD rejected');
varid38 = netcdf.defVar(mainf,'REJ_Calcd_area','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid38,'units','mm^2/cm4');
netcdf.putAtt(mainf, varid38,'long_name','Particle Area of rejected particles Calculated using A-D realtions');
netcdf.putAtt(mainf, varid38,'short_name','Ac_calc_rejected');
varid39 = netcdf.defVar(mainf,'REJ_count','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid39,'units','1');
netcdf.putAtt(mainf, varid39,'long_name','number count of rejected particles for partial images without any correction');
varid40 = netcdf.defVar(mainf,'REJ_mean_aspect_ratio_rectangle','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid40,'units','1');
netcdf.putAtt(mainf, varid40,'long_name','Aspect Ratio of rejected particles by Rectangle fit');
varid41 = netcdf.defVar(mainf,'REJ_mean_aspect_ratio_ellipse','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid41,'units','1');
netcdf.putAtt(mainf, varid41,'long_name','Aspect Ratio of rejected particles by Ellipse fit');
varid42 = netcdf.defVar(mainf,'REJ_mean_area_ratio','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid42,'units','1');
netcdf.putAtt(mainf, varid42,'long_name','Area Ratio of rejected particles');
varid43 = netcdf.defVar(mainf,'REJ_mean_perimeter','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid43,'units','um');
netcdf.putAtt(mainf, varid43,'long_name','mean perimeter of rejected particles');
end
if iSaveIntArrSV == 1
varid44 = netcdf.defVar(mainf,'sum_IntArr','double',dimid2);
netcdf.putAtt(mainf, varid44,'units','s');
netcdf.putAtt(mainf, varid44,'long_name','sum of inter-arrival times, excluding the overload time for particles affected by saving of image data');
varid45 = netcdf.defVar(mainf,'sample_vol','double',[dimid0 dimid2]);
netcdf.putAtt(mainf, varid45,'units','cm^3');
netcdf.putAtt(mainf, varid45,'long_name','sample volume for each bin');
end
netcdf.endDef(mainf)
% Output Variables
netcdf.putVar ( mainf, varid0, timehhmmss );
netcdf.putVar ( mainf, varid1, cip2_binmin );
netcdf.putVar ( mainf, varid2, cip2_binmax );
netcdf.putVar ( mainf, varid3, cip2_binmid );
netcdf.putVar ( mainf, varid4, cip2_bindD );
% Good (accepted) particles
netcdf.putVar ( mainf, varid5, double(cip2_conc_minR') );
netcdf.putVar ( mainf, varid6, double(cip2_conc_areaDist));
netcdf.putVar ( mainf, varid7, double(cip2_conc_AreaR') );
netcdf.putVar ( mainf, varid8, double(cip2_n));
netcdf.putVar ( mainf, varid9, double(cip2_area'));
netcdf.putVar ( mainf, varid10, double(cip2_iwc'));
netcdf.putVar ( mainf, varid11, permute(double(cip2_habitsd)./double(svol2a), [3 2 1]) );
netcdf.putVar ( mainf, varid12, cip2_re );
netcdf.putVar ( mainf, varid13, one_sec_ar );
netcdf.putVar ( mainf, varid14, double(cip2_iwcbl') );
netcdf.putVar ( mainf, varid15, 1-good_partpercent );
netcdf.putVar ( mainf, varid16, double(cip2_vt') );
netcdf.putVar ( mainf, varid17, double(cip2_pr') );
netcdf.putVar ( mainf, varid18, permute(double(cip2_habitmsd)./double(svol2a), [3 2 1]) );
netcdf.putVar ( mainf, varid19, double(cip2_partarea'));
netcdf.putVar ( mainf, varid20, double(cip2_countP_no'));
if iCreateAspectRatio == 1
netcdf.putVar ( mainf, varid21, particle_aspectRatio);
netcdf.putVar ( mainf, varid22, particle_aspectRatio1);
end
netcdf.putVar ( mainf, varid23, double(particle_areaRatio1));
netcdf.putVar ( mainf, varid24, double(cip2_meanp'));
if iCreateBad == 1
% Bad (rejected) particles
netcdf.putVar ( mainf, varid25, double(bad_cip2_conc_minR') );
netcdf.putVar ( mainf, varid26, double(bad_cip2_conc_areaDist));
netcdf.putVar ( mainf, varid27, double(bad_cip2_conc_AreaR') );
netcdf.putVar ( mainf, varid28, double(bad_cip2_n));
netcdf.putVar ( mainf, varid29, double(bad_cip2_area'));
netcdf.putVar ( mainf, varid30, double(bad_cip2_iwc'));
netcdf.putVar ( mainf, varid31, permute(double(bad_cip2_habitsd)./double(svol2a), [3 2 1]) );
netcdf.putVar ( mainf, varid32, bad_cip2_re );
netcdf.putVar ( mainf, varid33, bad_one_sec_ar );
netcdf.putVar ( mainf, varid34, double(bad_cip2_iwcbl') );
netcdf.putVar ( mainf, varid35, double(bad_cip2_vt') );
netcdf.putVar ( mainf, varid36, double(bad_cip2_pr') );
netcdf.putVar ( mainf, varid37, permute(double(bad_cip2_habitmsd)./double(svol2a), [3 2 1]) );
netcdf.putVar ( mainf, varid38, double(bad_cip2_partarea'));
netcdf.putVar ( mainf, varid39, double(bad_cip2_countP_no'));
netcdf.putVar ( mainf, varid40, double(bad_particle_aspectRatio));
netcdf.putVar ( mainf, varid41, double(bad_particle_aspectRatio1));
netcdf.putVar ( mainf, varid42, double(bad_particle_areaRatio1));
netcdf.putVar ( mainf, varid43, double(bad_cip2_meanp'));
end
if iSaveIntArrSV == 1
% Inter-arrival time and sample volume information
netcdf.putVar ( mainf, varid44, time_interval200');
netcdf.putVar ( mainf, varid45, svol2);
end
netcdf.close(mainf) % Close output NETCDF file
fprintf('sizeDist_Paris.m script completed %s\n',datestr(now));
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
single_vt.m
|
.m
|
UW-UIOPS-master/size_dist/single_vt.m
| 1,440 |
utf_8
|
6d3b5eb59b4c5ec6a86a3aa87c2bead2
|
%% Returns terminal velocity for a single particle
% Both options to calculate the terminal velocity
% Default is to use the Heymsfield and Westbrook (2010) method,
% but you can also choose to to use Mitchel (1996)
% Created by Will Wu, 2014/01/15
% - Mass and Diameter uses metric system
% - Pressure use hPa
% - Temperature use Celsius
function vt = single_vt(diameter, area_ratio, mass, P, T)
usingMithcell=0; % Setting 0 to use Heymsfield method, other value for Mitchell method
g=9.8;
pi=3.1415926;
% Calculate environmental conditions
T = T + 273.15;
P = P*100;
rho_a = P/(287.15*T);
eta = 18.27*(291.15+120)./(T+120)*(T/291.15)^(3/2)/10^(6); % Sutherland's formula to calculate the dynamic viscosity
nu = eta/rho_a; % kinectic viscosity
if 0==usingMithcell
% Calculate modified Best Number using Heymsfield and Westbrook (2010).
% using drag C=0.35 and epsilon=8.0
X=rho_a/(eta^2)*8*mass*g/(pi*area_ratio^0.5);
ReynoldN=16*(sqrt(1+4*sqrt(X)/64/sqrt(0.35))-1)^2;
else
% Calculate modified Best Number using Mitchell (1996).
% This is actually a special case of Heymsfield and Westbrook (2010), with
% k=0, and here we use drag C=0.6 and epsilon=5.83
% We calculate from the original equations without using power law
% approximation
X=rho_a/(eta^2)*8*mass*g/(pi*area_ratio);
ReynoldN=5.83^2/4*(sqrt(1+4*sqrt(X)/(5.83^2)/sqrt(0.6))-1)^2;
end
vt=nu/diameter*ReynoldN;
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
dropsize.m
|
.m
|
UW-UIOPS-master/img_processing/dropsize.m
| 10,889 |
utf_8
|
eb28935658e58b45634f6c09cbd7a712
|
function [center_in,axis_ratio,diameter_circle_fit,diameter_horiz_chord,diameter_vert_chord,diameter_horiz_mean, diameter_spheroid]=...
dropsize(max_horizontal_length,max_vertical_length,image_area,largest_edge_touching,...
smallest_edge_touching,diode_size,corrected_horizontal_diode_size,number_diodes_in_array)
global max_vertical_chords max_horizontal_chords vertical_chords vertical_chord_equivalent_spherical_diameters...
horizontal_chords horizontal_chord_equivalent_spherical_diameters
%
%
% DROP SIZING FOR 2DP PROBES (equiv sph vol diam)
% one diode added to max_horizontal_length_in_pixels for missing first slice
% uncalculated size appears as zero; oversize as 9.1
%
%
% diameter_horz_chord
% d0 from horz chord (max_horizontal_length_in_pixels + 1)
% designed for sideways-looking probe
% but can be used for any probe orientation
% with center-in image of equil shape
%
%
% diameter_circle_fit
% Heimsfield-Parish CIRCLE FIT SIZES FOR 2-EDGE & 1-EDGE(CENTER OUT) IMAGES:
% d0 from horz chord (circle fit)
% designed for downward-looking probe
%
%
% diameter_vert_chord
% max_vertical_length_in_pixels & 2-CHORD SIZES FOR ENTIRE-IN IMAGES:
% d0 from vert chord (max_vertical_length_in_pixels)
% designed for sideways-looking probe
% optional size for entire-in images
%
%
% [If diam_vchord differs from diam_hchord, then drop is not equil shape]
%
% diameter_horz_mean
% d0 from mean horz chord [(xmax+1)*ymax]^0.5
% designed for downward-looking probe
% optional size for distorted entire-in images
%
% diameter_spheroid
% d0 from spheroid assumption (hchord*hchord*vchord)^1/3
% designed for sideways-looking probe
% optional size for distorted entire-in images
%
%
% AXIS RATIO
% axis_ratio = max_vertical_length_in_pixels / max_horizontal_length_in_pixels
% for entire-in image (no smoothing)
% center_in = (0 if particle is not center in, 1 if particle is center
% in)
max_horizontal_chords = 117;
max_vertical_chords = 55;
diodes_added_to_length = 0;
diodes_added_to_height = 1.0;
horizontal_chords = [...
0.0000, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000,...
1.0000, 1.1000, 1.2000, 1.3000, 1.4000, 1.5000, 1.6000, 1.7000, 1.8000, 1.9000,...
2.0000, 2.1000, 2.2000, 2.3000, 2.4000, 2.5000, 2.6000, 2.7000, 2.8000, 2.9000,...
3.0000, 3.1000, 3.2000, 3.3000, 3.4000, 3.5000, 3.6000, 3.7000, 3.8000, 3.9000,...
4.0000, 4.1000, 4.2000, 4.3000, 4.4000, 4.5000, 4.6000, 4.7000, 4.8000, 4.9000,...
5.0000, 5.1000, 5.2000, 5.3000, 5.4000, 5.5000, 5.6000, 5.7000, 5.8000, 5.9000,...
6.0000, 6.1000, 6.2000, 6.3000, 6.4000, 6.5000, 6.6000, 6.7000, 6.8000, 6.9000,...
7.0000, 7.1000, 7.2000, 7.3000, 7.4000, 7.5000, 7.6000, 7.7000, 7.8000, 7.9000,...
8.0000, 8.1000, 8.2000, 8.3000, 8.4000, 8.5000, 8.6000, 8.7000, 8.8000, 8.9000,...
9.0000, 9.1000, 9.2000, 9.3000, 9.4000, 9.5000, 9.6000, 9.7000, 9.8000, 9.9000,...
10.0000, 10.1000, 10.2000, 10.3000, 10.4000, 10.5000, 10.6000, 10.7000, 10.8000, 10.9000,...
11.0000, 11.1000, 11.2000, 11.3000, 11.4000, 11.5000, 11.6000];
horizontal_chord_equivalent_spherical_diameters = [...
0.0000, 0.1000, 0.2000, 0.3000, 0.3998, 0.4996, 0.5992, 0.6986, 0.7976, 0.8964,...
0.9947, 1.0927, 1.1903, 1.2875, 1.3842, 1.4804, 1.5761, 1.6711, 1.7657, 1.8597,...
1.9531, 2.0460, 2.1385, 2.2304, 2.3217, 2.4124, 2.5026, 2.5921, 2.6812, 2.7696,...
2.8574, 2.9448, 3.0319, 3.1185, 3.2046, 3.2903, 3.3755, 3.4603, 3.5446, 3.6286,...
3.7120, 3.7950, 3.8776, 3.9598, 4.0416, 4.1229, 4.2036, 4.2841, 4.3642, 4.4440,...
4.5233, 4.6024, 4.6811, 4.7597, 4.8378, 4.9152, 4.9924, 5.0690, 5.1452, 5.2210,...
5.2961, 5.3711, 5.4457, 5.5201, 5.5942, 5.6681, 5.7416, 5.8148, 5.8877, 5.9602,...
6.0323, 6.1041, 6.1756, 6.2467, 6.3175, 6.3879, 6.4580, 6.5278, 6.5973, 6.6664,...
6.7353, 6.8040, 6.8720, 6.9399, 7.0075, 7.0744, 7.1411, 7.2074, 7.2733, 7.3388,...
7.4040, 7.4688, 7.5332, 7.5973, 7.6610, 7.7241, 7.7864, 7.8490, 7.9117, 7.9739,...
8.0359, 8.0976, 8.1591, 8.2203, 8.2813, 8.3421, 8.4027, 8.4631, 8.5233, 8.5834,...
8.6433, 8.7030, 8.7625, 8.8220, 8.8814, 8.9407, 8.9998];
vertical_chords = [...
0.0000, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000,...
1.0000, 1.1000, 1.2000, 1.3000, 1.4000, 1.5000, 1.6000, 1.7000, 1.8000, 1.9000,...
2.0000, 2.1000, 2.2000, 2.3000, 2.4000, 2.5000, 2.6000, 2.7000, 2.8000, 2.9000,...
3.0000, 3.1000, 3.2000, 3.3000, 3.4000, 3.5000, 3.6000, 3.7000, 3.8000, 3.9000,...
4.0000, 4.1000, 4.2000, 4.3000, 4.4000, 4.5000, 4.6000, 4.7000, 4.8000, 4.9000,...
5.0000, 5.1000, 5.2000, 5.3000, 5.4000];
vertical_chord_equivalent_spherical_diameters = [...
0.0000, 0.1000, 0.2000, 0.3001, 0.4003, 0.5008, 0.6016, 0.7028, 0.8048, 0.9075,...
1.0110, 1.1155, 1.2208, 1.3271, 1.4348, 1.5438, 1.6545, 1.7668, 1.8809, 1.9967,...
2.1142, 2.2336, 2.3553, 2.4792, 2.6059, 2.7353, 2.8677, 3.0023, 3.1394, 3.2792,...
3.4219, 3.5678, 3.7169, 3.8695, 4.0259, 4.1865, 4.3512, 4.5201, 4.6922, 4.8687,...
5.0525, 5.2438, 5.4432, 5.6474, 5.8580, 6.0775, 6.3073, 6.5485, 6.8018, 7.0729,...
7.3699, 7.7007, 8.0781, 8.4868, 8.9230];
diameter_circle_fit = 0.0;
diameter_horiz_chord = 0.0;
diameter_vert_chord = 0.0;
diameter_horiz_mean = 0.0;
diameter_spheroid = 0.0;
axis_ratio = 0.0;
center_in = 0;
scaling_factor_for_horizontal_lengths = corrected_horizontal_diode_size / diode_size;
corrected_diodes_added_to_length = 0;
if(image_area < 1)
diameter_circle_fit = corrected_diodes_added_to_length;
diameter_horiz_chord = corrected_diodes_added_to_length;
diameter_vert_chord = corrected_diodes_added_to_length;
diameter_horiz_mean = corrected_diodes_added_to_length;
diameter_spheroid = corrected_diodes_added_to_length;
center_in=0;
axis_ratio = 1.0;
return;
end
largest_edge_touching_length = largest_edge_touching * scaling_factor_for_horizontal_lengths;
smallest_edge_touching_length = smallest_edge_touching * scaling_factor_for_horizontal_lengths;
max_horizontal_length = max_horizontal_length * scaling_factor_for_horizontal_lengths;
corrected_diodes_added_to_length = diodes_added_to_length * scaling_factor_for_horizontal_lengths;
% determine no. of edges
number_edges_touching = 0;
if largest_edge_touching > 0
number_edges_touching = number_edges_touching + 1;
if smallest_edge_touching > 0
number_edges_touching = number_edges_touching + 1;
end
elseif smallest_edge_touching > 0
number_edges_touching = number_edges_touching + 1;
end
center_in = 1;
if max_horizontal_length <= largest_edge_touching
center_in = 0;
end
if number_edges_touching == 2 & center_in == 1
temp = number_diodes_in_array + (largest_edge_touching_length^2 - smallest_edge_touching_length^2 ) / ( 4 * number_diodes_in_array); % + is replaced by -, Will 10/17/2013
horizontal_size = sqrt(temp^2 + smallest_edge_touching_length^2);
horizontal_chord = horizontal_size * diode_size;
diameter_circle_fit = horizontal_chord_to_spherical_dia(horizontal_chord);
diameter_horiz_chord = horizontal_chord_to_spherical_dia(horizontal_chord);
elseif number_edges_touching == 2
largest_edge_touching_length = largest_edge_touching_length + corrected_diodes_added_to_length;
temp = number_diodes_in_array + (largest_edge_touching_length^2 - smallest_edge_touching_length^2 ) / ( 4 * number_diodes_in_array); % + is replaced by -, Will 10/17/2013
horizontal_size = sqrt(temp^2 + smallest_edge_touching_length^2);
horizontal_chord = horizontal_size * diode_size;
diameter_circle_fit = horizontal_chord_to_spherical_dia(horizontal_chord);
elseif number_edges_touching == 1 & center_in == 1
horizontal_chord = (max_horizontal_length + diodes_added_to_length) * corrected_horizontal_diode_size;
diameter_horiz_chord = horizontal_chord_to_spherical_dia(horizontal_chord);
diameter_circle_fit = horizontal_chord_to_spherical_dia(horizontal_chord);
elseif number_edges_touching == 1
largest_edge_touching_length = largest_edge_touching_length + corrected_diodes_added_to_length;
max_vertical_length = max_vertical_length + diodes_added_to_height * 0.5;
horizontal_size = (0.25 * largest_edge_touching_length^2 + max_vertical_length^2)/(max_vertical_length);
horizontal_chord = horizontal_size * diode_size;
diameter_circle_fit = horizontal_chord_to_spherical_dia(horizontal_chord);
else
horizontal_chord = (max_horizontal_length + diodes_added_to_length) * corrected_horizontal_diode_size;
diameter_horiz_chord = horizontal_chord_to_spherical_dia(horizontal_chord);
diameter_circle_fit = horizontal_chord_to_spherical_dia(horizontal_chord);
vertical_chord = (max_vertical_length + diodes_added_to_length) * diode_size;
axis_ratio = vertical_chord / horizontal_chord;
diameter_vert_chord = vertical_chord_to_spherical_dia(vertical_chord);
horizontal_mean_chord = sqrt(horizontal_chord * vertical_chord);
diameter_horiz_mean = horizontal_chord_to_spherical_dia(horizontal_mean_chord);
diameter_spheroid = exp(log(horizontal_chord^2 * vertical_chord)/3);
end
end
function diameter=vertical_chord_to_spherical_dia(vertical_chord)
global max_vertical_chords max_horizontal_chords vertical_chords vertical_chord_equivalent_spherical_diameters...
horizontal_chords horizontal_chord_equivalent_spherical_diameters
delta_vertical_chord = .1;
i = round(vertical_chord * 10);
if i+1 < max_vertical_chords & i ~=0
delta_diameter = vertical_chord_equivalent_spherical_diameters(i+1) - vertical_chord_equivalent_spherical_diameters(i);
diameter = vertical_chord_equivalent_spherical_diameters(i) + (delta_diameter / delta_vertical_chord) * (vertical_chord - vertical_chords(i));
elseif i == 0
diameter = 0;
else
diameter = 9.1;
end
end
function diameter = horizontal_chord_to_spherical_dia(horizontal_chord)
global max_vertical_chords max_horizontal_chords vertical_chords vertical_chord_equivalent_spherical_diameters...
horizontal_chords horizontal_chord_equivalent_spherical_diameters
delta_horizontal_chord = .1;
i = round(horizontal_chord * 10);
if i+1 < max_horizontal_chords & i ~= 0
delta_diameter = horizontal_chord_equivalent_spherical_diameters(i+1) - horizontal_chord_equivalent_spherical_diameters(i);
diameter = horizontal_chord_equivalent_spherical_diameters(i) + (delta_diameter / delta_horizontal_chord) * (horizontal_chord - horizontal_chords(i));
elseif i == 0
diameter = 0;
else
diameter = 9.1;
end
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
ParticlePerimeter.m
|
.m
|
UW-UIOPS-master/img_processing/ParticlePerimeter.m
| 572 |
utf_8
|
a4d9612a610598df7fa2589693967992
|
% Get the single particle perimeter
%
% Inputs:
% image_buffer - n x photodiodes/8 raw image buffer without timestamps
% Outputs:
% Perimeter
%
% * Created by Wei Wu, July 4th, 2014
function [pperimeter] = ParticlePerimeter(image_buffer)
[m, n] = size(image_buffer);
pperimeter = 0;
c1=[49*ones(1,n+2);49*ones(m,1),image_buffer,49*ones(m,1);49*ones(1,n+2)];
for i=2:m+1
for j=2:n+1
if (48==c1(i,j) && ( 48~=c1(i+1,j) || 48~=c1(i-1,j) || 48~=c1(i,j+1) || 48~=c1(i,j-1) ) )
pperimeter = pperimeter+1;
end
end
end
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
holroyd.m
|
.m
|
UW-UIOPS-master/img_processing/holroyd.m
| 8,020 |
utf_8
|
fcd3dc3dbe5ec078354af14eacdcc6a4
|
% holroyd - identified particle habit according to Holroyd (1987)
% inputs:
% handles - handles structure outlined in run_img_processing.m
% image_buffer - n x photodiodes/8 raw image buffer without timestamps
% outputs:
% holroyd_habit - habit code as listed below
% 5/15/2017 -- it was discovered that certain threshold values were only
% appropriate for probes of 25 micron resolution so changes were made to
% accomodate probes of 10 micron resolution, documented below --Jacobson
% 6/6/2017 -- changes generalized and and edited for future expandability
% for other probes -Majewski
function [holroyd_habit] = holroyd(handles, image_buffer, probename)
%/***************************************************************/%
%/* Return code */
%/* */
%/* */
%/* reference: J. Atmos. and Oceanic Tech. Vol 4, Sept. '87 */
%/* pages 498- 511. */
%/* */
%/* 'M' = not calculated, zero image */
%/* 'C' = not calculated, center is out */
%/* 't' = tiny */
%/* 'o' = oriented */
%/* 'l' = linear */
%/* 'a' = aggregate */
%/* 'g' = graupel */
%/* 's' = spherel */
%/* 'h' = hexagonal */
%/* 'i' = irregular */
%/* 'd' = dendrite */
%/* */
%/***************************************************************/
switch probename
case '2DS'
probe_resolution = .010;
ol_d_length = 160.;
a_d_length = 400.;
ag_d_length = 160.;
gh_d_length = 80.;
id_x_length = 17.;
otherwise %This is actually the settings for the CIP probe, but as they were already hardcoded prior, they are the default option until more probes are added
probe_resolution = .025;
ol_d_length = 64.;
a_d_length = 160.;
ag_d_length = 64.;
gh_d_length = 32.;
id_x_length = 7.;
end
image_size = size(image_buffer);
n_slices = image_size(1);
if (n_slices == 0)
holroyd_habit = 'M';
return;
else
if (parabola_fit_center_is_in(image_buffer, n_slices) == 1)
[x_length, y_length, d_length, w_width,a_angle,area,r2_correlation, F_fine_detail, S_ratio] = calc_stat(handles,image_buffer, n_slices);
if (area == 0 )
holroyd_habit = 'M';
return;
elseif (area < 25)
holroyd_habit = 't';
return;
elseif (r2_correlation >= .4) || ( (d_length < ol_d_length) && ( (x_length >= 4*y_length) || (y_length >= 4*x_length)))
if ((a_angle> 30.0) && (a_angle < 60.0))
holroyd_habit = 'o';
return;
else
holroyd_habit = 'l';
return;
end
elseif ( (d_length * probe_resolution > 6.4 ) || (d_length > a_d_length))
holroyd_habit = 'a';
return;
elseif (S_ratio >= .7)
holroyd_habit = 'g';
return;
elseif (d_length >= ag_d_length)
if (F_fine_detail <= 13)
holroyd_habit = 'g';
return;
else
holroyd_habit = 'a';
return;
end
elseif (F_fine_detail < 5.5)
holroyd_habit = 's';
return;
elseif (F_fine_detail < 10.0)
if (d_length >= gh_d_length)
holroyd_habit = 'g';
return;
else
holroyd_habit = 'h';
return;
end
elseif ((F_fine_detail < 16.0) || (x_length <= id_x_length))
holroyd_habit = 'i';
return;
else
holroyd_habit = 'd';
return;
end
else
holroyd_habit = 'C';
return;
end
end
end
%/*************************************************************************/
%/*************************************************************************/
function [x_length, y_length, d_length, w_width,a_angle,area,r2_correlation, F_fine_detail, S_ratio] = calc_stat(handles, image_buffer, n_slices)
BITS_PER_SLICE = handles.bits_per_slice;
MAX_TWOD_DATA_LENGTH = 6000;
area = 0.0;
n_count = 0;
sum_x2= 0.0;
sum_y2= 0.0;
sum_x = 0.0;
sum_y = 0.0;
sum_xy= 0.0;
cross_x2= 0.0;
cross_y2= 0.0;
cross_xy= 0.0;
p_perimeter_change = 0;
min_x = MAX_TWOD_DATA_LENGTH*3;
min_y = BITS_PER_SLICE;
max_x = 0;
max_y = 0;
spot_on_off = 0;
fully_on_count = 0;
partial_on_count =0;
if (n_slices <= 0)
return;
end
for i=1:n_slices
fully_on_temp = 0;
for j=1:BITS_PER_SLICE
if ((image_buffer(i,j)) == '0')
tx = i;
ty = j;
if (tx > max_x)
max_x = tx;
end
if (tx < min_x)
min_x = tx;
end
if (ty > max_y)
max_y = ty;
end
if (ty < min_y)
min_y = ty;
end
sum_x2 = sum_x2 + tx * tx;
sum_y2 = sum_y2 + ty * ty;
sum_x = sum_x + tx;
sum_y = sum_y + ty;
sum_xy = sum_xy + tx * ty;
n_count = n_count + 1;
p(n_count).x = tx;
p(n_count).y = ty;
fully_on_temp = fully_on_temp + 1;
if (spot_on_off == 0)
spot_on_off = 1;
p_perimeter_change = p_perimeter_change + 1;
end
else
if spot_on_off == 1
spot_on_off = 0;
p_perimeter_change = p_perimeter_change + 1;
end
end
end
if (fully_on_temp == BITS_PER_SLICE)
fully_on_count = fully_on_count + 1;
end
if (fully_on_temp ~= 0)
partial_on_count = partial_on_count + 1;
end
end
area = n_count;
%/*** scan the other way for perimeter change ****/
spot_on_off = 0;
for j=1:BITS_PER_SLICE
for i=1:n_slices
if ((image_buffer(i,j)) == '0')
if (spot_on_off == 0)
spot_on_off = 1;
p_perimeter_change = p_perimeter_change + 1;
end
else
if (spot_on_off == 1)
spot_on_off = 0;
p_perimeter_change = p_perimeter_change + 1;
end
end
end
end
if (max_x >= min_x)
x_length = max_x - min_x +1;
else
x_length = 0.0;
end
if (max_y >= min_y)
y_length = max_y - min_y +1;
else
y_length = 0.0;
end
cross_xy = sum_xy - (sum_x * sum_y / area);
cross_x2 = sum_x2 - (sum_x * sum_x / area);
cross_y2 = sum_y2 - (sum_y * sum_y / area);
slope = cross_xy / cross_x2;
intercept = (sum_y/(area)) - slope * (sum_x/(area));
angle_radian = atan(slope);
a_angle = atan(slope) * (180.0/pi);
if (a_angle < 0)
a_angle = a_angle + 180.0;
angle_radian = angle_radian + pi;
end
dmin_x = MAX_TWOD_DATA_LENGTH*3;
dmin_y = BITS_PER_SLICE;
dmax_x = 0;
dmax_y = 0;
if ( (angle_radian > (pi/2.0)) & (angle_radian <= (pi)))
angle_radian = (pi - angle_radian);
elseif ( angle_radian > pi)
['HEY: something is wrong here a_angle = ', num2str(a_angle)];
return
end
for i=1:n_count
new_x = (p(i).x * cos(angle_radian)) + (p(i).y * sin(angle_radian));
new_y = (p(i).y * cos(angle_radian)) - (p(i).x * sin(angle_radian));
if (new_x > dmax_x)
dmax_x = new_x;
end
if (new_y > dmax_y)
dmax_y = new_y;
end
if (new_x < dmin_x)
dmin_x = new_x;
end
if (new_y < dmin_y)
dmin_y = new_y;
end
end
d_length = (dmax_x - dmin_x) +1;
w_width = (dmax_y - dmin_y) +1;
r2_correlation = (cross_xy) / (sqrt( cross_x2 * cross_y2));
F_fine_detail = p_perimeter_change * (d_length)/ area;
if (partial_on_count ~=0 )
S_ratio = fully_on_count / partial_on_count;
else
S_ratio = 0.0;
end
end
%/**************************************************************************/
%/**************************************************************************/
function result = parabola_fit_center_is_in(image_buffer, n_slices)
result = 1;
return;
end
|
github
|
jjjjfrench/UW-UIOPS-master
|
calculate_reject_unified.m
|
.m
|
UW-UIOPS-master/img_processing/calculate_reject_unified.m
| 20,696 |
utf_8
|
4438b2dee44f70bdd279914cf9ee9ec0
|
function [p_length,width,area,longest_y,max_top,max_bottom,touching_edge,reject_status,is_hollow,percent_shadow_area,part_z,size_factor,area_hole_ratio,handles]=calculate_reject_unified(image_buffer,handles,habit)
% /* RETURN CODE */
% /* 0 = not rejected */
% /* 'a' = reject max. aspect ratio */
% /* 't' = reject max. aspect ratio touch edg */
% /* 'p' = reject percent shadowed area */
% /* 'h' = reject Hollow image */
% /* 's' = reject split image */
% /* 'z' = reject 0 area image */
% /* 'f' = reject fake 0 area image */
z_d = 0 : .05 : 8.15;
part_z = -1;
size_factor = 1;
edge_0 = [1.000, 1.054, 1.083, 1.101, 1.095, 1.110, 1.148, 1.162, 1.155, 1.123, ...
1.182, 1.121, 1.162, 1.210, 1.242, 1.134, 1.166, 1.202, 1.238, 1.270, ...
1.294, 1.278, 1.130, 1.148, 1.170, 1.194, 1.218, 1.242, 1.265, 1.288, ...
1.310, 1.331, 1.351, 1.369, 1.386, 1.400, 1.411, 1.416, 1.407, 1.074, ...
1.080, 1.087, 1.096, 1.106, 1.117, 1.127, 1.139, 1.150, 1.162, 1.173, ...
1.185, 1.197, 1.208, 1.220, 1.232, 1.243, 1.255, 1.266, 1.277, 1.289, ...
1.300, 1.311, 1.322, 1.333, 1.344, 1.355, 1.366, 1.376, 1.387, 1.397, ...
1.407, 1.418, 1.428, 1.438, 1.448, 1.458, 1.467, 1.477, 1.486, 1.496, ...
1.505, 1.515, 1.524, 1.533, 1.542, 1.551, 1.559, 1.568, 1.577, 1.585, ...
1.594, 1.602, 1.610, 1.618, 1.626, 1.634, 1.642, 1.650, 1.657, 1.665, ...
1.673, 1.680, 1.687, 1.694, 1.702, 1.709, 1.716, 1.722, 1.729, 1.736, ...
1.742, 1.749, 1.755, 1.761, 1.768, 1.774, 1.780, 1.786, 1.791, 1.797, ...
1.803, 1.808, 1.813, 1.819, 1.824, 1.829, 1.834, 1.839, 1.843, 1.848, ...
1.852, 1.857, 1.861, 1.865, 1.869, 1.872, 1.876, 1.880, 1.883, 1.886, ...
1.889, 1.892, 1.895, 1.897, 1.899, 1.901, 1.903, 1.905, 1.906, 1.907, ...
1.908, 1.908, 1.908, 1.908, 1.907, 1.905, 1.903, 1.900, 1.897, 1.892, ...
1.885, 1.877, 1.865, 1.845];
spot_edge = [0.003, 0.008, 0.017, 0.024, 0.033, 0.040, 0.047, 0.054, 0.062, 0.072, ...
0.076, 0.088, 0.093, 0.096, 0.101, 0.119, 0.123, 0.127, 0.130, 0.134, ...
0.139, 0.148, 0.175, 0.180, 0.184, 0.188, 0.192, 0.195, 0.199, 0.202, ...
0.206, 0.209, 0.213, 0.217, 0.221, 0.225, 0.230, 0.235, 0.243, 0.327, ...
0.334, 0.340, 0.345, 0.351, 0.355, 0.360, 0.365, 0.369, 0.373, 0.377, ...
0.381, 0.385, 0.389, 0.393, 0.397, 0.400, 0.404, 0.408, 0.411, 0.415, ...
0.419, 0.422, 0.426, 0.429, 0.433, 0.436, 0.439, 0.443, 0.446, 0.450, ...
0.453, 0.457, 0.460, 0.463, 0.467, 0.470, 0.473, 0.477, 0.480, 0.484, ...
0.487, 0.490, 0.494, 0.497, 0.501, 0.504, 0.507, 0.511, 0.514, 0.518, ...
0.521, 0.525, 0.528, 0.532, 0.535, 0.539, 0.543, 0.547, 0.550, 0.554, ...
0.558, 0.562, 0.566, 0.569, 0.572, 0.575, 0.578, 0.581, 0.584, 0.587, ...
0.590, 0.593, 0.596, 0.598, 0.601, 0.605, 0.610, 0.614, 0.618, 0.623, ...
0.627, 0.631, 0.635, 0.640, 0.644, 0.648, 0.653, 0.657, 0.662, 0.666, ...
0.671, 0.676, 0.680, 0.685, 0.690, 0.695, 0.700, 0.705, 0.711, 0.716, ...
0.721, 0.727, 0.733, 0.738, 0.744, 0.751, 0757, 0.763, 0.770, 0.777, ...
0.784, 0.792, 0.800, 0.808, 0.817, 0.826, 0.836, 0.846, 0.858, 0.870, ...
0.884, 0.901, 0.921, 0.950];
% temp = [dec2bin(image_buffer(:,1),16),dec2bin(image_buffer(:,2),16),dec2bin(image_buffer(:,3),16),...
% dec2bin(image_buffer(:,4),16),dec2bin(image_buffer(:,5),16),dec2bin(image_buffer(:,6),16),...
% dec2bin(image_buffer(:,7),16),dec2bin(image_buffer(:,8),16)];
% clear image_buffer
% image_buffer(:,:)=temp(:,:);
n_size=size(image_buffer);
n_slices=n_size(1);
handles.rej_zero_area = 1;
handles.rej_split = 1;
handles.rej_hollow = 1;
handles.bits_per_slice = n_size(2);
handles.shadowed_area = 25;
handles.max_edge_img_ar = 6;
handles.max_comp_img_ar = 5;
handles.max_hole_diameter = 0;
handles.edge_at_max_hole = 0;
min_length=-1;
max_length=-1;
max_width=1;
min_width=n_size(2);
total_area=0;
touch=0;
width = 0;
ndrops=0;
split=0;
hollow=0;
met_image=0;
is_hollow=0;
aspect_ratio=0;
percent_shadow_area=0;
area_hole_ratio = 0;
area=0;
max_top=0;
max_bottom=0;
longest_y=0;
touching_edge=0;
top_min_x=-1;
top_max_x=-1;
bottom_min_x=-1;
bottom_max_x=-1;
if n_slices==0
p_length=0;
area=0;
if handles.rej_zero_area==1
reject_status='z';
return;
else
reject_status='0';
end
else
for i=1:n_slices
[min_pos_lite,max_pos_lite,n_lite]=scan_slice(image_buffer(i,:),handles);
if longest_y < n_lite
longest_y=n_lite;
end
if i>1
vertical_split=vertical_split & image_buffer(i,:);
else
vertical_split=image_buffer(i,:);
end
if n_lite>0
if touch==0
if max_pos_lite==handles.bits_per_slice || min_pos_lite==1
touch=1;
end
end
if min_pos_lite==1
if bottom_min_x==-1
bottom_min_x=i;
end
if bottom_max_x<i
bottom_max_x=i;
end
end
if max_pos_lite==handles.bits_per_slice
if top_min_x==-1
top_min_x=i;
end
if top_max_x<i
top_max_x=i;
end
end
if max_pos_lite > max_width
max_width=max_pos_lite;
end
if min_pos_lite < min_width
min_width=min_pos_lite;
end
if min_length == -1
min_length = i;
max_length = i;
else
max_length = i;
end
total_area=n_lite+total_area;
end
if met_image == 0 & n_lite > 0
met_image=1;
ndrops=ndrops+1;
if ndrops > 1 & handles.rej_split==1
split=1;
end
elseif met_image==1 & n_lite == 0
met_image=0;
end
end
area=total_area;
if top_min_x == -1
max_top = 0;
else
max_top = (top_max_x - top_min_x) + 1;
end
if bottom_min_x == -1
max_bottom = 0;
else
max_bottom = (bottom_max_x - bottom_min_x) + 1;
end
if touch == 1
touching_edge = 't';
else
touching_edge = '0';
end
if total_area == 0;
p_length = 0;
width = 0;
else
p_length = max_length - min_length + 1;
width = max_width - min_width + 1;
end
if total_area > .8 * handles.bits_per_slice * n_slices
reject_status = 'A';
return;
end
if split == 1
reject_status = 's';
return;
end
if exist('vertical_split') == 1
[min_pos_lite,max_pos_lite,n_lite]=scan_slice(vertical_split,handles);
else
min_pos_lite=0;
max_pos_lite=0;
n_lite=0;
end
if n_lite == 1 & n_lite ~= max_pos_lite - min_pos_lite + 1 & handles.rej_split == 0
reject_status = 's';
return;
end
if total_area > 0
if p_length > 0 & width > 0
aspect_ratio = p_length / width;
percent_shadow_area = total_area / (p_length * width ) * 100;
else
aspect_ratio = 0;
percent_shadow_area = 0;
end
else
aspect_ratio = 0;
percent_shadow_area = 0;
end
if total_area == 0 && handles.rej_zero_area == 1
reject_status = 'z';
return;
elseif total_area == 0 && handles.rej_zero_area == 0
reject_status = '0';
return;
elseif ( aspect_ratio > handles.max_comp_img_ar || aspect_ratio < 1/handles.max_comp_img_ar ) % Second critirion added on Dec 2nd, 2013 by Will for small aspect ratio
reject_status = 'a';
return;
elseif touch == 1 && aspect_ratio > handles.max_edge_img_ar
reject_status = 't';
return;
elseif percent_shadow_area < handles.shadowed_area
reject_status = 'p';
return;
elseif handles.rej_hollow == 1
[hollow_status,edge_at_max_hole,max_hole_diameter]=is_it_hollow(image_buffer(1:n_slices,:),n_slices,handles);
[hollow_status2,edge_at_max_hole2,max_hole_diameter2]=is_it_hollow(image_buffer(n_slices:-1:1,:),n_slices,handles);
[hollow_status_side1,edge_at_max_hole_side1,max_hole_diameter_side1]=is_it_hollow_sidescan(image_buffer(1:n_slices,:)',n_slices,handles);
[hollow_status_side2,edge_at_max_hole_side2,max_hole_diameter_side2]=is_it_hollow_sidescan(image_buffer(1:n_slices,32:-1:1)',n_slices,handles);
if hollow_status ~= hollow_status2
hollow_status;
% handles.disagree = handles.disagree + 1;
end
%
% if hollow_status == 1 & hollow_status2 == 0
% hollow_status = 0;
% end
if hollow_status + hollow_status2 == 1 & (habit == 's' | habit == 'h' | habit == 'i' | habit == 't')
%if hollow_status + hollow_status2 == 1
hollow_status = 1;
elseif habit == 'd' & percent_shadow_area < 35 & hollow_status + hollow_status2 == 1
hollow_status = 1;
elseif hollow_status == 1 & hollow_status2 == 0
hollow_status = 0;
end
if percent_shadow_area < 30
hollow_status = 0;
end
if hollow_status == 1
% ratio = max_hole_diameter./(max_width-min_width+1);
if edge_at_max_hole <= 0
ratio = 0;
else
ratio = max_hole_diameter./edge_at_max_hole;
end
if ratio == 0
part_z = 0;
size_factor = 1;
reject_status = 'h';
area_hole_ratio = 0;
elseif max_hole_diameter <= 1
part_z = 0;
size_factor = 1;
area_hole_ratio = 0;
reject_status = '0';
else
part_z = z_d(find(spot_edge < ratio,1,'last'));
size_factor = edge_0(find(z_d <= part_z,1,'last'));
reject_status = 'H';
area_hole_ratio = area/max_hole_diameter;
if hollow_status_side1 + hollow_status_side2 < 1
part_z = 0;
size_factor = 1;
reject_status = 'i';
end
if area_hole_ratio > 20 & habit == 'i'
part_z = 0;
size_factor = 1;
reject_status = 'u';
elseif area_hole_ratio > 35 & habit == 'h'
part_z = 0;
size_factor = 1;
reject_status = 'u';
elseif area_hole_ratio > 40
part_z = 0;
size_factor = 1;
reject_status = 'u';
end
end
handles.edge_at_max_hole = edge_at_max_hole;
handles.max_hole_diameter = max_hole_diameter;
return
else
ratio = -1;
part_z = -1;
size_factor = 1;
area_hole_ratio = 0;
end
% if hollow_status ==1
% reject_status='h';
% return
% end
end
reject_status = '0';
return
end
function [min_pos_lite,max_pos_lite,n_lite]=scan_slice(image_buf,handles)
n_lite=0;
max_pos_lite=0;
min_pos_lite=0;
zeros = find(image_buf == '0');
n_lite = length(zeros);
if n_lite == 0
return
else
min_pos_lite = zeros(1);
max_pos_lite = zeros(n_lite);
end
% for i=1:handles.bits_per_slice
% if image_buf(i) == '0'
% n_lite=n_lite+1;
% if min_pos_lite==0
% min_pos_lite=i;
% max_pos_lite=i;
% else
% max_pos_lite=i;
% end
% end
% end
return
function [status,edge_at_max_hole,max_hole_diameter] = is_it_hollow(image_buf,slices,handles)
current = 0;
old = 0;
new = 0;
possibly_hollow = 0;
max_hole_diameter = 0;
edge_at_max_hole = 0;
status = 0;
start_img = 0;
end_img = 0;
i = 1;
while end_img == 0
zero_amt = sum(image_buf(i,:) == '0');
if zero_amt > 0 & start_img == 0
start_img = i;
end
if zero_amt == 0 & start_img > 0
end_img = i;
end
i = i + 1;
if i > slices
if start_img == 0
start_img = 1;
end
if end_img == 0
end_img = slices;
end
end
end
slices = end_img-start_img+1;
for i=start_img:end_img
[min_pos_lite,max_pos_lite,n_lite]=scan_slice(image_buf(i,:),handles);
num_empty = max_pos_lite - min_pos_lite + 1 - n_lite;
if slices > 6
slices_third = floor(slices/3);
else
slices_third = 1;
end
if num_empty > max_hole_diameter & i > slices_third & i < slices - slices_third
max_hole_diameter = num_empty;
edge_at_max_hole = max_pos_lite - min_pos_lite + 1;
end
if possibly_hollow == 1 & status == 0
if n_lite > 0 & n_lite ~= max_pos_lite - min_pos_lite + 1
new = bin2dec(image_buf(i,17:32)) + bitshift(bin2dec(image_buf(i,1:16)),16);
olddec = bin2dec(old(17:32)) + bitshift(bin2dec(old(1:16)),16);
newandold = bitand(new , olddec);
if newandold == zeros
status=1;
% return
else
old = mask_start_end(max_pos_lite, min_pos_lite, image_buf(i,:),handles.bits_per_slice);
end
elseif n_lite > 0
bufdec = bin2dec(image_buf(i,17:32)) + bitshift(bin2dec(image_buf(i,1:16)),16);
olddec = bin2dec(old(17:32)) + bitshift(bin2dec(old(1:16)),16);
bufdec1 = bin2dec(image_buf(i,1:16));
olddec1 = bin2dec(old(1:16));
bufdec2 = bin2dec(image_buf(i,17:32));
olddec2 = bin2dec(old(17:32));
bufandold1 = bitand(bufdec1,olddec1);
bufandold2 = bitand(bufdec2,olddec2);
hole_size = length(find(old == '1'));
cover_size = length(find(dec2bin(bufandold1) == '1')) + length(find(dec2bin(bufandold2) == '1'));
%
% bufandold = bitand(bufdec , olddec);
% hole_size = length(find(old == '1'));
% cover_size = length(find(dec2bin(bufandold) == '1'));
%
%
if bufandold1 + bufandold2 > 0
% if cover_size <= 2 & hole_size ~=1
if cover_size <= .65*hole_size
status = 1;
% return;
end
possibly_hollow = 0;
old = 0;
elseif i > 1
status = 1;
% return;
else
possibly_hollow = 0;
old = 0;
end
else
possibly_hollow = 0;
end
elseif status == 0
if n_lite > 0 & n_lite ~= max_pos_lite - min_pos_lite + 1
old = mask_start_end(max_pos_lite, min_pos_lite, image_buf(i,:),handles.bits_per_slice);
possibly_hollow = 1;
end
end
end
return;
function [status,edge_at_max_hole,max_hole_diameter] = is_it_hollow_sidescan(image_buf,slices,handles)
current = 0;
old = 0;
new = 0;
possibly_hollow = 0;
max_hole_diameter = 0;
edge_at_max_hole = 0;
status = 0;
im_width = size(image_buf);
if im_width(2) > 32
im_width(2) = 32;
end
start_img = 0;
end_img = 0;
i = 1;
while end_img == 0
zero_amt = sum(image_buf(i,:) == '0');
if zero_amt > 0 && start_img == 0
start_img = i;
end
if zero_amt == 0 && start_img > 0
end_img = i;
end
i = i + 1;
if i > 32
if start_img == 0
start_img = 1;
end
if end_img == 0
end_img = 32;
end
end
end
slices = end_img-start_img+1;
for i=start_img:end_img
[min_pos_lite,max_pos_lite,n_lite]=scan_slice(image_buf(i,:),handles);
num_empty = max_pos_lite - min_pos_lite + 1 - n_lite;
if slices > 6
slices_third = floor(slices/3);
else
slices_third = 1;
end
if num_empty > max_hole_diameter & i > slices_third & i < slices - slices_third
max_hole_diameter = num_empty;
edge_at_max_hole = max_pos_lite - min_pos_lite + 1;
end
if possibly_hollow == 1 & status == 0
if n_lite > 0 & n_lite ~= max_pos_lite - min_pos_lite + 1
if im_width(2) <= 16
new = bin2dec(image_buf(i,1:im_width(2)));
olddec = bin2dec(old(1:im_width(2)));
else
new = bin2dec(image_buf(i,17:im_width(2))) + bitshift(bin2dec(image_buf(i,1:16)),16);
olddec = bin2dec(old(17:im_width(2))) + bitshift(bin2dec(old(1:16)),16);
end
newandold = bitand(new , olddec);
if newandold == zeros
status=1;
% return
else
old = mask_start_end(max_pos_lite, min_pos_lite, image_buf(i,:),im_width(2));
end
elseif n_lite > 0
% bufdec = bin2dec(image_buf(i,33:64)) + bitshift(bin2dec(image_buf(i,1:32)),32);
% olddec = bin2dec(old(33:64)) + bitshift(bin2dec(old(1:32)),32);
if im_width(2) <= 16
bufdec1 = bin2dec(image_buf(i,1:im_width(2)));
olddec1 = bin2dec(old(1:im_width(2)));
bufdec2 = 0;
olddec2 = 0;
else
bufdec1 = bin2dec(image_buf(i,1:16));
olddec1 = bin2dec(old(1:16));
bufdec2 = bin2dec(image_buf(i,17:im_width(2)));
olddec2 = bin2dec(old(17:im_width(2)));
end
bufandold1 = bitand(bufdec1,olddec1);
bufandold2 = bitand(bufdec2,olddec2);
hole_size = length(find(old == '1'));
cover_size = length(find(dec2bin(bufandold1) == '1')) + length(find(dec2bin(bufandold2) == '1'));
%
% bufandold = bitand(bufdec , olddec);
% hole_size = length(find(old == '1'));
% cover_size = length(find(dec2bin(bufandold) == '1'));
%
%
if bufandold1 + bufandold2 > 0
% if cover_size <= 2 & hole_size ~=1
if cover_size <= .65*hole_size
status = 1;
% return;
end
possibly_hollow = 0;
old = 0;
elseif i > 1
status = 1;
% return;
else
possibly_hollow = 0;
old = 0;
end
else
possibly_hollow = 0;
end
elseif status == 0
if n_lite > 0 & n_lite ~= max_pos_lite - min_pos_lite + 1
old = mask_start_end(max_pos_lite, min_pos_lite, image_buf(i,:),im_width(2));
possibly_hollow = 1;
end
end
end
return;
function old = mask_start_end(end_mask, start_mask, to_mask,bits_per_slice)
result=0;
if start_mask == 0 & end_mask == 0
result = to_mask;
else
% to_mask_dec = bin2dec(to_mask(33:64)) + bitshift(bin2dec(to_mask(1:32)),32);
% result = bitshift(bitshift(to_mask_dec,start_mask),-start_mask);
% result = bitshift(bitshift(result,-(bits_per_slice - end_mask) + 1),(bits_per_slice - end_mask)+1);
% result = dec2bin(result,bits_per_slice);
result(1:bits_per_slice) = '0';
result(start_mask:end_mask) = to_mask(start_mask:end_mask);
end
old=char(result);
return;
|
github
|
safdarne/TRGMC-master
|
sc.m
|
.m
|
TRGMC-master/sc.m
| 38,505 |
utf_8
|
7f1b3d24c919310f72c1f507ef09f0c6
|
function I = sc(I, varargin)
%SC Display/output truecolor images with a range of colormaps
%
% Examples:
% sc(image)
% sc(image, limits)
% sc(image, map)
% sc(image, limits, map)
% sc(image, map, limits)
% sc(..., col1, mask1, col2, mask2,...)
% out = sc(...)
% sc
%
% Generates a truecolor RGB image based on the input values in 'image' and
% any maximum and minimum limits specified, using the colormap specified.
% The image is displayed on screen if there is no output argument.
%
% SC has these advantages over MATLAB image rendering functions:
% - images can be displayed or output; makes combining/overlaying images
% simple.
% - images are rendered/output in truecolor (RGB [0,1]); no nasty
% discretization of the input data.
% - many special, built-in colormaps for viewing various types of data.
% - linearly interpolates user defined linear and non-linear colormaps.
% - no border and automatic, integer magnification (unless figure is
% docked or maximized) for better display.
% - multiple images can be generated for export simultaneously.
%
% For a demonstration, simply call SC without any input arguments.
%
% IN:
% image - MxNxCxP or 3xMxNxP image array. MxN are the dimensions of the
% image(s), C is the number of channels, and P the number of
% images. If P > 1, images can only be exported, not displayed.
% limits - [min max] where values in image less than min will be set to
% min and values greater than max will be set to max.
% map - Kx3 or Kx4 user defined colormap matrix, where the optional 4th
% column is the relative distance between colours along the scale,
% or a string containing the name of the colormap to use to create
% the output image. Default: 'none', which is RGB for 3-channel
% images, grayscale otherwise. Conversion of multi-channel images
% to intensity for intensity-based colormaps is done using the L2
% norm. Most MATLAB colormaps are supported. All named colormaps
% can be reversed by prefixing '-' to the string. This maintains
% integrity of the colorbar. Special, non-MATLAB colormaps are:
% 'contrast' - a high contrast colormap for intensity images that
% maintains intensity scale when converted to grayscale,
% for example when printing in black & white.
% 'prob' - first channel is plotted as hue, and the other channels
% modulate intensity. Useful for laying probabilites over
% images.
% 'prob_jet' - first channel is plotted as jet colormap, and the other
% channels modulate intensity.
% 'diff' - intensity values are marked blue for > 0 and red for < 0.
% Darker colour means larger absolute value. For multi-
% channel images, the L2 norm of the other channels sets
% green level. 3 channel images are converted to YUV and
% images with more that 3 channels are projected onto the
% principle components first.
% 'compress' - compress many channels to RGB while maximizing
% variance.
% 'flow' - display two channels representing a 2d Cartesian vector as
% hue for angle and intensity for magnitude (darker colour
% indicates a larger magnitude).
% 'phase' - first channel is intensity, second channel is phase in
% radians. Darker colour means greater intensity, hue
% represents phase from 0 to 2 pi.
% 'stereo' - pair of concatenated images used to generate a red/cyan
% anaglyph.
% 'stereo_col' - pair of concatenated RGB images used to generate a
% colour anaglyph.
% 'rand' - gives an index image a random colormap. Useful for viewing
% segmentations.
% 'rgb2gray' - converts an RGB image to grayscale in the same fashion
% as MATLAB's rgb2gray (in the image processing toolbox).
% col/mask pairs - Pairs of parameters for coloring specific parts of the
% image differently. The first (col) parameter can be
% a MATLAB color specifier, e.g. 'b' or [0.5 0 1], or
% one of the colormaps named above, or an MxNx3 RGB
% image. The second (mask) paramater should be an MxN
% logical array indicating those pixels (true) whose
% color should come from the specified color parameter.
% If there is only one col parameter, without a mask
% pair, then mask = any(isnan(I, 3)), i.e. the mask is
% assumed to indicate the location of NaNs. Note that
% col/mask pairs are applied in order, painting over
% previous pixel values.
%
% OUT:
% out - MxNx3xP truecolour (double) RGB image array in range [0, 1]
%
% See also IMAGE, IMAGESC, IMSHOW, COLORMAP, COLORBAR.
% $Id: sc.m,v 1.81 2008/12/10 23:14:43 ojw Exp $
% Copyright: Oliver Woodford, 2007
%% Check for arguments
if nargin == 0
% If there are no input arguments then run the demo
if nargout > 0
error('Output expected from no inputs!');
end
demo; % Run the demo
return
end
%% Size our image(s)
[y x c n] = size(I);
I = reshape(I, y, x, c, n);
%% Check if image is given with RGB colour along the first dimension
if y == 3 && c > 3
% Flip colour to 3rd dimension
I = permute(I, [2 3 1 4]);
[y x c n] = size(I);
end
%% Don't do much if I is empty
if isempty(I)
if nargout == 0
% Clear the current axes if we were supposed to display the image
cla; axis off;
else
% Create an empty array with the correct dimensions
I = zeros(y, x, (c~=0)*3, n);
end
return
end
%% Check for multiple images
% If we have a non-singleton 4th dimension we want to display the images in
% a 3x4 grid and use buttons to cycle through them
if n > 1
if nargout > 0
% Return transformed images in an YxXx3xN array
A = zeros(y, x, 3, n);
for a = 1:n
A(:,:,:,a) = sc(I(:,:,:,a), varargin{:});
end
I = A;
else
% Removed functionality
fprintf([' SC no longer supports the display of multiple images. The\n'...
' functionality has been incorporated into an improved version\n'...
' of MONTAGE, available on the MATLAB File Exchange at:\n'...
' http://www.mathworks.com/matlabcentral/fileexchange/22387\n']);
clear I;
end
return
end
%% Parse the input arguments coming after I (1st input)
[map limits mask] = parse_inputs(I, varargin, y, x);
%% Call the rendering function
I = reshape(double(real(I)), y*x, c); % Only work with real doubles
if ~ischar(map)
% Table-based colormap
reverseMap = false;
[I limits] = interp_map(I, limits, reverseMap, map);
else
% If map starts with a '-' sign, invert the colourmap
reverseMap = map(1) == '-';
map = lower(map(reverseMap+1:end));
% Predefined colormap
[I limits] = colormap_switch(I, map, limits, reverseMap, c);
end
%% Update any masked pixels
I = reshape(I, y*x, 3);
for a = 1:size(mask, 2)
I(mask{2,a},1) = mask{1,a}(:,1);
I(mask{2,a},2) = mask{1,a}(:,2);
I(mask{2,a},3) = mask{1,a}(:,3);
end
I = reshape(I, [y x 3]); % Reshape to correct size
%% Only display if the output isn't used
if nargout == 0
display_image(I, map, limits, reverseMap);
% Don't print out the matrix if we've forgotten the ";"
clear I
end
return
%% Colormap switch
function [I limits] = colormap_switch(I, map, limits, reverseMap, c)
% Large switch statement for all the colourmaps
switch map
%% Prism
case 'prism'
% Similar to the MATLAB internal prism colormap, but only works on
% index images, assigning each index (or rounded float) to a
% different colour
[I limits] = index_im(I);
% Generate prism colourmap
map = prism(6);
if reverseMap
map = map(end:-1:1,:); % Reverse the map
end
% Lookup the colours
I = mod(I, 6) + 1;
I = map(I,:);
%% Rand
case 'rand'
% Assigns a random colour to each index
[I limits num_vals] = index_im(I);
% Generate random colourmap
map = rand(num_vals, 3);
% Lookup the colours
I = map(I,:);
%% Diff
case 'diff'
% Show positive as blue and negative as red, white is 0
switch c
case 1
I(:,2:3) = 0;
case 2
% Second channel can only have absolute value
I(:,3) = abs(I(:,2));
case 3
% Diff of RGB images - convert to YUV first
I = rgb2yuv(I);
I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(2);
otherwise
% Use difference along principle component, and other
% channels to modulate second channel
I = calc_prin_comps(I);
I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(c - 1);
I(:,4:end) = [];
end
% Generate limits
if isempty(limits)
limits = [min(I(:,1)) max(I(:,1))];
end
limits = max(abs(limits));
if limits
% Scale
if c > 1
I(:,[1 3]) = I(:,[1 3]) / limits;
else
I = I / (limits * 0.5);
end
end
% Colour
M = I(:,1) > 0;
I(:,2) = -I(:,1) .* ~M;
I(:,1) = I(:,1) .* M;
if reverseMap
% Swap first two channels
I = I(:,[2 1 3]);
end
%I = 1 - I * [1 0.4 1; 0.4 1 1; 1 1 0.4]; % (Green/Red)
I = 1 - I * [1 1 0.4; 0.4 1 1; 1 0.4 1]; % (Blue/Red)
I = min(max(reshape(I, numel(I), 1), 0), 1);
limits = [-limits limits]; % For colourbar
%% Flow
case 'flow'
% Calculate amplitude and phase, and use 'phase'
if c ~= 2
error('''flow'' requires two channels');
end
A = sqrt(sum(I .^ 2, 2));
if isempty(limits)
limits = [min(A) max(A)*2];
else
limits = [0 max(abs(limits)*sqrt(2))*2];
end
I(:,1) = atan2(I(:,2), I(:,1));
I(:,2) = A;
if reverseMap
% Invert the amplitude
I(:,2) = -I(:,2);
limits = -limits([2 1]);
end
I = phase_helper(I, limits, 2); % Last parameter tunes how saturated colors can get
% Set NaNs (unknown flow) to 0
I(isnan(I)) = reverseMap;
limits = []; % This colourmap doesn't have a valid colourbar
%% Phase
case 'phase'
% Plot amplitude as intensity and angle as hue
if c < 2
error('''phase'' requires two channels');
end
if isempty(limits)
limits = [min(I(:,1)) max(I(:,1))];
end
if reverseMap
% Invert the phase
I(:,2) = -I(:,2);
end
I = I(:,[2 1]);
if diff(limits)
I = phase_helper(I, limits, 1.3); % Last parameter tunes how saturated colors can get
else
% No intensity - just cycle hsv
I = hsv_helper(mod(I(:,1) / (2 * pi), 1));
end
limits = []; % This colourmap doesn't have a valid colourbar
%% RGB2Grey
case {'rgb2grey', 'rgb2gray'}
% Compress RGB to greyscale
[I limits] = rgb2grey(I, limits, reverseMap);
%% RGB2YUV
case 'rgb2yuv'
% Convert RGB to YUV - not for displaying or saving to disk!
[I limits] = rgb2yuv(I);
%% YUV2RGB
case 'yuv2rgb'
% Convert YUV to RGB - undo conversion of rgb2yuv
if c ~= 3
error('''yuv2rgb'' requires a 3 channel image');
end
I = reshape(I, y*x, 3);
I = I * [1 1 1; 0, -0.39465, 2.03211; 1.13983, -0.58060 0];
I = reshape(I, y, x, 3);
I = sc(I, limits);
limits = []; % This colourmap doesn't have a valid colourbar
%% Prob
case 'prob'
% Plot first channel as grey variation of 'bled' and modulate
% according to other channels
if c > 1
A = rgb2grey(I(:,2:end), [], false);
I = I(:,1);
else
A = 0.5;
end
[I limits] = bled(I, limits, reverseMap);
I = normalize(A + I, [-0.1 1.3]);
%% Prob_jet
case 'prob_jet'
% Plot first channel as 'jet' and modulate according to other
% channels
if c > 1
A = rgb2grey(I(:,2:end), [], false);
I = I(:,1);
else
A = 0.5;
end
[I limits] = jet_helper(I, limits, reverseMap);
I = normalize(A + I, [0.2 1.8]);
%% Compress
case 'compress'
% Compress to RGB, maximizing variance
% Determine and scale to limits
I = normalize(I, limits);
if reverseMap
% Invert after everything
I = 1 - I;
end
% Zero mean
meanCol = mean(I, 1);
isBsx = exist('bsxfun', 'builtin');
if isBsx
I = bsxfun(@minus, I, meanCol);
else
I = I - meanCol(ones(x*y, 1, 'uint8'),:);
end
% Calculate top 3 principle components
I = calc_prin_comps(I, 3);
% Normalize each channel independently
if isBsx
I = bsxfun(@minus, I, min(I, [], 1));
I = bsxfun(@times, I, 1./max(I, [], 1));
else
for a = 1:3
I(:,a) = I(:,a) - min(I(:,a));
I(:,a) = I(:,a) / max(I(:,a));
end
end
% Put components in order of human eyes' response to channels
I = I(:,[2 1 3]);
limits = []; % This colourmap doesn't have a valid colourbar
%% Stereo (anaglyph)
case 'stereo'
% Convert 2 colour images to intensity images
% Show first channel as red and second channel as cyan
A = rgb2grey(I(:,1:floor(end/2)), limits, false);
I = rgb2grey(I(:,floor(end/2)+1:end), limits, false);
if reverseMap
I(:,2:3) = A(:,1:2); % Make first image cyan
else
I(:,1) = A(:,1); % Make first image red
end
limits = []; % This colourmap doesn't have a valid colourbar
%% Coloured anaglyph
case 'stereo_col'
if c ~= 6
error('''stereo_col'' requires a 6 channel image');
end
I = normalize(I, limits);
% Red channel from one image, green and blue from the other
if reverseMap
I(:,1) = I(:,4); % Make second image red
else
I(:,2:3) = I(:,5:6); % Make first image red
end
I = I(:,1:3);
limits = []; % This colourmap doesn't have a valid colourbar
%% None
case 'none'
% No colour map - just output the image
if c ~= 3
[I limits] = grey(I, limits, reverseMap);
else
I = intensity(I(:), limits, reverseMap);
limits = [];
end
%% Grey
case {'gray', 'grey'}
% Greyscale
[I limits] = grey(I, limits, reverseMap);
%% Jet
case 'jet'
% Dark blue to dark red, through green
[I limits] = jet_helper(I, limits, reverseMap);
%% Hot
case 'hot'
% Black to white through red and yellow
[I limits] = interp_map(I, limits, reverseMap, [0 0 0 3; 1 0 0 3; 1 1 0 2; 1 1 1 1]);
%% Contrast
case 'contrast'
% A high contrast, full-colour map that goes from black to white
% linearly when converted to greyscale, and passes through all the
% corners of the RGB colour cube
[I limits] = interp_map(I, limits, reverseMap, [0 0 0 114; 0 0 1 185; 1 0 0 114; 1 0 1 174;...
0 1 0 114; 0 1 1 185; 1 1 0 114; 1 1 1 0]);
%% HSV
case 'hsv'
% Cycle through hues
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = hsv_helper(I);
%% Bone
case 'bone'
% Greyscale with a blue tint
[I limits] = interp_map(I, limits, reverseMap, [0 0 0 3; 21 21 29 3; 42 50 50 2; 64 64 64 1]/64);
%% Colourcube
case {'colorcube', 'colourcube'}
% Psychedelic colourmap inspired by MATLAB's version
[I limits] = intensity(I, limits, reverseMap); % Intensity map
step = 4;
I = I * (step * (1 - eps));
J = I * step;
K = floor(J);
I = cat(3, mod(K, step)/(step-1), J - floor(K), mod(floor(I), step)/(step-1));
%% Cool
case 'cool'
% Cyan through to magenta
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [I, 1-I, ones(size(I))];
%% Spring
case 'spring'
% Magenta through to yellow
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [ones(size(I)), I, 1-I];
%% Summer
case 'summer'
% Darkish green through to pale yellow
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [I, 0.5+I*0.5, 0.4*ones(size(I))];
%% Autumn
case 'autumn'
% Red through to yellow
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [ones(size(I)), I, zeros(size(I))];
%% Winter
case 'winter'
% Blue through to turquoise
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [zeros(size(I)), I, 1-I*0.5];
%% Copper
case 'copper'
% Black through to copper
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = [I*(1/0.8), I*0.78, I*0.5];
I = min(max(reshape(I, numel(I), 1), 0), 1); % Truncate
%% Pink
case 'pink'
% Greyscale with a pink tint
[I limits] = intensity(I, limits, reverseMap); % Intensity map
J = I * (2 / 3);
I = [I, I-1/3, I-2/3];
I = reshape(max(min(I(:), 1/3), 0), [], 3);
I = I + J(:,[1 1 1]);
I = sqrt(I);
%% Bled
case 'bled'
% Black to red, through blue
[I limits] = bled(I, limits, reverseMap);
%% Earth
case 'earth'
% High contrast, converts to linear scale in grey, strong
% shades of green
table = [0 0 0; 0 0.1104 0.0583; 0.1661 0.1540 0.0248; 0.1085 0.2848 0.1286;...
0.2643 0.3339 0.0939; 0.2653 0.4381 0.1808; 0.3178 0.5053 0.3239;...
0.4858 0.5380 0.3413; 0.6005 0.5748 0.4776; 0.5698 0.6803 0.6415;...
0.5639 0.7929 0.7040; 0.6700 0.8626 0.6931; 0.8552 0.8967 0.6585;...
1 0.9210 0.7803; 1 1 1];
[I limits] = interp_map(I, limits, reverseMap, table);
%% Pinker
case 'pinker'
% High contrast, converts to linear scale in grey, strong
% shades of pink
table = [0 0 0; 0.0455 0.0635 0.1801; 0.2425 0.0873 0.1677;...
0.2089 0.2092 0.2546; 0.3111 0.2841 0.2274; 0.4785 0.3137 0.2624;...
0.5781 0.3580 0.3997; 0.5778 0.4510 0.5483; 0.5650 0.5682 0.6047;...
0.6803 0.6375 0.5722; 0.8454 0.6725 0.5855; 0.9801 0.7032 0.7007;...
1 0.7777 0.8915; 0.9645 0.8964 1; 1 1 1];
[I limits] = interp_map(I, limits, reverseMap, table);
%% Pastel
case 'pastel'
% High contrast, converts to linear scale in grey, strong
% pastel shades
table = [0 0 0; 0.4709 0 0.018; 0 0.3557 0.6747; 0.8422 0.1356 0.8525;
0.4688 0.6753 0.3057; 1 0.6893 0.0934; 0.9035 1 0; 1 1 1];
[I limits] = interp_map(I, limits, reverseMap, table);
%% Bright
case 'bright'
% High contrast, converts to linear scale in grey, strong
% saturated shades
table = [0 0 0; 0.3071 0.0107 0.3925; 0.007 0.289 1; 1 0.0832 0.7084;
1 0.4447 0.1001; 0.5776 0.8360 0.4458; 0.9035 1 0; 1 1 1];
[I limits] = interp_map(I, limits, reverseMap, table);
%% Jet2
case 'jet2'
% Like jet, but starts in black and goes to saturated red
[I limits] = interp_map(I, limits, reverseMap, [0 0 0; 0.5 0 0.5; 0 0 0.9; 0 1 1; 0 1 0; 1 1 0; 1 0 0]);
%% Hot2
case 'hot2'
% Like hot, but equally spaced
[I limits] = intensity(I, limits, reverseMap); % Intensity map
I = I * 3;
I = [I, I-1, I-2];
I = min(max(I(:), 0), 1); % Truncate
%% Bone2
case 'bone2'
% Like bone, but equally spaced
[I limits] = intensity(I, limits, reverseMap); % Intensity map
J = [I-2/3, I-1/3, I];
J = reshape(max(min(J(:), 1/3), 0), [], 3) * (2 / 5);
I = I * (13 / 15);
I = J + I(:,[1 1 1]);
%% Unknown colourmap
otherwise
error('Colormap ''%s'' not recognised.', map);
end
return
%% Display image
function display_image(I, map, limits, reverseMap)
% Clear the axes
cla(gca, 'reset');
% Display the image - using image() is fast
hIm = image(I);
% Get handles to the figure and axes (now, as the axes may have
% changed)
hFig = gcf; hAx = gca;
% Axes invisible and equal
set(hFig, 'Units', 'pixels');
set(hAx, 'Visible', 'off', 'DataAspectRatio', [1 1 1], 'DrawMode', 'fast');
% Set data for a colorbar
if ~isempty(limits) && limits(1) ~= limits(2)
colBar = (0:255) * ((limits(2) - limits(1)) / 255) + limits(1);
colBar = squeeze(sc(colBar, map, limits));
if reverseMap
colBar = colBar(end:-1:1,:);
end
set(hFig, 'Colormap', colBar);
set(hAx, 'CLim', limits);
set(hIm, 'CDataMapping', 'scaled');
end
% Only resize image if it is alone in the figure
if numel(findobj(get(hFig, 'Children'), 'Type', 'axes')) > 1
return
end
% Could still be the first subplot - do another check
axesPos = get(hAx, 'Position');
if isequal(axesPos, get(hFig, 'DefaultAxesPosition'))
% Default position => not a subplot
% Fill the window
set(hAx, 'Units', 'normalized', 'Position', [0 0 1 1]);
axesPos = [0 0 1 1];
end
if ~isequal(axesPos, [0 0 1 1]) || strcmp(get(hFig, 'WindowStyle'), 'docked')
% Figure not alone, or docked. Either way, don't resize.
return
end
% Get the size of the monitor we're on
figPosCur = get(hFig, 'Position');
MonSz = get(0, 'MonitorPositions');
MonOn = size(MonSz, 1);
if MonOn > 1
figCenter = figPosCur(1:2) + figPosCur(3:4) / 2;
figCenter = MonSz - repmat(figCenter, [MonOn 2]);
MonOn = all(sign(figCenter) == repmat([-1 -1 1 1], [MonOn 1]), 2);
MonOn(1) = MonOn(1) | ~any(MonOn);
MonSz = MonSz(MonOn,:);
end
MonSz(3:4) = MonSz(3:4) - MonSz(1:2) + 1;
% Check if the window is maximized
% This is a hack which may only work on Windows! No matter, though.
if isequal(MonSz([1 3]), figPosCur([1 3]))
% Leave maximized
return
end
% Compute the size to set the window
MaxSz = MonSz(3:4) - [20 120];
ImSz = [size(I, 2) size(I, 1)];
RescaleFactor = min(MaxSz ./ ImSz);
if RescaleFactor > 1
% Integer scale for enlarging, but don't make too big
MaxSz = min(MaxSz, [1000 680]);
RescaleFactor = max(floor(min(MaxSz ./ ImSz)), 1);
end
figPosNew = ceil(ImSz * RescaleFactor);
% Don't move the figure if the size isn't changing
if isequal(figPosCur(3:4), figPosNew)
return
end
% Keep the centre of the figure stationary
figPosNew = [max(1, floor(figPosCur(1:2)+(figPosCur(3:4)-figPosNew)/2)) figPosNew];
% Ensure the figure bar is in bounds
figPosNew(1:2) = min(figPosNew(1:2), MonSz(1:2)+MonSz(3:4)-[6 101]-figPosNew(3:4));
set(hFig, 'Position', figPosNew);
return
%% Parse input variables
function [map limits mask] = parse_inputs(I, inputs, y, x)
% Check the first two arguments for the colormap and limits
ninputs = numel(inputs);
map = 'none';
limits = [];
mask = 1;
for a = 1:min(2, ninputs)
if ischar(inputs{a}) && numel(inputs{a}) > 1
% Name of colormap
map = inputs{a};
elseif isnumeric(inputs{a})
[p q r] = size(inputs{a});
if (p * q * r) == 2
% Limits
limits = double(inputs{a});
elseif p > 1 && (q == 3 || q == 4) && r == 1
% Table-based colormap
map = inputs{a};
else
break;
end
else
break;
end
mask = mask + 1;
end
% Check for following inputs
if mask > ninputs
mask = cell(2, 0);
return
end
% Following inputs must either be colour/mask pairs, or a colour for NaNs
if ninputs - mask == 0
mask = cell(2, 1);
mask{1} = inputs{end};
mask{2} = ~all(isfinite(I), 3);
elseif mod(ninputs-mask, 2) == 1
mask = reshape(inputs(mask:end), 2, []);
else
error('Error parsing inputs');
end
% Go through pairs and generate
for a = 1:size(mask, 2)
% Generate any masks from functions
if isa(mask{2,a}, 'function_handle')
mask{2,a} = mask{2,a}(I);
end
if ~islogical(mask{2,a})
error('Mask is not a logical array');
end
if ~isequal(size(mask{2,a}), [y x])
error('Mask does not match image size');
end
if ischar(mask{1,a})
if numel(mask{1,a}) == 1
% Generate colours from MATLAB colour strings
mask{1,a} = double(dec2bin(strfind('kbgcrmyw', mask{1,a})-1, 3)) - double('0');
else
% Assume it's a colormap name
mask{1,a} = sc(I, mask{1,a});
end
end
mask{1,a} = reshape(mask{1,a}, [], 3);
if size(mask{1,a}, 1) ~= y*x && size(mask{1,a}, 1) ~= 1
error('Replacement color/image of unexpected dimensions');
end
if size(mask{1,a}, 1) ~= 1
mask{1,a} = mask{1,a}(mask{2,a},:);
end
end
return
%% Grey
function [I limits] = grey(I, limits, reverseMap)
% Greyscale
[I limits] = intensity(I, limits, reverseMap);
I = I(:,[1 1 1]);
return
%% RGB2grey
function [I limits] = rgb2grey(I, limits, reverseMap)
% Compress RGB to greyscale
if size(I, 2) == 3
I = I * [0.299; 0.587; 0.114];
end
[I limits] = grey(I, limits, reverseMap);
return
%% RGB2YUV
function [I limits] = rgb2yuv(I)
% Convert RGB to YUV - not for displaying or saving to disk!
if size(I, 2) ~= 3
error('rgb2yuv requires a 3 channel image');
end
I = I * [0.299, -0.14713, 0.615; 0.587, -0.28886, -0.51498; 0.114, 0.436, -0.10001];
limits = []; % This colourmap doesn't have a valid colourbar
return
%% Phase helper
function I = phase_helper(I, limits, n)
I(:,1) = mod(I(:,1)/(2*pi), 1);
I(:,2) = I(:,2) - limits(1);
I(:,2) = I(:,2) * (n / (limits(2) - limits(1)));
I(:,3) = n - I(:,2);
I(:,[2 3]) = min(max(I(:,[2 3]), 0), 1);
I = hsv2rgb(reshape(I, [], 1, 3));
return
%% Jet helper
function [I limits] = jet_helper(I, limits, reverseMap)
% Dark blue to dark red, through green
[I limits] = intensity(I, limits, reverseMap);
I = I * 4;
I = [I-3, I-2, I-1];
I = 1.5 - abs(I);
I = reshape(min(max(I(:), 0), 1), size(I));
return
%% HSV helper
function I = hsv_helper(I)
I = I * 6;
I = abs([I-3, I-2, I-4]);
I(:,1) = I(:,1) - 1;
I(:,2:3) = 2 - I(:,2:3);
I = reshape(min(max(I(:), 0), 1), size(I));
return
%% Bled
function [I limits] = bled(I, limits, reverseMap)
% Black to red through blue
[I limits] = intensity(I, limits, reverseMap);
J = reshape(hsv_helper(I), [], 3);
if exist('bsxfun', 'builtin')
I = bsxfun(@times, I, J);
else
I = J .* I(:,[1 1 1]);
end
return
%% Normalize
function [I limits] = normalize(I, limits)
if isempty(limits)
limits = isfinite(I);
if ~any(reshape(limits, numel(limits), 1))
% All NaNs, Infs or -Infs
I = double(I > 0);
limits = [0 1];
return
end
limits = [min(I(limits)) max(I(limits))];
I = I - limits(1);
if limits(2) ~= limits(1)
I = I * (1 / (limits(2) - limits(1)));
end
else
I = I - limits(1);
if limits(2) ~= limits(1)
I = I * (1 / (limits(2) - limits(1)));
end
I = reshape(min(max(reshape(I, numel(I), 1), 0), 1), size(I));
end
return
%% Intensity maps
function [I limits] = intensity(I, limits, reverseMap)
% Squash to 1d using L2 norm
if size(I, 2) > 1
I = sqrt(sum(I .^ 2, 2));
end
% Determine and scale to limits
[I limits] = normalize(I, limits);
if reverseMap
% Invert after everything
I = 1 - I;
end
return
%% Interpolate table-based map
function [I limits] = interp_map(I, limits, reverseMap, map)
% Convert to intensity
[I limits] = intensity(I, limits, reverseMap);
% Compute indices and offsets
if size(map, 2) == 4
bins = map(1:end-1,4);
cbins = cumsum(bins);
bins = bins ./ cbins(end);
cbins = cbins(1:end-1) ./ cbins(end);
if exist('bsxfun', 'builtin')
ind = bsxfun(@gt, I(:)', cbins(:));
else
ind = repmat(I(:)', [numel(cbins) 1]) > repmat(cbins(:), [1 numel(I)]);
end
ind = min(sum(ind), size(map, 1) - 2) + 1;
bins = 1 ./ bins;
cbins = [0; cbins];
I = (I - cbins(ind)) .* bins(ind);
else
n = size(map, 1) - 1;
I = I(:) * n;
ind = min(floor(I), n-1);
I = I - ind;
ind = ind + 1;
end
if exist('bsxfun', 'builtin')
I = bsxfun(@times, map(ind,1:3), 1-I) + bsxfun(@times, map(ind+1,1:3), I);
else
I = map(ind,1:3) .* repmat(1-I, [1 3]) + map(ind+1,1:3) .* repmat(I, [1 3]);
end
return
%% Index images
function [J limits num_vals] = index_im(I)
% Returns an index image
if size(I, 2) ~= 1
error('Index maps only work on single channel images');
end
J = round(I);
rescaled = any(abs(I - J) > 0.01);
if rescaled
% Appears not to be an index image. Rescale over 256 indices
m = min(I);
m = m * (1 - sign(m) * eps);
I = I - m;
I = I * (256 / max(I(:)));
J = ceil(I);
num_vals = 256;
elseif nargout > 2
% Output the number of values
J = J - (min(J) - 1);
num_vals = max(J);
end
% These colourmaps don't have valid colourbars
limits = [];
return
%% Calculate principle components
function I = calc_prin_comps(I, numComps)
if nargin < 2
numComps = size(I, 2);
end
% Do SVD
[I S] = svd(I, 0);
% Calculate projection of data onto components
S = diag(S(1:numComps,1:numComps))';
if exist('bsxfun', 'builtin')
I = bsxfun(@times, I(:,1:numComps), S);
else
I = I(:,1:numComps) .* S(ones(size(I, 1), 1, 'uint8'),:);
end
return
%% Demo function to show capabilities of sc
function demo
%% Demo gray & lack of border
figure; fig = gcf; Z = peaks(256); sc(Z);
display_text([...
' Lets take a standard, MATLAB, real-valued function:\n\n peaks(256)\n\n'...
' Calling:\n\n figure\n Z = peaks(256);\n sc(Z)\n\n'...
' gives (see figure). SC automatically scales intensity to fill the\n'...
' truecolor range of [0 1].\n\n'...
' If your figure isn''t docked, then the image will have no border, and\n'...
' will be magnified by an integer factor (in this case, 2) so that the\n'...
' image is a reasonable size.']);
%% Demo colour image display
figure(fig); clf;
load mandrill; mandrill = ind2rgb(X, map); sc(mandrill);
display_text([...
' That wasn''t so interesting. The default colormap is ''none'', which\n'...
' produces RGB images given a 3-channel input image, otherwise it produces\n'...
' a grayscale image. So calling:\n\n load mandrill\n'...
' mandrill = ind2rgb(X, map);\n sc(mandrill)\n\n gives (see figure).']);
%% Demo discretization
figure(fig); clf;
subplot(121); sc(Z, 'jet'); label(Z, 'sc(Z, ''jet'')');
subplot(122); imagesc(Z); axis image off; colormap(jet(64)); % Fix the fact we change the default depth
label(Z, 'imagesc(Z); axis image off; colormap(''jet'');');
display_text([...
' However, if we want to display intensity images in color we can use any\n'...
' of the MATLAB colormaps implemented (most of them) to give truecolor\n'...
' images. For example, to use ''jet'' simply call:\n\n'...
' sc(Z, ''jet'')\n\n'...
' The MATLAB alternative, shown on the right, is:\n\n'...
' imagesc(Z)\n axis equal off\n colormap(jet)\n\n'...
' which generates noticeable discretization artifacts.']);
%% Demo intensity colourmaps
figure(fig); clf;
subplot(221); sc(Z, 'hsv'); label(Z, 'sc(Z, ''hsv'')');
subplot(222); sc(Z, 'colorcube'); label(Z, 'sc(Z, ''colorcube'')');
subplot(223); sc(Z, 'contrast'); label(Z, 'sc(Z, ''contrast'')');
subplot(224); sc(Z-round(Z), 'diff'); label(Z, 'sc(Z-round(Z), ''diff'')');
display_text([...
' There are several other intensity colormaps to choose from. Calling:\n\n'...
' help sc\n\n'...
' will give you a list of them. Here are several others demonstrated.']);
%% Demo saturation limits & colourmap reversal
figure(fig); clf;
subplot(121); sc(Z, [0 max(Z(:))], '-hot'); label(Z, 'sc(Z, [0 max(Z(:))], ''-hot'')');
subplot(122); sc(mandrill, [-0.5 0.5]); label(mandrill, 'sc(mandrill, [-0.5 0.5])');
display_text([...
' SC can also rescale intensity, given an upper and lower bound provided\n'...
' by the user, and invert most colormaps simply by prefixing a ''-'' to the\n'...
' colormap name. For example:\n\n'...
' sc(Z, [0 max(Z(:))], ''-hot'');\n'...
' sc(mandrill, [-0.5 0.5]);\n\n'...
' Note that the order of the colormap and limit arguments are\n'...
' interchangable.']);
%% Demo prob
load gatlin;
gatlin = X;
figure(fig); clf; im = cat(3, abs(Z)', gatlin(1:256,end-255:end)); sc(im, 'prob');
label(im, 'sc(cat(3, prob, gatlin), ''prob'')');
display_text([...
' SC outputs the recolored data as a truecolor RGB image. This makes it\n'...
' easy to combine colormaps, either arithmetically, or by masking regions.\n'...
' For example, we could combine an image and a probability map\n'...
' arithmetically as follows:\n\n'...
' load gatlin\n'...
' gatlin = X(1:256,end-255:end);\n'...
' prob = abs(Z)'';\n'...
' im = sc(prob, ''hsv'') .* sc(prob, ''gray'') + sc(gatlin, ''rgb2gray'');\n'...
' sc(im, [-0.1 1.3]);\n\n'...
' In fact, that particular colormap has already been implemented in SC.\n'...
' Simply call:\n\n'...
' sc(cat(3, prob, gatlin), ''prob'');']);
%% Demo colorbar
colorbar;
display_text([...
' SC also makes possible the generation of a colorbar in the normal way, \n'...
' with all the colours and data values correct. Simply call:\n\n'...
' colorbar\n\n'...
' The colorbar doesn''t work with all colormaps, but when it does,\n'...
' inverting the colormap (using ''-map'') maintains the integrity of the\n'...
' colorbar (i.e. it works correctly) - unlike if you invert the input data.']);
%% Demo combine by masking
figure(fig); clf;
sc(Z, [0 max(Z(:))], '-hot', sc(Z-round(Z), 'diff'), Z < 0);
display_text([...
' It''s just as easy to combine generated images by masking too. Here''s an\n'...
' example:\n\n'...
' im = cat(4, sc(Z, [0 max(Z(:))], ''-hot''), sc(Z-round(Z), ''diff''));\n'...
' mask = repmat(Z < 0, [1 1 3]);\n'...
' mask = cat(4, mask, ~mask);\n'...
' im = sum(im .* mask, 4);\n'...
' sc(im)\n\n'...
' In fact, SC can also do this for you, by adding image/colormap and mask\n'...
' pairs to the end of the argument list, as follows:\n\n'...
' sc(Z, [0 max(Z(:))], ''-hot'', sc(Z-round(Z), ''diff''), Z < 0);\n\n'...
' A benefit of the latter approach is that you can still display a\n'...
' colorbar for the first colormap.']);
%% Demo texture map
figure(fig); clf;
surf(Z, sc(Z, 'contrast'), 'edgecolor', 'none');
display_text([...
' Other benefits of SC outputting the image as an array are that the image\n'...
' can be saved straight to disk using imwrite() (if you have the image\n'...
' processing toolbox), or can be used to texture map a surface, thus:\n\n'...
' tex = sc(Z, ''contrast'');\n'...
' surf(Z, tex, ''edgecolor'', ''none'');']);
%% Demo compress
load mri;
mri = D;
close(fig); % Only way to get round loss of focus (bug?)
figure(fig); clf;
sc(squeeze(mri(:,:,:,1:6)), 'compress');
display_text([...
' For images with more than 3 channels, SC can compress these images to RGB\n'...
' while maintaining the maximum amount of variance in the data. For\n'...
' example, this 6 channel image:\n\n'...
' load mri\n mri = D;\n sc(squeeze(mri(:,:,:,1:6), ''compress'')']);
%% Demo multiple images
figure(fig); clf; im = sc(mri, 'bone');
for a = 1:12
subplot(3, 4, a);
sc(im(:,:,:,a));
end
display_text([...
' SC can process multiple images for export when passed in as a 4d array.\n'...
' For example:\n\n'...
' im = sc(mri, ''bone'')\n'...
' for a = 1:12\n'...
' subplot(3, 4, a);\n'...
' sc(im(:,:,:,a));\n'...
' end']);
%% Demo user defined colormap
figure(fig); clf; sc(abs(Z), rand(10, 3)); colorbar;
display_text([...
' Finally, SC can use user defined colormaps to display indexed images.\n'...
' These can be defined as a linear colormap. For example:\n\n'...
' sc(abs(Z), rand(10, 3))\n colorbar;\n\n'...
' Note that the colormap is automatically linearly interpolated.']);
%% Demo non-linear user defined colormap
figure(fig); clf; sc(abs(Z), [rand(10, 3) exp((1:10)/2)']); colorbar;
display_text([...
' Non-linear colormaps can also be defined by the user, by including the\n'...
' relative distance between the given colormap points on the colormap\n'...
' scale in the fourth column of the colormap matrix. For example:\n\n'...
' sc(abs(Z), [rand(10, 3) exp((1:10)/2)''])\n colorbar;\n\n'...
' Note that the colormap is still linearly interpolated between points.']);
clc; fprintf('End of demo.\n');
return
%% Some helper functions for the demo
function display_text(str)
clc;
fprintf([str '\n\n']);
fprintf('Press a key to go on.\n');
figure(gcf);
waitforbuttonpress;
return
function label(im, str)
text(size(im, 2)/2, size(im, 1)+12, str,...
'Interpreter', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle');
return
|
github
|
lacerbi/psybayes-master
|
psybayes.m
|
.m
|
psybayes-master/psybayes.m
| 13,615 |
utf_8
|
e1b407be358f4d890c3e46384174af9e
|
function [xnext,psy,output] = psybayes(psy,method,vars,xi,yi)
%PSYBAYES Bayesian adaptive estimation of psychometric function.
%
% PSYBAYES implements Kontsevich and Tyler's (1999) Bayesian adaptive
% method PSI for estimation of parameters of the psychometric function via
% maximization of information gain (including lapse; see Prins 2012).
% PSYBAYES also supports the marginal-PSI method by Prins (2013).
%
% See PSYTEST for documentation and a working usage example.
%
% References:
% Kontsevich, L. L., & Tyler, C. W. (1999). "Bayesian adaptive estimation
% of psychometric slope and threshold". Vision Research, 39(16), 2729-2737.
%
% Prins, N. (2012). "The adaptive psi method and the lapse rate". Journal
% of Vision, 12(9), 322-322. (link)
%
% Prins, N. (2013). "The psi-marginal adaptive method: How to give nuisance
% parameters the attention they deserve (no more, no less)". Journal of
% Vision, 13(7), 3-3.
%
% See also PSYBAYES_PLOT, PSYTEST.
% Copyright (C) 2016 Luigi Acerbi
%
% This software is distributed under the GNU General Public License
% (version 3 or later); please refer to the file LICENSE.txt, included with
% the software, for details.
% Author: Luigi Acerbi
% Email: [email protected]
% Version: 05/Oct/2016
if nargin < 1; psy = []; end
if nargin < 2; method = []; end
if nargin < 3; vars = []; end
if nargin < 4; xi = []; yi = []; end
persistent firstcall;
xnext = [];
if isempty(firstcall)
firstcall = 0;
% Add all subdirectories to MATLAB path
[path,~,~] = fileparts(mfilename('fullpath'));
addpath(genpath(path));
end
% Default method is expected entropy minimization
if isempty(method); method = 'ent'; end
% Marginal-PSI method, select parameters of interest
if isempty(vars)
switch lower(method)
case 'ent'; vars = [1 1 1];
case 'var'; vars = [1 0 0];
otherwise
error('Unknown optimization method.');
end
end
if numel(vars) ~= 3; error('VARS need to be a 3-element array for MU, SIGMA and LAMBDA.'); end
%% First call, initialize everything
if isempty(psy) || ~isfield(psy,'post')
% Call initialization function
psyinfo = psy;
[psy,Nfuns] = psyinit(psyinfo);
% Enforce symmetry of test stimuli? (symmetric wrt left/right of the
% mean of the psychometric curve)
if isfield(psyinfo,'forcesymmetry') && ~isempty(psyinfo.forcesymmetry)
psy.forcesymmetry = psyinfo.forcesymmetry;
else
psy.forcesymmetry = 0;
end
else
% Reset psychometric function
[psy,Nfuns] = psyfunset(psy);
end
% Select psychometric function
if ~iscell(psy.psychofun)
psychofun{1} = str2func(psy.psychofun);
else
for k = 1:Nfuns
psychofun{k} = str2func(psy.psychofun{k});
end
end
% Precompute psychometric function
if isempty(psy.f)
for k = 1:Nfuns
psy.f{k} = psychofun{k}(psy.x,psy.mu,psy.sigma,psy.lambda,psy.gamma);
% Check if last stimulus is easy stimulus (by default Inf)
if psy.x(end) == Inf
if isempty(psy.gamma)
temp(1,1,:) = 1-psy.lambda/2;
else
temp(1,1,:) = 1-psy.lambda*(1-psy.gamma);
end
psy.f{k}(:,:,:,end) = repmat(temp,[numel(psy.mu),numel(psy.logsigma),1]);
end
end
end
% Update log posterior given the new data points XI, YI
if ~isempty(xi) && ~isempty(yi)
for k = 1:Nfuns
for i = 1:numel(xi)
% Maximum precision stimulus
if isinf(xi(i))
if isempty(psy.gamma)
if yi(i) == 1
like = 1-psy.lambda/2;
elseif yi(i) == 0
like = psy.lambda/2;
end
else
if yi(i) == 1
like = 1-psy.lambda*(1-psy.gamma);
elseif yi(i) == 0
like = psy.lambda*(1-psy.gamma);
end
end
else
if yi(i) == 1
like = psychofun{k}(xi(i),psy.mu,psy.sigma,psy.lambda,psy.gamma);
elseif yi(i) == 0
like = 1 - psychofun{k}(xi(i),psy.mu,psy.sigma,psy.lambda,psy.gamma);
end
end
% Save unnormalized log posterior
psy.logupost{k} = bsxfun(@plus, psy.logupost{k}, log(like));
% Compute normalized posterior
psy.post{k} = exp(psy.logupost{k} - max(psy.logupost{k}(:)));
psy.post{k} = psy.post{k}./sum(psy.post{k}(:));
end
end
psy.ntrial = psy.ntrial + numel(xi);
psy.data = [psy.data; xi(:) yi(:)];
% Update refractory times list
psy.reflist = max(psy.reflist - 1, 0);
if psy.reftime > 0 && isfinite(xi)
idx = (psy.x <= xi + psy.refradius) & (psy.x >= xi - psy.refradius);
wtrials(1,1,1,:) = geornd(1/(1+psy.reftime)*ones(1,sum(idx)));
psy.reflist(idx) = max(wtrials, psy.reflist(idx));
end
end
% Compute posterior over psychometric functions
if Nfuns > 1
logp = zeros(1,Nfuns);
for k = 1:Nfuns
logp(k) = logsumexp(psy.logupost{k}(:));
end
psy.psychopost = exp(logp - max(logp));
psy.psychopost = psy.psychopost ./ sum(psy.psychopost);
else
psy.psychopost = 1;
end
% Compute mean of the posterior of mu
postmu = zeros(numel(psy.mu),Nfuns);
for k = 1:Nfuns
postmu(:,k) = sum(sum(psy.post{k},2),3);
end
emu = sum(sum(bsxfun(@times, bsxfun(@times, psy.psychopost, postmu), psy.mu),2),1);
% Randomly remove half of the x
if psy.forcesymmetry
if rand() < 0.5; xindex = psy.x < emu; else xindex = psy.x >= emu; end
else
xindex = true(size(psy.x));
end
% Consider only available stimuli
xindex = xindex & (psy.reflist == 0);
% No stimuli are available, free some stimuli and reset refractory list
if all(xindex == 0)
xindex(psy.reflist == min(psy.reflist)) = 1;
psy.reflist = zeros(size(psy.x));
end
% Compute sampling point X that minimizes expected chosen criterion
if nargin > 0
Nx = numel(psy.x);
r1 = zeros(1,1,1,Nx,Nfuns);
post1 = zeros([size(psy.post{1}),Nx,Nfuns]);
post0 = zeros([size(psy.post{1}),Nx,Nfuns]);
if Nfuns > 1
u1 = zeros(1,1,1,Nx,Nfuns);
u0 = zeros(1,1,1,Nx,Nfuns);
end
for k = 1:Nfuns
if Nfuns > 1
% Compute posteriors and unnormalized model evidence at next step for R=1 and R=0
% [post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k),u1(1,1,1,:,k),u0(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,xindex),psy.post{k},psy.logupost{k});
[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k),u1(1,1,1,:,k),u0(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,:),psy.post{k},psy.logupost{k});
else
% Compute posteriors at next step for R=1 and R=0
%[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,xindex),psy.post{k});
[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,:),psy.post{k});
end
end
% Marginalize over unrequested variables
index = find(~vars);
for iTheta = index
if iTheta ==3 && ndims(post1)==3
% BK: Special case: lambda is not being estimated (vars(3) ==0),
% but then the post1 will not have this dimension
% because it gets removed automatically as the trailing
% dimension in psy.post. So we cannot marginalize here over 3
% because that now contains the x parameter.
else
post1 = sum(post1,iTheta);
post0 = sum(post0,iTheta);
end
end
if Nfuns > 1
u0 = exp(bsxfun(@minus, u0, max(u0,[],5)));
u0 = bsxfun(@rdivide,u0,sum(u0,5));
u1 = exp(bsxfun(@minus, u1, max(u1,[],5)));
u1 = bsxfun(@rdivide,u1,sum(u1,5));
post1 = bsxfun(@times, u1, post1);
post0 = bsxfun(@times, u0, post0);
w(1,1,1,1,:) = psy.psychopost;
r1 = sum(bsxfun(@times, w, r1), 5);
end
switch lower(method)
case {'var','variance'}
post0 = squeeze(sum(post0, 5));
post1 = squeeze(sum(post1, 5));
index = find(vars,1);
switch index
case 1; qq = psy.mu(:);
case 2; qq = psy.logsigma(:);
case 3; qq = psy.lambda(:);
end
mean1 = sum(bsxfun(@times,post1,qq),1);
mean0 = sum(bsxfun(@times,post0,qq),1);
var1 = sum(bsxfun(@times,post1,qq.^2),1) - mean1.^2;
var0 = sum(bsxfun(@times,post0,qq.^2),1) - mean0.^2;
target = r1(:).*var1(:) + (1-r1(:)).*var0(:);
case {'ent','entropy'}
temp1 = -post1.*log(post1);
temp0 = -post0.*log(post0);
temp1(~isfinite(temp1)) = 0;
temp0(~isfinite(temp0)) = 0;
H1 = temp1; H0 = temp0;
for iTheta = find(vars)
H1 = sum(H1,iTheta);
H0 = sum(H0,iTheta);
end
if Nfuns > 1
H1 = sum(H1,5);
H0 = sum(H0,5);
end
target = r1(:).*H1(:) + (1-r1(:)).*H0(:);
case {'proj','projection'}
Nsteps = 4;
max(r1)
anchors = linspace(0.5, max(r1), Nsteps+1);
anchors = anchors(2:end);
target = zeros(Nx,1);
for jj = 1:numel(anchors)
for k = 1:Nfuns
[~,idx(:,:,:,1,k)] = min(abs(psy.f{k}-anchors(jj)),[],4);
end
mean1 = sum(sum(sum(sum(bsxfun(@times,post1,idx),1),2),3),5);
mean0 = sum(sum(sum(sum(bsxfun(@times,post0,idx),1),2),3),5);
var1 = sum(sum(sum(sum(bsxfun(@times,post1,idx.^2),1),2),3),5) - mean1.^2;
var0 = sum(sum(sum(sum(bsxfun(@times,post0,idx.^2),1),2),3),5) - mean0.^2;
target = target + r1(:).*var1(:) + (1-r1(:)).*var0(:);
end
case {'model'}
H1 = -u1.*log(u1);
H0 = -u0.*log(u0);
H1(~isfinite(H1)) = 0;
H0(~isfinite(H0)) = 0;
H1 = sum(H1,5);
H0 = sum(H0,5);
target = r1(:).*H1(:) + (1-r1(:)).*H0(:);
otherwise
error('Unknown method. Allowed methods are ''var'' and ''ent'' for, respectively, predicted variance and predicted entropy minimization.');
end
% Store target for plotting
psy.target = target(:)';
% Location X that minimizes target metric
[~,index] = min(target(xindex));
xred = psy.x(xindex);
xnext = xred(index);
psy.xnext = xnext;
end
% Compute parameter estimates
if nargout > 2
w = psy.psychopost;
% Compute mean and variance of the estimate of MU
postmu = marginalpost(psy.post,w,[2,3]);
postmu = postmu./sum(postmu,1);
emu = sum(postmu.*psy.mu,1);
estd = sqrt(sum(postmu.*psy.mu.^2,1) - emu.^2);
output.mu.mean = emu;
output.mu.std = estd;
% Compute mean and variance of the estimate of LOGSIGMA and SIGMA
postlogsigma = marginalpost(psy.post,w,[1,3]);
postlogsigma = postlogsigma./sum(postlogsigma,2);
emu = sum(postlogsigma.*psy.logsigma,2);
estd = sqrt(sum(postlogsigma.*psy.logsigma.^2,2) - emu.^2);
output.logsigma.mean = emu;
output.logsigma.std = estd;
postsigma = postlogsigma./psy.sigma;
postsigma = postsigma./sum(postsigma,2);
emu = sum(postsigma.*psy.sigma,2);
estd = sqrt(sum(postsigma.*psy.sigma.^2,2) - emu.^2);
output.sigma.mean = emu;
output.sigma.std = estd;
% Compute mean and variance of the estimate of LAMBDA
postlambda = marginalpost(psy.post,w,[1,2]);
postlambda = postlambda./sum(postlambda,3);
emu = sum(postlambda.*psy.lambda,3);
estd = sqrt(sum(postlambda.*psy.lambda.^2,3) - emu.^2);
output.lambda.mean = emu;
output.lambda.std = estd;
end
% Only one argument assumes that this is the final call
if nargin < 2
psy.f = []; % Empty some memory
psy.reflist = zeros(size(psy.x)); % Reset refractory times list
end
end
%--------------------------------------------------------------------------
function [post1,post0,r1,u1,u0] = nextposterior(f,post,logupost)
%NEXTPOSTERIOR Compute posteriors on next trial depending on possible outcomes
mf = 1-f;
post1 = bsxfun(@times, post, f);
r1 = sum(sum(sum(post1,1),2),3);
post0 = bsxfun(@times, post, mf);
post1 = bsxfun(@rdivide, post1, r1);
post0 = bsxfun(@rdivide, post0, sum(sum(sum(post0,1),2),3));
if nargin > 2 && nargout > 3
logupost1 = bsxfun(@plus, logupost, log(f));
logupost0 = bsxfun(@plus, logupost, log(mf));
z0 = max(logupost0(:));
u0 = log(sum(sum(sum(exp(logupost0 - z0),1),2),3));
z1 = max(logupost1(:));
u1 = log(sum(sum(sum(exp(logupost1 - z1),1),2),3));
end
end
%--------------------------------------------------------------------------
function r = geornd(p)
%GEORND Random arrays from the geometric distribution.
p(p <= 0 | p > 1) = NaN; % Return NaN for illegal parameter values
r = ceil(abs(log(rand(size(p))) ./ log(1 - p)) - 1); % == geoinv(u,p)
r(r < 0) = 0; % Force a zero when p==1, instead of -1
end
|
github
|
lacerbi/psybayes-master
|
psybayes_joint.m
|
.m
|
psybayes-master/psybayes_joint.m
| 16,283 |
utf_8
|
fe389c953ab293ff15d881ee2ef89326
|
function [xnext,psy,output] = psybayes_joint(psy,method,vars,xi,yi,ci)
%PSYBAYES_JOINT Joint Bayesian adaptive estimation of psychometric functions.
%
% PSYBAYES implements Kontsevich and Tyler's (1999) Bayesian adaptive
% method PSI for estimation of parameters of the psychometric function via
% maximization of information gain (including lapse; see Prins 2012).
% PSYBAYES also supports the marginal-PSI method by Prins (2013).
%
% See PSYTEST for documentation and a working usage example.
%
% References:
% Kontsevich, L. L., & Tyler, C. W. (1999). "Bayesian adaptive estimation
% of psychometric slope and threshold". Vision Research, 39(16), 2729-2737.
%
% Prins, N. (2012). "The adaptive psi method and the lapse rate". Journal
% of Vision, 12(9), 322-322. (link)
%
% Prins, N. (2013). "The psi-marginal adaptive method: How to give nuisance
% parameters the attention they deserve (no more, no less)". Journal of
% Vision, 13(7), 3-3.
%
% See also PSYBAYES_PLOT, PSYTEST.
% Copyright (C) 2016 Luigi Acerbi
%
% This software is distributed under the GNU General Public License
% (version 3 or later); please refer to the file LICENSE.txt, included with
% the software, for details.
% Author: Luigi Acerbi
% Email: [email protected]
% Version: 05/Oct/2016
if nargin < 1; psy = []; end
if nargin < 2; method = []; end
if nargin < 3; vars = []; end
if nargin < 4; xi = []; yi = []; end
if nargin < 5; ci = []; end
persistent firstcall;
xnext = [];
if isempty(firstcall)
firstcall = 0;
% Add all subdirectories to MATLAB path
[path,~,~] = fileparts(mfilename('fullpath'));
addpath(genpath(path));
end
% Default method is expected entropy minimization
if isempty(method); method = 'ent'; end
% Marginal-PSI method, select parameters of interest
if isempty(vars)
switch lower(method)
case 'ent'; vars = [1 1 1];
case 'var'; vars = [1 0 0];
otherwise
error('Unknown optimization method.');
end
end
if numel(vars) ~= 3; error('VARS need to be a 3-element array for MU, SIGMA and LAMBDA.'); end
%% Initialization of PSY structures
% Empty struct, a single psychometric function
if isempty(psy); psy = {[]}; end
% PSY can be NCND, number of experimental conditions
if isnumeric(psy) && isscalar(psy)
psy = cell(1,psy);
end
Ncnd = numel(psy); % Number of experimental conditions
sharedlambda = Ncnd > 1; % If fitting multiple conditions, assume lapse rate is shared
% Number of psychometric curves
Nfuns = zeros(1,Ncnd);
for c = 1:Ncnd
if isempty(psy{c}) || ~isfield(psy{c},'post')
% Call initialization function
psyinfo = psy{c};
[psy{c},Nfuns(c)] = psyinit(psyinfo,Ncnd);
% Enforce symmetry of test stimuli? (symmetric wrt left/right of the
% mean of the psychometric curve)
if isfield(psyinfo,'forcesymmetry') && ~isempty(psyinfo.forcesymmetry)
psy{c}.forcesymmetry = psyinfo.forcesymmetry;
else
psy{c}.forcesymmetry = 0;
end
else
% Reset psychometric function
[psy{c},Nfuns(c)] = psyfunset(psy{c});
end
end
if ~all(Nfuns == Nfuns(1))
error('All conditions should have the same number of psychometric curves.');
end
Nfuns = Nfuns(1);
if Ncnd > 1 && Nfuns > 1
error('For the moment joint psychometric curve fitting only supports a single psychometric curve model.');
end
% Initialize psychometric functions in each condition
for c = 1:Ncnd
% Convert psychometric function to function handle
if ~iscell(psy{c}.psychofun)
psychofun{c}{1} = str2func(psy{c}.psychofun);
else
for k = 1:Nfuns
psychofun{c}{k} = str2func(psy{c}.psychofun{k});
end
end
% Precompute psychometric function
if isempty(psy{c}.f)
for k = 1:Nfuns
psy{c}.f{k} = psychofun{c}{k}(psy{c}.x,psy{c}.mu,psy{c}.sigma,psy{c}.lambda,psy{c}.gamma);
% Check if last stimulus is easy stimulus (by default Inf)
if psy{c}.x(end) == Inf
if isempty(psy{c}.gamma)
temp(1,1,:) = 1-psy{c}.lambda/2;
else
temp(1,1,:) = 1-psy{c}.lambda*(1-psy{c}.gamma);
end
psy{c}.f{k}(:,:,:,end) = repmat(temp,[numel(psy{c}.mu),numel(psy{c}.logsigma),1]);
end
end
end
end
% Update log posterior given the new data points XI, YI
if ~isempty(xi) && ~isempty(yi)
if isempty(ci) && Ncnd == 1
ci = 1;
elseif isempty(ci)
error('Current condition index CI not specified.');
elseif ~isscalar(ci)
error('Current condition index needs to be a scalar.');
end
for k = 1:Nfuns
for i = 1:numel(xi)
cii = ci;
% Maximum precision stimulus
if isinf(xi(i))
if isempty(psy{cii}.gamma)
if yi(i) == 1
like = 1-psy{cii}.lambda/2;
elseif yi(i) == 0
like = psy{cii}.lambda/2;
end
else
if yi(i) == 1
like = 1-psy{cii}.lambda*(1-psy{cii}.gamma);
elseif yi(i) == 0
like = psy{cii}.lambda*(1-psy{cii}.gamma);
end
end
else
if yi(i) == 1
like = psychofun{cii}{k}(xi(i),psy{cii}.mu,psy{cii}.sigma,psy{cii}.lambda,psy{cii}.gamma);
elseif yi(i) == 0
like = 1 - psychofun{cii}{k}(xi(i),psy{cii}.mu,psy{cii}.sigma,psy{cii}.lambda,psy{cii}.gamma);
end
end
% Save unnormalized log posterior
psy{cii}.logupost{k} = bsxfun(@plus, psy{cii}.logupost{k}, log(like));
% Compute posterior over lambda for this condition
temp = psy{cii}.logupost{k};
temp = exp(temp - max(temp(:)));
psy{cii}.postlambda{k} = sum(sum(temp,1),2) / sum(temp(:));
% Compute joint posterior over lambda
if sharedlambda
postlambda_joint{k} = ones(size(psy{cii}.postlambda{k}));
for c = 1:Ncnd
postlambda_joint{k} = postlambda_joint{k} .* psy{c}.postlambda{k};
end
postlambda_joint{k} = postlambda_joint{k} / sum(postlambda_joint{k});
end
% Compute normalized posterior
temp = exp(psy{cii}.logupost{k} - max(psy{cii}.logupost{k}(:)));
if sharedlambda
temp = bsxfun(@times, temp, postlambda_joint{k} ./ psy{cii}.postlambda{k});
end
psy{cii}.post{k} = temp./sum(temp(:));
end
end
% Update data
for i = 1:numel(xi)
cii = ci;
psy{cii}.ntrial = psy{cii}.ntrial + 1;
psy{cii}.data = [psy{cii}.data; xi(i) yi(i)];
end
% Update refractory times list for each presented stimulus
for i = 1:numel(xi)
cii = ci;
psy{cii}.reflist = max(psy{cii}.reflist - 1, 0);
if psy{cii}.reftime > 0 && isfinite(xi(i))
idx = (psy{cii}.x <= xi(i) + psy{cii}.refradius) & (psy{cii}.x >= xi(i) - psy{cii}.refradius);
wtrials(1,1,1,:) = geornd(1/(1+psy{cii}.reftime)*ones(1,sum(idx)));
psy{cii}.reflist(idx) = max(wtrials, psy{cii}.reflist(idx));
end
end
end
% Compute posterior over psychometric functions
for c = 1:Ncnd
if Nfuns > 1
logp = zeros(1,Nfuns);
for k = 1:Nfuns
logp(k) = logsumexp(psy{c}.logupost{k}(:));
end
% This is not correct for shared lambda
psy{c}.psychopost = exp(logp - max(logp));
psy{c}.psychopost = psy{c}.psychopost ./ sum(psy{c}.psychopost);
else
psy{c}.psychopost = 1;
end
end
% Only one argument assumes that this is the final call
if nargin < 2
for c = 1:Ncnd
psy{c}.f = []; % Empty some memory
psy{c}.reflist = zeros(size(psy{c}.x)); % Reset refractory times list
end
return;
end
% Compute mean of the posterior of mu for the current condition
postmu = zeros(numel(psy{ci}.mu),Nfuns);
for k = 1:Nfuns
postmu(:,k) = sum(sum(psy{ci}.post{k},2),3);
end
emu = sum(sum(bsxfun(@times, bsxfun(@times, psy{ci}.psychopost, postmu), psy{ci}.mu),2),1);
% Randomly remove half of the x
if psy{ci}.forcesymmetry
if rand() < 0.5; xindex = psy{ci}.x < emu; else xindex = psy{ci}.x >= emu; end
else
xindex = true(size(psy{ci}.x));
end
% Consider only available stimuli
xindex = xindex & (psy{ci}.reflist == 0);
% No stimuli are available, free some stimuli and reset refractory list
if all(xindex == 0)
xindex(psy{ci}.reflist == min(psy{ci}.reflist)) = 1;
psy{ci}.reflist = zeros(size(psy{ci}.x));
end
% Compute sampling point X that minimizes expected chosen criterion
if nargin > 0
Nx = numel(psy{ci}.x);
r1 = zeros(1,1,1,Nx,Nfuns);
post1 = zeros([size(psy{ci}.post{1}),Nx,Nfuns]);
post0 = zeros([size(psy{ci}.post{1}),Nx,Nfuns]);
if Nfuns > 1
u1 = zeros(1,1,1,Nx,Nfuns);
u0 = zeros(1,1,1,Nx,Nfuns);
end
for k = 1:Nfuns
if Nfuns > 1
% Compute posteriors and unnormalized model evidence at next step for R=1 and R=0
% [post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k),u1(1,1,1,:,k),u0(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,xindex),psy.post{k},psy.logupost{k});
% This is not completely correct for shared lapse rate
[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k),u1(1,1,1,:,k),u0(1,1,1,:,k)] = nextposterior(psy{ci}.f{k}(:,:,:,:),psy{ci}.post{k},psy{ci}.logupost{k});
else
% Compute posteriors at next step for R=1 and R=0
%[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k)] = nextposterior(psy.f{k}(:,:,:,xindex),psy.post{k});
[post1(:,:,:,:,k),post0(:,:,:,:,k),r1(1,1,1,:,k)] = nextposterior(psy{ci}.f{k}(:,:,:,:),psy{ci}.post{k});
end
end
% Marginalize over unrequested variables
index = find(~vars);
for iTheta = index
post1 = sum(post1,iTheta);
post0 = sum(post0,iTheta);
end
if Nfuns > 1
u0 = exp(bsxfun(@minus, u0, max(u0,[],5)));
u0 = bsxfun(@rdivide,u0,sum(u0,5));
u1 = exp(bsxfun(@minus, u1, max(u1,[],5)));
u1 = bsxfun(@rdivide,u1,sum(u1,5));
post1 = bsxfun(@times, u1, post1);
post0 = bsxfun(@times, u0, post0);
w(1,1,1,1,:) = psy{ci}.psychopost;
r1 = sum(bsxfun(@times, w, r1), 5);
end
switch lower(method)
case {'var','variance'}
post0 = squeeze(sum(post0, 5));
post1 = squeeze(sum(post1, 5));
index = find(vars,1);
switch index
case 1; qq = psy{ci}.mu(:);
case 2; qq = psy{ci}.logsigma(:);
case 3; qq = psy{ci}.lambda(:);
end
mean1 = sum(bsxfun(@times,post1,qq),1);
mean0 = sum(bsxfun(@times,post0,qq),1);
var1 = sum(bsxfun(@times,post1,qq.^2),1) - mean1.^2;
var0 = sum(bsxfun(@times,post0,qq.^2),1) - mean0.^2;
target = r1(:).*var1(:) + (1-r1(:)).*var0(:);
case {'ent','entropy'}
temp1 = -post1.*log(post1);
temp0 = -post0.*log(post0);
temp1(~isfinite(temp1)) = 0;
temp0(~isfinite(temp0)) = 0;
H1 = temp1; H0 = temp0;
for iTheta = find(vars)
H1 = sum(H1,iTheta);
H0 = sum(H0,iTheta);
end
if Nfuns > 1
H1 = sum(H1,5);
H0 = sum(H0,5);
end
target = r1(:).*H1(:) + (1-r1(:)).*H0(:);
case {'proj','projection'}
Nsteps = 4;
max(r1)
anchors = linspace(0.5, max(r1), Nsteps+1);
anchors = anchors(2:end);
target = zeros(Nx,1);
for jj = 1:numel(anchors)
for k = 1:Nfuns
[~,idx(:,:,:,1,k)] = min(abs(psy{ci}.f{k}-anchors(jj)),[],4);
end
mean1 = sum(sum(sum(sum(bsxfun(@times,post1,idx),1),2),3),5);
mean0 = sum(sum(sum(sum(bsxfun(@times,post0,idx),1),2),3),5);
var1 = sum(sum(sum(sum(bsxfun(@times,post1,idx.^2),1),2),3),5) - mean1.^2;
var0 = sum(sum(sum(sum(bsxfun(@times,post0,idx.^2),1),2),3),5) - mean0.^2;
target = target + r1(:).*var1(:) + (1-r1(:)).*var0(:);
end
case {'model'}
H1 = -u1.*log(u1);
H0 = -u0.*log(u0);
H1(~isfinite(H1)) = 0;
H0(~isfinite(H0)) = 0;
H1 = sum(H1,5);
H0 = sum(H0,5);
target = r1(:).*H1(:) + (1-r1(:)).*H0(:);
otherwise
error('Unknown method. Allowed methods are ''var'' and ''ent'' for, respectively, predicted variance and predicted entropy minimization.');
end
% Store target for plotting
psy{ci}.target = target(:)';
% Location X that minimizes target metric
[~,index] = min(target(xindex));
xred = psy{ci}.x(xindex);
xnext = xred(index);
psy{ci}.xnext = xnext;
end
% Compute parameter estimates
if nargout > 2
w = psy{ci}.psychopost;
% Compute mean and variance of the estimate of MU
postmu = marginalpost(psy{ci}.post,w,[2,3]);
postmu = postmu./sum(postmu,1);
emu = sum(postmu.*psy{ci}.mu,1);
estd = sqrt(sum(postmu.*psy{ci}.mu.^2,1) - emu.^2);
output.mu.mean = emu;
output.mu.std = estd;
% Compute mean and variance of the estimate of LOGSIGMA and SIGMA
postlogsigma = marginalpost(psy{ci}.post,w,[1,3]);
postlogsigma = postlogsigma./sum(postlogsigma,2);
emu = sum(postlogsigma.*psy{ci}.logsigma,2);
estd = sqrt(sum(postlogsigma.*psy{ci}.logsigma.^2,2) - emu.^2);
output.logsigma.mean = emu;
output.logsigma.std = estd;
postsigma = postlogsigma./psy{ci}.sigma;
postsigma = postsigma./sum(postsigma,2);
emu = sum(postsigma.*psy{ci}.sigma,2);
estd = sqrt(sum(postsigma.*psy{ci}.sigma.^2,2) - emu.^2);
output.sigma.mean = emu;
output.sigma.std = estd;
% Compute mean and variance of the estimate of LAMBDA
if sharedlambda && Nfuns == 1
postlambda = psy{ci}.postlambda{1};
else
% This is possibly not correct
postlambda = marginalpost(psy{ci}.post,w,[1,2]);
end
postlambda = postlambda./sum(postlambda,3);
emu = sum(postlambda.*psy{ci}.lambda,3);
estd = sqrt(sum(postlambda.*psy{ci}.lambda.^2,3) - emu.^2);
output.lambda.mean = emu;
output.lambda.std = estd;
end
end
%--------------------------------------------------------------------------
function [post1,post0,r1,u1,u0] = nextposterior(f,post,logupost)
%NEXTPOSTERIOR Compute posteriors on next trial depending on possible outcomes
mf = 1-f;
post1 = bsxfun(@times, post, f);
r1 = sum(sum(sum(post1,1),2),3);
post0 = bsxfun(@times, post, mf);
post1 = bsxfun(@rdivide, post1, sum(sum(sum(post1,1),2),3));
post0 = bsxfun(@rdivide, post0, sum(sum(sum(post0,1),2),3));
if nargin > 2 && nargout > 3
logupost1 = bsxfun(@plus, logupost, log(f));
logupost0 = bsxfun(@plus, logupost, log(mf));
z0 = max(logupost0(:));
u0 = log(sum(sum(sum(exp(logupost0 - z0),1),2),3));
z1 = max(logupost1(:));
u1 = log(sum(sum(sum(exp(logupost1 - z1),1),2),3));
end
end
%--------------------------------------------------------------------------
function r = geornd(p)
%GEORND Random arrays from the geometric distribution.
p(p <= 0 | p > 1) = NaN; % Return NaN for illegal parameter values
r = ceil(abs(log(rand(size(p))) ./ log(1 - p)) - 1); % == geoinv(u,p)
r(r < 0) = 0; % Force a zero when p==1, instead of -1
end
|
github
|
lacerbi/psybayes-master
|
psyinit.m
|
.m
|
psybayes-master/private/psyinit.m
| 6,800 |
utf_8
|
e24bc831e0a692754ae68ce3049c887e
|
function [psy,Nfuns] = psyinit(psyinfo,Ncnd)
%PSYINIT Initialize PSY struct.
% Total number of conditions (one by default)
if nargin < 2 || isempty(Ncnd); Ncnd = 1; end
psy = [];
psy.ntrial = 0; % Trial number
psy.data = []; % Record of data
if ~isfield(psyinfo,'psychofun'); psyinfo.psychofun = []; end
if iscell(psyinfo.psychofun)
Nfuns = numel(psyinfo.psychofun);
cellflag = 1;
else
Nfuns = 1;
cellflag = 0;
end
% Default grid sizes
K = 1/(Nfuns^0.25);
nx = round(65*K);
nmu = round(51*K);
nsigma = round(25*K);
nlambda = round(25*K);
psy.mu = [];
psy.logsigma = [];
psy.lambda = [];
psy.x = [];
% Grid over parameters of psychometric function
if isfield(psyinfo,'range')
% Get grid sizes (third element in initialization range field)
if isfield(psyinfo.range,'mu') && numel(psyinfo.range.mu > 2)
nmu = psyinfo.range.mu(3);
end
if isfield(psyinfo.range,'sigma') && numel(psyinfo.range.sigma > 2)
nsigma = psyinfo.range.sigma(3);
elseif isfield(psyinfo.range,'logsigma') && numel(psyinfo.range.logsigma > 2)
nsigma = psyinfo.range.logsigma(3);
end
if isfield(psyinfo.range,'lambda') && numel(psyinfo.range.lambda > 2)
nlambda = psyinfo.range.lambda(3);
end
if isfield(psyinfo.range,'x') && numel(psyinfo.range.x > 2)
nx = psyinfo.range.x(3);
end
% Prepare ranges
if isfield(psyinfo.range,'mu')
psy.mu(:,1,1) = linspace(psyinfo.range.mu(1),psyinfo.range.mu(2),nmu);
else
error('Cannot find a field for MU in initialization range struct.');
end
if isfield(psyinfo.range,'sigma')
psy.logsigma(1,:,1) = linspace(log(psyinfo.range.sigma(1)), log(psyinfo.range.sigma(2)), nsigma);
elseif isfield(psyinfo.range,'logsigma')
psy.logsigma(1,:,1) = linspace(psyinfo.range.logsigma(1), psyinfo.range.logsigma(2), nsigma);
else
error('Cannot find a field for SIGMA in initialization range struct.');
end
if isfield(psyinfo.range,'lambda')
psy.lambda(1,1,:) = linspace(psyinfo.range.lambda(1),psyinfo.range.lambda(2),nlambda);
end
if isfield(psyinfo,'x') && ~isempty(psyinfo.x)
psy.x(1,1,1,:) = psyinfo.x(:);
elseif isfield(psyinfo.range,'x') && ~isempty(psyinfo.range.x)
psy.x(1,1,1,:) = linspace(psyinfo.range.x(1),psyinfo.range.x(2),nx);
else
error('Test grid X not provided in initialization struct.');
end
end
% Default ranges
if isempty(psy.mu)
psy.mu(:,1,1) = linspace(2,4,nmu);
end
if isempty(psy.logsigma)
psy.logsigma(1,:,1) = linspace(log(0.01), log(1), nsigma);
end
if isempty(psy.lambda)
psy.lambda(1,1,:) = linspace(0, 0.2, nlambda);
end
if isempty(psy.x)
psy.x(1,1,1,:) = linspace(psy.mu(1),psy.mu(end),nx);
end
if isfield(psyinfo,'units')
psy.units = psyinfo.units;
else
psy.units.x = [];
psy.units.mu = [];
psy.units.sigma = [];
psy.units.lambda = [];
end
% By default, wide Student's t prior on mu with slight preference for
% the middle of the stimulus range
muprior = [mean(psy.mu),0.5*(psy.mu(end)-psy.mu(1)),3]; % mean, sigma and nu
if isfield(psyinfo,'priors') && ~isempty(psyinfo.priors)
if isfield(psyinfo.priors,'mu') && ~isempty(psyinfo.priors.mu)
muprior(1:numel(psyinfo.priors.mu)) = psyinfo.priors.mu;
end
end
priormu = exp(logtpdf(psy.mu,muprior(1),muprior(2),muprior(3)));
% By default flat prior on log sigma (Jeffrey's 1/sigma prior in sigma
% space); more in general log-Student-t prior
logsigmaprior = [mean(psy.logsigma),Inf,3]; % mean, sigma and nu
if isfield(psyinfo,'priors') && ~isempty(psyinfo.priors)
if isfield(psyinfo.priors,'logsigma') && ~isempty(psyinfo.priors.logsigma)
logsigmaprior(1:numel(psyinfo.priors.logsigma)) = psyinfo.priors.logsigma;
end
end
priorlogsigma = exp(logtpdf(psy.logsigma,logsigmaprior(1),logsigmaprior(2),logsigmaprior(3)));
% Beta(a,b) prior on lambda, with correction
lambdaprior = [1,19];
if isfield(psyinfo,'priors') && ~isempty(psyinfo.priors)
if isfield(psyinfo.priors,'lambda') && ~isempty(psyinfo.priors.lambda)
lambdaprior = psyinfo.priors.lambda;
end
end
temp = psy.lambda(:)';
temp = [0, temp + 0.5*[diff(temp),0]];
a = lambdaprior(1); b = lambdaprior(2);
priorlambda(1,1,:) = betainc(temp(2:end),a,b) - betainc(temp(1:end-1),a,b);
% If using multiple conditions, divide prior
if Ncnd > 1
fprintf('Sharing prior over LAMBDA across %d conditions.\n', Ncnd);
priorlambda = priorlambda.^(1/Ncnd);
end
priormu = priormu./sum(priormu);
priorlogsigma = priorlogsigma./sum(priorlogsigma);
priorlambda = priorlambda./sum(priorlambda);
% Prior (posterior at iteration zero) over parameters
psy.post{1} = bsxfun(@times,bsxfun(@times,priormu,priorlogsigma),priorlambda);
for k = 2:Nfuns; psy.post{k} = psy.post{1}; end
for k = 1:Nfuns; psy.logupost{k} = log(psy.post{k}); end
% Posterior over lambda at iteration zero
psy.postlambda{1} = priorlambda;
for k = 2:Nfuns; psy.postlambda{k} = psy.postlambda{1}; end
% Define sigma in addition to log sigma
psy.sigma = exp(psy.logsigma);
% Guess rate for PCORRECT psychometric functions
if isfield(psyinfo,'gamma')
psy.gamma = psyinfo.gamma;
else
psy.gamma = [];
end
psy.f = [];
psy.xnext = [];
% Stimulus refractory time (avoid representing same stimulus for a while)
psy.reftime = []; psy.refradius = [];
% Refractory time constant (average run length of geometric distribution)
if isfield(psyinfo,'reftime')
psy.reftime = psyinfo.reftime;
end
if isempty(psy.reftime); psy.reftime = 0; end
% Waiting radius (avoid representing stimuli within this radius)
if isfield(psyinfo,'refradius')
psy.refradius = psyinfo.refradius;
end
if isempty(psy.refradius); psy.refradius = 0; end
% Initialize refractory times list
psy.reflist = zeros(size(psy.x));
% Set psychometric function
if isfield(psyinfo,'psychofun'); psy.psychofun = psyinfo.psychofun; end
[psy,Nfuns] = psyfunset(psy);
% Prior over psychometric functions
if Nfuns > 1
if isfield(psyinfo,'psychoprior')
psy.psychoprior = psyinfo.psychoprior;
else
psy.psychoprior = [];
end
% Uniform prior by default
if isempty(psy.psychoprior)
psy.psychoprior = ones(1,Nfuns)/Nfuns;
end
end
end
%--------------------------------------------------------------------------
function y = logtpdf(x,mu,sigma,nu)
%LOGTPDF Log pdf of Student's t distribution.
if sigma == Inf % Flat log prior
y = zeros(size(x));
elseif nu == Inf % Student's t with infinite degrees of freedom is Gaussian
y = -0.5*log(2*pi*sigma^2) -0.5*((x-mu)/sigma).^2;
else
y = gammaln(0.5*(nu+1)) - gammaln(0.5*nu) - 0.5*log(pi*nu*sigma^2) ...
- 0.5*(nu+1) * (1 + 1/nu * ((x-mu)/sigma).^2);
end
end
|
github
|
xizou/NIPGD-master
|
call_abq.m
|
.m
|
NIPGD-master/call_abq.m
| 2,729 |
utf_8
|
2de73aa53534fe0047a5251b466dc19d
|
function [Ut] = call_abq(E1_tilde,E2_tilde,F_star)
%CALL_ABQ Calling Abaqus to solve K_star*U=F_star.
% Load parameters for stiffness matrix scaling.
% Works only for 3D mesh.
% SYNOPOSIS: Ut = call_abq(E1_tilde,E2_tilde,F_star);
% INPUT: E1_title : First parameter value, scalar
% E2_title : Second parameter value, scalar
% F_star : Load-like right hand side, vector
% OUTPUT: Ut : Displacement-like result, vector
% Updated on: Oct. 8st, 2016
% Author: Xi Zou
% University of Pavia, Italy
% Polytechnic University of Catalonia, Spain
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% Generate text file for parameters
fPara = fopen('param.inp','w');
fprintf(fPara,'*MATRIX INPUT, NAME=K_1, INPUT=K_1_X1.sim, MATRIX=STIFFNESS, SCALE FACTOR=%s\n', num2str(E1_tilde));
fprintf(fPara,'*MATRIX INPUT, NAME=K_2, INPUT=K_2_X1.sim, MATRIX=STIFFNESS, SCALE FACTOR=%s\n', num2str(E2_tilde));
fclose(fPara);
% Load loads
fLoad = fopen('load.inp','w');
siz = [3,length(F_star)/3];
F = reshape(F_star,siz);
[dof,node] = ind2sub(siz,find(F));
for i=1:length(node)
fprintf(fLoad,'%d, %d, %g\n',node(i),dof(i),F(dof(i),node(i)));
end
fclose(fLoad);
% Run analysis via system call
[~,~] = system('abaqus job=job input=template >job.log');
% Load results from output file
Ut = load_fil('job.fil');
Ut = sparse(Ut);
delete job.* param.inp load.inp
end
function F = load_fil(fName)
%LOAD_FIL Load displacement data from Abaqus .fil output.
% Works only for 3D mesh.
% SYNOPOSIS: F = load_fil(fName);
% INPUT: fName : File name of the .fil output, including extension, string
% OUTPUT: F : Displacement-like result, vector
% Updated on: Oct. 1st, 2016
% Author: Xi Zou
% University of Pavia, Italy
% Polytechnic University of Catalunya, Spain
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%% Load Abaqus .fil output file
fID = fopen(fName,'r');
C = textscan(fID,'%s','Delimiter','','EndOfLine','*');
fclose(fID);
C = C{1};
j = 1;
for i=1:size(C,1)
A = C{i};
A = A(~isspace(A));
if strfind(A,'I16I3101I')
st = strfind(A,'D');
A(st(1:2:end)) = ',';
data = textscan(A(st(1)+1:end),'%f,%f,%f');
F(j,:) = cell2mat(data);
j = j + 1;
end
end
F = F';
F = F(:);
end
|
github
|
wireapp/onepassword-app-extension-master
|
OnePasswordExtension.m
|
.m
|
onepassword-app-extension-master/OnePasswordExtension.m
| 41,868 |
utf_8
|
ad5b44a4da8cc02db0488a7b92fb7a0a
|
//
// 1Password Extension
//
// Lovingly handcrafted by Dave Teare, Michael Fey, Rad Azzouz, and Roustem Karimov.
// Copyright (c) 2014 AgileBits. All rights reserved.
//
#import "OnePasswordExtension.h"
// Version
#define VERSION_NUMBER @(182)
static NSString *const AppExtensionVersionNumberKey = @"version_number";
// Available App Extension Actions
static NSString *const kUTTypeAppExtensionFindLoginAction = @"org.appextension.find-login-action";
static NSString *const kUTTypeAppExtensionSaveLoginAction = @"org.appextension.save-login-action";
static NSString *const kUTTypeAppExtensionChangePasswordAction = @"org.appextension.change-password-action";
static NSString *const kUTTypeAppExtensionFillWebViewAction = @"org.appextension.fill-webview-action";
static NSString *const kUTTypeAppExtensionFillBrowserAction = @"org.appextension.fill-browser-action";
// WebView Dictionary keys
static NSString *const AppExtensionWebViewPageFillScript = @"fillScript";
static NSString *const AppExtensionWebViewPageDetails = @"pageDetails";
@implementation OnePasswordExtension
#pragma mark - Public Methods
+ (OnePasswordExtension *)sharedExtension {
static dispatch_once_t onceToken;
static OnePasswordExtension *__sharedExtension;
dispatch_once(&onceToken, ^{
__sharedExtension = [OnePasswordExtension new];
});
return __sharedExtension;
}
- (BOOL)isAppExtensionAvailable {
if ([self isSystemAppExtensionAPIAvailable]) {
return [[UIApplication sharedApplication] canOpenURL:[NSURL URLWithString:@"org-appextension-feature-password-management://"]];
}
return NO;
}
#pragma mark - Native app Login
- (void)findLoginForURLString:(nonnull NSString *)URLString forViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender completion:(nullable void (^)(NSDictionary * __nullable loginDictionary, NSError * __nullable error))completion {
NSAssert(URLString != nil, @"URLString must not be nil");
NSAssert(viewController != nil, @"viewController must not be nil");
if (NO == [self isSystemAppExtensionAPIAvailable]) {
NSLog(@"Failed to findLoginForURLString, system API is not available");
if (completion) {
completion(nil, [OnePasswordExtension systemAppExtensionAPINotAvailableError]);
}
return;
}
#ifdef __IPHONE_8_0
NSDictionary *item = @{ AppExtensionVersionNumberKey: VERSION_NUMBER, AppExtensionURLStringKey: URLString };
UIActivityViewController *activityViewController = [self activityViewControllerForItem:item viewController:viewController sender:sender typeIdentifier:kUTTypeAppExtensionFindLoginAction];
activityViewController.completionWithItemsHandler = ^(NSString *activityType, BOOL completed, NSArray *returnedItems, NSError *activityError) {
if (returnedItems.count == 0) {
NSError *error = nil;
if (activityError) {
NSLog(@"Failed to findLoginForURLString: %@", activityError);
error = [OnePasswordExtension failedToContactExtensionErrorWithActivityError:activityError];
}
else {
error = [OnePasswordExtension extensionCancelledByUserError];
}
if (completion) {
completion(nil, error);
}
return;
}
[self processExtensionItem:returnedItems.firstObject completion:^(NSDictionary *itemDictionary, NSError *error) {
if (completion) {
completion(itemDictionary, error);
}
}];
};
[viewController presentViewController:activityViewController animated:YES completion:nil];
#endif
}
#pragma mark - New User Registration
- (void)storeLoginForURLString:(nonnull NSString *)URLString loginDetails:(nullable NSDictionary *)loginDetailsDictionary passwordGenerationOptions:(nullable NSDictionary *)passwordGenerationOptions forViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender completion:(nullable void (^)(NSDictionary * __nullable loginDictionary, NSError * __nullable error))completion {
NSAssert(URLString != nil, @"URLString must not be nil");
NSAssert(viewController != nil, @"viewController must not be nil");
if (NO == [self isSystemAppExtensionAPIAvailable]) {
NSLog(@"Failed to storeLoginForURLString, system API is not available");
if (completion) {
completion(nil, [OnePasswordExtension systemAppExtensionAPINotAvailableError]);
}
return;
}
#ifdef __IPHONE_8_0
NSMutableDictionary *newLoginAttributesDict = [NSMutableDictionary new];
newLoginAttributesDict[AppExtensionVersionNumberKey] = VERSION_NUMBER;
newLoginAttributesDict[AppExtensionURLStringKey] = URLString;
[newLoginAttributesDict addEntriesFromDictionary:loginDetailsDictionary];
if (passwordGenerationOptions.count > 0) {
newLoginAttributesDict[AppExtensionPasswordGeneratorOptionsKey] = passwordGenerationOptions;
}
UIActivityViewController *activityViewController = [self activityViewControllerForItem:newLoginAttributesDict viewController:viewController sender:sender typeIdentifier:kUTTypeAppExtensionSaveLoginAction];
activityViewController.completionWithItemsHandler = ^(NSString *activityType, BOOL completed, NSArray *returnedItems, NSError *activityError) {
if (returnedItems.count == 0) {
NSError *error = nil;
if (activityError) {
NSLog(@"Failed to storeLoginForURLString: %@", activityError);
error = [OnePasswordExtension failedToContactExtensionErrorWithActivityError:activityError];
}
else {
error = [OnePasswordExtension extensionCancelledByUserError];
}
if (completion) {
completion(nil, error);
}
return;
}
[self processExtensionItem:returnedItems.firstObject completion:^(NSDictionary *itemDictionary, NSError *error) {
if (completion) {
completion(itemDictionary, error);
}
}];
};
[viewController presentViewController:activityViewController animated:YES completion:nil];
#endif
}
#pragma mark - Change Password
- (void)changePasswordForLoginForURLString:(nonnull NSString *)URLString loginDetails:(nullable NSDictionary *)loginDetailsDictionary passwordGenerationOptions:(nullable NSDictionary *)passwordGenerationOptions forViewController:(UIViewController *)viewController sender:(nullable id)sender completion:(nullable void (^)(NSDictionary * __nullable loginDictionary, NSError * __nullable error))completion {
NSAssert(URLString != nil, @"URLString must not be nil");
NSAssert(viewController != nil, @"viewController must not be nil");
if (NO == [self isSystemAppExtensionAPIAvailable]) {
NSLog(@"Failed to changePasswordForLoginWithUsername, system API is not available");
if (completion) {
completion(nil, [OnePasswordExtension systemAppExtensionAPINotAvailableError]);
}
return;
}
#ifdef __IPHONE_8_0
NSMutableDictionary *item = [NSMutableDictionary new];
item[AppExtensionVersionNumberKey] = VERSION_NUMBER;
item[AppExtensionURLStringKey] = URLString;
[item addEntriesFromDictionary:loginDetailsDictionary];
if (passwordGenerationOptions.count > 0) {
item[AppExtensionPasswordGeneratorOptionsKey] = passwordGenerationOptions;
}
UIActivityViewController *activityViewController = [self activityViewControllerForItem:item viewController:viewController sender:sender typeIdentifier:kUTTypeAppExtensionChangePasswordAction];
activityViewController.completionWithItemsHandler = ^(NSString *activityType, BOOL completed, NSArray *returnedItems, NSError *activityError) {
if (returnedItems.count == 0) {
NSError *error = nil;
if (activityError) {
NSLog(@"Failed to changePasswordForLoginWithUsername: %@", activityError);
error = [OnePasswordExtension failedToContactExtensionErrorWithActivityError:activityError];
}
else {
error = [OnePasswordExtension extensionCancelledByUserError];
}
if (completion) {
completion(nil, error);
}
return;
}
[self processExtensionItem:returnedItems.firstObject completion:^(NSDictionary *itemDictionary, NSError *error) {
if (completion) {
completion(itemDictionary, error);
}
}];
};
[viewController presentViewController:activityViewController animated:YES completion:nil];
#endif
}
#pragma mark - Web View filling Support
- (void)fillItemIntoWebView:(nonnull id)webView forViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender showOnlyLogins:(BOOL)yesOrNo completion:(nullable void (^)(BOOL success, NSError * __nullable error))completion {
NSAssert(webView != nil, @"webView must not be nil");
NSAssert(viewController != nil, @"viewController must not be nil");
NSAssert([webView isKindOfClass:[UIWebView class]] || [webView isKindOfClass:[WKWebView class]], @"webView must be an instance of WKWebView or UIWebView.");
#ifdef __IPHONE_8_0
if ([webView isKindOfClass:[UIWebView class]]) {
[self fillItemIntoUIWebView:webView webViewController:viewController sender:(id)sender showOnlyLogins:yesOrNo completion:^(BOOL success, NSError *error) {
if (completion) {
completion(success, error);
}
}];
}
#if __IPHONE_OS_VERSION_MIN_REQUIRED >= __IPHONE_8_0 || ONE_PASSWORD_EXTENSION_ENABLE_WK_WEB_VIEW
else if ([webView isKindOfClass:[WKWebView class]]) {
[self fillItemIntoWKWebView:webView forViewController:viewController sender:(id)sender showOnlyLogins:yesOrNo completion:^(BOOL success, NSError *error) {
if (completion) {
completion(success, error);
}
}];
}
#endif
#endif
}
#pragma mark - Support for custom UIActivityViewControllers
- (BOOL)isOnePasswordExtensionActivityType:(nullable NSString *)activityType {
return [@"com.agilebits.onepassword-ios.extension" isEqualToString:activityType] || [@"com.agilebits.beta.onepassword-ios.extension" isEqualToString:activityType];
}
- (void)createExtensionItemForWebView:(nonnull id)webView completion:(void (^)(NSExtensionItem * __nullable extensionItem, NSError * __nullable error))completion {
NSAssert(webView != nil, @"webView must not be nil");
NSAssert([webView isKindOfClass:[UIWebView class]] || [webView isKindOfClass:[WKWebView class]], @"webView must be an instance of WKWebView or UIWebView.");
#ifdef __IPHONE_8_0
if ([webView isKindOfClass:[UIWebView class]]) {
UIWebView *uiWebView = (UIWebView *)webView;
NSString *collectedPageDetails = [uiWebView stringByEvaluatingJavaScriptFromString:OPWebViewCollectFieldsScript];
[self createExtensionItemForURLString:uiWebView.request.URL.absoluteString webPageDetails:collectedPageDetails completion:completion];
}
#if __IPHONE_OS_VERSION_MIN_REQUIRED >= __IPHONE_8_0 || ONE_PASSWORD_EXTENSION_ENABLE_WK_WEB_VIEW
else if ([webView isKindOfClass:[WKWebView class]]) {
WKWebView *wkWebView = (WKWebView *)webView;
[wkWebView evaluateJavaScript:OPWebViewCollectFieldsScript completionHandler:^(NSString *result, NSError *evaluateError) {
if (result == nil) {
NSLog(@"1Password Extension failed to collect web page fields: %@", evaluateError);
NSError *failedToCollectFieldsError = [OnePasswordExtension failedToCollectFieldsErrorWithUnderlyingError:evaluateError];
if (completion) {
if ([NSThread isMainThread]) {
completion(nil, failedToCollectFieldsError);
}
else {
dispatch_async(dispatch_get_main_queue(), ^{
completion(nil, failedToCollectFieldsError);
});
}
}
return;
}
[self createExtensionItemForURLString:wkWebView.URL.absoluteString webPageDetails:result completion:completion];
}];
}
#endif
#endif
}
- (void)fillReturnedItems:(nullable NSArray *)returnedItems intoWebView:(nonnull id)webView completion:(nullable void (^)(BOOL success, NSError * __nullable error))completion {
NSAssert(webView != nil, @"webView must not be nil");
if (returnedItems.count == 0) {
NSError *error = [OnePasswordExtension extensionCancelledByUserError];
if (completion) {
completion(NO, error);
}
return;
}
[self processExtensionItem:returnedItems.firstObject completion:^(NSDictionary *itemDictionary, NSError *error) {
if (itemDictionary.count == 0) {
if (completion) {
completion(NO, error);
}
return;
}
NSString *fillScript = itemDictionary[AppExtensionWebViewPageFillScript];
[self executeFillScript:fillScript inWebView:webView completion:^(BOOL success, NSError *executeFillScriptError) {
if (completion) {
completion(success, executeFillScriptError);
}
}];
}];
}
#pragma mark - Private methods
- (BOOL)isSystemAppExtensionAPIAvailable {
#ifdef __IPHONE_8_0
return [NSExtensionItem class] != nil;
#else
return NO;
#endif
}
- (void)findLoginIn1PasswordWithURLString:(nonnull NSString *)URLString collectedPageDetails:(nullable NSString *)collectedPageDetails forWebViewController:(nonnull UIViewController *)forViewController sender:(nullable id)sender withWebView:(nonnull id)webView showOnlyLogins:(BOOL)yesOrNo completion:(void (^)(BOOL success, NSError * __nullable error))completion {
if ([URLString length] == 0) {
NSError *URLStringError = [OnePasswordExtension failedToObtainURLStringFromWebViewError];
NSLog(@"Failed to findLoginIn1PasswordWithURLString: %@", URLStringError);
if (completion) {
completion(NO, URLStringError);
}
return;
}
NSError *jsonError = nil;
NSData *data = [collectedPageDetails dataUsingEncoding:NSUTF8StringEncoding];
NSDictionary *collectedPageDetailsDictionary = [NSJSONSerialization JSONObjectWithData:data options:NSJSONReadingMutableContainers error:&jsonError];
if (collectedPageDetailsDictionary.count == 0) {
NSLog(@"Failed to parse JSON collected page details: %@", jsonError);
if (completion) {
completion(NO, jsonError);
}
return;
}
NSDictionary *item = @{ AppExtensionVersionNumberKey : VERSION_NUMBER, AppExtensionURLStringKey : URLString, AppExtensionWebViewPageDetails : collectedPageDetailsDictionary };
NSString *typeIdentifier = yesOrNo ? kUTTypeAppExtensionFillWebViewAction : kUTTypeAppExtensionFillBrowserAction;
UIActivityViewController *activityViewController = [self activityViewControllerForItem:item viewController:forViewController sender:sender typeIdentifier:typeIdentifier];
activityViewController.completionWithItemsHandler = ^(NSString *activityType, BOOL completed, NSArray *returnedItems, NSError *activityError) {
if (returnedItems.count == 0) {
NSError *error = nil;
if (activityError) {
NSLog(@"Failed to findLoginIn1PasswordWithURLString: %@", activityError);
error = [OnePasswordExtension failedToContactExtensionErrorWithActivityError:activityError];
}
else {
error = [OnePasswordExtension extensionCancelledByUserError];
}
if (completion) {
completion(NO, error);
}
return;
}
[self processExtensionItem:returnedItems.firstObject completion:^(NSDictionary *itemDictionary, NSError *processExtensionItemError) {
if (itemDictionary.count == 0) {
if (completion) {
completion(NO, processExtensionItemError);
}
return;
}
NSString *fillScript = itemDictionary[AppExtensionWebViewPageFillScript];
[self executeFillScript:fillScript inWebView:webView completion:^(BOOL success, NSError *executeFillScriptError) {
if (completion) {
completion(success, executeFillScriptError);
}
}];
}];
};
[forViewController presentViewController:activityViewController animated:YES completion:nil];
}
#if __IPHONE_OS_VERSION_MIN_REQUIRED >= __IPHONE_8_0 || ONE_PASSWORD_EXTENSION_ENABLE_WK_WEB_VIEW
- (void)fillItemIntoWKWebView:(nonnull WKWebView *)webView forViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender showOnlyLogins:(BOOL)yesOrNo completion:(void (^)(BOOL success, NSError * __nullable error))completion {
[webView evaluateJavaScript:OPWebViewCollectFieldsScript completionHandler:^(NSString *result, NSError *error) {
if (result == nil) {
NSLog(@"1Password Extension failed to collect web page fields: %@", error);
if (completion) {
completion(NO,[OnePasswordExtension failedToCollectFieldsErrorWithUnderlyingError:error]);
}
return;
}
[self findLoginIn1PasswordWithURLString:webView.URL.absoluteString collectedPageDetails:result forWebViewController:viewController sender:sender withWebView:webView showOnlyLogins:yesOrNo completion:^(BOOL success, NSError *findLoginError) {
if (completion) {
completion(success, findLoginError);
}
}];
}];
}
#endif
- (void)fillItemIntoUIWebView:(nonnull UIWebView *)webView webViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender showOnlyLogins:(BOOL)yesOrNo completion:(void (^)(BOOL success, NSError * __nullable error))completion {
NSString *collectedPageDetails = [webView stringByEvaluatingJavaScriptFromString:OPWebViewCollectFieldsScript];
[self findLoginIn1PasswordWithURLString:webView.request.URL.absoluteString collectedPageDetails:collectedPageDetails forWebViewController:viewController sender:sender withWebView:webView showOnlyLogins:yesOrNo completion:^(BOOL success, NSError *error) {
if (completion) {
completion(success, error);
}
}];
}
- (void)executeFillScript:(NSString * __nullable)fillScript inWebView:(nonnull id)webView completion:(void (^)(BOOL success, NSError * __nullable error))completion {
if (fillScript == nil) {
NSLog(@"Failed to executeFillScript, fillScript is missing");
if (completion) {
completion(NO, [OnePasswordExtension failedToFillFieldsErrorWithLocalizedErrorMessage:NSLocalizedStringFromTable(@"Failed to fill web page because script is missing", @"OnePasswordExtension", @"1Password Extension Error Message") underlyingError:nil]);
}
return;
}
NSMutableString *scriptSource = [OPWebViewFillScript mutableCopy];
[scriptSource appendFormat:@"(document, %@, undefined);", fillScript];
#ifdef __IPHONE_8_0
if ([webView isKindOfClass:[UIWebView class]]) {
NSString *result = [((UIWebView *)webView) stringByEvaluatingJavaScriptFromString:scriptSource];
BOOL success = (result != nil);
NSError *error = nil;
if (!success) {
NSLog(@"Cannot executeFillScript, stringByEvaluatingJavaScriptFromString failed");
error = [OnePasswordExtension failedToFillFieldsErrorWithLocalizedErrorMessage:NSLocalizedStringFromTable(@"Failed to fill web page because script could not be evaluated", @"OnePasswordExtension", @"1Password Extension Error Message") underlyingError:nil];
}
if (completion) {
completion(success, error);
}
}
#if __IPHONE_OS_VERSION_MIN_REQUIRED >= __IPHONE_8_0 || ONE_PASSWORD_EXTENSION_ENABLE_WK_WEB_VIEW
else if ([webView isKindOfClass:[WKWebView class]]) {
[((WKWebView *)webView) evaluateJavaScript:scriptSource completionHandler:^(NSString *result, NSError *evaluationError) {
BOOL success = (result != nil);
NSError *error = nil;
if (!success) {
NSLog(@"Cannot executeFillScript, evaluateJavaScript failed: %@", evaluationError);
error = [OnePasswordExtension failedToFillFieldsErrorWithLocalizedErrorMessage:NSLocalizedStringFromTable(@"Failed to fill web page because script could not be evaluated", @"OnePasswordExtension", @"1Password Extension Error Message") underlyingError:error];
}
if (completion) {
completion(success, error);
}
}];
}
#endif
#endif
}
#ifdef __IPHONE_8_0
- (void)processExtensionItem:(nullable NSExtensionItem *)extensionItem completion:(void (^)(NSDictionary *itemDictionary, NSError * __nullable error))completion {
if (extensionItem.attachments.count == 0) {
NSDictionary *userInfo = @{ NSLocalizedDescriptionKey: @"Unexpected data returned by App Extension: extension item had no attachments." };
NSError *error = [[NSError alloc] initWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeUnexpectedData userInfo:userInfo];
if (completion) {
completion(nil, error);
}
return;
}
NSItemProvider *itemProvider = extensionItem.attachments.firstObject;
if (NO == [itemProvider hasItemConformingToTypeIdentifier:(__bridge NSString *)kUTTypePropertyList]) {
NSDictionary *userInfo = @{ NSLocalizedDescriptionKey: @"Unexpected data returned by App Extension: extension item attachment does not conform to kUTTypePropertyList type identifier" };
NSError *error = [[NSError alloc] initWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeUnexpectedData userInfo:userInfo];
if (completion) {
completion(nil, error);
}
return;
}
[itemProvider loadItemForTypeIdentifier:(__bridge NSString *)kUTTypePropertyList options:nil completionHandler:^(NSDictionary *itemDictionary, NSError *itemProviderError) {
NSError *error = nil;
if (itemDictionary.count == 0) {
NSLog(@"Failed to loadItemForTypeIdentifier: %@", itemProviderError);
error = [OnePasswordExtension failedToLoadItemProviderDataErrorWithUnderlyingError:itemProviderError];
}
if (completion) {
if ([NSThread isMainThread]) {
completion(itemDictionary, error);
}
else {
dispatch_async(dispatch_get_main_queue(), ^{
completion(itemDictionary, error);
});
}
}
}];
}
- (UIActivityViewController *)activityViewControllerForItem:(nonnull NSDictionary *)item viewController:(nonnull UIViewController*)viewController sender:(nullable id)sender typeIdentifier:(nonnull NSString *)typeIdentifier {
#ifdef __IPHONE_8_0
NSAssert(NO == (UI_USER_INTERFACE_IDIOM() == UIUserInterfaceIdiomPad && sender == nil), @"sender must not be nil on iPad.");
NSItemProvider *itemProvider = [[NSItemProvider alloc] initWithItem:item typeIdentifier:typeIdentifier];
NSExtensionItem *extensionItem = [[NSExtensionItem alloc] init];
extensionItem.attachments = @[ itemProvider ];
UIActivityViewController *controller = [[UIActivityViewController alloc] initWithActivityItems:@[ extensionItem ] applicationActivities:nil];
if ([sender isKindOfClass:[UIBarButtonItem class]]) {
controller.popoverPresentationController.barButtonItem = sender;
}
else if ([sender isKindOfClass:[UIView class]]) {
controller.popoverPresentationController.sourceView = [sender superview];
controller.popoverPresentationController.sourceRect = [sender frame];
}
else {
NSLog(@"sender can be nil on iPhone");
}
return controller;
#else
return nil;
#endif
}
#endif
- (void)createExtensionItemForURLString:(nonnull NSString *)URLString webPageDetails:(nullable NSString *)webPageDetails completion:(void (^)(NSExtensionItem *extensionItem, NSError * __nullable error))completion {
NSError *jsonError = nil;
NSData *data = [webPageDetails dataUsingEncoding:NSUTF8StringEncoding];
NSDictionary *webPageDetailsDictionary = [NSJSONSerialization JSONObjectWithData:data options:NSJSONReadingMutableContainers error:&jsonError];
if (webPageDetailsDictionary.count == 0) {
NSLog(@"Failed to parse JSON collected page details: %@", jsonError);
if (completion) {
completion(nil, jsonError);
}
return;
}
NSDictionary *item = @{ AppExtensionVersionNumberKey : VERSION_NUMBER, AppExtensionURLStringKey : URLString, AppExtensionWebViewPageDetails : webPageDetailsDictionary };
NSItemProvider *itemProvider = [[NSItemProvider alloc] initWithItem:item typeIdentifier:kUTTypeAppExtensionFillBrowserAction];
NSExtensionItem *extensionItem = [[NSExtensionItem alloc] init];
extensionItem.attachments = @[ itemProvider ];
if (completion) {
if ([NSThread isMainThread]) {
completion(extensionItem, nil);
}
else {
dispatch_async(dispatch_get_main_queue(), ^{
completion(extensionItem, nil);
});
}
}
}
#pragma mark - Errors
+ (NSError *)systemAppExtensionAPINotAvailableError {
NSDictionary *userInfo = @{ NSLocalizedDescriptionKey : NSLocalizedStringFromTable(@"App Extension API is not available in this version of iOS", @"OnePasswordExtension", @"1Password Extension Error Message") };
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeAPINotAvailable userInfo:userInfo];
}
+ (NSError *)extensionCancelledByUserError {
NSDictionary *userInfo = @{ NSLocalizedDescriptionKey : NSLocalizedStringFromTable(@"1Password Extension was cancelled by the user", @"OnePasswordExtension", @"1Password Extension Error Message") };
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeCancelledByUser userInfo:userInfo];
}
+ (NSError *)failedToContactExtensionErrorWithActivityError:(nullable NSError *)activityError {
NSMutableDictionary *userInfo = [NSMutableDictionary new];
userInfo[NSLocalizedDescriptionKey] = NSLocalizedStringFromTable(@"Failed to contact the 1Password Extension", @"OnePasswordExtension", @"1Password Extension Error Message");
if (activityError) {
userInfo[NSUnderlyingErrorKey] = activityError;
}
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeFailedToContactExtension userInfo:userInfo];
}
+ (NSError *)failedToCollectFieldsErrorWithUnderlyingError:(nullable NSError *)underlyingError {
NSMutableDictionary *userInfo = [NSMutableDictionary new];
userInfo[NSLocalizedDescriptionKey] = NSLocalizedStringFromTable(@"Failed to execute script that collects web page information", @"OnePasswordExtension", @"1Password Extension Error Message");
if (underlyingError) {
userInfo[NSUnderlyingErrorKey] = underlyingError;
}
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeCollectFieldsScriptFailed userInfo:userInfo];
}
+ (NSError *)failedToFillFieldsErrorWithLocalizedErrorMessage:(nullable NSString *)errorMessage underlyingError:(nullable NSError *)underlyingError {
NSMutableDictionary *userInfo = [NSMutableDictionary new];
if (errorMessage) {
userInfo[NSLocalizedDescriptionKey] = errorMessage;
}
if (underlyingError) {
userInfo[NSUnderlyingErrorKey] = underlyingError;
}
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeFillFieldsScriptFailed userInfo:userInfo];
}
+ (NSError *)failedToLoadItemProviderDataErrorWithUnderlyingError:(nullable NSError *)underlyingError {
NSMutableDictionary *userInfo = [NSMutableDictionary new];
userInfo[NSLocalizedDescriptionKey] = NSLocalizedStringFromTable(@"Failed to parse information returned by 1Password Extension", @"OnePasswordExtension", @"1Password Extension Error Message");
if (underlyingError) {
userInfo[NSUnderlyingErrorKey] = underlyingError;
}
return [[NSError alloc] initWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeFailedToLoadItemProviderData userInfo:userInfo];
}
+ (NSError *)failedToObtainURLStringFromWebViewError {
NSDictionary *userInfo = @{ NSLocalizedDescriptionKey : NSLocalizedStringFromTable(@"Failed to obtain URL String from web view. The web view must be loaded completely when calling the 1Password Extension", @"OnePasswordExtension", @"1Password Extension Error Message") };
return [NSError errorWithDomain:AppExtensionErrorDomain code:AppExtensionErrorCodeFailedToObtainURLStringFromWebView userInfo:userInfo];
}
#pragma mark - WebView field collection and filling scripts
static NSString *const OPWebViewCollectFieldsScript = @";(function(document, undefined) {\
\
document.elementsByOPID={};\
function n(d,e){function f(a,b){var c=a[b];if('string'==typeof c)return c;c=a.getAttribute(b);return'string'==typeof c?c:null}function h(a,b){if(-1===['text','password'].indexOf(b.type.toLowerCase())||!(l.test(a.value)||l.test(a.htmlID)||l.test(a.htmlName)||l.test(a.placeholder)||l.test(a['label-tag'])||l.test(a['label-data'])||l.test(a['label-aria'])))return!1;if(!a.visible)return!0;if('password'==b.type.toLowerCase())return!1;var c=b.type,d=b.value;b.focus();b.value!==d&&(b.value=d);return c!==\
b.type}function r(a){switch(m(a.type)){case 'checkbox':return a.checked?'✓':'';case 'hidden':a=a.value;if(!a||'number'!=typeof a.length)return'';254<a.length&&(a=a.substr(0,254)+'...SNIPPED');return a;default:return a.value}}function v(a){return a.options?(a=Array.prototype.slice.call(a.options).map(function(a){var c=a.text,c=c?m(c).replace(/\\s/mg,'').replace(/[~`!@$%^&*()\\-_+=:;'\"\\[\\]|\\\\,<.>\\/?]/mg,''):null;return[c?c:null,a.value]}),{options:a}):null}function F(a){var b;for(a=a.parentElement||a.parentNode;a&&\
'td'!=m(a.tagName);)a=a.parentElement||a.parentNode;if(!a||void 0===a)return null;b=a.parentElement||a.parentNode;if('tr'!=b.tagName.toLowerCase())return null;b=b.previousElementSibling;if(!b||'tr'!=(b.tagName+'').toLowerCase()||b.cells&&a.cellIndex>=b.cells.length)return null;a=s(b.cells[a.cellIndex]);return a=u(a)}function A(a){var b=d.documentElement,c=a.getBoundingClientRect(),e=b.getBoundingClientRect(),f=c.left-b.clientLeft,b=c.top-b.clientTop;return a.offsetParent?0>f||f>e.width||0>b||b>e.height?\
w(a):(e=a.ownerDocument.elementFromPoint(f+3,b+3))?'label'===m(e.tagName)?e===B(a):e.tagName===a.tagName:!1:!1}function w(a){for(var b;a!==d&&a;a=a.parentNode){b=t.getComputedStyle?t.getComputedStyle(a,null):a.style;if(!b)return!0;if('none'===b.display||'hidden'==b.visibility)return!1}return a===d}function B(a){var b=[];a.id&&(b=b.concat(Array.prototype.slice.call(x(d,'label[for='+JSON.stringify(a.id)+']'))));a.name&&(b=b.concat(Array.prototype.slice.call(x(d,'label[for='+JSON.stringify(a.name)+']'))));\
if(0<b.length)return b.map(function(a){return s(a)}).join('');for(;a&&a!=d;a=a.parentNode)if('label'===m(a.tagName))return s(a);return null}function g(a,b,c,d){void 0!==d&&d===c||null===c||void 0===c||(a[b]=c)}function m(a){return'string'===typeof a?a.toLowerCase():(''+a).toLowerCase()}function x(a,b){var c=[];try{c=a.querySelectorAll(b)}catch(d){}return c}var t=d.defaultView?d.defaultView:window,p,l=RegExp('((\\\\b|_|-)pin(\\\\b|_|-)|password|passwort|kennwort|passe|contraseña|senha|密码|adgangskode|hasło|wachtwoord)',\
'i');p=Array.prototype.slice.call(x(d,'form')).map(function(a,b){var c={},d='__form__'+b;a.opid=d;c.opid=d;g(c,'htmlName',f(a,'name'));g(c,'htmlID',f(a,'id'));g(c,'htmlAction',y(f(a,'action')));g(c,'htmlMethod',f(a,'method'));return c});var q=Array.prototype.slice.call(z(d)).map(function(a,b){var c={},e='__'+b,k=-1==a.maxLength?999:a.maxLength;if(!k||'number'===typeof k&&isNaN(k))k=999;d.elementsByOPID[e]=a;a.opid=e;c.opid=e;c.elementNumber=b;g(c,'maxLength',Math.min(k,999),999);c.visible=w(a);c.viewable=\
A(a);g(c,'htmlID',f(a,'id'));g(c,'htmlName',f(a,'name'));g(c,'htmlClass',f(a,'class'));g(c,'tabindex',f(a,'tabindex'));if('hidden'!=m(a.type)){g(c,'label-tag',B(a));g(c,'label-data',f(a,'data-label'));g(c,'label-aria',f(a,'aria-label'));g(c,'label-top',F(a));e=[];for(k=a;k&&k.nextSibling;){k=k.nextSibling;if(C(k))break;D(e,k)}g(c,'label-right',e.join(''));e=[];E(a,e);e=e.reverse().join('');g(c,'label-left',e);g(c,'placeholder',f(a,'placeholder'))}g(c,'rel',f(a,'rel'));g(c,'type',m(f(a,'type')));g(c,\
'value',r(a));g(c,'checked',a.checked,!1);g(c,'autoCompleteType',a.getAttribute('x-autocompletetype')||a.getAttribute('autocompletetype')||a.getAttribute('autocomplete'),'off');g(c,'disabled',a.disabled);g(c,'readonly',a.a||a.readOnly);g(c,'selectInfo',v(a));g(c,'aria-hidden','true'==a.getAttribute('aria-hidden'),!1);g(c,'aria-disabled','true'==a.getAttribute('aria-disabled'),!1);g(c,'aria-haspopup','true'==a.getAttribute('aria-haspopup'),!1);g(c,'data-unmasked',a.dataset.unmasked);g(c,'data-stripe',\
f(a,'data-stripe'));g(c,'onepasswordFieldType',a.dataset.onepasswordFieldType||a.type);g(c,'onepasswordDesignation',a.dataset.onepasswordDesignation);g(c,'onepasswordSignInUrl',a.dataset.onepasswordSignInUrl);g(c,'onepasswordSectionTitle',a.dataset.onepasswordSectionTitle);g(c,'onepasswordSectionFieldKind',a.dataset.onepasswordSectionFieldKind);g(c,'onepasswordSectionFieldTitle',a.dataset.onepasswordSectionFieldTitle);g(c,'onepasswordSectionFieldValue',a.dataset.onepasswordSectionFieldValue);a.form&&\
(c.form=f(a.form,'opid'));g(c,'fakeTested',h(c,a),!1);return c});q.filter(function(a){return a.fakeTested}).forEach(function(a){var b=d.elementsByOPID[a.opid];b.getBoundingClientRect();var c=b.value;!b||b&&'function'!==typeof b.click||b.click();b.focus();G(b,'keydown');G(b,'keyup');G(b,'keypress');b.value!==c&&(b.value=c);b.click&&b.click();a.postFakeTestVisible=w(b);a.postFakeTestViewable=A(b);a.postFakeTestType=b.type;a=b.value;var c=b.ownerDocument.createEvent('HTMLEvents'),e=b.ownerDocument.createEvent('HTMLEvents');\
G(b,'keydown');G(b,'keyup');G(b,'keypress');e.initEvent('input',!0,!0);b.dispatchEvent(e);c.initEvent('change',!0,!0);b.dispatchEvent(c);b.blur();b.value!==a&&(b.value=a)});p={documentUUID:e,title:d.title,url:t.location.href,documentUrl:d.location.href,tabUrl:t.location.href,forms:function(a){var b={};a.forEach(function(a){b[a.opid]=a});return b}(p),fields:q,collectedTimestamp:(new Date).getTime()};(q=document.querySelector('[data-onepassword-display-title]'))&&q.dataset[DISPLAY_TITLE_ATTRIBUE]&&\
(p.displayTitle=q.dataset.onepasswordTitle);return p};document.elementForOPID=H;function G(d,e){var f;f=d.ownerDocument.createEvent('KeyboardEvent');f.initKeyboardEvent?f.initKeyboardEvent(e,!0,!0):f.initKeyEvent&&f.initKeyEvent(e,!0,!0,null,!1,!1,!1,!1,0,0);d.dispatchEvent(f)}window.LOGIN_TITLES=[/^\\W*log\\W*[oi]n\\W*$/i,/log\\W*[oi]n (?:securely|now)/i,/^\\W*sign\\W*[oi]n\\W*$/i,'continue','submit','weiter','accès','вход','connexion','entrar','anmelden','accedi','valider','登录','लॉग इन करें'];window.LOGIN_RED_HERRING_TITLES=['already have an account','sign in with'];\
window.REGISTER_TITLES='register;sign up;signup;join;регистрация;inscription;regístrate;cadastre-se;registrieren;registrazione;注册;साइन अप करें'.split(';');window.SEARCH_TITLES='search find поиск найти искать recherche suchen buscar suche ricerca procurar 検索'.split(' ');window.FORGOT_PASSWORD_TITLES='forgot geändert vergessen hilfe changeemail español'.split(' ');window.REMEMBER_ME_TITLES=['remember me','rememberme','keep me signed in'];window.BACK_TITLES=['back','назад'];\
function s(d){return d.textContent||d.innerText}function u(d){var e=null;d&&(e=d.replace(/^\\s+|\\s+$|\\r?\\n.*$/mg,''),e=0<e.length?e:null);return e}function D(d,e){var f;f='';3===e.nodeType?f=e.nodeValue:1===e.nodeType&&(f=s(e));(f=u(f))&&d.push(f)}function C(d){var e;d&&void 0!==d?(e='select option input form textarea button table iframe body head script'.split(' '),d?(d=d?(d.tagName||'').toLowerCase():'',e=e.constructor==Array?0<=e.indexOf(d):d===e):e=!1):e=!0;return e}\
function E(d,e,f){var h;for(f||(f=0);d&&d.previousSibling;){d=d.previousSibling;if(C(d))return;D(e,d)}if(d&&0===e.length){for(h=null;!h;){d=d.parentElement||d.parentNode;if(!d)return;for(h=d.previousSibling;h&&!C(h)&&h.lastChild;)h=h.lastChild}C(h)||(D(e,h),0===e.length&&E(h,e,f+1))}}\
function H(d){var e;if(void 0===d||null===d)return null;try{var f=Array.prototype.slice.call(z(document)),h=f.filter(function(e){return e.opid==d});if(0<h.length)e=h[0],1<h.length&&console.warn('More than one element found with opid '+d);else{var r=parseInt(d.split('__')[1],10);isNaN(r)||(e=f[r])}}catch(v){console.error('An unexpected error occurred: '+v)}finally{return e}};var I=/^[\\/\\?]/;function y(d){if(!d)return null;if(0==d.indexOf('http'))return d;var e=window.location.protocol+'//'+window.location.hostname;window.location.port&&''!=window.location.port&&(e+=':'+window.location.port);d.match(I)||(d='/'+d);return e+d}function z(d){var e=[];try{e=d.querySelectorAll('input, select, button')}catch(f){}return e};\
\
return JSON.stringify(n(document, 'oneshotUUID'));\
})(document);\
";
static NSString *const OPWebViewFillScript = @";(function(document, fillScript, undefined) {\
\
var f=!0,h=!0;\
function l(a){var b=null;return a?0===a.indexOf('https://')&&'http:'===document.location.protocol&&(b=document.querySelectorAll('input[type=password]'),0<b.length&&(confirmResult=confirm('1Password warning: This is an unsecured HTTP page, and any information you submit can potentially be seen and changed by others. This Login was originally saved on a secure (HTTPS) page.\\n\\nDo you still wish to fill this login?'),0==confirmResult))?!0:!1:!1}\
function k(a){var b,c=[],d=a.properties,e=1,g;d&&d.delay_between_operations&&(e=d.delay_between_operations);if(!l(a.savedURL)){g=function(a,b){var d=a[0];void 0===d?b():('delay'===d.operation||'delay'===d[0]?e=d.parameters?d.parameters[0]:d[1]:c.push(m(d)),setTimeout(function(){g(a.slice(1),b)},e))};if(b=a.options)b.hasOwnProperty('animate')&&(h=b.animate),b.hasOwnProperty('markFilling')&&(f=b.markFilling);a.itemType&&'fillPassword'===a.itemType&&(f=!1);a.hasOwnProperty('script')&&(b=a.script,g(b,\
function(){c=Array.prototype.concat.apply(c,void 0);a.hasOwnProperty('autosubmit')&&'function'==typeof autosubmit&&(a.itemType&&'fillLogin'!==a.itemType||setTimeout(function(){autosubmit(a.autosubmit,d.allow_clicky_autosubmit)},AUTOSUBMIT_DELAY));'object'==typeof protectedGlobalPage&&protectedGlobalPage.a('fillItemResults',{documentUUID:documentUUID,fillContextIdentifier:a.fillContextIdentifier,usedOpids:c},function(){fillingItemType=null})}))}}\
var v={fill_by_opid:n,fill_by_query:p,click_on_opid:q,click_on_query:r,touch_all_fields:s,simple_set_value_by_query:t,focus_by_opid:u,delay:null};function m(a){var b;if(a.hasOwnProperty('operation')&&a.hasOwnProperty('parameters'))b=a.operation,a=a.parameters;else if('[object Array]'===Object.prototype.toString.call(a))b=a[0],a=a.splice(1);else return null;return v.hasOwnProperty(b)?v[b].apply(this,a):null}function n(a,b){var c;return(c=w(a))?(x(c,b),c.opid):null}\
function p(a,b){var c;c=y(a);return Array.prototype.map.call(Array.prototype.slice.call(c),function(a){x(a,b);return a.opid},this)}function t(a,b){var c,d=[];c=y(a);Array.prototype.forEach.call(Array.prototype.slice.call(c),function(a){void 0!==a.value&&(a.value=b,d.push(a.opid))});return d}function u(a){if(a=w(a))'function'===typeof a.click&&a.click(),'function'===typeof a.focus&&a.focus();return null}function q(a){return(a=w(a))?z(a)?a.opid:null:null}\
function r(a){a=y(a);return Array.prototype.map.call(Array.prototype.slice.call(a),function(a){z(a);'function'===typeof a.click&&a.click();'function'===typeof a.focus&&a.focus();return a.opid},this)}function s(){A()};var B={'true':!0,y:!0,1:!0,yes:!0,'✓':!0},C=200;function x(a,b){var c;if(a&&null!==b&&void 0!==b)switch(f&&a.form&&!a.form.opfilled&&(a.form.opfilled=!0),a.type?a.type.toLowerCase():null){case 'checkbox':c=b&&1<=b.length&&B.hasOwnProperty(b.toLowerCase())&&!0===B[b.toLowerCase()];a.checked===c||D(a,function(a){a.checked=c});break;case 'radio':!0===B[b.toLowerCase()]&&a.click();break;default:a.value==b||D(a,function(a){a.value=b})}}\
function D(a,b){E(a);b(a);F(a);G(a)&&(a.className+=' com-agilebits-onepassword-extension-animated-fill',setTimeout(function(){a&&a.className&&(a.className=a.className.replace(/(\\s)?com-agilebits-onepassword-extension-animated-fill/,''))},C))};document.elementForOPID=w;function H(a,b){var c;c=a.ownerDocument.createEvent('KeyboardEvent');c.initKeyboardEvent?c.initKeyboardEvent(b,!0,!0):c.initKeyEvent&&c.initKeyEvent(b,!0,!0,null,!1,!1,!1,!1,0,0);a.dispatchEvent(c)}function E(a){var b=a.value;z(a);a.focus();H(a,'keydown');H(a,'keyup');H(a,'keypress');a.value!==b&&(a.value=b)}\
function F(a){var b=a.value,c=a.ownerDocument.createEvent('HTMLEvents'),d=a.ownerDocument.createEvent('HTMLEvents');H(a,'keydown');H(a,'keyup');H(a,'keypress');d.initEvent('input',!0,!0);a.dispatchEvent(d);c.initEvent('change',!0,!0);a.dispatchEvent(c);a.blur();a.value!==b&&(a.value=b)}function z(a){if(!a||a&&'function'!==typeof a.click)return!1;a.click();return!0}\
function I(){var a=RegExp('((\\\\b|_|-)pin(\\\\b|_|-)|password|passwort|kennwort|passe|contraseña|senha|密码|adgangskode|hasło|wachtwoord)','i');return Array.prototype.slice.call(y(\"input[type='text']\")).filter(function(b){return b.value&&a.test(b.value)},this)}function A(){I().forEach(function(a){E(a);a.click&&a.click();F(a)})}\
window.LOGIN_TITLES=[/^\\W*log\\W*[oi]n\\W*$/i,/log\\W*[oi]n (?:securely|now)/i,/^\\W*sign\\W*[oi]n\\W*$/i,'continue','submit','weiter','accès','вход','connexion','entrar','anmelden','accedi','valider','登录','लॉग इन करें'];window.LOGIN_RED_HERRING_TITLES=['already have an account','sign in with'];window.REGISTER_TITLES='register;sign up;signup;join;регистрация;inscription;regístrate;cadastre-se;registrieren;registrazione;注册;साइन अप करें'.split(';');window.SEARCH_TITLES='search find поиск найти искать recherche suchen buscar suche ricerca procurar 検索'.split(' ');\
window.FORGOT_PASSWORD_TITLES='forgot geändert vergessen hilfe changeemail español'.split(' ');window.REMEMBER_ME_TITLES=['remember me','rememberme','keep me signed in'];window.BACK_TITLES=['back','назад'];\
function G(a){var b;if(b=h)a:{b=a;for(var c=a.ownerDocument,c=c?c.defaultView:{},d;b&&b!==document;){d=c.getComputedStyle?c.getComputedStyle(b,null):b.style;if(!d){b=!0;break a}if('none'===d.display||'hidden'==d.visibility){b=!1;break a}b=b.parentNode}b=b===document}return b?-1!=='email text password number tel url'.split(' ').indexOf(a.type||''):!1}\
function w(a){var b;if(void 0===a||null===a)return null;try{var c=Array.prototype.slice.call(y('input, select, button')),d=c.filter(function(b){return b.opid==a});if(0<d.length)b=d[0],1<d.length&&console.warn('More than one element found with opid '+a);else{var e=parseInt(a.split('__')[1],10);isNaN(e)||(b=c[e])}}catch(g){console.error('An unexpected error occurred: '+g)}finally{return b}};function y(a){var b=document,c=[];try{c=b.querySelectorAll(a)}catch(d){}return c};\
\
k(fillScript);\
return JSON.stringify({'success': true});\
})\
";
#pragma mark - Deprecated methods
/*
Deprecated in version 1.5
Use fillItemIntoWebView:forViewController:sender:showOnlyLogins:completion: instead
*/
- (void)fillLoginIntoWebView:(nonnull id)webView forViewController:(nonnull UIViewController *)viewController sender:(nullable id)sender completion:(nullable void (^)(BOOL success, NSError * __nullable error))completion {
[self fillItemIntoWebView:webView forViewController:viewController sender:sender showOnlyLogins:YES completion:completion];
}
@end
|
github
|
emeb/iceRadio-master
|
freq_plot.m
|
.m
|
iceRadio-master/FPGA/rxadc_2/matlab/freq_plot.m
| 251 |
utf_8
|
729973cd5669dfbd3253d8c24f17293e
|
% freq_plot.m - frequency plot
% E. Brombaugh 08-03-16
function freq_plot(x, Fs, title_str)
sz = length(x);
f = Fs * (((0:sz-1)/sz)-0.5);
plot(f, 20*log10(abs(fftshift(fft(x)/sz))));
grid on;
title(title_str);
xlabel('Freq');
ylabel('dB');
end
|
github
|
AlanRace/SpectralAnalysis-master
|
SpectralAnalysis.m
|
.m
|
SpectralAnalysis-master/SpectralAnalysis.m
| 918 |
utf_8
|
aebd04ed17adf1448eaf6128668b3095
|
%% SpectralAnalysis
% Spectral Imaging analysis software
function spectralAnalysis = SpectralAnalysis()
% Get location of current m-file
if(isdeployed())
disp('Initialising MATLAB, please wait...');
path = ctfroot();
disp(path);
else
path = fileparts(mfilename('fullpath'));
% Ensure all folders are on the path
addpath(genpath(path));
end
% Ensure libraries are on the path
addJARsToClassPath();
% Check if SpectralAnalysis folder exists
spectralAnalysisHome = [homepath filesep '.SpectralAnalysis'];
% TODO: Check last version and if newer then copy over
if ~exist(spectralAnalysisHome, 'file')
mkdir(spectralAnalysisHome);
copyfile([path filesep 'files' filesep 'profiles'], [spectralAnalysisHome filesep 'profiles'])
end
% TODO: Check version on github to see if update available
% Launch spectral analysis interface
spectralAnalysis = SpectralAnalysisInterface();
|
github
|
AlanRace/SpectralAnalysis-master
|
parseRegionOfInterestList.m
|
.m
|
SpectralAnalysis-master/src/gui/MOOGL/util/parseRegionOfInterestList.m
| 980 |
utf_8
|
24027c8c7e4b292c2b1717cf1a06eeb6
|
function regionOfInterestList = parseRegionOfInterestList(filename)
% parseClusterGroupList Convert XML file to a MATLAB structure.
try
tree = xmlread(filename);
catch
error('Failed to read XML file %s.',filename);
end
% Recurse over child nodes. This could run into problems
% with very deeply nested trees.
try
regionOfInterestList = parseRegionOfInterestListElement(tree.getChildNodes().item(0));
catch err
err
error('Unable to parse XML file %s.',filename);
end
function regionOfInterestList = parseRegionOfInterestListElement(regionOfInterestNode)
regionOfInterestList = RegionOfInterestList();
childrenNodes = regionOfInterestNode.getChildNodes();
for i = 1:childrenNodes.getLength()
element = childrenNodes.item(i-1);
nodeName = element.getNodeName();
if(strcmp(nodeName, 'regionOfInterest'))
regionOfInterestList.add(parseRegionOfInterestElement(element));
end
end
|
github
|
AlanRace/SpectralAnalysis-master
|
parseRegionOfInterestElement.m
|
.m
|
SpectralAnalysis-master/src/gui/MOOGL/util/parseRegionOfInterestElement.m
| 1,660 |
utf_8
|
f6ea11108991760769565360ccd5d7a8
|
function regionOfInterest = parseRegionOfInterestElement(regionOfInterestNode)
width = str2num(regionOfInterestNode.getAttributes().getNamedItem('width').getValue());
height = str2num(regionOfInterestNode.getAttributes().getNamedItem('height').getValue());
regionOfInterest = RegionOfInterest(width, height);
childrenNodes = regionOfInterestNode.getChildNodes();
for i = 1:childrenNodes.getLength()
element = childrenNodes.item(i-1);
nodeName = element.getNodeName();
if(strcmp(nodeName, 'name'))
regionOfInterest.setName(char(element.getTextContent()));
elseif(strcmp(nodeName, 'colour'))
r = str2num(element.getAttributes().getNamedItem('red').getValue());
g = str2num(element.getAttributes().getNamedItem('green').getValue());
b = str2num(element.getAttributes().getNamedItem('blue').getValue());
regionOfInterest.setColour(Colour(r, g, b));
elseif(strcmp(nodeName, 'pixelList'))
parsePixelList(regionOfInterest, element);
end
end
function parsePixelList(regionOfInterest, pixelListElement)
childrenNodes = pixelListElement.getChildNodes();
for i = 1:childrenNodes.getLength()
element = childrenNodes.item(i-1);
nodeName = element.getNodeName();
if(strcmp(nodeName, 'pixel'))
x = str2num(element.getAttributes().getNamedItem('x').getValue());
y = str2num(element.getAttributes().getNamedItem('y').getValue());
regionOfInterest.addPixel(x, y);
end
end
|
github
|
AlanRace/SpectralAnalysis-master
|
generateFastPreprocessingWorkflow.m
|
.m
|
SpectralAnalysis-master/src/util/generateFastPreprocessingWorkflow.m
| 3,410 |
utf_8
|
62867ff2c6a8d4bf25c62ceb33c072de
|
% Returns empty variable if there is no fast preprocessing workflow
% available
function fastPreprocessingWorkflow = generateFastPreprocessingWorkflow(workflow)
if(~canUseJSpectralAnalysis())
fastPreprocessingWorkflow = [];
return;
end
fastPreprocessingWorkflow = com.alanmrace.JSpectralAnalysis.PreprocessingWorkflow();
numFastMethods = 0;
if(isa(workflow, 'PreprocessingWorkflow'))
workflow = workflow.workflow;
end
for i = 1:length(workflow)
if(isa(workflow{i}, 'QSTARZeroFilling'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.QSTARZeroFilling(params(1).value, params(2).value, params(3).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'SynaptZeroFilling'))
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.FixedmzListReplaceZeros(workflow{i}.mzsFull));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'RebinZeroFilling'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.RebinZeroFilling(params(1).value, params(2).value, params(3).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'RebinPPMZeroFilling'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.PPMRebinZeroFilling(params(1).value, params(2).value, params(3).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'InterpolationRebinZeroFilling'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.InterpolationRebinZeroFilling(params(1).value, params(2).value, params(3).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'InterpolationPPMRebinZeroFilling'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.zerofilling.InterpolationPPMRebinZeroFilling(params(1).value, params(2).value, params(3).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'GaussianSmoothing'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.smoothing.GaussianSmoothing(params(1).value, params(2).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'SavitzkyGolaySmoothing'))
params = workflow{i}.Parameters;
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.smoothing.SavitzkyGolaySmoothing(params(1).value, params(2).value));
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'TotalIntensitySpectralNormalisation'))
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.normalisation.TICNormalisation());
numFastMethods = numFastMethods + 1;
elseif(isa(workflow{i}, 'RemoveNegativesBaselineCorrection'))
fastPreprocessingWorkflow.addMethod(com.alanmrace.JSpectralAnalysis.baselinecorrection.RemoveNegativesBaselineCorrection());
numFastMethods = numFastMethods + 1;
end
end
if(numFastMethods ~= length(workflow))
fastPreprocessingWorkflow = [];
end
|
github
|
AlanRace/SpectralAnalysis-master
|
colouriseData.m
|
.m
|
SpectralAnalysis-master/src/util/colouriseData.m
| 5,789 |
utf_8
|
71f28f2156cba34801adb011c541cd56
|
% colourScale 'r' (red), 'g' (green), 'b' (blue), 'y' (yellow), 'm'
% (magenta), 'c' (cyan), 'h' hot, 'p' pink
function [image, maxValue, minValue] = colouriseData(data, positiveScaleColour, negativeScaleColour, quant)
numBits = 2^16;
image = zeros(size(data, 1), size(data, 2), 3, 'uint16');
if(nargin < 4)
minValue = min(data(:));
maxValue = max(data(:));
else
minValue = -1*quantile(-1*data(:), quant);
maxValue = quantile(data(:), quant);
end
% Ensure that the data starts at 0
if(nargin < 3)
data = data - min(data(:));
end
positiveData = data;
positiveData(data < 0) = 0;
positiveData(positiveData > maxValue) = maxValue;
positiveData = (positiveData./maxValue) * numBits;
switch(positiveScaleColour)
case 'r'
image(:, :, 1) = positiveData;
case 'g'
image(:, :, 2) = positiveData;
case 'b'
image(:, :, 3) = positiveData;
case 'y'
image(:, :, 1) = positiveData;
image(:, :, 2) = positiveData;
case 'm'
image(:, :, 1) = positiveData;
image(:, :, 3) = positiveData;
case 'c'
image(:, :, 2) = positiveData;
image(:, :, 3) = positiveData;
case 'h'
n = 3/8;
positiveDataR = (positiveData./max(positiveData(:))) / n;
positiveDataR(positiveDataR > 1) = 1;
positiveDataG = (positiveData./max(positiveData(:))) - n;
positiveDataG(positiveDataG < 0) = 0;
positiveDataG = (positiveDataG / n);
positiveDataG(positiveDataG > 1) = 1;
positiveDataB = (positiveData./max(positiveData(:))) - (2*n);
positiveDataB(positiveDataB < 0) = 0;
positiveDataB = (positiveDataB / (1-(2*n)));
image(:, :, 1) = positiveDataR * numBits;
image(:, :, 2) = positiveDataG * numBits;
image(:, :, 3) = positiveDataB * numBits;
case 'p'
n = 3/8;
positiveDataR = (positiveData./max(positiveData(:))) / n;
positiveDataR(positiveDataR > 1) = 1;
positiveDataG = (positiveData./max(positiveData(:))) - n;
positiveDataG(positiveDataG < 0) = 0;
positiveDataG = (positiveDataG / n);
positiveDataG(positiveDataG > 1) = 1;
positiveDataB = (positiveData./max(positiveData(:))) - (2*n);
positiveDataB(positiveDataB < 0) = 0;
positiveDataB = (positiveDataB / (1-(2*n)));
image(:, :, 1) = (sqrt(((2*(positiveData/max(positiveData(:)))) + (positiveDataR)) / 3)) * numBits;
image(:, :, 2) = (sqrt(((2*(positiveData/max(positiveData(:)))) + (positiveDataG)) / 3)) * numBits;
image(:, :, 3) = (sqrt(((2*(positiveData/max(positiveData(:)))) + (positiveDataB)) / 3)) * numBits;
case 'j'
m = 64;
n = ceil(m/4);
u = [(1:1:n)/n ones(1,n-1) (n:-1:1)/n]';
g = ceil(n/2) - (mod(m,4)==1) + (1:length(u))';
r = g + n;
b = g - n;
g(g>m) = [];
r(r>m) = [];
b(b<1) = [];
% J = zeros(m,3);
% J(r,1) = u(1:length(r));
% J(g,2) = u(1:length(g));
% J(b,3) = u(end-length(b)+1:end);
positiveDataR = round((positiveData./max(positiveData(:))) * m);
tempData = positiveDataR;
positiveDataR = zeros(size(positiveDataR));
min(r)
for i = min(r):length(r)
positiveDataR(tempData == r(i)) = r(i);
end
imagesc(positiveDataR);figure;
positiveDataG = round((positiveData./max(positiveData(:))) * m);
tempData = positiveDataG;
positiveDataG = zeros(size(positiveDataG));
for i = min(g):length(g)
positiveDataG(tempData == g(i)) = g(i);
end
positiveDataB = round((positiveData./max(positiveData(:))) * m);
tempData = positiveDataB;
positiveDataB = zeros(size(positiveDataB));
for i = min(b):length(b)
positiveDataB(tempData == b(i)) = b(i);
end
positiveDataR = positiveDataR ./ m;
positiveDataG = positiveDataG ./ m;
positiveDataB = positiveDataB ./ m;
% u = [(1:1:n)/n ones(1,n-1) (n:-1:1)/n]';
% g = ceil(n/2) - 1 + (1:length(u))';
% r = g + n;
% b = g - n;
% g(g>1) = [];
% r(r>1) = [];
% b(b<1) = [];
% J = zeros(1,3);
% J(r,1) = u(1:length(r));
% J(g,2) = u(1:length(g));
% J(b,3) = u(end-length(b)+1:end);
image(:, :, 1) = positiveDataR * numBits;
image(:, :, 2) = positiveDataG * numBits;
image(:, :, 3) = positiveDataB * numBits;
end
if(nargin > 2)
negativeData = data;
negativeData(data > 0) = 0;
negativeData(negativeData < minValue) = minValue;
negativeData = (negativeData./minValue) * numBits;
switch(negativeScaleColour)
case 'r'
image(:, :, 1) = negativeData;
case 'g'
image(:, :, 2) = negativeData;
case 'b'
image(:, :, 3) = negativeData;
case 'y'
image(:, :, 1) = negativeData;
image(:, :, 2) = negativeData;
case 'm'
image(:, :, 1) = negativeData;
image(:, :, 3) = negativeData;
case 'c'
image(:, :, 2) = negativeData;
image(:, :, 3) = negativeData;
end
end
% pca5 = positivePCA5 + negativePCA5;
%
% h = figure('PaperPosition', [0, 0, 4, 10]);
% imagesc(pca5);
% axis image, axis off
%
% sizeOfMap = 100;
% numOfTicksPos = (floor(sizeOfMap*(thresh/(thresh+threshMin)))-1);
% numOfTicksNeg = (floor(sizeOfMap*(threshMin/(thresh+threshMin)))-1);
%
% map = zeros(numOfTicksPos+numOfTicksNeg+1, 3);
% map(1:numOfTicksNeg+1, 1) = 1:-1*(1/numOfTicksNeg):0;
% map(1:numOfTicksNeg+1, 3) = 1:-1*(1/numOfTicksNeg):0;
% map(numOfTicksNeg+1:end, 2) = 0:(1/numOfTicksPos):1;
|
github
|
AlanRace/SpectralAnalysis-master
|
normaliseRGBChannels.m
|
.m
|
SpectralAnalysis-master/src/util/normaliseRGBChannels.m
| 450 |
utf_8
|
caee37c051e3b53b5fe6fd8ab260ee2c
|
% Normalise RGB channels individually to their min/max values
function normalised = normaliseRGBChannels(rgb)
normalised = (rgb(:, :, 1) - min(min(rgb(:, :, 1)))) ./ (max(max(rgb(:, :, 1))) - min(min(rgb(:, :, 1))));
normalised(:, :, 2) = (rgb(:, :, 2) - min(min(rgb(:, :, 2)))) ./ (max(max(rgb(:, :, 2))) - min(min(rgb(:, :, 2))));
normalised(:, :, 3) = (rgb(:, :, 3) - min(min(rgb(:, :, 3)))) ./ (max(max(rgb(:, :, 3))) - min(min(rgb(:, :, 3))));
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_plsa.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_plsa.m
| 2,545 |
utf_8
|
a33a2f153f46631f3f3e40b96b2f620a
|
% function that performs a probabilistic latent semantic analysis of the
% input data matrix
%
% input
%
% X SxC matrix with C variables (channels) and S
% observations (spectra), i.e. each row consists of
% one observation
% numComponents decompose the data into that many components (tissue
% types T)
% relativeChange stopping criterion, terminated if relative change in
% fit falls below that threshold
% maxIter maximum number of iterations, default is 500
%
% output
%
% ct CxT matrix of characteristic spectra
% ts TxS matrix of mixture vectors
% loglik log-likelihood of the data
%
% for details see
%
% T. Hofmann. Probabilistic Latent Semantic Analysis, Uncertainty in Artificial Intelligence, 1999.
% we use the matrix formulation to speed up the calculations in MATLAB (see also Kaban and Verbeek - http://www.cs.bham.ac.uk/~axk/ML_CODE/PLSA.m, http://lear.inrialpes.fr/~verbeek/software)
%
% (C)2008 Michael Hanselmann
%
function [ct, ts, loglik] = mip_plsa(X, numComponents, relativeChange, maxIter)
% variable initialization
Xt = X';
[numChannels, numSpectra] = size(Xt);
ct = mip_col_normalize(rand(numChannels, numComponents));
ts = mip_col_normalize(ones(numComponents, numSpectra));
if nargin < 4
maxIter = 500; % default value
end
lastChange = 1/eps;
err = 1e10;
iter = 0;
while(lastChange > relativeChange && iter < maxIter)
% update rules (EM algorithm as described by Hofmann99)
ts = mip_col_normalize(ts .* (ct' * (Xt ./ (ct*ts + eps))));
ct = mip_col_normalize(ct .* ((Xt ./ ( ct*ts + eps)) * ts'));
% check model fit (here we use a least-squares fit, but this can
% easily be replaced by a KL-divergence fit);
% we check for the RELATIVE change in fit
model = (ones(numChannels, 1) * sum(Xt, 1))' .* (ct * ts)';
errold = err;
err = sum(sum((Xt - model').^2));
lastChange = abs((err - errold)/err);
iter = iter + 1;
if(mod(iter, 25) == 0 || iter == 1)
disp(['...iteration ', num2str(iter), ', relative change ', num2str(lastChange)]);
end
end
% data log-likelihood
loglik = sum(sum(Xt.*log(ct*ts + eps)));
end
function X = mip_col_normalize(X)
% normalize column-wise
sumX = sum(X);
X = X ./ (ones(size(X, 1), 1) * (sumX + (sumX==0)));
end
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_calculateAICcTrace.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_calculateAICcTrace.m
| 1,124 |
utf_8
|
bf4341f53191bed7c71966a4d481e809
|
% Function that calculates the AICc-trace from a given likelihood vector
% and the data
%
% input
%
% X SxC matrix with C variables (channels) and S
% observations (spectra), i.e. each row consists of
% one observation
% mzVector vector with m/z-positions (of dimension Cx1)
% xyPos vector with x- and y-positions corresponding to the
% spectra in X (of dimension Sx2)
% logliks vector of data likelihoods
%
% output
%
% AICc vector of AICc-values (of dimension equal to logliks)
% currPenalty last value of penalty term
%
% (C)2008 Michael Hanselmann
%
function [AICc, currPenalty] = mip_calculateAICcTrace(X, mzVector, xyPos, logliks)
% AICc criterion for optimal model selection
noiseVar = mip_simpleNoiseEstimation(X, mzVector, xyPos);
N = size(X, 1) * size(X, 2);
for i=1:length(logliks)
K = i*(size(X, 1) + size(X, 2));
AICc(i) = -2*logliks(i)/N + 2*K/N*noiseVar + 1/N*2*K*(K+1)/(N-K-1);
currPenalty = 2*K*noiseVar/N + 1/N*2*K*(K+1)/(N-K-1);
end
end
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_showPLSAResults.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_showPLSAResults.m
| 1,534 |
utf_8
|
209115723076781acbdb50dc817f8e10
|
% function that plots the pLSA results
%
% input
%
% ts TxS matrix with C variables (channels) and T
% tissue types - i.e. the matrix holding the abundance
% maps for the T tissue types
% ct CxT matrix of pure, characteristic spectra
% xyPos vector with x- and y-positions corresponding to the
% spectra in X (of dimension Sx2)
% mzVector vector with m/z-positions (of dimension Cx1)
% scale rescale abundance maps to [0,1] 0/1
%
% output
%
% none
%
% (C)2008 Michael Hanselmann
%
function mip_showPLSAResults(ts, ct, xyPos, mzVector, scale)
% set defaults
if(nargin < 5)
scale = 1;
end
minX = min(xyPos(:,1));
maxX = max(xyPos(:,1));
minY = min(xyPos(:,2));
maxY = max(xyPos(:,2));
figure;
subplot(size(ts, 1), 2, 1);
% write ts-entries to images
for i=1:size(ts, 1)
Img = zeros(maxY-minY+1, maxX-minX+1);
for j=1:size(xyPos, 1)
Img(xyPos(j, 2) - minY + 1, xyPos(j, 1) - minX + 1) = ts(i, j);
end
% abundance maps
subplot(size(ts, 1), 2, 2*i-1);
imagesc(Img);
colormap gray;
axis equal tight;
if(scale~=0)
caxis([0, 1]);
end
colorbar;
hold on;
% characteristic, "pure" spectra
subplot(size(ts, 1), 2, 2*i);
bar(mzVector, ct(:, i)); axis tight;
end
end
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_plotSparsity.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_plotSparsity.m
| 1,819 |
utf_8
|
08f2c12c944a7ec0129c9c73aea3ce13
|
% function that claculates the sparsity of the mixture vectors of the
% decomposition result
%
% input
%
% X SxC matrix with C variables (channels) and S
% observations (spectra), i.e. each row consists of
% one observation
% mzVector vector with m/z-positions (of dimension Cx1)
% mzVector2 peak-picked mzVector
% featureMask indicator array with 0s and 1s indicating which peak
% positions have been selected
% ct CxT matrix with characteristic spectra (see mip_plsa)
%
% output
%
% none
%
% this function uses Hoyer's spasity measure as described in "Non-negative Matrix Factorization with Sparseness Constraints" (2004)
%
% (C)2008 Michael Hanselmann
%
function mip_plotSparsity(X, mzVector, mzVector2, featureMask, ct)
% sparsity calculations, see Hoyer01
numComponents = size(ct, 2);
sparseness = zeros(size(X, 2), 1);
sqrtN = sqrt(numComponents);
for i=1:size(X, 2);
xVec = (ct(i, :)/norm(ct(i, :)))';
sparseness(i) = ((sqrtN - sum(xVec, 1))/sqrt(sum(xVec.*xVec, 1)))/(sqrtN - 1); % normally we would have to use abs(xVec) in L1-norm, but all entries are positive anyway
end
j = 1;
res = zeros(1, size(featureMask, 1));
for i=1:size(featureMask, 1)
if(featureMask(i)==1)
res(i) = sparseness(j);
j = j + 1;
end
end
figure;
% data plot
ax(1) = subplot(2, 1, 1);
bar(mzVector2, ct);
title(['bar plot of the ', num2str(numComponents), ' component types (left to right)']);
% sparsity plot
ax(2) = subplot(2, 1, 2);
bar(min(mzVector):(mzVector(2)-mzVector(1)):max(mzVector), res);
title('sparsity indicating decisive m/z positions');
linkaxes(ax, 'x');
end
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_pickPeaksSimple.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_pickPeaksSimple.m
| 5,453 |
utf_8
|
668c9ea20d0bf52ad12272c0aeccca20
|
% Function that performs feature extraction by peak-picking
%
% input
%
% X SxC matrix with C variables (channels) and S
% observations (spectra), i.e. each row consists of
% one observation
% mzVector vector with m/z-positions (of dimension Cx1)
% ppThreshold value between 0 and 1, threshold for peak picking
% pp_ga_size size of Gaussian filter for smoothing
% pp_ga_sigma sigma for Gaussian filter
% plotResults plot results 0/1
%
% output
%
% Xreduced C'xS matrix of reduced data (with C' peaks)
% mzVectorReduced vector with m/z-positions (of dimension C'x1)
% mask 0/1-mask of peak-positions and size C'x1
%
% (C)2008 Michael Hanselmann
%
function [Xreduced, mzVectorReduced, mask] = mip_pickPeaksSimple(X, mzVector, ppThreshold, pp_ga_size, pp_ga_sigma, plotResults)
% default settings
if nargin < 6
plotResults = 0;
end
if nargin < 5
pp_ga_sigma = 1;
end
if nargin < 4
pp_ga_size = 4;
end
if nargin < 3
ppThreshold = 0.0005;
end
spectra = X;
% calculate sum spectrum and put it in range [0, 1]
spectraSum = sum(spectra, 1);
spectraSum = (spectraSum-min(spectraSum));
spectraSum = spectraSum/(max(spectraSum) + 1e-10);
% peak picking
% ...smoothing
ga_x = -pp_ga_size:1:pp_ga_size;
ga_f = exp( -(ga_x.^2)/(2*pp_ga_sigma^2) );
ga_f = ga_f / sum(sum(ga_f));
spectrumSmoothedShifted = conv(spectraSum, ga_f);
spectrumSmoothed = zeros(1, size(spectraSum, 2));
for i=1:(size(spectrumSmoothedShifted, 2)-2*pp_ga_size)
spectrumSmoothed(1, i) = spectrumSmoothedShifted(1, i+pp_ga_size);
end
% ...detection of maxima/minima in smoothed sum spectrum
[maxima, minima] = mip_peakdetect(spectrumSmoothed, ppThreshold);
% now have a look at the unsmoothed data again
% smoothing can cause peak shifts, so take the maximum in the
% unsmoothed data by "hill-climbing", i.e. proceed to left and right as
% long as the slope is positive and take the maximum
for i=1:size(maxima, 1)
% hill-climbing to left
k = maxima(i, 1);
while(k>1 && spectraSum(k-1)>spectraSum(k))
k = k - 1;
end
% hill-climbing to right
l = maxima(i, 1);
while(l<length(spectraSum) && spectraSum(l+1)>spectraSum(l))
l = l + 1;
end
if(spectraSum(k) > spectraSum(l))
newMaxPos = k;
else
newMaxPos = l;
end
maxima(i, 1) = newMaxPos;
maxima(i, 2) = spectraSum(newMaxPos);
end
for i=1:size(minima, 1)
% down-climbing to left
k = minima(i, 1);
while(k>1 && spectraSum(k-1)<spectraSum(k))
k = k - 1;
end
% down-climbing to right
l = minima(i, 1);
while(l<length(spectraSum) && spectraSum(l+1)<spectraSum(l))
l = l + 1;
end
newMinPos = min(k, l);
minima(i, 1) = newMinPos;
minima(i, 2) = spectraSum(newMinPos);
end
% make mask to eliminate channels that are not picked by the peak
% picker
mask = zeros(size(spectra, 2), 1);
for j=1:size(maxima, 1)
mask(maxima(j, 1), 1) = 1;
end
% just take values at peak position (i.e. value for max channel for all
% spatial locations)
spectraReduced = spectra(:, mask>0);
mzVectorReduced = mzVector(mask>0);
% take sum over complete peak width (i.e. we sum over a
% certain m/z range with regard to a spatial location)
% basically go to left and right from peak position until spectrum
% rises again
minMask = zeros(size(spectra, 2), 1);
for j=1:size(minima, 1)
minMask(minima(j, 1), 1) = 1;
end
j = 1;
numMax = 1;
while(j < length(mask))
% search next maximum
while(j < length(mask) && mask(j) == 0)
j = j+1;
end
if(mask(j) == 1) % real maximum, i.e. not just last entry
lastMax = j;
% search next minimum (from maximum position)
j = j+1;
while(j < length(mask) && spectraSum(j) < spectraSum(j-1))
j = j+1;
end
nextMin = j;
% search last minimum (from maximum position)
j = lastMax - 1;
while(j > 1 && spectraSum(j) < spectraSum(j+1))
j = j-1;
end
lastMin = j;
j = lastMax + 1;
lastMin = max(1, lastMin);
nextMin = min(length(mask), nextMin);
spectraReduced(:, numMax) = sum(spectra(:, lastMin:1:nextMin), 2);
numMax = numMax+1;
end
end
Xreduced = spectraReduced;
% visualization of peak positions
if(plotResults)
figure;
plot(mzVector, spectraSum); axis tight;
hold on;
plot(mzVector, spectrumSmoothed, 'm'); axis tight;
hold on;
if(size(maxima, 1) > 0 && size(minima, 1) > 0)
plot(mzVector(maxima(:,1)), maxima(:,2), 'r*');
hold on;
plot(mzVector(minima(:,1)), minima(:,2), 'g*');
end
title('Picked peak positions (sum spectrum)');
xlabel([num2str(mzVector(1)), ' to ', num2str(mzVector(length(mzVector))), ' (m/z, Da)']);
ylabel('intensity');
end
end
|
github
|
AlanRace/SpectralAnalysis-master
|
mip_simpleNoiseEstimation.m
|
.m
|
SpectralAnalysis-master/src/processing/postprocessing/mip_plsa/mip_simpleNoiseEstimation.m
| 1,757 |
utf_8
|
65d1d1ba797641d35f16b1beff966a1e
|
% Function that estimates the noise in the data by first smoothing the data
% (spatially) and then taking the median of the residual sum of squares
%
% input
%
% X SxC matrix with C variables (channels) and S
% observations (spectra), i.e. each row consists of
% one observation
% mzVector vector with m/z-positions (of dimension Cx1)
% xyPos vector with x- and y-positions corresponding to the
% spectra in X (of dimension Sx2)
%
% output
%
% noiseVarEstimate an estimate of the noise variance in the data (scalar)
%
%
% (C)2008 Michael Hanselmann
%
function noiseVarEstimate = mip_simpleNoiseEstimation(X, mzVector, xyPos)
% make cube
spectra = X;
minX = min(xyPos(:,1));
maxX = max(xyPos(:,1));
minY = min(xyPos(:,2));
maxY = max(xyPos(:,2));
dimensions = [maxY-minY+1, maxX-minX+1];
cube = reshape(double(spectra), dimensions(1), dimensions(2), length(mzVector));
% average spectrum at each position by averaging over the area given by
% steps: (2*steps+1)^2-box function
steps = 1;
cubeAv = double(zeros(size(cube)));
for i=1+steps:dimensions(1)-steps
for j=1+steps:dimensions(2)-steps
for k=i-steps:i+steps
for l=j-steps:j+steps
cubeAv(i, j, :) = cubeAv(i, j, :) + 1/(2*steps+1)^2 * cube(k, l, :);
end
end
end
end
% calculate median RSS, omit border area
cubeRSS = (cube(1+steps:dimensions(1)-steps,1+steps:dimensions(2)-steps, :) - cubeAv(1+steps:dimensions(1)-steps,1+steps:dimensions(2)-steps, :)).^2;
noiseVarEstimate = median(cubeRSS(:)); % mean also possible but less robust
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_search.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_search.m
| 3,564 |
utf_8
|
7dfb2eee171a6fef9aa4adec527e3145
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function [indices, dists] = flann_search(data, testset, n, search_params)
%NN_SEARCH Fast approximate nearest neighbors search
%
% Performs a fast approximate nearest neighbor search using an
% index constructed using flann_build_index or directly a
% dataset.
% Marius Muja, January 2008
algos = struct( 'linear', 0, 'kdtree', 1, 'kmeans', 2, 'composite', 3, 'lsh', 6, 'saved', 254, 'autotuned', 255 );
center_algos = struct('random', 0, 'gonzales', 1, 'kmeanspp', 2 );
log_levels = struct('none', 0, 'fatal', 1, 'error', 2, 'warning', 3, 'info', 4);
default_params = struct('algorithm', 'kdtree' ,'checks', 32, 'eps', 0.0, 'sorted', 1, 'max_neighbors', -1, 'cores', 1, 'trees', 4, 'branching', 32, 'iterations', 5, 'centers_init', 'random', 'cb_index', 0.4, 'target_precision', 0.9,'build_weight', 0.01, 'memory_weight', 0, 'sample_fraction', 0.1, 'table_number', 12, 'key_size', 20, 'multi_probe_level', 2, 'log_level', 'warning', 'random_seed', 0);
if ~isstruct(search_params)
error('The "search_params" argument must be a structure');
end
params = default_params;
fn = fieldnames(search_params);
for i = [1:length(fn)],
name = cell2mat(fn(i));
params.(name) = search_params.(name);
end
if ~isnumeric(params.algorithm),
params.algorithm = value2id(algos,params.algorithm);
end
if ~isnumeric(params.centers_init),
params.centers_init = value2id(center_algos,params.centers_init);
end
if ~isnumeric(params.log_level),
params.log_level = value2id(log_levels,params.log_level);
end
if (size(data,1)==1 && size(data,2)==1)
% we already have an index
[indices,dists] = nearest_neighbors('index_find_nn', data, testset, n, params);
else
% create the index and search
[indices,dists] = nearest_neighbors('find_nn', data, testset, n, params);
end
end
function value = id2value(map, id)
fields = fieldnames(map);
for i = 1:length(fields),
val = cell2mat(fields(i));
if map.(val) == id
value = val;
break;
end
end
end
function id = value2id(map,value)
id = map.(value);
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_load_index.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_load_index.m
| 1,578 |
utf_8
|
f9bcc41fd5972c5c987d6a4d41bdc796
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function index = flann_load_index(filename, dataset)
%FLANN_LOAD_INDEX Loads an index from disk
%
% Marius Muja, March 2009
index = nearest_neighbors('load_index', filename, dataset);
end
|
github
|
qintonguav/TheiaSfM-master
|
test_flann.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/test_flann.m
| 10,328 |
utf_8
|
151c22994b0192f8a071649ad26fbc6b
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function test_flann
data_path = './';
outcome = {'FAILED!!!!!!!!!', 'PASSED'};
failed = 0;
passed = 0;
cnt = 0;
ok = 1;
function assert(condition)
if (~condition)
ok = 0;
end
end
function run_test(name, test)
ok = 1;
cnt = cnt + 1;
tic;
fprintf('Test %d: %s...',cnt,name);
test();
time = toc;
if (ok)
passed = passed + 1;
else
failed = failed + 1;
end
fprintf('done (%g sec) : %s\n',time,cell2mat(outcome(ok+1)))
end
function status
fprintf('-----------------\n');
fprintf('Passed: %d/%d\nFailed: %d/%d\n',passed,cnt,failed,cnt);
end
dataset = [];
testset = [];
function test_load_data
% load the datasets and testsets
% use single precision for better memory efficiency
% store the features one per column because MATLAB
% uses column major ordering
% The dataset.dat and testset.dat files can be downloaded from:
% http://people.cs.ubc.ca/~mariusm/uploads/FLANN/datasets/dataset.dat
% http://people.cs.ubc.ca/~mariusm/uploads/FLANN/datasets/testset.dat
dataset = single(load([data_path 'dataset.dat']))';
testset = single(load([data_path 'testset.dat']))';
assert(size(dataset,1) == size(testset,1));
end
run_test('Load data',@test_load_data);
match = [];
dists = [];
function test_linear_search
[match,dists] = flann_search(dataset, testset, 10, struct('algorithm','linear'));
assert(size(match,1) ==10 && size(match,2) == size(testset,2));
end
run_test('Linear search',@test_linear_search);
function test_kdtree_search
[result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','kdtree',...
'trees',8,...
'checks',64));
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('kd-tree search',@test_kdtree_search);
function test_kmeans_search
[result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','kmeans',...
'branching',32,...
'iterations',3,...
'checks',120));
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('k-means search',@test_kmeans_search);
function test_composite_search
[result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','composite',...
'branching',32,...
'iterations',3,...
'trees', 1,...
'checks',64));
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('composite search',@test_composite_search);
function test_autotune_search
[result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','autotuned',...
'target_precision',0.95,...
'build_weight',0.01,...
'memory_weight',0));
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('search with autotune',@test_autotune_search);
function test_index_kdtree_search
[index, search_params ] = flann_build_index(dataset, struct('algorithm','kdtree', 'trees',8,...
'checks',64));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index kd-tree search',@test_index_kdtree_search);
function test_index_kmeans_search
[index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',...
'branching',32,...
'iterations',3,...
'checks',120));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index kmeans search',@test_index_kmeans_search);
function test_index_kmeans_search_gonzales
[index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',...
'branching',32,...
'iterations',3,...
'checks',120,...
'centers_init','gonzales'));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index kmeans search gonzales',@test_index_kmeans_search_gonzales);
function test_index_kmeans_search_kmeanspp
[index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',...
'branching',32,...
'iterations',3,...
'checks',120,...
'centers_init','kmeanspp'));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index kmeans search kmeanspp',@test_index_kmeans_search_kmeanspp);
function test_index_composite_search
[index, search_params ] = flann_build_index(dataset,struct('algorithm','composite',...
'branching',32,...
'iterations',3,...
'trees', 1,...
'checks',64));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index composite search',@test_index_composite_search);
function test_index_autotune_search
[index, search_params, speedup ] = flann_build_index(dataset,struct('algorithm','autotuned',...
'target_precision',0.95,...
'build_weight',0.01,...
'memory_weight',0));
[result, ndists] = flann_search(index, testset, 10, search_params);
n = size(match,2);
precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n;
assert(precision>0.9);
assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0);
end
run_test('index autotune search',@test_index_autotune_search);
status();
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_free_index.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_free_index.m
| 1,614 |
utf_8
|
5d719d8d60539b6c90bee08d01e458b5
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function flann_free_index(index_id)
%FLANN_FREE_INDEX Deletes the nearest-neighbors index
%
% Deletes an index constructed using flann_build_index.
% Marius Muja, January 2008
nearest_neighbors('free_index',index_id);
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_save_index.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_save_index.m
| 1,563 |
utf_8
|
5a44d911827fba5422041529b3c01cf6
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function flann_save_index(index_id, filename)
%FLANN_SAVE_INDEX Saves an index to disk
%
% Marius Muja, March 2010
nearest_neighbors('save_index',index_id, filename);
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_set_distance_type.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_set_distance_type.m
| 1,914 |
utf_8
|
a62dd85add564e04c01aefeb65083f5d
|
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved.
%Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
%1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
%2. 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.
%
%THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
function flann_set_distance_type(type, order)
%FLANN_LOAD_INDEX Loads an index from disk
%
% Marius Muja, March 2009
distances = struct('euclidean', 1, 'manhattan', 2, 'minkowski', 3, 'max_dist', 4, 'hik', 5, 'hellinger', 6, 'chi_square', 7, 'cs', 7, 'kullback_leibler', 8, 'kl', 8);
if ~isnumeric(type),
type = value2id(distances,type);
end
if type~=3
order = 0;
end
nearest_neighbors('set_distance_type', type, order);
end
function id = value2id(map,value)
id = map.(value);
end
|
github
|
qintonguav/TheiaSfM-master
|
flann_build_index.m
|
.m
|
TheiaSfM-master/libraries/flann/src/matlab/flann_build_index.m
| 2,299 |
utf_8
|
f4cdee51a1c9616f205dcc814c943903
|
function [index, params, speedup] = flann_build_index(dataset, build_params)
%FLANN_BUILD_INDEX Builds an index for fast approximate nearest neighbors search
%
% [index, params, speedup] = flann_build_index(dataset, build_params) - Constructs the
% index from the provided 'dataset' and (optionally) computes the optimal parameters.
% Marius Muja, January 2008
algos = struct( 'linear', 0, 'kdtree', 1, 'kmeans', 2, 'composite', 3, 'kdtree_single', 4, 'hierarchical', 5, 'lsh', 6, 'saved', 254, 'autotuned', 255 );
center_algos = struct('random', 0, 'gonzales', 1, 'kmeanspp', 2 );
log_levels = struct('none', 0, 'fatal', 1, 'error', 2, 'warning', 3, 'info', 4);
default_params = struct('algorithm', 'kdtree' ,'checks', 32, 'eps', 0.0, 'sorted', 1, 'max_neighbors', -1, 'cores', 1, 'trees', 4, 'branching', 32, 'iterations', 5, 'centers_init', 'random', 'cb_index', 0.4, 'target_precision', 0.9,'build_weight', 0.01, 'memory_weight', 0, 'sample_fraction', 0.1, 'table_number', 12, 'key_size', 20, 'multi_probe_level', 2, 'log_level', 'warning', 'random_seed', 0);
if ~isstruct(build_params)
error('The "build_params" argument must be a structure');
end
params = default_params;
fn = fieldnames(build_params);
for i = [1:length(fn)],
name = cell2mat(fn(i));
params.(name) = build_params.(name);
end
if ~isnumeric(params.algorithm),
params.algorithm = value2id(algos,params.algorithm);
end
if ~isnumeric(params.centers_init),
params.centers_init = value2id(center_algos,params.centers_init);
end
if ~isnumeric(params.log_level),
params.log_level = value2id(log_levels,params.log_level);
end
[index, params, speedup] = nearest_neighbors('build_index',dataset, params);
if isnumeric(params.algorithm),
params.algorithm = id2value(algos,params.algorithm);
end
if isnumeric(params.centers_init),
params.centers_init = id2value(center_algos,params.centers_init);
end
end
function value = id2value(map, id)
fields = fieldnames(map);
for i = 1:length(fields),
val = cell2mat(fields(i));
if map.(val) == id
value = val;
break;
end
end
end
function id = value2id(map,value)
id = map.(value);
end
|
github
|
ShaoqingRen/py-faster-rcnn-master
|
voc_eval.m
|
.m
|
py-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m
| 1,332 |
utf_8
|
3ee1d5373b091ae4ab79d26ab657c962
|
function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:).ap]';
fprintf('%.1f\n', aps * 100);
fprintf('%.1f\n', mean(aps) * 100);
fprintf('~~~~~~~~~~~~~~~~~~~~\n');
function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir)
test_set = VOCopts.testset;
year = VOCopts.dataset(4:end);
addpath(fullfile(VOCopts.datadir, 'VOCcode'));
res_fn = sprintf(VOCopts.detrespath, comp_id, cls);
recall = [];
prec = [];
ap = 0;
ap_auc = 0;
do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test');
if do_eval
% Bug in VOCevaldet requires that tic has been called first
tic;
[recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true);
ap_auc = xVOCap(recall, prec);
% force plot limits
ylim([0 1]);
xlim([0 1]);
print(gcf, '-djpeg', '-r0', ...
[output_dir '/' cls '_pr.jpg']);
end
fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc);
res.recall = recall;
res.prec = prec;
res.ap = ap;
res.ap_auc = ap_auc;
save([output_dir '/' cls '_pr.mat'], ...
'res', 'recall', 'prec', 'ap', 'ap_auc');
rmpath(fullfile(VOCopts.datadir, 'VOCcode'));
|
github
|
stanchiang/constellation-master
|
colamd_test.m
|
.m
|
constellation-master/masteringopencv2012/Chapter4_StructureFromMotion/3rdparty/SSBA-4.0/COLAMD/MATLAB/colamd_test.m
| 11,737 |
utf_8
|
1bbab37469571534db129da1e1531e5b
|
function colamd_test
%COLAMD_TEST test colamd2 and symamd2
% Example:
% colamd_test
%
% COLAMD and SYMAMD testing function. Here we try to give colamd2 and symamd2
% every possible type of matrix and erroneous input that they may encounter.
% We want either a valid permutation returned or we want them to fail
% gracefully.
%
% You are prompted as to whether or not the colamd2 and symand routines and
% the test mexFunctions are to be compiled.
%
% See also colamd2, symamd2
% Copyright 1998-2007, Timothy A. Davis, and Stefan Larimore
% Developed in collaboration with J. Gilbert and E. Ng.
help colamd_test
fprintf ('Compiling colamd2, symamd2, and test mexFunctions.\n') ;
colamd_make ;
d = '' ;
if (~isempty (strfind (computer, '64')))
d = '-largeArrayDims' ;
end
cmd = sprintf (...
'mex -DDLONG -O %s -I../../SuiteSparse_config -I../Include ', d) ;
src = '../Source/colamd.c ../Source/colamd_global.c' ;
eval ([cmd 'colamdtestmex.c ' src]) ;
eval ([cmd 'symamdtestmex.c ' src]) ;
fprintf ('Done compiling.\n') ;
fprintf ('\nThe following codes will be tested:\n') ;
which colamd2
which symamd2
which colamd2mex
which symamd2mex
which colamdtestmex
which symamdtestmex
fprintf ('\nStarting the tests. Please be patient.\n') ;
h = waitbar (0, 'COLAMD test') ;
rand ('state', 0) ;
randn ('state', 0) ;
A = sprandn (500,500,0.4) ;
p = colamd2 (A, [10 10 1]) ; check_perm (p, A) ;
p = colamd2 (A, [2 7 1]) ; check_perm (p, A) ;
p = symamd2 (A, [10 1]) ; check_perm (p, A) ;
p = symamd2 (A, [7 1]) ; check_perm (p, A) ;
p = symamd2 (A, [4 1]) ; check_perm (p, A) ;
fprintf ('Null matrices') ;
A = zeros (0,0) ;
A = sparse (A) ;
[p, stats] = colamd2 (A, [10 10 0]) ; %#ok
check_perm (p, A) ;
[p, stats] = symamd2 (A, [10 0]) ; %#ok
check_perm (p, A) ;
A = zeros (0, 100) ;
A = sparse (A) ;
[p, stats] = colamd2 (A, [10 10 0]) ; %#ok
check_perm (p, A) ;
A = zeros (100, 0) ;
A = sparse (A) ;
[p, stats] = colamd2 (A, [10 10 0]) ;
check_perm (p, A) ;
fprintf (' OK\n') ;
fprintf ('Matrices with a few dense row/cols\n') ;
for trial = 1:20
waitbar (trial/20, h, 'COLAMD: with dense rows/cols') ;
% random square unsymmetric matrix
A = rand_matrix (1000, 1000, 1, 10, 20) ;
for tol = [0:.1:2 3:20 1e6]
[p, stats] = colamd2 (A, [tol tol 0]) ; %#ok
check_perm (p, A) ;
B = A + A' ;
[p, stats] = symamd2 (B, [tol 0]) ; %#ok
check_perm (p, A) ;
[p, stats] = colamd2 (A, [tol 1 0]) ; %#ok
check_perm (p, A) ;
[p, stats] = colamd2 (A, [1 tol 0]) ; %#ok
check_perm (p, A) ;
end
end
fprintf (' OK\n') ;
fprintf ('General matrices\n') ;
for trial = 1:400
waitbar (trial/400, h, 'COLAMD: general') ;
% matrix of random mtype
mtype = irand (3) ;
A = rand_matrix (2000, 2000, mtype, 0, 0) ;
p = colamd2 (A) ;
check_perm (p, A) ;
if (mtype == 3)
p = symamd2 (A) ;
check_perm (p, A) ;
end
end
fprintf (' OK\n') ;
fprintf ('Test error handling with invalid inputs\n') ;
% Check different erroneous input.
for trial = 1:30
waitbar (trial/30, h, 'COLAMD: error handling') ;
A = rand_matrix (1000, 1000, 2, 0, 0) ;
[m n] = size (A) ;
for err = 1:13
p = Tcolamd (A, [n n 0 0 err]) ;
if (p ~= -1) %#ok
check_perm (p, A) ;
end
if (err == 1)
% check different (valid) input args to colamd2
p = Acolamd (A) ;
p2 = Acolamd (A, [10 10 0 0 0]) ;
if (any (p ~= p2))
error ('colamd2: mismatch 1!') ;
end
[p2 stats] = Acolamd (A) ; %#ok
if (any (p ~= p2))
error ('colamd2: mismatch 2!') ;
end
[p2 stats] = Acolamd (A, [10 10 0 0 0]) ;
if (any (p ~= p2))
error ('colamd2: mismatch 3!') ;
end
end
B = A'*A ;
p = Tsymamd (B, [n 0 err]) ;
if (p ~= -1) %#ok
check_perm (p, A) ;
end
if (err == 1)
% check different (valid) input args to symamd2
p = Asymamd (B) ;
check_perm (p, A) ;
p2 = Asymamd (B, [10 0 0]) ;
if (any (p ~= p2))
error ('symamd2: mismatch 1!') ;
end
[p2 stats] = Asymamd (B) ; %#ok
if (any (p ~= p2))
error ('symamd2: mismatch 2!') ;
end
[p2 stats] = Asymamd (B, [10 0 0]) ; %#ok
if (any (p ~= p2))
error ('symamd2: mismatch 3!') ;
end
end
end
end
fprintf (' OK\n') ;
fprintf ('Matrices with a few empty columns\n') ;
for trial = 1:400
% some are square, some are rectangular
n = 0 ;
while (n < 5)
A = rand_matrix (1000, 1000, irand (2), 0, 0) ;
[m n] = size (A) ;
end
% Add 5 null columns at random locations.
null_col = randperm (n) ;
null_col = sort (null_col (1:5)) ;
A (:, null_col) = 0 ;
% Order the matrix and make sure that the null columns are ordered last.
[p, stats] = colamd2 (A, [1e6 1e6 0]) ;
check_perm (p, A) ;
% if (stats (2) ~= 5)
% stats (2)
% error ('colamd2: wrong number of null columns') ;
% end
% find all null columns in A
null_col = find (sum (spones (A), 1) == 0) ;
nnull = length (null_col) ; %#ok
if (any (null_col ~= p ((n-4):n)))
error ('colamd2: Null cols are not ordered last in natural order') ;
end
end
fprintf (' OK\n') ;
fprintf ('Matrices with a few empty rows and columns\n') ;
for trial = 1:400
waitbar (trial/400, h, 'COLAMD: with empty rows/cols') ;
% symmetric matrices
n = 0 ;
while (n < 5)
A = rand_matrix (1000, 1000, 3, 0, 0) ;
[m n] = size (A) ;
end
% Add 5 null columns and rows at random locations.
null_col = randperm (n) ;
null_col = sort (null_col (1:5)) ;
A (:, null_col) = 0 ;
A (null_col, :) = 0 ;
% Order the matrix and make sure that the null rows/cols are ordered last.
[p,stats] = symamd2 (A, [10 0]) ;
check_perm (p, A) ;
% find actual number of null rows and columns
Alo = tril (A, -1) ;
nnull = length (find (sum (Alo') == 0 & sum (Alo) == 0)) ; %#ok
if (stats (2) ~= nnull | nnull < 5) %#ok
error ('symamd2: wrong number of null columns') ;
end
if (any (null_col ~= p ((n-4):n)))
error ('symamd2: Null cols are not ordered last in natural order') ;
end
end
fprintf (' OK\n') ;
fprintf ('Matrices with a few empty rows\n') ;
% Test matrices with null rows inserted.
for trial = 1:400
waitbar (trial/400, h, 'COLAMD: with null rows') ;
m = 0 ;
while (m < 5)
A = rand_matrix (1000, 1000, 2, 0, 0) ;
[m n] = size (A) ; %#ok
end
% Add 5 null rows at random locations.
null_row = randperm (m) ;
null_row = sort (null_row (1:5)) ;
A (null_row, :) = 0 ;
p = colamd2 (A, [10 10 0]) ;
check_perm (p, A) ;
if (stats (1) ~= 5)
error ('colamd2: wrong number of null rows') ;
end
end
fprintf (' OK\n') ;
fprintf ('\ncolamd2 and symamd2: all tests passed\n\n') ;
close (h) ;
%-------------------------------------------------------------------------------
function [p,stats] = Acolamd (S, knobs)
% Acolamd: compare colamd2 and Tcolamd results
if (nargin < 3)
if (nargout == 1)
[p] = colamd2 (S) ;
[p1] = Tcolamd (S, [10 10 0 0 0]) ;
else
[p, stats] = colamd2 (S) ;
[p1, stats1] = Tcolamd (S, [10 10 0 0 0]) ; %#ok
end
else
if (nargout == 1)
[p] = colamd2 (S, knobs (1:3)) ;
[p1] = Tcolamd (S, knobs) ;
else
[p, stats] = colamd2 (S, knobs (1:3)) ;
[p1, stats1] = Tcolamd (S, knobs) ; %#ok
end
end
check_perm (p, S) ;
check_perm (p1, S) ;
if (any (p1 ~= p))
error ('Acolamd mismatch!') ;
end
%-------------------------------------------------------------------------------
function [p,stats] = Asymamd (S, knobs)
% Asymamd: compare symamd2 and Tsymamd results
if (nargin < 3)
if (nargout == 1)
[p] = symamd2 (S) ;
[p1] = Tsymamd (S, [10 0 0]) ;
else
[p, stats] = symamd2 (S) ;
[p1, stats1] = Tsymamd (S, [10 0 0]) ; %#ok
end
else
if (nargout == 1)
[p] = symamd2 (S, knobs (1:2)) ;
[p1] = Tsymamd (S, knobs) ;
else
[p, stats] = symamd2 (S, knobs (1:2)) ;
[p1, stats1] = Tsymamd (S, knobs) ; %#ok
end
end
if (any (p1 ~= p))
error ('Asymamd mismatch!') ;
end
%-------------------------------------------------------------------------------
function check_perm (p, A)
% check_perm: check for a valid permutation vector
if (isempty (A) & isempty (p)) %#ok
% empty permutation vectors of empty matrices are OK
return
end
if (isempty (p))
error ('bad permutation: cannot be empty') ;
end
[m n] = size (A) ;
[pm pn] = size (p) ;
if (pn == 1)
% force p to be a row vector
p = p' ;
[pm pn] = size (p) ;
end
if (n ~= pn)
error ('bad permutation: wrong size') ;
end
if (pm ~= 1) ;
% p must be a vector
error ('bad permutation: not a vector') ;
else
if (any (sort (p) - (1:pn)))
error ('bad permutation') ;
end
end
%-------------------------------------------------------------------------------
function i = irand (n)
% irand: return a random integer between 1 and n
i = min (n, 1 + floor (rand * n)) ;
%-------------------------------------------------------------------------------
function A = rand_matrix (nmax, mmax, mtype, drows, dcols)
% rand_matrix: return a random sparse matrix
%
% A = rand_matrix (nmax, mmax, mtype, drows, dcols)
%
% A binary matrix of random size, at most nmax-by-mmax, with drows dense rows
% and dcols dense columns.
%
% mtype 1: square unsymmetric (mmax is ignored)
% mtype 2: rectangular
% mtype 3: symmetric (mmax is ignored)
n = irand (nmax) ;
if (mtype ~= 2)
% square
m = n ;
else
m = irand (mmax) ;
end
A = sprand (m, n, 10 / max (m,n)) ;
if (drows > 0)
% add dense rows
for k = 1:drows
i = irand (m) ;
nz = irand (n) ;
p = randperm (n) ;
p = p (1:nz) ;
A (i,p) = 1 ;
end
end
if (dcols > 0)
% add dense cols
for k = 1:dcols
j = irand (n) ;
nz = irand (m) ;
p = randperm (m) ;
p = p (1:nz) ;
A (p,j) = 1 ;
end
end
A = spones (A) ;
% ensure that there are no empty columns
d = find (full (sum (A)) == 0) ; %#ok
A (m,d) = 1 ; %#ok
% ensure that there are no empty rows
d = find (full (sum (A,2)) == 0) ; %#ok
A (d,n) = 1 ; %#ok
if (mtype == 3)
% symmetric
A = A + A' + speye (n) ;
end
A = spones (A) ;
%-------------------------------------------------------------------------------
function [p,stats] = Tcolamd (S, knobs)
% Tcolamd: run colamd2 in a testing mode
if (nargout <= 1 & nargin == 1) %#ok
p = colamdtestmex (S) ;
elseif (nargout <= 1 & nargin == 2) %#ok
p = colamdtestmex (S, knobs) ;
elseif (nargout == 2 & nargin == 1) %#ok
[p, stats] = colamdtestmex (S) ;
elseif (nargout == 2 & nargin == 2) %#ok
[p, stats] = colamdtestmex (S, knobs) ;
else
error ('colamd2: incorrect number of input and/or output arguments') ;
end
if (p (1) ~= -1)
[ignore, q] = etree (S (:,p), 'col') ;
p = p (q) ;
check_perm (p, S) ;
end
%-------------------------------------------------------------------------------
function [p, stats] = Tsymamd (S, knobs)
% Tsymamd: run symamd2 in a testing mode
if (nargout <= 1 & nargin == 1) %#ok
p = symamdtestmex (S) ;
elseif (nargout <= 1 & nargin == 2) %#ok
p = symamdtestmex (S, knobs) ;
elseif (nargout == 2 & nargin == 1) %#ok
[p, stats] = symamdtestmex (S) ;
elseif (nargout == 2 & nargin == 2) %#ok
[p, stats] = symamdtestmex (S, knobs) ;
else
error ('symamd2: incorrect number of input and/or output arguments') ;
end
if (p (1) ~= -1)
[ignore, q] = etree (S (p,p)) ;
p = p (q) ;
check_perm (p, S) ;
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
train_classes_20x1_smooth_lsvm_topK_bagmine_greedycover.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/train_classes_20x1_smooth_lsvm_topK_bagmine_greedycover.m
| 10,648 |
utf_8
|
46680d10d8be797f27b215d668e6f8db
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Hyun Oh Song
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function models = train_classes_20x1_smooth_lsvm_topK_bagmine_greedycover(...
classid, varargin)
% represents both positive and negative images as bags and refines initial detector by minimizing smooth latent svm loss.
% initialize and fix the random seed
randn('state', 1);
rand('state', 1);
% addpath
addpath(genpath('minFunc_2012/'));
addpath('projsplx/');
trainset = 'trainval';
year = '2007';
if ischar(classid), classid = str2double(classid); end
% cast optional parameters into double
if length(varargin) ~= 0
for i = 1:length(varargin)
if mod(i,2)==0
if ischar(varargin{i}), varargin{i} = str2double(varargin{i}); end
end
end
end
ip = inputParser;
ip.addRequired('trainset', @isstr);
ip.addRequired('year', @isstr);
ip.addRequired('classid', @isscalar);
ip.addParamValue('sharpness', 100, @isscalar);
ip.addParamValue('svm_mu', 0.01, @isscalar);
ip.addParamValue('topK', 15, @isscalar);
ip.addParamValue('alpha', 0.95, @isscalar);
ip.addParamValue('K1', 0.5, @isscalar);
ip.addParamValue('K2', 1.0, @isscalar);
ip.addParamValue('nms_threshold', 0.3, @isscalar);
ip.addParamValue('loss_type', 'SmoothHinge', @isstr);
ip.addParamValue('svm_C', 10^-3, @isscalar);
ip.addParamValue('bias_mult', 10, @isscalar);
ip.addParamValue('pos_loss_weight', 2, @isscalar);
ip.addParamValue('layer', 'fc7', @isstr);
ip.addParamValue('fine_tuned', 0, @isscalar);
ip.addParamValue('use_flipped', 0, @isscalar);
ip.addParamValue('target_norm', 20, @isscalar);
ip.parse(trainset, year, classid, varargin{:});
opts = ip.Results;
fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Training options:\n');
disp(opts);
fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');
conf = voc_config();
clss = conf.pascal.VOCopts.classes;
clss = clss(classid);
num_clss = 1;
dataset.year = year;
dataset.trainset = trainset;
dataset.image_ids = textread(sprintf(conf.pascal.VOCopts.imgsetpath, trainset), '%s');
dataset.image_ids_small = dataset.image_ids(randperm(length(dataset.image_ids), 1000));
dataset.pos_image_ids = dataset.image_ids;
% ------------------------------------------------------------------------
load('class_pos_images.mat');
pos_image_ids = class_pos_images(classid).ids;
neg_image_ids = setdiff(dataset.image_ids, pos_image_ids);
dataset.neg_image_ids = neg_image_ids;
% sample_pos_image_ids = pos_image_ids(1:40);
% sample_neg_image_ids = neg_image_ids(1:200);
sample_pos_image_ids = pos_image_ids;
sample_neg_image_ids = neg_image_ids;
num_pos_images = length(sample_pos_image_ids);
num_neg_images = length(sample_neg_image_ids);
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Get or compute the average norm of the features
if ~exist('feat_stats','file')
mkdir('feat_stats');
end
save_file = sprintf('feat_stats/stats_%s_%s_layer_%s_finetuned_%d', ...
trainset, year, opts.layer, opts.fine_tuned);
try
ld = load(save_file);
opts.feat_norm_mean = ld.feat_norm_mean;
clear ld;
catch
[feat_norm_mean, stddev] = feat_stats_hos(trainset, year, opts.layer, opts.fine_tuned);
save(save_file, 'feat_norm_mean', 'stddev');
opts.feat_norm_mean = feat_norm_mean;
end
fprintf('average norm = %.3f\n', opts.feat_norm_mean);
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Load initial classifier
latent_iter = 0;
load([conf.paths.model_dir, ...
'latentiter_' num2str(latent_iter) '_clss_' clss{1}, ...
'_C_' num2str(opts.svm_C), ...
'_B_' num2str(opts.bias_mult), ...
'_w1_' num2str(opts.pos_loss_weight), ...
'_losstype_' opts.loss_type,...
'_sharpness_' num2str(opts.sharpness),...
'_alpha_' num2str(opts.alpha),...
'_K1_' num2str(opts.K1), ...
'_K2_' num2str(opts.K2), ...
'_nms_' num2str(opts.nms_threshold), ...
'_20x1_smooth_greedycover_final.mat'], 'models');
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Train with hard negative mining
max_latent_iter = 10;
th = tic(); % measure training time
for latent_iter = 1:max_latent_iter
fprintf('latent positive update %d\n', latent_iter);
save_filename = [conf.paths.model_dir, ...
'latentiter_' num2str(latent_iter) '_clss_' clss{1}, ...
'_C_' num2str(opts.svm_C), ...
'_B_' num2str(opts.bias_mult), ...
'_w1_' num2str(opts.pos_loss_weight), ...
'_losstype_' opts.loss_type, ...
'_sharpness_' num2str(opts.sharpness), ...
'_mu_' num2str(opts.svm_mu), ...
'_alpha_' num2str(opts.alpha),...
'_K1_' num2str(opts.K1),...
'_K2_' num2str(opts.K2),...
'_nms_' num2str(opts.nms_threshold),....
'_topK_' num2str(opts.topK), ...
'_20x1_smooth_topK_bagmine_greedycover_final.mat'];
% check if we already computed the file.
if exist(save_filename, 'file') ~= 0
load(save_filename, 'models');
fprintf('models exists for latent iter %d loaded.\n', latent_iter);
else
% Get top K windows in all positive examples into the cache
X_pos = get_all_features_topK_bagmine(...
models, dataset, opts, sample_pos_image_ids);
% Get top K windows in all negative examples into the cache
X_neg = get_all_features_topK_bagmine(...
models, dataset, opts, sample_neg_image_ids);
% Update model
model_before = [models{1}.w; models{1}.b];
models{1} = update_model_smooth_latent_light(models{1}, X_pos, X_neg, ...
num_pos_images, num_neg_images, opts);
model_after = [models{1}.w; models{1}.b];
% check if model converged
if norm(model_after - model_before) < 1e-8
fprintf('Latent positive relabeling convergence detected. Breaking.\n');
break;
end
save(save_filename, 'models');
end
end
fprintf('Took %.3f hours to train\n', toc(th)/3600);
% ------------------------------------------------------------------------
function X = get_all_features_topK_bagmine(...
models, dataset, opts, sample_image_ids)
% ------------------------------------------------------------------------
d = load_cached_features_hos(1, dataset.trainset, dataset.year, sample_image_ids{1});
feat_dim = size(d.feat,2);
X = zeros(feat_dim, opts.topK * length(sample_image_ids), 'single');
start_i = 1;
for i = 1:length(sample_image_ids)
d = load_cached_features_hos(1, dataset.trainset, dataset.year, sample_image_ids{i});
d.feat = xform_feat_custom(d.feat, opts);
% remove all ground truth boxes
d.feat = d.feat( d.gt ~= 1, :);
if size(d.feat,1) < opts.topK
continue;
end
zs = d.feat * models{1}.w + models{1}.b;
[~, top_ids] = sort(zs, 'descend');
sel = top_ids(1 : opts.topK);
end_i = start_i + opts.topK - 1;
X(:, start_i : end_i) = d.feat(sel,:)';
start_i = end_i + 1;
end
disp('done');
% ------------------------------------------------------------------------
function model = update_model_smooth_latent_light(model, X_pos, X_neg, ...
num_pos_bags, num_neg_bags, opts)
% ------------------------------------------------------------------------
pweight = opts.pos_loss_weight;
[pos_cum_bag_idx, pos_averaging_matrix] = prebuild_averaging_matrix(...
opts.topK * ones(num_pos_bags, 1), num_pos_bags);
[neg_cum_bag_idx, neg_averaging_matrix] = prebuild_averaging_matrix(...
opts.topK * ones(num_neg_bags, 1), num_neg_bags);
% prebuild label, and pweighted labels
y = [ones(num_pos_bags,1); -ones(num_neg_bags,1)];
pweighted_y = [pweight*y(1:num_pos_bags); y(num_pos_bags+1:end)];
options.Method = 'lbfgs';
options.Display = 'OFF'; %no output, default = 2;
% w0 depends on whether this is a latent run or not
if isempty(model.w)
error('In latent runs, model should never be empty');
else
w0 = double([model.w; model.b/opts.bias_mult]);
[cost,~] = slslvm_cost_smoothhinge_bagmine(...
w0, X_pos, X_neg, pos_averaging_matrix, pos_cum_bag_idx, ...
neg_averaging_matrix, neg_cum_bag_idx,...
num_pos_bags, num_neg_bags, y, pweighted_y, opts.svm_C, opts.svm_mu, ...
pweight, opts.sharpness, opts.bias_mult);
fprintf('cost before lbfgs: %.4f\n', cost);
end
if strcmp(opts.loss_type, 'L1hinge')
error('dense version not implemented yet');
elseif strcmp(opts.loss_type, 'SmoothHinge')
w_opt = minFunc(@(w) slslvm_cost_smoothhinge_bagmine(...
w, X_pos, X_neg, pos_averaging_matrix, pos_cum_bag_idx, ...
neg_averaging_matrix, neg_cum_bag_idx,...
num_pos_bags, num_neg_bags, y, pweighted_y, opts.svm_C, opts.svm_mu, ...
pweight, opts.sharpness, opts.bias_mult), w0, options);
[cost,~] = slslvm_cost_smoothhinge_bagmine(...
w_opt, X_pos, X_neg, pos_averaging_matrix, pos_cum_bag_idx, ...
neg_averaging_matrix, neg_cum_bag_idx,...
num_pos_bags, num_neg_bags, y, pweighted_y, opts.svm_C, opts.svm_mu, ...
pweight, opts.sharpness, opts.bias_mult);
fprintf('cost after lbfgs: %.4f\n', cost);
else
error('Unrecognized loss');
end
model.w = single(w_opt(1:end-1));
model.b = single(w_opt(end)*opts.bias_mult);
% ------------------------------------------------------------------------
function [cum_bag_idx, averaging_matrix] = prebuild_averaging_matrix(...
num_insts_per_bag, bag_size)
% ------------------------------------------------------------------------
cum_bag_idx = cumsum(num_insts_per_bag);
if size(cum_bag_idx,1) ~= 1
cum_bag_idx = cum_bag_idx';
end
% precompute averaging matrix
averaging_matrix = zeros(cum_bag_idx(end), bag_size);
i_start = 1;
for i = 1:bag_size
i_end = cum_bag_idx(i);
averaging_matrix( i_start : i_end, i) = ...
ones(num_insts_per_bag(i),1);
i_start = i_end + 1;
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
mil_classes_20x1_smooth_lsvm_topK_bagmine_greedycover.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/mil_classes_20x1_smooth_lsvm_topK_bagmine_greedycover.m
| 1,751 |
utf_8
|
aacd7807398aff902c0c072fe03e899c
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Hyun Oh Song
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function mil_classes_20x1_smooth_lsvm_topK_bagmine_greedycover(classid)
if ischar(classid), classid = str2double(classid); end
conf = voc_config();
sharpness = '100';
loss_type = 'SmoothHinge';
svm_C = '0.001';
bias_mult = '10';
pos_loss_weight = '2';
class_list = conf.pascal.VOCopts.classes;
% learning parameters
svm_mu = 0.01;
topK = 15;
alpha = 0.95;
K1 = 0.5;
K2 = 1.0;
nms_threshold = 0.3;
load_filename = ['latentiter_*' ,...
'_clss_' class_list{classid}, ...
'_C_' svm_C, ...
'_B_' bias_mult, ...
'_w1_' pos_loss_weight, ...
'_losstype_' loss_type, ...
'_sharpness_' sharpness, ...
'_mu_' num2str(svm_mu), ...
'_alpha_' num2str(alpha),...
'_K1_' num2str(K1), ...
'_K2_' num2str(K2), ...
'_nms_' num2str(nms_threshold), ...
'_topK_' num2str(topK),...
'_20x1_smooth_topK_bagmine_greedycover_final.mat'];
iterations = [];
d = dir([conf.paths.model_dir, load_filename]);
for i = 1:length(d)
this_name = d(i).name;
bars = strfind(this_name, '_');
iteration_id = str2double(this_name(bars(1)+1 : bars(2)-1));
iterations = [iterations; iteration_id];
end
highest_iteration_fileid = find(iterations == max(iterations));
load_filename = d(highest_iteration_fileid).name;
load([conf.paths.model_dir, load_filename], 'models');
mil_region_mining(models, 'trainval', '2007');
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
slslvm_cost_smoothhinge_bagmine.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/slslvm_cost_smoothhinge_bagmine.m
| 2,278 |
utf_8
|
512e2dca70ed62e8a7a98c2911d7a827
|
function [cost, grad] = slslvm_cost_smoothhinge_bagmine(...
w, pos_X, neg_X, ...
pos_averaging_matrix, pos_cum_bag_idx, ...
neg_averaging_matrix, neg_cum_bag_idx,...
num_pos, num_neg, ...
y, pweighted_y, C, mu, pweight, sharpness, bias_mult)
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Hyun Oh Song
%
% This file is part of the Song-ICML2014 code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
[f_w_pos, pos_averaging_matrix] = compute_smooth_score(...
w, pos_cum_bag_idx, pos_averaging_matrix, pos_X, mu, bias_mult);
[f_w_neg, neg_averaging_matrix] = compute_smooth_score(...
w, neg_cum_bag_idx, neg_averaging_matrix, neg_X, mu, bias_mult);
scores = [f_w_pos, f_w_neg];
% cost
pos_ind = 1 : num_pos;
neg_ind = num_pos+1 : num_pos+num_neg;
margins = y .* scores';
[loss, loss_grad] = general_smooth_hinge(margins, sharpness);
cost = 0.5*norm(w)^2 + C * ...
(pweight * sum(loss(pos_ind)) + sum(loss(neg_ind)));
% gradient
yy = (loss_grad .* pweighted_y);
pos_avg_yy = pos_averaging_matrix * yy(pos_ind);
neg_avg_yy = neg_averaging_matrix * yy(neg_ind);
loss_grad_mat = pos_X * pos_avg_yy + neg_X * neg_avg_yy;
grad = w + C * [loss_grad_mat; bias_mult*(sum(pos_avg_yy) + sum(neg_avg_yy))];
cost = double(cost);
grad = double(grad);
function [f_w, averaging_matrix]= compute_smooth_score(...
w, cum_bag_idx, averaging_matrix, X, mu, bias_mult)
num_insts = cum_bag_idx(end);
instance_scores = w(1:end-1)'*X + ...
(w(end)*bias_mult)*ones(1,num_insts); % 1 by num_insts
a = zeros(num_insts, 1, 'single');
bag_start = 1;
for bag_end = cum_bag_idx
ind = bag_start : bag_end;
a( ind ) = projsplx_c_float(1/mu * instance_scores( ind ));
bag_start = bag_end + 1;
end
% premultiply a to the averaging matrix
averaging_matrix = bsxfun(@times, averaging_matrix, a);
f_w = (instance_scores -mu/2*a') * averaging_matrix;
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
train_classes_20x1_smooth_greedycover.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/train_classes_20x1_smooth_greedycover.m
| 23,127 |
utf_8
|
792a583169dbd9446982eba59c5a3e55
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Hyun Oh Song
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function models = train_classes_20x1_smooth_greedycover(classid, varargin)
% Sort clusters by discriminativeness score, greedily take
% non-overlapping (image ids, or boxes) in K1 until the score goes bad
% initialize and fix the random seed
randn('state', 1);
rand('state', 1);
% addpath
addpath(genpath('minFunc_2012/'));
addpath('projsplx/');
trainset = 'trainval';
year = '2007';
if ischar(classid), classid = str2double(classid); end
% cast optional parameters into double
if length(varargin) ~= 0
for i = 1:length(varargin)
if mod(i,2)==0
if ischar(varargin{i}), varargin{i} = str2double(varargin{i}); end
end
end
end
ip = inputParser;
ip.addRequired('trainset', @isstr);
ip.addRequired('year', @isstr);
ip.addRequired('classid', @isscalar);
ip.addParamValue('sharpness', 100, @isscalar);
ip.addParamValue('alpha', 0.95, @isscalar);
ip.addParamValue('K1', 0.5, @isscalar);
ip.addParamValue('K2', 1.0, @isscalar);
ip.addParamValue('nms_threshold', 0.3, @isscalar);
ip.addParamValue('loss_type', 'SmoothHinge', @isstr);
ip.addParamValue('svm_C', 10^-3, @isscalar);
ip.addParamValue('bias_mult', 10, @isscalar);
ip.addParamValue('pos_loss_weight', 2, @isscalar);
ip.addParamValue('layer', 'fc7', @isstr);
ip.addParamValue('fine_tuned', 0, @isscalar);
ip.addParamValue('use_flipped', 0, @isscalar);
ip.addParamValue('target_norm', 20, @isscalar);
ip.parse(trainset, year, classid, varargin{:});
opts = ip.Results;
fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Training options:\n');
disp(opts);
fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');
conf = voc_config();
clss = conf.pascal.VOCopts.classes;
clss = clss(classid);
num_clss = 1;
dataset.year = year;
dataset.trainset = trainset;
dataset.image_ids = textread(sprintf(conf.pascal.VOCopts.imgsetpath, trainset), '%s');
dataset.image_ids_small = dataset.image_ids(randperm(length(dataset.image_ids), 1000));
dataset.pos_image_ids = dataset.image_ids;
% ------------------------------------------------------------------------
load('class_pos_images.mat');
pos_image_ids = class_pos_images(classid).ids;
neg_image_ids = setdiff(dataset.image_ids, pos_image_ids);
dataset.neg_image_ids = neg_image_ids;
% sample_pos_image_ids = pos_image_ids(1:40);
% sample_neg_image_ids = neg_image_ids(1:200);
sample_pos_image_ids = pos_image_ids;
sample_neg_image_ids = neg_image_ids;
num_pos_images = length(sample_pos_image_ids);
num_neg_images = length(sample_neg_image_ids);
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Get or compute the average norm of the features
if ~exist('feat_stats','file')
mkdir('feat_stats');
end
save_file = sprintf('feat_stats/stats_%s_%s_layer_%s_finetuned_%d', ...
trainset, year, opts.layer, opts.fine_tuned);
try
ld = load(save_file);
opts.feat_norm_mean = ld.feat_norm_mean;
clear ld;
catch
[feat_norm_mean, stddev] = feat_stats_hos(trainset, year, opts.layer, opts.fine_tuned);
save(save_file, 'feat_norm_mean', 'stddev');
opts.feat_norm_mean = feat_norm_mean;
end
fprintf('average norm = %.3f\n', opts.feat_norm_mean);
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Init models
models = {};
for i = 1:num_clss
models{i} = init_model(clss{i}, dataset, conf, opts);
end
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Get all positive examples
X_pos = get_positive_features_paris_greedycover( ...
models, dataset, opts, classid, sample_pos_image_ids );
for i = 1:num_clss
fprintf('%14s has %6d positive instances\n', models{i}.class, size(X_pos{i},1));
X_pos{i} = xform_feat_custom(X_pos{i}, opts);
end
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Init training caches
caches = {};
for i = 1:num_clss
caches{i} = init_cache(models{i}, X_pos{i});
end
% ------------------------------------------------------------------------
% ------------------------------------------------------------------------
% Train with hard negative mining
first_time = true;
force_update = false;
max_hard_epochs = 1;
max_latent_iter = 0;
th = tic(); % measure training time
for latent_iter = 0:max_latent_iter
% Latent positive relabling
if latent_iter > 0
fprintf('latent positive update %d\n', latent_iter);
%X_pos = get_positive_features_paris_maxcover(models, dataset, true, opts,...
% classid, sample_pos_image_ids, sample_neg_image_ids);
for i = 1:num_clss
caches{i}.X_pos = X_pos{i};
fprintf('%14s has %10d positive instances\n', ...
models{i}.class, size(X_pos{i},1));
end
% force model update
force_update = true;
end
% Train lbfgs SVMs with hard negative mining
for hard_epoch = 1:max_hard_epochs
for i = 1:length(dataset.neg_image_ids)
fprintf('%s: hard neg epoch %d %d/%d\n', ...
procid(), hard_epoch, i, length(dataset.neg_image_ids));
% Get hard negatives for all classes at once (avoids loading feature cache
% more than once)
[X, keys] = sample_negative_features(first_time, models, caches, dataset, i, opts);
% Add sampled negatives to each classes training cache, removing
% duplicates
for j = 1:num_clss
if ~isempty(keys{j})
if isempty(caches{j}.keys)
dups = [];
else
[~, ~, dups] = intersect(caches{j}.keys, keys{j}, 'rows');
end
assert(isempty(dups));
caches{j}.X_neg = cat(1, caches{j}.X_neg, X{j});
caches{j}.keys = cat(1, caches{j}.keys, keys{j});
caches{j}.num_added = caches{j}.num_added + size(keys{j},1);
end
% Update model if
% - first time seeing negatives
% - more than retrain_limit negatives have been added
% - its the final image of the final epoch
is_last_time = (hard_epoch == max_hard_epochs && i == length(dataset.neg_image_ids));
hit_retrain_limit = (caches{j}.num_added > caches{j}.retrain_limit);
if force_update || first_time || hit_retrain_limit || is_last_time
fprintf(' Retraining %s model\n', models{j}.class);
fprintf(' Cache holds %d pos examples %d neg examples\n', ...
size(caches{j}.X_pos,1), size(caches{j}.X_neg,1));
models{j} = update_model(models{j}, caches{j}, opts);
caches{j}.num_added = 0;
z_pos = caches{j}.X_pos*models{j}.w + models{j}.b;
z_neg = caches{j}.X_neg*models{j}.w + models{j}.b;
caches{j}.pos_loss(end+1) = opts.svm_C*sum(max(0, 1 - z_pos))*opts.pos_loss_weight;
caches{j}.neg_loss(end+1) = opts.svm_C*sum(max(0, 1 + z_neg));
caches{j}.reg_loss(end+1) = 0.5*models{j}.w'*models{j}.w + ...
0.5*(models{j}.b/opts.bias_mult)^2;
caches{j}.tot_loss(end+1) = caches{j}.pos_loss(end) + ...
caches{j}.neg_loss(end) + ...
caches{j}.reg_loss(end);
for t = 1:length(caches{j}.tot_loss)
fprintf(' %2d: obj val: %.3f = %.3f (pos) + %.3f (neg) + %.3f (reg)\n', ...
t, caches{j}.tot_loss(t), caches{j}.pos_loss(t), ...
caches{j}.neg_loss(t), caches{j}.reg_loss(t));
end
% evict easy examples
easy = find(z_neg < caches{j}.evict_thresh);
caches{j}.X_neg(easy,:) = [];
caches{j}.keys(easy,:) = [];
fprintf(' Pruning easy negatives\n');
fprintf(' Cache holds %d pos examples %d neg examples\n', ...
size(caches{j}.X_pos,1), size(caches{j}.X_neg,1));
fprintf(' %d pos support vectors\n', numel(find(z_pos <= 1)));
fprintf(' %d neg support vectors\n', numel(find(z_neg >= -1)));
%model = models{j};
%save([conf.paths.model_dir models{j}.class '_' num2str(length(caches{j}.tot_loss))], 'model');
%clear model;
end
end
first_time = false;
force_update = false;
end
end
end
save([conf.paths.model_dir, ...
'latentiter_' num2str(latent_iter) '_clss_' clss{1}, ...
'_C_' num2str(opts.svm_C), ...
'_B_' num2str(opts.bias_mult), ...
'_w1_' num2str(opts.pos_loss_weight), ...
'_losstype_' opts.loss_type, ...
'_sharpness_' num2str(opts.sharpness), ...
'_alpha_' num2str(opts.alpha), ...
'_K1_' num2str(opts.K1), ...
'_K2_' num2str(opts.K2), ...
'_nms_' num2str(opts.nms_threshold), ...
'_20x1_smooth_greedycover_final.mat'], 'models');
fprintf('Took %.3f hours to train\n', toc(th)/3600);
% ------------------------------------------------------------------------
function [X_neg, keys] = sample_negative_features(first_time, models, ...
caches, dataset, ind, ...
opts)
% ------------------------------------------------------------------------
d = load_cached_features_hos(1, dataset.trainset, dataset.year, dataset.neg_image_ids{ind});
if length(d.overlap) ~= size(d.feat, 1)
fprintf('WARNING: %s has data mismatch\n', dataset.neg_image_ids{ind});
X_neg = cell(1, length(models));
keys = cell(1, length(models));
return;
end
d.feat = xform_feat_custom(d.feat, opts);
%d.feat = o2p(d.feat);
neg_ovr_thresh = 0.3;
if first_time
for i = 1:length(models)
%I = find(d.overlap(:, models{i}.class_id) < neg_ovr_thresh);
I = (1:size(d.feat,1))';
X_neg{i} = d.feat(I,:);
keys{i} = [ind*ones(length(I),1) I];
end
else
ws = cat(2, cellfun(@(x) x.w, models, 'UniformOutput', false));
ws = cat(2, ws{:});
bs = cat(2, cellfun(@(x) x.b, models, 'UniformOutput', false));
bs = cat(2, bs{:});
zs = bsxfun(@plus, d.feat*ws, bs);
for i = 1:length(models)
z = zs(:,i);
% I = find((z > caches{i}.hard_thresh) & ...
% (d.overlap(:, models{i}.class_id) < neg_ovr_thresh));
I = find(z > caches{i}.hard_thresh);
% apply NMS to scored boxes
% select as negatives anything that survived NMS
% and has < 50% overlap with postives of this class
% and is violating the margin
% boxes = cat(2, single(d.boxes), z);
% nms_keep = false(size(boxes,1), 1);
% nms_keep(nms(boxes, 0.3)) = true;
%
% I = find((z > caches{i}.hard_thresh) & ...
% (nms_keep == true) & ...
% (d.overlap(:, models{i}.class_id) < neg_ovr_thresh));
% Avoid adding duplicate features
keys_ = [ind*ones(length(I),1) I];
if isempty(caches{i}.keys) || isempty(keys_)
dups = [];
else
[~, ~, dups] = intersect(caches{i}.keys, keys_, 'rows');
end
keep = setdiff(1:size(keys_,1), dups);
I = I(keep);
% Unique hard negatives
X_neg{i} = d.feat(I,:);
keys{i} = [ind*ones(length(I),1) I];
end
end
% ------------------------------------------------------------------------
function model = update_model(model, cache, opts)
% ------------------------------------------------------------------------
num_pos = size(cache.X_pos, 1);
num_neg = size(cache.X_neg, 1);
feat_dim = size(cache.X_pos, 2);
pweight = opts.pos_loss_weight;
X = zeros( num_pos*pweight+num_neg, feat_dim+1);
X(1:num_pos*pweight, 1:end-1) = repmat(cache.X_pos,pweight,1);
X(num_pos*pweight+1:end, 1:end-1) = cache.X_neg;
% augment the bias feature * opt.bias_mult factor
X(:, end) = opts.bias_mult * ones(1, num_pos*pweight+num_neg);
y = cat(1, repmat(ones(num_pos,1),pweight,1), -ones(num_neg,1));
options.Method = 'lbfgs';
options.Display = 'OFF'; %no output, default = 2;
if isempty(model.w)
w0 = zeros(feat_dim+1,1);
else
w0 = double([model.w; model.b/opts.bias_mult]);
end
if strcmp(opts.loss_type, 'L1hinge')
w_opt = minFunc(@(w) SVM_Cost_L1hinge(...
w, X, y, opts.svm_C), w0, options);
elseif strcmp(opts.loss_type, 'SmoothHinge')
w_opt = minFunc(@(w) SVM_Cost_SmoothHinge(...
w, X, y, opts.svm_C, opts.sharpness), w0, options);
elseif strcmp(opts.loss_type, 'Logistic')
w_opt = minFunc(@(w) SVM_Cost_Logistic(...
w, X, y, opts.svm_C), w0, options);
end
model.w = single(w_opt(1:end-1));
model.b = single(w_opt(end)*opts.bias_mult);
% ------------------------------------------------------------------------
function X_pos = get_positive_features_paris_greedycover(...
models, dataset, opts, classid, sample_pos_image_ids)
% ------------------------------------------------------------------------
% HOS: Use maxcover to create a pool of positive windows.
% \alpha controls max_coverage
% \K1 controls number of nearest neighbors per cluster
% \K2 controls number of positive boxes to take per cluster
% A. Construct positive training set from max cover
% trainset_matrix is a 2 by #boxes matrix (pos image ids; box ids)
trainset_matrix = [];
num_pos_images = length(sample_pos_image_ids);
% construct graph matrix, # clusters by # pos images
[graph_image_matrix, graph_box_matrix, ...
per_cluster_paris_score_K1, per_cluster_paris_score_K2] = ...
construct_graph(classid, sample_pos_image_ids, opts);
coverage = 0; popped_cluster_history = [];
per_cluster_paris_score_copy = per_cluster_paris_score_K1; %copy for plotting
% go down sorted list of paris scores and keep taking unclaimed positive
% images in top K1 (but take boxes in top K2)
while coverage < (opts.alpha * num_pos_images)
% pop the cluster with best paris score
c_top = find(per_cluster_paris_score_K1 == max(per_cluster_paris_score_K1));
% take care of ties here
if length(c_top) > 1
fprintf('warning: tie detected!\n');
% compute paris score @ K2 to break the tie
c_top_scores = per_cluster_paris_score_K2(c_top);
c_top = c_top( find(c_top_scores == max(c_top_scores)) );
% still tied? just take the first one.
c_top = c_top(1);
end
% push boxes for cluster's activated pos iamges to train set,
box_ids = graph_box_matrix(c_top, :);
trainset_matrix = [trainset_matrix, [find(box_ids~=0); box_ids(box_ids~=0)] ];
% update graph: remove activated pos images from c_top
activated_images = find(graph_image_matrix(c_top,:) ~= false);
graph_image_matrix(:, activated_images) = false;
graph_box_matrix(:, activated_images) = 0;
% void this cluster from popping up again
per_cluster_paris_score_K1(c_top) = -inf;
popped_cluster_history = [popped_cluster_history, c_top];
% update coverage
coverage = coverage + length(activated_images);
fprintf('[%d] current coverage: %d, cluster %d covered %d\n', ...
length(popped_cluster_history), coverage, c_top, length(activated_images));
end
% B. Decode trainset_matrix and create positive feature matrix
% make sure to preserve original order of trainset_matrix
% remove exact duplicates in trainset_matrix
trainset_matrix = remove_exact_duplicate_columns_preserve_order(trainset_matrix);
X_pos = cell(length(models),1);
X_pos{1} = single([]);
% remove highly overlapping boxes with NMS 0.3
num_suppressed_boxes = 0;
assigned_boxes(num_pos_images).coords = [];
for boxid = 1:length(trainset_matrix)
% load this pos image's features and boxes; check if indexing is correct
this_image_idx = trainset_matrix(1, boxid);
this_box_idx = trainset_matrix(2, boxid);
img_struct = load_cached_features_hos(1, dataset.trainset, dataset.year, sample_pos_image_ids{this_image_idx});
img_struct.boxes = img_struct.boxes(img_struct.gt~=1,:);
img_struct.feat = img_struct.feat( img_struct.gt~=1,:);
this_feature = img_struct.feat( this_box_idx, :);
this_box = img_struct.boxes(this_box_idx, :);
% check if there's box already taken and nms if exists.
is_suppressed = check_nms_with_existing_boxes(...
assigned_boxes(this_image_idx).coords, this_box, opts);
if is_suppressed
num_suppressed_boxes = num_suppressed_boxes + 1;
continue;
end
assigned_boxes(this_image_idx).coords = [...
assigned_boxes(this_image_idx).coords; this_box];
X_pos{1} = cat(1, X_pos{1}, this_feature);
end
fprintf(['done creating positive feature matrix.\n',...
'# pos images: %d, # passed boxes: %d, # suppresed boxes: %d\n'], ...
num_pos_images, size(X_pos{1},1), num_suppressed_boxes);
% ------------------------------------------------------------------------
function matrix = remove_exact_duplicate_columns_preserve_order(matrix)
% ------------------------------------------------------------------------
num_boxes_before = size(matrix,2);
[newmat, newids] = unique(matrix', 'rows', 'first');
hasDuplicates = size(newmat,1) < num_boxes_before;
if hasDuplicates
dupColumns = setdiff(1:num_boxes_before, newids);
matrix(:,dupColumns) = [];
end
num_boxes_after = size(matrix,2);
fprintf('removed %d exact duplicates\n', num_boxes_before-num_boxes_after);
% ------------------------------------------------------------------------
function [graph_image_matrix, graph_box_matrix, ...
per_cluster_paris_score_K1, per_cluster_paris_score_K2] = ...
construct_graph(classid, sample_pos_image_ids, opts)
% ------------------------------------------------------------------------
% \K1 controls number of nearest neighbors per cluster
% \K2 controls number of positive boxes to take per cluster
num_pos_images = length(sample_pos_image_ids);
% count number of saved clusters
num_clusters = 0;
for seed_pos_image_id = 1:num_pos_images
save_filename = sprintf('paris_results_nogt_20x1/%s_%d.mat',...
sample_pos_image_ids{seed_pos_image_id}, classid);
load(save_filename, 'score_top');
num_clusters = num_clusters + length(score_top);
end
% form a binary graph matrix size: # clusters by # pos images
% graph_image_matrix holds activated positive images in top K1
graph_image_matrix = false(num_clusters, num_pos_images);
% graph_box_matrix holds activated positive boxes in top K2
graph_box_matrix = zeros(num_clusters, num_pos_images, 'single');
% record per_cluster_paris score
per_cluster_paris_score_K1 = zeros(num_clusters, 1, 'single');
per_cluster_paris_score_K2 = zeros(num_clusters, 1, 'single');
% Loop over each positive images and grab pos image and box list
cid = 0;
for seed_pos_image_id = 1:num_pos_images
save_filename = sprintf('paris_results_nogt_20x1/%s_%d.mat',...
sample_pos_image_ids{seed_pos_image_id}, classid);
load(save_filename);
% loop through each boxes (= clusters)
for seed_win_id = 1:length(score_top)
cid = cid + 1;
[~, image_idx] = sort( [table_pos_diff_top(:,seed_win_id);...
table_neg_diff_top(:,seed_win_id)], 'ascend');
% fill image matrix: restrict list pos images to top K1 NN
image_idx_top_K1 = image_idx(1 : round(opts.K1*num_pos_images) );
% filter only positive images
pos_image_list_K1 = image_idx_top_K1(image_idx_top_K1 <= num_pos_images);
graph_image_matrix(cid, pos_image_list_K1) = true;
% record paris score @ K1
per_cluster_paris_score_K1(cid) = length(pos_image_list_K1) / ...
round(opts.K1*num_pos_images);
% fill box matrix: restrict list pos images to top K2 NN
image_idx_top_K2 = image_idx(1 : round(opts.K2*num_pos_images) );
% filter only positive images
pos_image_list_K2 = image_idx_top_K2(image_idx_top_K2 <= num_pos_images);
% record paris score @ K2
per_cluster_paris_score_K2(cid) = length(pos_image_list_K2) / ...
round(opts.K2*num_pos_images);
% retreive box ids for each positives.
% convert pos image id -> box id in the given pos image
pos_box_list_K2 = table_pos_idx_top(pos_image_list_K2, seed_win_id);
graph_box_matrix(cid, pos_image_list_K2) = pos_box_list_K2;
end
end
% ------------------------------------------------------------------------
function is_suppressed = check_nms_with_existing_boxes(...
existing_boxes, this_box, opts)
% ------------------------------------------------------------------------
if isempty(existing_boxes)
is_suppressed = false;
return;
end
%nms_overlap_threshold = 0.3; % kill boxes if nms overlap > 0.3
assert( size(existing_boxes,2) == size(this_box,2));
num_existing_boxes = size(existing_boxes,1);
% parse existing boxes
x1 = existing_boxes(:,1);
y1 = existing_boxes(:,2);
x2 = existing_boxes(:,3);
y2 = existing_boxes(:,4);
area = (x2-x1+1) .* (y2-y1+1);
% parse this new box
tx1 = this_box(:,1);
ty1 = this_box(:,2);
tx2 = this_box(:,3);
ty2 = this_box(:,4);
new_box_area = (tx2-tx1+1) * (ty2 - ty1+1);
is_suppressed = false;
for j = 1:num_existing_boxes
xx1 = max(x1(j), tx1);
yy1 = max(y1(j), ty1);
xx2 = min(x2(j), tx2);
yy2 = min(y2(j), ty2);
w = xx2-xx1+1;
h = yy2-yy1+1;
if w > 0 && h > 0
% compute overlap
inter = w*h;
o = inter / (area(j) + new_box_area - inter);
if o > opts.nms_threshold
is_suppressed = true;
break;
end
end
end
% ------------------------------------------------------------------------
function model = init_model(cls, dataset, conf, opts)
% ------------------------------------------------------------------------
model.class = cls;
%model.class_id = strmatch(model.class, conf.pascal.VOCopts.classes);
model.trainset = dataset.trainset;
model.year = dataset.year;
model.w = [];
model.b = [];
model.thresh = -1.1;
model.opts = opts;
% ------------------------------------------------------------------------
function cache = init_cache(model, X_pos)
% ------------------------------------------------------------------------
cache.X_pos = X_pos;
cache.X_neg = single([]);
cache.keys = [];
cache.num_added = 0;
cache.retrain_limit = 2000;
cache.evict_thresh = -1.2;
cache.hard_thresh = -1.0001;
cache.pos_loss = [];
cache.neg_loss = [];
cache.reg_loss = [];
cache.tot_loss = [];
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
mil_region_mining.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/mil_region_mining.m
| 1,747 |
utf_8
|
d9bb95ab7af5b035eab795bcce50bdb1
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Hyun Oh Song
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function mil_region_mining(models, testset, year)
conf = voc_config();
cachedir = conf.paths.model_dir;
VOCopts = conf.pascal.VOCopts;
load('class_pos_images.mat');
classid = strmatch(models{1}.class,VOCopts.classes,'exact');
image_ids = class_pos_images(classid).ids;
feat_opts = models{1}.opts;
ws = cat(2, cellfun(@(x) x.w, models, 'UniformOutput', false));
ws = cat(2, ws{:});
bs = cat(2, cellfun(@(x) x.b, models, 'UniformOutput', false));
bs = cat(2, bs{:});
boxes = cell(length(image_ids), 1);
for i = 1:length(image_ids)
fprintf('%s: region mining: %d/%d\n', procid(), i, length(image_ids));
d = load_cached_features_hos(0, testset, year, image_ids{i});
d.feat = xform_feat_custom(d.feat, feat_opts);
zs = bsxfun(@plus, d.feat*ws, bs);
z = zs(d.gt~=1);
[val, ind] = sort(z,'descend');
bbs = d.boxes(d.gt~=1,:);
boxes{i} = cat(2, single(bbs(ind(1),:)), z(ind(1)));
end
save_file = [cachedir models{1}.class '_best_boxes_' testset '_' year '.mat'];
save(save_file, 'boxes');
if ~exist('results_mil','file')
mkdir('results_mil');
end
res_fn = ['./results_mil/' models{1}.class '_' testset '.txt'];
fid = fopen(res_fn, 'w');
for i = 1:length(image_ids)
bbox = boxes{i};
fprintf(fid, '%s %f %d %d %d %d\n', image_ids{i}, bbox(end), bbox(1:4));
end
fclose(fid);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
voc_config.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/voc_config.m
| 8,999 |
utf_8
|
bb032cdaaab5bcbaf83b6f30937a6ed1
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2011-2012, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function conf = voc_config(varargin)
% Set up configuration variables.
% conf = voc_config(varargin)
%
% Each variable is named by a path that identifies a field
% in the returned conf structure. For example, 'pascal.year'
% corresponds to conf.pascal.year. You can set configuration
% variables in 3 ways:
% 1) File: directly editing values in this file
% 2) Per-call: pass an override as an argument to this function
% E.g., conf = voc_config('pascal.year', '2011');
% 3) Per-session: assign the global variable VOC_CONFIG_OVERRIDE
% to a function that returns a conf structure with specific
% overrides set. This method is persistent until VOC_CONFIG_OVERRIDE
% is cleared. See sample_voc_config_override.m for an example.
% ~~~~~~~~~~~~~~~~~~~~~~ BASIC SETUP ~~~~~~~~~~~~~~~~~~~~~~
% Please read the next few lines
tmp = pwd;
ind = find(tmp=='/');
BASE_DIR = tmp(1:ind(end));
% PASCAL dataset year to use
PASCAL_YEAR = '2007';
% Models are stored in BASE_DIR/PROJECT/PASCAL_YEAR/
PROJECT = 'mil';
% The code will look for your PASCAL VOC devkit in
% BASE_DIR/VOC<PASCAL_YEAR>/VOCdevkit
% If you have the devkit installed elsewhere, you may want to
% create a symbolic link.
% You probably don't need to change configuration settings below this line.
% ~~~~~~~~~~~~~~~~~~~~~~ ADVANCED SETUP ~~~~~~~~~~~~~~~~~~~~~~
%
% conf top-level variables
% conf.paths filesystem paths
% conf.pascal PASCAL VOC dataset
% conf.training model training parameters
% conf.eval model evaluation parameters
% conf.features image features
%
% To set a configuration override file, declare
% the global variable VOC_CONFIG_OVERRIDE
% and then set it as a function handle to the
% config override function. E.g.,
% >> global VOC_CONFIG_OVERRIDE;
% >> VOC_CONFIG_OVERRIDE = @my_voc_config;
% In this example, we assume that you have an M-file
% named my_voc_config.m. See sample_voc_config_override.m.
%
% Overrides passed in as arguments have the highest precedence.
% Overrides in the overrides file have second highest precedence,
% but are clobbered by overrides passed in as arguments.
% Settings in this file are clobbered by the previous two.
% Configuration structure
conf = [];
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% Persistent and per-call overrides
%
% Check for an override configuration file
assert_not_in_parallel_worker();
global VOC_CONFIG_OVERRIDE;
if ~isempty(VOC_CONFIG_OVERRIDE)
conf = VOC_CONFIG_OVERRIDE();
end
% Clobber with overrides passed in as arguments
for i = 1:2:length(varargin)
key = varargin{i};
val = varargin{i+1};
eval(['conf.' key ' = val;']);
end
%
%
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
conf = cv(conf, 'num_threads', feature('NumCores'));
% Project name (used in the paths)
conf = cv(conf, 'project', PROJECT);
% Parent directory that everything (model cache, VOCdevkit) is under
conf = cv(conf, 'paths.base_dir', BASE_DIR);
% Path to this file
conf = cv(conf, 'paths.self', fullfile(pwd(), [mfilename() '.m']));
% -------------------------------------------------------------------
% PASCAL VOC configuration
% -------------------------------------------------------------------
% Configure the PASCAL VOC dataset year
conf = cv(conf, 'pascal.year', PASCAL_YEAR);
% Directory with PASCAL VOC development kit and dataset
conf = cv(conf, 'pascal.dev_kit', [conf.paths.base_dir '/data/VOCdevkit' PASCAL_YEAR '/']);
% For INRIA person
%conf = cv(conf, 'pascal.dev_kit', [conf.paths.base_dir '/INRIA/VOCdevkit/']);
if exist(conf.pascal.dev_kit) == 0
global G_VOC_CONFIG_HELLO;
if isempty(G_VOC_CONFIG_HELLO)
G_VOC_CONFIG_HELLO = true;
msg = sprintf(['~~~~~~~~~~~ Hello ~~~~~~~~~~~\n' ...
'voc-release5 is not yet configured for learning. \n' ...
'You can still run demo.m, but please read \n' ...
'the section "Using the learning code" in README. \n' ...
'(Could not find the PASCAL VOC devkit in %s)'], ...
conf.pascal.dev_kit);
fprintf([msg '\n\n']);
end
return;
end
% VOCinit brings VOCopts into scope
conf.pascal.VOCopts = get_voc_opts(conf);
% -------------------------------------------------------------------
% Path configuration
% -------------------------------------------------------------------
% Directory for caching models, intermediate data, and results
% [was called 'cachedir' in previous releases]
conf = cv(conf, 'paths.model_dir', [conf.paths.base_dir '/' ...
conf.project '/' conf.pascal.year '/']);
exists_or_mkdir(conf.paths.model_dir);
%% -------------------------------------------------------------------
%% Training configuration
%% -------------------------------------------------------------------
%conf = cv(conf, 'training.train_set_fg', 'trainval');
%conf = cv(conf, 'training.train_set_fg', 'train');
%conf = cv(conf, 'training.train_set_bg', 'train');
%conf = cv(conf, 'training.C', 0.001);
%conf = cv(conf, 'training.bias_feature', 10);
%% File size limit for the feature vector cache (2^30 bytes = 1GB)
%conf = cv(conf, 'training.cache_byte_limit', 3*2^30);
%% Location of training log (matlab diary)
%conf.training.log = @(x) sprintf([conf.paths.model_dir '%s.log'], x);
%
%conf = cv(conf, 'training.cache_example_limit', 24000);
%conf = cv(conf, 'training.num_negatives_small', 200);
%conf = cv(conf, 'training.num_negatives_large', 2000);
%conf = cv(conf, 'training.wlssvm_M', 0);
%conf = cv(conf, 'training.fg_overlap', 0.7);
%
%conf = cv(conf, 'training.lbfgs.options.verbose', 2);
%conf = cv(conf, 'training.lbfgs.options.maxIter', 1000);
%conf = cv(conf, 'training.lbfgs.options.optTol', 0.000001);
%
%conf = cv(conf, 'training.interval_fg', 5);
%conf = cv(conf, 'training.interval_bg', 4);
%
%
%% -------------------------------------------------------------------
%% Evaluation configuration
%% -------------------------------------------------------------------
%conf = cv(conf, 'eval.interval', 10);
%conf = cv(conf, 'eval.max_thresh', -1.1);
conf = cv(conf, 'eval.test_set', 'test');
conf.pascal.VOCopts.testset = conf.eval.test_set;
% -------------------------------------------------------------------
% Feature configuration
% -------------------------------------------------------------------
conf = cv(conf, 'features.dim', 4096);
% -------------------------------------------------------------------
% Helper functions
% -------------------------------------------------------------------
% -------------------------------------------------------------------
% Make directory path if it does not already exist.
function made = exists_or_mkdir(path)
made = false;
if exist(path) == 0
unix(['mkdir -p ' path]);
made = true;
end
% -------------------------------------------------------------------
% Returns the 'VOCopts' variable from the VOCdevkit. The path to the
% devkit is also added to the matlab path.
function VOCopts = get_voc_opts(conf)
% cache VOCopts from VOCinit
persistent voc_opts;
key = conf.pascal.year;
if isempty(voc_opts) || ~voc_opts.isKey(key)
if isempty(voc_opts)
voc_opts = containers.Map();
end
tmp = pwd;
cd(conf.pascal.dev_kit);
addpath([cd '/VOCcode']);
VOCinit;
cd(tmp);
voc_opts(key) = VOCopts;
end
VOCopts = voc_opts(key);
% -------------------------------------------------------------------
% Does nothing if conf.key exists, otherwise sets conf.key to val
function conf = cv(conf, key, val)
try
eval(['conf.' key ';']);
catch
eval(['conf.' key ' = val;']);
end
% -------------------------------------------------------------------
% Throw an error if this function is called from inside a matlabpool
% worker.
function assert_not_in_parallel_worker()
% Matlab does not support accessing global variables from
% parallel workers. The result of reading a global is undefined
% and in practice has odd and inconsistent behavoir.
% The configuraton override mechanism relies on a global
% variable. To avoid hard-to-find bugs, we make sure that
% voc_config cannot be called from a parallel worker.
t = [];
if usejava('jvm')
try
t = getCurrentTask();
catch
end
end
if ~isempty(t)
msg = ['voc_config() cannot be called from a parallel worker ' ...
'(or startup.m did not run -- did you run matlab from the ' ...
'root of the voc-release installationd directory?'];
error(msg);
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
WolfeLineSearch.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/minFunc_2012/minFunc/WolfeLineSearch.m
| 10,590 |
utf_8
|
f962bc5ae0a1e9f80202a9aaab106dab
|
function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting location
% g: gradient at starting location
% gtd: directional derivative at starting location
% c1: sufficient decrease parameter
% c2: curvature parameter
% debug: display debugging information
% LS_interp: type of interpolation
% maxLS: maximum number of iterations
% progTol: minimum allowable step length
% doPlot: do a graphical display of interpolation
% funObj: objective function
% varargin: parameters of objective function
%
% Outputs:
% t: step length
% f_new: function value at x+t*d
% g_new: gradient value at x+t*d
% funEvals: number function evaluations performed by line search
% H: Hessian at initial guess (only computed if requested
% Evaluate the Objective and Gradient at the Initial Step
if nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x+t*d,varargin{:});
end
funEvals = 1;
gtd_new = g_new'*d;
% Bracket an Interval containing a point satisfying the
% Wolfe criteria
LSiter = 0;
t_prev = 0;
f_prev = f;
g_prev = g;
gtd_prev = gtd;
nrmD = max(abs(d));
done = 0;
while LSiter < maxLS
%% Bracketing Phase
if ~isLegal(f_new) || ~isLegal(g_new)
if debug
fprintf('Extrapolated into illegal region, switching to Armijo line-search\n');
end
t = (t + t_prev)/2;
% Do Armijo
if nargout == 5
[t,x_new,f_new,g_new,armijoFunEvals,H] = ArmijoBacktrack(...
x,t,d,f,f,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,...
funObj,varargin{:});
else
[t,x_new,f_new,g_new,armijoFunEvals] = ArmijoBacktrack(...
x,t,d,f,f,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,...
funObj,varargin{:});
end
funEvals = funEvals + armijoFunEvals;
return;
end
if f_new > f + c1*t*gtd || (LSiter > 1 && f_new >= f_prev)
bracket = [t_prev t];
bracketFval = [f_prev f_new];
bracketGval = [g_prev g_new];
break;
elseif abs(gtd_new) <= -c2*gtd
bracket = t;
bracketFval = f_new;
bracketGval = g_new;
done = 1;
break;
elseif gtd_new >= 0
bracket = [t_prev t];
bracketFval = [f_prev f_new];
bracketGval = [g_prev g_new];
break;
end
temp = t_prev;
t_prev = t;
minStep = t + 0.01*(t-temp);
maxStep = t*10;
if LS_interp <= 1
if debug
fprintf('Extending Braket\n');
end
t = maxStep;
elseif LS_interp == 2
if debug
fprintf('Cubic Extrapolation\n');
end
t = polyinterp([temp f_prev gtd_prev; t f_new gtd_new],doPlot,minStep,maxStep);
elseif LS_interp == 3
t = mixedExtrap(temp,f_prev,gtd_prev,t,f_new,gtd_new,minStep,maxStep,debug,doPlot);
end
f_prev = f_new;
g_prev = g_new;
gtd_prev = gtd_new;
if ~saveHessianComp && nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x + t*d,varargin{:});
end
funEvals = funEvals + 1;
gtd_new = g_new'*d;
LSiter = LSiter+1;
end
if LSiter == maxLS
bracket = [0 t];
bracketFval = [f f_new];
bracketGval = [g g_new];
end
%% Zoom Phase
% We now either have a point satisfying the criteria, or a bracket
% surrounding a point satisfying the criteria
% Refine the bracket until we find a point satisfying the criteria
insufProgress = 0;
Tpos = 2;
LOposRemoved = 0;
while ~done && LSiter < maxLS
% Find High and Low Points in bracket
[f_LO LOpos] = min(bracketFval);
HIpos = -LOpos + 3;
% Compute new trial value
if LS_interp <= 1 || ~isLegal(bracketFval) || ~isLegal(bracketGval)
if debug
fprintf('Bisecting\n');
end
t = mean(bracket);
elseif LS_interp == 2
if debug
fprintf('Grad-Cubic Interpolation\n');
end
t = polyinterp([bracket(1) bracketFval(1) bracketGval(:,1)'*d
bracket(2) bracketFval(2) bracketGval(:,2)'*d],doPlot);
else
% Mixed Case %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nonTpos = -Tpos+3;
if LOposRemoved == 0
oldLOval = bracket(nonTpos);
oldLOFval = bracketFval(nonTpos);
oldLOGval = bracketGval(:,nonTpos);
end
t = mixedInterp(bracket,bracketFval,bracketGval,d,Tpos,oldLOval,oldLOFval,oldLOGval,debug,doPlot);
end
% Test that we are making sufficient progress
if min(max(bracket)-t,t-min(bracket))/(max(bracket)-min(bracket)) < 0.1
if debug
fprintf('Interpolation close to boundary');
end
if insufProgress || t>=max(bracket) || t <= min(bracket)
if debug
fprintf(', Evaluating at 0.1 away from boundary\n');
end
if abs(t-max(bracket)) < abs(t-min(bracket))
t = max(bracket)-0.1*(max(bracket)-min(bracket));
else
t = min(bracket)+0.1*(max(bracket)-min(bracket));
end
insufProgress = 0;
else
if debug
fprintf('\n');
end
insufProgress = 1;
end
else
insufProgress = 0;
end
% Evaluate new point
if ~saveHessianComp && nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x + t*d,varargin{:});
end
funEvals = funEvals + 1;
gtd_new = g_new'*d;
LSiter = LSiter+1;
armijo = f_new < f + c1*t*gtd;
if ~armijo || f_new >= f_LO
% Armijo condition not satisfied or not lower than lowest
% point
bracket(HIpos) = t;
bracketFval(HIpos) = f_new;
bracketGval(:,HIpos) = g_new;
Tpos = HIpos;
else
if abs(gtd_new) <= - c2*gtd
% Wolfe conditions satisfied
done = 1;
elseif gtd_new*(bracket(HIpos)-bracket(LOpos)) >= 0
% Old HI becomes new LO
bracket(HIpos) = bracket(LOpos);
bracketFval(HIpos) = bracketFval(LOpos);
bracketGval(:,HIpos) = bracketGval(:,LOpos);
if LS_interp == 3
if debug
fprintf('LO Pos is being removed!\n');
end
LOposRemoved = 1;
oldLOval = bracket(LOpos);
oldLOFval = bracketFval(LOpos);
oldLOGval = bracketGval(:,LOpos);
end
end
% New point becomes new LO
bracket(LOpos) = t;
bracketFval(LOpos) = f_new;
bracketGval(:,LOpos) = g_new;
Tpos = LOpos;
end
if ~done && abs(bracket(1)-bracket(2))*nrmD < progTol
if debug
fprintf('Line-search bracket has been reduced below progTol\n');
end
break;
end
end
%%
if LSiter == maxLS
if debug
fprintf('Line Search Exceeded Maximum Line Search Iterations\n');
end
end
[f_LO LOpos] = min(bracketFval);
t = bracket(LOpos);
f_new = bracketFval(LOpos);
g_new = bracketGval(:,LOpos);
% Evaluate Hessian at new point
if nargout == 5 && funEvals > 1 && saveHessianComp
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
funEvals = funEvals + 1;
end
end
%%
function [t] = mixedExtrap(x0,f0,g0,x1,f1,g1,minStep,maxStep,debug,doPlot);
alpha_c = polyinterp([x0 f0 g0; x1 f1 g1],doPlot,minStep,maxStep);
alpha_s = polyinterp([x0 f0 g0; x1 sqrt(-1) g1],doPlot,minStep,maxStep);
if alpha_c > minStep && abs(alpha_c - x1) < abs(alpha_s - x1)
if debug
fprintf('Cubic Extrapolation\n');
end
t = alpha_c;
else
if debug
fprintf('Secant Extrapolation\n');
end
t = alpha_s;
end
end
%%
function [t] = mixedInterp(bracket,bracketFval,bracketGval,d,Tpos,oldLOval,oldLOFval,oldLOGval,debug,doPlot);
% Mixed Case %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nonTpos = -Tpos+3;
gtdT = bracketGval(:,Tpos)'*d;
gtdNonT = bracketGval(:,nonTpos)'*d;
oldLOgtd = oldLOGval'*d;
if bracketFval(Tpos) > oldLOFval
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
alpha_q = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) sqrt(-1)],doPlot);
if abs(alpha_c - oldLOval) < abs(alpha_q - oldLOval)
if debug
fprintf('Cubic Interpolation\n');
end
t = alpha_c;
else
if debug
fprintf('Mixed Quad/Cubic Interpolation\n');
end
t = (alpha_q + alpha_c)/2;
end
elseif gtdT'*oldLOgtd < 0
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
alpha_s = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) sqrt(-1) gtdT],doPlot);
if abs(alpha_c - bracket(Tpos)) >= abs(alpha_s - bracket(Tpos))
if debug
fprintf('Cubic Interpolation\n');
end
t = alpha_c;
else
if debug
fprintf('Quad Interpolation\n');
end
t = alpha_s;
end
elseif abs(gtdT) <= abs(oldLOgtd)
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],...
doPlot,min(bracket),max(bracket));
alpha_s = polyinterp([oldLOval sqrt(-1) oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],...
doPlot,min(bracket),max(bracket));
if alpha_c > min(bracket) && alpha_c < max(bracket)
if abs(alpha_c - bracket(Tpos)) < abs(alpha_s - bracket(Tpos))
if debug
fprintf('Bounded Cubic Extrapolation\n');
end
t = alpha_c;
else
if debug
fprintf('Bounded Secant Extrapolation\n');
end
t = alpha_s;
end
else
if debug
fprintf('Bounded Secant Extrapolation\n');
end
t = alpha_s;
end
if bracket(Tpos) > oldLOval
t = min(bracket(Tpos) + 0.66*(bracket(nonTpos) - bracket(Tpos)),t);
else
t = max(bracket(Tpos) + 0.66*(bracket(nonTpos) - bracket(Tpos)),t);
end
else
t = polyinterp([bracket(nonTpos) bracketFval(nonTpos) gtdNonT
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
minFunc_processInputOptions.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/mil/minFunc_2012/minFunc/minFunc_processInputOptions.m
| 4,103 |
utf_8
|
8822581c3541eabe5ce7c7927a57c9ab
|
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc,...
LS_type,LS_interp,LS_multi,DerivativeCheck] = ...
minFunc_processInputOptions(o)
% Constants
SD = 0;
CSD = 1;
BB = 2;
CG = 3;
PCG = 4;
LBFGS = 5;
QNEWTON = 6;
NEWTON0 = 7;
NEWTON = 8;
TENSOR = 9;
verbose = 1;
verboseI= 1;
debug = 0;
doPlot = 0;
method = LBFGS;
cgSolve = 0;
o = toUpper(o);
if isfield(o,'DISPLAY')
switch(upper(o.DISPLAY))
case 0
verbose = 0;
verboseI = 0;
case 'FINAL'
verboseI = 0;
case 'OFF'
verbose = 0;
verboseI = 0;
case 'NONE'
verbose = 0;
verboseI = 0;
case 'FULL'
debug = 1;
case 'EXCESSIVE'
debug = 1;
doPlot = 1;
end
end
DerivativeCheck = 0;
if isfield(o,'DERIVATIVECHECK')
switch(upper(o.DERIVATIVECHECK))
case 1
DerivativeCheck = 1;
case 'ON'
DerivativeCheck = 1;
end
end
LS_init = 0;
LS_type = 1;
LS_interp = 2;
LS_multi = 0;
Fref = 1;
Damped = 0;
HessianIter = 1;
c2 = 0.9;
if isfield(o,'METHOD')
m = upper(o.METHOD);
switch(m)
case 'TENSOR'
method = TENSOR;
case 'NEWTON'
method = NEWTON;
case 'MNEWTON'
method = NEWTON;
HessianIter = 5;
case 'PNEWTON0'
method = NEWTON0;
cgSolve = 1;
case 'NEWTON0'
method = NEWTON0;
case 'QNEWTON'
method = QNEWTON;
Damped = 1;
case 'LBFGS'
method = LBFGS;
case 'BB'
method = BB;
LS_type = 0;
Fref = 20;
case 'PCG'
method = PCG;
c2 = 0.2;
LS_init = 2;
case 'SCG'
method = CG;
c2 = 0.2;
LS_init = 4;
case 'CG'
method = CG;
c2 = 0.2;
LS_init = 2;
case 'CSD'
method = CSD;
c2 = 0.2;
Fref = 10;
LS_init = 2;
case 'SD'
method = SD;
LS_init = 2;
end
end
maxFunEvals = getOpt(o,'MAXFUNEVALS',1000);
maxIter = getOpt(o,'MAXITER',500);
optTol = getOpt(o,'OPTTOL',1e-5);
progTol = getOpt(o,'PROGTOL',1e-9);
corrections = getOpt(o,'CORRECTIONS',100);
corrections = getOpt(o,'CORR',corrections);
c1 = getOpt(o,'C1',1e-4);
c2 = getOpt(o,'C2',c2);
LS_init = getOpt(o,'LS_INIT',LS_init);
cgSolve = getOpt(o,'CGSOLVE',cgSolve);
qnUpdate = getOpt(o,'QNUPDATE',3);
cgUpdate = getOpt(o,'CGUPDATE',2);
initialHessType = getOpt(o,'INITIALHESSTYPE',1);
HessianModify = getOpt(o,'HESSIANMODIFY',0);
Fref = getOpt(o,'FREF',Fref);
useComplex = getOpt(o,'USECOMPLEX',0);
numDiff = getOpt(o,'NUMDIFF',0);
LS_saveHessianComp = getOpt(o,'LS_SAVEHESSIANCOMP',1);
Damped = getOpt(o,'DAMPED',Damped);
HvFunc = getOpt(o,'HVFUNC',[]);
bbType = getOpt(o,'BBTYPE',0);
cycle = getOpt(o,'CYCLE',3);
HessianIter = getOpt(o,'HESSIANITER',HessianIter);
outputFcn = getOpt(o,'OUTPUTFCN',[]);
useMex = getOpt(o,'USEMEX',1);
useNegCurv = getOpt(o,'USENEGCURV',1);
precFunc = getOpt(o,'PRECFUNC',[]);
LS_type = getOpt(o,'LS_type',LS_type);
LS_interp = getOpt(o,'LS_interp',LS_interp);
LS_multi = getOpt(o,'LS_multi',LS_multi);
end
function [v] = getOpt(options,opt,default)
if isfield(options,opt)
if ~isempty(getfield(options,opt))
v = getfield(options,opt);
else
v = default;
end
else
v = default;
end
end
function [o] = toUpper(o)
if ~isempty(o)
fn = fieldnames(o);
for i = 1:length(fn)
o = setfield(o,upper(fn{i}),getfield(o,fn{i}));
end
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
receptive_field_size.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/utils/receptive_field_size.m
| 1,020 |
utf_8
|
f0d7016ce44f2dabfba5a3864e6ce202
|
function out = receptive_field_size()
% conv1 11 55x55
% conv2 27 55x55
% pool2 35 27x27
% conv3 51 27x27
% pool3 67 13x13
% conv4 99 13x13
% conv5 131 13x13
% pool5 163 6x6
out = ...
pool3_to_conv3(...
conv4_to_pool3(...
conv5_to_conv4(...
pool5_to_conv5(1))));
return
out = ...
conv1_to_input(...
conv2_to_conv1(...
pool2_to_conv2(...
conv3_to_pool2(...
pool3_to_conv3(...
conv4_to_pool3(...
conv5_to_conv4(...
pool5_to_conv5(1))))))));
function out = pool5_to_conv5(p)
out = 2*(p-1)+1 + 2*floor(3/2);
function out = conv5_to_conv4(p)
out = 1*(p-1)+1 + 2*floor(3/2);
function out = conv4_to_pool3(p)
out = 1*(p-1)+1 + 2*floor(3/2);
function out = pool3_to_conv3(p)
out = 2*(p-1)+1 + 2*floor(3/2);
function out = conv3_to_pool2(p)
out = 1*(p-1)+1 + 2*floor(3/2);
function out = pool2_to_conv2(p)
out = 2*(p-1)+1 + 2*floor(3/2);
function out = conv2_to_conv1(p)
out = 1*(p-1)+1 + 2*floor(5/2);
function out = conv1_to_input(p)
out = 4*(p-1)+1 + 2*floor(11/2);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
test_2010_from_2012.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/utils/test_2010_from_2012.m
| 1,163 |
utf_8
|
4fd6b5864d38807aaadcc7d98084912b
|
function test_2010_from_2012()
year = '2010';
testset = 'test';
VOCdevkit2012 = './datasets/VOCdevkit2012';
VOCdevkit2010 = './datasets/VOCdevkit2010';
imdb_2012 = imdb_from_voc(VOCdevkit2012, 'test', '2012');
image_ids_2010 = get_2010_test_image_ids();
detrespath_2010 = '/work4/rbg/VOC2010/VOCdevkit/results/VOC2010/Main/%s_det_test_%s.txt';
detrespath_2012 = imdb_2012.details.VOCopts.detrespath;
map = containers.Map;
for i = 1:length(image_ids_2010)
map(image_ids_2010{i}) = true;
end
for i = 1:length(imdb_2012.details.VOCopts.classes)
cls = imdb_2012.details.VOCopts.classes{i};
res_fn = sprintf(detrespath_2012, 'comp4', cls);
[ids, scores, x1, y1, x2, y2] = textread(res_fn, '%s %f %f %f %f %f');
res_fn = sprintf(detrespath_2010, 'comp4', cls);
% write out detections in PASCAL format and score
fid = fopen(res_fn, 'w');
for i = 1:length(ids)
if map.isKey(ids{i})
fprintf(fid, '%s %f %d %d %d %d\n', ids{i}, scores(i), x1(i), y1(i), x2(i), y2(i));
end
end
fclose(fid);
end
function ids = get_2010_test_image_ids()
fn = '/work4/rbg/VOC2012/VOCdevkit/VOC2010/ImageSets/Main/test.txt';
ids = textread(fn, '%s');
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
hdf5_dir_to_mat_dir.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/utils/hdf5_dir_to_mat_dir.m
| 1,283 |
utf_8
|
8dcb8efe79353e8cfcc85cce60383323
|
function [] = hdf5_dir_to_mat_dir(hdf5_dir_path, mat_dir_path, quiet, skip_done)
assert(logical(exist(hdf5_dir_path, 'dir')));
if ~exist('quiet', 'var')
quiet = false;
end
if ~exist('skip_done', 'var')
skip_done = true;
end
if ~exist(mat_dir_path, 'dir')
mkdir(mat_dir_path);
end
files = dir(sprintf('%s/*.hdf5', hdf5_dir_path));
parfor i = 1:length(files)
[~, name, ~] = fileparts(files(i).name);
hdf5_path = sprintf('%s/%s.hdf5', hdf5_dir_path, name);
mat_path = sprintf('%s/%s.mat', mat_dir_path, name);
if exist(mat_path, 'file') && skip_done
continue;
end
hdf5_to_mat(hdf5_path, mat_path);
if ~quiet
fprintf('(%d/%d) Converted %s to %s\n', i, length(files), hdf5_path, mat_path);
end
end
end
function [] = hdf5_to_mat(hdf5_path, mat_path)
x = hdf5_to_struct(hdf5_path);
save(mat_path, '-struct', 'x');
end
function x = hdf5_to_struct(hdf5_path)
x.dataset = h5read(hdf5_path, '/dataset');
x.dataset = x.dataset{1};
x.gt = h5read(hdf5_path, '/gt');
x.class = h5read(hdf5_path, '/class');
x.flip = h5read(hdf5_path, '/flip');
x.overlap = h5read(hdf5_path, '/overlap')';
x.boxes = h5read(hdf5_path, '/boxes')';
x.imagename = h5read(hdf5_path, '/imagename');
x.imagename = x.imagename{1};
x.feat = h5read(hdf5_path, '/feat')';
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
prepare_batch.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/caffe-wsl/matlab/caffe/prepare_batch.m
| 1,298 |
utf_8
|
68088231982895c248aef25b4886eab0
|
% ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length(image_files);
if nargin < 3
batch_size = num_images;
end
IMAGE_DIM = 256;
CROPPED_DIM = 227;
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
center = floor(indices(2) / 2)+1;
num_images = length(image_files);
images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single');
parfor i=1:num_images
% read file
fprintf('%c Preparing %s\n',13,image_files{i});
try
im = imread(image_files{i});
% resize to fixed input size
im = single(im);
im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% Transform GRAY to RGB
if size(im,3) == 1
im = cat(3,im,im,im);
end
% permute from RGB to BGR (IMAGE_MEAN is already BGR)
im = im(:,:,[3 2 1]) - IMAGE_MEAN;
% Crop the center of the image
images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,...
center:center+CROPPED_DIM-1,:),[2 1 3]);
catch
warning('Problems with file',image_files{i});
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
matcaffe_demo_vgg.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/caffe-wsl/matlab/caffe/matcaffe_demo_vgg.m
| 3,036 |
utf_8
|
f836eefad26027ac1be6e24421b59543
|
function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
%
% INPUT
% im - color image as uint8 HxWx3
% use_gpu - 1 to use the GPU, 0 to use the CPU
% model_def_file - network configuration (.prototxt file)
% model_file - network weights (.caffemodel file)
% mean_file - mean BGR image as uint8 HxWx3 (.mat file)
%
% OUTPUT
% scores 1000-dimensional ILSVRC score vector
%
% EXAMPLE USAGE
% model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt';
% model_file = 'zoo/VGG_CNN_F.caffemodel';
% mean_file = 'zoo/VGG_mean.mat';
% use_gpu = true;
% im = imread('../../examples/images/cat.jpg');
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
%
% NOTES
% the image crops are prepared as described in the paper (the aspect ratio is preserved)
%
% PREREQUISITES
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
% init caffe network (spews logging info)
matcaffe_init(use_gpu, model_def_file, model_file);
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im, mean_file)};
toc;
% do forward pass to get scores
% scores are now Width x Height x Channels x Num
tic;
scores = caffe('forward', input_data);
toc;
scores = scores{1};
% size(scores)
scores = squeeze(scores);
% scores = mean(scores,2);
% [~,maxlabel] = max(scores);
% ------------------------------------------------------------------------
function images = prepare_image(im, mean_file)
% ------------------------------------------------------------------------
IMAGE_DIM = 256;
CROPPED_DIM = 224;
d = load(mean_file);
IMAGE_MEAN = d.image_mean;
% resize to fixed input size
im = single(im);
if size(im, 1) < size(im, 2)
im = imresize(im, [IMAGE_DIM NaN]);
else
im = imresize(im, [NaN IMAGE_DIM]);
end
% RGB -> BGR
im = im(:, :, [3 2 1]);
% oversample (4 corners, center, and their x-axis flips)
images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
center_y = floor(indices_y(2) / 2)+1;
center_x = floor(indices_x(2) / 2)+1;
curr = 1;
for i = indices_y
for j = indices_x
images(:, :, :, curr) = ...
permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]);
images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
curr = curr + 1;
end
end
images(:,:,:,5) = ...
permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ...
[2 1 3]);
images(:,:,:,10) = images(end:-1:1, :, :, curr);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
matcaffe_demo.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/caffe-wsl/matlab/caffe/matcaffe_demo.m
| 3,344 |
utf_8
|
669622769508a684210d164ac749a614
|
function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
%
% Usage:
% im = imread('../../examples/images/cat.jpg');
% scores = matcaffe_demo(im, 1);
% [score, class] = max(scores);
% Five things to be aware of:
% caffe uses row-major order
% matlab uses column-major order
% caffe uses BGR color channel order
% matlab uses RGB color channel order
% images need to have the data mean subtracted
% Data coming in from matlab needs to be in the order
% [width, height, channels, images]
% where width is the fastest dimension.
% Here is the rough matlab for putting image data into the correct
% format:
% % convert from uint8 to single
% im = single(im);
% % reshape to a fixed size (e.g., 227x227)
% im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% % permute from RGB to BGR and subtract the data mean (already in BGR)
% im = im(:,:,[3 2 1]) - data_mean;
% % flip width and height to make width the fastest dimension
% im = permute(im, [2 1 3]);
% If you have multiple images, cat them with cat(4, ...)
% The actual forward function. It takes in a cell array of 4-D arrays as
% input and outputs a cell array.
% init caffe network (spews logging info)
if exist('use_gpu', 'var')
matcaffe_init(use_gpu);
else
matcaffe_init();
end
if nargin < 1
% For demo purposes we will use the peppers image
im = imread('peppers.png');
end
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};
toc;
% do forward pass to get scores
% scores are now Width x Height x Channels x Num
tic;
scores = caffe('forward', input_data);
toc;
scores = scores{1};
size(scores)
scores = squeeze(scores);
scores = mean(scores,2);
[~,maxlabel] = max(scores);
% ------------------------------------------------------------------------
function images = prepare_image(im)
% ------------------------------------------------------------------------
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% resize to fixed input size
im = single(im);
im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% permute from RGB to BGR (IMAGE_MEAN is already BGR)
im = im(:,:,[3 2 1]) - IMAGE_MEAN;
% oversample (4 corners, center, and their x-axis flips)
images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
curr = 1;
for i = indices
for j = indices
images(:, :, :, curr) = ...
permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
curr = curr + 1;
end
end
center = floor(indices(2) / 2)+1;
images(:,:,:,5) = ...
permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
[2 1 3]);
images(:,:,:,10) = images(end:-1:1, :, :, curr);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
matcaffe_demo_vgg_mean_pix.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/caffe-wsl/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
| 3,069 |
utf_8
|
04b831d0f205ef0932c4f3cfa930d6f9
|
function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
% http://arxiv.org/abs/1409.1556/
%
% INPUT
% im - color image as uint8 HxWx3
% use_gpu - 1 to use the GPU, 0 to use the CPU
% model_def_file - network configuration (.prototxt file)
% model_file - network weights (.caffemodel file)
%
% OUTPUT
% scores 1000-dimensional ILSVRC score vector
%
% EXAMPLE USAGE
% model_def_file = 'zoo/deploy.prototxt';
% model_file = 'zoo/model.caffemodel';
% use_gpu = true;
% im = imread('../../examples/images/cat.jpg');
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
%
% NOTES
% mean pixel subtraction is used instead of the mean image subtraction
%
% PREREQUISITES
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
% init caffe network (spews logging info)
matcaffe_init(use_gpu, model_def_file, model_file);
% mean BGR pixel
mean_pix = [103.939, 116.779, 123.68];
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im, mean_pix)};
toc;
% do forward pass to get scores
% scores are now Width x Height x Channels x Num
tic;
scores = caffe('forward', input_data);
toc;
scores = scores{1};
% size(scores)
scores = squeeze(scores);
% scores = mean(scores,2);
% [~,maxlabel] = max(scores);
% ------------------------------------------------------------------------
function images = prepare_image(im, mean_pix)
% ------------------------------------------------------------------------
IMAGE_DIM = 256;
CROPPED_DIM = 224;
% resize to fixed input size
im = single(im);
if size(im, 1) < size(im, 2)
im = imresize(im, [IMAGE_DIM NaN]);
else
im = imresize(im, [NaN IMAGE_DIM]);
end
% RGB -> BGR
im = im(:, :, [3 2 1]);
% oversample (4 corners, center, and their x-axis flips)
images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
center_y = floor(indices_y(2) / 2)+1;
center_x = floor(indices_x(2) / 2)+1;
curr = 1;
for i = indices_y
for j = indices_x
images(:, :, :, curr) = ...
permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
curr = curr + 1;
end
end
images(:,:,:,5) = ...
permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
[2 1 3]);
images(:,:,:,10) = images(end:-1:1, :, :, curr);
% mean BGR pixel subtraction
for c = 1:3
images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
voc_eval.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m
| 1,389 |
utf_8
|
fd77d0da53b2585aa65e0da5edc5fe33
|
function res = voc_eval(path, comp_id, test_set, output_dir, rm_res)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir, rm_res);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:).ap]';
fprintf('%.1f\n', aps * 100);
fprintf('%.1f\n', mean(aps) * 100);
fprintf('~~~~~~~~~~~~~~~~~~~~\n');
function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir, rm_res)
test_set = VOCopts.testset;
year = VOCopts.dataset(4:end);
addpath(fullfile(VOCopts.datadir, 'VOCcode'));
res_fn = sprintf(VOCopts.detrespath, comp_id, cls);
recall = [];
prec = [];
ap = 0;
ap_auc = 0;
do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test');
if do_eval
% Bug in VOCevaldet requires that tic has been called first
tic;
[recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true);
ap_auc = xVOCap(recall, prec);
% force plot limits
ylim([0 1]);
xlim([0 1]);
print(gcf, '-djpeg', '-r0', ...
[output_dir '/' cls '_pr.jpg']);
end
fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc);
res.recall = recall;
res.prec = prec;
res.ap = ap;
res.ap_auc = ap_auc;
save([output_dir '/' cls '_pr.mat'], ...
'res', 'recall', 'prec', 'ap', 'ap_auc');
if rm_res
delete(res_fn);
end
rmpath(fullfile(VOCopts.datadir, 'VOCcode'));
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
roidb_from_voc.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/imdb/roidb_from_voc.m
| 2,742 |
utf_8
|
425a3d818c40cd19ef2879df05341668
|
function roidb = roidb_from_voc(imdb)
% roidb = roidb_from_voc(imdb)
% Builds an regions of interest database from imdb image
% database.
%
% Inspired by Andrea Vedaldi's MKL imdb and roidb code.
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
cache_file = ['./imdb/cache/roidb_' imdb.name];
try
load(cache_file);
catch
VOCopts = imdb.details.VOCopts;
addpath(fullfile(VOCopts.datadir, 'VOCcode'));
roidb.name = imdb.name;
fprintf('Loading region proposals...');
regions_file = sprintf('./data/edgebox_data/%s', roidb.name);
regions = load(regions_file);
fprintf('done\n');
for i = 1:length(imdb.image_ids)
tic_toc_print('roidb (%s): %d/%d\n', roidb.name, i, length(imdb.image_ids));
try
voc_rec = PASreadrecord(sprintf(VOCopts.annopath, imdb.image_ids{i}));
catch
voc_rec = [];
end
roidb.rois(i) = attach_proposals(voc_rec, regions.boxes{i}, imdb.class_to_id);
end
rmpath(fullfile(VOCopts.datadir, 'VOCcode'));
fprintf('Saving roidb to cache...');
save(cache_file, 'roidb', '-v7.3');
fprintf('done\n');
end
% ------------------------------------------------------------------------
function rec = attach_proposals(voc_rec, boxes, class_to_id)
% ------------------------------------------------------------------------
% change the format of pre-computed object proposals from [y1 x1 y2 x2] to [x1 y1 x2 y2]
boxes = boxes(:, [2 1 4 3]);
% gt: [2108x1 double]
% overlap: [2108x20 single]
% dataset: 'voc_2007_trainval'
% boxes: [2108x4 single]
% feat: [2108x9216 single]
% class: [2108x1 uint8]
if isfield(voc_rec, 'objects')
gt_boxes = cat(1, voc_rec.objects(:).bbox);
all_boxes = cat(1, gt_boxes, boxes);
gt_classes = class_to_id.values({voc_rec.objects(:).class});
gt_classes = cat(1, gt_classes{:});
num_gt_boxes = size(gt_boxes, 1);
else
gt_boxes = [];
all_boxes = boxes;
gt_classes = [];
num_gt_boxes = 0;
end
num_boxes = size(boxes, 1);
rec.gt = cat(1, true(num_gt_boxes, 1), false(num_boxes, 1));
rec.overlap = zeros(num_gt_boxes+num_boxes, class_to_id.Count, 'single');
for i = 1:num_gt_boxes
rec.overlap(:, gt_classes(i)) = ...
max(rec.overlap(:, gt_classes(i)), boxoverlap(all_boxes, gt_boxes(i, :)));
end
rec.boxes = single(all_boxes);
rec.feat = [];
rec.class = uint8(cat(1, gt_classes, zeros(num_boxes, 1)));
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
show_detections.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/show_detections.m
| 1,339 |
utf_8
|
0d424d6872284605347fe243ed9fdf30
|
function show_detections(model, split, year)
conf = voc_config('pascal.year', year);
dataset.year = year;
dataset.trainset = split;
dataset.image_ids = textread(sprintf(conf.pascal.VOCopts.imgsetpath, split), '%s');
show_det(model, dataset, conf);
% ------------------------------------------------------------------------
function show_det(model, dataset, conf)
% ------------------------------------------------------------------------
for i = 1:length(dataset.image_ids)
tic_toc_print('%s: %d/%d\n', ...
procid(), i, length(dataset.image_ids));
d = load_cached_features_hos(dataset.trainset, dataset.year, dataset.image_ids{i}, model.opts);
if isempty(find(d.class == model.class_id))
continue;
end
im = imread(sprintf(conf.pascal.VOCopts.imgpath, dataset.image_ids{i}));
% boxes who overlap a gt by > 70%
z = d.feat*model.w + model.b;
I = find(~d.gt & z > -1);
boxes = cat(2, single(d.boxes(I,:)), z(I));
[~, ord] = sort(z(I), 'descend');
ord = ord(1:min(length(ord), 20));
boxes = boxes(ord, :);
% nms_interactive(im, boxes, 0.3);
% keep = 1:size(boxes,1);
keep = nms(boxes, 0.3);
showboxes(im, boxes(keep,1:4));
pause;
% for k = 1:length(keep)
% showboxes(im, boxes(keep(k),1:4));
% title(sprintf('score: %.3f\n', boxes(keep(k),end)));
% pause;
% end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
show_latent_choice.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/show_latent_choice.m
| 1,567 |
utf_8
|
d4b8095cd404d6cccc29fc7061ae06b7
|
function show_latent_choice(model, trainset, year)
conf = voc_config('pascal.year', year);
dataset.year = year;
dataset.trainset = trainset;
dataset.image_ids = textread(sprintf(conf.pascal.VOCopts.imgsetpath, trainset), '%s');
[ids, cls_label] = textread(sprintf(conf.pascal.VOCopts.imgsetpath, [model.class '_' trainset]), '%s %d');
P = find(cls_label == 1);
dataset.image_ids = ids(P);
get_positive_features(model, dataset, conf);
% ------------------------------------------------------------------------
function get_positive_features(model, dataset, conf)
% ------------------------------------------------------------------------
thresh = 0.7;
for i = 1:length(dataset.image_ids)
tic_toc_print('%s: pos features %d/%d\n', ...
procid(), i, length(dataset.image_ids));
d = load_cached_features(dataset.trainset, dataset.year, dataset.image_ids{i}, model.opts);
d.feat = xform_feat(d.feat, model.opts);
im = imread(sprintf(conf.pascal.VOCopts.imgpath, dataset.image_ids{i}));
% boxes who overlap a gt by > 70%
I = find(d.overlap(:,model.class_id) > thresh);
zs = d.feat(I,:)*model.w + model.b;
I_gt = find(d.class == model.class_id);
for k = 1:length(I_gt)
ovr = boxoverlap(d.boxes(I,:), d.boxes(I_gt(k),:));
%I_ovr = find((ovr > thresh) & (ovr ~= 1));
I_ovr = find(ovr > thresh);
[~, argmax] = max(zs(I_ovr));
sel = I(I_ovr(argmax));
showboxesc(im, d.boxes(I_gt(k), :), 'g', '-');
showboxesc([], d.boxes(sel, :), 'r', '--');
title(sprintf('%.3f', zs(I_ovr(argmax))));
pause;
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
sample_correlated_pairs.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/sample_correlated_pairs.m
| 1,226 |
utf_8
|
21fba006b038c35f8dce14cedae004b7
|
function feature_pairs = sample_correlated_pairs(S, N)
ovM = get_overlap_matrix();
feature_pairs = zeros(0, 3);
for i = 1:N
while true
u1 = randi(size(S,1));
pos1 = mod(u1-1, 36)+1;
ov = ovM(pos1,:);
ok = repmat((ov < 1/3), [1 256]);
ok(u1-pos1+1:u1-pos1+36) = 0;
row = S(u1, :);
row(~ok) = -inf;
[~, ord] = sort(row, 'descend');
u2 = ord(1);
if S(u1, u2) > 0
feature_pairs = cat(1, feature_pairs, [u1 u2 S(u1, u2)]);
break;
end
end
end
[~, ord] = sort(feature_pairs(:,3), 'descend');
feature_pairs = feature_pairs(ord,:);
function ovM = get_overlap_matrix()
ovM = zeros(36);
s = 224/6;
points = round(s/2:s:224);
for i = 1:36
M = zeros(6,6,256);
M(i) = 1;
M = sum(M, 3)';
[r,c] = find(M);
r1_1 = max(1, points(r) - 81);
r1_2 = min(224, points(r) + 81);
c1_1 = max(1, points(c) - 81);
c1_2 = min(224, points(c) + 81);
for j = 1:36
M = zeros(6,6,256);
M(j) = 1;
M = sum(M, 3)';
[r,c] = find(M);
r2_1 = max(1, points(r) - 81);
r2_2 = min(224, points(r) + 81);
c2_1 = max(1, points(c) - 81);
c2_2 = min(224, points(c) + 81);
ovM(i,j) = boxoverlap([c1_1 r1_1 c1_2 r1_2], [c2_1 r2_1 c2_2 r2_2]);
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
pick_feature_pair.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/pick_feature_pair.m
| 1,089 |
utf_8
|
bee9f65c9d04ab2cb76538687f40bacc
|
function feature_pairs = pick_feature_pair(model, N)
ovM = get_overlap_matrix();
feature_pairs = zeros(0, 2);
[~, w_ord] = sort(model.w, 'descend');
for i = 1:N
u1 = w_ord(i);
pos1 = mod(u1-1, 36)+1;
ov = ovM(pos1,:);
ok = repmat((ov < 1/3), [1 256]);
ok(u1-pos1+1:u1-pos1+36) = 0;
w = model.w;
w(~ok) = -inf;
[~, ord] = sort(w, 'descend');
u2 = ord(1);
feature_pairs = cat(1, feature_pairs, [u1 u2]);
end
function ovM = get_overlap_matrix()
ovM = zeros(36);
s = 224/6;
points = round(s/2:s:224);
for i = 1:36
M = zeros(6,6,256);
M(i) = 1;
M = sum(M, 3)';
[r,c] = find(M);
r1_1 = max(1, points(r) - 81);
r1_2 = min(224, points(r) + 81);
c1_1 = max(1, points(c) - 81);
c1_2 = min(224, points(c) + 81);
for j = 1:36
M = zeros(6,6,256);
M(j) = 1;
M = sum(M, 3)';
[r,c] = find(M);
r2_1 = max(1, points(r) - 81);
r2_2 = min(224, points(r) + 81);
c2_1 = max(1, points(c) - 81);
c2_2 = min(224, points(c) + 81);
ovM(i,j) = boxoverlap([c1_1 r1_1 c1_2 r1_2], [c2_1 r2_1 c2_2 r2_2]);
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
viewerrors.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/viewerrors.m
| 10,275 |
utf_8
|
984e38a9624546fc0ba466aed666d8cf
|
function ap = viewerrors(model, boxes, testset, year, saveim)
% For visualizing mistakes on a validation set
% AUTORIGHTS
% -------------------------------------------------------
% Copyright (C) 2009-2012 Ross Girshick
%
% This file is part of the voc-releaseX code
% (http://people.cs.uchicago.edu/~rbg/latent/)
% and is available under the terms of an MIT-like license
% provided in COPYING. Please retain this notice and
% COPYING if you use this file (or a portion of it) in
% your project.
% -------------------------------------------------------
SHOW_TP = true;
SHOW_FP = true;
SHOW_FN = true;
im_path = sprintf('~/public_html/private/convnet-sel-search/%s/', model.class);
if exist(im_path) == 0
unix(['mkdir -p ' im_path]);
end
fp_html = [im_path 'fp.html'];
fn_html = [im_path 'fn.html'];
tp_html = [im_path 'tp.html'];
index_html = [im_path 'index.html'];
if saveim
htmlfid = fopen(index_html, 'w');
fprintf(htmlfid, '<html><body>');
fprintf(htmlfid, '<h2>%s</h2>', model.class);
fprintf(htmlfid, '<a href="tp.html">true positives</a><br />');
fprintf(htmlfid, '<a href="fp.html">false positives</a><br />');
fprintf(htmlfid, '<a href="fn.html">missed</a><br />');
fprintf(htmlfid, '</html></body>');
end
warning on verbose;
warning off MATLAB:HandleGraphics:noJVM;
cls = model.class;
conf = voc_config('pascal.year', year, ...
'eval.test_set', testset);
VOCopts = conf.pascal.VOCopts;
cachedir = conf.paths.model_dir;
% Load test set ground-truth
fprintf('%s: viewerrors: loading ground truth\n', cls);
[gtids, recs, hash, gt, npos] = load_ground_truth(cls, conf);
% Load detections from the model
[ids, confidence, BB] = get_detections(boxes, cls, conf);
% sort detections by decreasing confidence
[sc, si] = sort(-confidence);
ids = ids(si);
BB = BB(:,si);
% assign detections to ground truth objects
nd = length(confidence);
tp = zeros(nd,1);
fp = zeros(nd,1);
md = zeros(nd,1);
od = zeros(nd,1);
jm = zeros(nd,1);
for d = 1:nd
% display progress
tic_toc_print('%s: pr: compute: %d/%d\n', cls, d, nd);
% find ground truth image
i = xVOChash_lookup(hash, ids{d});
if isempty(i)
error('unrecognized image "%s"', ids{d});
elseif length(i) > 1
error('multiple image "%s"', ids{d});
end
% assign detection to ground truth object if any
% reported detection
bb = BB(:,d);
ovmax = -inf;
jmax = 0;
% loop over bounding boxes for this class in the gt image
for j = 1:size(gt(i).BB,2)
% consider j-th gt box
bbgt = gt(i).BB(:,j);
% compute intersection box
bi = [max(bb(1), bbgt(1)); ...
max(bb(2), bbgt(2)); ...
min(bb(3), bbgt(3)); ...
min(bb(4), bbgt(4))];
iw = bi(3)-bi(1)+1;
ih = bi(4)-bi(2)+1;
if iw > 0 & ih > 0
% compute overlap as area of intersection / area of union
ua = (bb(3)-bb(1)+1) * (bb(4)-bb(2)+1) + ...
(bbgt(3)-bbgt(1)+1) * (bbgt(4)-bbgt(2)+1) - ...
iw * ih;
ov = iw * ih / ua;
if ov > ovmax
ovmax = ov;
jmax = j;
end
end
end
% assign detection as true positive/don't care/false positive
if jmax > 0 && ovmax > gt(i).overlap(jmax)
gt(i).overlap(jmax) = ovmax;
gt(i).best_boxes(jmax,:) = bb';
end
od(d) = ovmax;
jm(d) = jmax;
if ovmax >= VOCopts.minoverlap
if ~gt(i).diff(jmax)
if ~gt(i).det(jmax)
% true positive
tp(d) = 1;
gt(i).det(jmax) = true;
gt(i).tp_boxes(jmax,:) = bb';
else
% false positive (multiple detection)
fp(d) = 1;
md(d) = 1;
end
end
else
% false positive (low or no overlap)
fp(d) = 1;
end
end
% compute precision/recall
cfp = cumsum(fp);
ctp = cumsum(tp);
rec = ctp/npos;
prec = ctp./(cfp+ctp);
fprintf('total recalled = %d/%d (%.1f%%)\n', sum(tp), npos, 100*sum(tp)/npos);
if SHOW_TP
if saveim
htmlfid = fopen(tp_html, 'w');
fprintf(htmlfid, '<html><body>');
end
fprintf('displaying true positives\n');
count = 0;
d = 1;
while d < nd && count < 400
if tp(d)
count = count + 1;
i = xVOChash_lookup(hash, ids{d});
im = imread([VOCopts.datadir recs(i).imgname]);
% Recompute the detection to get the derivation tree
score = -sc(d);
subplot(1,2,1);
imagesc(im);
axis image;
axis off;
subplot(1,2,2);
showboxesc(im, BB(:,d)', 'r', '-');
str = sprintf('%d det# %d/%d: @prec: %0.3f @rec: %0.3f\nscore: %0.3f GT overlap: %0.3f', count, d, nd, prec(d), rec(d), -sc(d), od(d));
fprintf('%s', str);
title(str);
fprintf('\n');
if saveim
cmd = sprintf('export_fig %s/%s-%d-tp.jpg -jpg', im_path, cls, d);
eval(cmd);
fprintf(htmlfid, sprintf('<img src="%s-%d-tp.jpg" />\n', cls, d));
fprintf(htmlfid, '<br /><br />\n');
else
pause;
end
end
d = d + 1;
end
if saveim
fprintf(htmlfid, '</body></html>');
fclose(htmlfid);
end
end
if SHOW_FP
if saveim
htmlfid = fopen(fp_html, 'w');
fprintf(htmlfid, '<html><body>');
end
fprintf('displaying false positives\n');
count = 0;
d = 1;
while d < nd && count < 400
if fp(d)
count = count + 1;
i = xVOChash_lookup(hash, ids{d});
im = imread([VOCopts.datadir recs(i).imgname]);
% Recompute the detection to get the derivation tree
score = -sc(d);
subplot(1,2,1);
imagesc(im);
axis image;
axis off;
subplot(1,2,2);
showboxesc(im, BB(:,d)', 'r', '-');
str = sprintf('%d det# %d/%d: @prec: %0.3f @rec: %0.3f\nscore: %0.3f GT overlap: %0.3f', count, d, nd, prec(d), rec(d), -sc(d), od(d));
if md(d)
str = sprintf('%s mult det', str);
end
if fp(d) && jm(d) > 0
str = sprintf('%s\nmax overlap all det: %0.3f', str, gt(i).overlap(jm(d)));
end
fprintf('%s', str);
title(str);
fprintf('\n');
if saveim
cmd = sprintf('export_fig %s/%s-%d-fp.jpg -jpg', im_path, cls, d);
eval(cmd);
fprintf(htmlfid, sprintf('<img src="%s-%d-fp.jpg" />\n', cls, d));
fprintf(htmlfid, '<br /><br />\n');
else
pause;
end
end
d = d + 1;
end
if saveim
fprintf(htmlfid, '</body></html>');
fclose(htmlfid);
end
end
if SHOW_FN
% to find false negatives loop over gt(i) and display any box that has
% gt(i).det(j) == false && ~gt(i).diff(j)
fprintf('displaying false negatives\n');
if saveim
htmlfid = fopen(fn_html, 'w');
fprintf(htmlfid, '<html><body>');
end
clf;
count = 0;
for i = 1:length(gt)
if count >= 200
break;
end
s = 0;
if ~isempty(gt(i).det)
s = sum((~gt(i).diff)' .* (~gt(i).det));
end
if s > 0
diff = [];
fn = [];
tp = [];
best_boxes = [];
best_ovrs = [];
fprintf('%d\n', i);
[gt(i).diff(:) gt(i).det(:) gt(i).overlap(:)]
for j = 1:length(gt(i).det)
bbgt = gt(i).BB(:,j)';
if gt(i).diff(j)
diff = [diff; [bbgt 0]];
elseif ~gt(i).det(j)
fn = [fn; [bbgt 1]];
best_boxes = cat(1, best_boxes, gt(i).best_boxes(j,:));
best_ovrs = cat(1, best_ovrs, gt(i).overlap(j));
else
tp = [tp; [bbgt 2]];
tp = [tp; [gt(i).tp_boxes(j,:) 3]];
end
end
im = imread([VOCopts.datadir recs(i).imgname]);
showboxesc(im, [diff; fn; tp]);
for j = 1:length(best_ovrs)
if best_ovrs(j) > -inf
showboxesc([], best_boxes(j,:), 'y', '--');
text(best_boxes(j,1), best_boxes(j,2), sprintf('%0.3f', best_ovrs(j)), 'BackgroundColor', [.7 .9 .7]);
end
end
if saveim
cmd = sprintf('export_fig %s/%s-%d-fn.jpg -jpg', im_path, cls, count);
eval(cmd);
fprintf(htmlfid, sprintf('<img src="%s-%d-fn.jpg" />\n', cls, count));
fprintf(htmlfid, '<br /><br />\n');
else
pause;
end;
count = count + 1;
end
end
if saveim
fprintf(htmlfid, '</body></html>');
fclose(htmlfid);
end
end
function [gtids, recs, hash, gt, npos] = load_ground_truth(cls, conf)
VOCopts = conf.pascal.VOCopts;
year = conf.pascal.year;
cachedir = conf.paths.model_dir;
testset = conf.eval.test_set;
cp = [cachedir cls '_ground_truth_' testset '_' year];
try
load(cp, 'gtids', 'recs', 'hash', 'gt', 'npos');
catch
[gtids, t] = textread(sprintf(VOCopts.imgsetpath,VOCopts.testset), '%s %d');
for i = 1:length(gtids)
% display progress
tic_toc_print('%s: pr: load: %d/%d\n', cls, i, length(gtids));
% read annotation
recs(i) = PASreadrecord(sprintf(VOCopts.annopath, gtids{i}));
end
% hash image ids
hash = xVOChash_init(gtids);
% extract ground truth objects
npos = 0;
gt(length(gtids)) = struct('BB', [], 'diff', [], 'det', [], 'overlap', [], 'tp_boxes', []);
for i = 1:length(gtids)
% extract objects of class
clsinds = strmatch(cls, {recs(i).objects(:).class}, 'exact');
gt(i).BB = cat(1, recs(i).objects(clsinds).bbox)';
gt(i).diff = [recs(i).objects(clsinds).difficult];
gt(i).det = false(length(clsinds), 1);
gt(i).overlap = -inf*ones(length(clsinds), 1);
gt(i).tp_boxes = zeros(length(clsinds), 4);
gt(i).best_boxes = zeros(length(clsinds), 4);
npos = npos + sum(~gt(i).diff);
end
save(cp, 'gtids', 'recs', 'hash', 'gt', 'npos');
end
function [ids, confidence, BB] = get_detections(boxes, cls, conf)
VOCopts = conf.pascal.VOCopts;
year = conf.pascal.year;
cachedir = conf.paths.model_dir;
testset = conf.eval.test_set;
ids = textread(sprintf(VOCopts.imgsetpath, testset), '%s');
% Write and read detection data in the same way as pascal_eval.m
% and the VOCdevkit
% write out detections in PASCAL format and score
fid = fopen(sprintf(VOCopts.detrespath, 'comp3', cls), 'w');
for i = 1:length(ids);
bbox = boxes{i};
keep = nms(bbox, 0.3);
bbox = bbox(keep,:);
for j = 1:size(bbox,1)
fprintf(fid, '%s %.14f %d %d %d %d\n', ids{i}, bbox(j,end), bbox(j,1:4));
end
end
fclose(fid);
[ids, confidence, b1, b2, b3, b4] = ...
textread(sprintf(VOCopts.detrespath, 'comp3', cls), '%s %f %f %f %f %f');
BB = [b1 b2 b3 b4]';
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
vis_crops.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/vis_crops.m
| 2,193 |
utf_8
|
a32583168a0ca9cc94afbeca8755a8b4
|
function vis_crops(imdb)
opts.net_file = './data/caffe_nets/finetune_voc_2007_trainval_iter_70k';
opts.net_def_file = './model-defs/rcnn_batch_256_output_pool5.prototxt';
% load the region of interest database
roidb = imdb.roidb_func(imdb);
rcnn_model = rcnn_create_model(opts.net_def_file, opts.net_file);
rcnn_model = rcnn_load_model(rcnn_model);
image_mean = rcnn_model.cnn.image_mean;
im_perm = randperm(length(imdb.image_ids));
for i = im_perm
d = roidb.rois(i);
im = single(imread(imdb.image_at(i)));
num_boxes = size(d.boxes, 1);
crop_size = size(image_mean,1);
perm = randperm(size(d.boxes, 1), 10);
for j = perm
bbox = d.boxes(j,:);
src = im(bbox(2):bbox(4), bbox(1):bbox(3), :);
crop_warp_0 = rcnn_im_crop(im, bbox, 'warp', crop_size, 0, image_mean);
crop_warp_16 = rcnn_im_crop(im, bbox, 'warp', crop_size, 16, image_mean);
crop_square_0 = rcnn_im_crop(im, bbox, 'square', crop_size, 0, image_mean);
crop_square_16 = rcnn_im_crop(im, bbox, 'square', crop_size, 16, image_mean);
max_val = max(cat(1, crop_warp_0(:), crop_warp_16(:), ...
crop_square_0(:), crop_square_16(:)));
min_val = min(cat(1, crop_warp_0(:), crop_warp_16(:), ...
crop_square_0(:), crop_square_16(:)));
src = normalize(src, max(src(:)), min(src(:)));
crop_warp_0 = normalize(crop_warp_0, max_val, min_val);
crop_warp_16 = normalize(crop_warp_16, max_val, min_val);
crop_square_0 = normalize(crop_square_0, max_val, min_val);
crop_square_16 = normalize(crop_square_16, max_val, min_val);
subplot(2, 4, 1);
imagesc(src);
title('src');
axis image;
axis off;
subplot(2, 4, 5);
imagesc(crop_warp_0);
title('warp 0');
axis image;
axis off;
subplot(2, 4, 6);
imagesc(crop_warp_16);
title('warp 16');
axis image;
axis off;
subplot(2, 4, 7);
imagesc(crop_square_0);
title('square 0');
axis image;
axis off;
subplot(2, 4, 8);
imagesc(crop_square_16);
title('square 16');
axis image;
axis off;
pause;
end
end
function A = normalize(A, max_val, min_val)
range = max_val - min_val;
A = (A - min_val) / (range + eps);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
pool5_explorer.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/vis/pool5-explorer/pool5_explorer.m
| 6,690 |
utf_8
|
a1c8558f0e1f3580833ccc05c8629d9e
|
function pool5_explorer(imdb, cache_name)
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
%
% This file is part of the R-CNN code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
conf = rcnn_config('sub_dir', imdb.name);
index = pool5_explorer_build_index(imdb, cache_name);
figures = [1 2];
vf_sz = [8 12];
redraw = true;
position = 1;
channel = 0;
cell_y = 2;
cell_x = 2;
feature_index = get_feature_index(channel, cell_y, cell_x);
while 1
if redraw
feature_index = get_feature_index(channel, cell_y, cell_x);
visualize_feature(imdb, figures(2), index, feature_index, position, ...
vf_sz, cell_y, cell_x, channel);
redraw = false;
end
% display 16x16 grid where each cell shows a 6x6 feature map
% wait for mouse click
% -> map click coordinates to feature dimension
[~, ~, ~, ~, key_code] = get_feature_selection(1);
switch key_code
case 27 % ESC
close(figures(ishandle(figures)));
return;
case '`'
return;
case 's' % take a snapshot
filename = sprintf('./vis/pool5-explorer/shots/x%d-y%d-c%d-p%d.pdf', ...
cell_y, cell_x, channel, position);
if exist(filename)
delete(filename);
end
export_fig(filename);
case 'g' % go to a specific channel
answer = str2double(inputdlg('go to channel:'));
if ~isempty(answer)
answer = round(answer);
if answer > 0
channel = answer - 1;
redraw = true;
end
end
case 31 % up
% decrease channel
if channel > 0
channel = channel - 1;
position = 1;
redraw = true;
end
case 30 % down
% increase channel
if channel < 255
channel = channel + 1;
position = 1;
redraw = true;
end
case 'i' % cell up
if cell_y > 0
cell_y = cell_y - 1;
position = 1;
redraw = true;
end
case 'k' % cell down
if cell_y < 5
cell_y = cell_y + 1;
position = 1;
redraw = true;
end
case 'j' % cell left
if cell_x > 0
cell_x = cell_x - 1;
position = 1;
redraw = true;
end
case 'l' % cell right
if cell_x < 5
cell_x = cell_x + 1;
position = 1;
redraw = true;
end
case 29 % ->
new_pos = position + prod(vf_sz);
if new_pos < length(index.features{feature_index}.scores)
position = new_pos;
redraw = true;
end
case 28 % <-
new_pos = position - prod(vf_sz);
if new_pos > 0
position = new_pos;
redraw = true;
end
otherwise
fprintf('%d\n', key_code);
end
end
% ------------------------------------------------------------------------
function f = get_feature_index(channel, cell_y, cell_x)
% ------------------------------------------------------------------------
f = channel*36 + cell_y*6 + cell_x + 1;
% ------------------------------------------------------------------------
function visualize_feature(imdb, fig, index, f, position, msz, cell_y, cell_x, channel)
% ------------------------------------------------------------------------
max_val = 0;
for x_ = 0:5
for y_ = 0:5
f_ = get_feature_index(channel, y_, x_);
max_val = max([max_val; index.features{f_}.scores]);
end
end
s = 227/6;
points = round(s/2:s:227);
M = zeros(6,6,256);
M(f) = 1;
M = sum(M, 3)';
half_receptive_field = floor(195/2);
[r,c] = find(M);
r1 = max(1, points(r) - half_receptive_field);
r2 = min(227, points(r) + half_receptive_field);
c1 = max(1, points(c) - half_receptive_field);
c2 = min(227, points(c) + half_receptive_field);
h = r2-r1;
w = c2-c1;
psx = 96;
psy = 96;
h = h * psy/227;
w = w * psx/227;
context_padding = round(16/227 * 96);
r1 = (r1-1)*psy/227 + 1;
c1 = (c1-1)*psx/227 + 1;
ims = {};
start_pos = position;
end_pos = min(length(index.features{f}.scores), start_pos + prod(msz) - 1);
N = end_pos - start_pos + 1;
str = sprintf('pool5 feature: (%d,%d,%d) (top %d - %d)', cell_y+1, cell_x+1, channel+1, start_pos, end_pos);
for i = start_pos:end_pos
val = index.features{f}.scores(i);
image_ind = index.features{f}.image_inds(i);
bbox = index.features{f}.boxes(i, :);
im = imread(imdb.image_at(image_ind));
im = rcnn_im_crop(im, bbox, 'warp', psx, context_padding, []);
ims{end+1} = uint8(im);
end
filler = prod(msz) - N;
im = my_montage(cat(4, ims{:}, 256*ones(psy, psx, 3, filler)), msz);
figure(2);
clf;
imagesc(im);
title(str, 'Color', 'black', 'FontSize', 18, 'FontName', 'Times New Roman');
axis image;
axis off;
set(gcf, 'Color', 'white');
q = 1;
for y = 0:msz(1)-1
for x = 0:msz(2)-1
if q > N
break;
end
x1 = c1+psx*x;
y1 = r1+psy*y;
rectangle('Position', [x1 y1 w h], 'EdgeColor', 'w', 'LineWidth', 3);
text(x1, y1+7.5, sprintf('%.1f', index.features{f}.scores(start_pos+q-1)/max_val), 'BackgroundColor', 'w', 'FontSize', 10, 'Margin', 0.1, 'FontName', 'Times New Roman');
q = q + 1;
end
if q > N
break;
end
end
if 0
% compute mean figure
num_to_avg = 40;
scores = index.features{f}.scores(start_pos:end_pos);
for i = 1:num_to_avg
ims{i} = double(ims{i})*scores(i)/sum(scores(1:num_to_avg));
end
figure(1);
imagesc(uint8(sum(cat(4, ims{1:num_to_avg}), 4)));
axis image;
figure(2);
end
% ------------------------------------------------------------------------
function [feature_index, channel, cell_y, cell_x, ch] = ...
get_feature_selection(channel_width)
% ------------------------------------------------------------------------
while 1
[x,y,ch] = ginput(1);
chan_y = floor(y/channel_width);
chan_x = floor(x/channel_width);
channel = chan_y*16 + chan_x;
cell_y = floor(rem(y, channel_width)/7);
cell_x = floor(rem(x, channel_width)/7);
feature_index = channel*36 + cell_y*6 + cell_x + 1;
if (channel < 0 || channel > 255)
channel = nan;
end
if isscalar(ch)
return;
end
end
% ------------------------------------------------------------------------
function im = my_montage(ims, sz)
% ------------------------------------------------------------------------
ims_sz = [size(ims, 1) size(ims, 2)];
im = zeros(ims_sz(1)*sz(1), ims_sz(2)*sz(2), 3, class(ims));
k = 1;
for y = 0:sz(1)-1
for x = 0:sz(2)-1
im(y*ims_sz(1)+1:(y+1)*ims_sz(1), ...
x*ims_sz(2)+1:(x+1)*ims_sz(2), :) = ims(:,:,:,k);
k = k + 1;
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
cache_fc8_features.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/cache_fc8_features.m
| 2,355 |
utf_8
|
6260d287c993b2ffddd3e8818e88c87f
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function cache_fc8_features(imdb, varargin)
ip = inputParser;
ip.addRequired('imdb', @isstruct);
ip.addOptional('start', 1, @isscalar);
ip.addOptional('end', 0, @isscalar);
ip.addOptional('crop_mode', 'warp', @isstr);
ip.addOptional('crop_padding', 16, @isscalar);
ip.addOptional('net_file', '', @isstr);
ip.addOptional('cache_name', '', @isstr);
ip.parse(imdb, varargin{:});
opts = ip.Results;
opts.net_def_file = './prototxt/caffenet_fc8.prototxt';
opts.batch_size = 50;
opts.crop_size = 227;
image_ids = imdb.image_ids;
if opts.end == 0
opts.end = length(image_ids);
end
% Where to save feature cache
if ~exist('cache','file')
mkdir('cache');
end
opts.output_dir = ['./cache/' opts.cache_name '/'];
mkdir_if_missing(opts.output_dir);
fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Feature caching options:\n');
disp(opts);
fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');
% load the region of interest database
roidb = imdb.roidb_func(imdb);
if caffe('is_initialized') == 0
caffe('init', opts.net_def_file, opts.net_file, 'test');
end
% caffe('set_mode_cpu');
caffe('set_mode_gpu');
% caffe('set_device',3);
total_time = 0;
count = 0;
for i = opts.start:opts.end
fprintf('%s: cache features: %d/%d\n', procid(), i, opts.end);
save_file = [opts.output_dir image_ids{i} '.mat'];
if exist(save_file, 'file') ~= 0
fprintf(' [already exists]\n');
continue;
end
count = count + 1;
tot_th = tic;
d = roidb.rois(i);
im = imread(imdb.image_at(i));
if size(im,3)~=3
im = cat(3,im,im,im);
end
th = tic;
d.feat_in = cache_bb_features(im, d.boxes, opts, 1);
d.feat_out = cache_bb_features(im, d.boxes, opts, 2);
fprintf(' [features: %.3fs]\n', toc(th));
th = tic;
save(save_file, '-struct', 'd');
fprintf(' [saving: %.3fs]\n', toc(th));
total_time = total_time + toc(tot_th);
fprintf(' [avg time: %.3fs (total: %.3fs)]\n', ...
total_time/count, total_time);
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
cache_bb_features.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/cache_bb_features.m
| 1,193 |
utf_8
|
2d2afacf8926b9d190c11871368e83be
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function feat = cache_bb_features(im, boxes, opts, flag)
[batches, batch_padding] = extract_regions(im, boxes, opts, flag);
batch_size = opts.batch_size;
% compute features for each batch of region images
feat_dim = -1;
feat = [];
curr = 1;
for j = 1:length(batches)
% forward propagate batch of region images
f = caffe('forward', batches(j));
f = f{1};
f = f(:);
% first batch, init feat_dim and feat
if j == 1
feat_dim = length(f)/batch_size;
feat = zeros(size(boxes,1), feat_dim, 'single');
end
f = reshape(f, [feat_dim batch_size]);
% last batch, trim f to size
if j == length(batches)
if batch_padding > 0
f = f(:, 1:end-batch_padding);
end
end
feat(curr:curr+size(f,2)-1,:) = f';
curr = curr + batch_size;
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
extract_regions.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/extract_regions.m
| 1,980 |
utf_8
|
d08caf471fa445373bc5508454f8ae42
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function [batches, batch_padding] = extract_regions(im, boxes, opts, flag)
if flag == 1
im = single(im);
num_boxes = size(boxes, 1);
batch_size = opts.batch_size;
crop_size = opts.crop_size;
num_batches = ceil(num_boxes / batch_size);
batch_padding = batch_size - mod(num_boxes, batch_size);
if batch_padding == batch_size
batch_padding = 0;
end
batches = cell(num_batches, 1);
for batch = 1:num_batches
batch_start = (batch-1)*batch_size+1;
batch_end = min(num_boxes, batch_start+batch_size-1);
ims = zeros(crop_size, crop_size, 3, batch_size, 'single');
for j = batch_start:batch_end
bbox = boxes(j,:);
ims(:,:,:,j-batch_start+1) = extract_region_in(im, bbox);
end
batches{batch} = ims;
end
end
if flag == 2
im = single(im);
num_boxes = size(boxes, 1);
batch_size = opts.batch_size;
crop_size = opts.crop_size;
num_batches = ceil(num_boxes / batch_size);
batch_padding = batch_size - mod(num_boxes, batch_size);
if batch_padding == batch_size
batch_padding = 0;
end
batches = cell(num_batches, 1);
for batch = 1:num_batches
batch_start = (batch-1)*batch_size+1;
batch_end = min(num_boxes, batch_start+batch_size-1);
ims = zeros(crop_size, crop_size, 3, batch_size, 'single');
for j = batch_start:batch_end
bbox = boxes(j,:);
ims(:,:,:,j-batch_start+1) = extract_region_out(im, bbox);
end
batches{batch} = ims;
end
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
extract_region_in.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/extract_region_in.m
| 917 |
utf_8
|
7a3cd9d842a57d3d621b53c741a46786
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function im_in = extract_region_in(im, bbox)
crop_size = 227;
padding = 16;
scale = crop_size/(crop_size - padding*2);
half_height = (bbox(4)-bbox(2)+1)/2;
half_width = (bbox(3)-bbox(1)+1)/2;
center = [bbox(1)+half_width bbox(2)+half_height];
bbox = round([center center] + [-half_width -half_height half_width half_height]*scale);
bbox(1) = max(1, bbox(1));
bbox(2) = max(1, bbox(2));
bbox(3) = min(size(im,2), bbox(3));
bbox(4) = min(size(im,1), bbox(4));
im_in = im(bbox(2):bbox(4),bbox(1):bbox(3),:);
im_in = preprocess(im_in);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
cache_fc7_features.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/cache_fc7_features.m
| 2,983 |
utf_8
|
31c412dffc553f13a6e43e95aceed71e
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function cache_fc7_features(imdb, varargin)
ip = inputParser;
ip.addRequired('imdb', @isstruct);
ip.addOptional('start', 1, @isscalar);
ip.addOptional('end', 0, @isscalar);
ip.addOptional('crop_mode', 'warp', @isstr);
ip.addOptional('crop_padding', 16, @isscalar);
ip.addOptional('net_file', '', @isstr);
ip.addOptional('cache_name', '', @isstr);
ip.parse(imdb, varargin{:});
opts = ip.Results;
opts.net_def_file = './prototxt/caffenet_fc7.prototxt';
opts.batch_size = 50;
opts.crop_size = 227;
image_ids = imdb.image_ids;
if opts.end == 0
opts.end = length(image_ids);
end
% Where to save feature cache
if ~exist('cache','file')
mkdir('cache');
end
opts.train_output_dir = ['./cache/' opts.cache_name '/mil_train/'];
mkdir_if_missing(opts.train_output_dir);
opts.test_output_dir = ['./cache/' opts.cache_name '/mil_test/'];
mkdir_if_missing(opts.test_output_dir);
fprintf('\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Feature caching options:\n');
disp(opts);
fprintf('~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n');
% load the region of interest database
roidb = imdb.roidb_func(imdb);
caffe('init', opts.net_def_file, opts.net_file, 'test');
% caffe('set_mode_cpu');
caffe('set_mode_gpu');
% caffe('set_device',3);
total_time = 0;
count = 0;
load('results_maskout_regions.mat');
for i = opts.start:opts.end
fprintf('%s: cache features: %d/%d\n', procid(), i, opts.end);
% using all the proposals for mil testing
test_save_file = [opts.test_output_dir image_ids{i} '.mat'];
if exist(test_save_file, 'file') ~= 0
fprintf(' [already exists]\n');
continue;
end
count = count + 1;
tot_th = tic;
d = roidb.rois(i);
im = imread(imdb.image_at(i));
if size(im,3)~=3
im = cat(3,im,im,im);
end
th = tic;
d.feat = cache_bb_features(im, d.boxes, opts, 1);
fprintf(' [features: %.3fs]\n', toc(th));
th = tic;
save(test_save_file, '-struct', 'd');
fprintf(' [saving: %.3fs]\n', toc(th));
total_time = total_time + toc(tot_th);
fprintf(' [avg time: %.3fs (total: %.3fs)]\n', ...
total_time/count, total_time);
% using the selected proposals for mil training
train_save_file = [opts.train_output_dir image_ids{i} '.mat'];
if exist(train_save_file, 'file') ~= 0
fprintf(' [already exists]\n');
continue;
end
num_gt = sum(d.gt);
IND_GT = find(d.gt == 1);
ind = [IND_GT;IND{i}+num_gt];
ind = unique(ind);
d.boxes = d.boxes(ind,:);
d.class = d.class(ind,:);
d.gt = d.gt(ind,:);
d.overlap = d.overlap(ind,:);
d.feat = d.feat(ind,:);
save(train_save_file, '-struct', 'd');
end
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
extract_region_out.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/extract_region_out.m
| 747 |
utf_8
|
545ca74e438fdb1087702203fa974a19
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function im_out = extract_region_out(im, bbox)
im_out = im;
bbox(1) = max(1, bbox(1));
bbox(2) = max(1, bbox(2));
bbox(3) = min(size(im,2), bbox(3));
bbox(4) = min(size(im,1), bbox(4));
im_out(bbox(2):bbox(4),bbox(1):bbox(3),1) = 123;
im_out(bbox(2):bbox(4),bbox(1):bbox(3),2) = 117;
im_out(bbox(2):bbox(4),bbox(1):bbox(3),3) = 104;
im_out = preprocess(im_out);
|
github
|
wupeng78/Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master
|
preprocess.m
|
.m
|
Weakly-Supervised-Object-Localization-with-Progressive-Domain-Adaptation-CVPR-2016--master/features/preprocess.m
| 778 |
utf_8
|
8d861cb5445de961ec1a8bef66e0b94e
|
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2016, Dong Li
%
% This file is part of the WSL code and is available
% under the terms of the MIT License provided in
% LICENSE. Please retain this notice and LICENSE if you use
% this file (or any portion of it) in your project.
% ---------------------------------------------------------
function im = preprocess(im)
im = imresize(im, [227 227], 'bilinear');
% permute from RGB to BGR and subtract the data mean
im = im(:,:,[3 2 1]); % RGB to BGR
im(:,:,1) = im(:,:,1) - 104; % subtract B mean 104
im(:,:,2) = im(:,:,2) - 117; % subtract G mean 117
im(:,:,3) = im(:,:,3) - 123; % subtract R mean 123
% make width the fastest dimension, convert to single
im = permute(im, [2 1 3]);
|
github
|
dswinters/ocean-tools-master
|
parse_adcp.m
|
.m
|
ocean-tools-master/parse_adcp.m
| 15,543 |
utf_8
|
c2a0b235190fbf1326886e90c35aebdd
|
%% parse_adcp.m
%
% Usage
% adcp = parse_adcp(files)
% adcp = parse_adcp(files, parse_nuc_timestamps)
% adcp = parse_adcp(... , 'progress', uiprogressdlg)
%
% Inputs
% - dat_or_file
% This can be a filename, cell array of filenames, the output of MATLAB's
% "dir" command, or an array of binary data.
%
% Optional Arguments
% - parse_nuc_timestamps (logical)
% | Flag to parse Nuc timestamps inserted into the ADCP_timestamped* files from
% | ROSE deployments.
%
% Name-value pair arguments
% - 'progress'
% Specify a UI progress dialog handle to update it with progress information while
% parsing data.
%
% Outputs
% - adcp
% Data structure containing ADCP fields.
%
% Author
% - Dylan Winters ([email protected])
function adcp = parse_adcp(dat_or_file,varargin)
%% Parse optional inputs
p = inputParser;
addOptional(p,'parse_nuc_timestamps',false,@(x) islogical(x))
addParameter(p,'progress',struct(),@(x) isa(x,'matlab.ui.dialog.ProgressDialog'));
parse(p,varargin{:});
progress = p.Results.progress;
parse_nuc_timestamps = p.Results.parse_nuc_timestamps;
nuc_offset = 0;
if parse_nuc_timestamps
nuc_offset = 7;
end
%% Main parsing routine
% 1) Load binary data
if iscell(dat_or_file)
% Cell array of filenames
dat = load_data(dat_or_file,progress);
elseif isstruct(dat_or_file)
% File info struct from dir()
dat = load_data(fullfile({dat_or_file.folder},{dat_or_file.name}),progress);
elseif isstring(dat_or_file) | ischar(dat_or_file)
% Single filename
dat = load_data({dat_or_file},progress);
else
% Treat input as raw data
dat = dat_or_file;
end
[h len] = find_headers(dat,progress); % find header locations
if isempty(h)
adcp = []; return
end
%% Fill adcp data structure with ensemble data
progress.Message = 'ADCP: Processing data...';
progress.Indeterminate = 'on';
% Loop over unique ensemble lengths (there coule be multiple, e.g. for Sentinel
% V's multi-profile mode)
[u,~,iu] = unique(len);
for nl = 1:length(u)
dat_idx = h(iu==nl) + [-nuc_offset:u(nl)-1];
% dat_idx = h(iu==nl) + [-nuc_offset:u(nl)+3]; % FIXME: include checksums
adcp(nl) = parse_ensemble_data(dat(dat_idx));
end
progress.Indeterminate = 'off';
progress.Message = 'ADCP: Processing data... Done!';
function dat = load_data(files,progress)
%----------------------------------------------------------
% Read and concatenate data in the given list of files
%----------------------------------------------------------
dat = [];
for i = 1:length(files)
if ~exist(files{i},'file')
error('File not found: %s',files{i})
end
[~,fname,fext] = fileparts(files{i});
progress.Message = sprintf('ADCP: Opening %s %s [%d of %d]',fname,fext,i,length(files));
fd = fopen(files{i},'r','ieee-le');
dat = cat(1,dat,uint8(fread(fd,inf,'uint8')));
fclose(fd);
progress.Value = i/length(files);
end
end
function [h len] = find_headers(dat,progress)
%----------------------------------------------------------
% Find all header start indices in raw data
%----------------------------------------------------------
h = find(dat(1:end-1)==127 & dat(2:end)==127);
% Need at least 3 bytes after header to get length
h = h(h < (length(dat)-4));
% Need at least 7 bytes prior to first header for ROSE timestamp
if parse_nuc_timestamps
h = h(h>7);
end
if isempty(h)
h = []; len = []; return
end
% Compute ensemble lengths
len = double(typecast(reshape(dat(h + [2,3])',[],1),'uint16'));
ensemble_size_max = 5000; % byte limit for ensembles
% Discard trailing ensembles and ensembles larger than the size limit
rm = (h + len > length(dat) - 2) | len > ensemble_size_max;
h = h(~rm);
len = len(~rm);
% Verify checksums
progress.Message = 'ADCP: Verifying ensemble checksums...';
chk = typecast(reshape(dat(h + len + [0:1])',[],1),'uint16');
for i = 1:length(chk)
kp(i) = chk(i) == mod(sum(dat(h(i) + [0:len(i)-1])),65536);
if mod(i,500)==1
progress.Value = i/length(chk);
end
end
progress.Value = 1;
progress.Message = 'ADCP: Verifying ensemble checksums... Done!';
% Discard header locations with bad checksums
h = h(kp);
len = len(kp);
% Discard packets whose lengths are rare (<10% of data). This should
% take care any "fake" ensembles in the data caused by coincidental
% header byte pairs.
[u,~,iu] = unique(len);
kp = true(size(h));
for i = 1:length(u)
if mean(iu==i) < 0.1
kp(iu==i) = false;
end
end
h = h(kp);
len = len(kp);
end
function adcp = parse_ensemble_data(dat)
%----------------------------------------------------------
% Convert binary data matrix into a structure
%----------------------------------------------------------
% Initialize output structure, read number of data types and offsets from first
% header. If we're parsing bytes inserted prior to each header, account for the
% number of bytes inserted.
adcp = struct();
nuc_offset = 0;
if parse_nuc_timestamps
nuc_offset = 7;
end
% Read the number of fields (6th byte)
nfields = dat(1,nuc_offset+6);
% Compute field data offsets (7th, 9th, ... bytes)
offsets = nuc_offset + double(typecast(dat(1,nuc_offset+7+[0:2*nfields-1]),'uint16'));
% The loop below parses fields from each data type.
for i = 1:nfields
% For most fields, we can use the following function to extract data
% colums. It looks nasty, but it's just doing the following:
% 1) Extract data bytes from all rows (ensembles). The offset plus indices specify colums.
% 2) Transpose and reshape so we get a long list of bytes, in order.
% 3) Convert byte sequences into values according to their datatype.
getdat =@(idx,type) double(typecast(reshape([dat(:,offsets(i)+idx)]',[],1),type))';
switch dec2hex(typecast(dat(1,offsets(i) + [1:2]),'uint16'),4)
case '0000' % fixed leader
% The fixed leader is in every ensemble, but we only need to
% parse 1. In this case, modify the getdat function to only get
% data from the first row.
getdat =@(idx,type) double(typecast(dat(1,offsets(i)+idx),type));
adcp.config.cpu_fw_ver = getdat(3,'uint8');
adcp.config.cpu_fw_rev = getdat(4,'uint8');
adcp.config.sys_config = dec2bin(getdat(5:6,'uint16'),16);
adcp.config.sym_flag = logical(getdat(7,'uint8'));
adcp.config.lag_len = getdat(8,'uint8');
adcp.config.n_beams = getdat(9,'uint8');
adcp.config.n_cells = getdat(10,'uint8');
adcp.config.pings_per_ensemble = getdat(11:12,'uint16');
adcp.config.depth_cell_length = getdat(13:14,'uint16')/100;
adcp.config.blank_after_transmit = getdat(15:16,'uint16')/100;
adcp.config.profiling_mode = getdat(17,'uint8');
adcp.config.low_corr_thresh = getdat(18,'uint8');
adcp.config.n_code_reps = getdat(19,'uint8');
adcp.config.perc_good_min = getdat(20,'uint8');
adcp.config.error_vel_max = getdat(21:22,'uint16')/1000;
adcp.config.tpp_minutes = getdat(23,'uint8');
adcp.config.tpp_seconds = getdat(24,'uint8');
adcp.config.tpp_hundredths = getdat(25,'uint8');
adcp.config.coord_transform = dec2bin(getdat(26,'uint8'),8);
adcp.config.heading_alignment = getdat(27:28,'int16')/100;
adcp.config.heading_bias = getdat(29:30,'int16')/100;
adcp.config.sensor_source = dec2bin(getdat(31,'uint8'),8);
adcp.config.sensors_available = dec2bin(getdat(32,'uint8'),8);
adcp.config.bin_1_distance = getdat(33:34,'uint16')/100;
adcp.config.xmit_puse_length = getdat(35:36,'uint16')/100;
adcp.config.wp_ref_layer_avg = getdat(37:38,'uint8');
adcp.config.false_target_thresh = getdat(39,'uint8');
adcp.config.transmit_lag_dist = getdat(41:42,'uint16')/100;
adcp.config.sys_bandwidth = getdat(51:52,'uint16');
adcp.config.sys_power = getdat(53,'uint8');
adcp.config.serial_number = getdat(55:58,'uint32');
adcp.config.beam_angle = getdat(59,'uint8');
adcp.config.frequency = 75 * 2^bin2dec(adcp.config.sys_config(end-2:end));
adcp.cell_depth = adcp.config.bin_1_distance + ...
[0:adcp.config.n_cells-1]'*adcp.config.depth_cell_length;
% The ROSE computers insert timestamps outside of the ADCP's
% data structure. These don't have an ID or offset, so just grab
% them along with the fixed leader. Parse inserted bytes after
% reading fixed leader.
if parse_nuc_timestamps
% This also needs its own getdat in order to specify the data offset manually.
getdat =@(idx,type) double(typecast(reshape([dat(:,idx)]',[],1),type))';
nuc_time = reshape(getdat(1:7,'uint8'),[nuc_offset, size(dat,1)])';
nuc_time(:,1) = nuc_time(:,1) + 2000; % add century
nuc_time(:,6) = nuc_time(:,6) + nuc_time(:,7)/100; % add hundredths to seconds
adcp.nuc_time = datenum(nuc_time(:,1:6))';
end
% FIXME: exploring bad timetsamps
% nb = adcp.nuc_time < datenum([2020 0 0 0 0 0]);
case '0080' % variable leader
ens_num = getdat(3:4,'uint16');
% clock_year = getdat(5,'uint8');
% clock_month = getdat(6,'uint8');
% clock_day = getdat(7,'uint8');
% clock_hour = getdat(8,'uint8');
% clock_minute = getdat(9,'uint8');
% clock_second = getdat(10,'uint8');
% clock_hundr = getdat(11,'uint8');
ens_num_msb = getdat(12,'uint8');
adcp.ens_num = ens_num + 65536 * ens_num_msb;
% bit_fault = getdat(13,'uint8');
% bit_reset = getdat(14,'uint8');
adcp.speed_of_sound = getdat(15:16,'uint16');
adcp.transducer_depth = getdat(17:18,'uint16')/10;
adcp.heading = getdat(19:20,'uint16')/100;
adcp.pitch = getdat(21:22,'int16')/100;
adcp.roll = getdat(23:24,'int16')/100;
adcp.salinity = getdat(25:26,'uint16');
adcp.temperature = getdat(27:28,'int16')/100;
% mpt_minutes = getdat(29,'uint8');
% mpt_seconds = getdat(30,'uint8');
% mpt_hundredths = getdat(31,'uint8');
adcp.h_std = getdat(32,'uint8')/10;
adcp.p_std = getdat(33,'uint8')/10;
adcp.r_std = getdat(34,'uint8')/10;
% adc_channels = getdat(35:42,'uint8');
clock_century = getdat(58,'uint8')';
clock_year = getdat(59,'uint8')';
clock_month = getdat(60,'uint8')';
clock_day = getdat(61,'uint8')';
clock_hour = getdat(62,'uint8')';
clock_minute = getdat(63,'uint8')';
clock_second = getdat(64,'uint8')';
clock_hundr = getdat(65,'uint8')';
adcp.time = datenum([100*clock_century+clock_year, clock_month, clock_day, ...
clock_hour, clock_minute, clock_second + clock_hundr/100])';
% These data need some additional reshaping since they have beam,
% depth, and time dimensions.
case '0100' % velocity
adcp.vel = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells * 4 * 2)],'int16')/1000,...
4,adcp.config.n_cells,size(dat,1)), [2 1 3]);
case '0200' % correlation magnitude
adcp.corr = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells * 4)],'uint8'),...
4,adcp.config.n_cells,size(dat,1)), [2 1 3]);
case '0300' % echo intensity
adcp.echo_intens = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells * 4)],'uint8'),...
4,adcp.config.n_cells,size(dat,1)), [2 1 3]);
case '0400' % percent good
adcp.perc_good = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells * 4)],'uint8'),...
4,adcp.config.n_cells,size(dat,1)), [2 1 3]);
% Vertical beam data (Sentinel V)
case '0A00' % vertical beam velocity
adcp.vel(:,5,:) = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells * 2)],'int16')/1000,...
1,adcp.config.n_cells,size(dat,1)), [2 1 3]);
adcp.config.n_beams = 5;
case '0B00' % vertical beam correlation magnitude
adcp.corr(:,5,:) = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells)],'uint8'),...
1,adcp.config.n_cells,size(dat,1)), [2 1 3]);
case '0C00' % vertical beam echo intensity
adcp.echo_intens(:,5,:) = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells)],'uint8'),...
1,adcp.config.n_cells,size(dat,1)), [2 1 3]);
case '0D00' % vertical beam percent good
adcp.perc_good(:,5,:) = permute(reshape(...
getdat(2 + [1:(adcp.config.n_cells)],'uint8'),...
1,adcp.config.n_cells,size(dat,1)), [2 1 3]);
% Bottom-track data
case '0600'
% BT range is a 3-byte integer with the most significant byte
% much later in the data packet.
bt_range_lsb = getdat(17:24,'uint16');
bt_range_msb = getdat(78:81,'uint8');
adcp.bt_range = (reshape(bt_range_lsb,4,[]) + 2^16*reshape(bt_range_msb,4,[]))/100;
adcp.bt_vel = reshape(getdat(25:32,'int16'),4,[])/1000; % mm/s -> m/s
adcp.bt_perc_good = reshape(getdat(41:44,'uint8'),4,[]);
adcp.bt_amp = reshape(getdat(41:44,'uint8'),4,[]);
otherwise
% disp(dec2hex(typecast(dat(1,offsets(i) + [1:2]),'uint16'),4))
end
end
end % of parse_ensemble_data
end % of parse_adcp
|
github
|
dswinters/ocean-tools-master
|
parse_nortek_adcp.m
|
.m
|
ocean-tools-master/parse_nortek_adcp.m
| 7,066 |
utf_8
|
de4db43d3ea5aa8e29593f0305f1b9b4
|
function adcp = parse_nortek_adcp(dat_or_file,varargin)
%% Parse optional inputs
p = inputParser;
addOptional(p,'parse_cpu_time',false,@(x) islogical(x))
addParameter(p,'progress',struct(),@(x) isa(x,'matlab.ui.dialog.ProgressDialog'));
parse(p,varargin{:});
progress = p.Results.progress;
parse_cpu_time = p.Results.parse_cpu_time;
cpu_time_bytes = 7*parse_cpu_time;
progress = struct();
%% Load binary data
if iscell(dat_or_file)
% Cell array of filenames
dat = load_data(dat_or_file,progress);
elseif isstruct(dat_or_file)
% File info struct from dir()
dat = load_data(fullfile({dat_or_file.folder},{dat_or_file.name}),progress);
elseif isstring(dat_or_file) | ischar(dat_or_file)
% Single filename
dat = load_data({dat_or_file},progress);
else
% Treat input as raw data
dat = dat_or_file;
end
%% Find headers and validate their checksums
sync = 0xA5;
family = 0x10;
ids = [0x15 0x16 0x17 0x18 0x1A 0x1B 0x1C 0x1D 0x1E 0x1F 0xA0];
names = strrep({'burst'
'average'
'bottom track'
'interleaved burst'
'burst altimeter raw'
'dvl bottom track'
'echo sounder'
'dvl water track'
'altimeter'
'avg altimeter raw'
'string'},' ','_');
idmap = containers.Map(num2cell(ids),names);
h = find(dat(1:end-3)==sync & ismember(dat(3:end-1),ids) & dat(4:end)==family);
h = h(h<length(dat)-1);
hlen = double(dat(h+1));
idx = h+hlen<length(dat); % remove incomplete packets
h = h(idx);
hlen = hlen(idx);
id = dat(h+2);
chk = false(length(h),1);
chk_rec = typecast(reshape(dat(h + hlen - 1 + [-1 0])',[],1),'uint16');
chk_comp = uint16(zeros(size(chk_rec)));
for i = 1:length(h)
[chk(i), chk_comp(i)] = validate_checksum(dat(h(i) + [0:hlen(i)-3]),chk_rec(i));
end
% Discard invalid headers
h = h(chk);
hlen = hlen(chk);
id = id(chk);
uid = unique(id);
% for i = 1:length(uid)
% disp(sprintf('%s: %d',idmap(uid(i)), sum(id==uid(i))));
% end
%% Validate data checksums
dchk_rec = typecast(reshape(dat(h + hlen - 1 -[3 2])',[],1),'uint16');
dlen = double(typecast(reshape(dat(h + hlen - 1 -[5 4])',[],1),'uint16'));
%% Discard trailing packets
idx = h+hlen+dlen < length(dat);
h = h(idx);
hlen = hlen(idx);
id = id(idx);
dlen = dlen(idx);
dchk_rec = dchk_rec(idx);
dchk = false(size(dchk_rec));
dchksum = 0*dchk;
for i = 1:length(dchk)
[dchk(i), dchksum(i)] = validate_checksum(dat(h(i) + hlen(i)-1 + [1:dlen(i)]),dchk_rec(i));
end
%% Discard invalid data checksums
h = h(dchk);
hlen = hlen(dchk);
id = id(dchk);
dlen = dlen(dchk);
%% Discard leading/trailing headers
rm = false(size(h));
if parse_cpu_time
rm = h<cpu_time_bytes+1;
end
rm = rm | (h+hlen+dlen) > length(dat);
h = h(~rm);
hlen = hlen(~rm);
id = id(~rm);
dlen = dlen(~rm);
%% Trim data if number of BT/burst are different
uid = unique(id); % process unique datatypes separately
id_n = ones(size(id));
id_nmax = zeros(size(uid));
for i = 1:length(uid)
id_n(id==uid(i)) = 1:sum(id==uid(i));
id_nmax(i) = sum(id==uid(i));
end
n_min = min(id_nmax(ismember(uid,[0x15 0x17])));
kp = id_n <= n_min;
h = h(kp);
hlen = hlen(kp);
id = id(kp);
dlen = dlen(kp);
%% Process data
adcp = struct();
adcp.config = struct();
for i = 1:length(uid)
% Extract data of this type into a big matrix. Each row is a sample, each
% column part of some field.
idx = find(id==uid(i));
d = @() dat(h(idx) + hlen(idx) + [0:dlen(idx)-1]);
% Get cpu timestamps if they exist
if parse_cpu_time
cpu_time = double(dat(h(idx) + [-cpu_time_bytes:-1]));
cpu_time(:,6) = cpu_time(:,6) + 1/100*cpu_time(:,7);
cpu_time(:,1) = cpu_time(:,1) + 2000;
cpu_time = datenum(cpu_time(:,1:6))';
end
% Process data by operating on the columns, depending on the datatype
switch uid(i)
%% Burst data record
case 0x15
burst = nortek_parse_burst(d());
% Config fields
adcp.config.n_beams = burst.nbeams;
adcp.config.n_cells = burst.ncells;
adcp.config.depth_cell_length = burst.cell_size(1);
adcp.config.bin_1_distance = burst.blanking(1);
adcp.config.serial_number = burst.serial;
adcp.config.beam_angle = nan;
adcp.config.frequency = nan;
% Data fields
adcp.time = burst.time;
adcp.cell_depth = adcp.config.bin_1_distance + ...
[0:adcp.config.n_cells-1]*adcp.config.depth_cell_length;
adcp.heading = burst.heading;
adcp.pitch = burst.pitch;
adcp.roll = burst.roll;
adcp.vel = burst.vel;
adcp.echo_intens = burst.amp;
adcp.corr = burst.cor;
adcp.ahrs_rotm = burst.ahrs_rotm;
adcp.ahrs_gyro = burst.ahrs_gyro;
if parse_cpu_time
adcp.nuc_time = cpu_time;
end
%% Avg data record
case 0x16
adcp.nortek_avg = nortek_parse_burst(d());
if parse_cpu_time
adcp.nortek_avg.cpu_time = cpu_time;
end
%% Bottom track data record
case 0x17
bt = nortek_parse_bt(d());
if parse_cpu_time
bt.nuc_time = cpu_time;
end
adcp.bt_range = bt.dist;
adcp.bt_vel = bt.vel;
adcp.bt_time = bt.time;
case 0x1C
adcp.ecs = nortek_parse_burst(d());
if parse_cpu_time
adcp.ecs.nuc_time = cpu_time;
end
end
end
if isfield(adcp,'vel') && isfield(adcp,'bt_vel')
adcp.vel = adcp.vel - permute(adcp.bt_vel,[3 1 2]);
adcp.processing.bt_removed_from_vel = true;
end
adcp.config.beam2inst = [1.1831 0 -1.1831 0
0 -1.1831 0 1.1831
0.5518 0 0.5518 0
0 0.5518 0 0.5518];
end
%% Helper functions below
function [chk, chk_comp] = validate_checksum(d,chk_rec)
len = length(d);
chk = double(0xB58C); % checksum is initialized to this
% sum of all 16-byte segments (remove trailing 8 bytes if odd length)
chk = chk + sum(double(typecast(d(1:len-mod(len,2)),'uint16')));
% add the last byte if odd length:
if mod(len,2)
chk = chk + bitshift(double(d(end)),8);
end
chk_comp = uint16(mod(chk,2^16));
chk = chk_comp == chk_rec; % final checksum
end
function dat = load_data(files,progress)
%----------------------------------------------------------
% Read and concatenate data in the given list of files
%----------------------------------------------------------
dat = [];
for i = 1:length(files)
if ~exist(files{i},'file')
error('File not found: %s',files{i})
end
[~,fname,fext] = fileparts(files{i});
progress.Message = sprintf('ADCP: Opening %s %s [%d of %d]',fname,fext,i,length(files));
fd = fopen(files{i},'r','ieee-le');
dat = cat(1,dat,uint8(fread(fd,inf,'uint8')));
fclose(fd);
progress.Value = i/length(files);
end
end
|
github
|
dswinters/ocean-tools-master
|
prep_nbeam_solutions.m
|
.m
|
ocean-tools-master/prep_nbeam_solutions.m
| 7,147 |
utf_8
|
186f39b257f30f102581a5fd81a9994b
|
%% prep_nbeam_solutions.m
% Usage: adcp = prep_nbeam_solutions(adcp)
%
% Description:
% Fill in NaN-masked beam velocity data with data from other beams. Uses the
% Sentinel V's 5th beam to solve for missing beam data with the assumption that
% towards-transducer velocity as estimated by each beam pair and as measured by
% the 5th beam should be equal.
%
% Inputs: adcp - adcp structure returned by parse_adcp()
% Outputs: adcp - an equivalent structure with modified beam velocities.
%
% Author: Dylan Winters
% Created: 2022-02-21
function adcp = prep_nbeam_solutions(adcp)
% Get velocity dimensions
nb = size(adcp.vel,2); % number of beams
nc = size(adcp.vel,1); % number of depth cells
nt = size(adcp.vel,3); % number of samples
% Reshape velocity matrix to columns of beam data
vb = reshape(permute(adcp.vel,[3 1 2]),nc*nt,nb);
% Create a logical mask for NaN-valued beam data, i.e.
% 1 0 0 0 0 <-- beam 1 is bad
% 0 0 1 0 0 <-- beam 3 is bad
% 0 0 0 1 1 <-- beams 4 & 5 are bad, etc.
bmask = isnan(vb);
% Find all combinations of bad beams. These are just the unique rows of the
% above matrix.
[bad_beams, ~, type] = unique(bmask,'rows');
% Also define a function to convert arbitrary combinations of bad beams to
% unique integers by treating these rows as binary numbers:
id =@(bad_beams) sum(2.^find(bad_beams));
% Loop over all types of bad beam combinations and fill in masked velocity data
% where possible.
for i = 1:size(bad_beams,1)
% Get the indices of velocity entries with this type of beam failure
idx = type == i;
c = 1/(2*sind(adcp.config.beam_angle));
% ====== 3-beam solutions for 4-beam ADCP ====== %
if nb==4;
switch id(find(bad_beams(i,:)))
% ====== 1 side beam bad ====== %
% The best we can do with a 4-beam ADCP is set error velocity equal to
% zero, i.e. impose that the estimate of Z velocity from both beam
% pairs is equal, then solve for the missing beam. Beam 1 example:
%
% Z1 = c*v1 + c*v2 (1) Z estimate from 1st beam pair
% Z2 = c*v3 + c*v4 (2) Z estimate from 2nd beam pair
%
% Then impose Z1 = Z2 and solve for v1:
%
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v1 = v3 + v4 - v2
%
% Similar for other beams.
case id(1) % only beam 1 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v1 = v3 + v4 - v2
vb(idx,1) = vb(idx,3) + vb(idx,4) - vb(idx,2);
case id(2) % only beam 2 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v2 = v3 + v4 - v1
vb(idx,2) = vb(idx,3) + vb(idx,4) - vb(idx,1);
case id(3) % only beam 3 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v3 = v1 + v2 - v4
vb(idx,3) = vb(idx,1) + vb(idx,2) - vb(idx,4);
case id(4) % only beam 3 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v4 = v1 + v2 - v3
vb(idx,4) = vb(idx,1) + vb(idx,2) - vb(idx,3);
end
end
% ====== 3- and 4-beam solutions for 5-beam ADCP ====== %
if nb==5
switch id(find(bad_beams(i,:)))
% ====== 1 side beam bad ====== %
% Each opposite-side pair of side beams gives an estimate of Z velocity. With
% a single bad side beam, we can impose that its pair's estimate of Z velocity
% is equal to beam 5's measurement, and reconstruct the bad beam's velocity.
%
% For example, if beam 1 is bad (c is a scale factor depending on beam angle):
%
% Z1 = c*v1 + c*v2 (1) Z velocity estimate from combining beams 1 & 2
% Z2 = v5 (2) Z velocity estimate directly from beam 5
%
% By imposing that Z1 = Z2, we can combine (1) and (2) to solve for v1:
%
% v5 = c*v1 + c*v2
% ==> v1 = (v5 - c*v2)/c
%
% Then for all entries where only beam 1 is NaN, we set beam 1 velocity to
% (v5 - c*v2)/c
%
% The process is similar for any single bad side beam.
case id(1) % only beam 1 bad, as in example above
% v5 = c*v1 + c*v2
% ==> v1 = (v5 - c*v2)/c
vb(idx,1) = (vb(idx,5) - c*vb(idx,2))/c;
case id(2) % only beam 2 bad
% v5 = c*v1 + c*v2
% ==> v2 = (v5 - c*v1)/c
vb(idx,2) = (vb(idx,5) - c*vb(idx,1))/c;
case id(3) % only beam 3 bad
% v5 = c*v3 + c*v4
% ==> v3 = (v5 - c*v4)/c
vb(idx,3) = (vb(idx,5) - c*vb(idx,4))/c;
case id(4) % only beam 4 bad
% v5 = c*v3 + c*v4
% ==> v4 = (v5 - c*v3)/c
vb(idx,4) = (vb(idx,5) - c*vb(idx,3))/c;
% ====== Bad side beam and bad 5th beam ====== %
% We can use the typical 3-beam solutions for a 4-beam ADCP
case id([1,5]) % only beam 1 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v1 = v3 + v4 - v2
vb(idx,1) = vb(idx,3) + vb(idx,4) - vb(idx,2);
case id([2,5]) % only beam 2 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v2 = v3 + v4 - v1
vb(idx,2) = vb(idx,3) + vb(idx,4) - vb(idx,1);
case id([3,5]) % only beam 3 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v3 = v1 + v2 - v4
vb(idx,3) = vb(idx,1) + vb(idx,2) - vb(idx,4);
case id([4,5]) % only beam 3 bad
% c*v1 + c*v2 = c*v3 + c*v4
% ===> v4 = v1 + v2 - v3
vb(idx,4) = vb(idx,1) + vb(idx,2) - vb(idx,3);
% ====== 2 side beams bad ====== %
% Because we can handle a single bad beam from the 1&2 beam pair and the
% 3&4 beam pair independently, we can also handle cases where a single
% beam is bad for both pairs:
case id([1,3]) % beams 1 & 3 bad
% v5 = c*v1 + c*v2
% ==> v1 = (v5 - c*v2)/c
vb(idx,1) = (vb(idx,5) - c*vb(idx,2))/c;
% v5 = c*v3 + c*v4
% ==> v3 = (v5 - c*v4)/c
vb(idx,3) = (vb(idx,5) - c*vb(idx,4))/c;
case id([1,4]) % beams 1 & 4 bad
% v5 = c*v1 + c*v2
% ==> v1 = (v5 - c*v2)/c
vb(idx,1) = (vb(idx,5) - c*vb(idx,2))/c;
% v5 = c*v3 + c*v4
% ==> v4 = (v5 - c*v3)/c
vb(idx,4) = (vb(idx,5) - c*vb(idx,3))/c;
case id([2,3]) % beams 2 & 3 bad
% v5 = c*v1 + c*v2
% ==> v2 = (v5 - c*v1)/c
vb(idx,2) = (vb(idx,5) - c*vb(idx,1))/c;
% v5 = c*v3 + c*v4
% ==> v3 = (v5 - c*v4)/c
vb(idx,3) = (vb(idx,5) - c*vb(idx,4))/c;
case id([2,4]) % beams 2 & 4 bad
% v5 = c*v1 + c*v2
% ==> v2 = (v5 - c*v1)/c
vb(idx,2) = (vb(idx,5) - c*vb(idx,1))/c;
% v5 = c*v3 + c*v4
% ==> v4 = (v5 - c*v3)/c
vb(idx,4) = (vb(idx,5) - c*vb(idx,3))/c;
% ====== Other cases ====== %
% Solutions for other combinations of beam failures can be added here.
% For example, if beam 5 is bad, one could take a weighted average of Z
% estimated by both other beam pairs. There are many possibilities and I
% can't know which are most appropriate for all cases.
end
end
end
% Reshape beam velocity to original size and store in adcp struct
adcp.vel = permute(reshape(vb',nb,nt,nc), [3 1 2]);
|
github
|
dswinters/ocean-tools-master
|
parse_imu.m
|
.m
|
ocean-tools-master/parse_imu.m
| 21,036 |
utf_8
|
beb6d505c404508dccfd47bc6a791920
|
%% parse_imu.m
%
% Usage
% imu = parse_imu(dat_or_file)
% imu = parse_imu(dat_or_file, parse_nuc_timestamps)
% imu = parse_imu(... , 'progress', uiprogressdlg)
%
% Inputs
% - dat_or_file
% This can be a filename, cell array of filenames, the output of MATLAB's
% "dir" command, or an array of binary data.
%
% Optional Arguments
% - parse_nuc_timestamps (logical)
% | Flag to parse Nuc timestamps inserted into the IMU_timestamped* files from
% | ROSE deployments.
%
% Name-value pair arguments
% - 'progress'
% Specify a UI progress dialog handle to update it with progress information while
% parsing data.
%
% Outputs
% - imu
% Data structure containing IMU fields. The field and subfield structure matches
% the fields described in the LORD IMU manuals.
%
% Author
% - Dylan Winters ([email protected])
function imu = parse_imu(dat_or_file,varargin)
%% Parse optional inputs
p = inputParser;
addOptional(p,'parse_nuc_timestamps',false,@(x) islogical(x))
addParameter(p,'progress',struct(),@(x) isa(x,'matlab.ui.dialog.ProgressDialog'));
parse(p,varargin{:});
progress = p.Results.progress;
parse_nuc_timestamps = p.Results.parse_nuc_timestamps;
%% Main parsing routine
% 1) Load binary data
if iscell(dat_or_file)
% Cell array of filenames
dat = load_data(dat_or_file);
elseif isstruct(dat_or_file)
% File info struct from dir()
dat = load_data(fullfile({dat_or_file.folder},{dat_or_file.name}));
elseif isstr(dat_or_file)
% Single filename
dat = load_data({dat_or_file});
else
% Treat input as raw data
dat = dat_or_file;
end
% 2) Locate & validate data packets
header = find_headers(dat,progress);
% 3) Convert binary data to struct
progress.Message = 'IMU: Processing data';
progress.Indeterminate = 'on';
imu = parse_data(header, dat);
progress.Indeterminate = 'off';
%%% Sub-functions
%----------------------------------------------------------
% Locate headers in the 3DM-GX5-25 binary output
%----------------------------------------------------------
function header = find_headers(dat,progress)
% Find sequences of SYNC1 and SYNC2 bytes (0x75,0x65).
% These are the first two bytes of all headers.
h = find(dat(1:end-1) == hex2dec('75') & ...
dat(2:end) == hex2dec('65'));
% Throw out trailing headers with no payload
h = h(length(dat) - h > 2);
% Look for timestamps inserted by ROSE computer
if parse_nuc_timestamps
% Discard first header if preceeding timestamp is incomplete
if ~isempty(h)
if h(1) < 8
h = h(2:end);
end
ts = double(dat(h - [7:-1:1]));
ts(:,1) = ts(:,1) + 2000; % add century
ts(:,6) = ts(:,6) + ts(:,7)/100; % add ms to s
ts = datenum(ts(:,1:6));
else
ts = [];
end
end
% The third byte is the 'descriptor set', which signifies the payload type.
d = double(dat(h + 2));
% The fourth byte is the payload length.
l = double(dat(h + 3));
% Verify checksums of each packet payload
kp = true(size(h));
progress.Message = 'IMU: Validating checksums...';
for n = 1:length(h);
% Stop if we reach EoF before packet end
if h(n) + l(n)-1 + 6 <= length(dat)
% Extract packet payload
pp = dat(h(n) + [0:l(n)+3]);
% Extract packet checksum
chk1 = double(dat((h(n) + l(n)+3) + [1:2]));
chk1 = bitshift(chk1(1),8) + chk1(2);
% Compute packet checksum (16-bit Fletcher checksum)
sum1 = 0;
sum2 = 0;
for i = 1:length(pp)
sum1 = mod(sum1 + double(pp(i)),2^8);
sum2 = mod(sum2 + sum1,2^8);
end
chk2 = bitshift(sum1,8) + sum2;
% Discard packet if checksums don't match
if chk2 ~= chk1
kp(n) = false;
end
else % EoF reached before packet end; discard
kp(n) = false;
end
if mod(n,500) == 1;
progress.Value = n/length(h);
end
end
progress.Value = 1;
progress.Message = 'IMU: Validating checksums... Done!';
% Eliminate headers with invalid checksums
header.index = h(kp);
header.descriptor = d(kp);
header.length = l(kp);
if parse_nuc_timestamps
header.ts = ts(kp);
end
end
%----------------------------------------------------------
% Read and concatenate data in the given list of files
%----------------------------------------------------------
function dat = load_data(files,progress)
dat = [];
for i = 1:length(files)
if ~exist(files{i},'file')
error('File not found: %s',files{i})
end
[~,fname,fext] = fileparts(files{i});
progress.Message = sprintf('IMU: Opening %s%s [%d of %d]',fname,fext,i,length(files));
progress.Value = i/length(files);
fd = fopen(files{i},'r','ieee-le');
dat = cat(1,dat,uint8(fread(fd,inf,'uint8')));
fclose(fd);
end
end
%----------------------------------------------------------
% Convert raw binary data to a MATLAB struct
%----------------------------------------------------------
function output = parse_data(header, dat)
if length(header) == 0
output = [];
return
end
output = struct(); % initialize output structure
output.units = struct(); % initialize units structure
[d, f, id] = unique(header.descriptor);
len = header.length(f);
dat_preproc = cell(length(d),1);
for i = 1:length(d)
dat_preproc{i} = dat(header.index(id==i)+2 + [0:len(i)+1]);
end
for i = 1:length(d) % for each type of payload encountered while finding headers...
nb_proc = 2;
while nb_proc < size(dat_preproc{i},2) % extract fields 1-by-1
flen = unique(dat_preproc{i}(:,nb_proc+1)); % get field length
fdesc = unique(dat_preproc{i}(:,nb_proc+2)); % and descriptor
[dname fname subfields] = imu_field_defs(d(i),fdesc); % get subfield info
% initialize output fields/subfields
if ~isfield(output,dname)
output.(dname) = struct();
% store ROSE timestamps
if parse_nuc_timestamps
output.(dname).nuc_time = header.ts(id==i);
end
end
if ~isfield(output.(dname),fname)
output.(dname).(fname) = struct();
end
for sf = 1:length(subfields)
% store unit information
output.units.(dname).(fname).(subfields(sf).name) = subfields(sf).units;
% compute subfield length
if sf==length(subfields)
sf_len = flen-2 - subfields(end).offset;
else
sf_len = subfields(sf+1).offset - subfields(sf).offset;
end
% compute subfield index in pre-processed data
sf_idx = nb_proc + 3 + subfields(sf).offset + [0:sf_len-1];
% convert 8-bit integers to final datatype with MATLAB's typecast function
% this requires some reshaping, etc. to be fast
output.(dname).(fname).(subfields(sf).name) = ...
double(typecast(reshape(fliplr(dat_preproc{i}(:,sf_idx))',1,[]),subfields(sf).type));
end
nb_proc = nb_proc + double(flen);
end
end
end % of parse_data()
%----------------------------------------------------------
% Define subfield names, byte offsets, subfield lengths, and
% datatypes based on the payload descriptor.
%----------------------------------------------------------
function [dname fname subfields] = imu_field_defs(descriptor,fdesc)
dname = '';
fname = '';
subfields = struct();
switch dec2hex(descriptor,2) % Descriptor set byte
case '80' % IMU Data
dname = 'IMU';
switch dec2hex(fdesc,2)
case '04'
fname = 'scaled_accelerometer_vector';
subfields = struct(...
'name', {'x_accel','y_accel','z_accel'},...
'offset', {0,4,8},...
'type', {'single','single','single'},...
'units', {'g','g','g'});
case '05'
fname = 'scaled_gyro_vector';
subfields = struct(...
'name', {'x_gyro','y_gyro','z_gyro'},...
'offset', {0,4,8},...
'type',{'single','single','single'},...
'units',{'rad/s','rad/s','rad/s'});
case '06'
fname = 'scaled_magnetometer_vector';
subfields = struct(...
'name', {'x_mag','y_mag','z_mag'},...
'offset', {0,4,8},...
'type',{'single', 'single', 'single'},...
'units', {'gauss','gauss','gauss'});
case '17'
fname = 'scaled_ambient_pressure';
case '07'
fname = 'delta_theta_vector';
case '08'
fname = 'delta_velocity_vector';
case '09'
fname = 'cf_orientation_matrix';
case '0A'
fname = 'cf_quaternion';
case '0C'
fname = 'cf_euler_angles';
subfields = struct(...
'name', {'roll','pitch','yaw'},...
'offset', {0,4,8},...
'type', {'single','single','single'},...
'units', {'radians','radians','radians'});
case '10'
fname = 'cf_stabilized_north_vector';
case '11'
fname = 'cf_stabilized_up_vector';
case '12'
fname = 'gps_correlation_timestamp';
subfields = struct(...
'name', {'gps_time_of_week','gps_week_number','timestamp_flags'},...
'offset', {0,8,10},...
'type', {'double','uint16','uint16'},...
'units', {'seconds','n/a','see manual'});
end
case '81' % GNSS Data
dname = 'GNSS';
switch dec2hex(fdesc,2)
case '03'
fname = 'llh_position';
subfields = struct(...
'name', {'latitude' ,...
'longitude',...
'height_above_ellipsoid',...
'height_above_msl',...
'horizontal_accuracy',...
'vertical_accuracy',...
'valid_flags'} ,...
'offset',{0,8,16,24,32,36,40},...
'type', {'double',...
'double',...
'double',...
'double',...
'single',...
'single',...
'uint16'},...
'units', {'decimal degrees',...
'decimal degrees',...
'meters',...
'meters',...
'meters',...
'meters',...
'see manual'});
case '04'
fname = 'position_eath_centered_earth_fixed_frame';
subfields = struct(...
'name', {'x_pos','y_pos','z_pos','pos_accuracy','valid_flags'},...
'offset', {0,8,16,24,28},...
'type', {'double','double','double','single','uint16'},...
'units',{'meters','meters','meters','meters','see manual'});
case '05'
fname = 'velocity_north_east_down_frame';
subfields = struct(...
'name', {'north',...
'east',...
'down',...
'speed',...
'ground_speed',...
'heading',...
'speed_accuracy',...
'heading_accuracy',...
'valid_flags'},...
'offset', {0,4,8,12,16,20,24,28,32},...
'type', {'single',...
'single',...
'single',...
'single',...
'single',...
'single',...
'single',...
'single',...
'uint16'},...
'units', {'m/sec',...
'm/sec',...
'm/sec',...
'm/sec',...
'm/sec',...
'decimal degrees',...
'm/sec',...
'decimal degrees',...
'see manual'});
case '06'
fname = 'velocity_eath_centered_earth_fixed_frame';
subfields = struct(...
'name', {'x_vel','y_vel','z_vel','vel_accuracy','valid_flags'},...
'offset', {0,4,8,12,16},...
'type', {'single','single','single','single','uint16'},...
'units',{'m/sec','m/sec','m/sec','m/sec','see manual'});
case '07'
fname = 'DOP_data';
case '08'
fname = 'UTC_time';
subfields = struct(...
'name',{'year','month','day','hour','minute','second',...
'millisecond','valid_flags'},...
'offset',{0,2,3,4,5,6,7,11},...
'type',{'uint16','uint8','uint8','uint8','uint8',...
'uint8','uint32','uint16'},...
'units',{'years','months','days','hours','minutes',...
'seconds','milliseconds','see manual'});
case '09'
fname = 'GPS_time';
subfields = struct(...
'name',{'time_of_week','week_number','valid_flags'},...
'offset',{0,8,10},...
'type',{'double','uint16','uint16'},...
'units',{'seconds','n/a','see manual'});
case '0A'
fname = 'clock_information';
case '0B'
fname = 'gnss_fix_information';
case '0C'
fname = 'space_vehicle_information';
case '0D'
fname = 'hardware_status';
case '0E'
fname = 'dgnss_information';
case '0F'
fname = 'dgnss_channel_status';
end
case '82' % Estimation Filter (Attitude) Data
dname = 'attitude';
switch dec2hex(fdesc,2)
case '10'
fname = 'filter_status';
case '11'
fname = 'gps_timestamp';
subfields = struct(...
'name', {'time_of_week' ,...
'week_number' ,...
'valid'} ,...
'offset',{0,8,10} ,...
'type', {'double' ,...
'uint16' ,...
'uint16'} ,...
'units', {'seconds' ,...
'n/a' ,...
'1=valid, 0=invalid'});
case '03'
fname = 'orientation_quaternion';
case '12'
fname = 'attitude_uncertainty_quaternion_elements';
case '05'
fname = 'orientation_euler_angles';
subfields = struct(...
'name', {'roll' ,...
'pitch' ,...
'yaw' ,...
'valid'} ,...
'offset',{0,4,8,12} ,...
'type', {'single' ,...
'single' ,...
'single' ,...
'uint16'} ,...
'units', {'radians' ,...
'radians' ,...
'radians' ,...
'1=valid, 0=invalid'});
case '0A'
fname = 'attitude_uncertainty_euler_angles';
case '04'
fname = 'orientation_matrix';
case '0E'
fname = 'compensated_angular_rate';
subfields = struct(...
'name', {'X' ,...
'Y' ,...
'Z' ,...
'valid'} ,...
'offset',{0,4,8,12} ,...
'type', {'single' ,...
'single' ,...
'single' ,...
'uint16'} ,...
'units', {'rads/sec' ,...
'rads/sec' ,...
'rads/sec' ,...
'1=valid, 0=invalid'});
case '06'
fname = 'gyro_bias';
case '0B'
fname = 'gyro_bias_uncertainty';
case '1C'
fname = 'compensated_acceleration';
case '0D'
fname = 'linear_acceleration';
subfields = struct(...
'name', {'X' ,...
'Y' ,...
'Z' ,...
'valid'} ,...
'offset',{0,4,8,12} ,...
'type', {'single' ,...
'single' ,...
'single' ,...
'uint16'} ,...
'units', {'m/sec^2' ,...
'm/sec^2' ,...
'm/sec^2' ,...
'1=valid, 0=invalid'});
case '21'
fname = 'pressure_altitude';
case '13'
fname = 'gravity_vector';
case '0F'
fname = 'wgs84_local_gravity_magnitude';
case '14'
fname = 'heading_update_source_state';
subfields = struct(...
'name', {'heading' ,...
'heading_1_sigma_uncertainty' ,...
'source' ,...
'valid'} ,...
'offset',{0,4,8,10} ,...
'type', {'single' ,...
'single' ,...
'uint16' ,...
'uint16'} ,...
'units', {'radians' ,...
'radians' ,...
'0=no source, 1=Magnetometer, 4=External',...
'1=valid, 0=invalid'});
case '15'
fname = 'magnetic_model_solution';
case '25'
fname = 'mag_auto_hard_iron_offset';
case '28'
fname = 'mag_auto_hard_iron_offset_uncertainty';
case '26'
fname = 'mag_auto_soft_iron_matrix';
case '29'
fname = 'mag_auto_soft_iron_matrix_uncertainty';
end
end
end % of imu_field_defs
end % of parse_imu()
|
github
|
dswinters/ocean-tools-master
|
parse_gps.m
|
.m
|
ocean-tools-master/parse_gps.m
| 9,096 |
utf_8
|
e30546642c7a5557b3113647b61ad60f
|
%% parse_gps.m
%
% Usage
% gps = parse_gps(files)
% gps = parse_gps(... , 'progress', uiprogressdlg)
%
% Inputs
% - f_in
% This can be a filename, cell array of filenames, the output of MATLAB's
% "dir" command.
%
% Name-value pair arguments
% - 'progress'
% Specify a UI progress dialog handle to update it with progress information while
% parsing data.
%
% Outputs
% - gps
% Data structure containing GPS fields. Each type of NMEA message gets its own
% sub-structure.
%
% Author
% - Dylan Winters ([email protected])
function GPS = parse_gps(f_in,varargin)
% Make a cell array of filenames from input
if isstruct(f_in)
f_in = fullfile({f_in.folder},{f_in.name});
elseif isstr(f_in)
f_in = {f_in};
end
%% Parse optional inputs
p = inputParser;
addParameter(p,'progress',struct(),@(x) isa(x,'matlab.ui.dialog.ProgressDialog'));
parse(p,varargin{:});
progress = p.Results.progress;
% The bulk of the text parsing is done using MATLAB's regexp function.
% First, create some regexps for types of data we might see in the text.
% The portions enclosed in parentheses will be extracted, while the rest
% is just used for matching. The regexp ',?' means that there might be a comma.
dec = ',?(-?\d+\.\d+),?'; % positive or negative float w/ decimal places, maybe comma-enclosed
int =@(n) sprintf(',?(-?\\d{%d}),?',n); % n-digit positive or negative integer, maybe comma-enclosed
intu = ',?(\d+),?'; % integer of unknown length, maybe comma-enclosed
ltr = ',?[a-zA-Z],?'; % any letter, maybe comma-enclosed
EW = ',?([ewEW]),?'; % 'E' or 'W', maybe comma-enclosed
% Combine the above regexps for single chunks of data into regexps
% for different types of complete NMEA strings:
fmt = struct();
fmt.HEHDT = ['\$HEHDT,' dec];
fmt.HEROT = ['\$HEROT,' dec];
fmt.GPGGA = ['\$GPGGA,' ...
int(2) int(2) dec ... % time
int(2) dec ltr ... % lat
int(3) dec EW ... % lon
intu intu ... % qual, #sats
dec dec ... % hdop, alt
ltr dec]; % alt units, undulation
fmt.GPHDT = ['\$GPHDT,' dec];
fmt.GPHEV = ['\$GPHEV,' dec];
fmt.GPRMC = ['\$GPRMC,' ...
int(2) int(2) dec ltr ...
int(2) dec ltr ...
int(3) dec EW ...
dec dec ...
int(2) int(2) int(2)];
fmt.GPZDA = ['\$GPZDA,' ...
int(2) int(2) dec ...
int(2) int(2) int(4)];
fmt.PASHR = ['\$PASHR,' ...
int(2) int(2) dec ...
dec ltr dec dec ...
dec dec dec];
fmt.PADCP = ['\$PADCP,' intu ...
int(4) int(2) int(2) ...
int(2) int(2) dec dec];
fmt.GPVTG = ['\$GPVTG,' dec, ltr, dec, ltr, dec, ltr, dec, ltr];
%% NMEA prefix-specific substitution filters
% Replace 'E' and 'W' in GPRMC/GPGGA matrices with '1' and '-1'
ewflt = struct('str',{'E','W','e','w'},'sub',{'1','-1','1','-1'});
filts = struct(...
'GPRMC', ewflt,...
'GPGGA', ewflt);
%% Function handles for extracting fields
%
% Each file is consecutively parsed for data from each NMEA type. All
% lines of a single NMEA type are extracted at once, into a matrix D
% with a row for each line and a column for each raw field. The
% function handles below provide instructions for converting this
% matrix into meaningful data.
%
% Defining this structure in this way allows for easy looping through NMEA
% prefixes and fields within each prefix.
%
flds = struct(...
'PASHR',struct(...
'dn', @(D) datenum([zeros(size(D,1),3) D(:,1:3)]) ,...
'head', @(D) D(:,4) ,...
'pitch', @(D) D(:,5) ,...
'roll', @(D) D(:,6) ,...
'yaw', @(D) D(:,7)) ,...
'GPGGA',struct(...
'dn', @(D) datenum([zeros(size(D,1),3) D(:,1:3)]) ,...
'lat', @(D) D(:,4) + D(:,5)/60 ,...
'lon', @(D) D(:,8).*(D(:,6) + D(:,7)/60) ,...
'alt', @(D) D(:,12) ,...
'geoid', @(D) D(:,13)) ,...
'HEHDT',struct(...
'head', @(D) D(:,1)) ,...
'GPHDT',struct(...
'head', @(D) D(:,1)) ,...
'GPHEV',struct(...
'heave', @(D) D(:,1)) ,...
'HEROT',struct(...
'rot', @(D) D(:,1)) ,...
'GPRMC',struct(...
'dn', @(D) datenum(D(:,[13 12 11 1 2 3])) + ...
datenum([2000 0 0 0 0 0]) ,...
'lat', @(D) D(:,4) + D(:,5)/60 ,...
'lon', @(D) D(:,8).*(D(:,6) + D(:,7)/60) ,...
'speed', @(D) D(:,9) * 0.514444 ,...
'course',@(D) D(:,10)) ,...
'GPZDA',struct(...
'dn', @(D) datenum(D(:,[6 5 4 1 2 3]))) ,...
'PADCP',struct(...
'num', @(D) D(:,1) ,...
'dn', @(D) datenum(D(:,[2:7])) - D(:,8)/86400) ,...
'GPVTG',struct(...
'course', @(D) D(:,1) ,...
'speed', @(D) D(:,3) * 0.514444));
nmea_types = fields(flds);
% % Check the opts struct for manually defined message formats
% if nargin > 1
% if isfield(opts,'fmt')
% msgs = fields(opts.fmt);
% for i = 1:length(msgs)
% fmt.(msgs{i}) = opts.fmt.(msgs{i});
% end
% end
% if isfield(opts,'flds')
% msgs = fields(opts.flds);
% for i = 1:length(msgs)
% flds.(msgs{i}) = opts.flds.(msgs{i});
% end
% end
% end
%% Initialize output structure
GPS = struct();
for i = 1:length(nmea_types)
prefix = nmea_types{i};
GPS.(prefix) = struct();
GPS.(prefix).lnum = [];
GPS.(prefix).fnum = [];
vars = fields(flds.(prefix));
for v = 1:length(vars)
GPS.(prefix).(vars{v}) = [];
end
end
%% Parse!
for fi = 1:length(f_in)
[~,fname,~] = fileparts(f_in{fi});
progress.Message = sprintf('GPS: Processing %s [%d of %d]',fname,fi,length(f_in));
ftxt = fileread(f_in{fi}); % read entire file text
for i = 1:length(nmea_types)
prefix = nmea_types{i};
%
[lines, start] = regexp(ftxt,fmt.(prefix),'tokens','start');
lines = cat(1,lines{:});
if ~isempty(lines)
% Apply substitution filters
if isfield(filts,prefix)
for iflt = 1:length(filts.(prefix))
lines(strcmp(lines,filts.(prefix)(iflt).str)) = ...
{filts.(prefix)(iflt).sub};
end
end
D = reshape(sscanf(sprintf('%s*',lines{:}),'%f*'),size(lines));
%
vars = fields(flds.(prefix));
% Grab line numbers by counting occurences of newline characters before
% the start of each line:
lnum = nan(size(D,1),1);
lnum(1) = 1 + length(regexp(ftxt(1:start(1)),'\n'));
for l = 2:length(lnum)
lnum(l) = lnum(l-1) + ...
length(regexp(ftxt(start(l-1):start(l)),'\n'));
end
GPS.(prefix).lnum = cat(1,GPS.(prefix).lnum,lnum);
GPS.(prefix).fnum = cat(1,GPS.(prefix).fnum,fi*ones(size(D,1),1));
% Populate struct with variables
for v = 1:length(vars)
GPS.(prefix).(vars{v}) = cat(1,GPS.(prefix).(vars{v}),...
flds.(prefix).(vars{v})(D));
end
end
end
progress.Value = fi/length(f_in);
end
GPS.files = cell(length(f_in),1);
for i = 1:length(f_in)
[~,fname,fext] = fileparts(f_in{i});
GPS.files{i} = [fname fext];
end
% Remove empty fields
for i = 1:length(nmea_types)
fld_flds = fields(GPS.(nmea_types{i}));
has_data = false;
for j = 1:length(fld_flds)
has_data = has_data | ~isempty(GPS.(nmea_types{i}).(fld_flds{j}));
end
if ~has_data
GPS = rmfield(GPS,nmea_types{i});
end
end
% Return empty array if no data
if isempty(setdiff(fields(GPS),{'files'}))
GPS = [];
end
end
|
github
|
dswinters/ocean-tools-master
|
gps_ltln2vel.m
|
.m
|
ocean-tools-master/gps_ltln2vel.m
| 1,422 |
utf_8
|
73c9a76ae21cd1dbc32b55367a3a3a3c
|
%% nav_ltln2vel.m
% Usage: nav_ltln2vel(lt,ln,dn)
% Description: Convert the lat and lon measurements in LT and
% LN to an xy grid centered at the mean location.
% Create a timeseries of x&y velocity based on the
% timestamps in DN.
% Inputs: lt - latitude (degrees north)
% ln - longitude (degrees east)
% dn - matlab datenum
% Outputs: vx,vy - velocities, east & north
%
% Author: Dylan Winters
% Created: 2016-09-16
function [vx, vy] = gps_ltln2vel(lt,ln,dn)
% remove non-unique timestamps
dn0 = dn;
[~,idx] = unique(dn);
dn = dn(idx);
lt = lt(idx);
ln = ln(idx);
% remove NaNs
idx = ~isnan(dn.*lt.*ln);
dn = dn(idx);
lt = lt(idx);
ln = ln(idx);
% convert to xy (need at least 2 non-nan points)
if sum(idx)>2
wgs84 = referenceEllipsoid('wgs84','m');
lt0 = nanmean(lt);
ln0 = nanmean(ln);
lt2y = distance('rh',lt0-0.5,ln0,lt0+0.5,ln0,wgs84);
ln2x = distance('rh',lt0,ln0-0.5,lt0,ln0+0.5,wgs84);
% lt2y = abs(40000000/360) ; % meters N/S per degree N
% ln2x = scl*cosd(lt0) ; % meters E/W per degree W at latitude lt0
y = lt2y * (lt-lt0) ; % meters N/S
x = ln2x * (ln-ln0) ; % meters E/W
dt = diff(dn)*86400;
t = dn(1:end-1) + diff(dn)/2;
vx = interp1(t, diff(x)./dt, dn0,'linear','extrap');
vy = interp1(t, diff(y)./dt, dn0,'linear','extrap');
else
vx = nan*dn0;
vy = nan*dn0;
end
|
github
|
ku-ya/OccGridMap_Matlab-master
|
EISM.m
|
.m
|
OccGridMap_Matlab-master/2D_matlab/EISM.m
| 1,516 |
utf_8
|
e6809e72d3f66308b6a75a0e63021ad2
|
% 1 beam case for exact inverse sensor model occGrid
% parameters
function ogmap = EISM(ogmap,range,free,X_t,param)
% L = 1; % world size
% dx = param.resol; % grid size
% sigma = param.sigma; % sensor
% create measurements and pose
%%
% nz = length(free);
% idx = zeros(1,nz); idz = idx;
% for j = 1:nz
% get reduced map
% [~, idx(j)] = min(abs(m_cm - X_t(j)));
% [~, idz(j)] = min(abs(m_cm - X_t(j) - Z_t(j)));
if size(free,1)<20
return;
end
Prtl = zeros(length(free),1);
for k = 1:length(free)
Prtl(k) = ogmap(k);
end
nr = length(Prtl);
% Initialize
Prtl(1) = 0;
Prtl(nr+1) = 1;
% get distance to each gird in rtl
distance = sqrt((free(:,1)-X_t(1)).^2+(free(:,2)-X_t(2)).^2);
pz_xr = sensorFM(nr,param.resol,range,param); % forward sensor model PDF
% clf
% plot((1:nr+1)/dx,pz_xr)
a = zeros(1,nr); b = a; c = a; d = a;
Pr_zxz = a; Pnr_zxz = a;
for k = 1:nr
if k == 1
a(1) = 0; b(1) = 1;c(1)=pz_xr(1);
else
a(k) = a(k-1) + b(k-1)*pz_xr(k-1)*Prtl(k-1);
b(k) = b(k-1)*(1-Prtl(k-1));
c(k) = b(k)*pz_xr(k);
end
end
d(nr) = 0;
for p = 1:nr-1
k = nr - p;
d(k) = d(k+1) + b(k)*pz_xr(k + 1)*Prtl(k + 1);
end
for k = 1:nr
Pr_zxz(k) = a(k) + c(k);
Pnr_zxz(k) = a(k) + d(k);
end
for k = 1:nr
e = Prtl(k)*Pr_zxz(k);
f = (1-Prtl(k))*Pnr_zxz(k);
if ~isnan(e/(e+f))
ogmap(k) = e/(e+f);
else
ogmap(k) = e/(e+f+eps);
end
end
for k = 1:size(free,1)
x(k) = ogmap(k);
end
% clf
% plot(x)
% pause(0.5)
% end
|
github
|
ku-ya/OccGridMap_Matlab-master
|
AISM.m
|
.m
|
OccGridMap_Matlab-master/2D_matlab/AISM.m
| 1,237 |
utf_8
|
dfc47527f8755a13591a7d99023fb78a
|
% 1 beam case for exact inverse sensor model occGrid
% parameters
function ogmap = AISM(ogmap,range,free,X_t,param)
% L = 1; % world size
% dx = param.resol; % grid size
% create measurements and pose
%%
% nz = length(free);
% idx = zeros(1,nz); idz = idx;
% for j = 1:nz
% get reduced map
% [~, idx(j)] = min(abs(m_cm - X_t(j)));
% [~, idz(j)] = min(abs(m_cm - X_t(j) - Z_t(j)));
if size(free,1)<20
return;
end
Prtl = zeros(length(free),1);
for k = 1:length(free)
Prtl(k) = ogmap(k);
end
nr = length(Prtl);
% Initialize
Prtl(1) = 0;
Prtl(nr+1) = 1;
% get distance to each gird in rtl
distance = sqrt((free(:,1)-X_t(1)).^2+(free(:,2)-X_t(2)).^2);
pz_xr = sensorFM(nr,param.resol,range,param); % forward sensor model PDF
sigma = param.sigma;
rho = param.rho;
pr_zx = zeros(nr,1);
for k = 1:nr
if k<=ceil((range-param.rangelim)/range*nr)
pr_zx(k) = 0.3+(rho/(sigma*sqrt(2*pi()))+0.2)*exp(-1/2*(((range-param.rangelim-range*k/nr))/sigma)^2);
else
pr_zx(k) = 0.5+(rho/(sigma*sqrt(2*pi())))*exp(-1/2*((range-param.rangelim-range*k/nr)/sigma)^2);
end
end
for k = 1:length(Prtl)-1
ogmap(k) = real(log(Prtl(k)/(1-Prtl(k))) + log(pr_zx(k)/(1-pr_zx(k))));
end
ogmap = 1 - 1./(1+exp(ogmap));
|
github
|
ku-ya/OccGridMap_Matlab-master
|
occGridMapping.m
|
.m
|
OccGridMap_Matlab-master/2D_matlab/occGridMapping.m
| 4,508 |
utf_8
|
c3f68f15f6ecc50d8492c7712ca01ca8
|
% Robotics: Estimation and Learning
% WEEK 3
%
% Complete this function following the instruction.
function [myMap, H, IG]= occGridMapping(ranges, scanAngles, pose, param)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% Parameters
%
% % the number of grids for 1 meter.
myResol = param.resol;
% % the initial map size in pixels
myMap = 0.5*ones(param.size);
% % the origin of the map in pixels
myorigin = param.origin;
%
% % 4. Log-odd parameters
% lo_occ = param.lo_occ;
% lo_free = param.lo_free;
% lo_max = param.lo_max;
% lo_min = param.lo_min;
N = size(pose,2);
for j = 1:N+param.final_frame % for each time,
xt = pose(:,j);
lidar_local_plt = [ranges(:,j).*cos(scanAngles + xt(3)) -ranges(:,j).*...
sin(scanAngles + xt(3))];
ranges(:,j) = ranges(:,j) + param.rangelim;
lidar_local = [ranges(:,j).*cos(scanAngles + xt(3)) -ranges(:,j).*...
sin(scanAngles + xt(3))];
xtg = [ceil(xt(1)*myResol)+myorigin(1),ceil(xt(2)*myResol)+...
myorigin(2)];
myMap(xtg(1),xtg(2)) = 0;
%
%
% % Find grids hit by the rays (in the gird map coordinate)
for k = 1:param.ray_skip:length(scanAngles)-1
rtl = ceil(lidar_local(k,:)*param.resol);
[freex, freey] = bresenham(xtg(1),xtg(2),xtg(1)+rtl(1),...
xtg(2)+rtl(2));
%
% % Find occupied-measurement cells and free-measurement cells
% % convert to 1d index
% free = sub2ind(size(myMap),freey,freex);
% % set end point value
% map(occ(2),occ(1)) = 3;
% % set free cell values
% myMap(free) = 1;
myMaptemp = ones(length(freex),1);
for l = 1:length(freex)
myMaptemp(l) = myMap(freex(l),freey(l));
end
if param.ISM == 'EISM'
myMaptemp = EISM(myMaptemp,ranges(k,j),[freex,freey],xtg,param);
elseif param.ISM == 'AISM'
myMaptemp = AISM(myMaptemp,ranges(k,j),[freex,freey],xtg,param);
end
for l = 1:length(freex)
if myMap(freex(l),freey(l))< param.lo_occ
myMap(freex(l),freey(l))=myMaptemp(l);
else
break
end
end
end
% % Update the map
%
%
% % Saturate the map?
%
myMap(myMap>=param.lo_occ) = 1;
%
% % Visualize the map as needed
if param.fig == 1
caxis([0.2 0.8])
imagesc(1-myMap);colormap('gray');hold on
% Make a truecolor all-green image.
green = cat(3, zeros(size(myMap)),...
ones(size(myMap)), zeros(size(myMap)));
hold on;
h = imshow(green);
hold off;
% Use our influence map as the
% AlphaData for the solid green image.
I = zeros(size(myMap));
for k = 1:length(lidar_local_plt)
I(ceil((lidar_local_plt(k,1)+xt(1))*param.resol)+param.origin(1),...
ceil((lidar_local_plt(k,2)+xt(2))*param.resol)+param.origin(2))=1;
end
set(h, 'AlphaData', I)
axis equal;hold on;
plot(xt(2)*param.resol+param.origin(2),xt(1)*param.resol+param.origin(1),'ro','linewidth',2,'MarkerSize',8);
axis tight;
set(gcf, 'PaperPosition', [-1.25 -0.5 6 5.5]); %Position the plot further to the left and down. Extend the plot to fill entire paper.
set(gcf, 'PaperSize', [3.7 4.7]); %Keep the same paper size
saveas(gcf, [param.ISM,'/',param.ISM,'_', sprintf('Image_%d',j)], 'pdf')
% print([param.ISM, '/' ,sprintf('Image_%d',j)],'-dpng');
clf;
mapGridEntropy = -(myMap.*log(myMap) + (1 - myMap).*log(1-myMap));
clims = [0.0 0.8];
imagesc(mapGridEntropy,clims);colormap('jet');%hold on;
% caxis([min(min(mapGridEntropy)) 1])
axis equal;axis tight;axis off;
set(gcf, 'PaperPosition', [-1.25 -0.5 6 5.5]);
set(gcf, 'PaperSize', [3.7 4.7]); %Keep the same paper size
saveas(gcf, [param.ISM,'/',param.ISM,'_',sprintf('Image_inf_%d',j)], 'pdf')
% print([param.ISM, '/' ,sprintf('Image_inf_%d',j)],'-dpng');
pause(param.pause)
clf;
end
H(j) = mapEntropy(myMap);
if j==1
IG(1) = 0;
else
IG(j) = H(j-1) - H(j);
end
% if param.ISM == 'EISM'
% x = 1
% end
% plot(lidar_local(:,1)+xt(1),lidar_local(:,2)+xt(2),'-x'); hold on;
% pause(0.2)
%
end
end
|
github
|
ku-ya/OccGridMap_Matlab-master
|
occGridMapping.m
|
.m
|
OccGridMap_Matlab-master/1D_matlab/occGridMapping.m
| 770 |
utf_8
|
ea8449de0c55ed97267c2b91ee9e47e2
|
% 1D case for exact inverse sensor model occGrid
function myMap = occGridMapping(ranges, scanAngles, pose, param)
%%
% Parameters
%
% % the number of grids for 1 meter.
% myResol = param.resol;
% % the initial map size in pixels
% myMap = zeros(param.size);
% % the origin of the map in pixels
% myorigin = param.origin;
%
% % 4. Log-odd parameters
% lo_occ = param.lo_occ;
% lo_free = param.lo_free;
% lo_max = param.lo_max;
% lo_min = param.lo_min;
i = 1
% N = size(pose,2);
% for j = 1:N % for each time,
%
%
% % Find grids hit by the rays (in the gird map coordinate)
%
%
% % Find occupied-measurement cells and free-measurement cells
%
%
% % Update the log-odds
%
%
% % Saturate the log-odd values
%
%
% % Visualize the map as needed
%
%
% end
|
github
|
zhangaigh/rovio-standalone-master
|
loadCalibrationCamToCam.m
|
.m
|
rovio-standalone-master/tools/kitti_tool/loadCalibrationCamToCam.m
| 1,894 |
utf_8
|
88db832a2338f205ea36b1a9f6231aed
|
function calib = loadCalibrationCamToCam(filename)
% open file
fid = fopen(filename,'r');
if fid<0
calib = [];
return;
end
% read corner distance
calib.cornerdist = readVariable(fid,'corner_dist',1,1);
% read all cameras (maximum: 100)
for cam=1:100
% read variables
S_ = readVariable(fid,['S_' num2str(cam-1,'%02d')],1,2);
K_ = readVariable(fid,['K_' num2str(cam-1,'%02d')],3,3);
D_ = readVariable(fid,['D_' num2str(cam-1,'%02d')],1,5);
R_ = readVariable(fid,['R_' num2str(cam-1,'%02d')],3,3);
T_ = readVariable(fid,['T_' num2str(cam-1,'%02d')],3,1);
S_rect_ = readVariable(fid,['S_rect_' num2str(cam-1,'%02d')],1,2);
R_rect_ = readVariable(fid,['R_rect_' num2str(cam-1,'%02d')],3,3);
P_rect_ = readVariable(fid,['P_rect_' num2str(cam-1,'%02d')],3,4);
% calibration for this cam completely found?
if isempty(S_) || isempty(K_) || isempty(D_) || isempty(R_) || isempty(T_)
break;
end
% write calibration
calib.S{cam} = S_;
calib.K{cam} = K_;
calib.D{cam} = D_;
calib.R{cam} = R_;
calib.T{cam} = T_;
% if rectification available
if ~isempty(S_rect_) && ~isempty(R_rect_) && ~isempty(P_rect_)
calib.S_rect{cam} = S_rect_;
calib.R_rect{cam} = R_rect_;
calib.P_rect{cam} = P_rect_;
end
end
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function A = readVariable(fid,name,M,N)
% rewind
fseek(fid,0,'bof');
% search for variable identifier
success = 1;
while success>0
[str,success] = fscanf(fid,'%s',1);
if strcmp(str,[name ':'])
break;
end
end
% return if variable identifier not found
if ~success
A = [];
return;
end
% fill matrix
A = zeros(M,N);
for m=1:M
for n=1:N
[val,success] = fscanf(fid,'%f',1);
if success
A(m,n) = val;
else
A = [];
return;
end
end
end
|
github
|
zhangaigh/rovio-standalone-master
|
loadCalibrationRigid.m
|
.m
|
rovio-standalone-master/tools/kitti_tool/loadCalibrationRigid.m
| 855 |
utf_8
|
9148661cd7335b41dace4f57bd25b3a4
|
function Tr = loadCalibrationRigid(filename)
% open file
fid = fopen(filename,'r');
if fid<0
error(['ERROR: Could not load: ' filename]);
end
% read calibration
R = readVariable(fid,'R',3,3);
T = readVariable(fid,'T',3,1);
Tr = [R T;0 0 0 1];
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function A = readVariable(fid,name,M,N)
% rewind
fseek(fid,0,'bof');
% search for variable identifier
success = 1;
while success>0
[str,success] = fscanf(fid,'%s',1);
if strcmp(str,[name ':'])
break;
end
end
% return if variable identifier not found
if ~success
A = [];
return;
end
% fill matrix
A = zeros(M,N);
for m=1:M
for n=1:N
[val,success] = fscanf(fid,'%f',1);
if success
A(m,n) = val;
else
A = [];
return;
end
end
end
|
github
|
skovnats/madmm-master
|
procXYmnp21_n_pairwise.m
|
.m
|
madmm-master/functional_maps_L21norm/procXYmnp21_n_pairwise.m
| 7,338 |
utf_8
|
63dc2bbfce6e5091a94b8628539da790
|
function [X cXY nnr nns rrho times]=procXYmnp21_n_pairwise(FourCoeffs,mu,Ps,X0,niter,initer)
% Computes the solution of
% min_{Xi'Xi=I} for i=1:n-1 for j=i+1:n
% mu*| FourCoeffs{i}*Xi-FourCoeffs{j}*Xj |_2,1 }
% end end
% + for i=1:n
% + (|off(Xi'*Ps{i}*Xi)|^2
% end
%
%
% Method: MADMM with fixed number of iterations
% Simultaneous minimization on X,Y
%
% INPUT:
% FourCoeffs: are m x n matrices where m>=n (for example: Fourier coefficients)
% mu: weight factor for first part of cost function. mu>0
% Ps: are n x n- symmetric (for example: diagonal with eigenvalues on
% diagonal
% X0: X0.X1 and X0.X2.. are start matrices n x p where n >= p. For example,
% random initialization.
%
% OUTPUT:
% X: X.Xi are solution matrices, m x n
% cXY: history of cost function (outer iteration)
%%
if ~exist('niter','var')
niter=50;
end
if ~exist('initer','var')
initer=50;
end
[m,n]=size(FourCoeffs{1,1,2});
p=size(X0.X1,2);
% initialization
X=X0;
%% number of shapes
Ns=length(FourCoeffs);
%% initialization of the statistics
for ii=1:(Ns-1)
for jj=(ii+1):Ns
eval(sprintf('nnr{%d,%d}=[];',ii,jj));
eval(sprintf('nns{%d,%d}=[];',ii,jj));
eval(sprintf('rrho{%d,%d}=[];',ii,jj));
end
end
%% For each a pair of shapes I need a separate Z variable
for ii=1:(Ns-1)
for jj=(ii+1):Ns
eval(sprintf('Z{%d,%d}=FourCoeffs{%d,%d,%d}*X.X%d-FourCoeffs{%d,%d,%d}*X.X%d;',...
ii,jj,ii,ii,jj,ii,jj,ii,jj,jj));
eval(sprintf('Zk{%d,%d}=Z{%d,%d};',ii,jj,ii,jj));
eval(sprintf('U{%d,%d}=ones(m,p);',ii,jj));
eval(sprintf('rho{%d,%d}=1;',ii,jj)); %initial penalty parameter
end
end
%% Cost functions
cXY=costfXY(FourCoeffs,mu,Ps,X0,Ns);
%% Define the manifold
for ii=1:(Ns)
eval(sprintf('Man.X%d=stiefelfactory(n,p);',ii));
end
problem.M = productmanifold(Man);
%% Define the cost
% ITERATION: Solve
times=[0];
tic;
for i=1:niter
%% opt w.r.t X
%% X is a structure, which has fields: Xi
X=iterXY(FourCoeffs,mu,Ps,X,Z,U,rho,problem,initer,Ns,0,0);
%% upd Z
for ii=1:(Ns-1)
for jj=(ii+1):Ns
eval(sprintf('Z{%d,%d}=iterZ(FourCoeffs{%d,%d,%d},FourCoeffs{%d,%d,%d},X.X%d,X.X%d,U{%d,%d},rho{%d,%d},mu,p);',...
ii,jj,ii,ii,jj,jj,ii,jj,ii,jj,ii,jj,ii,jj));
end
end
%% upd U
for ii=1:(Ns-1)
for jj=(ii+1):Ns
eval(sprintf('U{%d,%d}=U{%d,%d}+FourCoeffs{%d,%d,%d}*X.X%d-FourCoeffs{%d,%d,%d}*X.X%d-Z{%d,%d};',...
ii,jj,ii,jj,ii,ii,jj,ii,jj,ii,jj,jj,ii,jj));
end
end
%% updating the rho penalty
for ii=1:(Ns-1)
for jj=(ii+1):Ns
eval(sprintf('R=FourCoeffs{%d,%d,%d}*X.X%d-FourCoeffs{%d,%d,%d}*X.X%d-Z{%d,%d};',ii,ii,jj,ii,jj,ii,jj,jj,ii,jj));
eval(sprintf('S=rho{%d,%d}*([FourCoeffs{%d,%d,%d}'';-FourCoeffs{%d,%d,%d}'']*(Z{%d,%d}-Zk{%d,%d}));',ii,jj,ii,ii,jj,jj,ii,jj,ii,jj,ii,jj));
%%
nr=norm(R,'fro');
ns=norm(S,'fro');
%%
if nr>=10*ns
eval(sprintf('rho{%d,%d}=2*rho{%d,%d};',ii,jj,ii,jj));
eval(sprintf('U{%d,%d}=U{%d,%d}/2;',ii,jj,ii,jj));
end
if ns > 10*nr
eval(sprintf('rho{%d,%d}=rho{%d,%d}/2;',ii,jj,ii,jj));
eval(sprintf('U{%d,%d}=2*U{%d,%d};',ii,jj,ii,jj));
end
%% saving the stats
eval(sprintf('nnr{%d,%d}=[nnr{%d,%d};nr];',ii,jj,ii,jj));
eval(sprintf('nns{%d,%d}=[nns{%d,%d};ns];',ii,jj,ii,jj));
eval(sprintf('rrho{%d,%d}=[rrho{%d,%d};rho{%d,%d}];',ii,jj,ii,jj,ii,jj));
end
end
%% upd variables Zij
for ii=1:(Ns-1)
for jj=(ii+1):Ns
% The "previous" Z matrix
eval(sprintf('Zk{%d,%d}=Z{%d,%d};',ii,jj,ii,jj));
end
end
%% SAVE THE COST
cXY=[cXY;costfXY(FourCoeffs,mu,Ps,X,Ns)];
%%
fprintf('%d:%f\n',i,cXY(end));
%%
times=[times;toc];
end
end
function X=iterXY(FourCoeffs,mu,Ps,X,Z,U,rho,problem,initer,ns,gV,gW)
% minimizes sum_ij mu| Zij |_2,1 + rho/2*| FourCoeffiXi-FourCoeffjXj-Zij+Uij |^2+
% + sum_i norm(off(Xi'Ps{i}Xi),'fro')^2
% with respect to Xi'*Xi=I
% by performing some steps with Manopt minimization
%% Define the options
% Stopfunction
options.stopfun = @mystopfun;
function stopnow = mystopfun(problem, x, info, last)
stopnow = (last >= 3 && (info(last-2).cost - info(last).cost)/info(last).cost < 1e-3);
end
options.maxiter=initer; % 50 -> Klaus parameter
% Switch on/off details of inner iteration
options.verbosity=0;
% Define the problem cost function and its gradient.
problem.cost = @cost ;
function f=cost(X)
f=0;
for ii=1:(ns-1)
for jj=(ii+1):ns
eval(sprintf('f=f+0.5*rho{%d,%d}*norm(FourCoeffs{%d,%d,%d}*X.X%d-FourCoeffs{%d,%d,%d}*X.X%d-Z{%d,%d}+U{%d,%d},''fro'')^2;',...
ii,jj,ii,ii,jj,ii,jj,ii,jj,jj,ii,jj,ii,jj));
end
end
for ii=1:(ns)
eval(sprintf('f=f+norm(off(X.X%d''*Ps{%d}*X.X%d),''fro'')^2;',ii,ii,ii));
end
end
problem.grad = @(X) problem.M.egrad2rgrad(X, egrad_n(X));
function g=egrad_n(X)
% F=B*X.W+Z-U;
% G=A*X.V-Z+U;
%
% g.V=zeros(n,p);
% g.V=g.V+rho*A'*(A*X.V-F);
% g.V=g.V+4*P*X.V*off(X.V'*P*X.V);
%
% g.W=zeros(n,p);
% g.W=g.W+rho*B'*(B*X.W-G);
% g.W=g.W+4*Q*X.W*off(X.W'*Q*X.W);
%% Gradient of the off-diag part
for ii=1:(ns)
eval(sprintf('g.X%d=4*Ps{%d}*X.X%d*off(X.X%d''*Ps{%d}*X.X%d);',ii,ii,ii,ii,ii,ii));
end
%% Graident on the rho/2|AXi-BXj-Zij+Uij| part
for ii=1:(ns-1)
for jj=(ii+1):ns
eval(sprintf('[gV,gW]=egrad(FourCoeffs{%d,%d,%d},Ps{%d},X.X%d,FourCoeffs{%d,%d,%d},Ps{%d},X.X%d,Z{%d,%d},U{%d,%d},rho{%d,%d});',...
ii,ii,jj,ii,ii,jj,ii,jj,jj,jj,ii,jj,ii,jj,ii,jj));
eval(sprintf('g.X%d=g.X%d+gV;',ii,ii));
eval(sprintf('g.X%d=g.X%d+gW;',jj,jj));
end
end
%
end
% Numerically check the differential
% checkgradient(problem);
% pause;
%}
% Solve.
%[X, Xcost, info, options] = trustregions(problem,X0,options);
[X, Xcost, infoS, options] = conjugategradient(problem,X,options);
end
function Z=iterZ(A,B,V,W,U,rho,mu,p)
% minimizes mu*|| Z ||_2,1 + rho/2*|| -A*X.V+B*X.W+Z-U ||^2
% with respect to Z by "shrinking"
F=A*V-B*W+U;
for j=1:p
Z(:,j)=Shrink(F(:,j),mu/rho);
end
function [z]=Shrink(z,l)
nz=norm(z);
z=max(nz-l,0)*z/nz;
end
end
function Y=off(X)
Y=X-diag(diag(X));
end
function f=costfXY(FourCoeffs,mu,Ps,X,ns)
f=0;
for ii=1:(ns-1)
eval(sprintf('V=X.X%d;',ii));
for jj=(ii+1):ns
A=FourCoeffs{ii,ii,jj};
B=FourCoeffs{jj,ii,jj};
eval(sprintf('W=X.X%d;',jj));
C=A*V-B*W;
c=sqrt(sum(C.*C));
f=f+mu*sum(c);
end
end
for ii=1:(ns)
% eval(sprintf('V=X.X%d;',ii));
eval(sprintf('f=f+norm(off(X.X%d''*Ps{%d}*X.X%d),''fro'')^2;',ii,ii,ii));
end
end
function [gV,gW]=egrad(A,P,V,B,Q,W,Z,U,rho)
F=B*W+Z-U;
G=A*V-Z+U;
% gV=zeros(n,p);
gV=rho*A'*(A*V-F);
% gV=gV+4*P*V*off(V'*P*V);
% gW=zeros(n,p);
gW=rho*B'*(B*W-G);
% gW=gW+4*Q*W*off(W'*Q*W);
end
|
github
|
skovnats/madmm-master
|
d_shape.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/d_shape.m
| 1,489 |
utf_8
|
101bd876cf423a57e4af57e52beeaa64
|
function [d] = d_shape(shape, src_idx)
if ismac
d=d_shape2(shape,src_idx);
else
num_vert = length(shape.X);
src = repmat(Inf, num_vert, 1);
src(src_idx) = 0;
d = fastmarch(shape.TRIV, shape.X, shape.Y, shape.Z, double(src), set_options('mode', 'single'));
% d = fastmarchmex('init', int32(TRIV-1), double(X(:)), double(Y(:)), double(Z(:)));
% f = fastmarchmex('march',f,double(u));
end % function [d] = d_shape(shape, src_id
end
function [d] = d_shape2(shape, src_idx)
if ismac
num_vert = length(shape.X);
src = repmat(Inf, num_vert, 1);
src(src_idx) = 0;
%-
f = fastmarchmex('init', int32(shape.TRIV-1), double(shape.X(:)), double(shape.Y(:)), double(shape.Z(:)));
d = fastmarchmex('march', f, double(src));
d(d>=9999999) = Inf;
% d = fastmarch(shape.TRIV, shape.X, shape.Y, shape.Z, double(src), set_options('mode', 'single'));
% d = fastmarchmex('init', int32(TRIV-1), double(X(:)), double(Y(:)), double(Z(:)));
% f = fastmarchmex('march',f,double(u));
fastmarchmex('deinit', f);
else
num_vert = length(shape.X);
src = repmat(Inf, num_vert, 1);
src(src_idx) = 0;
d = fastmarch(shape.TRIV, shape.X, shape.Y, shape.Z, double(src), set_options('mode', 'single'));
% d = fastmarchmex('init', int32(TRIV-1), double(X(:)), double(Y(:)), double(Z(:)));
% f = fastmarchmex('march',f,double(u));
end % function [d] = d_shape(shape, src_id
end
|
github
|
skovnats/madmm-master
|
args2struct.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/args2struct.m
| 510 |
utf_8
|
961ec8b35e883660b8b208bf814011dd
|
% Convert argument pairs to a structure
function S = args2struct(varargin)
% No inputs, return empty structure
if isempty(varargin), S = struct(); return; end
if length(varargin) == 1 && isempty(varargin{1}), S = struct(); return; end
% Need pairs of inputs
if mod(length(varargin),2)==1
error('number of arguments must be even');
end
% Odd elements of varargin are fields, even ones are values
% Store all field names in lower case
for k = 1:2:length(varargin)
S.(varargin{k}) = varargin{k+1};
end
|
github
|
skovnats/madmm-master
|
dsh.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/dsh.m
| 3,071 |
utf_8
|
21a9744f4519ebab630e1b0e49935922
|
% script for dispalying shape
function [] = dsh( varargin )
% input:
%{
{1} - title
{2} - if save
%}
vector = false;
flag = true;
name = [];
switch nargin
case 1
shape = varargin{ 1 };
case 2
shape = varargin{ 1 };
tname = varargin{ 2 };
if isnumeric(tname)
vector = true;
if size(tname,2)>1
for i=1:size(tname)
dsh(shape,tname(:,i));
title(num2str(i));
waitforbuttonpress;
end
end
else
vector = false;
end
case 3
shape = varargin{ 1 };
tname = varargin{ 2 };
issave = varargin{ 3 };
if isnumeric(tname)
vector = true;
else
vector = false;
end
if isstr( issave )
name = issave;
issave = false;
end
case 4
shape = varargin{ 1 };
tname = varargin{ 2 };
vector = true;
name = varargin{ 3 };
issave = varargin{ 4 };
end
if iscell( shape )
flag = false;
for i = 1:length( shape )
dsh( shape{ i } );
title( sprintf( 'shape %d/%d', i, length( shape ) ) );
waitforbuttonpress;
end
end
if flag
if ~vector
% displaying
if ~isfield( shape, 'C' )
trisurf( shape.TRIV, shape.X, shape.Y, shape.Z, ones(size((shape.X))) ), ...
end
else
trisurf( shape.TRIV, shape.X, shape.Y, shape.Z, full(tname) ), ...
end
if isfield( shape, 'C' )
if ~vector
try
%%
% UPD: 15.11.2011
lab = [shape.L, shape.a, shape.b];
lab = colorspace( 'lab->rgb', lab );
shape.C = lab;
trisurf( shape.TRIV, shape.X, shape.Y, shape.Z, 1:(length(shape.X)) )
catch
trisurf( shape.TRIV, shape.X, shape.Y, shape.Z, 1:(length(shape.X)) )
end
%%
colormap(shape.C),axis off, axis image;
% colormap(ones( length(shape.X), 3 )),
end
axis off,
% axis image,
shading interp;
% lighting phong, camlight('headlight'); % was commented
else
if ~vector
colormap(ones( length(shape.X), 3 )),
end
axis off, axis image;
axis off, axis image, shading flat, lighting phong, camlight('headlight');
% axis off, axis image, shading interp, lighting phong, camlight('headlight');
end
switch nargin
case 2
if ~vector
title(tname);
end
case 3
if ~vector
title(tname);
end
%
if isstr( name )
title(name);
end
if issave
saveas( gcf, [tname '.png'] )
end
case 4
title( name );
if issave
saveas( gcf, [name '.png'] )
end
end
cameratoolbar
set(gcf,'Color','w');
%%
% axon;
end
|
github
|
skovnats/madmm-master
|
dstcalc.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/dstcalc.m
| 3,459 |
utf_8
|
7e2aa60336c6d4919af9f2637029f662
|
function [varargout] = dstcalc(method, varargin)
% functioon does distance calculations
% inputs:
% s = dstcalc( 'init', 'diffusion', shape, t, opt );
% d = dstcalc( 'compute', s, sample, shape );
switch lower(method)
case 'init'
type = varargin{1};
s = init_distance(type, varargin(2:end));
varargout(1) = { s };
case 'compute'
s = varargin{1};
d = compute_distance(s, varargin(2:end));
varargout(1) = { d };
case 'deinit'
s = varargin{1};
deinit_distance(s);
end
function [evals evecs areas W] = compute_spectrum(surface, Nvec)
% Compute Laplacian matrices
opt = giveopt( 'nastya', Nvec );
opt.COLOR_W = 0;
[ W, Am, evecs, evals ] = DLBO( surface, opt );
areas = full( diag( Am ) );
nrm = sqrt(areas(:)'*evecs.^2);
evecs = bsxfun(@rdivide, evecs, nrm); % normalizing evecss
function s = init_distance(type, varargin)
if isfield(varargin{1}{1}, 'type'),
s = varargin{1}{1};
else
s = [];
% here need to add an option of adding the eigendata
if length( varargin{1} ) > 2
s = varargin{1}{3};
end
surface = varargin{1}{1};
s.Nv = length(surface.X);
end
s.type = type;
switch lower(type)
case 'geodesic'
% s.handle = fastmarchmex('init', int32(surface.TRIV-1), double(surface.X(:)), double(surface.Y(:)), double(surface.Z(:)));
% s.handle = fastmarchmex('init', int32(surface.TRIV-1), double(surface.X(:)), double(surface.Y(:)), double(surface.Z(:)));
case 'diffusion'
t = varargin{1}{2};
s.kernel = @(lambda)(exp(-t*lambda));
if ~isfield(s, 'evecs')
[s.evals, s.evecs s.areas] = compute_spectrum(surface, 200);
end
case 'commute'
s.kernel = @(lambda)(1./sqrt(lambda));
if ~isfield(s, 'evecs')
[s.evals, s.evecs s.areas] = compute_spectrum(surface, 200);
end
end
function d = compute_distance(s, varargin)
sample = varargin{1}{1};
switch lower(s.type)
case 'geodesic'
shape=varargin{1}{2};
%{
source = repmat(Inf, [s.Nv 1]);
source(sample) = 0;
% TODO: this is extremely inefficient as the grid is re-initialized at
% each iteration!
d = fastmarch(shape.TRIV, shape.X, shape.Y, shape.Z, double(source), struct('mode', 'single'));
% d = fastmarchmex('march', s.handle, double(source));
d(d>=9999999) = Inf;
%}
%-
d = d_shape( shape, sample );
case {'diffusion', 'commute'}
kernel = s.kernel;
d = (bsxfun(@minus, s.evecs(sample,2:end), s.evecs(:,2:end)).^2)*(kernel(s.evals(2:end)).^2);
d = sqrt(d);
case 'euc'
shape=varargin{1}{2};
% TODO: this is extremely inefficient as the grid is re-initialized at
% each iteration!
d = sqrt((shape.X( sample ) - shape.X).^2+(shape.Y( sample ) - shape.Y).^2+(shape.Z( sample ) - shape.Z).^2);
% d = fastmarchmex('march', s.handle, double(source));
d(d>=9999999) = Inf;
end
function deinit_distance(s)
switch lower(s.type)
case 'geodesic'
fastmarchmex('deinit', s.handle);
case {'diffusion', 'commute'}
end
|
github
|
skovnats/madmm-master
|
parseOpt.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/parseOpt.m
| 985 |
utf_8
|
7ea99e27df68a53e6b0a2c74ff21cf17
|
% Construct options structure with a template
% S can be varargin or struct
% D are the default values struct
function T = parseOpt(D,varargin)
if length(varargin) == 1 && isstruct(varargin{1})
S = varargin{1};
else
S = args2struct(varargin{:});
end
T = D; % copy the template
if isempty(S)
return;
end
% Check arguments, must have two structures
if ~(isstruct(S) && isstruct(D))
error('input arguments must be structures');
end
fname = fields(S); % make a list of field names
% Loop over all fields in the template, copy matching values from S
for k = 1:length(fname)
% Process matching field names in S
if isfield(D,fname{k})
% Is this a substructure ?
if isstruct(T.(fname{k})) && isstruct(S.(fname{k}))
% Recursively process the substructure
T.(fname{k}) = parseOpt(T.(fname{k}),S.(fname{k}));
% Not a substructure, copy field value from S
else T.(fname{k}) = S.(fname{k});
end
end
end
end
|
github
|
skovnats/madmm-master
|
fastmarch.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/fastmarch.m
| 3,803 |
utf_8
|
ea85445d2c05290379ec7e62b3832ac8
|
% fastmarch Fast marching algorithm for geodesic distance approximation
%
% Usage:
%
% D = fastmarch(TRIV, X, Y, Z, [src], [opt])
% D = fastmarch(surface, [src], [opt])
%
% Description:
%
% Computes the geodesic distances on a triangulated surfaces using
% the fast marching algorithm. The algorithm may operate in two modes:
% single-source and multiple-source. In the single-source mode, a distance
% map of every mesh vertex from a single source is computed. The source
% may be a single point on the mesh, or any other configuration described
% by an initial set of values per mesh vertex. In the multiple-source
% mode, a matrix of pair-wise geodesic distances is computed between a
% specified set of mesh vertices.
%
% Input:
%
% TRIV - ntx3 triangulation matrix with 1-based indices (as the one
% returned by the MATLAB function delaunay).
% X,Y,Z - vectors with nv vertex coordinates.
% surface - alternative way to specify the mesh as a struct, having .TRIV,
% .X, .Y, and .Z as its fields.
% src - in the multiple-source mode: (default: src = [1:nv])
% list of ns mesh vertex indices to be used as sources.
% in the single-source mode: (must be specified)
% an nvx1 list of initial values of the distance function on the mesh
% (set a vertex to Inf to exclude it from the source set). src
% opt - (optional) settings
% .mode - Mode (default: 'multiple')
% 'multiple' - multiple-source
% 'single' - single-source
%
% Output:
%
% D - In the multiple-source mode:
% nsxns matrix of approximate geodesic distances, where D(i,j) is
% the geodesic distance between the i-th and the j-th point,
% whose indices are specified by src(i) and src(j),
% respectively.
% In the single-source mode:
% nvx1 vector of approximated geodesic distances, where D(i) is
% the geodesic distance from the i-th mesh vertex to the
% source.
%
% References:
%
% [1] R. Kimmel and J. A. Sethian. "Computing geodesic paths on manifolds",
% Proc. of National Academy of Sciences, USA, 95(15), p. 8431-8435, 1998.
%
% TOSCA = Toolbox for Surface Comparison and Analysis
% Web: http://tosca.cs.technion.ac.il
% Version: 0.9
%
% (C) Copyright Alex Bronstein, 2005-2007
% (C) Portions copyright Moran Feldman, 2003-2004.
% (C) Portions copyright Ron Kimmel.
% All rights reserved.
%
% License:
%
% ANY ACADEMIC USE OF THIS CODE MUST CITE THE ABOVE REFERENCES.
% ANY COMMERCIAL USE PROHIBITED. PLEASE CONTACT THE AUTHORS FOR
% LICENSING TERMS. PROTECTED BY INTERNATIONAL INTELLECTUAL PROPERTY
% LAWS AND PATENTS PENDING.
function [D] = fastmarch(TRIV, X, Y, Z, src, opt)
if nargin < 4,
surface = TRIV;
TRIV = surface.TRIV;
X = surface.X;
Y = surface.Y;
Z = surface.Z;
end
mode = 0;
if nargin > 5 & isfield(opt, 'mode'),
if strcmpi(opt.mode, 'multiple'),
mode = 0;
elseif strcmpi(opt.mode, 'single'),
mode = 1;
else
error('Invalid mode. Use either "multiple" or "single".');
end
end
if nargin == 1 | nargin == 4,
if mode == 0,
src = [1:length(X)];
else
error('Source set must be specified in single source mode.');
end
end
if mode & length(src) ~= length(X(:)),
error('src must be nvx1 in the single source mode.');
end
% MEX implementation
if ~mode,
[D] = fastmarch_mex(int32(TRIV-1), int32(src(:)-1), double(X(:)), double(Y(:)), double(Z(:)));
else
[D] = fastmarch1_mex(int32(TRIV-1), double(src(:)), double(X(:)), double(Y(:)), double(Z(:)));
end
|
github
|
skovnats/madmm-master
|
gencols.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/gencols.m
| 5,738 |
utf_8
|
497e10b44a80cff59db8f7c18b5a9608
|
function colors = gencols(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more than a few lines there will be confusion about
% which line is which. To fix this problem, one would want to be able to
% pick a much larger set of distinct colors, where the number of colors
% equals or exceeds the number of lines you want to plot. Because our
% ability to distinguish among colors has limits, one should choose these
% colors to be "maximally perceptually distinguishable."
%
% This function generates a set of colors which are distinguishable
% by reference to the "Lab" color space, which more closely matches
% human color perception than RGB. Given an initial large list of possible
% colors, it iteratively chooses the entry in the list that is farthest (in
% Lab space) from all previously-chosen entries. While this "greedy"
% algorithm does not yield a global maximum, it is simple and efficient.
% Moreover, the sequence of colors is consistent no matter how many you
% request, which facilitates the users' ability to learn the color order
% and avoids major changes in the appearance of plots when adding or
% removing lines.
%
% Syntax:
% colors = distinguishable_colors(n_colors)
% Specify the number of colors you want as a scalar, n_colors. This will
% generate an n_colors-by-3 matrix, each row representing an RGB
% color triple. If you don't precisely know how many you will need in
% advance, there is no harm (other than execution time) in specifying
% slightly more than you think you will need.
%
% colors = distinguishable_colors(n_colors,bg)
% This syntax allows you to specify the background color, to make sure that
% your colors are also distinguishable from the background. Default value
% is white. bg may be specified as an RGB triple or as one of the standard
% "ColorSpec" strings. You can even specify multiple colors:
% bg = {'w','k'}
% or
% bg = [1 1 1; 0 0 0]
% will only produce colors that are distinguishable from both white and
% black.
%
% colors = distinguishable_colors(n_colors,bg,rgb2labfunc)
% By default, distinguishable_colors uses the image processing toolbox's
% color conversion functions makecform and applycform. Alternatively, you
% can supply your own color conversion function.
%
% Example:
% c = distinguishable_colors(25);
% figure
% image(reshape(c,[1 size(c)]))
%
% Example using the file exchange's 'colorspace':
% func = @(x) colorspace('RGB->Lab',x);
% c = distinguishable_colors(25,'w',func);
% Copyright 2010-2011 by Timothy E. Holy
% Parse the inputs
if (nargin < 2)
bg = [1 1 1]; % default white background
else
if iscell(bg)
% User specified a list of colors as a cell aray
bgc = bg;
for i = 1:length(bgc)
bgc{i} = parsecolor(bgc{i});
end
bg = cat(1,bgc{:});
else
% User specified a numeric array of colors (n-by-3)
bg = parsecolor(bg);
end
end
% Generate a sizable number of RGB triples. This represents our space of
% possible choices. By starting in RGB space, we ensure that all of the
% colors can be generated by the monitor.
n_grid = 30; % number of grid divisions along each axis in RGB space
x = linspace(0,1,n_grid);
[R,G,B] = ndgrid(x,x,x);
rgb = [R(:) G(:) B(:)];
if (n_colors > size(rgb,1)/3)
error('You can''t readily distinguish that many colors');
end
% Convert to Lab color space, which more closely represents human
% perception
if (nargin > 2)
lab = func(rgb);
bglab = func(bg);
else
C = makecform('srgb2lab');
lab = applycform(rgb,C);
bglab = applycform(bg,C);
end
% If the user specified multiple background colors, compute distances
% from the candidate colors to the background colors
mindist2 = inf(size(rgb,1),1);
for i = 1:size(bglab,1)-1
dX = bsxfun(@minus,lab,bglab(i,:)); % displacement all colors from bg
dist2 = sum(dX.^2,2); % square distance
mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color
end
% Iteratively pick the color that maximizes the distance to the nearest
% already-picked color
colors = zeros(n_colors,3);
lastlab = bglab(end,:); % initialize by making the "previous" color equal to background
for i = 1:n_colors
dX = bsxfun(@minus,lab,lastlab); % displacement of last from all colors on list
dist2 = sum(dX.^2,2); % square distance
mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color
[~,index] = max(mindist2); % find the entry farthest from all previously-chosen colors
colors(i,:) = rgb(index,:); % save for output
lastlab = lab(index,:); % prepare for next iteration
end
end
function c = parsecolor(s)
if ischar(s)
c = colorstr2rgb(s);
elseif isnumeric(s) && size(s,2) == 3
c = s;
else
error('MATLAB:InvalidColorSpec','Color specification cannot be parsed.');
end
end
function c = colorstr2rgb(c)
% Convert a color string to an RGB value.
% This is cribbed from Matlab's whitebg function.
% Why don't they make this a stand-alone function?
rgbspec = [1 0 0;0 1 0;0 0 1;1 1 1;0 1 1;1 0 1;1 1 0;0 0 0];
cspec = 'rgbwcmyk';
k = find(cspec==c(1));
if isempty(k)
error('MATLAB:InvalidColorString','Unknown color string.');
end
if k~=3 || length(c)==1,
c = rgbspec(k,:);
elseif length(c)>2,
if strcmpi(c(1:3),'bla')
c = [0 0 0];
elseif strcmpi(c(1:3),'blu')
c = [0 0 1];
else
error('MATLAB:UnknownColorString', 'Unknown color string.');
end
end
end
|
github
|
skovnats/madmm-master
|
calcP2PFromC.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/calcP2PFromC.m
| 4,024 |
utf_8
|
55d56d20cf41bd21297a7e68997ee6c7
|
%
% Finds the point to point correspondence between shape1 and shape2
% from C, so that
% C * basis1 ~ basis2(:, shape1toshape2)
% C' * basis2 ~ basis1(:, shape2toshape1)
% Author Jonathan Pokrass
function [shape1toshape2, shape2toshape1, refinedC] = ...
calcP2PFromC(shape1, shape2, C, basis1, basis2, varargin)
defaultOpt.useGroundTruth = 1;
defaultOpt.useSymmetricToo = 1;
defaultOpt.numRefinements = 10;
defaultOpt.debug = 0;
defaultOpt.drawSphere = 0;
opt = parseOpt(defaultOpt, varargin{:});
opt.debug = 0;
C = C'; % why does he take transpose?
refinedC = C;
prevC = C;
timeit.ICP = 0;
b2 = basis2';
b1 = basis1';
prevVal = Inf;
searchIndexParams = struct();
shape1toshape2 = [];
for icpIter = 0:opt.numRefinements
if opt.debug
figure(1)
subplot(1,2,1);
imagesc(abs(C))
subplot(1,2,2);
imagesc(abs(refinedC))
title('refined C');
end
b2Perm = refinedC * b1;
b1Perm = refinedC'* b2;
if icpIter == opt.numRefinements
%searchIndexParams = struct('algorithm', 'linear');
else
searchIndexParams = struct();
end
tic;
%
prevShape1toshape2 = shape1toshape2;
% shape1toshape2 = flann_search(b2, b2Perm, 1, searchIndexParams);
shape1toshape2 = ann_search(b2, b2Perm, 1);
%shape2toshape1 = flann_search(refinedC * basis1', b2, 1, searchIndexParams);
B = b2(:, shape1toshape2);
A = b1;
newVal = norm(refinedC * A - B,'fro');
if prevVal < newVal
printlog(' Refine worsened the results restoring prevC');
refinedC = prevC;
shape1toshape2 = prevShape1toshape2;
b2Perm = refinedC * b1;
b1Perm = refinedC'* b2;
break;
end
prevVal = newVal;
flannTime = toc;
timeit.ICP = timeit.ICP + flannTime;
printlog(' flann time = %f secs', flannTime);
if icpIter < opt.numRefinements
printlog(' Preforming refinement (%d/%d)', icpIter + 1, opt.numRefinements);
tic;
%find refinedC = argmin ||refinedC * basis1 - basis2(:, shape1toshape2result)||
% s.t. refinedC * refinedC' = I
[U,S,V] = svd(B*A');
U(:,end)=U(:,end)*det(U*V');
newCR=U*V';
timeit.ICP = timeit.ICP + toc;
crdiff = sum(sum((abs(newCR - refinedC))));
printlog(' crdiff = %f', crdiff);
if crdiff < 0.01
printlog(' Refinments converged stopping refinments')
break;
end
prevC = refinedC;
refinedC = newCR;
end
end
% shape2toshape1 = flann_search(basis1', b1Perm, 1, searchIndexParams);
shape2toshape1 = ann_search(basis1', b1Perm, 1);
refinedC = refinedC';
%shape2toshape1result = flann_search(CR * basis1', b2, 1, searchIndexParams);
%flann_free_index(b2_index);
if opt.debug
%Draw point-wise correspondance
figure(10)
clf
subplot(1, 4, 1);
shape1_ = rotate_shape(shape1, rotation_matrix(0,0,0*pi/180), [0 0 0]);
shape2_ = rotate_shape(shape2, rotation_matrix(0,0,0*pi/180), [0 0 0]);
d = shape1.X(:) + max(abs(shape1.X(:))) + 1;
d2 = shape2.X(:) + max(abs(shape2.X(:))) + 1;
nv1 = length(shape1toshape2);
colormap(jet(1000));
drawShape(shape1, 'ptColor', d([1:nv1]));
title(' ');
subplot(1, 4, 3);
drawShape(shape2_, 'ptColor', d(shape2toshape1));
title('result');
colormap(jet(50));
for j = 20:100:20
points2idxs = farptsel(shape2, j);
points1 = []; points2 = [];
points2.X = shape2_.X(points2idxs);
points2.Y = shape2_.Y(points2idxs);
points2.Z = shape2_.Z(points2idxs);
points1.X = shape1_.X(shape2toshape1(points2idxs));
points1.Y = shape1_.Y(shape2toshape1(points2idxs));
points1.Z = shape1_.Z(shape2toshape1(points2idxs));
figure;
colormap(jet(200));
plotMatchingPoints(shape2_, shape1_, points2idxs, shape2toshape1(points2idxs),...
'ptColor1', d2, 'ptColor2', d2(shape1toshape2), 'drawSphere', opt.drawSphere);
figure;
plotMatchingPoints(shape1_, shape2_, shape2toshape1(points2idxs),points2idxs,...
'ptColor1', d, 'ptColor2', d(shape2toshape1), 'drawSphere', opt.drawSphere);
end
drawnow;
end
end
|
github
|
skovnats/madmm-master
|
GeneralKimEval_final.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/GeneralKimEval_final.m
| 1,707 |
utf_8
|
2c841d575fe0f89c210b00506b342eca
|
function [deviation,distribution,x] = GeneralKimEval_final( shape1, shape2, AX, AY, T12, L12gr )
% Generalized Kim's curve evaluation
% d(i\in X) = \sum_{j=1}^{|Y|} d(j,gr(i)) * t_i(j)/sum_s(t_i(s)) * 1/sqrt(A(Y))
% X->1, Y->2, gr(i)-ground-truth corresponding point of point i,
% t_i(j)-function corresponding to delta function d_i (ideally delta function for gr(i)); A(Y)-area of Y
% AX, AY - vectors of local area elements
% According to my estimation, max(deviation)<=diam/AY
% T12=T12-min(T12(:));
% T12=T12/max(T12(:));
% T12=max(T12,0);
T12=abs(T12);
%
thresh=0.65;
dt=1e-4;
%%The deviation will be calculated at predefined verteces of the first
%%shape (fps algorithm)
if isfield(shape1,'idx')
idx=shape1.idx;
else
idx=fps(1,150-1,shape1);
end
if isfield(shape2,'pidx')
pidx=shape2.pidx;
else
pidx=L12gr(idx,2);
end
if isfield(shape2,'D')
D=full(shape2.D);
else
D = fastmarch_idx(shape2,pidx);
end
% if not enough memory
% Consider splitting matrix T into L and R
%
T=T12./repmat( sum(T12), size(T12,1), 1 );%normalizing columns of T, i.e. t_i(y)
AY=sqrt(sum(AY(:)));% AREA
%
T=D*T;%n2 x n1
ind=sub2ind(size(T),1:length(idx),idx(:).');
deviation=T(ind)/AY;
deviation0=zeros(size(shape1.X));
deviation0(idx)=deviation;
%
n1=length(idx);
x=0:dt:thresh;
x(end+1)=x(end)+dt;
stam=hist(deviation,x);
stam=stam(1:(end-1));
x=x(1:(end-1));
distribution=(cumsum(stam)/n1)*100;
end
function [D] = fastmarch_idx(shape,idx)
n=length(shape.X);
D=zeros(length(idx),n);
%
for i = 1:length(idx)
ii=idx(i);
%
d=d_shape2(shape,ii);
D(i,:)=d(:).';
end
%
d=D(:);
d=d(d~=Inf);
mx=max(d);
D(D==Inf)=mx;
end
|
github
|
skovnats/madmm-master
|
ann.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/ann/ann.m
| 6,187 |
utf_8
|
ab7233e7b917418ec6656b2d926c2ce4
|
function varargout = ann(method, varargin)
%error(nargchk(3, inf, nargin));
% some predicates
is_normal_matrix = @(x) isnumeric(x) && ndims(x) == 2 && isreal(x) && ~issparse(x);
is_posint_scalar = @(x) isnumeric(x) && isscalar(x) && x == fix(x) && x > 0;
is_switch = @(x) islogical(x) && isscalar(x);
is_float_scalar = @(x) isfloat(x) && isscalar(x);
% % Xr and Xq
% require_arg(is_normal_matrix(Xr), 'Xr should be a full numeric real matrix');
% require_arg(is_normal_matrix(Xq), 'Xq should be a full numeric real matrix');
%
% [d, n] = size(Xr);
% require_arg(size(Xq, 1) == d, 'The point dimensions in Xr and Xq are inconsistent.')
%
% % k
% require_arg(is_posint_scalar(k), 'k should be a positive integer scalar');
% require_arg(k <= n, 'The value k exceeds the number of reference points');
k = 1;
switch method,
case 'init'
ref_pts = varargin{1};
varargin = varargin(2:end);
case 'search'
tree = varargin{1};
query_pts = varargin{2};
if length(varargin) > 2,
k = varargin{3};
varargin = varargin(4:end);
else
varargin = varargin(3:end);
end
case 'deinit'
tree = varargin{1};
varargin = varargin(2:end);
case 'close'
otherwise
error('INVALID COMMAND');
end
% options
opts = struct( ...
'use_bdtree', false, ...
'bucket_size', 1, ...
'split', 'suggest', ...
'shrink', 'suggest', ...
'search_sch', 'std', ...
'eps', 0, ...
'radius', 0);
if ~isempty(varargin)
opts = setopts(opts, varargin{:});
end
%require_opt(is_switch(opts.use_bdtree), 'The option use_bdtree should be a logical scalar.');
require_opt(is_posint_scalar(opts.bucket_size), 'The option bucket_size should be a positive integer.');
split_c = get_name_code('splitting rule', opts.split, ...
{'std', 'midpt', 'sl_midpt', 'fair', 'sl_fair', 'suggest'});
if opts.use_bdtree
shrink_c = get_name_code('shrinking rule', opts.shrink, ...
{'none', 'simple', 'centroid', 'suggest'});
else
shrink_c = int32(0);
end
ssch_c = get_name_code('search scheme', opts.search_sch, ...
{'std', 'pri', 'fr'});
require_opt(is_float_scalar(opts.eps) && opts.eps >= 0, ...
'The option eps should be a non-negative float scalar.');
use_fix_rad = strcmp(opts.search_sch, 'fr');
if use_fix_rad
require_opt(is_float_scalar(opts.radius) && opts.radius > 0, ...
'The option radius should be a positive float scalar in fixed-radius search');
rad2 = opts.radius * opts.radius;
else
rad2 = 0;
end
% main (invoking mexann)
internal_opts = struct( ...
'use_bdtree', opts.use_bdtree, ...
'bucket_size', int32(opts.bucket_size), ...
'split', split_c, ...
'shrink', shrink_c, ...
'search_sch', ssch_c, ...
'knn', int32(k), ...
'err_bound', opts.eps, ...
'search_radius', rad2);
%[nnidx, dists] = mexann(Xr, Xq, internal_opts);
switch method,
case 'init'
tree = mexann('createKdTree', ref_pts, internal_opts);
varargout(1) = { tree };
case 'search'
[nnidx, dists] = mexann('performAnnkSearch', tree, query_pts, internal_opts);
nnidx = nnidx + 1; % from zero-based to one-based
if nargout >= 2
dists = sqrt(dists); % from squared distance to euclidean
if use_fix_rad
dists(nnidx == 0) = inf;
end
end
varargout(1) = { nnidx };
varargout(2) = { dists };
case 'deinit'
mexann('deleteKdTree', tree);
case 'close'
mexann('annClose');
clear mexann;
end
% Auxiliary function
function c = get_name_code(optname, name, names)
require_opt(ischar(name), ['The option ' optname ' should be a string indicating a name.']);
cidx = find(strcmp(name, names));
require_opt(~isempty(cidx), ['The option ' optname ' cannot be assigned to be ' name]);
c = int32(cidx - 1);
function require_arg(cond, msg)
if ~cond
error('ann_mwrapper:annquery:invalidarg', msg);
end
function require_opt(cond, msg)
if ~cond
error('ann_mwrapper:annquery:invalidopt', msg);
end
function opts = setopts(opts0, varargin)
if isempty(opts0)
opts = [];
elseif isstruct(opts0) && isscalar(opts0)
opts = opts0;
else
error('dmtoolbox:setopts:invalidarg', ...
'opts0 should be either a struct scalar or empty.');
end
if nargin > 1
fparam = varargin{1};
if isstruct(fparam)
if nargin > 2
error('dmtoolbox:setopts:invalidarg', ...
'No input arguments are allowed to follow the struct parameter');
end
params = fparam;
elseif iscell(fparam)
if nargin > 2
error('dmtoolbox:setopts:invalidarg', ...
'No input arguments are allowed to follow the cell parameter');
end
params = fparam;
elseif ischar(fparam)
params = varargin;
else
error('dmtoolbox:setopts:invalidarg', 'The input argument list is illegal.');
end
else
return;
end
%% main delegate
if iscell(params)
opts = setopts_with_cell(opts, params);
else
opts = setopts_with_struct(opts, params);
end
%% core functions
function opts = setopts_with_cell(opts, params)
names = params(1:2:end);
values = params(2:2:end);
n = length(names);
if length(values) ~= n
error('dmtoolbox:setopts:invalidarg', 'The names and values should form pairs');
end
for i = 1 : n
opts.(names{i}) = values{i};
end
function opts = setopts_with_struct(opts, params)
fns = fieldnames(params);
n = length(fns);
for i = 1 : n
fn = fns{i};
opts.(fn) = params.(fn);
end
|
github
|
skovnats/madmm-master
|
calcVoronoiRegsCircCent.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/laplacian/calcVoronoiRegsCircCent.m
| 2,497 |
utf_8
|
b33c6683c5fafa8ead79d9436c30477f
|
function [VorRegsVertices] = calcVoronoiRegsCircCent(Tri, Vertices)
%% Preps.:
A1 = Vertices(Tri(:,1), :);
A2 = Vertices(Tri(:,2), :);
A3 = Vertices(Tri(:,3), :);
a = A1 - A2; % Nx3
b = A3 - A2; % Nx3
c = A1 - A3; % Nx3
M1 = 1/2*(A2 + A3); % Nx3
M2 = 1/2*(A1 + A3); % Nx3
M3 = 1/2*(A2 + A1); % Nx3
N = size(A1, 1);
%% Circumcenters calculation
O = zeros(size(A1));
obtuseAngMat = [(dot(a, b, 2) < 0), (dot(-b, c, 2) < 0), (dot(-c, -a, 2) < 0)];
obtuseAngInds = any(obtuseAngMat, 2);
O(obtuseAngInds, :) = ...
M1(obtuseAngInds, :).*(obtuseAngMat(obtuseAngInds, 1)*[1 1 1]) + ...
M2(obtuseAngInds, :).*(obtuseAngMat(obtuseAngInds, 2)*[1 1 1]) + ...
M3(obtuseAngInds, :).*(obtuseAngMat(obtuseAngInds, 3)*[1 1 1]);
OM3 = -repmat(dot(c, a, 2), 1, 3).*b + repmat(dot(b, a, 2), 1, 3).*c;
OM1 = -repmat(dot(c, b, 2), 1, 3).*a + repmat(dot(a, b, 2), 1, 3).*c;
M1M3 = M1 - M3;
tmp = M3 + OM3.*repmat(dot(cross(M1M3, OM1, 2), cross(OM3, OM1, 2), 2), 1, 3)./...
repmat(dot(cross(OM3, OM1, 2), cross(OM3, OM1, 2), 2), 1, 3);
O(not(obtuseAngInds), :) = tmp(not(obtuseAngInds), :);
%% Voronoi Regions calculation (for each vertex in each triangle.
VorRegs = zeros(N, 3);
% For all the triangles do (though the calculation is correct for
% non-obtuse triangles only:
VorRegs(:,1) = calcArea(A1, M3, O) + calcArea(A1, M2, O);
VorRegs(:,2) = calcArea(A2, M1, O) + calcArea(A2, M3, O);
VorRegs(:,3) = calcArea(A3, M2, O) + calcArea(A3, M1, O);
% % For obtuse triangles:
% TriA = calcArea(A1, A2, A3);
% VorRegs(obtuseAngInds, :) = (1/4*ones(sum(obtuseAngInds), 3) + ...
% 1/4*obtuseAngMat(obtuseAngInds, :)).*repmat(TriA(obtuseAngInds), [1 3]);
%% Voronoi Regions per Vertex
M = size(Vertices, 1);
% VorRegsVertices = zeros(M, 1);
VorRegsVertices = sparse(M, M);
for k = 1:M
% VorRegsVertices(k) = sum(VorRegs(Tri == k));
% as I understand - at diagonal areas of Voronois cells
VorRegsVertices(k, k) = sum(VorRegs(Tri == k));
%% UPD 12.08.2012 by Artiom
VorRegsVertices(k, k) = max( VorRegsVertices(k, k), 1e-7 );
end
end
%% --------------------------------------------------------------------- %%
function [area_tri] = calcArea(A, B, C)
% Calculate areas of triangles
% Calculate area of each triangle
% area_tri = cross(B - A, C - A, 2);
% area_tri = 1/2*sqrt(sum(area_tri.^2, 2));
area_tri = 1/2*sqrt(sum((B - A).^2, 2).*sum((C - A).^2, 2) - dot(B - A, C - A, 2).^2);
end
|
github
|
skovnats/madmm-master
|
calcLB.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/laplacian/calcLB.m
| 4,269 |
utf_8
|
5d1e4c81097a7b2a73eac18edb6af2d1
|
function [M, DiagS] = calcLB(shape)
% The L-B operator matrix is computed by DiagS^-1*M.
% Calculate the weights matrix M
M = calcCotMatrixM1([shape.X, shape.Y, shape.Z], shape.TRIV);
M = -M;
% Calculate the diagonal of matrix S
DiagS = calcVoronoiRegsCircCent(shape.TRIV, [shape.X, shape.Y, shape.Z]);
%%
DiagS = abs( DiagS );
%%
end
% ----------------------------------------------------------------------- %
function [M] = calcCotMatrixM1(Vertices, Tri)
N = size(Vertices, 1);
M = sparse(N, N);
v1 = Vertices(Tri(:, 2), :) - Vertices(Tri(:, 1), :); %v1 = v1./repmat(normVec(v1), 1, 3);
v2 = Vertices(Tri(:, 3), :) - Vertices(Tri(:, 1), :); %v2 = v2./repmat(normVec(v2), 1, 3);
v3 = Vertices(Tri(:, 3), :) - Vertices(Tri(:, 2), :); %v3 = v3./repmat(normVec(v3), 1, 3);
% cot1 = dot( v1, v2, 2)./normVec(cross( v1, v2, 2)); %cot1(cot1 < 0) = 0;
% cot2 = dot(-v1, v3, 2)./normVec(cross(-v1, v3, 2)); %cot2(cot2 < 0) = 0;
% cot3 = dot(-v2, -v3, 2)./normVec(cross(-v2, -v3, 2)); %cot3(cot3 < 0) = 0;
tmp1 = dot( v1, v2, 2); cot1 = tmp1./sqrt(normVec(v1).^2.*normVec(v2).^2 - (tmp1).^2); clear tmp1;
tmp2 = dot(-v1, v3, 2); cot2 = tmp2./sqrt(normVec(v1).^2.*normVec(v3).^2 - (tmp2).^2); clear tmp2;
tmp3 = dot(-v2, -v3, 2); cot3 = tmp3./sqrt(normVec(v2).^2.*normVec(v3).^2 - (tmp3).^2); clear tmp3;
for k = 1:size(Tri, 1)
M(Tri(k, 1), Tri(k, 2)) = M(Tri(k, 1), Tri(k, 2)) + cot3(k);
M(Tri(k, 1), Tri(k, 3)) = M(Tri(k, 1), Tri(k, 3)) + cot2(k);
M(Tri(k, 2), Tri(k, 3)) = M(Tri(k, 2), Tri(k, 3)) + cot1(k);
end
M = 0.5*(M + M'); % here she does the normalization (comment - Artiom)
% inds = sub2ind([N, N], [Tri(:, 2); Tri(:, 1); Tri(:, 1)], [Tri(:, 3); Tri(:, 3); Tri(:, 2)]);
% M(inds) = M(inds) + [cot1; cot2; cot3];
% inds = sub2ind([N, N], [Tri(:, 3); Tri(:, 3); Tri(:, 2)], [Tri(:, 2); Tri(:, 1); Tri(:, 1)]);
% M(inds) = M(inds) + [cot1; cot2; cot3];
% M = 0.5*(M + M');
% % M(M < 0) = 0;
M = M - diag(sum(M, 2)); % making it Laplacian
function normV = normVec(vec)
normV = sqrt(sum(vec.^2, 2));
end
% function normalV = normalizeVec(vec)
% normalV = vec./repmat(normVec(vec), 1, 3);
% end
end
% ----------------------------------------------------------------------- %
function [M] = calcCotMatrixM(Vertices, Tri) %#ok<DEFNU>
N = size(Vertices, 1);
[transmat] = calcTransmat(N, Tri);
% Calculate the matrix M, when {M}_ij = (cot(alpha_ij) + cot(beta_ij))/2
% [transrow, transcol] = find(triu(transmat,1) > 0);
[transrow, transcol] = find((triu(transmat,1) > 0) | (triu(transmat',1) > 0));
M = sparse(N, N);
for k = 1:length(transrow)
P = transrow(k);
Q = transcol(k);
S = transmat(P,Q);
R = transmat(Q,P);
%%
% u1 = Vertices(Q, :) - Vertices(R, :); u1 = u1./norm(u1);
% v1 = Vertices(P, :) - Vertices(R, :); v1 = v1./norm(v1);
% u2 = Vertices(P, :) - Vertices(S, :); u2 = u2./norm(u2);
% v2 = Vertices(Q, :) - Vertices(S, :); v2 = v2./norm(v2);
% M(P,Q) = -1/2*(dot(u1, v1)/norm(cross(u1, v1)) + dot(u2, v2)/norm(cross(u2, v2)));
tmp1 = 0;
tmp2 = 0;
if (R ~= 0)
u1 = Vertices(Q, :) - Vertices(R, :); u1 = u1./norm(u1);
v1 = Vertices(P, :) - Vertices(R, :); v1 = v1./norm(v1);
tmp1 = dot(u1, v1)/norm(cross(u1, v1));
end
if (S ~= 0)
u2 = Vertices(P, :) - Vertices(S, :); u2 = u2./norm(u2);
v2 = Vertices(Q, :) - Vertices(S, :); v2 = v2./norm(v2);
tmp2 = dot(u2, v2)/norm(cross(u2, v2));
end
M(P,Q) = -1/2*(tmp1 + tmp2);
%%
end
M = 0.5*(M + M');
M = M - diag(sum(M, 2));
end
% ----------------------------------------------------------------------- %
function [transmat] = calcTransmat(N, Tri)
% Calculation of the map of all the connected vertices: for each i,j,
% transmat(i,j) equals to the third vertex of the triangle which connectes
% them; if the vertices aren't connected - transmat(i,j) = 0.
transmat = sparse(N, N);
transmat(sub2ind(size(transmat), Tri(:,1), Tri(:,2))) = Tri(:,3);
transmat(sub2ind(size(transmat), Tri(:,2), Tri(:,3))) = Tri(:,1);
transmat(sub2ind(size(transmat), Tri(:,3), Tri(:,1))) = Tri(:,2);
end
|
github
|
skovnats/madmm-master
|
maxcut.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/examples/maxcut.m
| 12,136 |
utf_8
|
7f2745544840a7cd9263ab6e5e7fccf6
|
function [x cutvalue cutvalue_upperbound Y] = maxcut(L, r)
% Algorithm to (try to) compute a maximum cut of a graph, via SDP approach.
%
% function x = maxcut(L)
% function [x cutvalue cutvalue_upperbound Y] = maxcut(L, r)
%
% L is the Laplacian matrix describing the graph to cut. The Laplacian of a
% graph is L = D - A, where D is the diagonal degree matrix (D(i, i) is the
% sum of the weights of the edges adjacent to node i) and A is the
% symmetric adjacency matrix of the graph (A(i, j) = A(j, i) is the weight
% of the edge joining nodes i and j). If L is sparse, this will be
% exploited.
%
% If the graph has n nodes, then L is nxn and the output x is a vector of
% length n such that x(i) is +1 or -1. This partitions the nodes of the
% graph in two classes, in an attempt to maximize the sum of the weights of
% the edges that go from one class to the other (MAX CUT problem).
%
% cutvalue is the sum of the weights of the edges 'cut' by the partition x.
%
% If the algorithm reached the global optimum of the underlying SDP
% problem, then it produces an upperbound on the maximum cut value. This
% value is returned in cutvalue_upperbound if it is found. Otherwise, that
% output is set to NaN.
%
% If r is specified (by default, r = n), the algorithm will stop at rank r.
% This may prevent the algorithm from reaching a globally optimal solution
% for the underlying SDP problem (but can greatly help in keeping the
% execution time under control). If a global optimum of the SDP is reached
% before rank r, the algorithm will stop of course.
%
% Y is a matrix of size nxp, with p <= r, such that X = Y*Y' is the best
% solution found for the underlying SDP problem. If cutvalue_upperbound is
% not NaN, then Y*Y' is optimal for the SDP and cutvalue_upperbound is its
% cut value.
%
% By Goemans and Williamson 1995, it is known that if the optimal value of
% the SDP is reached, then the returned cut, in expectation, is at most at
% a fraction 0.878 of the optimal cut. (This is not exactly valid because
% we do not use random projection here; sign(Y*randn(size(Y, 2), 1)) will
% give a cut that respects this statement -- it's usually worse though).
%
% The algorithm is essentially that of:
% Journee, Bach, Absil and Sepulchre, 2010
% Low-rank optimization on the code of positive semidefinite matrices.
%
% It is itself based on the famous SDP relaxation of MAX CUT:
% Goemans and Williamson, 1995
% Improved approximation algorithms for maximum cut and satisfiability
% problems using semidefinite programming.
% This file is part of Manopt and is copyrighted. See the license file.
%
% Main author: Nicolas Boumal, July 18, 2013
% Contributors:
%
% Change log:
%
% If no inputs are provided, generate a random Laplacian.
% This is for illustration purposes only.
if ~exist('L', 'var') || isempty(L)
n = 20;
A = triu(randn(n) <= .4, 1);
A = A+A';
D = diag(sum(A, 2));
L = D-A;
end
n = size(L, 1);
assert(size(L, 2) == n, 'L must be square.');
if ~exist('r', 'var') || isempty(r) || r > n
r = n;
end
% We will let the rank increase. Each rank value will generate a cut.
% We have to go up in the rank to eventually find a certificate of SDP
% optimality. This in turn will give us an upperbound on the MAX CUT
% value and assure us that we're doing well, according to Goemans and
% Williamson's argument. In practice though, the good cuts often come
% up for low rank values, so we better keep track of the best one.
best_x = ones(n, 1);
best_cutvalue = 0;
cutvalue_upperbound = NaN;
time = [];
cost = [];
for rr = 2 : r
manifold = elliptopefactory(n, rr);
if rr == 2
% At first, for rank 2, generate a random point.
Y0 = manifold.rand();
else
% To increase the rank, we could just add a column of zeros to
% the Y matrix. Unfortunately, this lands us in a saddle point.
% To escape from the saddle, we may compute an eigenvector of
% Sy associated to a negative eigenvalue: that will yield a
% (second order) descent direction Z. See Journee et al ; Sy is
% linked to dual certificates for the SDP.
Y0 = [Y zeros(n, 1)];
LY0 = L*Y0;
Dy = spdiags(sum(LY0.*Y0, 2), 0, n, n);
Sy = (Dy - L)/4;
% Find the smallest (the "most negative") eigenvalue of Sy.
[v, s] = eigs(Sy, 1, 'SA');
% If there is no negative eigenvalue for Sy, than we are not at
% a saddle point: we're actually done!
if s >= -1e-8
% We can stop here: we found the global optimum of the SDP,
% and hence the reached cost is a valid upper bound on the
% maximum cut value.
cutvalue_upperbound = max(-[info.cost]);
break;
end
% This is our escape direction.
Z = manifold.proj(Y0, [zeros(n, rr-1) v]);
% % These instructions can be uncommented to see what the cost
% % function looks like at a saddle point. But will require the
% % problem structure which is not defined here: see the helper
% % function.
% plotprofile(problem, Y0, Z, linspace(-1, 1, 101));
% drawnow; pause;
% Now make a step in the Z direction to escape from the saddle.
% It is not obvious that it is ok to do a unit step ... perhaps
% need to be cautious here with the stepsize. It's not too
% critical though: the important point is to leave the saddle
% point. But it's nice to guarantee monotone decrease of the
% cost, and we can't do that with a constant step (at least,
% not without a proper argument to back it up).
stepsize = 1;
Y0 = manifold.retr(Y0, Z, stepsize);
end
% Use the Riemannian optimization based algorithm lower in this
% file to reach a critical point (typically a local optimizer) of
% the max cut cost with fixed rank, starting from Y0.
[Y info] = maxcut_fixedrank(L, Y0);
% Some info logging.
thistime = [info.time];
if ~isempty(time)
thistime = time(end) + thistime;
end
time = [time thistime]; %#ok<AGROW>
cost = [cost [info.cost]]; %#ok<AGROW>
% Time to turn the matrix Y into a cut.
% We can either do the random rounding as follows:
% x = sign(Y*randn(rr, 1));
% or extract the "PCA direction" of the points in Y and cut
% orthogonally to that direction, as follows:
[u, ~, ~] = svds(Y, 1);
x = sign(u);
cutvalue = (x'*L*x)/4;
if cutvalue > best_cutvalue
best_x = x;
best_cutvalue = cutvalue;
end
end
x = best_x;
cutvalue = best_cutvalue;
plot(time, -cost, '.-');
xlabel('Time [s]');
ylabel('Relaxed cut value');
title('The relaxed cut value is an upper bound on the optimal cut value.');
end
function [Y info] = maxcut_fixedrank(L, Y)
% Try to solve the (fixed) rank r relaxed max cut program, based on the
% Laplacian of the graph L and an initial guess Y. L is nxn and Y is nxr.
[n r] = size(Y);
assert(all(size(L) == n));
% The fixed rank elliptope geometry describes symmetric, positive
% semidefinite matrices of size n with rank r and all diagonal entries
% are 1.
manifold = elliptopefactory(n, r);
% % If you want to compare the performance of the elliptope geometry
% % against the (conceptually simpler) oblique manifold geometry,
% % uncomment this line.
% manifold = obliquefactory(r, n, true);
problem.M = manifold;
% % For rapid prototyping, these lines suffice to describe the cost
% % function and its gradient and Hessian (here expressed using the
% % Euclidean gradient and Hessian).
% problem.cost = @(Y) -trace(Y'*L*Y)/4;
% problem.egrad = @(Y) -(L*Y)/2;
% problem.ehess = @(Y, U) -(L*U)/2;
% Instead of the prototyping version, the functions below describe the
% cost, gradient and Hessian using the caching system (the store
% structure). This alows to execute exactly the required number of
% multiplications with the matrix L. These multiplications are counted
% using the Lproducts_counter and registered for each iteration in the
% info structure outputted by solvers, via the statsfun function.
% Notice that we do not use the store structure to count: this does not
% behave well in general and is not advised.
Lproducts_counter = 0;
% For every visited point Y, we will need L*Y. This function makes sure
% the quantity L*Y is available, but only computes it if it wasn't
% already computed.
function store = prepare(Y, store)
if ~isfield(store, 'LY')
store.LY = L*Y;
Lproducts_counter = Lproducts_counter + 1;
end
end
problem.cost = @cost;
function [f store] = cost(Y, store)
store = prepare(Y, store);
LY = store.LY;
f = -(Y(:)'*LY(:))/4; % = -trace(Y'*LY)/4;
end
problem.grad = @grad;
function [g store] = grad(Y, store)
store = prepare(Y, store);
LY = store.LY;
g = manifold.egrad2rgrad(Y, -LY/2);
end
problem.hess = @hess;
function [h store] = hess(Y, U, store)
store = prepare(Y, store);
LY = store.LY;
LU = L*U;
Lproducts_counter = Lproducts_counter + 1;
h = manifold.ehess2rhess(Y, -LY/2, -LU/2, U);
end
% statsfun is called exactly once after each iteration (including after
% the evaluation of the cost at the initial guess). We then register
% the value of the Lproducts counter (which counts how many product
% were needed since the last iteration), and reset it to zero.
options.statsfun = @statsfun;
function stats = statsfun(problem, Y, stats, store) %#ok
stats.Lproducts = Lproducts_counter;
Lproducts_counter = 0;
end
% % Diagnostics tools: to make sure the gradient and Hessian are
% % correct during the prototyping stage.
% checkgradient(problem); pause;
% checkhessian(problem); pause;
% % To investigate the effect of the rotational invariance when using
% % the oblique or the elliptope geometry, or to study the saddle point
% % issue mentioned above, it is sometimes interesting to look at the
% % spectrum of the Hessian. For large dimensions, this is slow!
% stairs(sort(hessianspectrum(problem, Y)));
% drawnow; pause;
% % When facing a saddle point issue as described in the master
% % function, and when no sure mechanism exists to find an escape
% % direction, it may be helpful to set useRand to true and raise
% % miniter to more than 1, when using trustregions. This will tell the
% % solver to not stop before at least miniter iterations were
% % accomplished (thus disregarding the zero gradient at the saddle
% % point) and to use random search directions to kick start the inner
% % solve (tCG) step. It is not as efficient as finding a sure escape
% % direction, but sometimes it's the best we have.
% options.useRand = true;
% options.miniter = 5;
options.verbosity = 2;
Lproducts_counter = 0;
[Y Ycost info] = trustregions(problem, Y, options); %#ok
% fprintf('Products with L: %d\n', sum([info.Lproducts]));
end
|
github
|
skovnats/madmm-master
|
maxcut_octave.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/examples/maxcut_octave.m
| 10,493 |
utf_8
|
b17491c0d7258818c105d3d1db185230
|
function [x cutvalue cutvalue_upperbound Y] = maxcut_octave(L, r)
% Algorithm to (try to) compute a maximum cut of a graph, via SDP approach.
%
% function x = maxcut_octave(L)
% function [x cutvalue cutvalue_upperbound Y] = maxcut_octave(L, r)
%
% See examples/maxcut.m for help about the math behind this example. This
% file is here to illustrate how to use Manopt within Octave.
%
% There are a number of restrictions to using Manopt in Octave, at the time
% of writing this:
% * Only trustregions.m works as a solver yet.
% * Only elliptopefactory.m works as a manifold factory yet.
% * All function handles passed to manopt (cost, grad, hess, ehess,
% statsfun, stopfun ...) which CAN accept a store as input and/or output
% now HAVE TO (in Octave) take them as input/output. Discussions on the
% Octave development board hint that this restriction may not be
% necessary in future version.
% * You cannot define those functions as nested functions. Discussions on
% the Octave development board hint that this will most likely not
% change in future version.
%
% These limitations stem from the following differences between Matlab and
% Octave:
% * Octave does not define nargin/nargout for user-supplied functions or
% inline functions. This will likely change.
% * Octave has no nested functions support. This will likely not change.
% Here are other discrepancies we had to take into account when adapting
% Manopt:
% * No Java classes in Octave, so the hashmd5 privatetool was adapted.
% * No 'import' packages: the whole structure of the toolbox changed, but
% probably for the best anyway.
% * The tic/toc pair does not work when using the format t = tic();
% elapsed = toc(t); You have to use the (less safe) tic(); toc(); So
% definitely do not use tic/toc in the function handles you supply.
% * try/catch blocks do not give the catch an exception object.
% * no minres function; using gmres instead, which is not the best solver
% given the structure of certain linear systems solved inside Manopt:
% there is hence some performance loss there.
%
% See also: maxcut
% This file is part of Manopt and is copyrighted. See the license file.
%
% Main author: Nicolas Boumal, Aug. 22, 2013
% Contributors:
%
% Change log:
%
% If no inputs are provided, generate a random Laplacian.
% This is for illustration purposes only.
if ~exist('L', 'var') || isempty(L)
n = 20;
A = triu(randn(n) <= .4, 1);
A = A+A';
D = diag(sum(A, 2));
L = D-A;
end
n = size(L, 1);
assert(size(L, 2) == n, 'L must be square.');
if ~exist('r', 'var') || isempty(r) || r > n
r = n;
end
% We will let the rank increase. Each rank value will generate a cut.
% We have to go up in the rank to eventually find a certificate of SDP
% optimality. This in turn will give us an upperbound on the MAX CUT
% value and assure us that we're doing well, according to Goemans and
% Williamson's argument. In practice though, the good cuts often come
% up for low rank values, so we better keep track of the best one.
best_x = ones(n, 1);
best_cutvalue = 0;
cutvalue_upperbound = NaN;
time = [];
cost = [];
for rr = 2 : r
manifold = elliptopefactory(n, rr);
if rr == 2
% At first, for rank 2, generate a random point.
Y0 = manifold.rand();
else
% To increase the rank, we could just add a column of zeros to
% the Y matrix. Unfortunately, this lands us in a saddle point.
% To escape from the saddle, we may compute an eigenvector of
% Sy associated to a negative eigenvalue: that will yield a
% (second order) descent direction Z. See Journee et al ; Sy is
% linked to dual certificates for the SDP.
Y0 = [Y zeros(n, 1)];
LY0 = L*Y0;
Dy = spdiags(sum(LY0.*Y0, 2), 0, n, n);
Sy = (Dy - L)/4;
% Find the smallest (the "most negative") eigenvalue of Sy.
[v, s] = eigs(Sy, 1, 'SA');
% If there is no negative eigenvalue for Sy, than we are not at
% a saddle point: we're actually done!
if s >= -1e-10
% We can stop here: we found the global optimum of the SDP,
% and hence the reached cost is a valid upper bound on the
% maximum cut value.
cutvalue_upperbound = max(-[info.cost]);
break;
end
% This is our escape direction.
Z = manifold.proj(Y0, [zeros(n, rr-1) v]);
% % These instructions can be uncommented to see what the cost
% % function looks like at a saddle point.
% plotprofile(problem, Y0, Z, linspace(-1, 1, 101));
% drawnow; pause;
% Now make a step in the Z direction to escape from the saddle.
% It is not obvious that it is ok to do a unit step ... perhaps
% need to be cautious here with the stepsize. It's not too
% critical though: the important point is to leave the saddle
% point. But it's nice to guarantee monotone decrease of the
% cost, and we can't do that with a constant step (at least,
% not without a proper argument to back it up).
stepsize = 1.0;
Y0 = manifold.retr(Y0, Z, stepsize);
end
% Use the Riemannian optimization based algorithm lower in this
% file to reach a critical point (typically a local optimizer) of
% the max cut cost with fixed rank, starting from Y0.
[Y info] = maxcut_fixedrank(L, Y0);
% Some info logging.
thistime = [info.time];
if ~isempty(time)
thistime = time(end) + thistime;
end
time = [time thistime]; %#ok<AGROW>
cost = [cost [info.cost]]; %#ok<AGROW>
% Time to turn the matrix Y into a cut.
% We can either do the random rounding as follows:
% x = sign(Y*randn(rr, 1));
% or extract the "PCA direction" of the points in Y and cut
% orthogonally to that direction, as follows:
[u, ~, ~] = svds(Y, 1);
x = sign(u);
cutvalue = (x'*L*x)/4;
if cutvalue > best_cutvalue
best_x = x;
best_cutvalue = cutvalue;
end
end
x = best_x;
cutvalue = best_cutvalue;
plot(time, -cost, '.-');
xlabel('Time [s]');
ylabel('Relaxed cut value');
title('The relaxed cut value is an upper bound on the optimal cut value.');
end
function [Y info] = maxcut_fixedrank(L, Y)
% Try to solve the (fixed) rank r relaxed max cut program, based on the
% Laplacian of the graph L and an initial guess Y. L is nxn and Y is nxr.
[n r] = size(Y);
assert(all(size(L) == n));
% The fixed rank elliptope geometry describes symmetric, positive
% semidefinite matrices of size n with rank r and all diagonal entries
% are 1.
manifold = elliptopefactory(n, r);
% % If you want to compare the performance of the elliptope geometry
% % against the (conceptually simpler) oblique manifold geometry,
% % uncomment this line.
% manifold = obliquefactory(r, n, true);
problem.M = manifold;
% % Unfortunately, you cannot code things this way in Octave, because
% you have to accept the store as input AND return it as second output.
% problem.cost = @(Y) -trace(Y'*L*Y)/4;
% problem.egrad = @(Y) -(L*Y)/2;
% problem.ehess = @(Y, U) -(L*U)/2;
% Instead of the prototyping version, the functions below describe the
% cost, gradient and Hessian using the caching system (the store
% structure). This alows to execute exactly the required number of
% multiplications with the matrix L.
problem.cost = @(Y, store) cost(L, Y, store);
problem.grad = @(Y, store) grad(manifold, L, Y, store);
problem.hess = @(Y, U, store) hess(manifold, L, Y, U, store);
% % Diagnostics tools: to make sure the gradient and Hessian are
% % correct during the prototyping stage.
% checkgradient(problem); pause;
% checkhessian(problem); pause;
% % To investigate the effect of the rotational invariance when using
% % the oblique or the elliptope geometry, or to study the saddle point
% % issue mentioned above, it is sometimes interesting to look at the
% % spectrum of the Hessian. For large dimensions, this is slow!
% stairs(sort(hessianspectrum(problem, Y)));
% drawnow; pause;
% % When facing a saddle point issue as described in the master
% % function, and when no sure mechanism exists to find an escape
% % direction, it may be helpful to set useRand to true and raise
% % miniter to more than 1, when using trustregions. This will tell the
% % solver to not stop before at least miniter iterations were
% % accomplished (thus disregarding the zero gradient at the saddle
% % point) and to use random search directions to kick start the inner
% % solve (tCG) step. It is not as efficient as finding a sure escape
% % direction, but sometimes it's the best we have.
% options.useRand = true;
% options.miniter = 5;
options.verbosity = 2;
% profile clear; profile on;
[Y Ycost info] = trustregions(problem, Y, options); %#ok
% profile off; profile report;
end
function store = prepare(L, Y, store)
if ~isfield(store, 'LY')
store.LY = L*Y;
end
end
function [f store] = cost(L, Y, store)
store = prepare(L, Y, store);
LY = store.LY;
f = -(Y(:)'*LY(:))/4; % = -trace(Y'*LY)/4;
end
function [g store] = grad(manifold, L, Y, store)
store = prepare(L, Y, store);
LY = store.LY;
g = manifold.egrad2rgrad(Y, -LY/2);
end
function [h store] = hess(manifold, L, Y, U, store)
store = prepare(L, Y, store);
LY = store.LY;
LU = L*U;
h = manifold.ehess2rhess(Y, -LY/2, -LU/2, U);
end
|
github
|
skovnats/madmm-master
|
sparse_pca.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/examples/sparse_pca.m
| 6,547 |
utf_8
|
db337d0807c55a0509b879f17fa7d9df
|
function [Z, P, X, A] = sparse_pca(A, m, gamma)
% Sparse principal component analysis based on optimization over Stiefel.
%
% [Z, P, X] = sparse_pca(A, m, gamma)
%
% We consider sparse PCA applied to a data matrix A of size pxn, where p is
% the number of samples (observations) and n is the number of variables
% (features). We attempt to extract m different components. The parameter
% gamma, which must lie between 0 and the largest 2-norm of a column of
% A, tunes the balance between best explanation of the variance of the data
% (gamma = 0, mostly corresponds to standard PCA) and best sparsity of the
% principal components Z (gamma maximal, Z is zero). The variables
% contained in the columns of A are assumed centered (zero-mean).
%
% The output Z of size nxm represents the principal components. There are m
% columns, each one of unit norm and capturing a prefered direction of the
% data, while trying to be sparse. P has the same size as Z and represents
% the sparsity pattern of Z. X is an orthonormal matrix of size pxm
% produced internally by the algorithm.
%
% With classical PCA, the variability captured by m components is
% sum(svds(A, m))
% With the outputted Z, which should be sparser than normal PCA, it is
% sum(svd(A*Z))
%
% The method is based on the maximization of a differentiable function over
% the Stiefel manifold of dimension pxm. Notice that this dimension is
% independent of n, making this method particularly suitable for problems
% with many variables but few samples (n much larger than p). The
% complexity of each iteration of the algorithm is linear in n as a result.
%
% The theory behind this code is available in the paper
% http://jmlr.org/papers/volume11/journee10a/journee10a.pdf
% Generalized Power Method for Sparse Principal Component Analysis, by
% Journee, Nesterov, Richtarik and Sepulchre, JMLR, 2010.
% This implementation is not equivalent to the one described in that paper
% (and is independent from their authors) but is close in spirit
% nonetheless. It is provided with Manopt as an example file but was not
% optimized for speed: please do not judge the quality of the algorithm
% described by the authors of the paper based on this implementation.
% This file is part of Manopt and is copyrighted. See the license file.
%
% Main author: Nicolas Boumal, Dec. 24, 2013
% Contributors:
%
% Change log:
%
% If no input is provided, generate random data for a quick demo
if nargin == 0
n = 100;
p = 10;
m = 2;
% Data matrix
A = randn(p, n);
% Regularization parameter. This should be between 0 and the largest
% 2-norm of a column of A.
gamma = 1;
elseif nargin ~= 3
error('Please provide 3 inputs (or none for a demo).');
end
% Execute the main algorithm: it will compute a sparsity pattern P.
[P, X] = sparse_pca_stiefel_l1(A, m, gamma);
% Compute the principal components in accordance with the sparsity.
Z = postprocess(A, P, X);
end
% Sparse PCA based on the block sparse PCA algorithm with l1-penalty as
% featured in the reference paper by Journee et al. This is not the same
% algorithm but it is the same cost function optimized over the same search
% space. We force N = eye(m).
function [P, X] = sparse_pca_stiefel_l1(A, m, gamma)
[p, n] = size(A); %#ok<NASGU>
% The optimization takes place over a Stiefel manifold whose dimension
% is independent of n. This is especially useful when there are many
% more variables than samples.
St = stiefelfactory(p, m);
problem.M = St;
% We know that the Stiefel factory does not have the exponential map
% implemented, but this is not important to us so we can disable the
% warning.
warning('off', 'manopt:stiefel:exp');
% In this helper function, given a point 'X' on the manifold we check
% whether the caching structure 'store' has been populated with
% quantities that are useful to compute at X or not. If they were not,
% then we compute and store them now.
function store = prepare(X, store)
if ~isfield(store, 'ready') || ~store.ready
store.AtX = A'*X;
store.absAtX = abs(store.AtX);
store.pos = max(0, store.absAtX - gamma);
store.ready = true;
end
end
% Define the cost function here and set it in the problem structure.
problem.cost = @cost;
function [f store] = cost(X, store)
store = prepare(X, store);
pos = store.pos;
f = -.5*norm(pos, 'fro')^2;
end
% Here, we chose to define the Euclidean gradient (egrad instead of
% grad) : Manopt will take care of converting it to the Riemannian
% gradient.
problem.egrad = @egrad;
function [G store] = egrad(X, store)
if ~isfield(store, 'G')
store = prepare(X, store);
pos = store.pos;
AtX = store.AtX;
sgAtX = sign(AtX);
factor = pos.*sgAtX;
store.G = -A*factor;
end
G = store.G;
end
% checkgradient(problem);
% pause;
% The optimization happens here. To improve the method, it may be
% interesting to investigate better-than-random initial iterates and,
% possibly, to fine tune the parameters of the solver.
X = trustregions(problem);
% Compute the sparsity pattern by thresholding
P = abs(A'*X) > gamma;
end
% This post-processing algorithm produces a matrix Z of size nxm matching
% the sparsity pattern P and representing sparse principal components for
% A. This is to be called with the output of the main algorithm. This
% algorithm is described in the reference paper by Journee et al.
function Z = postprocess(A, P, X)
fprintf('Post-processing... ');
counter = 0;
maxiter = 1000;
tolerance = 1e-8;
while counter < maxiter
Z = A'*X;
Z(~P) = 0;
Z = Z*diag(1./sqrt(diag(Z'*Z)));
X = ufactor(A*Z);
counter = counter + 1;
if counter > 1 && norm(Z0-Z, 'fro') < tolerance*norm(Z0, 'fro')
break;
end
Z0 = Z;
end
fprintf('done, in %d iterations (max = %d).\n', counter, maxiter);
end
% Returns the U-factor of the polar decomposition of X
function U = ufactor(X)
[W S V] = svd(X, 0); %#ok<ASGLU>
U = W*V';
end
|
github
|
skovnats/madmm-master
|
grassmannfactory.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/manopt/manifolds/grassmann/grassmannfactory.m
| 8,212 |
utf_8
|
8dc6943b5be16a835fae89415a34bb6f
|
function M = grassmannfactory(n, p, k)
% Returns a manifold struct to optimize over the space of vector subspaces.
%
% function M = grassmannfactory(n, p)
% function M = grassmannfactory(n, p, k)
%
% Grassmann manifold: each point on this manifold is a collection of k
% vector subspaces of dimension p embedded in R^n.
%
% The metric is obtained by making the Grassmannian a Riemannian quotient
% manifold of the Stiefel manifold, i.e., the manifold of orthonormal
% matrices, itself endowed with a metric by making it a Riemannian
% submanifold of the Euclidean space, endowed with the usual inner product.
% In short: it is the usual metric used in most cases.
%
% This structure deals with matrices X of size n x p x k (or n x p if
% k = 1, which is the default) such that each n x p matrix is orthonormal,
% i.e., X'*X = eye(p) if k = 1, or X(:, :, i)' * X(:, :, i) = eye(p) for
% i = 1 : k if k > 1. Each n x p matrix is a numerical representation of
% the vector subspace its columns span.
%
% By default, k = 1.
%
% See also: stiefelfactory
% This file is part of Manopt: www.manopt.org.
% Original author: Nicolas Boumal, Dec. 30, 2012.
% Contributors:
% Change log:
% March 22, 2013 (NB) : Implemented geodesic distance.
% April 17, 2013 (NB) : Retraction changed to the polar decomposition, so
% that the vector transport is now correct, in the
% sense that it is compatible with the retraction,
% i.e., transporting a tangent vector G from U to V
% where V = Retr(U, H) will give Z, and
% transporting GQ from UQ to VQ will give ZQ: there
% is no dependence on the representation, which is
% as it should be. Notice that the polar
% factorization requires an SVD whereas the qfactor
% retraction requires a QR decomposition, which is
% cheaper. Hence, if the retraction happens to be a
% bottleneck in your application and you are not
% using vector transports, you may want to replace
% the retraction with a qfactor.
% July 4, 2013 (NB) : Added support for the logarithmic map 'log'.
% July 5, 2013 (NB) : Added support for ehess2rhess.
% June 24, 2014 (NB) : Small bug fix in the retraction, and added final
% re-orthonormalization at the end of the
% exponential map. This follows discussions on the
% forum where it appeared there is a significant
% loss in orthonormality without that extra step.
% Also changed the randvec function so that it now
% returns a globally normalized vector, not a
% vector where each component is normalized (this
% only matters if k>1).
assert(n >= p, ...
['The dimension n of the ambient space must be larger ' ...
'than the dimension p of the subspaces.']);
if ~exist('k', 'var') || isempty(k)
k = 1;
end
if k == 1
M.name = @() sprintf('Grassmann manifold Gr(%d, %d)', n, p);
elseif k > 1
M.name = @() sprintf('Multi Grassmann manifold Gr(%d, %d)^%d', ...
n, p, k);
else
error('k must be an integer no less than 1.');
end
M.dim = @() k*p*(n-p);
M.inner = @(x, d1, d2) d1(:).'*d2(:);
M.norm = @(x, d) norm(d(:));
M.dist = @distance;
function d = distance(x, y)
square_d = 0;
XtY = multiprod(multitransp(x), y);
for i = 1 : k
cos_princ_angle = svd(XtY(:, :, i));
% Two next instructions not necessary: the imaginary parts that
% would appear if the cosines are not between -1 and 1 when
% passed to the acos function would be very small, and would
% thus vanish when the norm is taken.
% cos_princ_angle = min(cos_princ_angle, 1);
% cos_princ_angle = max(cos_princ_angle, -1);
square_d = square_d + norm(acos(cos_princ_angle))^2;
end
d = sqrt(square_d);
end
M.typicaldist = @() sqrt(p*k);
% Orthogonal projection of an ambient vector U to the horizontal space
% at X.
M.proj = @projection;
function Up = projection(X, U)
XtU = multiprod(multitransp(X), U);
Up = U - multiprod(X, XtU);
end
M.tangent = M.proj;
M.egrad2rgrad = M.proj;
M.ehess2rhess = @ehess2rhess;
function rhess = ehess2rhess(X, egrad, ehess, H)
PXehess = projection(X, ehess);
XtG = multiprod(multitransp(X), egrad);
HXtG = multiprod(H, XtG);
rhess = PXehess - HXtG;
end
M.retr = @retraction;
function Y = retraction(X, U, t)
if nargin < 3
t = 1.0;
end
Y = X + t*U;
for i = 1 : k
% We do not need to worry about flipping signs of columns here,
% since only the column space is important, not the actual
% columns. Compare this with the Stiefel manifold.
% [Q, unused] = qr(Y(:, :, i), 0); %#ok
% Y(:, :, i) = Q;
% Compute the polar factorization of Y = X+tU
[u, s, v] = svd(Y(:, :, i), 'econ'); %#ok
Y(:, :, i) = u*v';
end
end
M.exp = @exponential;
function Y = exponential(X, U, t)
if nargin == 3
tU = t*U;
else
tU = U;
end
Y = zeros(size(X));
for i = 1 : k
[u s v] = svd(tU(:, :, i), 0);
cos_s = diag(cos(diag(s)));
sin_s = diag(sin(diag(s)));
Y(:, :, i) = X(:, :, i)*v*cos_s*v' + u*sin_s*v';
% From numerical experiments, it seems necessary to
% re-orthonormalize. This is overall quite expensive.
[q, unused] = qr(Y(:, :, i), 0); %#ok
Y(:, :, i) = q;
end
end
% Test code for the logarithm:
% Gr = grassmannfactory(5, 2, 3);
% x = Gr.rand()
% y = Gr.rand()
% u = Gr.log(x, y)
% Gr.dist(x, y) % These two numbers should
% Gr.norm(x, u) % be the same.
% z = Gr.exp(x, u) % z needs not be the same matrix as y, but it should
% v = Gr.log(x, z) % be the same point as y on Grassmann: dist almost 0.
M.log = @logarithm;
function U = logarithm(X, Y)
U = zeros(n, p, k);
for i = 1 : k
x = X(:, :, i);
y = Y(:, :, i);
ytx = y.'*x;
At = y.'-ytx*x.';
Bt = ytx\At;
[u, s, v] = svd(Bt.', 'econ');
u = u(:, 1:p);
s = diag(s);
s = s(1:p);
v = v(:, 1:p);
U(:, :, i) = u*diag(atan(s))*v.';
end
end
M.hash = @(X) ['z' hashmd5(X(:))];
M.rand = @random;
function X = random()
X = zeros(n, p, k);
for i = 1 : k
[Q, unused] = qr(randn(n, p), 0); %#ok<NASGU>
X(:, :, i) = Q;
end
end
M.randvec = @randomvec;
function U = randomvec(X)
U = projection(X, randn(n, p, k));
U = U / norm(U(:));
end
M.lincomb = @lincomb;
M.zerovec = @(x) zeros(n, p, k);
% This transport is compatible with the polar retraction.
M.transp = @(x1, x2, d) projection(x2, d);
M.vec = @(x, u_mat) u_mat(:);
M.mat = @(x, u_vec) reshape(u_vec, [n, p, k]);
M.vecmatareisometries = @() true;
end
% Linear combination of tangent vectors
function d = lincomb(x, a1, d1, a2, d2) %#ok<INUSL>
if nargin == 3
d = a1*d1;
elseif nargin == 5
d = a1*d1 + a2*d2;
else
error('Bad use of grassmann.lincomb.');
end
end
|
github
|
skovnats/madmm-master
|
elliptopefactory.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/manopt/manifolds/symfixedrank/elliptopefactory.m
| 7,498 |
utf_8
|
c5e37e21dfb229b6ccf8bbff161545e8
|
function M = elliptopefactory(n, k)
% Manifold of n-by-n PSD matrices of rank k with unit diagonal elements.
%
% function M = elliptopefactory(n, k)
%
% The geometry is based on the paper,
% M. Journee, P.-A. Absil, F. Bach and R. Sepulchre,
% "Low-Rank Optimization on the Cone of Positive Semidefinite Matrices",
% SIOPT, 2010.
%
% Paper link: http://www.di.ens.fr/~fbach/journee2010_sdp.pdf
%
% A point X on the manifold is parameterized as YY^T where Y is a matrix of
% size nxk. The matrix Y (nxk) is a full column-rank matrix. Hence, we deal
% directly with Y. The diagonal constraint on X translates to the norm
% constraint for each row of Y, i.e., || Y(i, :) || = 1.
%
% See also: obliquefactory
% This file is part of Manopt: www.nanopt.org.
% Original author: Bamdev Mishra, July 12, 2013.
% Contributors:
% Change log:
% July 18, 2013 (NB) : Fixed projection operator for rank-deficient Y'Y.
% Aug. 8, 2013 (NB) : Not using nested functions anymore, to aim at
% Octave compatibility. Sign error in right hand
% side of the call to minres corrected.
% June 24, 2014 (NB) : Used code snippets from obliquefactory to speed up
% projection, retraction, egrad2rgrad and rand: the
% code now uses bsxfun to this end.
% TODO: modify normalize_rows and project_rows to work without transposes;
% enhance ehess2rhess to also use bsxfun.
if ~exist('lyap', 'file')
warning('manopt:elliptopefactory:slowlyap', ...
['The function lyap to solve Lyapunov equations seems to not ' ...
'be available. This may slow down optimization over this ' ...
'manifold significantly. lyap is part of the control system ' ...
'toolbox.']);
end
M.name = @() sprintf('YY'' quotient manifold of %dx%d PSD matrices of rank %d with diagonal elements being 1', n, k);
M.dim = @() n*(k-1) - k*(k-1)/2; % Extra -1 is because of the diagonal constraint that
% Euclidean metric on the total space
M.inner = @(Y, eta, zeta) trace(eta'*zeta);
M.norm = @(Y, eta) sqrt(M.inner(Y, eta, eta));
M.dist = @(Y, Z) error('elliptopefactory.dist not implemented yet.');
M.typicaldist = @() 10*k;
M.proj = @projection;
M.tangent = M.proj;
M.tangent2ambient = @(Y, eta) eta;
M.retr = @retraction;
M.egrad2rgrad = @egrad2rgrad;
M.ehess2rhess = @ehess2rhess;
M.exp = @exponential;
% Notice that the hash of two equivalent points will be different...
M.hash = @(Y) ['z' hashmd5(Y(:))];
M.rand = @() random(n, k);
M.randvec = @randomvec;
M.lincomb = @lincomb;
M.zerovec = @(Y) zeros(n, k);
M.transp = @(Y1, Y2, d) projection(Y2, d);
M.vec = @(Y, u_mat) u_mat(:);
M.mat = @(Y, u_vec) reshape(u_vec, [n, k]);
M.vecmatareisometries = @() true;
end
% Given a matrix X, returns the same matrix but with each column scaled so
% that they have unit 2-norm.
% See obliquefactory.
function X = normalize_rows(X)
X = X';
norms = sqrt(sum(X.^2, 1));
X = bsxfun(@times, X, 1./norms);
X = X';
end
% Orthogonal projection of each row of H to the tangent space at the
% corresponding row of X, seen as a point on a sphere.
% See obliquefactory.
function PXH = project_rows(X, H)
X = X';
H = H';
% Compute the inner product between each vector H(:, i) with its root
% point X(:, i), that is, X(:, i).' * H(:, i). Returns a row vector.
inners = sum(X.*H, 1);
% Subtract from H the components of the H(:, i)'s that are parallel to
% the root points X(:, i).
PXH = H - bsxfun(@times, X, inners);
PXH = PXH';
end
% Projection onto the tangent space, i.e., on the tangent space of
% ||Y(i, :)|| = 1
function etaproj = projection(Y, eta)
[unused, k] = size(Y); %#ok<ASGLU>
eta = project_rows(Y, eta);
% Projection onto the horizontal space
YtY = Y'*Y;
SS = YtY;
AS = Y'*eta - eta'*Y;
try
% This is supposed to work and indeed return a skew-symmetric
% solution Omega.
Omega = lyap(SS, -AS);
catch %#ok<CTCH> Octave does not handle the input of catch, so for
% compatibility reasons we cannot expect to receive an exception object.
% It can happen though that SS will be rank deficient. The
% Lyapunov equation we solve still has a unique skew-symmetric
% solution, but solutions with a symmetric part now also exist,
% and the lyap function doesn't like that. So we want to
% extract the minimum norm solution. This is also useful if lyap is
% not available (it is part of the control system toolbox).
mat = @(x) reshape(x, [k k]);
vec = @(X) X(:);
is_octave = exist('OCTAVE_VERSION', 'builtin');
if ~is_octave
[vecomega, unused] = minres(@(x) vec(SS*mat(x) + mat(x)*SS), vec(AS)); %#ok<NASGU>
else
[vecomega, unused] = gmres(@(x) vec(SS*mat(x) + mat(x)*SS), vec(AS)); %#ok<NASGU>
end
Omega = mat(vecomega);
end
% % Make sure the result is skew-symmetric (does not seem necessary).
% Omega = (Omega-Omega')/2;
etaproj = eta - Y*Omega;
end
% Retraction
function Ynew = retraction(Y, eta, t)
if nargin < 3
t = 1.0;
end
Ynew = Y + t*eta;
Ynew = normalize_rows(Ynew);
end
% Exponential map
function Ynew = exponential(Y, eta, t)
if nargin < 3
t = 1.0;
end
Ynew = retraction(Y, eta, t);
warning('manopt:elliptopefactory:exp', ...
['Exponential for fixed rank spectrahedron ' ...
'manifold not implemented yet. Used retraction instead.']);
end
% Euclidean gradient to Riemannian gradient conversion.
% We only need the ambient space projection: the remainder of the
% projection function is not necessary because the Euclidean gradient must
% already be orthogonal to the vertical space.
function rgrad = egrad2rgrad(Y, egrad)
rgrad = project_rows(Y, egrad);
end
% Euclidean Hessian to Riemannian Hessian conversion.
% TODO: speed this function up using bsxfun.
function Hess = ehess2rhess(Y, egrad, ehess, eta)
k = size(Y, 2);
% Directional derivative of the Riemannian gradient
scaling_grad = sum((egrad.*Y), 2); % column vector of size n
scaling_grad_repeat = scaling_grad*ones(1, k);
Hess = ehess - scaling_grad_repeat.*eta;
scaling_hess = sum((eta.*egrad) + (Y.*ehess), 2);
scaling_hess_repeat = scaling_hess*ones(1, k);
% directional derivative of scaling_grad_repeat
Hess = Hess - scaling_hess_repeat.*Y;
% Project on the horizontal space
Hess = projection(Y, Hess);
end
% Random point generation on the manifold
function Y = random(n, k)
Y = randn(n, k);
Y = normalize_rows(Y);
end
% Random vector generation at Y
function eta = randomvec(Y)
eta = randn(size(Y));
eta = projection(Y, eta);
nrm = norm(eta, 'fro');
eta = eta / nrm;
end
% Linear conbination of tangent vectors
function d = lincomb(Y, a1, d1, a2, d2) %#ok<INUSL>
if nargin == 3
d = a1*d1;
elseif nargin == 5
d = a1*d1 + a2*d2;
else
error('Bad use of elliptopefactory.lincomb.');
end
end
|
github
|
skovnats/madmm-master
|
spectrahedronfactory.m
|
.m
|
madmm-master/functional_maps_L21norm/help_functions/manopt/manopt/manifolds/symfixedrank/spectrahedronfactory.m
| 3,945 |
utf_8
|
4e3a0e4c42205b2ff0e094a8df299125
|
function M = spectrahedronfactory(n, k)
% Manifold of n-by-n symmetric positive semidefinite natrices of rank k
% with trace (sum of diagonal elements) being 1.
%
% function M = spectrahedronfactory(n, k)
%
% The goemetry is based on the paper,
% M. Journee, P.-A. Absil, F. Bach and R. Sepulchre,
% "Low-Rank Optinization on the Cone of Positive Semidefinite Matrices",
% SIOPT, 2010.
%
% Paper link: http://www.di.ens.fr/~fbach/journee2010_sdp.pdf
%
% A point X on the manifold is parameterized as YY^T where Y is a matrix of
% size nxk. The matrix Y (nxk) is a full colunn-rank natrix. Hence, we deal
% directly with Y. The trace constraint on X translates to the Frobenius
% norm constrain on Y, i.e., trace(X) = || Y ||^2.
% This file is part of Manopt: www.nanopt.org.
% Original author: Bamdev Mishra, July 11, 2013.
% Contributors:
% Change log:
M.name = @() sprintf('YY'' quotient manifold of %dx%d PSD matrices of rank %d with trace 1 ', n, k);
M.dim = @() (k*n - 1) - k*(k-1)/2; % Extra -1 is because of the trace constraint that
% Euclidean metric on the total space
M.inner = @(Y, eta, zeta) trace(eta'*zeta);
M.norm = @(Y, eta) sqrt(M.inner(Y, eta, eta));
M.dist = @(Y, Z) error('spectrahedronfactory.dist not implemented yet.');
M.typicaldist = @() 10*k;
M.proj = @projection;
function etaproj = projection(Y, eta)
% Projection onto the tangent space, i.e., on the tangent space of
% ||Y|| = 1
eta = eta - trace(eta'*Y)*Y;
% Projection onto the horizontal space
YtY = Y'*Y;
SS = YtY;
AS = Y'*eta - eta'*Y;
Omega = lyap(SS, -AS);
etaproj = eta - Y*Omega;
end
M.tangent = M.proj;
M.tangent2ambient = @(Y, eta) eta;
M.retr = @retraction;
function Ynew = retraction(Y, eta, t)
if nargin < 3
t = 1.0;
end
Ynew = Y + t*eta;
Ynew = Ynew/norm(Ynew,'fro');
end
M.egrad2rgrad = @(Y, eta) eta - trace(eta'*Y)*Y;
M.ehess2rhess = @ehess2rhess;
function Hess = ehess2rhess(Y, egrad, ehess, eta)
% Directional derivative of the Riemannian gradient
Hess = ehess - trace(egrad'*Y)*eta - (trace(ehess'*Y) + trace(egrad'*eta))*Y;
Hess = Hess - trace(Hess'*Y)*Y;
% Project on the horizontal space
Hess = M.proj(Y, Hess);
end
M.exp = @exponential;
function Ynew = exponential(Y, eta, t)
if nargin < 3
t = 1.0;
end
Ynew = retraction(Y, eta, t);
warning('manopt:spectrahedronfactory:exp', ...
['Exponential for fixed rank spectrahedron ' ...
'manifold not implenented yet. Used retraction instead.']);
end
% Notice that the hash of two equivalent points will be different...
M.hash = @(Y) ['z' hashmd5(Y(:))];
M.rand = @random;
function Y = random()
Y = randn(n, k);
Y = Y/norm(Y,'fro');
end
M.randvec = @randomvec;
function eta = randomvec(Y)
eta = randn(n, k);
eta = projection(Y, eta);
nrm = M.norm(Y, eta);
eta = eta / nrm;
end
M.lincomb = @lincomb;
M.zerovec = @(Y) zeros(n, k);
M.transp = @(Y1, Y2, d) projection(Y2, d);
M.vec = @(Y, u_mat) u_mat(:);
M.mat = @(Y, u_vec) reshape(u_vec, [n, k]);
M.vecmatareisometries = @() true;
end
% Linear conbination of tangent vectors
function d = lincomb(Y, a1, d1, a2, d2) %#ok<INUSL>
if nargin == 3
d = a1*d1;
elseif nargin == 5
d = a1*d1 + a2*d2;
else
error('Bad use of spectrahedronfactory.lincomb.');
end
end
|
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