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import cv2 as cv
import numpy as np
W = 400
## [my_ellipse]
def my_ellipse(img, angle):
thickness = 2
line_type = 8
cv.ellipse(img,
(W // 2, W // 2),
(W // 4, W // 16),
angle,
0,
360,
(255, 0, 0),
thickness,
line_type)
## [my_ellipse]
## [my_filled_circle]
def my_filled_circle(img, center):
thickness = -1
line_type = 8
cv.circle(img,
center,
W // 32,
(0, 0, 255),
thickness,
line_type)
## [my_filled_circle]
## [my_polygon]
def my_polygon(img):
line_type = 8
# Create some points
ppt = np.array([[W / 4, 7 * W / 8], [3 * W / 4, 7 * W / 8],
[3 * W / 4, 13 * W / 16], [11 * W / 16, 13 * W / 16],
[19 * W / 32, 3 * W / 8], [3 * W / 4, 3 * W / 8],
[3 * W / 4, W / 8], [26 * W / 40, W / 8],
[26 * W / 40, W / 4], [22 * W / 40, W / 4],
[22 * W / 40, W / 8], [18 * W / 40, W / 8],
[18 * W / 40, W / 4], [14 * W / 40, W / 4],
[14 * W / 40, W / 8], [W / 4, W / 8],
[W / 4, 3 * W / 8], [13 * W / 32, 3 * W / 8],
[5 * W / 16, 13 * W / 16], [W / 4, 13 * W / 16]], np.int32)
ppt = ppt.reshape((-1, 1, 2))
cv.fillPoly(img, [ppt], (255, 255, 255), line_type)
# Only drawind the lines would be:
# cv.polylines(img, [ppt], True, (255, 0, 255), line_type)
## [my_polygon]
## [my_line]
def my_line(img, start, end):
thickness = 2
line_type = 8
cv.line(img,
start,
end,
(0, 0, 0),
thickness,
line_type)
## [my_line]
## [create_images]
# Windows names
atom_window = "Drawing 1: Atom"
rook_window = "Drawing 2: Rook"
# Create black empty images
size = W, W, 3
atom_image = np.zeros(size, dtype=np.uint8)
rook_image = np.zeros(size, dtype=np.uint8)
## [create_images]
## [draw_atom]
# 1. Draw a simple atom:
# -----------------------
# 1.a. Creating ellipses
my_ellipse(atom_image, 90)
my_ellipse(atom_image, 0)
my_ellipse(atom_image, 45)
my_ellipse(atom_image, -45)
# 1.b. Creating circles
my_filled_circle(atom_image, (W // 2, W // 2))
## [draw_atom]
## [draw_rook]
# 2. Draw a rook
# ------------------
# 2.a. Create a convex polygon
my_polygon(rook_image)
## [rectangle]
# 2.b. Creating rectangles
cv.rectangle(rook_image,
(0, 7 * W // 8),
(W, W),
(0, 255, 255),
-1,
8)
## [rectangle]
# 2.c. Create a few lines
my_line(rook_image, (0, 15 * W // 16), (W, 15 * W // 16))
my_line(rook_image, (W // 4, 7 * W // 8), (W // 4, W))
my_line(rook_image, (W // 2, 7 * W // 8), (W // 2, W))
my_line(rook_image, (3 * W // 4, 7 * W // 8), (3 * W // 4, W))
## [draw_rook]
cv.imshow(atom_window, atom_image)
cv.moveWindow(atom_window, 0, 200)
cv.imshow(rook_window, rook_image)
cv.moveWindow(rook_window, W, 200)
cv.waitKey(0)
cv.destroyAllWindows()
|
import sys
import cv2 as cv
def main(argv):
print("""
Zoom In-Out demo
------------------
* [i] -> Zoom [i]n
* [o] -> Zoom [o]ut
* [ESC] -> Close program
""")
## [load]
filename = argv[0] if len(argv) > 0 else 'chicky_512.png'
# Load the image
src = cv.imread(cv.samples.findFile(filename))
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: pyramids.py [image_name -- default ../data/chicky_512.png] \n')
return -1
## [load]
## [loop]
while 1:
rows, cols, _channels = map(int, src.shape)
## [show_image]
cv.imshow('Pyramids Demo', src)
## [show_image]
k = cv.waitKey(0)
if k == 27:
break
## [pyrup]
elif chr(k) == 'i':
src = cv.pyrUp(src, dstsize=(2 * cols, 2 * rows))
print ('** Zoom In: Image x 2')
## [pyrup]
## [pyrdown]
elif chr(k) == 'o':
src = cv.pyrDown(src, dstsize=(cols // 2, rows // 2))
print ('** Zoom Out: Image / 2')
## [pyrdown]
## [loop]
cv.destroyAllWindows()
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
erosion_size = 0
max_elem = 2
max_kernel_size = 21
title_trackbar_element_type = 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse'
title_trackbar_kernel_size = 'Kernel size:\n 2n +1'
title_erosion_window = 'Erosion Demo'
title_dilatation_window = 'Dilation Demo'
def erosion(val):
erosion_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_erosion_window)
erosion_type = 0
val_type = cv.getTrackbarPos(title_trackbar_element_type, title_erosion_window)
if val_type == 0:
erosion_type = cv.MORPH_RECT
elif val_type == 1:
erosion_type = cv.MORPH_CROSS
elif val_type == 2:
erosion_type = cv.MORPH_ELLIPSE
element = cv.getStructuringElement(erosion_type, (2*erosion_size + 1, 2*erosion_size+1), (erosion_size, erosion_size))
erosion_dst = cv.erode(src, element)
cv.imshow(title_erosion_window, erosion_dst)
def dilatation(val):
dilatation_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_dilatation_window)
dilatation_type = 0
val_type = cv.getTrackbarPos(title_trackbar_element_type, title_dilatation_window)
if val_type == 0:
dilatation_type = cv.MORPH_RECT
elif val_type == 1:
dilatation_type = cv.MORPH_CROSS
elif val_type == 2:
dilatation_type = cv.MORPH_ELLIPSE
element = cv.getStructuringElement(dilatation_type, (2*dilatation_size + 1, 2*dilatation_size+1), (dilatation_size, dilatation_size))
dilatation_dst = cv.dilate(src, element)
cv.imshow(title_dilatation_window, dilatation_dst)
parser = argparse.ArgumentParser(description='Code for Eroding and Dilating tutorial.')
parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
cv.namedWindow(title_erosion_window)
cv.createTrackbar(title_trackbar_element_type, title_erosion_window , 0, max_elem, erosion)
cv.createTrackbar(title_trackbar_kernel_size, title_erosion_window , 0, max_kernel_size, erosion)
cv.namedWindow(title_dilatation_window)
cv.createTrackbar(title_trackbar_element_type, title_dilatation_window , 0, max_elem, dilatation)
cv.createTrackbar(title_trackbar_kernel_size, title_dilatation_window , 0, max_kernel_size, dilatation)
erosion(0)
dilatation(0)
cv.waitKey()
|
"""
@file morph_lines_detection.py
@brief Use morphology transformations for extracting horizontal and vertical lines sample code
"""
import numpy as np
import sys
import cv2 as cv
def show_wait_destroy(winname, img):
cv.imshow(winname, img)
cv.moveWindow(winname, 500, 0)
cv.waitKey(0)
cv.destroyWindow(winname)
def main(argv):
# [load_image]
# Check number of arguments
if len(argv) < 1:
print ('Not enough parameters')
print ('Usage:\nmorph_lines_detection.py < path_to_image >')
return -1
# Load the image
src = cv.imread(argv[0], cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image: ' + argv[0])
return -1
# Show source image
cv.imshow("src", src)
# [load_image]
# [gray]
# Transform source image to gray if it is not already
if len(src.shape) != 2:
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
else:
gray = src
# Show gray image
show_wait_destroy("gray", gray)
# [gray]
# [bin]
# Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
gray = cv.bitwise_not(gray)
bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 15, -2)
# Show binary image
show_wait_destroy("binary", bw)
# [bin]
# [init]
# Create the images that will use to extract the horizontal and vertical lines
horizontal = np.copy(bw)
vertical = np.copy(bw)
# [init]
# [horiz]
# Specify size on horizontal axis
cols = horizontal.shape[1]
horizontal_size = cols // 30
# Create structure element for extracting horizontal lines through morphology operations
horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
# Apply morphology operations
horizontal = cv.erode(horizontal, horizontalStructure)
horizontal = cv.dilate(horizontal, horizontalStructure)
# Show extracted horizontal lines
show_wait_destroy("horizontal", horizontal)
# [horiz]
# [vert]
# Specify size on vertical axis
rows = vertical.shape[0]
verticalsize = rows // 30
# Create structure element for extracting vertical lines through morphology operations
verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
# Apply morphology operations
vertical = cv.erode(vertical, verticalStructure)
vertical = cv.dilate(vertical, verticalStructure)
# Show extracted vertical lines
show_wait_destroy("vertical", vertical)
# [vert]
# [smooth]
# Inverse vertical image
vertical = cv.bitwise_not(vertical)
show_wait_destroy("vertical_bit", vertical)
'''
Extract edges and smooth image according to the logic
1. extract edges
2. dilate(edges)
3. src.copyTo(smooth)
4. blur smooth img
5. smooth.copyTo(src, edges)
'''
# Step 1
edges = cv.adaptiveThreshold(vertical, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 3, -2)
show_wait_destroy("edges", edges)
# Step 2
kernel = np.ones((2, 2), np.uint8)
edges = cv.dilate(edges, kernel)
show_wait_destroy("dilate", edges)
# Step 3
smooth = np.copy(vertical)
# Step 4
smooth = cv.blur(smooth, (2, 2))
# Step 5
(rows, cols) = np.where(edges != 0)
vertical[rows, cols] = smooth[rows, cols]
# Show final result
show_wait_destroy("smooth - final", vertical)
# [smooth]
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
import cv2 as cv
import numpy as np
import argparse
W = 52 # window size is WxW
C_Thr = 0.43 # threshold for coherency
LowThr = 35 # threshold1 for orientation, it ranges from 0 to 180
HighThr = 57 # threshold2 for orientation, it ranges from 0 to 180
## [calcGST]
## [calcJ_header]
## [calcGST_proto]
def calcGST(inputIMG, w):
## [calcGST_proto]
img = inputIMG.astype(np.float32)
# GST components calculation (start)
# J = (J11 J12; J12 J22) - GST
imgDiffX = cv.Sobel(img, cv.CV_32F, 1, 0, 3)
imgDiffY = cv.Sobel(img, cv.CV_32F, 0, 1, 3)
imgDiffXY = cv.multiply(imgDiffX, imgDiffY)
## [calcJ_header]
imgDiffXX = cv.multiply(imgDiffX, imgDiffX)
imgDiffYY = cv.multiply(imgDiffY, imgDiffY)
J11 = cv.boxFilter(imgDiffXX, cv.CV_32F, (w,w))
J22 = cv.boxFilter(imgDiffYY, cv.CV_32F, (w,w))
J12 = cv.boxFilter(imgDiffXY, cv.CV_32F, (w,w))
# GST components calculations (stop)
# eigenvalue calculation (start)
# lambda1 = J11 + J22 + sqrt((J11-J22)^2 + 4*J12^2)
# lambda2 = J11 + J22 - sqrt((J11-J22)^2 + 4*J12^2)
tmp1 = J11 + J22
tmp2 = J11 - J22
tmp2 = cv.multiply(tmp2, tmp2)
tmp3 = cv.multiply(J12, J12)
tmp4 = np.sqrt(tmp2 + 4.0 * tmp3)
lambda1 = tmp1 + tmp4 # biggest eigenvalue
lambda2 = tmp1 - tmp4 # smallest eigenvalue
# eigenvalue calculation (stop)
# Coherency calculation (start)
# Coherency = (lambda1 - lambda2)/(lambda1 + lambda2)) - measure of anisotropism
# Coherency is anisotropy degree (consistency of local orientation)
imgCoherencyOut = cv.divide(lambda1 - lambda2, lambda1 + lambda2)
# Coherency calculation (stop)
# orientation angle calculation (start)
# tan(2*Alpha) = 2*J12/(J22 - J11)
# Alpha = 0.5 atan2(2*J12/(J22 - J11))
imgOrientationOut = cv.phase(J22 - J11, 2.0 * J12, angleInDegrees = True)
imgOrientationOut = 0.5 * imgOrientationOut
# orientation angle calculation (stop)
return imgCoherencyOut, imgOrientationOut
## [calcGST]
parser = argparse.ArgumentParser(description='Code for Anisotropic image segmentation tutorial.')
parser.add_argument('-i', '--input', help='Path to input image.', required=True)
args = parser.parse_args()
imgIn = cv.imread(args.input, cv.IMREAD_GRAYSCALE)
if imgIn is None:
print('Could not open or find the image: {}'.format(args.input))
exit(0)
## [main_extra]
## [main]
imgCoherency, imgOrientation = calcGST(imgIn, W)
## [thresholding]
_, imgCoherencyBin = cv.threshold(imgCoherency, C_Thr, 255, cv.THRESH_BINARY)
_, imgOrientationBin = cv.threshold(imgOrientation, LowThr, HighThr, cv.THRESH_BINARY)
## [thresholding]
## [combining]
imgBin = cv.bitwise_and(imgCoherencyBin, imgOrientationBin)
## [combining]
## [main]
imgCoherency = cv.normalize(imgCoherency, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
imgOrientation = cv.normalize(imgOrientation, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
cv.imshow('result.jpg', np.uint8(0.5*(imgIn + imgBin)))
cv.imshow('Coherency.jpg', imgCoherency)
cv.imshow('Orientation.jpg', imgOrientation)
cv.waitKey(0)
## [main_extra]
|
print('Not showing this text because it is outside the snippet')
## [hello_world]
print('Hello world!')
## [hello_world]
|
from __future__ import print_function
import cv2 as cv
import argparse
def detectAndDisplay(frame):
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
frame_gray = cv.equalizeHist(frame_gray)
#-- Detect faces
faces = face_cascade.detectMultiScale(frame_gray)
for (x,y,w,h) in faces:
center = (x + w//2, y + h//2)
frame = cv.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
faceROI = frame_gray[y:y+h,x:x+w]
#-- In each face, detect eyes
eyes = eyes_cascade.detectMultiScale(faceROI)
for (x2,y2,w2,h2) in eyes:
eye_center = (x + x2 + w2//2, y + y2 + h2//2)
radius = int(round((w2 + h2)*0.25))
frame = cv.circle(frame, eye_center, radius, (255, 0, 0 ), 4)
cv.imshow('Capture - Face detection', frame)
parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
args = parser.parse_args()
face_cascade_name = args.face_cascade
eyes_cascade_name = args.eyes_cascade
face_cascade = cv.CascadeClassifier()
eyes_cascade = cv.CascadeClassifier()
#-- 1. Load the cascades
if not face_cascade.load(cv.samples.findFile(face_cascade_name)):
print('--(!)Error loading face cascade')
exit(0)
if not eyes_cascade.load(cv.samples.findFile(eyes_cascade_name)):
print('--(!)Error loading eyes cascade')
exit(0)
camera_device = args.camera
#-- 2. Read the video stream
cap = cv.VideoCapture(camera_device)
if not cap.isOpened:
print('--(!)Error opening video capture')
exit(0)
while True:
ret, frame = cap.read()
if frame is None:
print('--(!) No captured frame -- Break!')
break
detectAndDisplay(frame)
if cv.waitKey(10) == 27:
break
|
"""
@file copy_make_border.py
@brief Sample code that shows the functionality of copyMakeBorder
"""
import sys
from random import randint
import cv2 as cv
def main(argv):
## [variables]
# First we declare the variables we are going to use
borderType = cv.BORDER_CONSTANT
window_name = "copyMakeBorder Demo"
## [variables]
## [load]
imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
# Loads an image
src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: copy_make_border.py [image_name -- default lena.jpg] \n')
return -1
## [load]
# Brief how-to for this program
print ('\n'
'\t copyMakeBorder Demo: \n'
' -------------------- \n'
' ** Press \'c\' to set the border to a random constant value \n'
' ** Press \'r\' to set the border to be replicated \n'
' ** Press \'ESC\' to exit the program ')
## [create_window]
cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
## [create_window]
## [init_arguments]
# Initialize arguments for the filter
top = int(0.05 * src.shape[0]) # shape[0] = rows
bottom = top
left = int(0.05 * src.shape[1]) # shape[1] = cols
right = left
## [init_arguments]
while 1:
## [update_value]
value = [randint(0, 255), randint(0, 255), randint(0, 255)]
## [update_value]
## [copymakeborder]
dst = cv.copyMakeBorder(src, top, bottom, left, right, borderType, None, value)
## [copymakeborder]
## [display]
cv.imshow(window_name, dst)
## [display]
## [check_keypress]
c = cv.waitKey(500)
if c == 27:
break
elif c == 99: # 99 = ord('c')
borderType = cv.BORDER_CONSTANT
elif c == 114: # 114 = ord('r')
borderType = cv.BORDER_REPLICATE
## [check_keypress]
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
## [load_image]
# Load the image
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
Sample code showing how to segment overlapping objects using Laplacian filtering, \
in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Show source image
cv.imshow('Source Image', src)
## [load_image]
## [black_bg]
# Change the background from white to black, since that will help later to extract
# better results during the use of Distance Transform
src[np.all(src == 255, axis=2)] = 0
# Show output image
cv.imshow('Black Background Image', src)
## [black_bg]
## [sharp]
# Create a kernel that we will use to sharpen our image
# an approximation of second derivative, a quite strong kernel
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=np.float32)
# do the laplacian filtering as it is
# well, we need to convert everything in something more deeper then CV_8U
# because the kernel has some negative values,
# and we can expect in general to have a Laplacian image with negative values
# BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
# so the possible negative number will be truncated
imgLaplacian = cv.filter2D(src, cv.CV_32F, kernel)
sharp = np.float32(src)
imgResult = sharp - imgLaplacian
# convert back to 8bits gray scale
imgResult = np.clip(imgResult, 0, 255)
imgResult = imgResult.astype('uint8')
imgLaplacian = np.clip(imgLaplacian, 0, 255)
imgLaplacian = np.uint8(imgLaplacian)
#cv.imshow('Laplace Filtered Image', imgLaplacian)
cv.imshow('New Sharped Image', imgResult)
## [sharp]
## [bin]
# Create binary image from source image
bw = cv.cvtColor(imgResult, cv.COLOR_BGR2GRAY)
_, bw = cv.threshold(bw, 40, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow('Binary Image', bw)
## [bin]
## [dist]
# Perform the distance transform algorithm
dist = cv.distanceTransform(bw, cv.DIST_L2, 3)
# Normalize the distance image for range = {0.0, 1.0}
# so we can visualize and threshold it
cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX)
cv.imshow('Distance Transform Image', dist)
## [dist]
## [peaks]
# Threshold to obtain the peaks
# This will be the markers for the foreground objects
_, dist = cv.threshold(dist, 0.4, 1.0, cv.THRESH_BINARY)
# Dilate a bit the dist image
kernel1 = np.ones((3,3), dtype=np.uint8)
dist = cv.dilate(dist, kernel1)
cv.imshow('Peaks', dist)
## [peaks]
## [seeds]
# Create the CV_8U version of the distance image
# It is needed for findContours()
dist_8u = dist.astype('uint8')
# Find total markers
contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# Create the marker image for the watershed algorithm
markers = np.zeros(dist.shape, dtype=np.int32)
# Draw the foreground markers
for i in range(len(contours)):
cv.drawContours(markers, contours, i, (i+1), -1)
# Draw the background marker
cv.circle(markers, (5,5), 3, (255,255,255), -1)
cv.imshow('Markers', markers*10000)
## [seeds]
## [watershed]
# Perform the watershed algorithm
cv.watershed(imgResult, markers)
#mark = np.zeros(markers.shape, dtype=np.uint8)
mark = markers.astype('uint8')
mark = cv.bitwise_not(mark)
# uncomment this if you want to see how the mark
# image looks like at that point
#cv.imshow('Markers_v2', mark)
# Generate random colors
colors = []
for contour in contours:
colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))
# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)
# Fill labeled objects with random colors
for i in range(markers.shape[0]):
for j in range(markers.shape[1]):
index = markers[i,j]
if index > 0 and index <= len(contours):
dst[i,j,:] = colors[index-1]
# Visualize the final image
cv.imshow('Final Result', dst)
## [watershed]
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
## [Update]
def update_map(ind, map_x, map_y):
if ind == 0:
for i in range(map_x.shape[0]):
for j in range(map_x.shape[1]):
if j > map_x.shape[1]*0.25 and j < map_x.shape[1]*0.75 and i > map_x.shape[0]*0.25 and i < map_x.shape[0]*0.75:
map_x[i,j] = 2 * (j-map_x.shape[1]*0.25) + 0.5
map_y[i,j] = 2 * (i-map_y.shape[0]*0.25) + 0.5
else:
map_x[i,j] = 0
map_y[i,j] = 0
elif ind == 1:
for i in range(map_x.shape[0]):
map_x[i,:] = [x for x in range(map_x.shape[1])]
for j in range(map_y.shape[1]):
map_y[:,j] = [map_y.shape[0]-y for y in range(map_y.shape[0])]
elif ind == 2:
for i in range(map_x.shape[0]):
map_x[i,:] = [map_x.shape[1]-x for x in range(map_x.shape[1])]
for j in range(map_y.shape[1]):
map_y[:,j] = [y for y in range(map_y.shape[0])]
elif ind == 3:
for i in range(map_x.shape[0]):
map_x[i,:] = [map_x.shape[1]-x for x in range(map_x.shape[1])]
for j in range(map_y.shape[1]):
map_y[:,j] = [map_y.shape[0]-y for y in range(map_y.shape[0])]
## [Update]
parser = argparse.ArgumentParser(description='Code for Remapping tutorial.')
parser.add_argument('--input', help='Path to input image.', default='chicky_512.png')
args = parser.parse_args()
## [Load]
src = cv.imread(cv.samples.findFile(args.input), cv.IMREAD_COLOR)
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
## [Load]
## [Create]
map_x = np.zeros((src.shape[0], src.shape[1]), dtype=np.float32)
map_y = np.zeros((src.shape[0], src.shape[1]), dtype=np.float32)
## [Create]
## [Window]
window_name = 'Remap demo'
cv.namedWindow(window_name)
## [Window]
## [Loop]
ind = 0
while True:
update_map(ind, map_x, map_y)
ind = (ind + 1) % 4
dst = cv.remap(src, map_x, map_y, cv.INTER_LINEAR)
cv.imshow(window_name, dst)
c = cv.waitKey(1000)
if c == 27:
break
## [Loop]
|
from __future__ import print_function
import cv2 as cv
import argparse
max_lowThreshold = 100
window_name = 'Edge Map'
title_trackbar = 'Min Threshold:'
ratio = 3
kernel_size = 3
def CannyThreshold(val):
low_threshold = val
img_blur = cv.blur(src_gray, (3,3))
detected_edges = cv.Canny(img_blur, low_threshold, low_threshold*ratio, kernel_size)
mask = detected_edges != 0
dst = src * (mask[:,:,None].astype(src.dtype))
cv.imshow(window_name, dst)
parser = argparse.ArgumentParser(description='Code for Canny Edge Detector tutorial.')
parser.add_argument('--input', help='Path to input image.', default='fruits.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
cv.namedWindow(window_name)
cv.createTrackbar(title_trackbar, window_name , 0, max_lowThreshold, CannyThreshold)
CannyThreshold(0)
cv.waitKey()
|
"""
@file filter2D.py
@brief Sample code that shows how to implement your own linear filters by using filter2D function
"""
import sys
import cv2 as cv
import numpy as np
def main(argv):
window_name = 'filter2D Demo'
## [load]
imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
# Loads an image
src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: filter2D.py [image_name -- default lena.jpg] \n')
return -1
## [load]
## [init_arguments]
# Initialize ddepth argument for the filter
ddepth = -1
## [init_arguments]
# Loop - Will filter the image with different kernel sizes each 0.5 seconds
ind = 0
while True:
## [update_kernel]
# Update kernel size for a normalized box filter
kernel_size = 3 + 2 * (ind % 5)
kernel = np.ones((kernel_size, kernel_size), dtype=np.float32)
kernel /= (kernel_size * kernel_size)
## [update_kernel]
## [apply_filter]
# Apply filter
dst = cv.filter2D(src, ddepth, kernel)
## [apply_filter]
cv.imshow(window_name, dst)
c = cv.waitKey(500)
if c == 27:
break
ind += 1
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
"""
@file laplace_demo.py
@brief Sample code showing how to detect edges using the Laplace operator
"""
import sys
import cv2 as cv
def main(argv):
# [variables]
# Declare the variables we are going to use
ddepth = cv.CV_16S
kernel_size = 3
window_name = "Laplace Demo"
# [variables]
# [load]
imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR) # Load an image
# Check if image is loaded fine
if src is None:
print ('Error opening image')
print ('Program Arguments: [image_name -- default lena.jpg]')
return -1
# [load]
# [reduce_noise]
# Remove noise by blurring with a Gaussian filter
src = cv.GaussianBlur(src, (3, 3), 0)
# [reduce_noise]
# [convert_to_gray]
# Convert the image to grayscale
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
# [convert_to_gray]
# Create Window
cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
# [laplacian]
# Apply Laplace function
dst = cv.Laplacian(src_gray, ddepth, ksize=kernel_size)
# [laplacian]
# [convert]
# converting back to uint8
abs_dst = cv.convertScaleAbs(dst)
# [convert]
# [display]
cv.imshow(window_name, abs_dst)
cv.waitKey(0)
# [display]
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
"""
@file sobel_demo.py
@brief Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
"""
import sys
import cv2 as cv
def main(argv):
## [variables]
# First we declare the variables we are going to use
window_name = ('Sobel Demo - Simple Edge Detector')
scale = 1
delta = 0
ddepth = cv.CV_16S
## [variables]
## [load]
# As usual we load our source image (src)
# Check number of arguments
if len(argv) < 1:
print ('Not enough parameters')
print ('Usage:\nmorph_lines_detection.py < path_to_image >')
return -1
# Load the image
src = cv.imread(argv[0], cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image: ' + argv[0])
return -1
## [load]
## [reduce_noise]
# Remove noise by blurring with a Gaussian filter ( kernel size = 3 )
src = cv.GaussianBlur(src, (3, 3), 0)
## [reduce_noise]
## [convert_to_gray]
# Convert the image to grayscale
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
## [convert_to_gray]
## [sobel]
# Gradient-X
# grad_x = cv.Scharr(gray,ddepth,1,0)
grad_x = cv.Sobel(gray, ddepth, 1, 0, ksize=3, scale=scale, delta=delta, borderType=cv.BORDER_DEFAULT)
# Gradient-Y
# grad_y = cv.Scharr(gray,ddepth,0,1)
grad_y = cv.Sobel(gray, ddepth, 0, 1, ksize=3, scale=scale, delta=delta, borderType=cv.BORDER_DEFAULT)
## [sobel]
## [convert]
# converting back to uint8
abs_grad_x = cv.convertScaleAbs(grad_x)
abs_grad_y = cv.convertScaleAbs(grad_y)
## [convert]
## [blend]
## Total Gradient (approximate)
grad = cv.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)
## [blend]
## [display]
cv.imshow(window_name, grad)
cv.waitKey(0)
## [display]
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
"""
@file hough_lines.py
@brief This program demonstrates line finding with the Hough transform
"""
import sys
import math
import cv2 as cv
import numpy as np
def main(argv):
## [load]
default_file = 'sudoku.png'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: hough_lines.py [image_name -- default ' + default_file + '] \n')
return -1
## [load]
## [edge_detection]
# Edge detection
dst = cv.Canny(src, 50, 200, None, 3)
## [edge_detection]
# Copy edges to the images that will display the results in BGR
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
cdstP = np.copy(cdst)
## [hough_lines]
# Standard Hough Line Transform
lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
## [hough_lines]
## [draw_lines]
# Draw the lines
if lines is not None:
for i in range(0, len(lines)):
rho = lines[i][0][0]
theta = lines[i][0][1]
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))
cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
## [draw_lines]
## [hough_lines_p]
# Probabilistic Line Transform
linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
## [hough_lines_p]
## [draw_lines_p]
# Draw the lines
if linesP is not None:
for i in range(0, len(linesP)):
l = linesP[i][0]
cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)
## [draw_lines_p]
## [imshow]
# Show results
cv.imshow("Source", src)
cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
## [imshow]
## [exit]
# Wait and Exit
cv.waitKey()
return 0
## [exit]
if __name__ == "__main__":
main(sys.argv[1:])
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
## [Load the image]
parser = argparse.ArgumentParser(description='Code for Affine Transformations tutorial.')
parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [Load the image]
## [Set your 3 points to calculate the Affine Transform]
srcTri = np.array( [[0, 0], [src.shape[1] - 1, 0], [0, src.shape[0] - 1]] ).astype(np.float32)
dstTri = np.array( [[0, src.shape[1]*0.33], [src.shape[1]*0.85, src.shape[0]*0.25], [src.shape[1]*0.15, src.shape[0]*0.7]] ).astype(np.float32)
## [Set your 3 points to calculate the Affine Transform]
## [Get the Affine Transform]
warp_mat = cv.getAffineTransform(srcTri, dstTri)
## [Get the Affine Transform]
## [Apply the Affine Transform just found to the src image]
warp_dst = cv.warpAffine(src, warp_mat, (src.shape[1], src.shape[0]))
## [Apply the Affine Transform just found to the src image]
# Rotating the image after Warp
## [Compute a rotation matrix with respect to the center of the image]
center = (warp_dst.shape[1]//2, warp_dst.shape[0]//2)
angle = -50
scale = 0.6
## [Compute a rotation matrix with respect to the center of the image]
## [Get the rotation matrix with the specifications above]
rot_mat = cv.getRotationMatrix2D( center, angle, scale )
## [Get the rotation matrix with the specifications above]
## [Rotate the warped image]
warp_rotate_dst = cv.warpAffine(warp_dst, rot_mat, (warp_dst.shape[1], warp_dst.shape[0]))
## [Rotate the warped image]
## [Show what you got]
cv.imshow('Source image', src)
cv.imshow('Warp', warp_dst)
cv.imshow('Warp + Rotate', warp_rotate_dst)
## [Show what you got]
## [Wait until user exits the program]
cv.waitKey()
## [Wait until user exits the program]
|
import sys
import cv2 as cv
import numpy as np
def main(argv):
## [load]
default_file = 'smarties.png'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: hough_circle.py [image_name -- default ' + default_file + '] \n')
return -1
## [load]
## [convert_to_gray]
# Convert it to gray
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
## [convert_to_gray]
## [reduce_noise]
# Reduce the noise to avoid false circle detection
gray = cv.medianBlur(gray, 5)
## [reduce_noise]
## [houghcircles]
rows = gray.shape[0]
circles = cv.HoughCircles(gray, cv.HOUGH_GRADIENT, 1, rows / 8,
param1=100, param2=30,
minRadius=1, maxRadius=30)
## [houghcircles]
## [draw]
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
center = (i[0], i[1])
# circle center
cv.circle(src, center, 1, (0, 100, 100), 3)
# circle outline
radius = i[2]
cv.circle(src, center, radius, (255, 0, 255), 3)
## [draw]
## [display]
cv.imshow("detected circles", src)
cv.waitKey(0)
## [display]
return 0
if __name__ == "__main__":
main(sys.argv[1:])
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
# Find contours
contours, hierarchy = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# Draw contours
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color, 2, cv.LINE_8, hierarchy, 0)
# Show in a window
cv.imshow('Contours', drawing)
# Load source image
parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
parser.add_argument('--input', help='Path to input image.', default='HappyFish.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
cv.waitKey()
|
from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
## [Canny]
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
## [Canny]
## [findContours]
# Find contours
contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
## [findContours]
# Get the moments
mu = [None]*len(contours)
for i in range(len(contours)):
mu[i] = cv.moments(contours[i])
# Get the mass centers
mc = [None]*len(contours)
for i in range(len(contours)):
# add 1e-5 to avoid division by zero
mc[i] = (mu[i]['m10'] / (mu[i]['m00'] + 1e-5), mu[i]['m01'] / (mu[i]['m00'] + 1e-5))
# Draw contours
## [zeroMat]
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
## [zeroMat]
## [forContour]
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color, 2)
cv.circle(drawing, (int(mc[i][0]), int(mc[i][1])), 4, color, -1)
## [forContour]
## [showDrawings]
# Show in a window
cv.imshow('Contours', drawing)
## [showDrawings]
# Calculate the area with the moments 00 and compare with the result of the OpenCV function
for i in range(len(contours)):
print(' * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f' % (i, mu[i]['m00'], cv.contourArea(contours[i]), cv.arcLength(contours[i], True)))
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Image Moments tutorial.')
parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
## [setup]
## [createWindow]
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
## [createWindow]
## [trackbar]
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
## [trackbar]
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
## [Canny]
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
## [Canny]
## [findContours]
# Find contours
contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
## [findContours]
# Find the rotated rectangles and ellipses for each contour
minRect = [None]*len(contours)
minEllipse = [None]*len(contours)
for i, c in enumerate(contours):
minRect[i] = cv.minAreaRect(c)
if c.shape[0] > 5:
minEllipse[i] = cv.fitEllipse(c)
# Draw contours + rotated rects + ellipses
## [zeroMat]
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
## [zeroMat]
## [forContour]
for i, c in enumerate(contours):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
# contour
cv.drawContours(drawing, contours, i, color)
# ellipse
if c.shape[0] > 5:
cv.ellipse(drawing, minEllipse[i], color, 2)
# rotated rectangle
box = cv.boxPoints(minRect[i])
box = np.intp(box) #np.intp: Integer used for indexing (same as C ssize_t; normally either int32 or int64)
cv.drawContours(drawing, [box], 0, color)
## [forContour]
## [showDrawings]
# Show in a window
cv.imshow('Contours', drawing)
## [showDrawings]
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Creating Bounding rotated boxes and ellipses for contours tutorial.')
parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
## [setup]
## [createWindow]
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
## [createWindow]
## [trackbar]
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
## [trackbar]
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
# Find contours
contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# Find the convex hull object for each contour
hull_list = []
for i in range(len(contours)):
hull = cv.convexHull(contours[i])
hull_list.append(hull)
# Draw contours + hull results
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color)
cv.drawContours(drawing, hull_list, i, color)
# Show in a window
cv.imshow('Contours', drawing)
# Load source image
parser = argparse.ArgumentParser(description='Code for Convex Hull tutorial.')
parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
cv.waitKey()
|
from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
# Create an image
r = 100
src = np.zeros((4*r, 4*r), dtype=np.uint8)
# Create a sequence of points to make a contour
vert = [None]*6
vert[0] = (3*r//2, int(1.34*r))
vert[1] = (1*r, 2*r)
vert[2] = (3*r//2, int(2.866*r))
vert[3] = (5*r//2, int(2.866*r))
vert[4] = (3*r, 2*r)
vert[5] = (5*r//2, int(1.34*r))
# Draw it in src
for i in range(6):
cv.line(src, vert[i], vert[(i+1)%6], ( 255 ), 3)
# Get the contours
contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# Calculate the distances to the contour
raw_dist = np.empty(src.shape, dtype=np.float32)
for i in range(src.shape[0]):
for j in range(src.shape[1]):
raw_dist[i,j] = cv.pointPolygonTest(contours[0], (j,i), True)
minVal, maxVal, _, maxDistPt = cv.minMaxLoc(raw_dist)
minVal = abs(minVal)
maxVal = abs(maxVal)
# Depicting the distances graphically
drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)
for i in range(src.shape[0]):
for j in range(src.shape[1]):
if raw_dist[i,j] < 0:
drawing[i,j,0] = 255 - abs(raw_dist[i,j]) * 255 / minVal
elif raw_dist[i,j] > 0:
drawing[i,j,2] = 255 - raw_dist[i,j] * 255 / maxVal
else:
drawing[i,j,0] = 255
drawing[i,j,1] = 255
drawing[i,j,2] = 255
cv.circle(drawing,maxDistPt, int(maxVal),tuple(255,255,255), 1, cv.LINE_8, 0)
cv.imshow('Source', src)
cv.imshow('Distance and inscribed circle', drawing)
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
## [Canny]
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
## [Canny]
## [findContours]
# Find contours
contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
## [findContours]
## [allthework]
# Approximate contours to polygons + get bounding rects and circles
contours_poly = [None]*len(contours)
boundRect = [None]*len(contours)
centers = [None]*len(contours)
radius = [None]*len(contours)
for i, c in enumerate(contours):
contours_poly[i] = cv.approxPolyDP(c, 3, True)
boundRect[i] = cv.boundingRect(contours_poly[i])
centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])
## [allthework]
## [zeroMat]
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
## [zeroMat]
## [forContour]
# Draw polygonal contour + bonding rects + circles
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours_poly, i, color)
cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
(int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
cv.circle(drawing, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), color, 2)
## [forContour]
## [showDrawings]
# Show in a window
cv.imshow('Contours', drawing)
## [showDrawings]
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Creating Bounding boxes and circles for contours tutorial.')
parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
## [setup]
## [createWindow]
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
## [createWindow]
## [trackbar]
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
## [trackbar]
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import random as rng
NTRAINING_SAMPLES = 100 # Number of training samples per class
FRAC_LINEAR_SEP = 0.9 # Fraction of samples which compose the linear separable part
# Data for visual representation
WIDTH = 512
HEIGHT = 512
I = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)
# --------------------- 1. Set up training data randomly ---------------------------------------
trainData = np.empty((2*NTRAINING_SAMPLES, 2), dtype=np.float32)
labels = np.empty((2*NTRAINING_SAMPLES, 1), dtype=np.int32)
rng.seed(100) # Random value generation class
# Set up the linearly separable part of the training data
nLinearSamples = int(FRAC_LINEAR_SEP * NTRAINING_SAMPLES)
## [setup1]
# Generate random points for the class 1
trainClass = trainData[0:nLinearSamples,:]
# The x coordinate of the points is in [0, 0.4)
c = trainClass[:,0:1]
c[:] = np.random.uniform(0.0, 0.4 * WIDTH, c.shape)
# The y coordinate of the points is in [0, 1)
c = trainClass[:,1:2]
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
# Generate random points for the class 2
trainClass = trainData[2*NTRAINING_SAMPLES-nLinearSamples:2*NTRAINING_SAMPLES,:]
# The x coordinate of the points is in [0.6, 1]
c = trainClass[:,0:1]
c[:] = np.random.uniform(0.6*WIDTH, WIDTH, c.shape)
# The y coordinate of the points is in [0, 1)
c = trainClass[:,1:2]
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
## [setup1]
#------------------ Set up the non-linearly separable part of the training data ---------------
## [setup2]
# Generate random points for the classes 1 and 2
trainClass = trainData[nLinearSamples:2*NTRAINING_SAMPLES-nLinearSamples,:]
# The x coordinate of the points is in [0.4, 0.6)
c = trainClass[:,0:1]
c[:] = np.random.uniform(0.4*WIDTH, 0.6*WIDTH, c.shape)
# The y coordinate of the points is in [0, 1)
c = trainClass[:,1:2]
c[:] = np.random.uniform(0.0, HEIGHT, c.shape)
## [setup2]
#------------------------- Set up the labels for the classes ---------------------------------
labels[0:NTRAINING_SAMPLES,:] = 1 # Class 1
labels[NTRAINING_SAMPLES:2*NTRAINING_SAMPLES,:] = 2 # Class 2
#------------------------ 2. Set up the support vector machines parameters --------------------
print('Starting training process')
## [init]
svm = cv.ml.SVM_create()
svm.setType(cv.ml.SVM_C_SVC)
svm.setC(0.1)
svm.setKernel(cv.ml.SVM_LINEAR)
svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, int(1e7), 1e-6))
## [init]
#------------------------ 3. Train the svm ----------------------------------------------------
## [train]
svm.train(trainData, cv.ml.ROW_SAMPLE, labels)
## [train]
print('Finished training process')
#------------------------ 4. Show the decision regions ----------------------------------------
## [show]
green = (0,100,0)
blue = (100,0,0)
for i in range(I.shape[0]):
for j in range(I.shape[1]):
sampleMat = np.matrix([[j,i]], dtype=np.float32)
response = svm.predict(sampleMat)[1]
if response == 1:
I[i,j] = green
elif response == 2:
I[i,j] = blue
## [show]
#----------------------- 5. Show the training data --------------------------------------------
## [show_data]
thick = -1
# Class 1
for i in range(NTRAINING_SAMPLES):
px = trainData[i,0]
py = trainData[i,1]
cv.circle(I, (px, py), 3, (0, 255, 0), thick)
# Class 2
for i in range(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES):
px = trainData[i,0]
py = trainData[i,1]
cv.circle(I, (px, py), 3, (255, 0, 0), thick)
## [show_data]
#------------------------- 6. Show support vectors --------------------------------------------
## [show_vectors]
thick = 2
sv = svm.getUncompressedSupportVectors()
for i in range(sv.shape[0]):
cv.circle(I, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thick)
## [show_vectors]
cv.imwrite('result.png', I) # save the Image
cv.imshow('SVM for Non-Linear Training Data', I) # show it to the user
cv.waitKey()
|
import cv2 as cv
import numpy as np
# Set up training data
## [setup1]
labels = np.array([1, -1, -1, -1])
trainingData = np.matrix([[501, 10], [255, 10], [501, 255], [10, 501]], dtype=np.float32)
## [setup1]
# Train the SVM
## [init]
svm = cv.ml.SVM_create()
svm.setType(cv.ml.SVM_C_SVC)
svm.setKernel(cv.ml.SVM_LINEAR)
svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, 100, 1e-6))
## [init]
## [train]
svm.train(trainingData, cv.ml.ROW_SAMPLE, labels)
## [train]
# Data for visual representation
width = 512
height = 512
image = np.zeros((height, width, 3), dtype=np.uint8)
# Show the decision regions given by the SVM
## [show]
green = (0,255,0)
blue = (255,0,0)
for i in range(image.shape[0]):
for j in range(image.shape[1]):
sampleMat = np.matrix([[j,i]], dtype=np.float32)
response = svm.predict(sampleMat)[1]
if response == 1:
image[i,j] = green
elif response == -1:
image[i,j] = blue
## [show]
# Show the training data
## [show_data]
thickness = -1
cv.circle(image, (501, 10), 5, ( 0, 0, 0), thickness)
cv.circle(image, (255, 10), 5, (255, 255, 255), thickness)
cv.circle(image, (501, 255), 5, (255, 255, 255), thickness)
cv.circle(image, ( 10, 501), 5, (255, 255, 255), thickness)
## [show_data]
# Show support vectors
## [show_vectors]
thickness = 2
sv = svm.getUncompressedSupportVectors()
for i in range(sv.shape[0]):
cv.circle(image, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thickness)
## [show_vectors]
cv.imwrite('result.png', image) # save the image
cv.imshow('SVM Simple Example', image) # show it to the user
cv.waitKey()
|
#!/usr/bin/env python
import cv2 as cv
import numpy as np
SZ=20
bin_n = 16 # Number of bins
affine_flags = cv.WARP_INVERSE_MAP|cv.INTER_LINEAR
## [deskew]
def deskew(img):
m = cv.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
return img
## [deskew]
## [hog]
def hog(img):
gx = cv.Sobel(img, cv.CV_32F, 1, 0)
gy = cv.Sobel(img, cv.CV_32F, 0, 1)
mag, ang = cv.cartToPolar(gx, gy)
bins = np.int32(bin_n*ang/(2*np.pi)) # quantizing binvalues in (0...16)
bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
hist = np.hstack(hists) # hist is a 64 bit vector
return hist
## [hog]
img = cv.imread('digits.png',0)
if img is None:
raise Exception("we need the digits.png image from samples/data here !")
cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]
# First half is trainData, remaining is testData
train_cells = [ i[:50] for i in cells ]
test_cells = [ i[50:] for i in cells]
###### Now training ########################
deskewed = [list(map(deskew,row)) for row in train_cells]
hogdata = [list(map(hog,row)) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1,64)
responses = np.repeat(np.arange(10),250)[:,np.newaxis]
svm = cv.ml.SVM_create()
svm.setKernel(cv.ml.SVM_LINEAR)
svm.setType(cv.ml.SVM_C_SVC)
svm.setC(2.67)
svm.setGamma(5.383)
svm.train(trainData, cv.ml.ROW_SAMPLE, responses)
svm.save('svm_data.dat')
###### Now testing ########################
deskewed = [list(map(deskew,row)) for row in test_cells]
hogdata = [list(map(hog,row)) for row in deskewed]
testData = np.float32(hogdata).reshape(-1,bin_n*4)
result = svm.predict(testData)[1]
####### Check Accuracy ########################
mask = result==responses
correct = np.count_nonzero(mask)
print(correct*100.0/result.size)
|
from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi
def drawAxis(img, p_, q_, colour, scale):
p = list(p_)
q = list(q_)
## [visualization1]
angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))
# Here we lengthen the arrow by a factor of scale
q[0] = p[0] - scale * hypotenuse * cos(angle)
q[1] = p[1] - scale * hypotenuse * sin(angle)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
# create the arrow hooks
p[0] = q[0] + 9 * cos(angle + pi / 4)
p[1] = q[1] + 9 * sin(angle + pi / 4)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
p[0] = q[0] + 9 * cos(angle - pi / 4)
p[1] = q[1] + 9 * sin(angle - pi / 4)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
## [visualization1]
def getOrientation(pts, img):
## [pca]
# Construct a buffer used by the pca analysis
sz = len(pts)
data_pts = np.empty((sz, 2), dtype=np.float64)
for i in range(data_pts.shape[0]):
data_pts[i,0] = pts[i,0,0]
data_pts[i,1] = pts[i,0,1]
# Perform PCA analysis
mean = np.empty((0))
mean, eigenvectors, eigenvalues = cv.PCACompute2(data_pts, mean)
# Store the center of the object
cntr = (int(mean[0,0]), int(mean[0,1]))
## [pca]
## [visualization]
# Draw the principal components
cv.circle(img, cntr, 3, (255, 0, 255), 2)
p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0])
p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0])
drawAxis(img, cntr, p1, (0, 255, 0), 1)
drawAxis(img, cntr, p2, (255, 255, 0), 5)
angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians
## [visualization]
return angle
## [pre-process]
# Load image
parser = argparse.ArgumentParser(description='Code for Introduction to Principal Component Analysis (PCA) tutorial.\
This program demonstrates how to use OpenCV PCA to extract the orientation of an object.')
parser.add_argument('--input', help='Path to input image.', default='pca_test1.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
# Check if image is loaded successfully
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
cv.imshow('src', src)
# Convert image to grayscale
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
# Convert image to binary
_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
## [pre-process]
## [contours]
# Find all the contours in the thresholded image
contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
for i, c in enumerate(contours):
# Calculate the area of each contour
area = cv.contourArea(c)
# Ignore contours that are too small or too large
if area < 1e2 or 1e5 < area:
continue
# Draw each contour only for visualisation purposes
cv.drawContours(src, contours, i, (0, 0, 255), 2)
# Find the orientation of each shape
getOrientation(c, src)
## [contours]
cv.imshow('output', src)
cv.waitKey()
|
from __future__ import print_function
from __future__ import division
import cv2 as cv
import argparse
alpha_slider_max = 100
title_window = 'Linear Blend'
## [on_trackbar]
def on_trackbar(val):
alpha = val / alpha_slider_max
beta = ( 1.0 - alpha )
dst = cv.addWeighted(src1, alpha, src2, beta, 0.0)
cv.imshow(title_window, dst)
## [on_trackbar]
parser = argparse.ArgumentParser(description='Code for Adding a Trackbar to our applications tutorial.')
parser.add_argument('--input1', help='Path to the first input image.', default='LinuxLogo.jpg')
parser.add_argument('--input2', help='Path to the second input image.', default='WindowsLogo.jpg')
args = parser.parse_args()
## [load]
# Read images ( both have to be of the same size and type )
src1 = cv.imread(cv.samples.findFile(args.input1))
src2 = cv.imread(cv.samples.findFile(args.input2))
## [load]
if src1 is None:
print('Could not open or find the image: ', args.input1)
exit(0)
if src2 is None:
print('Could not open or find the image: ', args.input2)
exit(0)
## [window]
cv.namedWindow(title_window)
## [window]
## [create_trackbar]
trackbar_name = 'Alpha x %d' % alpha_slider_max
cv.createTrackbar(trackbar_name, title_window , 0, alpha_slider_max, on_trackbar)
## [create_trackbar]
# Show some stuff
on_trackbar(0)
# Wait until user press some key
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
parser.add_argument('--input', help='Path to input image.', default='box.png')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input), cv.IMREAD_GRAYSCALE)
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
#-- Step 1: Detect the keypoints using SURF Detector
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints = detector.detect(src)
#-- Draw keypoints
img_keypoints = np.empty((src.shape[0], src.shape[1], 3), dtype=np.uint8)
cv.drawKeypoints(src, keypoints, img_keypoints)
#-- Show detected (drawn) keypoints
cv.imshow('SURF Keypoints', img_keypoints)
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)
#-- Step 2: Matching descriptor vectors with a FLANN based matcher
# Since SURF is a floating-point descriptor NORM_L2 is used
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)
knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.7
good_matches = []
for m,n in knn_matches:
if m.distance < ratio_thresh * n.distance:
good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
#-- Show detected matches
cv.imshow('Good Matches', img_matches)
cv.waitKey()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
img_object = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
img_scene = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img_object is None or img_scene is None:
print('Could not open or find the images!')
exit(0)
#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints_obj, descriptors_obj = detector.detectAndCompute(img_object, None)
keypoints_scene, descriptors_scene = detector.detectAndCompute(img_scene, None)
#-- Step 2: Matching descriptor vectors with a FLANN based matcher
# Since SURF is a floating-point descriptor NORM_L2 is used
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)
knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.75
good_matches = []
for m,n in knn_matches:
if m.distance < ratio_thresh * n.distance:
good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]+img_scene.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img_object, keypoints_obj, img_scene, keypoints_scene, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
#-- Localize the object
obj = np.empty((len(good_matches),2), dtype=np.float32)
scene = np.empty((len(good_matches),2), dtype=np.float32)
for i in range(len(good_matches)):
#-- Get the keypoints from the good matches
obj[i,0] = keypoints_obj[good_matches[i].queryIdx].pt[0]
obj[i,1] = keypoints_obj[good_matches[i].queryIdx].pt[1]
scene[i,0] = keypoints_scene[good_matches[i].trainIdx].pt[0]
scene[i,1] = keypoints_scene[good_matches[i].trainIdx].pt[1]
H, _ = cv.findHomography(obj, scene, cv.RANSAC)
#-- Get the corners from the image_1 ( the object to be "detected" )
obj_corners = np.empty((4,1,2), dtype=np.float32)
obj_corners[0,0,0] = 0
obj_corners[0,0,1] = 0
obj_corners[1,0,0] = img_object.shape[1]
obj_corners[1,0,1] = 0
obj_corners[2,0,0] = img_object.shape[1]
obj_corners[2,0,1] = img_object.shape[0]
obj_corners[3,0,0] = 0
obj_corners[3,0,1] = img_object.shape[0]
scene_corners = cv.perspectiveTransform(obj_corners, H)
#-- Draw lines between the corners (the mapped object in the scene - image_2 )
cv.line(img_matches, (int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])),\
(int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])),\
(int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])),\
(int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])),\
(int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])), (0,255,0), 4)
#-- Show detected matches
cv.imshow('Good Matches & Object detection', img_matches)
cv.waitKey()
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
def basicPanoramaStitching(img1Path, img2Path):
img1 = cv.imread(cv.samples.findFile(img1Path))
img2 = cv.imread(cv.samples.findFile(img2Path))
# [camera-pose-from-Blender-at-location-1]
c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],
[ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
[-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
[0, 0, 0, 1]],dtype=np.float64)
# [camera-pose-from-Blender-at-location-1]
# [camera-pose-from-Blender-at-location-2]
c2Mo = np.array([[0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112],
[-0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
[0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
[0, 0, 0, 1]],dtype=np.float64)
# [camera-pose-from-Blender-at-location-2]
# [camera-intrinsics-from-Blender]
cameraMatrix = np.array([[700.0, 0.0, 320.0], [0.0, 700.0, 240.0], [0, 0, 1]], dtype=np.float32)
# [camera-intrinsics-from-Blender]
# [extract-rotation]
R1 = c1Mo[0:3, 0:3]
R2 = c2Mo[0:3, 0:3]
#[extract-rotation]
# [compute-rotation-displacement]
R2 = R2.transpose()
R_2to1 = np.dot(R1,R2)
# [compute-rotation-displacement]
# [compute-homography]
H = cameraMatrix.dot(R_2to1).dot(np.linalg.inv(cameraMatrix))
H = H / H[2][2]
# [compute-homography]
# [stitch]
img_stitch = cv.warpPerspective(img2, H, (img2.shape[1]*2, img2.shape[0]))
img_stitch[0:img1.shape[0], 0:img1.shape[1]] = img1
# [stitch]
img_space = np.zeros((img1.shape[0],50,3), dtype=np.uint8)
img_compare = cv.hconcat([img1,img_space, img2])
cv.imshow("Final", img_compare)
cv.imshow("Panorama", img_stitch)
cv.waitKey(0)
def main():
import argparse
parser = argparse.ArgumentParser(description="Code for homography tutorial. Example 5: basic panorama stitching from a rotating camera.")
parser.add_argument("-I1","--image1", help = "path to first image", default="Blender_Suzanne1.jpg")
parser.add_argument("-I2","--image2", help = "path to second image", default="Blender_Suzanne2.jpg")
args = parser.parse_args()
print("Panorama Stitching Started")
basicPanoramaStitching(args.image1, args.image2)
print("Panorama Stitching Completed Successfully")
if __name__ == '__main__':
main()
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import sys
def randomColor():
color = np.random.randint(0, 255,(1, 3))
return color[0].tolist()
def perspectiveCorrection(img1Path, img2Path ,patternSize ):
img1 = cv.imread(cv.samples.findFile(img1Path))
img2 = cv.imread(cv.samples.findFile(img2Path))
# [find-corners]
ret1, corners1 = cv.findChessboardCorners(img1, patternSize)
ret2, corners2 = cv.findChessboardCorners(img2, patternSize)
# [find-corners]
if not ret1 or not ret2:
print("Error, cannot find the chessboard corners in both images.")
sys.exit(-1)
# [estimate-homography]
H, _ = cv.findHomography(corners1, corners2)
print(H)
# [estimate-homography]
# [warp-chessboard]
img1_warp = cv.warpPerspective(img1, H, (img1.shape[1], img1.shape[0]))
# [warp-chessboard]
img_draw_warp = cv.hconcat([img2, img1_warp])
cv.imshow("Desired chessboard view / Warped source chessboard view", img_draw_warp )
corners1 = corners1.tolist()
corners1 = [a[0] for a in corners1]
# [compute-transformed-corners]
img_draw_matches = cv.hconcat([img1, img2])
for i in range(len(corners1)):
pt1 = np.array([corners1[i][0], corners1[i][1], 1])
pt1 = pt1.reshape(3, 1)
pt2 = np.dot(H, pt1)
pt2 = pt2/pt2[2]
end = (int(img1.shape[1] + pt2[0]), int(pt2[1]))
cv.line(img_draw_matches, tuple([int(j) for j in corners1[i]]), end, randomColor(), 2)
cv.imshow("Draw matches", img_draw_matches)
cv.waitKey(0)
# [compute-transformed-corners]
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-I1', "--image1", help="Path to the first image", default="left02.jpg")
parser.add_argument('-I2', "--image2", help="Path to the second image", default="left01.jpg")
parser.add_argument('-H', "--height", help="Height of pattern size", default=6)
parser.add_argument('-W', "--width", help="Width of pattern size", default=9)
args = parser.parse_args()
img1Path = args.image1
img2Path = args.image2
h = args.height
w = args.width
perspectiveCorrection(img1Path, img2Path, (w, h))
if __name__ == "__main__":
main()
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt
## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')
parser.add_argument('--input2', help='Path to input image 2.', default='graf3.png')
parser.add_argument('--homography', help='Path to the homography matrix.', default='H1to3p.xml')
args = parser.parse_args()
img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
fs = cv.FileStorage(cv.samples.findFile(args.homography), cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()
## [load]
## [AKAZE]
akaze = cv.AKAZE_create()
kpts1, desc1 = akaze.detectAndCompute(img1, None)
kpts2, desc2 = akaze.detectAndCompute(img2, None)
## [AKAZE]
## [2-nn matching]
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE_HAMMING)
nn_matches = matcher.knnMatch(desc1, desc2, 2)
## [2-nn matching]
## [ratio test filtering]
matched1 = []
matched2 = []
nn_match_ratio = 0.8 # Nearest neighbor matching ratio
for m, n in nn_matches:
if m.distance < nn_match_ratio * n.distance:
matched1.append(kpts1[m.queryIdx])
matched2.append(kpts2[m.trainIdx])
## [ratio test filtering]
## [homography check]
inliers1 = []
inliers2 = []
good_matches = []
inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
for i, m in enumerate(matched1):
col = np.ones((3,1), dtype=np.float64)
col[0:2,0] = m.pt
col = np.dot(homography, col)
col /= col[2,0]
dist = sqrt(pow(col[0,0] - matched2[i].pt[0], 2) +\
pow(col[1,0] - matched2[i].pt[1], 2))
if dist < inlier_threshold:
good_matches.append(cv.DMatch(len(inliers1), len(inliers2), 0))
inliers1.append(matched1[i])
inliers2.append(matched2[i])
## [homography check]
## [draw final matches]
res = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, inliers1, img2, inliers2, good_matches, res)
cv.imwrite("akaze_result.png", res)
inlier_ratio = len(inliers1) / float(len(matched1))
print('A-KAZE Matching Results')
print('*******************************')
print('# Keypoints 1: \t', len(kpts1))
print('# Keypoints 2: \t', len(kpts2))
print('# Matches: \t', len(matched1))
print('# Inliers: \t', len(inliers1))
print('# Inliers Ratio: \t', inlier_ratio)
cv.imshow('result', res)
cv.waitKey()
## [draw final matches]
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 400
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)
#-- Step 2: Matching descriptor vectors with a brute force matcher
# Since SURF is a floating-point descriptor NORM_L2 is used
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE)
matches = matcher.match(descriptors1, descriptors2)
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches)
#-- Show detected matches
cv.imshow('Matches', img_matches)
cv.waitKey()
|
from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
import os
def loadExposureSeq(path):
images = []
times = []
with open(os.path.join(path, 'list.txt')) as f:
content = f.readlines()
for line in content:
tokens = line.split()
images.append(cv.imread(os.path.join(path, tokens[0])))
times.append(1 / float(tokens[1]))
return images, np.asarray(times, dtype=np.float32)
parser = argparse.ArgumentParser(description='Code for High Dynamic Range Imaging tutorial.')
parser.add_argument('--input', type=str, help='Path to the directory that contains images and exposure times.')
args = parser.parse_args()
if not args.input:
parser.print_help()
exit(0)
## [Load images and exposure times]
images, times = loadExposureSeq(args.input)
## [Load images and exposure times]
## [Estimate camera response]
calibrate = cv.createCalibrateDebevec()
response = calibrate.process(images, times)
## [Estimate camera response]
## [Make HDR image]
merge_debevec = cv.createMergeDebevec()
hdr = merge_debevec.process(images, times, response)
## [Make HDR image]
## [Tonemap HDR image]
tonemap = cv.createTonemap(2.2)
ldr = tonemap.process(hdr)
## [Tonemap HDR image]
## [Perform exposure fusion]
merge_mertens = cv.createMergeMertens()
fusion = merge_mertens.process(images)
## [Perform exposure fusion]
## [Write results]
cv.imwrite('fusion.png', fusion * 255)
cv.imwrite('ldr.png', ldr * 255)
cv.imwrite('hdr.hdr', hdr)
## [Write results]
|
#!/usr/bin/env python
'''
You can download the converted pb model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
or convert the model yourself.
Follow these steps if you want to convert the original model yourself:
To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
Change script evaluate_parsing_JPPNet-s2.py for human parsing
1. Remove preprocessing to create image_batch_origin:
with tf.name_scope("create_inputs"):
...
Add
image_batch_origin = tf.placeholder(tf.float32, shape=(2, None, None, 3), name='input')
2. Create input
image = cv2.imread(path/to/image)
image_rev = np.flip(image, axis=1)
input = np.stack([image, image_rev], axis=0)
3. Hardcode image_h and image_w shapes to determine output shapes.
We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py.
parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE),
tf.image.resize_images(parsing_out1_075, INPUT_SIZE),
tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0)
Do similarly with parsing_out2, parsing_out3
4. Remove postprocessing. Last net operation:
raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0)
Change:
parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
5. To save model after sess.run(...) add:
input_graph_def = tf.get_default_graph().as_graph_def()
output_node = "Mean_3"
output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
output_graph = "LIP_JPPNet.pb"
with tf.gfile.GFile(output_graph, "wb") as f:
f.write(output_graph_def.SerializeToString())'
'''
import argparse
import os.path
import numpy as np
import cv2 as cv
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
def preprocess(image):
"""
Create 4-dimensional blob from image and flip image
:param image: input image
"""
image_rev = np.flip(image, axis=1)
input = cv.dnn.blobFromImages([image, image_rev], mean=(104.00698793, 116.66876762, 122.67891434))
return input
def run_net(input, model_path, backend, target):
"""
Read network and infer model
:param model_path: path to JPPNet model
:param backend: computation backend
:param target: computation device
"""
net = cv.dnn.readNet(model_path)
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
net.setInput(input)
out = net.forward()
return out
def postprocess(out, input_shape):
"""
Create a grayscale human segmentation
:param out: network output
:param input_shape: input image width and height
"""
# LIP classes
# 0 Background
# 1 Hat
# 2 Hair
# 3 Glove
# 4 Sunglasses
# 5 UpperClothes
# 6 Dress
# 7 Coat
# 8 Socks
# 9 Pants
# 10 Jumpsuits
# 11 Scarf
# 12 Skirt
# 13 Face
# 14 LeftArm
# 15 RightArm
# 16 LeftLeg
# 17 RightLeg
# 18 LeftShoe
# 19 RightShoe
head_output, tail_output = np.split(out, indices_or_sections=[1], axis=0)
head_output = head_output.squeeze(0)
tail_output = tail_output.squeeze(0)
head_output = np.stack([cv.resize(img, dsize=input_shape) for img in head_output[:, ...]])
tail_output = np.stack([cv.resize(img, dsize=input_shape) for img in tail_output[:, ...]])
tail_list = np.split(tail_output, indices_or_sections=list(range(1, 20)), axis=0)
tail_list = [arr.squeeze(0) for arr in tail_list]
tail_list_rev = [tail_list[i] for i in range(14)]
tail_list_rev.extend([tail_list[15], tail_list[14], tail_list[17], tail_list[16], tail_list[19], tail_list[18]])
tail_output_rev = np.stack(tail_list_rev, axis=0)
tail_output_rev = np.flip(tail_output_rev, axis=2)
raw_output_all = np.mean(np.stack([head_output, tail_output_rev], axis=0), axis=0, keepdims=True)
raw_output_all = np.argmax(raw_output_all, axis=1)
raw_output_all = raw_output_all.transpose(1, 2, 0)
return raw_output_all
def decode_labels(gray_image):
"""
Colorize image according to labels
:param gray_image: grayscale human segmentation result
"""
height, width, _ = gray_image.shape
colors = [(0, 0, 0), (128, 0, 0), (255, 0, 0), (0, 85, 0), (170, 0, 51), (255, 85, 0),
(0, 0, 85), (0, 119, 221), (85, 85, 0), (0, 85, 85), (85, 51, 0), (52, 86, 128),
(0, 128, 0), (0, 0, 255), (51, 170, 221), (0, 255, 255),(85, 255, 170),
(170, 255, 85), (255, 255, 0), (255, 170, 0)]
segm = np.stack([colors[idx] for idx in gray_image.flatten()])
segm = segm.reshape(height, width, 3).astype(np.uint8)
segm = cv.cvtColor(segm, cv.COLOR_BGR2RGB)
return segm
def parse_human(image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
"""
Prepare input for execution, run net and postprocess output to parse human.
:param image: input image
:param model_path: path to JPPNet model
:param backend: name of computation backend
:param target: name of computation target
"""
input = preprocess(image)
input_h, input_w = input.shape[2:]
output = run_net(input, model_path, backend, target)
grayscale_out = postprocess(output, (input_w, input_h))
segmentation = decode_labels(grayscale_out)
return segmentation
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', required=True, help='Path to input image.')
parser.add_argument('--model', '-m', default='lip_jppnet_384.pb', help='Path to pb model.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
if not os.path.isfile(args.model):
raise OSError("Model not exist")
image = cv.imread(args.input)
output = parse_human(image, args.model, args.backend, args.target)
winName = 'Deep learning human parsing in OpenCV'
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
cv.imshow(winName, output)
cv.waitKey()
|
# Import required modules
import cv2 as cv
import math
import argparse
############ Add argument parser for command line arguments ############
parser = argparse.ArgumentParser(description='Use this script to run TensorFlow implementation (https://github.com/argman/EAST) of EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True,
help='Path to a binary .pb file of model contains trained weights.')
parser.add_argument('--width', type=int, default=320,
help='Preprocess input image by resizing to a specific width. It should be multiple by 32.')
parser.add_argument('--height',type=int, default=320,
help='Preprocess input image by resizing to a specific height. It should be multiple by 32.')
parser.add_argument('--thr',type=float, default=0.5,
help='Confidence threshold.')
parser.add_argument('--nms',type=float, default=0.4,
help='Non-maximum suppression threshold.')
args = parser.parse_args()
############ Utility functions ############
def decode(scores, geometry, scoreThresh):
detections = []
confidences = []
############ CHECK DIMENSIONS AND SHAPES OF geometry AND scores ############
assert len(scores.shape) == 4, "Incorrect dimensions of scores"
assert len(geometry.shape) == 4, "Incorrect dimensions of geometry"
assert scores.shape[0] == 1, "Invalid dimensions of scores"
assert geometry.shape[0] == 1, "Invalid dimensions of geometry"
assert scores.shape[1] == 1, "Invalid dimensions of scores"
assert geometry.shape[1] == 5, "Invalid dimensions of geometry"
assert scores.shape[2] == geometry.shape[2], "Invalid dimensions of scores and geometry"
assert scores.shape[3] == geometry.shape[3], "Invalid dimensions of scores and geometry"
height = scores.shape[2]
width = scores.shape[3]
for y in range(0, height):
# Extract data from scores
scoresData = scores[0][0][y]
x0_data = geometry[0][0][y]
x1_data = geometry[0][1][y]
x2_data = geometry[0][2][y]
x3_data = geometry[0][3][y]
anglesData = geometry[0][4][y]
for x in range(0, width):
score = scoresData[x]
# If score is lower than threshold score, move to next x
if(score < scoreThresh):
continue
# Calculate offset
offsetX = x * 4.0
offsetY = y * 4.0
angle = anglesData[x]
# Calculate cos and sin of angle
cosA = math.cos(angle)
sinA = math.sin(angle)
h = x0_data[x] + x2_data[x]
w = x1_data[x] + x3_data[x]
# Calculate offset
offset = ([offsetX + cosA * x1_data[x] + sinA * x2_data[x], offsetY - sinA * x1_data[x] + cosA * x2_data[x]])
# Find points for rectangle
p1 = (-sinA * h + offset[0], -cosA * h + offset[1])
p3 = (-cosA * w + offset[0], sinA * w + offset[1])
center = (0.5*(p1[0]+p3[0]), 0.5*(p1[1]+p3[1]))
detections.append((center, (w,h), -1*angle * 180.0 / math.pi))
confidences.append(float(score))
# Return detections and confidences
return [detections, confidences]
def main():
# Read and store arguments
confThreshold = args.thr
nmsThreshold = args.nms
inpWidth = args.width
inpHeight = args.height
model = args.model
# Load network
net = cv.dnn.readNet(model)
# Create a new named window
kWinName = "EAST: An Efficient and Accurate Scene Text Detector"
cv.namedWindow(kWinName, cv.WINDOW_NORMAL)
outNames = []
outNames.append("feature_fusion/Conv_7/Sigmoid")
outNames.append("feature_fusion/concat_3")
# Open a video file or an image file or a camera stream
cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
# Read frame
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
# Get frame height and width
height_ = frame.shape[0]
width_ = frame.shape[1]
rW = width_ / float(inpWidth)
rH = height_ / float(inpHeight)
# Create a 4D blob from frame.
blob = cv.dnn.blobFromImage(frame, 1.0, (inpWidth, inpHeight), (123.68, 116.78, 103.94), True, False)
# Run the model
net.setInput(blob)
outs = net.forward(outNames)
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
# Get scores and geometry
scores = outs[0]
geometry = outs[1]
[boxes, confidences] = decode(scores, geometry, confThreshold)
# Apply NMS
indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold,nmsThreshold)
for i in indices:
# get 4 corners of the rotated rect
vertices = cv.boxPoints(boxes[i[0]])
# scale the bounding box coordinates based on the respective ratios
for j in range(4):
vertices[j][0] *= rW
vertices[j][1] *= rH
for j in range(4):
p1 = (vertices[j][0], vertices[j][1])
p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])
cv.line(frame, p1, p2, (0, 255, 0), 1)
# Put efficiency information
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
# Display the frame
cv.imshow(kWinName,frame)
if __name__ == "__main__":
main()
|
import cv2 as cv
import argparse
import numpy as np
from common import *
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
help='An optional path to file with preprocessing parameters.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
help='Optional name of an origin framework of the model. '
'Detect it automatically if it does not set.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'classification')
parser = argparse.ArgumentParser(parents=[parser],
description='Use this script to run classification deep learning networks using OpenCV.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
args.model = findFile(args.model)
args.config = findFile(args.config)
args.classes = findFile(args.classes)
# Load names of classes
classes = None
if args.classes:
with open(args.classes, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Load a network
net = cv.dnn.readNet(args.model, args.config, args.framework)
net.setPreferableBackend(args.backend)
net.setPreferableTarget(args.target)
winName = 'Deep learning image classification in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
# Create a 4D blob from a frame.
inpWidth = args.width if args.width else frame.shape[1]
inpHeight = args.height if args.height else frame.shape[0]
blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
# Run a model
net.setInput(blob)
out = net.forward()
# Get a class with a highest score.
out = out.flatten()
classId = np.argmax(out)
confidence = out[classId]
# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
# Print predicted class.
label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence)
cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
cv.imshow(winName, frame)
|
#!/usr/bin/env python3
'''
You can download the Geometric Matching Module model from https://www.dropbox.com/s/tyhc73xa051grjp/cp_vton_gmm.onnx?dl=0
You can download the Try-On Module model from https://www.dropbox.com/s/q2x97ve2h53j66k/cp_vton_tom.onnx?dl=0
You can download the cloth segmentation model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
You can find the OpenPose proto in opencv_extra/testdata/dnn/openpose_pose_coco.prototxt
and get .caffemodel using opencv_extra/testdata/dnn/download_models.py
'''
import argparse
import os.path
import numpy as np
import cv2 as cv
from numpy import linalg
from common import findFile
from human_parsing import parse_human
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(description='Use this script to run virtial try-on using CP-VTON',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input_image', '-i', required=True, help='Path to image with person.')
parser.add_argument('--input_cloth', '-c', required=True, help='Path to target cloth image')
parser.add_argument('--gmm_model', '-gmm', default='cp_vton_gmm.onnx', help='Path to Geometric Matching Module .onnx model.')
parser.add_argument('--tom_model', '-tom', default='cp_vton_tom.onnx', help='Path to Try-On Module .onnx model.')
parser.add_argument('--segmentation_model', default='lip_jppnet_384.pb', help='Path to cloth segmentation .pb model.')
parser.add_argument('--openpose_proto', default='openpose_pose_coco.prototxt', help='Path to OpenPose .prototxt model was trained on COCO dataset.')
parser.add_argument('--openpose_model', default='openpose_pose_coco.caffemodel', help='Path to OpenPose .caffemodel model was trained on COCO dataset.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
def get_pose_map(image, proto_path, model_path, backend, target, height=256, width=192):
radius = 5
inp = cv.dnn.blobFromImage(image, 1.0 / 255, (width, height))
net = cv.dnn.readNet(proto_path, model_path)
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
net.setInput(inp)
out = net.forward()
threshold = 0.1
_, out_c, out_h, out_w = out.shape
pose_map = np.zeros((height, width, out_c - 1))
# last label: Background
for i in range(0, out.shape[1] - 1):
heatMap = out[0, i, :, :]
keypoint = np.full((height, width), -1)
_, conf, _, point = cv.minMaxLoc(heatMap)
x = width * point[0] // out_w
y = height * point[1] // out_h
if conf > threshold and x > 0 and y > 0:
keypoint[y - radius:y + radius, x - radius:x + radius] = 1
pose_map[:, :, i] = keypoint
pose_map = pose_map.transpose(2, 0, 1)
return pose_map
class BilinearFilter(object):
"""
PIL bilinear resize implementation
image = image.resize((image_width // 16, image_height // 16), Image.BILINEAR)
"""
def _precompute_coeffs(self, inSize, outSize):
filterscale = max(1.0, inSize / outSize)
ksize = int(np.ceil(filterscale)) * 2 + 1
kk = np.zeros(shape=(outSize * ksize, ), dtype=np.float32)
bounds = np.empty(shape=(outSize * 2, ), dtype=np.int32)
centers = (np.arange(outSize) + 0.5) * filterscale + 0.5
bounds[::2] = np.where(centers - filterscale < 0, 0, centers - filterscale)
bounds[1::2] = np.where(centers + filterscale > inSize, inSize, centers + filterscale) - bounds[::2]
xmins = bounds[::2] - centers + 1
points = np.array([np.arange(row) + xmins[i] for i, row in enumerate(bounds[1::2])]) / filterscale
for xx in range(0, outSize):
point = points[xx]
bilinear = np.where(point < 1.0, 1.0 - abs(point), 0.0)
ww = np.sum(bilinear)
kk[xx * ksize : xx * ksize + bilinear.size] = np.where(ww == 0.0, bilinear, bilinear / ww)
return bounds, kk, ksize
def _resample_horizontal(self, out, img, ksize, bounds, kk):
for yy in range(0, out.shape[0]):
for xx in range(0, out.shape[1]):
xmin = bounds[xx * 2 + 0]
xmax = bounds[xx * 2 + 1]
k = kk[xx * ksize : xx * ksize + xmax]
out[yy, xx] = np.round(np.sum(img[yy, xmin : xmin + xmax] * k))
def _resample_vertical(self, out, img, ksize, bounds, kk):
for yy in range(0, out.shape[0]):
ymin = bounds[yy * 2 + 0]
ymax = bounds[yy * 2 + 1]
k = kk[yy * ksize: yy * ksize + ymax]
out[yy] = np.round(np.sum(img[ymin : ymin + ymax, 0:out.shape[1]] * k[:, np.newaxis], axis=0))
def imaging_resample(self, img, xsize, ysize):
height, width, *args = img.shape
bounds_horiz, kk_horiz, ksize_horiz = self._precompute_coeffs(width, xsize)
bounds_vert, kk_vert, ksize_vert = self._precompute_coeffs(height, ysize)
out_hor = np.empty((img.shape[0], xsize), dtype=np.uint8)
self._resample_horizontal(out_hor, img, ksize_horiz, bounds_horiz, kk_horiz)
out = np.empty((ysize, xsize), dtype=np.uint8)
self._resample_vertical(out, out_hor, ksize_vert, bounds_vert, kk_vert)
return out
class CpVton(object):
def __init__(self, gmm_model, tom_model, backend, target):
super(CpVton, self).__init__()
self.gmm_net = cv.dnn.readNet(gmm_model)
self.tom_net = cv.dnn.readNet(tom_model)
self.gmm_net.setPreferableBackend(backend)
self.gmm_net.setPreferableTarget(target)
self.tom_net.setPreferableBackend(backend)
self.tom_net.setPreferableTarget(target)
def prepare_agnostic(self, segm_image, input_image, pose_map, height=256, width=192):
palette = {
'Background' : (0, 0, 0),
'Hat' : (128, 0, 0),
'Hair' : (255, 0, 0),
'Glove' : (0, 85, 0),
'Sunglasses' : (170, 0, 51),
'UpperClothes' : (255, 85, 0),
'Dress' : (0, 0, 85),
'Coat' : (0, 119, 221),
'Socks' : (85, 85, 0),
'Pants' : (0, 85, 85),
'Jumpsuits' : (85, 51, 0),
'Scarf' : (52, 86, 128),
'Skirt' : (0, 128, 0),
'Face' : (0, 0, 255),
'Left-arm' : (51, 170, 221),
'Right-arm' : (0, 255, 255),
'Left-leg' : (85, 255, 170),
'Right-leg' : (170, 255, 85),
'Left-shoe' : (255, 255, 0),
'Right-shoe' : (255, 170, 0)
}
color2label = {val: key for key, val in palette.items()}
head_labels = ['Hat', 'Hair', 'Sunglasses', 'Face', 'Pants', 'Skirt']
segm_image = cv.cvtColor(segm_image, cv.COLOR_BGR2RGB)
phead = np.zeros((1, height, width), dtype=np.float32)
pose_shape = np.zeros((height, width), dtype=np.uint8)
for r in range(height):
for c in range(width):
pixel = tuple(segm_image[r, c])
if tuple(pixel) in color2label:
if color2label[pixel] in head_labels:
phead[0, r, c] = 1
if color2label[pixel] != 'Background':
pose_shape[r, c] = 255
input_image = cv.dnn.blobFromImage(input_image, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
input_image = input_image.squeeze(0)
img_head = input_image * phead - (1 - phead)
downsample = BilinearFilter()
down = downsample.imaging_resample(pose_shape, width // 16, height // 16)
res_shape = cv.resize(down, (width, height), cv.INTER_LINEAR)
res_shape = cv.dnn.blobFromImage(res_shape, 1.0 / 127.5, mean=(127.5, 127.5, 127.5), swapRB=True)
res_shape = res_shape.squeeze(0)
agnostic = np.concatenate((res_shape, img_head, pose_map), axis=0)
agnostic = np.expand_dims(agnostic, axis=0)
return agnostic
def get_warped_cloth(self, cloth_img, agnostic, height=256, width=192):
cloth = cv.dnn.blobFromImage(cloth_img, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
self.gmm_net.setInput(agnostic, "input.1")
self.gmm_net.setInput(cloth, "input.18")
theta = self.gmm_net.forward()
grid = self._generate_grid(theta)
warped_cloth = self._bilinear_sampler(cloth, grid).astype(np.float32)
return warped_cloth
def get_tryon(self, agnostic, warp_cloth):
inp = np.concatenate([agnostic, warp_cloth], axis=1)
self.tom_net.setInput(inp)
out = self.tom_net.forward()
p_rendered, m_composite = np.split(out, [3], axis=1)
p_rendered = np.tanh(p_rendered)
m_composite = 1 / (1 + np.exp(-m_composite))
p_tryon = warp_cloth * m_composite + p_rendered * (1 - m_composite)
rgb_p_tryon = cv.cvtColor(p_tryon.squeeze(0).transpose(1, 2, 0), cv.COLOR_BGR2RGB)
rgb_p_tryon = (rgb_p_tryon + 1) / 2
return rgb_p_tryon
def _compute_L_inverse(self, X, Y):
N = X.shape[0]
Xmat = np.tile(X, (1, N))
Ymat = np.tile(Y, (1, N))
P_dist_squared = np.power(Xmat - Xmat.transpose(1, 0), 2) + np.power(Ymat - Ymat.transpose(1, 0), 2)
P_dist_squared[P_dist_squared == 0] = 1
K = np.multiply(P_dist_squared, np.log(P_dist_squared))
O = np.ones([N, 1], dtype=np.float32)
Z = np.zeros([3, 3], dtype=np.float32)
P = np.concatenate([O, X, Y], axis=1)
first = np.concatenate((K, P), axis=1)
second = np.concatenate((P.transpose(1, 0), Z), axis=1)
L = np.concatenate((first, second), axis=0)
Li = linalg.inv(L)
return Li
def _prepare_to_transform(self, out_h=256, out_w=192, grid_size=5):
grid = np.zeros([out_h, out_w, 3], dtype=np.float32)
grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
grid_X = np.expand_dims(np.expand_dims(grid_X, axis=0), axis=3)
grid_Y = np.expand_dims(np.expand_dims(grid_Y, axis=0), axis=3)
axis_coords = np.linspace(-1, 1, grid_size)
N = grid_size ** 2
P_Y, P_X = np.meshgrid(axis_coords, axis_coords)
P_X = np.reshape(P_X,(-1, 1))
P_Y = np.reshape(P_Y,(-1, 1))
P_X = np.expand_dims(np.expand_dims(np.expand_dims(P_X, axis=2), axis=3), axis=4).transpose(4, 1, 2, 3, 0)
P_Y = np.expand_dims(np.expand_dims(np.expand_dims(P_Y, axis=2), axis=3), axis=4).transpose(4, 1, 2, 3, 0)
return grid_X, grid_Y, N, P_X, P_Y
def _expand_torch(self, X, shape):
if len(X.shape) != len(shape):
return X.flatten().reshape(shape)
else:
axis = [1 if src == dst else dst for src, dst in zip(X.shape, shape)]
return np.tile(X, axis)
def _apply_transformation(self, theta, points, N, P_X, P_Y):
if len(theta.shape) == 2:
theta = np.expand_dims(np.expand_dims(theta, axis=2), axis=3)
batch_size = theta.shape[0]
P_X_base = np.copy(P_X)
P_Y_base = np.copy(P_Y)
Li = self._compute_L_inverse(np.reshape(P_X, (N, -1)), np.reshape(P_Y, (N, -1)))
Li = np.expand_dims(Li, axis=0)
# split theta into point coordinates
Q_X = np.squeeze(theta[:, :N, :, :], axis=3)
Q_Y = np.squeeze(theta[:, N:, :, :], axis=3)
Q_X += self._expand_torch(P_X_base, Q_X.shape)
Q_Y += self._expand_torch(P_Y_base, Q_Y.shape)
points_b = points.shape[0]
points_h = points.shape[1]
points_w = points.shape[2]
P_X = self._expand_torch(P_X, (1, points_h, points_w, 1, N))
P_Y = self._expand_torch(P_Y, (1, points_h, points_w, 1, N))
W_X = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_X
W_Y = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_Y
W_X = np.expand_dims(np.expand_dims(W_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
W_X = np.repeat(W_X, points_h, axis=1)
W_X = np.repeat(W_X, points_w, axis=2)
W_Y = np.expand_dims(np.expand_dims(W_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
W_Y = np.repeat(W_Y, points_h, axis=1)
W_Y = np.repeat(W_Y, points_w, axis=2)
A_X = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_X
A_Y = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_Y
A_X = np.expand_dims(np.expand_dims(A_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
A_X = np.repeat(A_X, points_h, axis=1)
A_X = np.repeat(A_X, points_w, axis=2)
A_Y = np.expand_dims(np.expand_dims(A_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
A_Y = np.repeat(A_Y, points_h, axis=1)
A_Y = np.repeat(A_Y, points_w, axis=2)
points_X_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 0], axis=3), axis=4)
points_X_for_summation = self._expand_torch(points_X_for_summation, points[:, :, :, 0].shape + (1, N))
points_Y_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 1], axis=3), axis=4)
points_Y_for_summation = self._expand_torch(points_Y_for_summation, points[:, :, :, 0].shape + (1, N))
if points_b == 1:
delta_X = points_X_for_summation - P_X
delta_Y = points_Y_for_summation - P_Y
else:
delta_X = points_X_for_summation - self._expand_torch(P_X, points_X_for_summation.shape)
delta_Y = points_Y_for_summation - self._expand_torch(P_Y, points_Y_for_summation.shape)
dist_squared = np.power(delta_X, 2) + np.power(delta_Y, 2)
dist_squared[dist_squared == 0] = 1
U = np.multiply(dist_squared, np.log(dist_squared))
points_X_batch = np.expand_dims(points[:,:,:,0], axis=3)
points_Y_batch = np.expand_dims(points[:,:,:,1], axis=3)
if points_b == 1:
points_X_batch = self._expand_torch(points_X_batch, (batch_size, ) + points_X_batch.shape[1:])
points_Y_batch = self._expand_torch(points_Y_batch, (batch_size, ) + points_Y_batch.shape[1:])
points_X_prime = A_X[:,:,:,:,0]+ \
np.multiply(A_X[:,:,:,:,1], points_X_batch) + \
np.multiply(A_X[:,:,:,:,2], points_Y_batch) + \
np.sum(np.multiply(W_X, self._expand_torch(U, W_X.shape)), 4)
points_Y_prime = A_Y[:,:,:,:,0]+ \
np.multiply(A_Y[:,:,:,:,1], points_X_batch) + \
np.multiply(A_Y[:,:,:,:,2], points_Y_batch) + \
np.sum(np.multiply(W_Y, self._expand_torch(U, W_Y.shape)), 4)
return np.concatenate((points_X_prime, points_Y_prime), 3)
def _generate_grid(self, theta):
grid_X, grid_Y, N, P_X, P_Y = self._prepare_to_transform()
warped_grid = self._apply_transformation(theta, np.concatenate((grid_X, grid_Y), axis=3), N, P_X, P_Y)
return warped_grid
def _bilinear_sampler(self, img, grid):
x, y = grid[:,:,:,0], grid[:,:,:,1]
H = img.shape[2]
W = img.shape[3]
max_y = H - 1
max_x = W - 1
# rescale x and y to [0, W-1/H-1]
x = 0.5 * (x + 1.0) * (max_x - 1)
y = 0.5 * (y + 1.0) * (max_y - 1)
# grab 4 nearest corner points for each (x_i, y_i)
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
# calculate deltas
wa = (x1 - x) * (y1 - y)
wb = (x1 - x) * (y - y0)
wc = (x - x0) * (y1 - y)
wd = (x - x0) * (y - y0)
# clip to range [0, H-1/W-1] to not violate img boundaries
x0 = np.clip(x0, 0, max_x)
x1 = np.clip(x1, 0, max_x)
y0 = np.clip(y0, 0, max_y)
y1 = np.clip(y1, 0, max_y)
# get pixel value at corner coords
img = img.reshape(-1, H, W)
Ia = img[:, y0, x0].swapaxes(0, 1)
Ib = img[:, y1, x0].swapaxes(0, 1)
Ic = img[:, y0, x1].swapaxes(0, 1)
Id = img[:, y1, x1].swapaxes(0, 1)
wa = np.expand_dims(wa, axis=0)
wb = np.expand_dims(wb, axis=0)
wc = np.expand_dims(wc, axis=0)
wd = np.expand_dims(wd, axis=0)
# compute output
out = wa*Ia + wb*Ib + wc*Ic + wd*Id
return out
class CorrelationLayer(object):
def __init__(self, params, blobs):
super(CorrelationLayer, self).__init__()
def getMemoryShapes(self, inputs):
fetureAShape = inputs[0]
b, c, h, w = fetureAShape
return [[b, h * w, h, w]]
def forward(self, inputs):
feature_A, feature_B = inputs
b, c, h, w = feature_A.shape
feature_A = feature_A.transpose(0, 1, 3, 2)
feature_A = np.reshape(feature_A, (b, c, h * w))
feature_B = np.reshape(feature_B, (b, c, h * w))
feature_B = feature_B.transpose(0, 2, 1)
feature_mul = feature_B @ feature_A
feature_mul= np.reshape(feature_mul, (b, h, w, h * w))
feature_mul = feature_mul.transpose(0, 1, 3, 2)
correlation_tensor = feature_mul.transpose(0, 2, 1, 3)
correlation_tensor = np.ascontiguousarray(correlation_tensor)
return [correlation_tensor]
if __name__ == "__main__":
if not os.path.isfile(args.gmm_model):
raise OSError("GMM model not exist")
if not os.path.isfile(args.tom_model):
raise OSError("TOM model not exist")
if not os.path.isfile(args.segmentation_model):
raise OSError("Segmentation model not exist")
if not os.path.isfile(findFile(args.openpose_proto)):
raise OSError("OpenPose proto not exist")
if not os.path.isfile(findFile(args.openpose_model)):
raise OSError("OpenPose model not exist")
person_img = cv.imread(args.input_image)
ratio = 256 / 192
inp_h, inp_w, _ = person_img.shape
current_ratio = inp_h / inp_w
if current_ratio > ratio:
center_h = inp_h // 2
out_h = inp_w * ratio
start = int(center_h - out_h // 2)
end = int(center_h + out_h // 2)
person_img = person_img[start:end, ...]
else:
center_w = inp_w // 2
out_w = inp_h / ratio
start = int(center_w - out_w // 2)
end = int(center_w + out_w // 2)
person_img = person_img[:, start:end, :]
cloth_img = cv.imread(args.input_cloth)
pose = get_pose_map(person_img, findFile(args.openpose_proto),
findFile(args.openpose_model), args.backend, args.target)
segm_image = parse_human(person_img, args.segmentation_model)
segm_image = cv.resize(segm_image, (192, 256), cv.INTER_LINEAR)
cv.dnn_registerLayer('Correlation', CorrelationLayer)
model = CpVton(args.gmm_model, args.tom_model, args.backend, args.target)
agnostic = model.prepare_agnostic(segm_image, person_img, pose)
warped_cloth = model.get_warped_cloth(cloth_img, agnostic)
output = model.get_tryon(agnostic, warped_cloth)
cv.dnn_unregisterLayer('Correlation')
winName = 'Virtual Try-On'
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
cv.imshow(winName, output)
cv.waitKey()
|
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(
description='This sample shows how to define custom OpenCV deep learning layers in Python. '
'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to hed_pretrained_bsds.caffemodel', required=True)
parser.add_argument('--width', help='Resize input image to a specific width', default=500, type=int)
parser.add_argument('--height', help='Resize input image to a specific height', default=500, type=int)
args = parser.parse_args()
#! [CropLayer]
class CropLayer(object):
def __init__(self, params, blobs):
self.xstart = 0
self.xend = 0
self.ystart = 0
self.yend = 0
# Our layer receives two inputs. We need to crop the first input blob
# to match a shape of the second one (keeping batch size and number of channels)
def getMemoryShapes(self, inputs):
inputShape, targetShape = inputs[0], inputs[1]
batchSize, numChannels = inputShape[0], inputShape[1]
height, width = targetShape[2], targetShape[3]
self.ystart = (inputShape[2] - targetShape[2]) // 2
self.xstart = (inputShape[3] - targetShape[3]) // 2
self.yend = self.ystart + height
self.xend = self.xstart + width
return [[batchSize, numChannels, height, width]]
def forward(self, inputs):
return [inputs[0][:,:,self.ystart:self.yend,self.xstart:self.xend]]
#! [CropLayer]
#! [Register]
cv.dnn_registerLayer('Crop', CropLayer)
#! [Register]
# Load the model.
net = cv.dnn.readNet(cv.samples.findFile(args.prototxt), cv.samples.findFile(args.caffemodel))
kWinName = 'Holistically-Nested Edge Detection'
cv.namedWindow('Input', cv.WINDOW_NORMAL)
cv.namedWindow(kWinName, cv.WINDOW_NORMAL)
cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
cv.imshow('Input', frame)
inp = cv.dnn.blobFromImage(frame, scalefactor=1.0, size=(args.width, args.height),
mean=(104.00698793, 116.66876762, 122.67891434),
swapRB=False, crop=False)
net.setInput(inp)
out = net.forward()
out = out[0, 0]
out = cv.resize(out, (frame.shape[1], frame.shape[0]))
cv.imshow(kWinName, out)
|
# This file is a part of OpenCV project.
# It is a subject to the license terms in the LICENSE file found in the top-level directory
# of this distribution and at http://opencv.org/license.html.
#
# Copyright (C) 2018, Intel Corporation, all rights reserved.
# Third party copyrights are property of their respective owners.
#
# Use this script to get the text graph representation (.pbtxt) of SSD-based
# deep learning network trained in TensorFlow Object Detection API.
# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
import argparse
import re
from math import sqrt
from tf_text_graph_common import *
class SSDAnchorGenerator:
def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
reduce_boxes_in_lowest_layer, image_width, image_height):
self.min_scale = min_scale
self.aspect_ratios = aspect_ratios
self.reduce_boxes_in_lowest_layer = reduce_boxes_in_lowest_layer
self.image_width = image_width
self.image_height = image_height
self.scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
for i in range(num_layers)] + [1.0]
def get(self, layer_id):
if layer_id == 0 and self.reduce_boxes_in_lowest_layer:
widths = [0.1, self.min_scale * sqrt(2.0), self.min_scale * sqrt(0.5)]
heights = [0.1, self.min_scale / sqrt(2.0), self.min_scale / sqrt(0.5)]
else:
widths = [self.scales[layer_id] * sqrt(ar) for ar in self.aspect_ratios]
heights = [self.scales[layer_id] / sqrt(ar) for ar in self.aspect_ratios]
widths += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
heights += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
min_size = min(self.image_width, self.image_height)
widths = [w * min_size for w in widths]
heights = [h * min_size for h in heights]
return widths, heights
class MultiscaleAnchorGenerator:
def __init__(self, min_level, aspect_ratios, scales_per_octave, anchor_scale):
self.min_level = min_level
self.aspect_ratios = aspect_ratios
self.anchor_scale = anchor_scale
self.scales = [2**(float(s) / scales_per_octave) for s in range(scales_per_octave)]
def get(self, layer_id):
widths = []
heights = []
for a in self.aspect_ratios:
for s in self.scales:
base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale
ar = sqrt(a)
heights.append(base_anchor_size * s / ar)
widths.append(base_anchor_size * s * ar)
return widths, heights
def createSSDGraph(modelPath, configPath, outputPath):
# Nodes that should be kept.
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'AddV2', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm',
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity',
'Sub', 'ResizeNearestNeighbor', 'Pad', 'FusedBatchNormV3']
# Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Concatenate/', 'Postprocessor/', 'Preprocessor/map')
# Load a config file.
config = readTextMessage(configPath)
config = config['model'][0]['ssd'][0]
num_classes = int(config['num_classes'][0])
fixed_shape_resizer = config['image_resizer'][0]['fixed_shape_resizer'][0]
image_width = int(fixed_shape_resizer['width'][0])
image_height = int(fixed_shape_resizer['height'][0])
box_predictor = 'convolutional' if 'convolutional_box_predictor' in config['box_predictor'][0] else 'weight_shared_convolutional'
anchor_generator = config['anchor_generator'][0]
if 'ssd_anchor_generator' in anchor_generator:
ssd_anchor_generator = anchor_generator['ssd_anchor_generator'][0]
min_scale = float(ssd_anchor_generator['min_scale'][0])
max_scale = float(ssd_anchor_generator['max_scale'][0])
num_layers = int(ssd_anchor_generator['num_layers'][0])
aspect_ratios = [float(ar) for ar in ssd_anchor_generator['aspect_ratios']]
reduce_boxes_in_lowest_layer = True
if 'reduce_boxes_in_lowest_layer' in ssd_anchor_generator:
reduce_boxes_in_lowest_layer = ssd_anchor_generator['reduce_boxes_in_lowest_layer'][0] == 'true'
priors_generator = SSDAnchorGenerator(min_scale, max_scale, num_layers,
aspect_ratios, reduce_boxes_in_lowest_layer,
image_width, image_height)
print('Scale: [%f-%f]' % (min_scale, max_scale))
print('Aspect ratios: %s' % str(aspect_ratios))
print('Reduce boxes in the lowest layer: %s' % str(reduce_boxes_in_lowest_layer))
elif 'multiscale_anchor_generator' in anchor_generator:
multiscale_anchor_generator = anchor_generator['multiscale_anchor_generator'][0]
min_level = int(multiscale_anchor_generator['min_level'][0])
max_level = int(multiscale_anchor_generator['max_level'][0])
anchor_scale = float(multiscale_anchor_generator['anchor_scale'][0])
aspect_ratios = [float(ar) for ar in multiscale_anchor_generator['aspect_ratios']]
scales_per_octave = int(multiscale_anchor_generator['scales_per_octave'][0])
num_layers = max_level - min_level + 1
priors_generator = MultiscaleAnchorGenerator(min_level, aspect_ratios,
scales_per_octave, anchor_scale)
print('Levels: [%d-%d]' % (min_level, max_level))
print('Anchor scale: %f' % anchor_scale)
print('Scales per octave: %d' % scales_per_octave)
print('Aspect ratios: %s' % str(aspect_ratios))
else:
print('Unknown anchor_generator')
exit(0)
print('Number of classes: %d' % num_classes)
print('Number of layers: %d' % num_layers)
print('box predictor: %s' % box_predictor)
print('Input image size: %dx%d' % (image_width, image_height))
# Read the graph.
_inpNames = ['image_tensor']
outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
writeTextGraph(modelPath, outputPath, outNames)
graph_def = parseTextGraph(outputPath)
def getUnconnectedNodes():
unconnected = []
for node in graph_def.node:
unconnected.append(node.name)
for inp in node.input:
if inp in unconnected:
unconnected.remove(inp)
return unconnected
def fuse_nodes(nodesToKeep):
# Detect unfused batch normalization nodes and fuse them.
# Add_0 <-- moving_variance, add_y
# Rsqrt <-- Add_0
# Mul_0 <-- Rsqrt, gamma
# Mul_1 <-- input, Mul_0
# Mul_2 <-- moving_mean, Mul_0
# Sub_0 <-- beta, Mul_2
# Add_1 <-- Mul_1, Sub_0
nodesMap = {node.name: node for node in graph_def.node}
subgraphBatchNorm = ['Add',
['Mul', 'input', ['Mul', ['Rsqrt', ['Add', 'moving_variance', 'add_y']], 'gamma']],
['Sub', 'beta', ['Mul', 'moving_mean', 'Mul_0']]]
subgraphBatchNormV2 = ['AddV2',
['Mul', 'input', ['Mul', ['Rsqrt', ['AddV2', 'moving_variance', 'add_y']], 'gamma']],
['Sub', 'beta', ['Mul', 'moving_mean', 'Mul_0']]]
# Detect unfused nearest neighbor resize.
subgraphResizeNN = ['Reshape',
['Mul', ['Reshape', 'input', ['Pack', 'shape_1', 'shape_2', 'shape_3', 'shape_4', 'shape_5']],
'ones'],
['Pack', ['StridedSlice', ['Shape', 'input'], 'stack', 'stack_1', 'stack_2'],
'out_height', 'out_width', 'out_channels']]
def checkSubgraph(node, targetNode, inputs, fusedNodes):
op = targetNode[0]
if node.op == op and (len(node.input) >= len(targetNode) - 1):
fusedNodes.append(node)
for i, inpOp in enumerate(targetNode[1:]):
if isinstance(inpOp, list):
if not node.input[i] in nodesMap or \
not checkSubgraph(nodesMap[node.input[i]], inpOp, inputs, fusedNodes):
return False
else:
inputs[inpOp] = node.input[i]
return True
else:
return False
nodesToRemove = []
for node in graph_def.node:
inputs = {}
fusedNodes = []
if checkSubgraph(node, subgraphBatchNorm, inputs, fusedNodes) or \
checkSubgraph(node, subgraphBatchNormV2, inputs, fusedNodes):
name = node.name
node.Clear()
node.name = name
node.op = 'FusedBatchNorm'
node.input.append(inputs['input'])
node.input.append(inputs['gamma'])
node.input.append(inputs['beta'])
node.input.append(inputs['moving_mean'])
node.input.append(inputs['moving_variance'])
node.addAttr('epsilon', 0.001)
nodesToRemove += fusedNodes[1:]
inputs = {}
fusedNodes = []
if checkSubgraph(node, subgraphResizeNN, inputs, fusedNodes):
name = node.name
node.Clear()
node.name = name
node.op = 'ResizeNearestNeighbor'
node.input.append(inputs['input'])
node.input.append(name + '/output_shape')
out_height_node = nodesMap[inputs['out_height']]
out_width_node = nodesMap[inputs['out_width']]
out_height = int(out_height_node.attr['value']['tensor'][0]['int_val'][0])
out_width = int(out_width_node.attr['value']['tensor'][0]['int_val'][0])
shapeNode = NodeDef()
shapeNode.name = name + '/output_shape'
shapeNode.op = 'Const'
shapeNode.addAttr('value', [out_height, out_width])
graph_def.node.insert(graph_def.node.index(node), shapeNode)
nodesToKeep.append(shapeNode.name)
nodesToRemove += fusedNodes[1:]
for node in nodesToRemove:
graph_def.node.remove(node)
nodesToKeep = []
fuse_nodes(nodesToKeep)
removeIdentity(graph_def)
def to_remove(name, op):
return (not name in nodesToKeep) and \
(op == 'Const' or (not op in keepOps) or name.startswith(prefixesToRemove))
removeUnusedNodesAndAttrs(to_remove, graph_def)
# Connect input node to the first layer
assert(graph_def.node[0].op == 'Placeholder')
# assert(graph_def.node[1].op == 'Conv2D')
weights = graph_def.node[1].input[0]
for i in range(len(graph_def.node[1].input)):
graph_def.node[1].input.pop()
graph_def.node[1].input.append(graph_def.node[0].name)
graph_def.node[1].input.append(weights)
# Create SSD postprocessing head ###############################################
# Concatenate predictions of classes, predictions of bounding boxes and proposals.
def addConcatNode(name, inputs, axisNodeName):
concat = NodeDef()
concat.name = name
concat.op = 'ConcatV2'
for inp in inputs:
concat.input.append(inp)
concat.input.append(axisNodeName)
graph_def.node.extend([concat])
addConstNode('concat/axis_flatten', [-1], graph_def)
addConstNode('PriorBox/concat/axis', [-2], graph_def)
for label in ['ClassPredictor', 'BoxEncodingPredictor' if box_predictor is 'convolutional' else 'BoxPredictor']:
concatInputs = []
for i in range(num_layers):
# Flatten predictions
flatten = NodeDef()
if box_predictor is 'convolutional':
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
else:
if i == 0:
inpName = 'WeightSharedConvolutionalBoxPredictor/%s/BiasAdd' % label
else:
inpName = 'WeightSharedConvolutionalBoxPredictor_%d/%s/BiasAdd' % (i, label)
flatten.input.append(inpName)
flatten.name = inpName + '/Flatten'
flatten.op = 'Flatten'
concatInputs.append(flatten.name)
graph_def.node.extend([flatten])
addConcatNode('%s/concat' % label, concatInputs, 'concat/axis_flatten')
num_matched_layers = 0
for node in graph_def.node:
if re.match('BoxPredictor_\d/BoxEncodingPredictor/convolution', node.name) or \
re.match('BoxPredictor_\d/BoxEncodingPredictor/Conv2D', node.name) or \
re.match('WeightSharedConvolutionalBoxPredictor(_\d)*/BoxPredictor/Conv2D', node.name):
node.addAttr('loc_pred_transposed', True)
num_matched_layers += 1
assert(num_matched_layers == num_layers)
# Add layers that generate anchors (bounding boxes proposals).
priorBoxes = []
boxCoder = config['box_coder'][0]
fasterRcnnBoxCoder = boxCoder['faster_rcnn_box_coder'][0]
boxCoderVariance = [1.0/float(fasterRcnnBoxCoder['x_scale'][0]), 1.0/float(fasterRcnnBoxCoder['y_scale'][0]), 1.0/float(fasterRcnnBoxCoder['width_scale'][0]), 1.0/float(fasterRcnnBoxCoder['height_scale'][0])]
for i in range(num_layers):
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
priorBox.op = 'PriorBox'
if box_predictor is 'convolutional':
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
else:
if i == 0:
priorBox.input.append('WeightSharedConvolutionalBoxPredictor/BoxPredictor/Conv2D')
else:
priorBox.input.append('WeightSharedConvolutionalBoxPredictor_%d/BoxPredictor/BiasAdd' % i)
priorBox.input.append(graph_def.node[0].name) # image_tensor
priorBox.addAttr('flip', False)
priorBox.addAttr('clip', False)
widths, heights = priors_generator.get(i)
priorBox.addAttr('width', widths)
priorBox.addAttr('height', heights)
priorBox.addAttr('variance', boxCoderVariance)
graph_def.node.extend([priorBox])
priorBoxes.append(priorBox.name)
# Compare this layer's output with Postprocessor/Reshape
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
# Sigmoid for classes predictions and DetectionOutput layer
addReshape('ClassPredictor/concat', 'ClassPredictor/concat3d', [0, -1, num_classes + 1], graph_def)
sigmoid = NodeDef()
sigmoid.name = 'ClassPredictor/concat/sigmoid'
sigmoid.op = 'Sigmoid'
sigmoid.input.append('ClassPredictor/concat3d')
graph_def.node.extend([sigmoid])
addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def)
detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
if box_predictor == 'convolutional':
detectionOut.input.append('BoxEncodingPredictor/concat')
else:
detectionOut.input.append('BoxPredictor/concat')
detectionOut.input.append(sigmoid.name + '/Flatten')
detectionOut.input.append('PriorBox/concat')
detectionOut.addAttr('num_classes', num_classes + 1)
detectionOut.addAttr('share_location', True)
detectionOut.addAttr('background_label_id', 0)
postProcessing = config['post_processing'][0]
batchNMS = postProcessing['batch_non_max_suppression'][0]
if 'iou_threshold' in batchNMS:
detectionOut.addAttr('nms_threshold', float(batchNMS['iou_threshold'][0]))
else:
detectionOut.addAttr('nms_threshold', 0.6)
if 'score_threshold' in batchNMS:
detectionOut.addAttr('confidence_threshold', float(batchNMS['score_threshold'][0]))
else:
detectionOut.addAttr('confidence_threshold', 0.01)
if 'max_detections_per_class' in batchNMS:
detectionOut.addAttr('top_k', int(batchNMS['max_detections_per_class'][0]))
else:
detectionOut.addAttr('top_k', 100)
if 'max_total_detections' in batchNMS:
detectionOut.addAttr('keep_top_k', int(batchNMS['max_total_detections'][0]))
else:
detectionOut.addAttr('keep_top_k', 100)
detectionOut.addAttr('code_type', "CENTER_SIZE")
graph_def.node.extend([detectionOut])
while True:
unconnectedNodes = getUnconnectedNodes()
unconnectedNodes.remove(detectionOut.name)
if not unconnectedNodes:
break
for name in unconnectedNodes:
for i in range(len(graph_def.node)):
if graph_def.node[i].name == name:
del graph_def.node[i]
break
# Save as text.
graph_def.save(outputPath)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
'SSD model from TensorFlow Object Detection API. '
'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
parser.add_argument('--output', required=True, help='Path to output text graph.')
parser.add_argument('--config', required=True, help='Path to a *.config file is used for training.')
args = parser.parse_args()
createSSDGraph(args.input, args.config, args.output)
|
import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile
parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')
# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):
# - out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# - zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
# - out.size(2), out.size(3),
# - out.size(4)).zero_()
# - if isinstance(out.data, torch.cuda.FloatTensor):
# - zero_pads = zero_pads.cuda()
# -
# - out = Variable(torch.cat([out.data, zero_pads], dim=1))
# - return out
# To
# + def downsample_basic_block(x, planes, stride):
# + out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# + out = F.pad(out, (0, 0, 0, 0, 0, 0, 0, int(planes - out.size(1)), 0, 0), "constant", 0)
# + return out
# To ONNX export use torch.onnx.export(model, inputs, model_name)
def get_class_names(path):
class_names = []
with open(path) as f:
for row in f:
class_names.append(row[:-1])
return class_names
def classify_video(video_path, net_path):
SAMPLE_DURATION = 16
SAMPLE_SIZE = 112
mean = (114.7748, 107.7354, 99.4750)
class_names = get_class_names(args.classes)
net = cv.dnn.readNet(net_path)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
winName = 'Deep learning image classification in OpenCV'
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
cap = cv.VideoCapture(video_path)
while cv.waitKey(1) < 0:
frames = []
for _ in range(SAMPLE_DURATION):
hasFrame, frame = cap.read()
if not hasFrame:
exit(0)
frames.append(frame)
inputs = cv.dnn.blobFromImages(frames, 1, (SAMPLE_SIZE, SAMPLE_SIZE), mean, True, crop=True)
inputs = np.transpose(inputs, (1, 0, 2, 3))
inputs = np.expand_dims(inputs, axis=0)
net.setInput(inputs)
outputs = net.forward()
class_pred = np.argmax(outputs)
label = class_names[class_pred]
for frame in frames:
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv.rectangle(frame, (0, 10 - labelSize[1]),
(labelSize[0], 10 + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (0, 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
cv.imshow(winName, frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
if __name__ == "__main__":
args, _ = parser.parse_known_args()
classify_video(args.input if args.input else 0, args.model)
|
from __future__ import print_function
# Script to evaluate MobileNet-SSD object detection model trained in TensorFlow
# using both TensorFlow and OpenCV. Example:
#
# python mobilenet_ssd_accuracy.py \
# --weights=frozen_inference_graph.pb \
# --prototxt=ssd_mobilenet_v1_coco.pbtxt \
# --images=val2017 \
# --annotations=annotations/instances_val2017.json
#
# Tested on COCO 2017 object detection dataset, http://cocodataset.org/#download
import os
import cv2 as cv
import json
import argparse
parser = argparse.ArgumentParser(
description='Evaluate MobileNet-SSD model using both TensorFlow and OpenCV. '
'COCO evaluation framework is required: http://cocodataset.org')
parser.add_argument('--weights', required=True,
help='Path to frozen_inference_graph.pb of MobileNet-SSD model. '
'Download it from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz')
parser.add_argument('--prototxt', help='Path to ssd_mobilenet_v1_coco.pbtxt from opencv_extra.', required=True)
parser.add_argument('--images', help='Path to COCO validation images directory.', required=True)
parser.add_argument('--annotations', help='Path to COCO annotations file.', required=True)
args = parser.parse_args()
### Get OpenCV predictions #####################################################
net = cv.dnn.readNetFromTensorflow(cv.samples.findFile(args.weights), cv.samples.findFile(args.prototxt))
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
detections = []
for imgName in os.listdir(args.images):
inp = cv.imread(cv.samples.findFile(os.path.join(args.images, imgName)))
rows = inp.shape[0]
cols = inp.shape[1]
inp = cv.resize(inp, (300, 300))
net.setInput(cv.dnn.blobFromImage(inp, 1.0/127.5, (300, 300), (127.5, 127.5, 127.5), True))
out = net.forward()
for i in range(out.shape[2]):
score = float(out[0, 0, i, 2])
# Confidence threshold is in prototxt.
classId = int(out[0, 0, i, 1])
x = out[0, 0, i, 3] * cols
y = out[0, 0, i, 4] * rows
w = out[0, 0, i, 5] * cols - x
h = out[0, 0, i, 6] * rows - y
detections.append({
"image_id": int(imgName.rstrip('0')[:imgName.rfind('.')]),
"category_id": classId,
"bbox": [x, y, w, h],
"score": score
})
with open('cv_result.json', 'wt') as f:
json.dump(detections, f)
### Get TensorFlow predictions #################################################
import tensorflow as tf
with tf.gfile.FastGFile(args.weights) as f:
# Load the model
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Session() as sess:
# Restore session
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
detections = []
for imgName in os.listdir(args.images):
inp = cv.imread(os.path.join(args.images, imgName))
rows = inp.shape[0]
cols = inp.shape[1]
inp = cv.resize(inp, (300, 300))
inp = inp[:, :, [2, 1, 0]] # BGR2RGB
out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
sess.graph.get_tensor_by_name('detection_scores:0'),
sess.graph.get_tensor_by_name('detection_boxes:0'),
sess.graph.get_tensor_by_name('detection_classes:0')],
feed_dict={'image_tensor:0': inp.reshape(1, inp.shape[0], inp.shape[1], 3)})
num_detections = int(out[0][0])
for i in range(num_detections):
classId = int(out[3][0][i])
score = float(out[1][0][i])
bbox = [float(v) for v in out[2][0][i]]
if score > 0.01:
x = bbox[1] * cols
y = bbox[0] * rows
w = bbox[3] * cols - x
h = bbox[2] * rows - y
detections.append({
"image_id": int(imgName.rstrip('0')[:imgName.rfind('.')]),
"category_id": classId,
"bbox": [x, y, w, h],
"score": score
})
with open('tf_result.json', 'wt') as f:
json.dump(detections, f)
### Evaluation part ############################################################
# %matplotlib inline
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
annType = ['segm','bbox','keypoints']
annType = annType[1] #specify type here
prefix = 'person_keypoints' if annType=='keypoints' else 'instances'
print('Running demo for *%s* results.'%(annType))
#initialize COCO ground truth api
cocoGt=COCO(args.annotations)
#initialize COCO detections api
for resFile in ['tf_result.json', 'cv_result.json']:
print(resFile)
cocoDt=cocoGt.loadRes(resFile)
cocoEval = COCOeval(cocoGt,cocoDt,annType)
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
|
import argparse
import numpy as np
from tf_text_graph_common import *
def createFasterRCNNGraph(modelPath, configPath, outputPath):
scopesToKeep = ('FirstStageFeatureExtractor', 'Conv',
'FirstStageBoxPredictor/BoxEncodingPredictor',
'FirstStageBoxPredictor/ClassPredictor',
'CropAndResize',
'MaxPool2D',
'SecondStageFeatureExtractor',
'SecondStageBoxPredictor',
'Preprocessor/sub',
'Preprocessor/mul',
'image_tensor')
scopesToIgnore = ('FirstStageFeatureExtractor/Assert',
'FirstStageFeatureExtractor/Shape',
'FirstStageFeatureExtractor/strided_slice',
'FirstStageFeatureExtractor/GreaterEqual',
'FirstStageFeatureExtractor/LogicalAnd')
# Load a config file.
config = readTextMessage(configPath)
config = config['model'][0]['faster_rcnn'][0]
num_classes = int(config['num_classes'][0])
grid_anchor_generator = config['first_stage_anchor_generator'][0]['grid_anchor_generator'][0]
scales = [float(s) for s in grid_anchor_generator['scales']]
aspect_ratios = [float(ar) for ar in grid_anchor_generator['aspect_ratios']]
width_stride = float(grid_anchor_generator['width_stride'][0])
height_stride = float(grid_anchor_generator['height_stride'][0])
feature_extractor = config['feature_extractor'][0]
if 'type' in feature_extractor and feature_extractor['type'][0] == 'faster_rcnn_nas':
features_stride = 16.0
else:
features_stride = float(feature_extractor['first_stage_features_stride'][0])
first_stage_nms_iou_threshold = float(config['first_stage_nms_iou_threshold'][0])
first_stage_max_proposals = int(config['first_stage_max_proposals'][0])
print('Number of classes: %d' % num_classes)
print('Scales: %s' % str(scales))
print('Aspect ratios: %s' % str(aspect_ratios))
print('Width stride: %f' % width_stride)
print('Height stride: %f' % height_stride)
print('Features stride: %f' % features_stride)
# Read the graph.
writeTextGraph(modelPath, outputPath, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes'])
graph_def = parseTextGraph(outputPath)
removeIdentity(graph_def)
nodesToKeep = []
def to_remove(name, op):
if name in nodesToKeep:
return False
return op == 'Const' or name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
(name.startswith('CropAndResize') and op != 'CropAndResize')
# Fuse atrous convolutions (with dilations).
nodesMap = {node.name: node for node in graph_def.node}
for node in reversed(graph_def.node):
if node.op == 'BatchToSpaceND':
del node.input[2]
conv = nodesMap[node.input[0]]
spaceToBatchND = nodesMap[conv.input[0]]
# Extract paddings
stridedSlice = nodesMap[spaceToBatchND.input[2]]
assert(stridedSlice.op == 'StridedSlice')
pack = nodesMap[stridedSlice.input[0]]
assert(pack.op == 'Pack')
padNodeH = nodesMap[nodesMap[pack.input[0]].input[0]]
padNodeW = nodesMap[nodesMap[pack.input[1]].input[0]]
padH = int(padNodeH.attr['value']['tensor'][0]['int_val'][0])
padW = int(padNodeW.attr['value']['tensor'][0]['int_val'][0])
paddingsNode = NodeDef()
paddingsNode.name = conv.name + '/paddings'
paddingsNode.op = 'Const'
paddingsNode.addAttr('value', [padH, padH, padW, padW])
graph_def.node.insert(graph_def.node.index(spaceToBatchND), paddingsNode)
nodesToKeep.append(paddingsNode.name)
spaceToBatchND.input[2] = paddingsNode.name
removeUnusedNodesAndAttrs(to_remove, graph_def)
# Connect input node to the first layer
assert(graph_def.node[0].op == 'Placeholder')
graph_def.node[1].input.insert(0, graph_def.node[0].name)
# Temporarily remove top nodes.
topNodes = []
while True:
node = graph_def.node.pop()
topNodes.append(node)
if node.op == 'CropAndResize':
break
addReshape('FirstStageBoxPredictor/ClassPredictor/BiasAdd',
'FirstStageBoxPredictor/ClassPredictor/reshape_1', [0, -1, 2], graph_def)
addSoftMax('FirstStageBoxPredictor/ClassPredictor/reshape_1',
'FirstStageBoxPredictor/ClassPredictor/softmax', graph_def) # Compare with Reshape_4
addFlatten('FirstStageBoxPredictor/ClassPredictor/softmax',
'FirstStageBoxPredictor/ClassPredictor/softmax/flatten', graph_def)
# Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
addFlatten('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd',
'FirstStageBoxPredictor/BoxEncodingPredictor/flatten', graph_def)
proposals = NodeDef()
proposals.name = 'proposals' # Compare with ClipToWindow/Gather/Gather (NOTE: normalized)
proposals.op = 'PriorBox'
proposals.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd')
proposals.input.append(graph_def.node[0].name) # image_tensor
proposals.addAttr('flip', False)
proposals.addAttr('clip', True)
proposals.addAttr('step', features_stride)
proposals.addAttr('offset', 0.0)
proposals.addAttr('variance', [0.1, 0.1, 0.2, 0.2])
widths = []
heights = []
for a in aspect_ratios:
for s in scales:
ar = np.sqrt(a)
heights.append((height_stride**2) * s / ar)
widths.append((width_stride**2) * s * ar)
proposals.addAttr('width', widths)
proposals.addAttr('height', heights)
graph_def.node.extend([proposals])
# Compare with Reshape_5
detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/flatten')
detectionOut.input.append('FirstStageBoxPredictor/ClassPredictor/softmax/flatten')
detectionOut.input.append('proposals')
detectionOut.addAttr('num_classes', 2)
detectionOut.addAttr('share_location', True)
detectionOut.addAttr('background_label_id', 0)
detectionOut.addAttr('nms_threshold', first_stage_nms_iou_threshold)
detectionOut.addAttr('top_k', 6000)
detectionOut.addAttr('code_type', "CENTER_SIZE")
detectionOut.addAttr('keep_top_k', first_stage_max_proposals)
detectionOut.addAttr('clip', False)
graph_def.node.extend([detectionOut])
addConstNode('clip_by_value/lower', [0.0], graph_def)
addConstNode('clip_by_value/upper', [1.0], graph_def)
clipByValueNode = NodeDef()
clipByValueNode.name = 'detection_out/clip_by_value'
clipByValueNode.op = 'ClipByValue'
clipByValueNode.input.append('detection_out')
clipByValueNode.input.append('clip_by_value/lower')
clipByValueNode.input.append('clip_by_value/upper')
graph_def.node.extend([clipByValueNode])
# Save as text.
for node in reversed(topNodes):
graph_def.node.extend([node])
addSoftMax('SecondStageBoxPredictor/Reshape_1', 'SecondStageBoxPredictor/Reshape_1/softmax', graph_def)
addSlice('SecondStageBoxPredictor/Reshape_1/softmax',
'SecondStageBoxPredictor/Reshape_1/slice',
[0, 0, 1], [-1, -1, -1], graph_def)
addReshape('SecondStageBoxPredictor/Reshape_1/slice',
'SecondStageBoxPredictor/Reshape_1/Reshape', [1, -1], graph_def)
# Replace Flatten subgraph onto a single node.
cropAndResizeNodeName = ''
for i in reversed(range(len(graph_def.node))):
if graph_def.node[i].op == 'CropAndResize':
graph_def.node[i].input.insert(1, 'detection_out/clip_by_value')
cropAndResizeNodeName = graph_def.node[i].name
if graph_def.node[i].name == 'SecondStageBoxPredictor/Reshape':
addConstNode('SecondStageBoxPredictor/Reshape/shape2', [1, -1, 4], graph_def)
graph_def.node[i].input.pop()
graph_def.node[i].input.append('SecondStageBoxPredictor/Reshape/shape2')
if graph_def.node[i].name in ['SecondStageBoxPredictor/Flatten/flatten/Shape',
'SecondStageBoxPredictor/Flatten/flatten/strided_slice',
'SecondStageBoxPredictor/Flatten/flatten/Reshape/shape',
'SecondStageBoxPredictor/Flatten_1/flatten/Shape',
'SecondStageBoxPredictor/Flatten_1/flatten/strided_slice',
'SecondStageBoxPredictor/Flatten_1/flatten/Reshape/shape']:
del graph_def.node[i]
for node in graph_def.node:
if node.name == 'SecondStageBoxPredictor/Flatten/flatten/Reshape' or \
node.name == 'SecondStageBoxPredictor/Flatten_1/flatten/Reshape':
node.op = 'Flatten'
node.input.pop()
if node.name in ['FirstStageBoxPredictor/BoxEncodingPredictor/Conv2D',
'SecondStageBoxPredictor/BoxEncodingPredictor/MatMul']:
node.addAttr('loc_pred_transposed', True)
if node.name.startswith('MaxPool2D'):
assert(node.op == 'MaxPool')
assert(cropAndResizeNodeName)
node.input = [cropAndResizeNodeName]
################################################################################
### Postprocessing
################################################################################
addSlice('detection_out/clip_by_value', 'detection_out/slice', [0, 0, 0, 3], [-1, -1, -1, 4], graph_def)
variance = NodeDef()
variance.name = 'proposals/variance'
variance.op = 'Const'
variance.addAttr('value', [0.1, 0.1, 0.2, 0.2])
graph_def.node.extend([variance])
varianceEncoder = NodeDef()
varianceEncoder.name = 'variance_encoded'
varianceEncoder.op = 'Mul'
varianceEncoder.input.append('SecondStageBoxPredictor/Reshape')
varianceEncoder.input.append(variance.name)
varianceEncoder.addAttr('axis', 2)
graph_def.node.extend([varianceEncoder])
addReshape('detection_out/slice', 'detection_out/slice/reshape', [1, 1, -1], graph_def)
addFlatten('variance_encoded', 'variance_encoded/flatten', graph_def)
detectionOut = NodeDef()
detectionOut.name = 'detection_out_final'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('variance_encoded/flatten')
detectionOut.input.append('SecondStageBoxPredictor/Reshape_1/Reshape')
detectionOut.input.append('detection_out/slice/reshape')
detectionOut.addAttr('num_classes', num_classes)
detectionOut.addAttr('share_location', False)
detectionOut.addAttr('background_label_id', num_classes + 1)
detectionOut.addAttr('nms_threshold', 0.6)
detectionOut.addAttr('code_type', "CENTER_SIZE")
detectionOut.addAttr('keep_top_k', 100)
detectionOut.addAttr('clip', True)
detectionOut.addAttr('variance_encoded_in_target', True)
graph_def.node.extend([detectionOut])
def getUnconnectedNodes():
unconnected = [node.name for node in graph_def.node]
for node in graph_def.node:
for inp in node.input:
if inp in unconnected:
unconnected.remove(inp)
return unconnected
while True:
unconnectedNodes = getUnconnectedNodes()
unconnectedNodes.remove(detectionOut.name)
if not unconnectedNodes:
break
for name in unconnectedNodes:
for i in range(len(graph_def.node)):
if graph_def.node[i].name == name:
del graph_def.node[i]
break
# Save as text.
graph_def.save(outputPath)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
'Faster-RCNN model from TensorFlow Object Detection API. '
'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
parser.add_argument('--output', required=True, help='Path to output text graph.')
parser.add_argument('--config', required=True, help='Path to a *.config file is used for training.')
args = parser.parse_args()
createFasterRCNNGraph(args.input, args.config, args.output)
|
# Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py
# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/colorization/models
# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/colorization/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv
def parse_args():
parser = argparse.ArgumentParser(description='iColor: deep interactive colorization')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True)
parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True)
args = parser.parse_args()
return args
if __name__ == '__main__':
W_in = 224
H_in = 224
imshowSize = (640, 480)
args = parse_args()
# Select desired model
net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
pts_in_hull = np.load(args.kernel) # load cluster centers
# populate cluster centers as 1x1 convolution kernel
pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1)
net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)]
net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, np.float32)]
if args.input:
cap = cv.VideoCapture(args.input)
else:
cap = cv.VideoCapture(0)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
img_rgb = (frame[:,:,[2, 1, 0]] * 1.0 / 255).astype(np.float32)
img_lab = cv.cvtColor(img_rgb, cv.COLOR_RGB2Lab)
img_l = img_lab[:,:,0] # pull out L channel
(H_orig,W_orig) = img_rgb.shape[:2] # original image size
# resize image to network input size
img_rs = cv.resize(img_rgb, (W_in, H_in)) # resize image to network input size
img_lab_rs = cv.cvtColor(img_rs, cv.COLOR_RGB2Lab)
img_l_rs = img_lab_rs[:,:,0]
img_l_rs -= 50 # subtract 50 for mean-centering
net.setInput(cv.dnn.blobFromImage(img_l_rs))
ab_dec = net.forward()[0,:,:,:].transpose((1,2,0)) # this is our result
(H_out,W_out) = ab_dec.shape[:2]
ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig))
img_lab_out = np.concatenate((img_l[:,:,np.newaxis],ab_dec_us),axis=2) # concatenate with original image L
img_bgr_out = np.clip(cv.cvtColor(img_lab_out, cv.COLOR_Lab2BGR), 0, 1)
frame = cv.resize(frame, imshowSize)
cv.imshow('origin', frame)
cv.imshow('gray', cv.cvtColor(frame, cv.COLOR_RGB2GRAY))
cv.imshow('colorized', cv.resize(img_bgr_out, imshowSize))
|
import sys
import os
import cv2 as cv
def add_argument(zoo, parser, name, help, required=False, default=None, type=None, action=None, nargs=None):
if len(sys.argv) <= 1:
return
modelName = sys.argv[1]
if os.path.isfile(zoo):
fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ)
node = fs.getNode(modelName)
if not node.empty():
value = node.getNode(name)
if not value.empty():
if value.isReal():
default = value.real()
elif value.isString():
default = value.string()
elif value.isInt():
default = int(value.real())
elif value.isSeq():
default = []
for i in range(value.size()):
v = value.at(i)
if v.isInt():
default.append(int(v.real()))
elif v.isReal():
default.append(v.real())
else:
print('Unexpected value format')
exit(0)
else:
print('Unexpected field format')
exit(0)
required = False
if action == 'store_true':
default = 1 if default == 'true' else (0 if default == 'false' else default)
assert(default is None or default == 0 or default == 1)
parser.add_argument('--' + name, required=required, help=help, default=bool(default),
action=action)
else:
parser.add_argument('--' + name, required=required, help=help, default=default,
action=action, nargs=nargs, type=type)
def add_preproc_args(zoo, parser, sample):
aliases = []
if os.path.isfile(zoo):
fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ)
root = fs.root()
for name in root.keys():
model = root.getNode(name)
if model.getNode('sample').string() == sample:
aliases.append(name)
parser.add_argument('alias', nargs='?', choices=aliases,
help='An alias name of model to extract preprocessing parameters from models.yml file.')
add_argument(zoo, parser, 'model', required=True,
help='Path to a binary file of model contains trained weights. '
'It could be a file with extensions .caffemodel (Caffe), '
'.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO)')
add_argument(zoo, parser, 'config',
help='Path to a text file of model contains network configuration. '
'It could be a file with extensions .prototxt (Caffe), .pbtxt or .config (TensorFlow), .cfg (Darknet), .xml (OpenVINO)')
add_argument(zoo, parser, 'mean', nargs='+', type=float, default=[0, 0, 0],
help='Preprocess input image by subtracting mean values. '
'Mean values should be in BGR order.')
add_argument(zoo, parser, 'scale', type=float, default=1.0,
help='Preprocess input image by multiplying on a scale factor.')
add_argument(zoo, parser, 'width', type=int,
help='Preprocess input image by resizing to a specific width.')
add_argument(zoo, parser, 'height', type=int,
help='Preprocess input image by resizing to a specific height.')
add_argument(zoo, parser, 'rgb', action='store_true',
help='Indicate that model works with RGB input images instead BGR ones.')
add_argument(zoo, parser, 'classes',
help='Optional path to a text file with names of classes to label detected objects.')
def findFile(filename):
if filename:
if os.path.exists(filename):
return filename
fpath = cv.samples.findFile(filename, False)
if fpath:
return fpath
samplesDataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'..',
'data',
'dnn')
if os.path.exists(os.path.join(samplesDataDir, filename)):
return os.path.join(samplesDataDir, filename)
for path in ['OPENCV_DNN_TEST_DATA_PATH', 'OPENCV_TEST_DATA_PATH']:
try:
extraPath = os.environ[path]
absPath = os.path.join(extraPath, 'dnn', filename)
if os.path.exists(absPath):
return absPath
except KeyError:
pass
print('File ' + filename + ' not found! Please specify a path to '
'/opencv_extra/testdata in OPENCV_DNN_TEST_DATA_PATH environment '
'variable or pass a full path to model.')
exit(0)
|
import argparse
import numpy as np
from tf_text_graph_common import *
parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
'Mask-RCNN model from TensorFlow Object Detection API. '
'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
parser.add_argument('--output', required=True, help='Path to output text graph.')
parser.add_argument('--config', required=True, help='Path to a *.config file is used for training.')
args = parser.parse_args()
scopesToKeep = ('FirstStageFeatureExtractor', 'Conv',
'FirstStageBoxPredictor/BoxEncodingPredictor',
'FirstStageBoxPredictor/ClassPredictor',
'CropAndResize',
'MaxPool2D',
'SecondStageFeatureExtractor',
'SecondStageBoxPredictor',
'Preprocessor/sub',
'Preprocessor/mul',
'image_tensor')
scopesToIgnore = ('FirstStageFeatureExtractor/Assert',
'FirstStageFeatureExtractor/Shape',
'FirstStageFeatureExtractor/strided_slice',
'FirstStageFeatureExtractor/GreaterEqual',
'FirstStageFeatureExtractor/LogicalAnd',
'Conv/required_space_to_batch_paddings')
# Load a config file.
config = readTextMessage(args.config)
config = config['model'][0]['faster_rcnn'][0]
num_classes = int(config['num_classes'][0])
grid_anchor_generator = config['first_stage_anchor_generator'][0]['grid_anchor_generator'][0]
scales = [float(s) for s in grid_anchor_generator['scales']]
aspect_ratios = [float(ar) for ar in grid_anchor_generator['aspect_ratios']]
width_stride = float(grid_anchor_generator['width_stride'][0])
height_stride = float(grid_anchor_generator['height_stride'][0])
features_stride = float(config['feature_extractor'][0]['first_stage_features_stride'][0])
first_stage_nms_iou_threshold = float(config['first_stage_nms_iou_threshold'][0])
first_stage_max_proposals = int(config['first_stage_max_proposals'][0])
print('Number of classes: %d' % num_classes)
print('Scales: %s' % str(scales))
print('Aspect ratios: %s' % str(aspect_ratios))
print('Width stride: %f' % width_stride)
print('Height stride: %f' % height_stride)
print('Features stride: %f' % features_stride)
# Read the graph.
writeTextGraph(args.input, args.output, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes', 'detection_masks'])
graph_def = parseTextGraph(args.output)
removeIdentity(graph_def)
nodesToKeep = []
def to_remove(name, op):
if name in nodesToKeep:
return False
return op == 'Const' or name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
(name.startswith('CropAndResize') and op != 'CropAndResize')
# Fuse atrous convolutions (with dilations).
nodesMap = {node.name: node for node in graph_def.node}
for node in reversed(graph_def.node):
if node.op == 'BatchToSpaceND':
del node.input[2]
conv = nodesMap[node.input[0]]
spaceToBatchND = nodesMap[conv.input[0]]
paddingsNode = NodeDef()
paddingsNode.name = conv.name + '/paddings'
paddingsNode.op = 'Const'
paddingsNode.addAttr('value', [2, 2, 2, 2])
graph_def.node.insert(graph_def.node.index(spaceToBatchND), paddingsNode)
nodesToKeep.append(paddingsNode.name)
spaceToBatchND.input[2] = paddingsNode.name
removeUnusedNodesAndAttrs(to_remove, graph_def)
# Connect input node to the first layer
assert(graph_def.node[0].op == 'Placeholder')
graph_def.node[1].input.insert(0, graph_def.node[0].name)
# Temporarily remove top nodes.
topNodes = []
numCropAndResize = 0
while True:
node = graph_def.node.pop()
topNodes.append(node)
if node.op == 'CropAndResize':
numCropAndResize += 1
if numCropAndResize == 2:
break
addReshape('FirstStageBoxPredictor/ClassPredictor/BiasAdd',
'FirstStageBoxPredictor/ClassPredictor/reshape_1', [0, -1, 2], graph_def)
addSoftMax('FirstStageBoxPredictor/ClassPredictor/reshape_1',
'FirstStageBoxPredictor/ClassPredictor/softmax', graph_def) # Compare with Reshape_4
addFlatten('FirstStageBoxPredictor/ClassPredictor/softmax',
'FirstStageBoxPredictor/ClassPredictor/softmax/flatten', graph_def)
# Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
addFlatten('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd',
'FirstStageBoxPredictor/BoxEncodingPredictor/flatten', graph_def)
proposals = NodeDef()
proposals.name = 'proposals' # Compare with ClipToWindow/Gather/Gather (NOTE: normalized)
proposals.op = 'PriorBox'
proposals.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd')
proposals.input.append(graph_def.node[0].name) # image_tensor
proposals.addAttr('flip', False)
proposals.addAttr('clip', True)
proposals.addAttr('step', features_stride)
proposals.addAttr('offset', 0.0)
proposals.addAttr('variance', [0.1, 0.1, 0.2, 0.2])
widths = []
heights = []
for a in aspect_ratios:
for s in scales:
ar = np.sqrt(a)
heights.append((height_stride**2) * s / ar)
widths.append((width_stride**2) * s * ar)
proposals.addAttr('width', widths)
proposals.addAttr('height', heights)
graph_def.node.extend([proposals])
# Compare with Reshape_5
detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/flatten')
detectionOut.input.append('FirstStageBoxPredictor/ClassPredictor/softmax/flatten')
detectionOut.input.append('proposals')
detectionOut.addAttr('num_classes', 2)
detectionOut.addAttr('share_location', True)
detectionOut.addAttr('background_label_id', 0)
detectionOut.addAttr('nms_threshold', first_stage_nms_iou_threshold)
detectionOut.addAttr('top_k', 6000)
detectionOut.addAttr('code_type', "CENTER_SIZE")
detectionOut.addAttr('keep_top_k', first_stage_max_proposals)
detectionOut.addAttr('clip', True)
graph_def.node.extend([detectionOut])
# Save as text.
cropAndResizeNodesNames = []
for node in reversed(topNodes):
if node.op != 'CropAndResize':
graph_def.node.extend([node])
topNodes.pop()
else:
cropAndResizeNodesNames.append(node.name)
if numCropAndResize == 1:
break
else:
graph_def.node.extend([node])
topNodes.pop()
numCropAndResize -= 1
addSoftMax('SecondStageBoxPredictor/Reshape_1', 'SecondStageBoxPredictor/Reshape_1/softmax', graph_def)
addSlice('SecondStageBoxPredictor/Reshape_1/softmax',
'SecondStageBoxPredictor/Reshape_1/slice',
[0, 0, 1], [-1, -1, -1], graph_def)
addReshape('SecondStageBoxPredictor/Reshape_1/slice',
'SecondStageBoxPredictor/Reshape_1/Reshape', [1, -1], graph_def)
# Replace Flatten subgraph onto a single node.
for i in reversed(range(len(graph_def.node))):
if graph_def.node[i].op == 'CropAndResize':
graph_def.node[i].input.insert(1, 'detection_out')
if graph_def.node[i].name == 'SecondStageBoxPredictor/Reshape':
addConstNode('SecondStageBoxPredictor/Reshape/shape2', [1, -1, 4], graph_def)
graph_def.node[i].input.pop()
graph_def.node[i].input.append('SecondStageBoxPredictor/Reshape/shape2')
if graph_def.node[i].name in ['SecondStageBoxPredictor/Flatten/flatten/Shape',
'SecondStageBoxPredictor/Flatten/flatten/strided_slice',
'SecondStageBoxPredictor/Flatten/flatten/Reshape/shape',
'SecondStageBoxPredictor/Flatten_1/flatten/Shape',
'SecondStageBoxPredictor/Flatten_1/flatten/strided_slice',
'SecondStageBoxPredictor/Flatten_1/flatten/Reshape/shape']:
del graph_def.node[i]
for node in graph_def.node:
if node.name == 'SecondStageBoxPredictor/Flatten/flatten/Reshape' or \
node.name == 'SecondStageBoxPredictor/Flatten_1/flatten/Reshape':
node.op = 'Flatten'
node.input.pop()
if node.name in ['FirstStageBoxPredictor/BoxEncodingPredictor/Conv2D',
'SecondStageBoxPredictor/BoxEncodingPredictor/MatMul']:
node.addAttr('loc_pred_transposed', True)
if node.name.startswith('MaxPool2D'):
assert(node.op == 'MaxPool')
assert(len(cropAndResizeNodesNames) == 2)
node.input = [cropAndResizeNodesNames[0]]
del cropAndResizeNodesNames[0]
################################################################################
### Postprocessing
################################################################################
addSlice('detection_out', 'detection_out/slice', [0, 0, 0, 3], [-1, -1, -1, 4], graph_def)
variance = NodeDef()
variance.name = 'proposals/variance'
variance.op = 'Const'
variance.addAttr('value', [0.1, 0.1, 0.2, 0.2])
graph_def.node.extend([variance])
varianceEncoder = NodeDef()
varianceEncoder.name = 'variance_encoded'
varianceEncoder.op = 'Mul'
varianceEncoder.input.append('SecondStageBoxPredictor/Reshape')
varianceEncoder.input.append(variance.name)
varianceEncoder.addAttr('axis', 2)
graph_def.node.extend([varianceEncoder])
addReshape('detection_out/slice', 'detection_out/slice/reshape', [1, 1, -1], graph_def)
addFlatten('variance_encoded', 'variance_encoded/flatten', graph_def)
detectionOut = NodeDef()
detectionOut.name = 'detection_out_final'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('variance_encoded/flatten')
detectionOut.input.append('SecondStageBoxPredictor/Reshape_1/Reshape')
detectionOut.input.append('detection_out/slice/reshape')
detectionOut.addAttr('num_classes', num_classes)
detectionOut.addAttr('share_location', False)
detectionOut.addAttr('background_label_id', num_classes + 1)
detectionOut.addAttr('nms_threshold', 0.6)
detectionOut.addAttr('code_type', "CENTER_SIZE")
detectionOut.addAttr('keep_top_k',100)
detectionOut.addAttr('clip', True)
detectionOut.addAttr('variance_encoded_in_target', True)
detectionOut.addAttr('confidence_threshold', 0.3)
detectionOut.addAttr('group_by_classes', False)
graph_def.node.extend([detectionOut])
for node in reversed(topNodes):
graph_def.node.extend([node])
if node.name.startswith('MaxPool2D'):
assert(node.op == 'MaxPool')
assert(len(cropAndResizeNodesNames) == 1)
node.input = [cropAndResizeNodesNames[0]]
for i in reversed(range(len(graph_def.node))):
if graph_def.node[i].op == 'CropAndResize':
graph_def.node[i].input.insert(1, 'detection_out_final')
break
graph_def.node[-1].name = 'detection_masks'
graph_def.node[-1].op = 'Sigmoid'
graph_def.node[-1].input.pop()
def getUnconnectedNodes():
unconnected = [node.name for node in graph_def.node]
for node in graph_def.node:
for inp in node.input:
if inp in unconnected:
unconnected.remove(inp)
return unconnected
while True:
unconnectedNodes = getUnconnectedNodes()
unconnectedNodes.remove(graph_def.node[-1].name)
if not unconnectedNodes:
break
for name in unconnectedNodes:
for i in range(len(graph_def.node)):
if graph_def.node[i].name == name:
del graph_def.node[i]
break
# Save as text.
graph_def.save(args.output)
|
import cv2 as cv
import argparse
import numpy as np
parser = argparse.ArgumentParser(description=
'Use this script to run Mask-RCNN object detection and semantic '
'segmentation network from TensorFlow Object Detection API.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to a .pb file with weights.')
parser.add_argument('--config', required=True, help='Path to a .pxtxt file contains network configuration.')
parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
'An every color is represented with three values from 0 to 255 in BGR channels order.')
parser.add_argument('--width', type=int, default=800,
help='Preprocess input image by resizing to a specific width.')
parser.add_argument('--height', type=int, default=800,
help='Preprocess input image by resizing to a specific height.')
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
args = parser.parse_args()
np.random.seed(324)
# Load names of classes
classes = None
if args.classes:
with open(args.classes, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Load colors
colors = None
if args.colors:
with open(args.colors, 'rt') as f:
colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
legend = None
def showLegend(classes):
global legend
if not classes is None and legend is None:
blockHeight = 30
assert(len(classes) == len(colors))
legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
for i in range(len(classes)):
block = legend[i * blockHeight:(i + 1) * blockHeight]
block[:,:] = colors[i]
cv.putText(block, classes[i], (0, blockHeight/2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
cv.namedWindow('Legend', cv.WINDOW_NORMAL)
cv.imshow('Legend', legend)
classes = None
def drawBox(frame, classId, conf, left, top, right, bottom):
# Draw a bounding box.
cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
label = '%.2f' % conf
# Print a label of class.
if classes:
assert(classId < len(classes))
label = '%s: %s' % (classes[classId], label)
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, labelSize[1])
cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
# Load a network
net = cv.dnn.readNet(cv.samples.findFile(args.model), cv.samples.findFile(args.config))
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
winName = 'Mask-RCNN in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
cap = cv.VideoCapture(cv.samples.findFileOrKeep(args.input) if args.input else 0)
legend = None
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
frameH = frame.shape[0]
frameW = frame.shape[1]
# Create a 4D blob from a frame.
blob = cv.dnn.blobFromImage(frame, size=(args.width, args.height), swapRB=True, crop=False)
# Run a model
net.setInput(blob)
boxes, masks = net.forward(['detection_out_final', 'detection_masks'])
numClasses = masks.shape[1]
numDetections = boxes.shape[2]
# Draw segmentation
if not colors:
# Generate colors
colors = [np.array([0, 0, 0], np.uint8)]
for i in range(1, numClasses + 1):
colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
del colors[0]
boxesToDraw = []
for i in range(numDetections):
box = boxes[0, 0, i]
mask = masks[i]
score = box[2]
if score > args.thr:
classId = int(box[1])
left = int(frameW * box[3])
top = int(frameH * box[4])
right = int(frameW * box[5])
bottom = int(frameH * box[6])
left = max(0, min(left, frameW - 1))
top = max(0, min(top, frameH - 1))
right = max(0, min(right, frameW - 1))
bottom = max(0, min(bottom, frameH - 1))
boxesToDraw.append([frame, classId, score, left, top, right, bottom])
classMask = mask[classId]
classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))
mask = (classMask > 0.5)
roi = frame[top:bottom+1, left:right+1][mask]
frame[top:bottom+1, left:right+1][mask] = (0.7 * colors[classId] + 0.3 * roi).astype(np.uint8)
for box in boxesToDraw:
drawBox(*box)
# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
showLegend(classes)
cv.imshow(winName, frame)
|
def tokenize(s):
tokens = []
token = ""
isString = False
isComment = False
for symbol in s:
isComment = (isComment and symbol != '\n') or (not isString and symbol == '#')
if isComment:
continue
if symbol == ' ' or symbol == '\t' or symbol == '\r' or symbol == '\'' or \
symbol == '\n' or symbol == ':' or symbol == '\"' or symbol == ';' or \
symbol == ',':
if (symbol == '\"' or symbol == '\'') and isString:
tokens.append(token)
token = ""
else:
if isString:
token += symbol
elif token:
tokens.append(token)
token = ""
isString = (symbol == '\"' or symbol == '\'') ^ isString
elif symbol == '{' or symbol == '}' or symbol == '[' or symbol == ']':
if token:
tokens.append(token)
token = ""
tokens.append(symbol)
else:
token += symbol
if token:
tokens.append(token)
return tokens
def parseMessage(tokens, idx):
msg = {}
assert(tokens[idx] == '{')
isArray = False
while True:
if not isArray:
idx += 1
if idx < len(tokens):
fieldName = tokens[idx]
else:
return None
if fieldName == '}':
break
idx += 1
fieldValue = tokens[idx]
if fieldValue == '{':
embeddedMsg, idx = parseMessage(tokens, idx)
if fieldName in msg:
msg[fieldName].append(embeddedMsg)
else:
msg[fieldName] = [embeddedMsg]
elif fieldValue == '[':
isArray = True
elif fieldValue == ']':
isArray = False
else:
if fieldName in msg:
msg[fieldName].append(fieldValue)
else:
msg[fieldName] = [fieldValue]
return msg, idx
def readTextMessage(filePath):
if not filePath:
return {}
with open(filePath, 'rt') as f:
content = f.read()
tokens = tokenize('{' + content + '}')
msg = parseMessage(tokens, 0)
return msg[0] if msg else {}
def listToTensor(values):
if all([isinstance(v, float) for v in values]):
dtype = 'DT_FLOAT'
field = 'float_val'
elif all([isinstance(v, int) for v in values]):
dtype = 'DT_INT32'
field = 'int_val'
else:
raise Exception('Wrong values types')
msg = {
'tensor': {
'dtype': dtype,
'tensor_shape': {
'dim': {
'size': len(values)
}
}
}
}
msg['tensor'][field] = values
return msg
def addConstNode(name, values, graph_def):
node = NodeDef()
node.name = name
node.op = 'Const'
node.addAttr('value', values)
graph_def.node.extend([node])
def addSlice(inp, out, begins, sizes, graph_def):
beginsNode = NodeDef()
beginsNode.name = out + '/begins'
beginsNode.op = 'Const'
beginsNode.addAttr('value', begins)
graph_def.node.extend([beginsNode])
sizesNode = NodeDef()
sizesNode.name = out + '/sizes'
sizesNode.op = 'Const'
sizesNode.addAttr('value', sizes)
graph_def.node.extend([sizesNode])
sliced = NodeDef()
sliced.name = out
sliced.op = 'Slice'
sliced.input.append(inp)
sliced.input.append(beginsNode.name)
sliced.input.append(sizesNode.name)
graph_def.node.extend([sliced])
def addReshape(inp, out, shape, graph_def):
shapeNode = NodeDef()
shapeNode.name = out + '/shape'
shapeNode.op = 'Const'
shapeNode.addAttr('value', shape)
graph_def.node.extend([shapeNode])
reshape = NodeDef()
reshape.name = out
reshape.op = 'Reshape'
reshape.input.append(inp)
reshape.input.append(shapeNode.name)
graph_def.node.extend([reshape])
def addSoftMax(inp, out, graph_def):
softmax = NodeDef()
softmax.name = out
softmax.op = 'Softmax'
softmax.addAttr('axis', -1)
softmax.input.append(inp)
graph_def.node.extend([softmax])
def addFlatten(inp, out, graph_def):
flatten = NodeDef()
flatten.name = out
flatten.op = 'Flatten'
flatten.input.append(inp)
graph_def.node.extend([flatten])
class NodeDef:
def __init__(self):
self.input = []
self.name = ""
self.op = ""
self.attr = {}
def addAttr(self, key, value):
assert(not key in self.attr)
if isinstance(value, bool):
self.attr[key] = {'b': value}
elif isinstance(value, int):
self.attr[key] = {'i': value}
elif isinstance(value, float):
self.attr[key] = {'f': value}
elif isinstance(value, str):
self.attr[key] = {'s': value}
elif isinstance(value, list):
self.attr[key] = listToTensor(value)
else:
raise Exception('Unknown type of attribute ' + key)
def Clear(self):
self.input = []
self.name = ""
self.op = ""
self.attr = {}
class GraphDef:
def __init__(self):
self.node = []
def save(self, filePath):
with open(filePath, 'wt') as f:
def printAttr(d, indent):
indent = ' ' * indent
for key, value in sorted(d.items(), key=lambda x:x[0].lower()):
value = value if isinstance(value, list) else [value]
for v in value:
if isinstance(v, dict):
f.write(indent + key + ' {\n')
printAttr(v, len(indent) + 2)
f.write(indent + '}\n')
else:
isString = False
if isinstance(v, str) and not v.startswith('DT_'):
try:
float(v)
except:
isString = True
if isinstance(v, bool):
printed = 'true' if v else 'false'
elif v == 'true' or v == 'false':
printed = 'true' if v == 'true' else 'false'
elif isString:
printed = '\"%s\"' % v
else:
printed = str(v)
f.write(indent + key + ': ' + printed + '\n')
for node in self.node:
f.write('node {\n')
f.write(' name: \"%s\"\n' % node.name)
f.write(' op: \"%s\"\n' % node.op)
for inp in node.input:
f.write(' input: \"%s\"\n' % inp)
for key, value in sorted(node.attr.items(), key=lambda x:x[0].lower()):
f.write(' attr {\n')
f.write(' key: \"%s\"\n' % key)
f.write(' value {\n')
printAttr(value, 6)
f.write(' }\n')
f.write(' }\n')
f.write('}\n')
def parseTextGraph(filePath):
msg = readTextMessage(filePath)
graph = GraphDef()
for node in msg['node']:
graphNode = NodeDef()
graphNode.name = node['name'][0]
graphNode.op = node['op'][0]
graphNode.input = node['input'] if 'input' in node else []
if 'attr' in node:
for attr in node['attr']:
graphNode.attr[attr['key'][0]] = attr['value'][0]
graph.node.append(graphNode)
return graph
# Removes Identity nodes
def removeIdentity(graph_def):
identities = {}
for node in graph_def.node:
if node.op == 'Identity':
identities[node.name] = node.input[0]
graph_def.node.remove(node)
for node in graph_def.node:
for i in range(len(node.input)):
if node.input[i] in identities:
node.input[i] = identities[node.input[i]]
def removeUnusedNodesAndAttrs(to_remove, graph_def):
unusedAttrs = ['T', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
'Index', 'Tperm', 'is_training', 'Tpaddings']
removedNodes = []
for i in reversed(range(len(graph_def.node))):
op = graph_def.node[i].op
name = graph_def.node[i].name
if to_remove(name, op):
if op != 'Const':
removedNodes.append(name)
del graph_def.node[i]
else:
for attr in unusedAttrs:
if attr in graph_def.node[i].attr:
del graph_def.node[i].attr[attr]
# Remove references to removed nodes except Const nodes.
for node in graph_def.node:
for i in reversed(range(len(node.input))):
if node.input[i] in removedNodes:
del node.input[i]
def writeTextGraph(modelPath, outputPath, outNodes):
try:
import cv2 as cv
cv.dnn.writeTextGraph(modelPath, outputPath)
except:
import tensorflow as tf
from tensorflow.tools.graph_transforms import TransformGraph
with tf.gfile.FastGFile(modelPath, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph_def = TransformGraph(graph_def, ['image_tensor'], outNodes, ['sort_by_execution_order'])
for node in graph_def.node:
if node.op == 'Const':
if 'value' in node.attr and node.attr['value'].tensor.tensor_content:
node.attr['value'].tensor.tensor_content = b''
tf.train.write_graph(graph_def, "", outputPath, as_text=True)
|
# To use Inference Engine backend, specify location of plugins:
# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(
description='This script is used to demonstrate OpenPose human pose estimation network '
'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. '
'The sample and model are simplified and could be used for a single person on the frame.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--proto', help='Path to .prototxt')
parser.add_argument('--model', help='Path to .caffemodel')
parser.add_argument('--dataset', help='Specify what kind of model was trained. '
'It could be (COCO, MPI, HAND) depends on dataset.')
parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
parser.add_argument('--scale', default=0.003922, type=float, help='Scale for blob.')
args = parser.parse_args()
if args.dataset == 'COCO':
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
"LEye": 15, "REar": 16, "LEar": 17, "Background": 18 }
POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
elif args.dataset == 'MPI':
BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
"Background": 15 }
POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
else:
assert(args.dataset == 'HAND')
BODY_PARTS = { "Wrist": 0,
"ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
"IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
"MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
"RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
"LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
}
POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],
["ThumbProximal", "ThumbMiddle"], ["ThumbMiddle", "ThumbDistal"],
["Wrist", "IndexFingerMetacarpal"], ["IndexFingerMetacarpal", "IndexFingerProximal"],
["IndexFingerProximal", "IndexFingerMiddle"], ["IndexFingerMiddle", "IndexFingerDistal"],
["Wrist", "MiddleFingerMetacarpal"], ["MiddleFingerMetacarpal", "MiddleFingerProximal"],
["MiddleFingerProximal", "MiddleFingerMiddle"], ["MiddleFingerMiddle", "MiddleFingerDistal"],
["Wrist", "RingFingerMetacarpal"], ["RingFingerMetacarpal", "RingFingerProximal"],
["RingFingerProximal", "RingFingerMiddle"], ["RingFingerMiddle", "RingFingerDistal"],
["Wrist", "LittleFingerMetacarpal"], ["LittleFingerMetacarpal", "LittleFingerProximal"],
["LittleFingerProximal", "LittleFingerMiddle"], ["LittleFingerMiddle", "LittleFingerDistal"] ]
inWidth = args.width
inHeight = args.height
inScale = args.scale
net = cv.dnn.readNet(cv.samples.findFile(args.proto), cv.samples.findFile(args.model))
cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
inp = cv.dnn.blobFromImage(frame, inScale, (inWidth, inHeight),
(0, 0, 0), swapRB=False, crop=False)
net.setInput(inp)
out = net.forward()
assert(len(BODY_PARTS) <= out.shape[1])
points = []
for i in range(len(BODY_PARTS)):
# Slice heatmap of corresponding body's part.
heatMap = out[0, i, :, :]
# Originally, we try to find all the local maximums. To simplify a sample
# we just find a global one. However only a single pose at the same time
# could be detected this way.
_, conf, _, point = cv.minMaxLoc(heatMap)
x = (frameWidth * point[0]) / out.shape[3]
y = (frameHeight * point[1]) / out.shape[2]
# Add a point if it's confidence is higher than threshold.
points.append((int(x), int(y)) if conf > args.thr else None)
for pair in POSE_PAIRS:
partFrom = pair[0]
partTo = pair[1]
assert(partFrom in BODY_PARTS)
assert(partTo in BODY_PARTS)
idFrom = BODY_PARTS[partFrom]
idTo = BODY_PARTS[partTo]
if points[idFrom] and points[idTo]:
cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 3)
cv.ellipse(frame, points[idFrom], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)
cv.ellipse(frame, points[idTo], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED)
t, _ = net.getPerfProfile()
freq = cv.getTickFrequency() / 1000
cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
cv.imshow('OpenPose using OpenCV', frame)
|
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
parser = argparse.ArgumentParser(
description='This script is used to run style transfer models from '
'https://github.com/jcjohnson/fast-neural-style using OpenCV')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--model', help='Path to .t7 model')
parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
args = parser.parse_args()
net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
if args.input:
cap = cv.VideoCapture(args.input)
else:
cap = cv.VideoCapture(0)
cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
inWidth = args.width if args.width != -1 else frame.shape[1]
inHeight = args.height if args.height != -1 else frame.shape[0]
inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
(103.939, 116.779, 123.68), swapRB=False, crop=False)
net.setInput(inp)
out = net.forward()
out = out.reshape(3, out.shape[2], out.shape[3])
out[0] += 103.939
out[1] += 116.779
out[2] += 123.68
out /= 255
out = out.transpose(1, 2, 0)
t, _ = net.getPerfProfile()
freq = cv.getTickFrequency() / 1000
print(t / freq, 'ms')
if args.median_filter:
out = cv.medianBlur(out, args.median_filter)
cv.imshow('Styled image', out)
|
import cv2 as cv
import argparse
import numpy as np
import sys
import time
from threading import Thread
if sys.version_info[0] == 2:
import Queue as queue
else:
import queue
from common import *
from tf_text_graph_common import readTextMessage
from tf_text_graph_ssd import createSSDGraph
from tf_text_graph_faster_rcnn import createFasterRCNNGraph
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
help='An optional path to file with preprocessing parameters.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--out_tf_graph', default='graph.pbtxt',
help='For models from TensorFlow Object Detection API, you may '
'pass a .config file which was used for training through --config '
'argument. This way an additional .pbtxt file with TensorFlow graph will be created.')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet', 'dldt'],
help='Optional name of an origin framework of the model. '
'Detect it automatically if it does not set.')
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
parser.add_argument('--nms', type=float, default=0.4, help='Non-maximum suppression threshold')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
parser.add_argument('--async', type=int, default=0,
dest='asyncN',
help='Number of asynchronous forwards at the same time. '
'Choose 0 for synchronous mode')
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'object_detection')
parser = argparse.ArgumentParser(parents=[parser],
description='Use this script to run object detection deep learning networks using OpenCV.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
args.model = findFile(args.model)
args.config = findFile(args.config)
args.classes = findFile(args.classes)
# If config specified, try to load it as TensorFlow Object Detection API's pipeline.
config = readTextMessage(args.config)
if 'model' in config:
print('TensorFlow Object Detection API config detected')
if 'ssd' in config['model'][0]:
print('Preparing text graph representation for SSD model: ' + args.out_tf_graph)
createSSDGraph(args.model, args.config, args.out_tf_graph)
args.config = args.out_tf_graph
elif 'faster_rcnn' in config['model'][0]:
print('Preparing text graph representation for Faster-RCNN model: ' + args.out_tf_graph)
createFasterRCNNGraph(args.model, args.config, args.out_tf_graph)
args.config = args.out_tf_graph
# Load names of classes
classes = None
if args.classes:
with open(args.classes, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Load a network
net = cv.dnn.readNet(cv.samples.findFile(args.model), cv.samples.findFile(args.config), args.framework)
net.setPreferableBackend(args.backend)
net.setPreferableTarget(args.target)
outNames = net.getUnconnectedOutLayersNames()
confThreshold = args.thr
nmsThreshold = args.nms
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
def drawPred(classId, conf, left, top, right, bottom):
# Draw a bounding box.
cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
label = '%.2f' % conf
# Print a label of class.
if classes:
assert(classId < len(classes))
label = '%s: %s' % (classes[classId], label)
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, labelSize[1])
cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
layerNames = net.getLayerNames()
lastLayerId = net.getLayerId(layerNames[-1])
lastLayer = net.getLayer(lastLayerId)
classIds = []
confidences = []
boxes = []
if lastLayer.type == 'DetectionOutput':
# Network produces output blob with a shape 1x1xNx7 where N is a number of
# detections and an every detection is a vector of values
# [batchId, classId, confidence, left, top, right, bottom]
for out in outs:
for detection in out[0, 0]:
confidence = detection[2]
if confidence > confThreshold:
left = int(detection[3])
top = int(detection[4])
right = int(detection[5])
bottom = int(detection[6])
width = right - left + 1
height = bottom - top + 1
if width <= 2 or height <= 2:
left = int(detection[3] * frameWidth)
top = int(detection[4] * frameHeight)
right = int(detection[5] * frameWidth)
bottom = int(detection[6] * frameHeight)
width = right - left + 1
height = bottom - top + 1
classIds.append(int(detection[1]) - 1) # Skip background label
confidences.append(float(confidence))
boxes.append([left, top, width, height])
elif lastLayer.type == 'Region':
# Network produces output blob with a shape NxC where N is a number of
# detected objects and C is a number of classes + 4 where the first 4
# numbers are [center_x, center_y, width, height]
classIds = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
classId = np.argmax(scores)
confidence = scores[classId]
if confidence > confThreshold:
center_x = int(detection[0] * frameWidth)
center_y = int(detection[1] * frameHeight)
width = int(detection[2] * frameWidth)
height = int(detection[3] * frameHeight)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
classIds.append(classId)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
else:
print('Unknown output layer type: ' + lastLayer.type)
exit()
indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
for i in indices:
i = i[0]
box = boxes[i]
left = box[0]
top = box[1]
width = box[2]
height = box[3]
drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
def callback(pos):
global confThreshold
confThreshold = pos / 100.0
cv.createTrackbar('Confidence threshold, %', winName, int(confThreshold * 100), 99, callback)
cap = cv.VideoCapture(cv.samples.findFileOrKeep(args.input) if args.input else 0)
class QueueFPS(queue.Queue):
def __init__(self):
queue.Queue.__init__(self)
self.startTime = 0
self.counter = 0
def put(self, v):
queue.Queue.put(self, v)
self.counter += 1
if self.counter == 1:
self.startTime = time.time()
def getFPS(self):
return self.counter / (time.time() - self.startTime)
process = True
#
# Frames capturing thread
#
framesQueue = QueueFPS()
def framesThreadBody():
global framesQueue, process
while process:
hasFrame, frame = cap.read()
if not hasFrame:
break
framesQueue.put(frame)
#
# Frames processing thread
#
processedFramesQueue = queue.Queue()
predictionsQueue = QueueFPS()
def processingThreadBody():
global processedFramesQueue, predictionsQueue, args, process
futureOutputs = []
while process:
# Get a next frame
frame = None
try:
frame = framesQueue.get_nowait()
if args.asyncN:
if len(futureOutputs) == args.asyncN:
frame = None # Skip the frame
else:
framesQueue.queue.clear() # Skip the rest of frames
except queue.Empty:
pass
if not frame is None:
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
# Create a 4D blob from a frame.
inpWidth = args.width if args.width else frameWidth
inpHeight = args.height if args.height else frameHeight
blob = cv.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_8U)
processedFramesQueue.put(frame)
# Run a model
net.setInput(blob, scalefactor=args.scale, mean=args.mean)
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
frame = cv.resize(frame, (inpWidth, inpHeight))
net.setInput(np.array([[inpHeight, inpWidth, 1.6]], dtype=np.float32), 'im_info')
if args.asyncN:
futureOutputs.append(net.forwardAsync())
else:
outs = net.forward(outNames)
predictionsQueue.put(np.copy(outs))
while futureOutputs and futureOutputs[0].wait_for(0):
out = futureOutputs[0].get()
predictionsQueue.put(np.copy([out]))
del futureOutputs[0]
framesThread = Thread(target=framesThreadBody)
framesThread.start()
processingThread = Thread(target=processingThreadBody)
processingThread.start()
#
# Postprocessing and rendering loop
#
while cv.waitKey(1) < 0:
try:
# Request prediction first because they put after frames
outs = predictionsQueue.get_nowait()
frame = processedFramesQueue.get_nowait()
postprocess(frame, outs)
# Put efficiency information.
if predictionsQueue.counter > 1:
label = 'Camera: %.2f FPS' % (framesQueue.getFPS())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
label = 'Network: %.2f FPS' % (predictionsQueue.getFPS())
cv.putText(frame, label, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
label = 'Skipped frames: %d' % (framesQueue.counter - predictionsQueue.counter)
cv.putText(frame, label, (0, 45), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
cv.imshow(winName, frame)
except queue.Empty:
pass
process = False
framesThread.join()
processingThread.join()
|
# This file is part of OpenCV project.
# It is subject to the license terms in the LICENSE file found in the top-level directory
# of this distribution and at http://opencv.org/license.html.
#
# Copyright (C) 2017, Intel Corporation, all rights reserved.
# Third party copyrights are property of their respective owners.
import tensorflow as tf
import struct
import argparse
import numpy as np
parser = argparse.ArgumentParser(description='Convert weights of a frozen TensorFlow graph to fp16.')
parser.add_argument('--input', required=True, help='Path to frozen graph.')
parser.add_argument('--output', required=True, help='Path to output graph.')
parser.add_argument('--ops', default=['Conv2D', 'MatMul'], nargs='+',
help='List of ops which weights are converted.')
args = parser.parse_args()
DT_FLOAT = 1
DT_HALF = 19
# For the frozen graphs, an every node that uses weights connected to Const nodes
# through an Identity node. Usually they're called in the same way with '/read' suffix.
# We'll replace all of them to Cast nodes.
# Load the model
with tf.gfile.FastGFile(args.input) as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Set of all inputs from desired nodes.
inputs = []
for node in graph_def.node:
if node.op in args.ops:
inputs += node.input
weightsNodes = []
for node in graph_def.node:
# From the whole inputs we need to keep only an Identity nodes.
if node.name in inputs and node.op == 'Identity' and node.attr['T'].type == DT_FLOAT:
weightsNodes.append(node.input[0])
# Replace Identity to Cast.
node.op = 'Cast'
node.attr['DstT'].type = DT_FLOAT
node.attr['SrcT'].type = DT_HALF
del node.attr['T']
del node.attr['_class']
# Convert weights to halfs.
for node in graph_def.node:
if node.name in weightsNodes:
node.attr['dtype'].type = DT_HALF
node.attr['value'].tensor.dtype = DT_HALF
floats = node.attr['value'].tensor.tensor_content
floats = struct.unpack('f' * (len(floats) / 4), floats)
halfs = np.array(floats).astype(np.float16).view(np.uint16)
node.attr['value'].tensor.tensor_content = struct.pack('H' * len(halfs), *halfs)
tf.train.write_graph(graph_def, "", args.output, as_text=False)
|
import cv2 as cv
import argparse
import numpy as np
import sys
from common import *
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
help='An optional path to file with preprocessing parameters.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
help='Optional name of an origin framework of the model. '
'Detect it automatically if it does not set.')
parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
'An every color is represented with three values from 0 to 255 in BGR channels order.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'segmentation')
parser = argparse.ArgumentParser(parents=[parser],
description='Use this script to run semantic segmentation deep learning networks using OpenCV.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
args.model = findFile(args.model)
args.config = findFile(args.config)
args.classes = findFile(args.classes)
np.random.seed(324)
# Load names of classes
classes = None
if args.classes:
with open(args.classes, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Load colors
colors = None
if args.colors:
with open(args.colors, 'rt') as f:
colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
legend = None
def showLegend(classes):
global legend
if not classes is None and legend is None:
blockHeight = 30
assert(len(classes) == len(colors))
legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
for i in range(len(classes)):
block = legend[i * blockHeight:(i + 1) * blockHeight]
block[:,:] = colors[i]
cv.putText(block, classes[i], (0, blockHeight/2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
cv.namedWindow('Legend', cv.WINDOW_NORMAL)
cv.imshow('Legend', legend)
classes = None
# Load a network
net = cv.dnn.readNet(args.model, args.config, args.framework)
net.setPreferableBackend(args.backend)
net.setPreferableTarget(args.target)
winName = 'Deep learning image classification in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
cap = cv.VideoCapture(args.input if args.input else 0)
legend = None
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
# Create a 4D blob from a frame.
inpWidth = args.width if args.width else frameWidth
inpHeight = args.height if args.height else frameHeight
blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
# Run a model
net.setInput(blob)
score = net.forward()
numClasses = score.shape[1]
height = score.shape[2]
width = score.shape[3]
# Draw segmentation
if not colors:
# Generate colors
colors = [np.array([0, 0, 0], np.uint8)]
for i in range(1, numClasses):
colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
classIds = np.argmax(score[0], axis=0)
segm = np.stack([colors[idx] for idx in classIds.flatten()])
segm = segm.reshape(height, width, 3)
segm = cv.resize(segm, (frameWidth, frameHeight), interpolation=cv.INTER_NEAREST)
frame = (0.1 * frame + 0.9 * segm).astype(np.uint8)
# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
showLegend(classes)
cv.imshow(winName, frame)
|
#!/usr/bin/env python
from __future__ import print_function
import hashlib
import time
import sys
import xml.etree.ElementTree as ET
if sys.version_info[0] < 3:
from urllib2 import urlopen
else:
from urllib.request import urlopen
class HashMismatchException(Exception):
def __init__(self, expected, actual):
Exception.__init__(self)
self.expected = expected
self.actual = actual
def __str__(self):
return 'Hash mismatch: {} vs {}'.format(self.expected, self.actual)
class MetalinkDownloader(object):
BUFSIZE = 10*1024*1024
NS = {'ml': 'urn:ietf:params:xml:ns:metalink'}
tick = 0
def download(self, metalink_file):
status = True
for file_elem in ET.parse(metalink_file).getroot().findall('ml:file', self.NS):
url = file_elem.find('ml:url', self.NS).text
fname = file_elem.attrib['name']
hash_sum = file_elem.find('ml:hash', self.NS).text
print('*** {}'.format(fname))
try:
self.verify(hash_sum, fname)
except Exception as ex:
print(' {}'.format(ex))
try:
print(' {}'.format(url))
with open(fname, 'wb') as file_stream:
self.buffered_read(urlopen(url), file_stream.write)
self.verify(hash_sum, fname)
except Exception as ex:
print(' {}'.format(ex))
print(' FAILURE')
status = False
continue
print(' SUCCESS')
return status
def print_progress(self, msg, timeout = 0):
if time.time() - self.tick > timeout:
print(msg, end='')
sys.stdout.flush()
self.tick = time.time()
def buffered_read(self, in_stream, processing):
self.print_progress(' >')
while True:
buf = in_stream.read(self.BUFSIZE)
if not buf:
break
processing(buf)
self.print_progress('>', 5)
print(' done')
def verify(self, hash_sum, fname):
sha = hashlib.sha1()
with open(fname, 'rb') as file_stream:
self.buffered_read(file_stream, sha.update)
if hash_sum != sha.hexdigest():
raise HashMismatchException(hash_sum, sha.hexdigest())
if __name__ == '__main__':
sys.exit(0 if MetalinkDownloader().download('weights.meta4') else 1)
|
"""
This code adds Python/Java signatures to the docs.
TODO: Do the same thing for Java
* using javadoc/ get all the methods/classes/constants to a json file
TODO:
* clarify when there are several C++ signatures corresponding to a single Python function.
i.e: calcHist():
http://docs.opencv.org/3.2.0/d6/dc7/group__imgproc__hist.html#ga4b2b5fd75503ff9e6844cc4dcdaed35d
* clarify special case:
http://docs.opencv.org/3.2.0/db/de0/group__core__utils.html#ga4910d7f86336cd4eff9dd05575667e41
"""
from __future__ import print_function
import sys
sys.dont_write_bytecode = True # Don't generate .pyc files / __pycache__ directories
import os
from pprint import pprint
import re
import logging
import json
import html_functions
import doxygen_scan
loglevel=os.environ.get("LOGLEVEL", None)
if loglevel:
logging.basicConfig(level=loglevel)
ROOT_DIR = sys.argv[1]
PYTHON_SIGNATURES_FILE = sys.argv[2]
JAVA_OR_PYTHON = sys.argv[3]
ADD_JAVA = False
ADD_PYTHON = False
if JAVA_OR_PYTHON == "python":
ADD_PYTHON = True
python_signatures = dict()
with open(PYTHON_SIGNATURES_FILE, "rt") as f:
python_signatures = json.load(f)
print("Loaded Python signatures: %d" % len(python_signatures))
import xml.etree.ElementTree as ET
root = ET.parse(ROOT_DIR + 'opencv.tag')
files_dict = {}
# constants and function from opencv.tag
namespaces = root.findall("./compound[@kind='namespace']")
#print("Found {} namespaces".format(len(namespaces)))
for ns in namespaces:
ns_name = ns.find("./name").text
#print('NS: {}'.format(ns_name))
doxygen_scan.scan_namespace_constants(ns, ns_name, files_dict)
doxygen_scan.scan_namespace_functions(ns, ns_name, files_dict)
# class methods from opencv.tag
classes = root.findall("./compound[@kind='class']")
#print("Found {} classes".format(len(classes)))
for c in classes:
c_name = c.find("./name").text
file = c.find("./filename").text
#print('Class: {} => {}'.format(c_name, file))
doxygen_scan.scan_class_methods(c, c_name, files_dict)
print('Doxygen files to scan: %s' % len(files_dict))
files_processed = 0
files_skipped = 0
symbols_processed = 0
for file in files_dict:
#if file != "dd/d9e/classcv_1_1VideoWriter.html":
#if file != "d4/d86/group__imgproc__filter.html":
#if file != "df/dfb/group__imgproc__object.html":
# continue
#print('File: ' + file)
anchor_list = files_dict[file]
active_anchors = [a for a in anchor_list if a.cppname in python_signatures]
if len(active_anchors) == 0: # no linked Python symbols
#print('Skip: ' + file)
files_skipped = files_skipped + 1
continue
active_anchors_dict = {a.anchor: a for a in active_anchors}
if len(active_anchors_dict) != len(active_anchors):
logging.info('Duplicate entries detected: %s -> %s (%s)' % (len(active_anchors), len(active_anchors_dict), file))
files_processed = files_processed + 1
#pprint(active_anchors)
symbols_processed = symbols_processed + len(active_anchors_dict)
logging.info('File: %r' % file)
html_functions.insert_python_signatures(python_signatures, active_anchors_dict, ROOT_DIR + file)
print('Done (processed files %d, symbols %d, skipped %d files)' % (files_processed, symbols_processed, files_skipped))
|
from __future__ import print_function
import sys
import logging
import os
import re
from pprint import pprint
import traceback
try:
import bs4
from bs4 import BeautifulSoup
except ImportError:
raise ImportError('Error: '
'Install BeautifulSoup (bs4) for adding'
' Python & Java signatures documentation')
def load_html_file(file_dir):
""" Uses BeautifulSoup to load an html """
with open(file_dir, 'rb') as fp:
data = fp.read()
if os.name == 'nt' or sys.version_info[0] == 3:
data = data.decode(encoding='utf-8', errors='strict')
data = re.sub(r'(\>)([ ]+)', lambda match: match.group(1) + ('!space!' * len(match.group(2))), data)
data = re.sub(r'([ ]+)(\<)', lambda match: ('!space!' * len(match.group(1))) + match.group(2), data)
if os.name == 'nt' or sys.version_info[0] == 3:
data = data.encode('utf-8', 'ignore')
soup = BeautifulSoup(data, 'html.parser')
return soup
def update_html(file, soup):
s = str(soup)
s = s.replace('!space!', ' ')
if os.name == 'nt' or sys.version_info[0] == 3:
s = s.encode('utf-8', 'ignore')
with open(file, 'wb') as f:
f.write(s)
def insert_python_signatures(python_signatures, symbols_dict, filepath):
soup = load_html_file(filepath)
entries = soup.find_all(lambda tag: tag.name == "a" and tag.has_attr('id'))
for e in entries:
anchor = e['id']
if anchor in symbols_dict:
s = symbols_dict[anchor]
logging.info('Process: %r' % s)
if s.type == 'fn' or s.type == 'method':
process_fn(soup, e, python_signatures[s.cppname], s)
elif s.type == 'const':
process_const(soup, e, python_signatures[s.cppname], s)
else:
logging.error('unsupported type: %s' % s);
update_html(filepath, soup)
def process_fn(soup, anchor, python_signature, symbol):
try:
r = anchor.find_next_sibling(class_='memitem').find(class_='memproto').find('table')
insert_python_fn_signature(soup, r, python_signature, symbol)
except:
logging.error("Can't process: %s" % symbol)
traceback.print_exc()
pprint(anchor)
def process_const(soup, anchor, python_signature, symbol):
try:
#pprint(anchor.parent)
description = append(soup.new_tag('div', **{'class' : ['python_language']}),
'Python: ' + python_signature[0]['name'])
old = anchor.find_next_sibling('div', class_='python_language')
if old is None:
anchor.parent.append(description)
else:
old.replace_with(description)
#pprint(anchor.parent)
except:
logging.error("Can't process: %s" % symbol)
traceback.print_exc()
pprint(anchor)
def insert_python_fn_signature(soup, table, variants, symbol):
description = create_python_fn_description(soup, variants)
description['class'] = 'python_language'
soup = insert_or_replace(table, description, 'table', 'python_language')
return soup
def create_python_fn_description(soup, variants):
language = 'Python:'
table = soup.new_tag('table')
heading_row = soup.new_tag('th')
table.append(
append(soup.new_tag('tr'),
append(soup.new_tag('th', colspan=999, style="text-align:left"), language)))
for v in variants:
#logging.debug(v)
add_signature_to_table(soup, table, v, language, type)
#print(table)
return table
def add_signature_to_table(soup, table, signature, language, type):
""" Add a signature to an html table"""
row = soup.new_tag('tr')
row.append(soup.new_tag('td', style='width: 20px;'))
if 'ret' in signature:
row.append(append(soup.new_tag('td'), signature['ret']))
row.append(append(soup.new_tag('td'), '='))
else:
row.append(soup.new_tag('td')) # return values
row.append(soup.new_tag('td')) # '='
row.append(append(soup.new_tag('td'), signature['name'] + '('))
row.append(append(soup.new_tag('td', **{'class': 'paramname'}), signature['arg']))
row.append(append(soup.new_tag('td'), ')'))
table.append(row)
def append(target, obj):
target.append(obj)
return target
def insert_or_replace(element_before, new_element, tag, tag_class):
old = element_before.find_next_sibling(tag, class_=tag_class)
if old is None:
element_before.insert_after(new_element)
else:
old.replace_with(new_element)
|
import traceback
class Symbol(object):
def __init__(self, anchor, type, cppname):
self.anchor = anchor
self.type = type
self.cppname = cppname
#if anchor == 'ga586ebfb0a7fb604b35a23d85391329be':
# print(repr(self))
# traceback.print_stack()
def __repr__(self):
return '%s:%s@%s' % (self.type, self.cppname, self.anchor)
def add_to_file(files_dict, file, anchor):
anchors = files_dict.setdefault(file, [])
anchors.append(anchor)
def scan_namespace_constants(ns, ns_name, files_dict):
constants = ns.findall("./member[@kind='enumvalue']")
for c in constants:
c_name = c.find("./name").text
name = ns_name + '::' + c_name
file = c.find("./anchorfile").text
anchor = c.find("./anchor").text
#print(' CONST: {} => {}#{}'.format(name, file, anchor))
add_to_file(files_dict, file, Symbol(anchor, "const", name))
def scan_namespace_functions(ns, ns_name, files_dict):
functions = ns.findall("./member[@kind='function']")
for f in functions:
f_name = f.find("./name").text
name = ns_name + '::' + f_name
file = f.find("./anchorfile").text
anchor = f.find("./anchor").text
#print(' FN: {} => {}#{}'.format(name, file, anchor))
add_to_file(files_dict, file, Symbol(anchor, "fn", name))
def scan_class_methods(c, c_name, files_dict):
methods = c.findall("./member[@kind='function']")
for m in methods:
m_name = m.find("./name").text
name = c_name + '::' + m_name
file = m.find("./anchorfile").text
anchor = m.find("./anchor").text
#print(' Method: {} => {}#{}'.format(name, file, anchor))
add_to_file(files_dict, file, Symbol(anchor, "method", name))
|
#!/usr/bin/env python
"""gen_pattern.py
Usage example:
python gen_pattern.py -o out.svg -r 11 -c 8 -T circles -s 20.0 -R 5.0 -u mm -w 216 -h 279
-o, --output - output file (default out.svg)
-r, --rows - pattern rows (default 11)
-c, --columns - pattern columns (default 8)
-T, --type - type of pattern, circles, acircles, checkerboard (default circles)
-s, --square_size - size of squares in pattern (default 20.0)
-R, --radius_rate - circles_radius = square_size/radius_rate (default 5.0)
-u, --units - mm, inches, px, m (default mm)
-w, --page_width - page width in units (default 216)
-h, --page_height - page height in units (default 279)
-a, --page_size - page size (default A4), supersedes -h -w arguments
-H, --help - show help
"""
from svgfig import *
import sys
import getopt
class PatternMaker:
def __init__(self, cols,rows,output,units,square_size,radius_rate,page_width,page_height):
self.cols = cols
self.rows = rows
self.output = output
self.units = units
self.square_size = square_size
self.radius_rate = radius_rate
self.width = page_width
self.height = page_height
self.g = SVG("g") # the svg group container
def makeCirclesPattern(self):
spacing = self.square_size
r = spacing / self.radius_rate
for x in range(1,self.cols+1):
for y in range(1,self.rows+1):
dot = SVG("circle", cx=x * spacing, cy=y * spacing, r=r, fill="black", stroke="none")
self.g.append(dot)
def makeACirclesPattern(self):
spacing = self.square_size
r = spacing / self.radius_rate
for i in range(0,self.rows):
for j in range(0,self.cols):
dot = SVG("circle", cx= ((j*2 + i%2)*spacing) + spacing, cy=self.height - (i * spacing + spacing), r=r, fill="black", stroke="none")
self.g.append(dot)
def makeCheckerboardPattern(self):
spacing = self.square_size
xspacing = (self.width - self.cols * self.square_size) / 2.0
yspacing = (self.height - self.rows * self.square_size) / 2.0
for x in range(0,self.cols):
for y in range(0,self.rows):
if x%2 == y%2:
square = SVG("rect", x=x * spacing + xspacing, y=y * spacing + yspacing, width=spacing, height=spacing, fill="black", stroke="none")
self.g.append(square)
def save(self):
c = canvas(self.g,width="%d%s"%(self.width,self.units),height="%d%s"%(self.height,self.units),viewBox="0 0 %d %d"%(self.width,self.height))
c.save(self.output)
def main():
# parse command line options, TODO use argparse for better doc
try:
opts, args = getopt.getopt(sys.argv[1:], "Ho:c:r:T:u:s:R:w:h:a:", ["help","output=","columns=","rows=",
"type=","units=","square_size=","radius_rate=",
"page_width=","page_height=", "page_size="])
except getopt.error as msg:
print(msg)
print("for help use --help")
sys.exit(2)
output = "out.svg"
columns = 8
rows = 11
p_type = "circles"
units = "mm"
square_size = 20.0
radius_rate = 5.0
page_size = "A4"
# page size dict (ISO standard, mm) for easy lookup. format - size: [width, height]
page_sizes = {"A0": [840, 1188], "A1": [594, 840], "A2": [420, 594], "A3": [297, 420], "A4": [210, 297], "A5": [148, 210]}
page_width = page_sizes[page_size.upper()][0]
page_height = page_sizes[page_size.upper()][1]
# process options
for o, a in opts:
if o in ("-H", "--help"):
print(__doc__)
sys.exit(0)
elif o in ("-r", "--rows"):
rows = int(a)
elif o in ("-c", "--columns"):
columns = int(a)
elif o in ("-o", "--output"):
output = a
elif o in ("-T", "--type"):
p_type = a
elif o in ("-u", "--units"):
units = a
elif o in ("-s", "--square_size"):
square_size = float(a)
elif o in ("-R", "--radius_rate"):
radius_rate = float(a)
elif o in ("-w", "--page_width"):
page_width = float(a)
elif o in ("-h", "--page_height"):
page_height = float(a)
elif o in ("-a", "--page_size"):
units = "mm"
page_size = a.upper()
page_width = page_sizes[page_size][0]
page_height = page_sizes[page_size][1]
pm = PatternMaker(columns,rows,output,units,square_size,radius_rate,page_width,page_height)
#dict for easy lookup of pattern type
mp = {"circles":pm.makeCirclesPattern,"acircles":pm.makeACirclesPattern,"checkerboard":pm.makeCheckerboardPattern}
mp[p_type]()
#this should save pattern to output
pm.save()
if __name__ == "__main__":
main()
|
# svgfig.py copyright (C) 2008 Jim Pivarski <[email protected]>
#
# 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 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
#
# Full licence is in the file COPYING and at http://www.gnu.org/copyleft/gpl.html
import re, codecs, os, platform, copy, itertools, math, cmath, random, sys, copy
_epsilon = 1e-5
if sys.version_info >= (3,0):
long = int
basestring = (str,bytes)
# Fix Python 2.x.
try:
UNICODE_EXISTS = bool(type(unicode))
except NameError:
unicode = lambda s: str(s)
try:
xrange # Python 2
except NameError:
xrange = range # Python 3
if re.search("windows", platform.system(), re.I):
try:
import _winreg
_default_directory = _winreg.QueryValueEx(_winreg.OpenKey(_winreg.HKEY_CURRENT_USER,
r"Software\Microsoft\Windows\Current Version\Explorer\Shell Folders"), "Desktop")[0]
# tmpdir = _winreg.QueryValueEx(_winreg.OpenKey(_winreg.HKEY_CURRENT_USER, "Environment"), "TEMP")[0]
# if tmpdir[0:13] != "%USERPROFILE%":
# tmpdir = os.path.expanduser("~") + tmpdir[13:]
except:
_default_directory = os.path.expanduser("~") + os.sep + "Desktop"
_default_fileName = "tmp.svg"
_hacks = {}
_hacks["inkscape-text-vertical-shift"] = False
def rgb(r, g, b, maximum=1.):
"""Create an SVG color string "#xxyyzz" from r, g, and b.
r,g,b = 0 is black and r,g,b = maximum is white.
"""
return "#%02x%02x%02x" % (max(0, min(r*255./maximum, 255)),
max(0, min(g*255./maximum, 255)),
max(0, min(b*255./maximum, 255)))
def attr_preprocess(attr):
attrCopy = attr.copy()
for name in attr.keys():
name_colon = re.sub("__", ":", name)
if name_colon != name:
attrCopy[name_colon] = attrCopy[name]
del attrCopy[name]
name = name_colon
name_dash = re.sub("_", "-", name)
if name_dash != name:
attrCopy[name_dash] = attrCopy[name]
del attrCopy[name]
name = name_dash
return attrCopy
class SVG:
"""A tree representation of an SVG image or image fragment.
SVG(t, sub, sub, sub..., attribute=value)
t required SVG type name
sub optional list nested SVG elements or text/Unicode
attribute=value pairs optional keywords SVG attributes
In attribute names, "__" becomes ":" and "_" becomes "-".
SVG in XML
<g id="mygroup" fill="blue">
<rect x="1" y="1" width="2" height="2" />
<rect x="3" y="3" width="2" height="2" />
</g>
SVG in Python
>>> svg = SVG("g", SVG("rect", x=1, y=1, width=2, height=2), \
... SVG("rect", x=3, y=3, width=2, height=2), \
... id="mygroup", fill="blue")
Sub-elements and attributes may be accessed through tree-indexing:
>>> svg = SVG("text", SVG("tspan", "hello there"), stroke="none", fill="black")
>>> svg[0]
<tspan (1 sub) />
>>> svg[0, 0]
'hello there'
>>> svg["fill"]
'black'
Iteration is depth-first:
>>> svg = SVG("g", SVG("g", SVG("line", x1=0, y1=0, x2=1, y2=1)), \
... SVG("text", SVG("tspan", "hello again")))
...
>>> for ti, s in svg:
... print ti, repr(s)
...
(0,) <g (1 sub) />
(0, 0) <line x2=1 y1=0 x1=0 y2=1 />
(0, 0, 'x2') 1
(0, 0, 'y1') 0
(0, 0, 'x1') 0
(0, 0, 'y2') 1
(1,) <text (1 sub) />
(1, 0) <tspan (1 sub) />
(1, 0, 0) 'hello again'
Use "print" to navigate:
>>> print svg
None <g (2 sub) />
[0] <g (1 sub) />
[0, 0] <line x2=1 y1=0 x1=0 y2=1 />
[1] <text (1 sub) />
[1, 0] <tspan (1 sub) />
"""
def __init__(self, *t_sub, **attr):
if len(t_sub) == 0:
raise TypeError( "SVG element must have a t (SVG type)")
# first argument is t (SVG type)
self.t = t_sub[0]
# the rest are sub-elements
self.sub = list(t_sub[1:])
# keyword arguments are attributes
# need to preprocess to handle differences between SVG and Python syntax
self.attr = attr_preprocess(attr)
def __getitem__(self, ti):
"""Index is a list that descends tree, returning a sub-element if
it ends with a number and an attribute if it ends with a string."""
obj = self
if isinstance(ti, (list, tuple)):
for i in ti[:-1]:
obj = obj[i]
ti = ti[-1]
if isinstance(ti, (int, long, slice)):
return obj.sub[ti]
else:
return obj.attr[ti]
def __setitem__(self, ti, value):
"""Index is a list that descends tree, returning a sub-element if
it ends with a number and an attribute if it ends with a string."""
obj = self
if isinstance(ti, (list, tuple)):
for i in ti[:-1]:
obj = obj[i]
ti = ti[-1]
if isinstance(ti, (int, long, slice)):
obj.sub[ti] = value
else:
obj.attr[ti] = value
def __delitem__(self, ti):
"""Index is a list that descends tree, returning a sub-element if
it ends with a number and an attribute if it ends with a string."""
obj = self
if isinstance(ti, (list, tuple)):
for i in ti[:-1]:
obj = obj[i]
ti = ti[-1]
if isinstance(ti, (int, long, slice)):
del obj.sub[ti]
else:
del obj.attr[ti]
def __contains__(self, value):
"""x in svg == True iff x is an attribute in svg."""
return value in self.attr
def __eq__(self, other):
"""x == y iff x represents the same SVG as y."""
if id(self) == id(other):
return True
return (isinstance(other, SVG) and
self.t == other.t and self.sub == other.sub and self.attr == other.attr)
def __ne__(self, other):
"""x != y iff x does not represent the same SVG as y."""
return not (self == other)
def append(self, x):
"""Appends x to the list of sub-elements (drawn last, overlaps
other primitives)."""
self.sub.append(x)
def prepend(self, x):
"""Prepends x to the list of sub-elements (drawn first may be
overlapped by other primitives)."""
self.sub[0:0] = [x]
def extend(self, x):
"""Extends list of sub-elements by a list x."""
self.sub.extend(x)
def clone(self, shallow=False):
"""Deep copy of SVG tree. Set shallow=True for a shallow copy."""
if shallow:
return copy.copy(self)
else:
return copy.deepcopy(self)
### nested class
class SVGDepthIterator:
"""Manages SVG iteration."""
def __init__(self, svg, ti, depth_limit):
self.svg = svg
self.ti = ti
self.shown = False
self.depth_limit = depth_limit
def __iter__(self):
return self
def next(self):
if not self.shown:
self.shown = True
if self.ti != ():
return self.ti, self.svg
if not isinstance(self.svg, SVG):
raise StopIteration
if self.depth_limit is not None and len(self.ti) >= self.depth_limit:
raise StopIteration
if "iterators" not in self.__dict__:
self.iterators = []
for i, s in enumerate(self.svg.sub):
self.iterators.append(self.__class__(s, self.ti + (i,), self.depth_limit))
for k, s in self.svg.attr.items():
self.iterators.append(self.__class__(s, self.ti + (k,), self.depth_limit))
self.iterators = itertools.chain(*self.iterators)
return self.iterators.next()
### end nested class
def depth_first(self, depth_limit=None):
"""Returns a depth-first generator over the SVG. If depth_limit
is a number, stop recursion at that depth."""
return self.SVGDepthIterator(self, (), depth_limit)
def breadth_first(self, depth_limit=None):
"""Not implemented yet. Any ideas on how to do it?
Returns a breadth-first generator over the SVG. If depth_limit
is a number, stop recursion at that depth."""
raise NotImplementedError( "Got an algorithm for breadth-first searching a tree without effectively copying the tree?")
def __iter__(self):
return self.depth_first()
def items(self, sub=True, attr=True, text=True):
"""Get a recursively-generated list of tree-index, sub-element/attribute pairs.
If sub == False, do not show sub-elements.
If attr == False, do not show attributes.
If text == False, do not show text/Unicode sub-elements.
"""
output = []
for ti, s in self:
show = False
if isinstance(ti[-1], (int, long)):
if isinstance(s, basestring):
show = text
else:
show = sub
else:
show = attr
if show:
output.append((ti, s))
return output
def keys(self, sub=True, attr=True, text=True):
"""Get a recursively-generated list of tree-indexes.
If sub == False, do not show sub-elements.
If attr == False, do not show attributes.
If text == False, do not show text/Unicode sub-elements.
"""
return [ti for ti, s in self.items(sub, attr, text)]
def values(self, sub=True, attr=True, text=True):
"""Get a recursively-generated list of sub-elements and attributes.
If sub == False, do not show sub-elements.
If attr == False, do not show attributes.
If text == False, do not show text/Unicode sub-elements.
"""
return [s for ti, s in self.items(sub, attr, text)]
def __repr__(self):
return self.xml(depth_limit=0)
def __str__(self):
"""Print (actually, return a string of) the tree in a form useful for browsing."""
return self.tree(sub=True, attr=False, text=False)
def tree(self, depth_limit=None, sub=True, attr=True, text=True, tree_width=20, obj_width=80):
"""Print (actually, return a string of) the tree in a form useful for browsing.
If depth_limit == a number, stop recursion at that depth.
If sub == False, do not show sub-elements.
If attr == False, do not show attributes.
If text == False, do not show text/Unicode sub-elements.
tree_width is the number of characters reserved for printing tree indexes.
obj_width is the number of characters reserved for printing sub-elements/attributes.
"""
output = []
line = "%s %s" % (("%%-%ds" % tree_width) % repr(None),
("%%-%ds" % obj_width) % (repr(self))[0:obj_width])
output.append(line)
for ti, s in self.depth_first(depth_limit):
show = False
if isinstance(ti[-1], (int, long)):
if isinstance(s, basestring):
show = text
else:
show = sub
else:
show = attr
if show:
line = "%s %s" % (("%%-%ds" % tree_width) % repr(list(ti)),
("%%-%ds" % obj_width) % (" "*len(ti) + repr(s))[0:obj_width])
output.append(line)
return "\n".join(output)
def xml(self, indent=u" ", newl=u"\n", depth_limit=None, depth=0):
"""Get an XML representation of the SVG.
indent string used for indenting
newl string used for newlines
If depth_limit == a number, stop recursion at that depth.
depth starting depth (not useful for users)
print svg.xml()
"""
attrstr = []
for n, v in self.attr.items():
if isinstance(v, dict):
v = u"; ".join([u"%s:%s" % (ni, vi) for ni, vi in v.items()])
elif isinstance(v, (list, tuple)):
v = u", ".join(v)
attrstr.append(u" %s=%s" % (n, repr(v)))
attrstr = u"".join(attrstr)
if len(self.sub) == 0:
return u"%s<%s%s />" % (indent * depth, self.t, attrstr)
if depth_limit is None or depth_limit > depth:
substr = []
for s in self.sub:
if isinstance(s, SVG):
substr.append(s.xml(indent, newl, depth_limit, depth + 1) + newl)
elif isinstance(s, basestring):
substr.append(u"%s%s%s" % (indent * (depth + 1), s, newl))
else:
substr.append("%s%s%s" % (indent * (depth + 1), repr(s), newl))
substr = u"".join(substr)
return u"%s<%s%s>%s%s%s</%s>" % (indent * depth, self.t, attrstr, newl, substr, indent * depth, self.t)
else:
return u"%s<%s (%d sub)%s />" % (indent * depth, self.t, len(self.sub), attrstr)
def standalone_xml(self, indent=u" ", newl=u"\n", encoding=u"utf-8"):
"""Get an XML representation of the SVG that can be saved/rendered.
indent string used for indenting
newl string used for newlines
"""
if self.t == "svg":
top = self
else:
top = canvas(self)
return u"""\
<?xml version="1.0" encoding="%s" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
""" % encoding + (u"".join(top.__standalone_xml(indent, newl))) # end of return statement
def __standalone_xml(self, indent, newl):
output = [u"<%s" % self.t]
for n, v in self.attr.items():
if isinstance(v, dict):
v = u"; ".join([u"%s:%s" % (ni, vi) for ni, vi in v.items()])
elif isinstance(v, (list, tuple)):
v = u", ".join(v)
output.append(u' %s="%s"' % (n, v))
if len(self.sub) == 0:
output.append(u" />%s%s" % (newl, newl))
return output
elif self.t == "text" or self.t == "tspan" or self.t == "style":
output.append(u">")
else:
output.append(u">%s%s" % (newl, newl))
for s in self.sub:
if isinstance(s, SVG):
output.extend(s.__standalone_xml(indent, newl))
else:
output.append(unicode(s))
if self.t == "tspan":
output.append(u"</%s>" % self.t)
else:
output.append(u"</%s>%s%s" % (self.t, newl, newl))
return output
def interpret_fileName(self, fileName=None):
if fileName is None:
fileName = _default_fileName
if re.search("windows", platform.system(), re.I) and not os.path.isabs(fileName):
fileName = _default_directory + os.sep + fileName
return fileName
def save(self, fileName=None, encoding="utf-8", compresslevel=None):
"""Save to a file for viewing. Note that svg.save() overwrites the file named _default_fileName.
fileName default=None note that _default_fileName will be overwritten if
no fileName is specified. If the extension
is ".svgz" or ".gz", the output will be gzipped
encoding default="utf-8" file encoding
compresslevel default=None if a number, the output will be gzipped with that
compression level (1-9, 1 being fastest and 9 most
thorough)
"""
fileName = self.interpret_fileName(fileName)
if compresslevel is not None or re.search(r"\.svgz$", fileName, re.I) or re.search(r"\.gz$", fileName, re.I):
import gzip
if compresslevel is None:
f = gzip.GzipFile(fileName, "w")
else:
f = gzip.GzipFile(fileName, "w", compresslevel)
f = codecs.EncodedFile(f, "utf-8", encoding)
f.write(self.standalone_xml(encoding=encoding))
f.close()
else:
f = codecs.open(fileName, "w", encoding=encoding)
f.write(self.standalone_xml(encoding=encoding))
f.close()
def inkview(self, fileName=None, encoding="utf-8"):
"""View in "inkview", assuming that program is available on your system.
fileName default=None note that any file named _default_fileName will be
overwritten if no fileName is specified. If the extension
is ".svgz" or ".gz", the output will be gzipped
encoding default="utf-8" file encoding
"""
fileName = self.interpret_fileName(fileName)
self.save(fileName, encoding)
os.spawnvp(os.P_NOWAIT, "inkview", ("inkview", fileName))
def inkscape(self, fileName=None, encoding="utf-8"):
"""View in "inkscape", assuming that program is available on your system.
fileName default=None note that any file named _default_fileName will be
overwritten if no fileName is specified. If the extension
is ".svgz" or ".gz", the output will be gzipped
encoding default="utf-8" file encoding
"""
fileName = self.interpret_fileName(fileName)
self.save(fileName, encoding)
os.spawnvp(os.P_NOWAIT, "inkscape", ("inkscape", fileName))
def firefox(self, fileName=None, encoding="utf-8"):
"""View in "firefox", assuming that program is available on your system.
fileName default=None note that any file named _default_fileName will be
overwritten if no fileName is specified. If the extension
is ".svgz" or ".gz", the output will be gzipped
encoding default="utf-8" file encoding
"""
fileName = self.interpret_fileName(fileName)
self.save(fileName, encoding)
os.spawnvp(os.P_NOWAIT, "firefox", ("firefox", fileName))
######################################################################
_canvas_defaults = {"width": "400px",
"height": "400px",
"viewBox": "0 0 100 100",
"xmlns": "http://www.w3.org/2000/svg",
"xmlns:xlink": "http://www.w3.org/1999/xlink",
"version": "1.1",
"style": {"stroke": "black",
"fill": "none",
"stroke-width": "0.5pt",
"stroke-linejoin": "round",
"text-anchor": "middle",
},
"font-family": ["Helvetica", "Arial", "FreeSans", "Sans", "sans", "sans-serif"],
}
def canvas(*sub, **attr):
"""Creates a top-level SVG object, allowing the user to control the
image size and aspect ratio.
canvas(sub, sub, sub..., attribute=value)
sub optional list nested SVG elements or text/Unicode
attribute=value pairs optional keywords SVG attributes
Default attribute values:
width "400px"
height "400px"
viewBox "0 0 100 100"
xmlns "http://www.w3.org/2000/svg"
xmlns:xlink "http://www.w3.org/1999/xlink"
version "1.1"
style "stroke:black; fill:none; stroke-width:0.5pt; stroke-linejoin:round; text-anchor:middle"
font-family "Helvetica,Arial,FreeSans?,Sans,sans,sans-serif"
"""
attributes = dict(_canvas_defaults)
attributes.update(attr)
if sub is None or sub == ():
return SVG("svg", **attributes)
else:
return SVG("svg", *sub, **attributes)
def canvas_outline(*sub, **attr):
"""Same as canvas(), but draws an outline around the drawable area,
so that you know how close your image is to the edges."""
svg = canvas(*sub, **attr)
match = re.match(r"[, \t]*([0-9e.+\-]+)[, \t]+([0-9e.+\-]+)[, \t]+([0-9e.+\-]+)[, \t]+([0-9e.+\-]+)[, \t]*", svg["viewBox"])
if match is None:
raise ValueError( "canvas viewBox is incorrectly formatted")
x, y, width, height = [float(x) for x in match.groups()]
svg.prepend(SVG("rect", x=x, y=y, width=width, height=height, stroke="none", fill="cornsilk"))
svg.append(SVG("rect", x=x, y=y, width=width, height=height, stroke="black", fill="none"))
return svg
def template(fileName, svg, replaceme="REPLACEME"):
"""Loads an SVG image from a file, replacing instances of
<REPLACEME /> with a given svg object.
fileName required name of the template SVG
svg required SVG object for replacement
replaceme default="REPLACEME" fake SVG element to be replaced by the given object
>>> print load("template.svg")
None <svg (2 sub) style=u'stroke:black; fill:none; stroke-width:0.5pt; stroke-linejoi
[0] <rect height=u'100' width=u'100' stroke=u'none' y=u'0' x=u'0' fill=u'yellow'
[1] <REPLACEME />
>>>
>>> print template("template.svg", SVG("circle", cx=50, cy=50, r=30))
None <svg (2 sub) style=u'stroke:black; fill:none; stroke-width:0.5pt; stroke-linejoi
[0] <rect height=u'100' width=u'100' stroke=u'none' y=u'0' x=u'0' fill=u'yellow'
[1] <circle cy=50 cx=50 r=30 />
"""
output = load(fileName)
for ti, s in output:
if isinstance(s, SVG) and s.t == replaceme:
output[ti] = svg
return output
######################################################################
def load(fileName):
"""Loads an SVG image from a file."""
return load_stream(open(fileName))
def load_stream(stream):
"""Loads an SVG image from a stream (can be a string or a file object)."""
from xml.sax import handler, make_parser
from xml.sax.handler import feature_namespaces, feature_external_ges, feature_external_pes
class ContentHandler(handler.ContentHandler):
def __init__(self):
self.stack = []
self.output = None
self.all_whitespace = re.compile(r"^\s*$")
def startElement(self, name, attr):
s = SVG(name)
s.attr = dict(attr.items())
if len(self.stack) > 0:
last = self.stack[-1]
last.sub.append(s)
self.stack.append(s)
def characters(self, ch):
if not isinstance(ch, basestring) or self.all_whitespace.match(ch) is None:
if len(self.stack) > 0:
last = self.stack[-1]
if len(last.sub) > 0 and isinstance(last.sub[-1], basestring):
last.sub[-1] = last.sub[-1] + "\n" + ch
else:
last.sub.append(ch)
def endElement(self, name):
if len(self.stack) > 0:
last = self.stack[-1]
if (isinstance(last, SVG) and last.t == "style" and
"type" in last.attr and last.attr["type"] == "text/css" and
len(last.sub) == 1 and isinstance(last.sub[0], basestring)):
last.sub[0] = "<![CDATA[\n" + last.sub[0] + "]]>"
self.output = self.stack.pop()
ch = ContentHandler()
parser = make_parser()
parser.setContentHandler(ch)
parser.setFeature(feature_namespaces, 0)
parser.setFeature(feature_external_ges, 0)
parser.parse(stream)
return ch.output
######################################################################
def set_func_name(f, name):
"""try to patch the function name string into a function object"""
try:
f.func_name = name
except TypeError:
# py 2.3 raises: TypeError: readonly attribute
pass
def totrans(expr, vars=("x", "y"), globals=None, locals=None):
"""Converts to a coordinate transformation (a function that accepts
two arguments and returns two values).
expr required a string expression or a function
of two real or one complex value
vars default=("x", "y") independent variable names; a singleton
("z",) is interpreted as complex
globals default=None dict of global variables
locals default=None dict of local variables
"""
if locals is None:
locals = {} # python 2.3's eval() won't accept None
if callable(expr):
if expr.func_code.co_argcount == 2:
return expr
elif expr.func_code.co_argcount == 1:
split = lambda z: (z.real, z.imag)
output = lambda x, y: split(expr(x + y*1j))
set_func_name(output, expr.func_name)
return output
else:
raise TypeError( "must be a function of 2 or 1 variables")
if len(vars) == 2:
g = math.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s, %s: (%s)" % (vars[0], vars[1], expr), g, locals)
set_func_name(output, "%s,%s -> %s" % (vars[0], vars[1], expr))
return output
elif len(vars) == 1:
g = cmath.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s: (%s)" % (vars[0], expr), g, locals)
split = lambda z: (z.real, z.imag)
output2 = lambda x, y: split(output(x + y*1j))
set_func_name(output2, "%s -> %s" % (vars[0], expr))
return output2
else:
raise TypeError( "vars must have 2 or 1 elements")
def window(xmin, xmax, ymin, ymax, x=0, y=0, width=100, height=100,
xlogbase=None, ylogbase=None, minusInfinity=-1000, flipx=False, flipy=True):
"""Creates and returns a coordinate transformation (a function that
accepts two arguments and returns two values) that transforms from
(xmin, ymin), (xmax, ymax)
to
(x, y), (x + width, y + height).
xlogbase, ylogbase default=None, None if a number, transform
logarithmically with given base
minusInfinity default=-1000 what to return if
log(0 or negative) is attempted
flipx default=False if true, reverse the direction of x
flipy default=True if true, reverse the direction of y
(When composing windows, be sure to set flipy=False.)
"""
if flipx:
ox1 = x + width
ox2 = x
else:
ox1 = x
ox2 = x + width
if flipy:
oy1 = y + height
oy2 = y
else:
oy1 = y
oy2 = y + height
ix1 = xmin
iy1 = ymin
ix2 = xmax
iy2 = ymax
if xlogbase is not None and (ix1 <= 0. or ix2 <= 0.):
raise ValueError ("x range incompatible with log scaling: (%g, %g)" % (ix1, ix2))
if ylogbase is not None and (iy1 <= 0. or iy2 <= 0.):
raise ValueError ("y range incompatible with log scaling: (%g, %g)" % (iy1, iy2))
def maybelog(t, it1, it2, ot1, ot2, logbase):
if t <= 0.:
return minusInfinity
else:
return ot1 + 1.*(math.log(t, logbase) - math.log(it1, logbase))/(math.log(it2, logbase) - math.log(it1, logbase)) * (ot2 - ot1)
xlogstr, ylogstr = "", ""
if xlogbase is None:
xfunc = lambda x: ox1 + 1.*(x - ix1)/(ix2 - ix1) * (ox2 - ox1)
else:
xfunc = lambda x: maybelog(x, ix1, ix2, ox1, ox2, xlogbase)
xlogstr = " xlog=%g" % xlogbase
if ylogbase is None:
yfunc = lambda y: oy1 + 1.*(y - iy1)/(iy2 - iy1) * (oy2 - oy1)
else:
yfunc = lambda y: maybelog(y, iy1, iy2, oy1, oy2, ylogbase)
ylogstr = " ylog=%g" % ylogbase
output = lambda x, y: (xfunc(x), yfunc(y))
set_func_name(output, "(%g, %g), (%g, %g) -> (%g, %g), (%g, %g)%s%s" % (
ix1, ix2, iy1, iy2, ox1, ox2, oy1, oy2, xlogstr, ylogstr))
return output
def rotate(angle, cx=0, cy=0):
"""Creates and returns a coordinate transformation which rotates
around (cx,cy) by "angle" degrees."""
angle *= math.pi/180.
return lambda x, y: (cx + math.cos(angle)*(x - cx) - math.sin(angle)*(y - cy), cy + math.sin(angle)*(x - cx) + math.cos(angle)*(y - cy))
class Fig:
"""Stores graphics primitive objects and applies a single coordinate
transformation to them. To compose coordinate systems, nest Fig
objects.
Fig(obj, obj, obj..., trans=function)
obj optional list a list of drawing primitives
trans default=None a coordinate transformation function
>>> fig = Fig(Line(0,0,1,1), Rect(0.2,0.2,0.8,0.8), trans="2*x, 2*y")
>>> print fig.SVG().xml()
<g>
<path d='M0 0L2 2' />
<path d='M0.4 0.4L1.6 0.4ZL1.6 1.6ZL0.4 1.6ZL0.4 0.4ZZ' />
</g>
>>> print Fig(fig, trans="x/2., y/2.").SVG().xml()
<g>
<path d='M0 0L1 1' />
<path d='M0.2 0.2L0.8 0.2ZL0.8 0.8ZL0.2 0.8ZL0.2 0.2ZZ' />
</g>
"""
def __repr__(self):
if self.trans is None:
return "<Fig (%d items)>" % len(self.d)
elif isinstance(self.trans, basestring):
return "<Fig (%d items) x,y -> %s>" % (len(self.d), self.trans)
else:
return "<Fig (%d items) %s>" % (len(self.d), self.trans.func_name)
def __init__(self, *d, **kwds):
self.d = list(d)
defaults = {"trans": None, }
defaults.update(kwds)
kwds = defaults
self.trans = kwds["trans"]; del kwds["trans"]
if len(kwds) != 0:
raise TypeError ("Fig() got unexpected keyword arguments %s" % kwds.keys())
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object.
Coordinate transformations in nested Figs will be composed.
"""
if trans is None:
trans = self.trans
if isinstance(trans, basestring):
trans = totrans(trans)
output = SVG("g")
for s in self.d:
if isinstance(s, SVG):
output.append(s)
elif isinstance(s, Fig):
strans = s.trans
if isinstance(strans, basestring):
strans = totrans(strans)
if trans is None:
subtrans = strans
elif strans is None:
subtrans = trans
else:
subtrans = lambda x, y: trans(*strans(x, y))
output.sub += s.SVG(subtrans).sub
elif s is None:
pass
else:
output.append(s.SVG(trans))
return output
class Plot:
"""Acts like Fig, but draws a coordinate axis. You also need to supply plot ranges.
Plot(xmin, xmax, ymin, ymax, obj, obj, obj..., keyword options...)
xmin, xmax required minimum and maximum x values (in the objs' coordinates)
ymin, ymax required minimum and maximum y values (in the objs' coordinates)
obj optional list drawing primitives
keyword options keyword list options defined below
The following are keyword options, with their default values:
trans None transformation function
x, y 5, 5 upper-left corner of the Plot in SVG coordinates
width, height 90, 90 width and height of the Plot in SVG coordinates
flipx, flipy False, True flip the sign of the coordinate axis
minusInfinity -1000 if an axis is logarithmic and an object is plotted at 0 or
a negative value, -1000 will be used as a stand-in for NaN
atx, aty 0, 0 the place where the coordinate axes cross
xticks -10 request ticks according to the standard tick specification
(see help(Ticks))
xminiticks True request miniticks according to the standard minitick
specification
xlabels True request tick labels according to the standard tick label
specification
xlogbase None if a number, the axis and transformation are logarithmic
with ticks at the given base (10 being the most common)
(same for y)
arrows None if a new identifier, create arrow markers and draw them
at the ends of the coordinate axes
text_attr {} a dictionary of attributes for label text
axis_attr {} a dictionary of attributes for the axis lines
"""
def __repr__(self):
if self.trans is None:
return "<Plot (%d items)>" % len(self.d)
else:
return "<Plot (%d items) %s>" % (len(self.d), self.trans.func_name)
def __init__(self, xmin, xmax, ymin, ymax, *d, **kwds):
self.xmin, self.xmax, self.ymin, self.ymax = xmin, xmax, ymin, ymax
self.d = list(d)
defaults = {"trans": None,
"x": 5, "y": 5, "width": 90, "height": 90,
"flipx": False, "flipy": True,
"minusInfinity": -1000,
"atx": 0, "xticks": -10, "xminiticks": True, "xlabels": True, "xlogbase": None,
"aty": 0, "yticks": -10, "yminiticks": True, "ylabels": True, "ylogbase": None,
"arrows": None,
"text_attr": {}, "axis_attr": {},
}
defaults.update(kwds)
kwds = defaults
self.trans = kwds["trans"]; del kwds["trans"]
self.x = kwds["x"]; del kwds["x"]
self.y = kwds["y"]; del kwds["y"]
self.width = kwds["width"]; del kwds["width"]
self.height = kwds["height"]; del kwds["height"]
self.flipx = kwds["flipx"]; del kwds["flipx"]
self.flipy = kwds["flipy"]; del kwds["flipy"]
self.minusInfinity = kwds["minusInfinity"]; del kwds["minusInfinity"]
self.atx = kwds["atx"]; del kwds["atx"]
self.xticks = kwds["xticks"]; del kwds["xticks"]
self.xminiticks = kwds["xminiticks"]; del kwds["xminiticks"]
self.xlabels = kwds["xlabels"]; del kwds["xlabels"]
self.xlogbase = kwds["xlogbase"]; del kwds["xlogbase"]
self.aty = kwds["aty"]; del kwds["aty"]
self.yticks = kwds["yticks"]; del kwds["yticks"]
self.yminiticks = kwds["yminiticks"]; del kwds["yminiticks"]
self.ylabels = kwds["ylabels"]; del kwds["ylabels"]
self.ylogbase = kwds["ylogbase"]; del kwds["ylogbase"]
self.arrows = kwds["arrows"]; del kwds["arrows"]
self.text_attr = kwds["text_attr"]; del kwds["text_attr"]
self.axis_attr = kwds["axis_attr"]; del kwds["axis_attr"]
if len(kwds) != 0:
raise TypeError ("Plot() got unexpected keyword arguments %s" % kwds.keys())
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if trans is None:
trans = self.trans
if isinstance(trans, basestring):
trans = totrans(trans)
self.last_window = window(self.xmin, self.xmax, self.ymin, self.ymax,
x=self.x, y=self.y, width=self.width, height=self.height,
xlogbase=self.xlogbase, ylogbase=self.ylogbase,
minusInfinity=self.minusInfinity, flipx=self.flipx, flipy=self.flipy)
d = ([Axes(self.xmin, self.xmax, self.ymin, self.ymax, self.atx, self.aty,
self.xticks, self.xminiticks, self.xlabels, self.xlogbase,
self.yticks, self.yminiticks, self.ylabels, self.ylogbase,
self.arrows, self.text_attr, **self.axis_attr)]
+ self.d)
return Fig(Fig(*d, **{"trans": trans})).SVG(self.last_window)
class Frame:
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
axis_defaults = {}
tick_length = 1.5
minitick_length = 0.75
text_xaxis_offset = 1.
text_yaxis_offset = 2.
text_xtitle_offset = 6.
text_ytitle_offset = 12.
def __repr__(self):
return "<Frame (%d items)>" % len(self.d)
def __init__(self, xmin, xmax, ymin, ymax, *d, **kwds):
"""Acts like Fig, but draws a coordinate frame around the data. You also need to supply plot ranges.
Frame(xmin, xmax, ymin, ymax, obj, obj, obj..., keyword options...)
xmin, xmax required minimum and maximum x values (in the objs' coordinates)
ymin, ymax required minimum and maximum y values (in the objs' coordinates)
obj optional list drawing primitives
keyword options keyword list options defined below
The following are keyword options, with their default values:
x, y 20, 5 upper-left corner of the Frame in SVG coordinates
width, height 75, 80 width and height of the Frame in SVG coordinates
flipx, flipy False, True flip the sign of the coordinate axis
minusInfinity -1000 if an axis is logarithmic and an object is plotted at 0 or
a negative value, -1000 will be used as a stand-in for NaN
xtitle None if a string, label the x axis
xticks -10 request ticks according to the standard tick specification
(see help(Ticks))
xminiticks True request miniticks according to the standard minitick
specification
xlabels True request tick labels according to the standard tick label
specification
xlogbase None if a number, the axis and transformation are logarithmic
with ticks at the given base (10 being the most common)
(same for y)
text_attr {} a dictionary of attributes for label text
axis_attr {} a dictionary of attributes for the axis lines
"""
self.xmin, self.xmax, self.ymin, self.ymax = xmin, xmax, ymin, ymax
self.d = list(d)
defaults = {"x": 20, "y": 5, "width": 75, "height": 80,
"flipx": False, "flipy": True, "minusInfinity": -1000,
"xtitle": None, "xticks": -10, "xminiticks": True, "xlabels": True,
"x2labels": None, "xlogbase": None,
"ytitle": None, "yticks": -10, "yminiticks": True, "ylabels": True,
"y2labels": None, "ylogbase": None,
"text_attr": {}, "axis_attr": {},
}
defaults.update(kwds)
kwds = defaults
self.x = kwds["x"]; del kwds["x"]
self.y = kwds["y"]; del kwds["y"]
self.width = kwds["width"]; del kwds["width"]
self.height = kwds["height"]; del kwds["height"]
self.flipx = kwds["flipx"]; del kwds["flipx"]
self.flipy = kwds["flipy"]; del kwds["flipy"]
self.minusInfinity = kwds["minusInfinity"]; del kwds["minusInfinity"]
self.xtitle = kwds["xtitle"]; del kwds["xtitle"]
self.xticks = kwds["xticks"]; del kwds["xticks"]
self.xminiticks = kwds["xminiticks"]; del kwds["xminiticks"]
self.xlabels = kwds["xlabels"]; del kwds["xlabels"]
self.x2labels = kwds["x2labels"]; del kwds["x2labels"]
self.xlogbase = kwds["xlogbase"]; del kwds["xlogbase"]
self.ytitle = kwds["ytitle"]; del kwds["ytitle"]
self.yticks = kwds["yticks"]; del kwds["yticks"]
self.yminiticks = kwds["yminiticks"]; del kwds["yminiticks"]
self.ylabels = kwds["ylabels"]; del kwds["ylabels"]
self.y2labels = kwds["y2labels"]; del kwds["y2labels"]
self.ylogbase = kwds["ylogbase"]; del kwds["ylogbase"]
self.text_attr = dict(self.text_defaults)
self.text_attr.update(kwds["text_attr"]); del kwds["text_attr"]
self.axis_attr = dict(self.axis_defaults)
self.axis_attr.update(kwds["axis_attr"]); del kwds["axis_attr"]
if len(kwds) != 0:
raise TypeError( "Frame() got unexpected keyword arguments %s" % kwds.keys())
def SVG(self):
"""Apply the window transformation and return an SVG object."""
self.last_window = window(self.xmin, self.xmax, self.ymin, self.ymax,
x=self.x, y=self.y, width=self.width, height=self.height,
xlogbase=self.xlogbase, ylogbase=self.ylogbase,
minusInfinity=self.minusInfinity, flipx=self.flipx, flipy=self.flipy)
left = YAxis(self.ymin, self.ymax, self.xmin, self.yticks, self.yminiticks, self.ylabels, self.ylogbase,
None, None, None, self.text_attr, **self.axis_attr)
right = YAxis(self.ymin, self.ymax, self.xmax, self.yticks, self.yminiticks, self.y2labels, self.ylogbase,
None, None, None, self.text_attr, **self.axis_attr)
bottom = XAxis(self.xmin, self.xmax, self.ymin, self.xticks, self.xminiticks, self.xlabels, self.xlogbase,
None, None, None, self.text_attr, **self.axis_attr)
top = XAxis(self.xmin, self.xmax, self.ymax, self.xticks, self.xminiticks, self.x2labels, self.xlogbase,
None, None, None, self.text_attr, **self.axis_attr)
left.tick_start = -self.tick_length
left.tick_end = 0
left.minitick_start = -self.minitick_length
left.minitick_end = 0.
left.text_start = self.text_yaxis_offset
right.tick_start = 0.
right.tick_end = self.tick_length
right.minitick_start = 0.
right.minitick_end = self.minitick_length
right.text_start = -self.text_yaxis_offset
right.text_attr["text-anchor"] = "start"
bottom.tick_start = 0.
bottom.tick_end = self.tick_length
bottom.minitick_start = 0.
bottom.minitick_end = self.minitick_length
bottom.text_start = -self.text_xaxis_offset
top.tick_start = -self.tick_length
top.tick_end = 0.
top.minitick_start = -self.minitick_length
top.minitick_end = 0.
top.text_start = self.text_xaxis_offset
top.text_attr["dominant-baseline"] = "text-after-edge"
output = Fig(*self.d).SVG(self.last_window)
output.prepend(left.SVG(self.last_window))
output.prepend(bottom.SVG(self.last_window))
output.prepend(right.SVG(self.last_window))
output.prepend(top.SVG(self.last_window))
if self.xtitle is not None:
output.append(SVG("text", self.xtitle, transform="translate(%g, %g)" % ((self.x + self.width/2.), (self.y + self.height + self.text_xtitle_offset)), dominant_baseline="text-before-edge", **self.text_attr))
if self.ytitle is not None:
output.append(SVG("text", self.ytitle, transform="translate(%g, %g) rotate(-90)" % ((self.x - self.text_ytitle_offset), (self.y + self.height/2.)), **self.text_attr))
return output
######################################################################
def pathtoPath(svg):
"""Converts SVG("path", d="...") into Path(d=[...])."""
if not isinstance(svg, SVG) or svg.t != "path":
raise TypeError ("Only SVG <path /> objects can be converted into Paths")
attr = dict(svg.attr)
d = attr["d"]
del attr["d"]
for key in attr.keys():
if not isinstance(key, str):
value = attr[key]
del attr[key]
attr[str(key)] = value
return Path(d, **attr)
class Path:
"""Path represents an SVG path, an arbitrary set of curves and
straight segments. Unlike SVG("path", d="..."), Path stores
coordinates as a list of numbers, rather than a string, so that it is
transformable in a Fig.
Path(d, attribute=value)
d required path data
attribute=value pairs keyword list SVG attributes
See http://www.w3.org/TR/SVG/paths.html for specification of paths
from text.
Internally, Path data is a list of tuples with these definitions:
* ("Z/z",): close the current path
* ("H/h", x) or ("V/v", y): a horizontal or vertical line
segment to x or y
* ("M/m/L/l/T/t", x, y, global): moveto, lineto, or smooth
quadratic curveto point (x, y). If global=True, (x, y) should
not be transformed.
* ("S/sQ/q", cx, cy, cglobal, x, y, global): polybezier or
smooth quadratic curveto point (x, y) using (cx, cy) as a
control point. If cglobal or global=True, (cx, cy) or (x, y)
should not be transformed.
* ("C/c", c1x, c1y, c1global, c2x, c2y, c2global, x, y, global):
cubic curveto point (x, y) using (c1x, c1y) and (c2x, c2y) as
control points. If c1global, c2global, or global=True, (c1x, c1y),
(c2x, c2y), or (x, y) should not be transformed.
* ("A/a", rx, ry, rglobal, x-axis-rotation, angle, large-arc-flag,
sweep-flag, x, y, global): arcto point (x, y) using the
aforementioned parameters.
* (",/.", rx, ry, rglobal, angle, x, y, global): an ellipse at
point (x, y) with radii (rx, ry). If angle is 0, the whole
ellipse is drawn; otherwise, a partial ellipse is drawn.
"""
defaults = {}
def __repr__(self):
return "<Path (%d nodes) %s>" % (len(self.d), self.attr)
def __init__(self, d=[], **attr):
if isinstance(d, basestring):
self.d = self.parse(d)
else:
self.d = list(d)
self.attr = dict(self.defaults)
self.attr.update(attr)
def parse_whitespace(self, index, pathdata):
"""Part of Path's text-command parsing algorithm; used internally."""
while index < len(pathdata) and pathdata[index] in (" ", "\t", "\r", "\n", ","):
index += 1
return index, pathdata
def parse_command(self, index, pathdata):
"""Part of Path's text-command parsing algorithm; used internally."""
index, pathdata = self.parse_whitespace(index, pathdata)
if index >= len(pathdata):
return None, index, pathdata
command = pathdata[index]
if "A" <= command <= "Z" or "a" <= command <= "z":
index += 1
return command, index, pathdata
else:
return None, index, pathdata
def parse_number(self, index, pathdata):
"""Part of Path's text-command parsing algorithm; used internally."""
index, pathdata = self.parse_whitespace(index, pathdata)
if index >= len(pathdata):
return None, index, pathdata
first_digit = pathdata[index]
if "0" <= first_digit <= "9" or first_digit in ("-", "+", "."):
start = index
while index < len(pathdata) and ("0" <= pathdata[index] <= "9" or pathdata[index] in ("-", "+", ".", "e", "E")):
index += 1
end = index
index = end
return float(pathdata[start:end]), index, pathdata
else:
return None, index, pathdata
def parse_boolean(self, index, pathdata):
"""Part of Path's text-command parsing algorithm; used internally."""
index, pathdata = self.parse_whitespace(index, pathdata)
if index >= len(pathdata):
return None, index, pathdata
first_digit = pathdata[index]
if first_digit in ("0", "1"):
index += 1
return int(first_digit), index, pathdata
else:
return None, index, pathdata
def parse(self, pathdata):
"""Parses text-commands, converting them into a list of tuples.
Called by the constructor."""
output = []
index = 0
while True:
command, index, pathdata = self.parse_command(index, pathdata)
index, pathdata = self.parse_whitespace(index, pathdata)
if command is None and index == len(pathdata):
break # this is the normal way out of the loop
if command in ("Z", "z"):
output.append((command,))
######################
elif command in ("H", "h", "V", "v"):
errstring = "Path command \"%s\" requires a number at index %d" % (command, index)
num1, index, pathdata = self.parse_number(index, pathdata)
if num1 is None:
raise ValueError ( errstring)
while num1 is not None:
output.append((command, num1))
num1, index, pathdata = self.parse_number(index, pathdata)
######################
elif command in ("M", "m", "L", "l", "T", "t"):
errstring = "Path command \"%s\" requires an x,y pair at index %d" % (command, index)
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
if num1 is None:
raise ValueError ( errstring)
while num1 is not None:
if num2 is None:
raise ValueError ( errstring)
output.append((command, num1, num2, False))
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
######################
elif command in ("S", "s", "Q", "q"):
errstring = "Path command \"%s\" requires a cx,cy,x,y quadruplet at index %d" % (command, index)
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_number(index, pathdata)
if num1 is None:
raise ValueError ( errstring )
while num1 is not None:
if num2 is None or num3 is None or num4 is None:
raise ValueError (errstring)
output.append((command, num1, num2, False, num3, num4, False))
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_number(index, pathdata)
######################
elif command in ("C", "c"):
errstring = "Path command \"%s\" requires a c1x,c1y,c2x,c2y,x,y sextuplet at index %d" % (command, index)
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_number(index, pathdata)
num5, index, pathdata = self.parse_number(index, pathdata)
num6, index, pathdata = self.parse_number(index, pathdata)
if num1 is None:
raise ValueError(errstring)
while num1 is not None:
if num2 is None or num3 is None or num4 is None or num5 is None or num6 is None:
raise ValueError(errstring)
output.append((command, num1, num2, False, num3, num4, False, num5, num6, False))
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_number(index, pathdata)
num5, index, pathdata = self.parse_number(index, pathdata)
num6, index, pathdata = self.parse_number(index, pathdata)
######################
elif command in ("A", "a"):
errstring = "Path command \"%s\" requires a rx,ry,angle,large-arc-flag,sweep-flag,x,y septuplet at index %d" % (command, index)
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_boolean(index, pathdata)
num5, index, pathdata = self.parse_boolean(index, pathdata)
num6, index, pathdata = self.parse_number(index, pathdata)
num7, index, pathdata = self.parse_number(index, pathdata)
if num1 is None:
raise ValueError(errstring)
while num1 is not None:
if num2 is None or num3 is None or num4 is None or num5 is None or num6 is None or num7 is None:
raise ValueError(errstring)
output.append((command, num1, num2, False, num3, num4, num5, num6, num7, False))
num1, index, pathdata = self.parse_number(index, pathdata)
num2, index, pathdata = self.parse_number(index, pathdata)
num3, index, pathdata = self.parse_number(index, pathdata)
num4, index, pathdata = self.parse_boolean(index, pathdata)
num5, index, pathdata = self.parse_boolean(index, pathdata)
num6, index, pathdata = self.parse_number(index, pathdata)
num7, index, pathdata = self.parse_number(index, pathdata)
return output
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans)
x, y, X, Y = None, None, None, None
output = []
for datum in self.d:
if not isinstance(datum, (tuple, list)):
raise TypeError("pathdata elements must be tuples/lists")
command = datum[0]
######################
if command in ("Z", "z"):
x, y, X, Y = None, None, None, None
output.append("Z")
######################
elif command in ("H", "h", "V", "v"):
command, num1 = datum
if command == "H" or (command == "h" and x is None):
x = num1
elif command == "h":
x += num1
elif command == "V" or (command == "v" and y is None):
y = num1
elif command == "v":
y += num1
if trans is None:
X, Y = x, y
else:
X, Y = trans(x, y)
output.append("L%g %g" % (X, Y))
######################
elif command in ("M", "m", "L", "l", "T", "t"):
command, num1, num2, isglobal12 = datum
if trans is None or isglobal12:
if command.isupper() or X is None or Y is None:
X, Y = num1, num2
else:
X += num1
Y += num2
x, y = X, Y
else:
if command.isupper() or x is None or y is None:
x, y = num1, num2
else:
x += num1
y += num2
X, Y = trans(x, y)
COMMAND = command.capitalize()
output.append("%s%g %g" % (COMMAND, X, Y))
######################
elif command in ("S", "s", "Q", "q"):
command, num1, num2, isglobal12, num3, num4, isglobal34 = datum
if trans is None or isglobal12:
if command.isupper() or X is None or Y is None:
CX, CY = num1, num2
else:
CX = X + num1
CY = Y + num2
else:
if command.isupper() or x is None or y is None:
cx, cy = num1, num2
else:
cx = x + num1
cy = y + num2
CX, CY = trans(cx, cy)
if trans is None or isglobal34:
if command.isupper() or X is None or Y is None:
X, Y = num3, num4
else:
X += num3
Y += num4
x, y = X, Y
else:
if command.isupper() or x is None or y is None:
x, y = num3, num4
else:
x += num3
y += num4
X, Y = trans(x, y)
COMMAND = command.capitalize()
output.append("%s%g %g %g %g" % (COMMAND, CX, CY, X, Y))
######################
elif command in ("C", "c"):
command, num1, num2, isglobal12, num3, num4, isglobal34, num5, num6, isglobal56 = datum
if trans is None or isglobal12:
if command.isupper() or X is None or Y is None:
C1X, C1Y = num1, num2
else:
C1X = X + num1
C1Y = Y + num2
else:
if command.isupper() or x is None or y is None:
c1x, c1y = num1, num2
else:
c1x = x + num1
c1y = y + num2
C1X, C1Y = trans(c1x, c1y)
if trans is None or isglobal34:
if command.isupper() or X is None or Y is None:
C2X, C2Y = num3, num4
else:
C2X = X + num3
C2Y = Y + num4
else:
if command.isupper() or x is None or y is None:
c2x, c2y = num3, num4
else:
c2x = x + num3
c2y = y + num4
C2X, C2Y = trans(c2x, c2y)
if trans is None or isglobal56:
if command.isupper() or X is None or Y is None:
X, Y = num5, num6
else:
X += num5
Y += num6
x, y = X, Y
else:
if command.isupper() or x is None or y is None:
x, y = num5, num6
else:
x += num5
y += num6
X, Y = trans(x, y)
COMMAND = command.capitalize()
output.append("%s%g %g %g %g %g %g" % (COMMAND, C1X, C1Y, C2X, C2Y, X, Y))
######################
elif command in ("A", "a"):
command, num1, num2, isglobal12, angle, large_arc_flag, sweep_flag, num3, num4, isglobal34 = datum
oldx, oldy = x, y
OLDX, OLDY = X, Y
if trans is None or isglobal34:
if command.isupper() or X is None or Y is None:
X, Y = num3, num4
else:
X += num3
Y += num4
x, y = X, Y
else:
if command.isupper() or x is None or y is None:
x, y = num3, num4
else:
x += num3
y += num4
X, Y = trans(x, y)
if x is not None and y is not None:
centerx, centery = (x + oldx)/2., (y + oldy)/2.
CENTERX, CENTERY = (X + OLDX)/2., (Y + OLDY)/2.
if trans is None or isglobal12:
RX = CENTERX + num1
RY = CENTERY + num2
else:
rx = centerx + num1
ry = centery + num2
RX, RY = trans(rx, ry)
COMMAND = command.capitalize()
output.append("%s%g %g %g %d %d %g %g" % (COMMAND, RX - CENTERX, RY - CENTERY, angle, large_arc_flag, sweep_flag, X, Y))
elif command in (",", "."):
command, num1, num2, isglobal12, angle, num3, num4, isglobal34 = datum
if trans is None or isglobal34:
if command == "." or X is None or Y is None:
X, Y = num3, num4
else:
X += num3
Y += num4
x, y = None, None
else:
if command == "." or x is None or y is None:
x, y = num3, num4
else:
x += num3
y += num4
X, Y = trans(x, y)
if trans is None or isglobal12:
RX = X + num1
RY = Y + num2
else:
rx = x + num1
ry = y + num2
RX, RY = trans(rx, ry)
RX, RY = RX - X, RY - Y
X1, Y1 = X + RX * math.cos(angle*math.pi/180.), Y + RX * math.sin(angle*math.pi/180.)
X2, Y2 = X + RY * math.sin(angle*math.pi/180.), Y - RY * math.cos(angle*math.pi/180.)
X3, Y3 = X - RX * math.cos(angle*math.pi/180.), Y - RX * math.sin(angle*math.pi/180.)
X4, Y4 = X - RY * math.sin(angle*math.pi/180.), Y + RY * math.cos(angle*math.pi/180.)
output.append("M%g %gA%g %g %g 0 0 %g %gA%g %g %g 0 0 %g %gA%g %g %g 0 0 %g %gA%g %g %g 0 0 %g %g" % (
X1, Y1, RX, RY, angle, X2, Y2, RX, RY, angle, X3, Y3, RX, RY, angle, X4, Y4, RX, RY, angle, X1, Y1))
return SVG("path", d="".join(output), **self.attr)
######################################################################
def funcRtoC(expr, var="t", globals=None, locals=None):
"""Converts a complex "z(t)" string to a function acceptable for Curve.
expr required string in the form "z(t)"
var default="t" name of the independent variable
globals default=None dict of global variables used in the expression;
you may want to use Python's builtin globals()
locals default=None dict of local variables
"""
if locals is None:
locals = {} # python 2.3's eval() won't accept None
g = cmath.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s: (%s)" % (var, expr), g, locals)
split = lambda z: (z.real, z.imag)
output2 = lambda t: split(output(t))
set_func_name(output2, "%s -> %s" % (var, expr))
return output2
def funcRtoR2(expr, var="t", globals=None, locals=None):
"""Converts a "f(t), g(t)" string to a function acceptable for Curve.
expr required string in the form "f(t), g(t)"
var default="t" name of the independent variable
globals default=None dict of global variables used in the expression;
you may want to use Python's builtin globals()
locals default=None dict of local variables
"""
if locals is None:
locals = {} # python 2.3's eval() won't accept None
g = math.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s: (%s)" % (var, expr), g, locals)
set_func_name(output, "%s -> %s" % (var, expr))
return output
def funcRtoR(expr, var="x", globals=None, locals=None):
"""Converts a "f(x)" string to a function acceptable for Curve.
expr required string in the form "f(x)"
var default="x" name of the independent variable
globals default=None dict of global variables used in the expression;
you may want to use Python's builtin globals()
locals default=None dict of local variables
"""
if locals is None:
locals = {} # python 2.3's eval() won't accept None
g = math.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s: (%s, %s)" % (var, var, expr), g, locals)
set_func_name(output, "%s -> %s" % (var, expr))
return output
class Curve:
"""Draws a parametric function as a path.
Curve(f, low, high, loop, attribute=value)
f required a Python callable or string in
the form "f(t), g(t)"
low, high required left and right endpoints
loop default=False if True, connect the endpoints
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
random_sampling = True
recursion_limit = 15
linearity_limit = 0.05
discontinuity_limit = 5.
def __repr__(self):
return "<Curve %s [%s, %s] %s>" % (self.f, self.low, self.high, self.attr)
def __init__(self, f, low, high, loop=False, **attr):
self.f = f
self.low = low
self.high = high
self.loop = loop
self.attr = dict(self.defaults)
self.attr.update(attr)
### nested class Sample
class Sample:
def __repr__(self):
t, x, y, X, Y = self.t, self.x, self.y, self.X, self.Y
if t is not None:
t = "%g" % t
if x is not None:
x = "%g" % x
if y is not None:
y = "%g" % y
if X is not None:
X = "%g" % X
if Y is not None:
Y = "%g" % Y
return "<Curve.Sample t=%s x=%s y=%s X=%s Y=%s>" % (t, x, y, X, Y)
def __init__(self, t):
self.t = t
def link(self, left, right):
self.left, self.right = left, right
def evaluate(self, f, trans):
self.x, self.y = f(self.t)
if trans is None:
self.X, self.Y = self.x, self.y
else:
self.X, self.Y = trans(self.x, self.y)
### end Sample
### nested class Samples
class Samples:
def __repr__(self):
return "<Curve.Samples (%d samples)>" % len(self)
def __init__(self, left, right):
self.left, self.right = left, right
def __len__(self):
count = 0
current = self.left
while current is not None:
count += 1
current = current.right
return count
def __iter__(self):
self.current = self.left
return self
def next(self):
current = self.current
if current is None:
raise StopIteration
self.current = self.current.right
return current
### end nested class
def sample(self, trans=None):
"""Adaptive-sampling algorithm that chooses the best sample points
for a parametric curve between two endpoints and detects
discontinuities. Called by SVG()."""
oldrecursionlimit = sys.getrecursionlimit()
sys.setrecursionlimit(self.recursion_limit + 100)
try:
# the best way to keep all the information while sampling is to make a linked list
if not (self.low < self.high):
raise ValueError("low must be less than high")
low, high = self.Sample(float(self.low)), self.Sample(float(self.high))
low.link(None, high)
high.link(low, None)
low.evaluate(self.f, trans)
high.evaluate(self.f, trans)
# adaptive sampling between the low and high points
self.subsample(low, high, 0, trans)
# Prune excess points where the curve is nearly linear
left = low
while left.right is not None:
# increment mid and right
mid = left.right
right = mid.right
if (right is not None and
left.X is not None and left.Y is not None and
mid.X is not None and mid.Y is not None and
right.X is not None and right.Y is not None):
numer = left.X*(right.Y - mid.Y) + mid.X*(left.Y - right.Y) + right.X*(mid.Y - left.Y)
denom = math.sqrt((left.X - right.X)**2 + (left.Y - right.Y)**2)
if denom != 0. and abs(numer/denom) < self.linearity_limit:
# drop mid (the garbage collector will get it)
left.right = right
right.left = left
else:
# increment left
left = left.right
else:
left = left.right
self.last_samples = self.Samples(low, high)
finally:
sys.setrecursionlimit(oldrecursionlimit)
def subsample(self, left, right, depth, trans=None):
"""Part of the adaptive-sampling algorithm that chooses the best
sample points. Called by sample()."""
if self.random_sampling:
mid = self.Sample(left.t + random.uniform(0.3, 0.7) * (right.t - left.t))
else:
mid = self.Sample(left.t + 0.5 * (right.t - left.t))
left.right = mid
right.left = mid
mid.link(left, right)
mid.evaluate(self.f, trans)
# calculate the distance of closest approach of mid to the line between left and right
numer = left.X*(right.Y - mid.Y) + mid.X*(left.Y - right.Y) + right.X*(mid.Y - left.Y)
denom = math.sqrt((left.X - right.X)**2 + (left.Y - right.Y)**2)
# if we haven't sampled enough or left fails to be close enough to right, or mid fails to be linear enough...
if (depth < 3 or
(denom == 0 and left.t != right.t) or
denom > self.discontinuity_limit or
(denom != 0. and abs(numer/denom) > self.linearity_limit)):
# and we haven't sampled too many points
if depth < self.recursion_limit:
self.subsample(left, mid, depth+1, trans)
self.subsample(mid, right, depth+1, trans)
else:
# We've sampled many points and yet it's still not a small linear gap.
# Break the line: it's a discontinuity
mid.y = mid.Y = None
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
return self.Path(trans).SVG()
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
if isinstance(trans, basestring):
trans = totrans(trans)
if isinstance(self.f, basestring):
self.f = funcRtoR2(self.f)
self.sample(trans)
output = []
for s in self.last_samples:
if s.X is not None and s.Y is not None:
if s.left is None or s.left.Y is None:
command = "M"
else:
command = "L"
if local:
output.append((command, s.x, s.y, False))
else:
output.append((command, s.X, s.Y, True))
if self.loop:
output.append(("Z",))
return Path(output, **self.attr)
######################################################################
class Poly:
"""Draws a curve specified by a sequence of points. The curve may be
piecewise linear, like a polygon, or a Bezier curve.
Poly(d, mode, loop, attribute=value)
d required list of tuples representing points
and possibly control points
mode default="L" "lines", "bezier", "velocity",
"foreback", "smooth", or an abbreviation
loop default=False if True, connect the first and last
point, closing the loop
attribute=value pairs keyword list SVG attributes
The format of the tuples in d depends on the mode.
"lines"/"L" d=[(x,y), (x,y), ...]
piecewise-linear segments joining the (x,y) points
"bezier"/"B" d=[(x, y, c1x, c1y, c2x, c2y), ...]
Bezier curve with two control points (control points
precede (x,y), as in SVG paths). If (c1x,c1y) and
(c2x,c2y) both equal (x,y), you get a linear
interpolation ("lines")
"velocity"/"V" d=[(x, y, vx, vy), ...]
curve that passes through (x,y) with velocity (vx,vy)
(one unit of arclength per unit time); in other words,
(vx,vy) is the tangent vector at (x,y). If (vx,vy) is
(0,0), you get a linear interpolation ("lines").
"foreback"/"F" d=[(x, y, bx, by, fx, fy), ...]
like "velocity" except that there is a left derivative
(bx,by) and a right derivative (fx,fy). If (bx,by)
equals (fx,fy) (with no minus sign), you get a
"velocity" curve
"smooth"/"S" d=[(x,y), (x,y), ...]
a "velocity" interpolation with (vx,vy)[i] equal to
((x,y)[i+1] - (x,y)[i-1])/2: the minimal derivative
"""
defaults = {}
def __repr__(self):
return "<Poly (%d nodes) mode=%s loop=%s %s>" % (
len(self.d), self.mode, repr(self.loop), self.attr)
def __init__(self, d=[], mode="L", loop=False, **attr):
self.d = list(d)
self.mode = mode
self.loop = loop
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
return self.Path(trans).SVG()
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
if isinstance(trans, basestring):
trans = totrans(trans)
if self.mode[0] == "L" or self.mode[0] == "l":
mode = "L"
elif self.mode[0] == "B" or self.mode[0] == "b":
mode = "B"
elif self.mode[0] == "V" or self.mode[0] == "v":
mode = "V"
elif self.mode[0] == "F" or self.mode[0] == "f":
mode = "F"
elif self.mode[0] == "S" or self.mode[0] == "s":
mode = "S"
vx, vy = [0.]*len(self.d), [0.]*len(self.d)
for i in xrange(len(self.d)):
inext = (i+1) % len(self.d)
iprev = (i-1) % len(self.d)
vx[i] = (self.d[inext][0] - self.d[iprev][0])/2.
vy[i] = (self.d[inext][1] - self.d[iprev][1])/2.
if not self.loop and (i == 0 or i == len(self.d)-1):
vx[i], vy[i] = 0., 0.
else:
raise ValueError("mode must be \"lines\", \"bezier\", \"velocity\", \"foreback\", \"smooth\", or an abbreviation")
d = []
indexes = list(range(len(self.d)))
if self.loop and len(self.d) > 0:
indexes.append(0)
for i in indexes:
inext = (i+1) % len(self.d)
iprev = (i-1) % len(self.d)
x, y = self.d[i][0], self.d[i][1]
if trans is None:
X, Y = x, y
else:
X, Y = trans(x, y)
if d == []:
if local:
d.append(("M", x, y, False))
else:
d.append(("M", X, Y, True))
elif mode == "L":
if local:
d.append(("L", x, y, False))
else:
d.append(("L", X, Y, True))
elif mode == "B":
c1x, c1y = self.d[i][2], self.d[i][3]
if trans is None:
C1X, C1Y = c1x, c1y
else:
C1X, C1Y = trans(c1x, c1y)
c2x, c2y = self.d[i][4], self.d[i][5]
if trans is None:
C2X, C2Y = c2x, c2y
else:
C2X, C2Y = trans(c2x, c2y)
if local:
d.append(("C", c1x, c1y, False, c2x, c2y, False, x, y, False))
else:
d.append(("C", C1X, C1Y, True, C2X, C2Y, True, X, Y, True))
elif mode == "V":
c1x, c1y = self.d[iprev][2]/3. + self.d[iprev][0], self.d[iprev][3]/3. + self.d[iprev][1]
c2x, c2y = self.d[i][2]/-3. + x, self.d[i][3]/-3. + y
if trans is None:
C1X, C1Y = c1x, c1y
else:
C1X, C1Y = trans(c1x, c1y)
if trans is None:
C2X, C2Y = c2x, c2y
else:
C2X, C2Y = trans(c2x, c2y)
if local:
d.append(("C", c1x, c1y, False, c2x, c2y, False, x, y, False))
else:
d.append(("C", C1X, C1Y, True, C2X, C2Y, True, X, Y, True))
elif mode == "F":
c1x, c1y = self.d[iprev][4]/3. + self.d[iprev][0], self.d[iprev][5]/3. + self.d[iprev][1]
c2x, c2y = self.d[i][2]/-3. + x, self.d[i][3]/-3. + y
if trans is None:
C1X, C1Y = c1x, c1y
else:
C1X, C1Y = trans(c1x, c1y)
if trans is None:
C2X, C2Y = c2x, c2y
else:
C2X, C2Y = trans(c2x, c2y)
if local:
d.append(("C", c1x, c1y, False, c2x, c2y, False, x, y, False))
else:
d.append(("C", C1X, C1Y, True, C2X, C2Y, True, X, Y, True))
elif mode == "S":
c1x, c1y = vx[iprev]/3. + self.d[iprev][0], vy[iprev]/3. + self.d[iprev][1]
c2x, c2y = vx[i]/-3. + x, vy[i]/-3. + y
if trans is None:
C1X, C1Y = c1x, c1y
else:
C1X, C1Y = trans(c1x, c1y)
if trans is None:
C2X, C2Y = c2x, c2y
else:
C2X, C2Y = trans(c2x, c2y)
if local:
d.append(("C", c1x, c1y, False, c2x, c2y, False, x, y, False))
else:
d.append(("C", C1X, C1Y, True, C2X, C2Y, True, X, Y, True))
if self.loop and len(self.d) > 0:
d.append(("Z",))
return Path(d, **self.attr)
######################################################################
class Text:
"""Draws a text string at a specified point in local coordinates.
x, y required location of the point in local coordinates
d required text/Unicode string
attribute=value pairs keyword list SVG attributes
"""
defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
def __repr__(self):
return "<Text %s at (%g, %g) %s>" % (repr(self.d), self.x, self.y, self.attr)
def __init__(self, x, y, d, **attr):
self.x = x
self.y = y
self.d = unicode(d)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans)
X, Y = self.x, self.y
if trans is not None:
X, Y = trans(X, Y)
return SVG("text", self.d, x=X, y=Y, **self.attr)
class TextGlobal:
"""Draws a text string at a specified point in global coordinates.
x, y required location of the point in global coordinates
d required text/Unicode string
attribute=value pairs keyword list SVG attributes
"""
defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
def __repr__(self):
return "<TextGlobal %s at (%s, %s) %s>" % (repr(self.d), str(self.x), str(self.y), self.attr)
def __init__(self, x, y, d, **attr):
self.x = x
self.y = y
self.d = unicode(d)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
return SVG("text", self.d, x=self.x, y=self.y, **self.attr)
######################################################################
_symbol_templates = {"dot": SVG("symbol", SVG("circle", cx=0, cy=0, r=1, stroke="none", fill="black"), viewBox="0 0 1 1", overflow="visible"),
"box": SVG("symbol", SVG("rect", x1=-1, y1=-1, x2=1, y2=1, stroke="none", fill="black"), viewBox="0 0 1 1", overflow="visible"),
"uptri": SVG("symbol", SVG("path", d="M -1 0.866 L 1 0.866 L 0 -0.866 Z", stroke="none", fill="black"), viewBox="0 0 1 1", overflow="visible"),
"downtri": SVG("symbol", SVG("path", d="M -1 -0.866 L 1 -0.866 L 0 0.866 Z", stroke="none", fill="black"), viewBox="0 0 1 1", overflow="visible"),
}
def make_symbol(id, shape="dot", **attr):
"""Creates a new instance of an SVG symbol to avoid cross-linking objects.
id required a new identifier (string/Unicode)
shape default="dot" the shape name from _symbol_templates
attribute=value list keyword list modify the SVG attributes of the new symbol
"""
output = copy.deepcopy(_symbol_templates[shape])
for i in output.sub:
i.attr.update(attr_preprocess(attr))
output["id"] = id
return output
_circular_dot = make_symbol("circular_dot")
class Dots:
"""Dots draws SVG symbols at a set of points.
d required list of (x,y) points
symbol default=None SVG symbol or a new identifier to
label an auto-generated symbol;
if None, use pre-defined _circular_dot
width, height default=1, 1 width and height of the symbols
in SVG coordinates
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
return "<Dots (%d nodes) %s>" % (len(self.d), self.attr)
def __init__(self, d=[], symbol=None, width=1., height=1., **attr):
self.d = list(d)
self.width = width
self.height = height
self.attr = dict(self.defaults)
self.attr.update(attr)
if symbol is None:
self.symbol = _circular_dot
elif isinstance(symbol, SVG):
self.symbol = symbol
else:
self.symbol = make_symbol(symbol)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans)
output = SVG("g", SVG("defs", self.symbol))
id = "#%s" % self.symbol["id"]
for p in self.d:
x, y = p[0], p[1]
if trans is None:
X, Y = x, y
else:
X, Y = trans(x, y)
item = SVG("use", x=X, y=Y, xlink__href=id)
if self.width is not None:
item["width"] = self.width
if self.height is not None:
item["height"] = self.height
output.append(item)
return output
######################################################################
_marker_templates = {"arrow_start": SVG("marker", SVG("path", d="M 9 3.6 L 10.5 0 L 0 3.6 L 10.5 7.2 L 9 3.6 Z"), viewBox="0 0 10.5 7.2", refX="9", refY="3.6", markerWidth="10.5", markerHeight="7.2", markerUnits="strokeWidth", orient="auto", stroke="none", fill="black"),
"arrow_end": SVG("marker", SVG("path", d="M 1.5 3.6 L 0 0 L 10.5 3.6 L 0 7.2 L 1.5 3.6 Z"), viewBox="0 0 10.5 7.2", refX="1.5", refY="3.6", markerWidth="10.5", markerHeight="7.2", markerUnits="strokeWidth", orient="auto", stroke="none", fill="black"),
}
def make_marker(id, shape, **attr):
"""Creates a new instance of an SVG marker to avoid cross-linking objects.
id required a new identifier (string/Unicode)
shape required the shape name from _marker_templates
attribute=value list keyword list modify the SVG attributes of the new marker
"""
output = copy.deepcopy(_marker_templates[shape])
for i in output.sub:
i.attr.update(attr_preprocess(attr))
output["id"] = id
return output
class Line(Curve):
"""Draws a line between two points.
Line(x1, y1, x2, y2, arrow_start, arrow_end, attribute=value)
x1, y1 required the starting point
x2, y2 required the ending point
arrow_start default=None if an identifier string/Unicode,
draw a new arrow object at the
beginning of the line; if a marker,
draw that marker instead
arrow_end default=None same for the end of the line
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
return "<Line (%g, %g) to (%g, %g) %s>" % (
self.x1, self.y1, self.x2, self.y2, self.attr)
def __init__(self, x1, y1, x2, y2, arrow_start=None, arrow_end=None, **attr):
self.x1, self.y1, self.x2, self.y2 = x1, y1, x2, y2
self.arrow_start, self.arrow_end = arrow_start, arrow_end
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
line = self.Path(trans).SVG()
if ((self.arrow_start != False and self.arrow_start is not None) or
(self.arrow_end != False and self.arrow_end is not None)):
defs = SVG("defs")
if self.arrow_start != False and self.arrow_start is not None:
if isinstance(self.arrow_start, SVG):
defs.append(self.arrow_start)
line.attr["marker-start"] = "url(#%s)" % self.arrow_start["id"]
elif isinstance(self.arrow_start, basestring):
defs.append(make_marker(self.arrow_start, "arrow_start"))
line.attr["marker-start"] = "url(#%s)" % self.arrow_start
else:
raise TypeError("arrow_start must be False/None or an id string for the new marker")
if self.arrow_end != False and self.arrow_end is not None:
if isinstance(self.arrow_end, SVG):
defs.append(self.arrow_end)
line.attr["marker-end"] = "url(#%s)" % self.arrow_end["id"]
elif isinstance(self.arrow_end, basestring):
defs.append(make_marker(self.arrow_end, "arrow_end"))
line.attr["marker-end"] = "url(#%s)" % self.arrow_end
else:
raise TypeError("arrow_end must be False/None or an id string for the new marker")
return SVG("g", defs, line)
return line
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
self.f = lambda t: (self.x1 + t*(self.x2 - self.x1), self.y1 + t*(self.y2 - self.y1))
self.low = 0.
self.high = 1.
self.loop = False
if trans is None:
return Path([("M", self.x1, self.y1, not local), ("L", self.x2, self.y2, not local)], **self.attr)
else:
return Curve.Path(self, trans, local)
class LineGlobal:
"""Draws a line between two points, one or both of which is in
global coordinates.
Line(x1, y1, x2, y2, lcoal1, local2, arrow_start, arrow_end, attribute=value)
x1, y1 required the starting point
x2, y2 required the ending point
local1 default=False if True, interpret first point as a
local coordinate (apply transform)
local2 default=False if True, interpret second point as a
local coordinate (apply transform)
arrow_start default=None if an identifier string/Unicode,
draw a new arrow object at the
beginning of the line; if a marker,
draw that marker instead
arrow_end default=None same for the end of the line
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
local1, local2 = "", ""
if self.local1:
local1 = "L"
if self.local2:
local2 = "L"
return "<LineGlobal %s(%s, %s) to %s(%s, %s) %s>" % (
local1, str(self.x1), str(self.y1), local2, str(self.x2), str(self.y2), self.attr)
def __init__(self, x1, y1, x2, y2, local1=False, local2=False, arrow_start=None, arrow_end=None, **attr):
self.x1, self.y1, self.x2, self.y2 = x1, y1, x2, y2
self.local1, self.local2 = local1, local2
self.arrow_start, self.arrow_end = arrow_start, arrow_end
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans)
X1, Y1, X2, Y2 = self.x1, self.y1, self.x2, self.y2
if self.local1:
X1, Y1 = trans(X1, Y1)
if self.local2:
X2, Y2 = trans(X2, Y2)
line = SVG("path", d="M%s %s L%s %s" % (X1, Y1, X2, Y2), **self.attr)
if ((self.arrow_start != False and self.arrow_start is not None) or
(self.arrow_end != False and self.arrow_end is not None)):
defs = SVG("defs")
if self.arrow_start != False and self.arrow_start is not None:
if isinstance(self.arrow_start, SVG):
defs.append(self.arrow_start)
line.attr["marker-start"] = "url(#%s)" % self.arrow_start["id"]
elif isinstance(self.arrow_start, basestring):
defs.append(make_marker(self.arrow_start, "arrow_start"))
line.attr["marker-start"] = "url(#%s)" % self.arrow_start
else:
raise TypeError("arrow_start must be False/None or an id string for the new marker")
if self.arrow_end != False and self.arrow_end is not None:
if isinstance(self.arrow_end, SVG):
defs.append(self.arrow_end)
line.attr["marker-end"] = "url(#%s)" % self.arrow_end["id"]
elif isinstance(self.arrow_end, basestring):
defs.append(make_marker(self.arrow_end, "arrow_end"))
line.attr["marker-end"] = "url(#%s)" % self.arrow_end
else:
raise TypeError("arrow_end must be False/None or an id string for the new marker")
return SVG("g", defs, line)
return line
class VLine(Line):
"""Draws a vertical line.
VLine(y1, y2, x, attribute=value)
y1, y2 required y range
x required x position
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
return "<VLine (%g, %g) at x=%s %s>" % (self.y1, self.y2, self.x, self.attr)
def __init__(self, y1, y2, x, **attr):
self.x = x
self.attr = dict(self.defaults)
self.attr.update(attr)
Line.__init__(self, x, y1, x, y2, **self.attr)
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
self.x1 = self.x
self.x2 = self.x
return Line.Path(self, trans, local)
class HLine(Line):
"""Draws a horizontal line.
HLine(x1, x2, y, attribute=value)
x1, x2 required x range
y required y position
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
return "<HLine (%g, %g) at y=%s %s>" % (self.x1, self.x2, self.y, self.attr)
def __init__(self, x1, x2, y, **attr):
self.y = y
self.attr = dict(self.defaults)
self.attr.update(attr)
Line.__init__(self, x1, y, x2, y, **self.attr)
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
self.y1 = self.y
self.y2 = self.y
return Line.Path(self, trans, local)
######################################################################
class Rect(Curve):
"""Draws a rectangle.
Rect(x1, y1, x2, y2, attribute=value)
x1, y1 required the starting point
x2, y2 required the ending point
attribute=value pairs keyword list SVG attributes
"""
defaults = {}
def __repr__(self):
return "<Rect (%g, %g), (%g, %g) %s>" % (
self.x1, self.y1, self.x2, self.y2, self.attr)
def __init__(self, x1, y1, x2, y2, **attr):
self.x1, self.y1, self.x2, self.y2 = x1, y1, x2, y2
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
return self.Path(trans).SVG()
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
if trans is None:
return Path([("M", self.x1, self.y1, not local), ("L", self.x2, self.y1, not local), ("L", self.x2, self.y2, not local), ("L", self.x1, self.y2, not local), ("Z",)], **self.attr)
else:
self.low = 0.
self.high = 1.
self.loop = False
self.f = lambda t: (self.x1 + t*(self.x2 - self.x1), self.y1)
d1 = Curve.Path(self, trans, local).d
self.f = lambda t: (self.x2, self.y1 + t*(self.y2 - self.y1))
d2 = Curve.Path(self, trans, local).d
del d2[0]
self.f = lambda t: (self.x2 + t*(self.x1 - self.x2), self.y2)
d3 = Curve.Path(self, trans, local).d
del d3[0]
self.f = lambda t: (self.x1, self.y2 + t*(self.y1 - self.y2))
d4 = Curve.Path(self, trans, local).d
del d4[0]
return Path(d=(d1 + d2 + d3 + d4 + [("Z",)]), **self.attr)
######################################################################
class Ellipse(Curve):
"""Draws an ellipse from a semimajor vector (ax,ay) and a semiminor
length (b).
Ellipse(x, y, ax, ay, b, attribute=value)
x, y required the center of the ellipse/circle
ax, ay required a vector indicating the length
and direction of the semimajor axis
b required the length of the semiminor axis.
If equal to sqrt(ax2 + ay2), the
ellipse is a circle
attribute=value pairs keyword list SVG attributes
(If sqrt(ax**2 + ay**2) is less than b, then (ax,ay) is actually the
semiminor axis.)
"""
defaults = {}
def __repr__(self):
return "<Ellipse (%g, %g) a=(%g, %g), b=%g %s>" % (
self.x, self.y, self.ax, self.ay, self.b, self.attr)
def __init__(self, x, y, ax, ay, b, **attr):
self.x, self.y, self.ax, self.ay, self.b = x, y, ax, ay, b
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
return self.Path(trans).SVG()
def Path(self, trans=None, local=False):
"""Apply the transformation "trans" and return a Path object in
global coordinates. If local=True, return a Path in local coordinates
(which must be transformed again)."""
angle = math.atan2(self.ay, self.ax) + math.pi/2.
bx = self.b * math.cos(angle)
by = self.b * math.sin(angle)
self.f = lambda t: (self.x + self.ax*math.cos(t) + bx*math.sin(t), self.y + self.ay*math.cos(t) + by*math.sin(t))
self.low = -math.pi
self.high = math.pi
self.loop = True
return Curve.Path(self, trans, local)
######################################################################
def unumber(x):
"""Converts numbers to a Unicode string, taking advantage of special
Unicode characters to make nice minus signs and scientific notation.
"""
output = u"%g" % x
if output[0] == u"-":
output = u"\u2013" + output[1:]
index = output.find(u"e")
if index != -1:
uniout = unicode(output[:index]) + u"\u00d710"
saw_nonzero = False
for n in output[index+1:]:
if n == u"+":
pass # uniout += u"\u207a"
elif n == u"-":
uniout += u"\u207b"
elif n == u"0":
if saw_nonzero:
uniout += u"\u2070"
elif n == u"1":
saw_nonzero = True
uniout += u"\u00b9"
elif n == u"2":
saw_nonzero = True
uniout += u"\u00b2"
elif n == u"3":
saw_nonzero = True
uniout += u"\u00b3"
elif u"4" <= n <= u"9":
saw_nonzero = True
if saw_nonzero:
uniout += eval("u\"\\u%x\"" % (0x2070 + ord(n) - ord(u"0")))
else:
uniout += n
if uniout[:2] == u"1\u00d7":
uniout = uniout[2:]
return uniout
return output
class Ticks:
"""Superclass for all graphics primitives that draw ticks,
miniticks, and tick labels. This class only draws the ticks.
Ticks(f, low, high, ticks, miniticks, labels, logbase, arrow_start,
arrow_end, text_attr, attribute=value)
f required parametric function along which ticks
will be drawn; has the same format as
the function used in Curve
low, high required range of the independent variable
ticks default=-10 request ticks according to the standard
tick specification (see below)
miniticks default=True request miniticks according to the
standard minitick specification (below)
labels True request tick labels according to the
standard tick label specification (below)
logbase default=None if a number, the axis is logarithmic with
ticks at the given base (usually 10)
arrow_start default=None if a new string identifier, draw an arrow
at the low-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
arrow_end default=None if a new string identifier, draw an arrow
at the high-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes for the tick marks
Standard tick specification:
* True: same as -10 (below).
* Positive number N: draw exactly N ticks, including the endpoints. To
subdivide an axis into 10 equal-sized segments, ask for 11 ticks.
* Negative number -N: draw at least N ticks. Ticks will be chosen with
"natural" values, multiples of 2 or 5.
* List of values: draw a tick mark at each value.
* Dict of value, label pairs: draw a tick mark at each value, labeling
it with the given string. This lets you say things like {3.14159: "pi"}.
* False or None: no ticks.
Standard minitick specification:
* True: draw miniticks with "natural" values, more closely spaced than
the ticks.
* Positive number N: draw exactly N miniticks, including the endpoints.
To subdivide an axis into 100 equal-sized segments, ask for 101 miniticks.
* Negative number -N: draw at least N miniticks.
* List of values: draw a minitick mark at each value.
* False or None: no miniticks.
Standard tick label specification:
* True: use the unumber function (described below)
* Format string: standard format strings, e.g. "%5.2f" for 12.34
* Python callable: function that converts numbers to strings
* False or None: no labels
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
tick_start = -1.5
tick_end = 1.5
minitick_start = -0.75
minitick_end = 0.75
text_start = 2.5
text_angle = 0.
def __repr__(self):
return "<Ticks %s from %s to %s ticks=%s labels=%s %s>" % (
self.f, self.low, self.high, str(self.ticks), str(self.labels), self.attr)
def __init__(self, f, low, high, ticks=-10, miniticks=True, labels=True, logbase=None,
arrow_start=None, arrow_end=None, text_attr={}, **attr):
self.f = f
self.low = low
self.high = high
self.ticks = ticks
self.miniticks = miniticks
self.labels = labels
self.logbase = logbase
self.arrow_start = arrow_start
self.arrow_end = arrow_end
self.attr = dict(self.defaults)
self.attr.update(attr)
self.text_attr = dict(self.text_defaults)
self.text_attr.update(text_attr)
def orient_tickmark(self, t, trans=None):
"""Return the position, normalized local x vector, normalized
local y vector, and angle of a tick at position t.
Normally only used internally.
"""
if isinstance(trans, basestring):
trans = totrans(trans)
if trans is None:
f = self.f
else:
f = lambda t: trans(*self.f(t))
eps = _epsilon * abs(self.high - self.low)
X, Y = f(t)
Xprime, Yprime = f(t + eps)
xhatx, xhaty = (Xprime - X)/eps, (Yprime - Y)/eps
norm = math.sqrt(xhatx**2 + xhaty**2)
if norm != 0:
xhatx, xhaty = xhatx/norm, xhaty/norm
else:
xhatx, xhaty = 1., 0.
angle = math.atan2(xhaty, xhatx) + math.pi/2.
yhatx, yhaty = math.cos(angle), math.sin(angle)
return (X, Y), (xhatx, xhaty), (yhatx, yhaty), angle
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans)
self.last_ticks, self.last_miniticks = self.interpret()
tickmarks = Path([], **self.attr)
minitickmarks = Path([], **self.attr)
output = SVG("g")
if ((self.arrow_start != False and self.arrow_start is not None) or
(self.arrow_end != False and self.arrow_end is not None)):
defs = SVG("defs")
if self.arrow_start != False and self.arrow_start is not None:
if isinstance(self.arrow_start, SVG):
defs.append(self.arrow_start)
elif isinstance(self.arrow_start, basestring):
defs.append(make_marker(self.arrow_start, "arrow_start"))
else:
raise TypeError("arrow_start must be False/None or an id string for the new marker")
if self.arrow_end != False and self.arrow_end is not None:
if isinstance(self.arrow_end, SVG):
defs.append(self.arrow_end)
elif isinstance(self.arrow_end, basestring):
defs.append(make_marker(self.arrow_end, "arrow_end"))
else:
raise TypeError("arrow_end must be False/None or an id string for the new marker")
output.append(defs)
eps = _epsilon * (self.high - self.low)
for t, label in self.last_ticks.items():
(X, Y), (xhatx, xhaty), (yhatx, yhaty), angle = self.orient_tickmark(t, trans)
if ((not self.arrow_start or abs(t - self.low) > eps) and
(not self.arrow_end or abs(t - self.high) > eps)):
tickmarks.d.append(("M", X - yhatx*self.tick_start, Y - yhaty*self.tick_start, True))
tickmarks.d.append(("L", X - yhatx*self.tick_end, Y - yhaty*self.tick_end, True))
angle = (angle - math.pi/2.)*180./math.pi + self.text_angle
########### a HACK! ############ (to be removed when Inkscape handles baselines)
if _hacks["inkscape-text-vertical-shift"]:
if self.text_start > 0:
X += math.cos(angle*math.pi/180. + math.pi/2.) * 2.
Y += math.sin(angle*math.pi/180. + math.pi/2.) * 2.
else:
X += math.cos(angle*math.pi/180. + math.pi/2.) * 2. * 2.5
Y += math.sin(angle*math.pi/180. + math.pi/2.) * 2. * 2.5
########### end hack ###########
if label != "":
output.append(SVG("text", label, transform="translate(%g, %g) rotate(%g)" %
(X - yhatx*self.text_start, Y - yhaty*self.text_start, angle), **self.text_attr))
for t in self.last_miniticks:
skip = False
for tt in self.last_ticks.keys():
if abs(t - tt) < eps:
skip = True
break
if not skip:
(X, Y), (xhatx, xhaty), (yhatx, yhaty), angle = self.orient_tickmark(t, trans)
if ((not self.arrow_start or abs(t - self.low) > eps) and
(not self.arrow_end or abs(t - self.high) > eps)):
minitickmarks.d.append(("M", X - yhatx*self.minitick_start, Y - yhaty*self.minitick_start, True))
minitickmarks.d.append(("L", X - yhatx*self.minitick_end, Y - yhaty*self.minitick_end, True))
output.prepend(tickmarks.SVG(trans))
output.prepend(minitickmarks.SVG(trans))
return output
def interpret(self):
"""Evaluate and return optimal ticks and miniticks according to
the standard minitick specification.
Normally only used internally.
"""
if self.labels is None or self.labels == False:
format = lambda x: ""
elif self.labels == True:
format = unumber
elif isinstance(self.labels, basestring):
format = lambda x: (self.labels % x)
elif callable(self.labels):
format = self.labels
else:
raise TypeError("labels must be None/False, True, a format string, or a number->string function")
# Now for the ticks
ticks = self.ticks
# Case 1: ticks is None/False
if ticks is None or ticks == False:
return {}, []
# Case 2: ticks is the number of desired ticks
elif isinstance(ticks, (int, long)):
if ticks == True:
ticks = -10
if self.logbase is None:
ticks = self.compute_ticks(ticks, format)
else:
ticks = self.compute_logticks(self.logbase, ticks, format)
# Now for the miniticks
if self.miniticks == True:
if self.logbase is None:
return ticks, self.compute_miniticks(ticks)
else:
return ticks, self.compute_logminiticks(self.logbase)
elif isinstance(self.miniticks, (int, long)):
return ticks, self.regular_miniticks(self.miniticks)
elif getattr(self.miniticks, "__iter__", False):
return ticks, self.miniticks
elif self.miniticks == False or self.miniticks is None:
return ticks, []
else:
raise TypeError("miniticks must be None/False, True, a number of desired miniticks, or a list of numbers")
# Cases 3 & 4: ticks is iterable
elif getattr(ticks, "__iter__", False):
# Case 3: ticks is some kind of list
if not isinstance(ticks, dict):
output = {}
eps = _epsilon * (self.high - self.low)
for x in ticks:
if format == unumber and abs(x) < eps:
output[x] = u"0"
else:
output[x] = format(x)
ticks = output
# Case 4: ticks is a dict
else:
pass
# Now for the miniticks
if self.miniticks == True:
if self.logbase is None:
return ticks, self.compute_miniticks(ticks)
else:
return ticks, self.compute_logminiticks(self.logbase)
elif isinstance(self.miniticks, (int, long)):
return ticks, self.regular_miniticks(self.miniticks)
elif getattr(self.miniticks, "__iter__", False):
return ticks, self.miniticks
elif self.miniticks == False or self.miniticks is None:
return ticks, []
else:
raise TypeError("miniticks must be None/False, True, a number of desired miniticks, or a list of numbers")
else:
raise TypeError("ticks must be None/False, a number of desired ticks, a list of numbers, or a dictionary of explicit markers")
def compute_ticks(self, N, format):
"""Return less than -N or exactly N optimal linear ticks.
Normally only used internally.
"""
if self.low >= self.high:
raise ValueError("low must be less than high")
if N == 1:
raise ValueError("N can be 0 or >1 to specify the exact number of ticks or negative to specify a maximum")
eps = _epsilon * (self.high - self.low)
if N >= 0:
output = {}
x = self.low
for i in xrange(N):
if format == unumber and abs(x) < eps:
label = u"0"
else:
label = format(x)
output[x] = label
x += (self.high - self.low)/(N-1.)
return output
N = -N
counter = 0
granularity = 10**math.ceil(math.log10(max(abs(self.low), abs(self.high))))
lowN = math.ceil(1.*self.low / granularity)
highN = math.floor(1.*self.high / granularity)
while lowN > highN:
countermod3 = counter % 3
if countermod3 == 0:
granularity *= 0.5
elif countermod3 == 1:
granularity *= 0.4
else:
granularity *= 0.5
counter += 1
lowN = math.ceil(1.*self.low / granularity)
highN = math.floor(1.*self.high / granularity)
last_granularity = granularity
last_trial = None
while True:
trial = {}
for n in range(int(lowN), int(highN)+1):
x = n * granularity
if format == unumber and abs(x) < eps:
label = u"0"
else:
label = format(x)
trial[x] = label
if int(highN)+1 - int(lowN) >= N:
if last_trial is None:
v1, v2 = self.low, self.high
return {v1: format(v1), v2: format(v2)}
else:
low_in_ticks, high_in_ticks = False, False
for t in last_trial.keys():
if 1.*abs(t - self.low)/last_granularity < _epsilon:
low_in_ticks = True
if 1.*abs(t - self.high)/last_granularity < _epsilon:
high_in_ticks = True
lowN = 1.*self.low / last_granularity
highN = 1.*self.high / last_granularity
if abs(lowN - round(lowN)) < _epsilon and not low_in_ticks:
last_trial[self.low] = format(self.low)
if abs(highN - round(highN)) < _epsilon and not high_in_ticks:
last_trial[self.high] = format(self.high)
return last_trial
last_granularity = granularity
last_trial = trial
countermod3 = counter % 3
if countermod3 == 0:
granularity *= 0.5
elif countermod3 == 1:
granularity *= 0.4
else:
granularity *= 0.5
counter += 1
lowN = math.ceil(1.*self.low / granularity)
highN = math.floor(1.*self.high / granularity)
def regular_miniticks(self, N):
"""Return exactly N linear ticks.
Normally only used internally.
"""
output = []
x = self.low
for i in xrange(N):
output.append(x)
x += (self.high - self.low)/(N-1.)
return output
def compute_miniticks(self, original_ticks):
"""Return optimal linear miniticks, given a set of ticks.
Normally only used internally.
"""
if len(original_ticks) < 2:
original_ticks = ticks(self.low, self.high) # XXX ticks is undefined!
original_ticks = original_ticks.keys()
original_ticks.sort()
if self.low > original_ticks[0] + _epsilon or self.high < original_ticks[-1] - _epsilon:
raise ValueError("original_ticks {%g...%g} extend beyond [%g, %g]" % (original_ticks[0], original_ticks[-1], self.low, self.high))
granularities = []
for i in range(len(original_ticks)-1):
granularities.append(original_ticks[i+1] - original_ticks[i])
spacing = 10**(math.ceil(math.log10(min(granularities)) - 1))
output = []
x = original_ticks[0] - math.ceil(1.*(original_ticks[0] - self.low) / spacing) * spacing
while x <= self.high:
if x >= self.low:
already_in_ticks = False
for t in original_ticks:
if abs(x-t) < _epsilon * (self.high - self.low):
already_in_ticks = True
if not already_in_ticks:
output.append(x)
x += spacing
return output
def compute_logticks(self, base, N, format):
"""Return less than -N or exactly N optimal logarithmic ticks.
Normally only used internally.
"""
if self.low >= self.high:
raise ValueError("low must be less than high")
if N == 1:
raise ValueError("N can be 0 or >1 to specify the exact number of ticks or negative to specify a maximum")
eps = _epsilon * (self.high - self.low)
if N >= 0:
output = {}
x = self.low
for i in xrange(N):
if format == unumber and abs(x) < eps:
label = u"0"
else:
label = format(x)
output[x] = label
x += (self.high - self.low)/(N-1.)
return output
N = -N
lowN = math.floor(math.log(self.low, base))
highN = math.ceil(math.log(self.high, base))
output = {}
for n in range(int(lowN), int(highN)+1):
x = base**n
label = format(x)
if self.low <= x <= self.high:
output[x] = label
for i in range(1, len(output)):
keys = output.keys()
keys.sort()
keys = keys[::i]
values = map(lambda k: output[k], keys)
if len(values) <= N:
for k in output.keys():
if k not in keys:
output[k] = ""
break
if len(output) <= 2:
output2 = self.compute_ticks(N=-int(math.ceil(N/2.)), format=format)
lowest = min(output2)
for k in output:
if k < lowest:
output2[k] = output[k]
output = output2
return output
def compute_logminiticks(self, base):
"""Return optimal logarithmic miniticks, given a set of ticks.
Normally only used internally.
"""
if self.low >= self.high:
raise ValueError("low must be less than high")
lowN = math.floor(math.log(self.low, base))
highN = math.ceil(math.log(self.high, base))
output = []
num_ticks = 0
for n in range(int(lowN), int(highN)+1):
x = base**n
if self.low <= x <= self.high:
num_ticks += 1
for m in range(2, int(math.ceil(base))):
minix = m * x
if self.low <= minix <= self.high:
output.append(minix)
if num_ticks <= 2:
return []
else:
return output
######################################################################
class CurveAxis(Curve, Ticks):
"""Draw an axis with tick marks along a parametric curve.
CurveAxis(f, low, high, ticks, miniticks, labels, logbase, arrow_start, arrow_end,
text_attr, attribute=value)
f required a Python callable or string in
the form "f(t), g(t)", just like Curve
low, high required left and right endpoints
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=True request miniticks according to the
standard minitick specification
labels True request tick labels according to the
standard tick label specification
logbase default=None if a number, the x axis is logarithmic
with ticks at the given base (10 being
the most common)
arrow_start default=None if a new string identifier, draw an
arrow at the low-end of the axis,
referenced by that identifier; if an
SVG marker object, use that marker
arrow_end default=None if a new string identifier, draw an
arrow at the high-end of the axis,
referenced by that identifier; if an
SVG marker object, use that marker
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
def __repr__(self):
return "<CurveAxis %s [%s, %s] ticks=%s labels=%s %s>" % (
self.f, self.low, self.high, str(self.ticks), str(self.labels), self.attr)
def __init__(self, f, low, high, ticks=-10, miniticks=True, labels=True, logbase=None,
arrow_start=None, arrow_end=None, text_attr={}, **attr):
tattr = dict(self.text_defaults)
tattr.update(text_attr)
Curve.__init__(self, f, low, high)
Ticks.__init__(self, f, low, high, ticks, miniticks, labels, logbase, arrow_start, arrow_end, tattr, **attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
func = Curve.SVG(self, trans)
ticks = Ticks.SVG(self, trans) # returns a <g />
if self.arrow_start != False and self.arrow_start is not None:
if isinstance(self.arrow_start, basestring):
func.attr["marker-start"] = "url(#%s)" % self.arrow_start
else:
func.attr["marker-start"] = "url(#%s)" % self.arrow_start.id
if self.arrow_end != False and self.arrow_end is not None:
if isinstance(self.arrow_end, basestring):
func.attr["marker-end"] = "url(#%s)" % self.arrow_end
else:
func.attr["marker-end"] = "url(#%s)" % self.arrow_end.id
ticks.append(func)
return ticks
class LineAxis(Line, Ticks):
"""Draws an axis with tick marks along a line.
LineAxis(x1, y1, x2, y2, start, end, ticks, miniticks, labels, logbase,
arrow_start, arrow_end, text_attr, attribute=value)
x1, y1 required starting point
x2, y2 required ending point
start, end default=0, 1 values to start and end labeling
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=True request miniticks according to the
standard minitick specification
labels True request tick labels according to the
standard tick label specification
logbase default=None if a number, the x axis is logarithmic
with ticks at the given base (usually 10)
arrow_start default=None if a new string identifier, draw an arrow
at the low-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
arrow_end default=None if a new string identifier, draw an arrow
at the high-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
def __repr__(self):
return "<LineAxis (%g, %g) to (%g, %g) ticks=%s labels=%s %s>" % (
self.x1, self.y1, self.x2, self.y2, str(self.ticks), str(self.labels), self.attr)
def __init__(self, x1, y1, x2, y2, start=0., end=1., ticks=-10, miniticks=True, labels=True,
logbase=None, arrow_start=None, arrow_end=None, exclude=None, text_attr={}, **attr):
self.start = start
self.end = end
self.exclude = exclude
tattr = dict(self.text_defaults)
tattr.update(text_attr)
Line.__init__(self, x1, y1, x2, y2, **attr)
Ticks.__init__(self, None, None, None, ticks, miniticks, labels, logbase, arrow_start, arrow_end, tattr, **attr)
def interpret(self):
if self.exclude is not None and not (isinstance(self.exclude, (tuple, list)) and len(self.exclude) == 2 and
isinstance(self.exclude[0], (int, long, float)) and isinstance(self.exclude[1], (int, long, float))):
raise TypeError("exclude must either be None or (low, high)")
ticks, miniticks = Ticks.interpret(self)
if self.exclude is None:
return ticks, miniticks
ticks2 = {}
for loc, label in ticks.items():
if self.exclude[0] <= loc <= self.exclude[1]:
ticks2[loc] = ""
else:
ticks2[loc] = label
return ticks2, miniticks
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
line = Line.SVG(self, trans) # must be evaluated first, to set self.f, self.low, self.high
f01 = self.f
self.f = lambda t: f01(1. * (t - self.start) / (self.end - self.start))
self.low = self.start
self.high = self.end
if self.arrow_start != False and self.arrow_start is not None:
if isinstance(self.arrow_start, basestring):
line.attr["marker-start"] = "url(#%s)" % self.arrow_start
else:
line.attr["marker-start"] = "url(#%s)" % self.arrow_start.id
if self.arrow_end != False and self.arrow_end is not None:
if isinstance(self.arrow_end, basestring):
line.attr["marker-end"] = "url(#%s)" % self.arrow_end
else:
line.attr["marker-end"] = "url(#%s)" % self.arrow_end.id
ticks = Ticks.SVG(self, trans) # returns a <g />
ticks.append(line)
return ticks
class XAxis(LineAxis):
"""Draws an x axis with tick marks.
XAxis(xmin, xmax, aty, ticks, miniticks, labels, logbase, arrow_start, arrow_end,
exclude, text_attr, attribute=value)
xmin, xmax required the x range
aty default=0 y position to draw the axis
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=True request miniticks according to the
standard minitick specification
labels True request tick labels according to the
standard tick label specification
logbase default=None if a number, the x axis is logarithmic
with ticks at the given base (usually 10)
arrow_start default=None if a new string identifier, draw an arrow
at the low-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
arrow_end default=None if a new string identifier, draw an arrow
at the high-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
exclude default=None if a (low, high) pair, don't draw text
labels within this range
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes for all lines
The exclude option is provided for Axes to keep text from overlapping
where the axes cross. Normal users are not likely to need it.
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, "dominant-baseline": "text-before-edge", }
text_start = -1.
text_angle = 0.
def __repr__(self):
return "<XAxis (%g, %g) at y=%g ticks=%s labels=%s %s>" % (
self.xmin, self.xmax, self.aty, str(self.ticks), str(self.labels), self.attr) # XXX self.xmin/xmax undefd!
def __init__(self, xmin, xmax, aty=0, ticks=-10, miniticks=True, labels=True, logbase=None,
arrow_start=None, arrow_end=None, exclude=None, text_attr={}, **attr):
self.aty = aty
tattr = dict(self.text_defaults)
tattr.update(text_attr)
LineAxis.__init__(self, xmin, aty, xmax, aty, xmin, xmax, ticks, miniticks, labels, logbase, arrow_start, arrow_end, exclude, tattr, **attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
self.y1 = self.aty
self.y2 = self.aty
return LineAxis.SVG(self, trans)
class YAxis(LineAxis):
"""Draws a y axis with tick marks.
YAxis(ymin, ymax, atx, ticks, miniticks, labels, logbase, arrow_start, arrow_end,
exclude, text_attr, attribute=value)
ymin, ymax required the y range
atx default=0 x position to draw the axis
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=True request miniticks according to the
standard minitick specification
labels True request tick labels according to the
standard tick label specification
logbase default=None if a number, the y axis is logarithmic
with ticks at the given base (usually 10)
arrow_start default=None if a new string identifier, draw an arrow
at the low-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
arrow_end default=None if a new string identifier, draw an arrow
at the high-end of the axis, referenced by
that identifier; if an SVG marker object,
use that marker
exclude default=None if a (low, high) pair, don't draw text
labels within this range
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes for all lines
The exclude option is provided for Axes to keep text from overlapping
where the axes cross. Normal users are not likely to need it.
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, "text-anchor": "end", "dominant-baseline": "middle", }
text_start = 2.5
text_angle = 90.
def __repr__(self):
return "<YAxis (%g, %g) at x=%g ticks=%s labels=%s %s>" % (
self.ymin, self.ymax, self.atx, str(self.ticks), str(self.labels), self.attr) # XXX self.ymin/ymax undefd!
def __init__(self, ymin, ymax, atx=0, ticks=-10, miniticks=True, labels=True, logbase=None,
arrow_start=None, arrow_end=None, exclude=None, text_attr={}, **attr):
self.atx = atx
tattr = dict(self.text_defaults)
tattr.update(text_attr)
LineAxis.__init__(self, atx, ymin, atx, ymax, ymin, ymax, ticks, miniticks, labels, logbase, arrow_start, arrow_end, exclude, tattr, **attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
self.x1 = self.atx
self.x2 = self.atx
return LineAxis.SVG(self, trans)
class Axes:
"""Draw a pair of intersecting x-y axes.
Axes(xmin, xmax, ymin, ymax, atx, aty, xticks, xminiticks, xlabels, xlogbase,
yticks, yminiticks, ylabels, ylogbase, arrows, text_attr, attribute=value)
xmin, xmax required the x range
ymin, ymax required the y range
atx, aty default=0, 0 point where the axes try to cross;
if outside the range, the axes will
cross at the closest corner
xticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
xminiticks default=True request miniticks according to the
standard minitick specification
xlabels True request tick labels according to the
standard tick label specification
xlogbase default=None if a number, the x axis is logarithmic
with ticks at the given base (usually 10)
yticks default=-10 request ticks according to the standard
tick specification
yminiticks default=True request miniticks according to the
standard minitick specification
ylabels True request tick labels according to the
standard tick label specification
ylogbase default=None if a number, the y axis is logarithmic
with ticks at the given base (usually 10)
arrows default=None if a new string identifier, draw arrows
referenced by that identifier
text_attr default={} SVG attributes for the text labels
attribute=value pairs keyword list SVG attributes for all lines
"""
defaults = {"stroke-width": "0.25pt", }
text_defaults = {"stroke": "none", "fill": "black", "font-size": 5, }
def __repr__(self):
return "<Axes x=(%g, %g) y=(%g, %g) at (%g, %g) %s>" % (
self.xmin, self.xmax, self.ymin, self.ymax, self.atx, self.aty, self.attr)
def __init__(self, xmin, xmax, ymin, ymax, atx=0, aty=0,
xticks=-10, xminiticks=True, xlabels=True, xlogbase=None,
yticks=-10, yminiticks=True, ylabels=True, ylogbase=None,
arrows=None, text_attr={}, **attr):
self.xmin, self.xmax = xmin, xmax
self.ymin, self.ymax = ymin, ymax
self.atx, self.aty = atx, aty
self.xticks, self.xminiticks, self.xlabels, self.xlogbase = xticks, xminiticks, xlabels, xlogbase
self.yticks, self.yminiticks, self.ylabels, self.ylogbase = yticks, yminiticks, ylabels, ylogbase
self.arrows = arrows
self.text_attr = dict(self.text_defaults)
self.text_attr.update(text_attr)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
atx, aty = self.atx, self.aty
if atx < self.xmin:
atx = self.xmin
if atx > self.xmax:
atx = self.xmax
if aty < self.ymin:
aty = self.ymin
if aty > self.ymax:
aty = self.ymax
xmargin = 0.1 * abs(self.ymin - self.ymax)
xexclude = atx - xmargin, atx + xmargin
ymargin = 0.1 * abs(self.xmin - self.xmax)
yexclude = aty - ymargin, aty + ymargin
if self.arrows is not None and self.arrows != False:
xarrow_start = self.arrows + ".xstart"
xarrow_end = self.arrows + ".xend"
yarrow_start = self.arrows + ".ystart"
yarrow_end = self.arrows + ".yend"
else:
xarrow_start = xarrow_end = yarrow_start = yarrow_end = None
xaxis = XAxis(self.xmin, self.xmax, aty, self.xticks, self.xminiticks, self.xlabels, self.xlogbase, xarrow_start, xarrow_end, exclude=xexclude, text_attr=self.text_attr, **self.attr).SVG(trans)
yaxis = YAxis(self.ymin, self.ymax, atx, self.yticks, self.yminiticks, self.ylabels, self.ylogbase, yarrow_start, yarrow_end, exclude=yexclude, text_attr=self.text_attr, **self.attr).SVG(trans)
return SVG("g", *(xaxis.sub + yaxis.sub))
######################################################################
class HGrid(Ticks):
"""Draws the horizontal lines of a grid over a specified region
using the standard tick specification (see help(Ticks)) to place the
grid lines.
HGrid(xmin, xmax, low, high, ticks, miniticks, logbase, mini_attr, attribute=value)
xmin, xmax required the x range
low, high required the y range
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=False request miniticks according to the
standard minitick specification
logbase default=None if a number, the axis is logarithmic
with ticks at the given base (usually 10)
mini_attr default={} SVG attributes for the minitick-lines
(if miniticks != False)
attribute=value pairs keyword list SVG attributes for the major tick lines
"""
defaults = {"stroke-width": "0.25pt", "stroke": "gray", }
mini_defaults = {"stroke-width": "0.25pt", "stroke": "lightgray", "stroke-dasharray": "1,1", }
def __repr__(self):
return "<HGrid x=(%g, %g) %g <= y <= %g ticks=%s miniticks=%s %s>" % (
self.xmin, self.xmax, self.low, self.high, str(self.ticks), str(self.miniticks), self.attr)
def __init__(self, xmin, xmax, low, high, ticks=-10, miniticks=False, logbase=None, mini_attr={}, **attr):
self.xmin, self.xmax = xmin, xmax
self.mini_attr = dict(self.mini_defaults)
self.mini_attr.update(mini_attr)
Ticks.__init__(self, None, low, high, ticks, miniticks, None, logbase)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
self.last_ticks, self.last_miniticks = Ticks.interpret(self)
ticksd = []
for t in self.last_ticks.keys():
ticksd += Line(self.xmin, t, self.xmax, t).Path(trans).d
miniticksd = []
for t in self.last_miniticks:
miniticksd += Line(self.xmin, t, self.xmax, t).Path(trans).d
return SVG("g", Path(d=ticksd, **self.attr).SVG(), Path(d=miniticksd, **self.mini_attr).SVG())
class VGrid(Ticks):
"""Draws the vertical lines of a grid over a specified region
using the standard tick specification (see help(Ticks)) to place the
grid lines.
HGrid(ymin, ymax, low, high, ticks, miniticks, logbase, mini_attr, attribute=value)
ymin, ymax required the y range
low, high required the x range
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=False request miniticks according to the
standard minitick specification
logbase default=None if a number, the axis is logarithmic
with ticks at the given base (usually 10)
mini_attr default={} SVG attributes for the minitick-lines
(if miniticks != False)
attribute=value pairs keyword list SVG attributes for the major tick lines
"""
defaults = {"stroke-width": "0.25pt", "stroke": "gray", }
mini_defaults = {"stroke-width": "0.25pt", "stroke": "lightgray", "stroke-dasharray": "1,1", }
def __repr__(self):
return "<VGrid y=(%g, %g) %g <= x <= %g ticks=%s miniticks=%s %s>" % (
self.ymin, self.ymax, self.low, self.high, str(self.ticks), str(self.miniticks), self.attr)
def __init__(self, ymin, ymax, low, high, ticks=-10, miniticks=False, logbase=None, mini_attr={}, **attr):
self.ymin, self.ymax = ymin, ymax
self.mini_attr = dict(self.mini_defaults)
self.mini_attr.update(mini_attr)
Ticks.__init__(self, None, low, high, ticks, miniticks, None, logbase)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
self.last_ticks, self.last_miniticks = Ticks.interpret(self)
ticksd = []
for t in self.last_ticks.keys():
ticksd += Line(t, self.ymin, t, self.ymax).Path(trans).d
miniticksd = []
for t in self.last_miniticks:
miniticksd += Line(t, self.ymin, t, self.ymax).Path(trans).d
return SVG("g", Path(d=ticksd, **self.attr).SVG(), Path(d=miniticksd, **self.mini_attr).SVG())
class Grid(Ticks):
"""Draws a grid over a specified region using the standard tick
specification (see help(Ticks)) to place the grid lines.
Grid(xmin, xmax, ymin, ymax, ticks, miniticks, logbase, mini_attr, attribute=value)
xmin, xmax required the x range
ymin, ymax required the y range
ticks default=-10 request ticks according to the standard
tick specification (see help(Ticks))
miniticks default=False request miniticks according to the
standard minitick specification
logbase default=None if a number, the axis is logarithmic
with ticks at the given base (usually 10)
mini_attr default={} SVG attributes for the minitick-lines
(if miniticks != False)
attribute=value pairs keyword list SVG attributes for the major tick lines
"""
defaults = {"stroke-width": "0.25pt", "stroke": "gray", }
mini_defaults = {"stroke-width": "0.25pt", "stroke": "lightgray", "stroke-dasharray": "1,1", }
def __repr__(self):
return "<Grid x=(%g, %g) y=(%g, %g) ticks=%s miniticks=%s %s>" % (
self.xmin, self.xmax, self.ymin, self.ymax, str(self.ticks), str(self.miniticks), self.attr)
def __init__(self, xmin, xmax, ymin, ymax, ticks=-10, miniticks=False, logbase=None, mini_attr={}, **attr):
self.xmin, self.xmax = xmin, xmax
self.ymin, self.ymax = ymin, ymax
self.mini_attr = dict(self.mini_defaults)
self.mini_attr.update(mini_attr)
Ticks.__init__(self, None, None, None, ticks, miniticks, None, logbase)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
self.low, self.high = self.xmin, self.xmax
self.last_xticks, self.last_xminiticks = Ticks.interpret(self)
self.low, self.high = self.ymin, self.ymax
self.last_yticks, self.last_yminiticks = Ticks.interpret(self)
ticksd = []
for t in self.last_xticks.keys():
ticksd += Line(t, self.ymin, t, self.ymax).Path(trans).d
for t in self.last_yticks.keys():
ticksd += Line(self.xmin, t, self.xmax, t).Path(trans).d
miniticksd = []
for t in self.last_xminiticks:
miniticksd += Line(t, self.ymin, t, self.ymax).Path(trans).d
for t in self.last_yminiticks:
miniticksd += Line(self.xmin, t, self.xmax, t).Path(trans).d
return SVG("g", Path(d=ticksd, **self.attr).SVG(), Path(d=miniticksd, **self.mini_attr).SVG())
######################################################################
class XErrorBars:
"""Draws x error bars at a set of points. This is usually used
before (under) a set of Dots at the same points.
XErrorBars(d, attribute=value)
d required list of (x,y,xerr...) points
attribute=value pairs keyword list SVG attributes
If points in d have
* 3 elements, the third is the symmetric error bar
* 4 elements, the third and fourth are the asymmetric lower and
upper error bar. The third element should be negative,
e.g. (5, 5, -1, 2) is a bar from 4 to 7.
* more than 4, a tick mark is placed at each value. This lets
you nest errors from different sources, correlated and
uncorrelated, statistical and systematic, etc.
"""
defaults = {"stroke-width": "0.25pt", }
def __repr__(self):
return "<XErrorBars (%d nodes)>" % len(self.d)
def __init__(self, d=[], **attr):
self.d = list(d)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans) # only once
output = SVG("g")
for p in self.d:
x, y = p[0], p[1]
if len(p) == 3:
bars = [x - p[2], x + p[2]]
else:
bars = [x + pi for pi in p[2:]]
start, end = min(bars), max(bars)
output.append(LineAxis(start, y, end, y, start, end, bars, False, False, **self.attr).SVG(trans))
return output
class YErrorBars:
"""Draws y error bars at a set of points. This is usually used
before (under) a set of Dots at the same points.
YErrorBars(d, attribute=value)
d required list of (x,y,yerr...) points
attribute=value pairs keyword list SVG attributes
If points in d have
* 3 elements, the third is the symmetric error bar
* 4 elements, the third and fourth are the asymmetric lower and
upper error bar. The third element should be negative,
e.g. (5, 5, -1, 2) is a bar from 4 to 7.
* more than 4, a tick mark is placed at each value. This lets
you nest errors from different sources, correlated and
uncorrelated, statistical and systematic, etc.
"""
defaults = {"stroke-width": "0.25pt", }
def __repr__(self):
return "<YErrorBars (%d nodes)>" % len(self.d)
def __init__(self, d=[], **attr):
self.d = list(d)
self.attr = dict(self.defaults)
self.attr.update(attr)
def SVG(self, trans=None):
"""Apply the transformation "trans" and return an SVG object."""
if isinstance(trans, basestring):
trans = totrans(trans) # only once
output = SVG("g")
for p in self.d:
x, y = p[0], p[1]
if len(p) == 3:
bars = [y - p[2], y + p[2]]
else:
bars = [y + pi for pi in p[2:]]
start, end = min(bars), max(bars)
output.append(LineAxis(x, start, x, end, start, end, bars, False, False, **self.attr).SVG(trans))
return output
|
#!/usr/bin/env python
'''
Lucas-Kanade tracker
====================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate, isPointInRect
lk_params = dict( winSize = (15, 15),
maxLevel = 2,
criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 500,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
def getRectFromPoints(points):
distances = []
for point in points:
distances.append(cv.norm(point, cv.NORM_L2))
x0, y0 = points[np.argmin(distances)]
x1, y1 = points[np.argmax(distances)]
return np.array([x0, y0, x1, y1])
class lk_track_test(NewOpenCVTests):
track_len = 10
detect_interval = 5
tracks = []
frame_idx = 0
render = None
def test_lk_track(self):
self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'), self.get_sample('samples/data/box.png'))
self.runTracker()
def runTracker(self):
foregroundPointsNum = 0
while True:
frame = self.render.getNextFrame()
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append([(x, y), self.frame_idx])
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
self.tracks = new_tracks
if self.frame_idx % self.detect_interval == 0:
goodTracksCount = 0
for tr in self.tracks:
oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
goodTracksCount += 1
if self.frame_idx == self.detect_interval:
foregroundPointsNum = goodTracksCount
fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
if self.frame_idx > 0:
self.assertGreater(fgIndex, 0.9)
self.assertGreater(fgRate, 0.2)
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]:
cv.circle(mask, (x, y), 5, 0, -1)
p = cv.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([[(x, y), self.frame_idx]])
self.frame_idx += 1
self.prev_gray = frame_gray
if self.frame_idx > 300:
break
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Lucas-Kanade homography tracker test
===============================
Uses goodFeaturesToTrack for track initialization and back-tracking for match verification
between frames. Finds homography between reference and current views.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
#local modules
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, isPointInRect
lk_params = dict( winSize = (19, 19),
maxLevel = 2,
criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 1000,
qualityLevel = 0.01,
minDistance = 8,
blockSize = 19 )
def checkedTrace(img0, img1, p0, back_threshold = 1.0):
p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
status = d < back_threshold
return p1, status
class lk_homography_test(NewOpenCVTests):
render = None
framesCounter = 0
frame = frame0 = None
p0 = None
p1 = None
gray0 = gray1 = None
numFeaturesInRectOnStart = 0
def test_lk_homography(self):
self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'),
self.get_sample('samples/data/box.png'), noise = 0.1, speed = 1.0)
frame = self.render.getNextFrame()
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
self.frame0 = frame.copy()
self.p0 = cv.goodFeaturesToTrack(frame_gray, **feature_params)
isForegroundHomographyFound = False
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
currRect = self.render.getCurrentRect()
for (x,y) in self.p0[:,0]:
if isPointInRect((x,y), currRect):
self.numFeaturesInRectOnStart += 1
while self.framesCounter < 200:
self.framesCounter += 1
frame = self.render.getNextFrame()
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
if self.p0 is not None:
p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
self.p1 = p2[trace_status].copy()
self.p0 = self.p0[trace_status].copy()
self.gray1 = frame_gray
if len(self.p0) < 4:
self.p0 = None
continue
_H, status = cv.findHomography(self.p0, self.p1, cv.RANSAC, 5.0)
goodPointsInRect = 0
goodPointsOutsideRect = 0
for (_x0, _y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
if isPointInRect((x1,y1), self.render.getCurrentRect()):
goodPointsInRect += 1
else: goodPointsOutsideRect += 1
if goodPointsOutsideRect < goodPointsInRect:
isForegroundHomographyFound = True
self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6)
else:
self.p0 = cv.goodFeaturesToTrack(frame_gray, **feature_params)
self.assertEqual(isForegroundHomographyFound, True)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/bin/python
# usage:
# cat clAmdBlas.h | $0
from __future__ import print_function
import sys, re;
from common import remove_comments, getTokens, getParameters, postProcessParameters
try:
if len(sys.argv) > 1:
f = open(sys.argv[1], "r")
else:
f = sys.stdin
except:
sys.exit("ERROR. Can't open input file")
fns = []
while True:
line = f.readline()
if len(line) == 0:
break
assert isinstance(line, str)
line = line.strip()
parts = line.split();
if (line.startswith('clAmd') or line.startswith('cl_') or line == 'void') and len(line.split()) == 1 and line.find('(') == -1:
fn = {}
modifiers = []
ret = []
calling = []
i = 0
while (i < len(parts)):
if parts[i].startswith('CL_'):
modifiers.append(parts[i])
else:
break
i += 1
while (i < len(parts)):
if not parts[i].startswith('CL_'):
ret.append(parts[i])
else:
break
i += 1
while (i < len(parts)):
calling.append(parts[i])
i += 1
fn['modifiers'] = [] # modifiers
fn['ret'] = ret
fn['calling'] = calling
# print 'modifiers='+' '.join(modifiers)
# print 'ret='+' '.join(type)
# print 'calling='+' '.join(calling)
# read block of lines
line = f.readline()
while True:
nl = f.readline()
nl = nl.strip()
nl = re.sub(r'\n', r'', nl)
if len(nl) == 0:
break;
line += ' ' + nl
line = remove_comments(line)
parts = getTokens(line)
i = 0;
name = parts[i]; i += 1;
fn['name'] = name
print('name=' + name)
params = getParameters(i, parts)
fn['params'] = params
# print 'params="'+','.join(params)+'"'
fns.append(fn)
f.close()
print('Found %d functions' % len(fns))
postProcessParameters(fns)
from pprint import pprint
pprint(fns)
from common import *
filterFileName='./filter/opencl_clamdblas_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)
functionsFilter = generateFilterNames(fns)
filter_file = open(filterFileName, 'wb')
filter_file.write(functionsFilter)
ctx = {}
ctx['CLAMDBLAS_REMAP_ORIGIN'] = generateRemapOrigin(fns)
ctx['CLAMDBLAS_REMAP_DYNAMIC'] = generateRemapDynamic(fns)
ctx['CLAMDBLAS_FN_DECLARATIONS'] = generateFnDeclaration(fns)
sys.stdout = open('../../../../include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp', 'wb')
ProcessTemplate('template/opencl_clamdblas.hpp.in', ctx)
ctx['CL_FN_ENUMS'] = generateEnums(fns, 'OPENCLAMDBLAS_FN', )
ctx['CL_FN_SWITCH'] = generateTemplates(23, 'openclamdblas_fn', 'openclamdblas_check_fn', '')
ctx['CL_FN_ENTRY_DEFINITIONS'] = generateStructDefinitions(fns, 'openclamdblas_fn', 'OPENCLAMDBLAS_FN')
ctx['CL_FN_ENTRY_LIST'] = generateListOfDefinitions(fns, 'openclamdblas_fn')
ctx['CL_NUMBER_OF_ENABLED_FUNCTIONS'] = '// number of enabled functions: %d' % (numEnabled)
sys.stdout = open('../autogenerated/opencl_clamdblas_impl.hpp', 'wb')
ProcessTemplate('template/opencl_clamdblas_impl.hpp.in', ctx)
|
from __future__ import print_function
import sys, os, re
#
# Parser helpers
#
def remove_comments(s):
def replacer(match):
s = match.group(0)
if s.startswith('/'):
return ""
else:
return s
pattern = re.compile(
r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"',
re.DOTALL | re.MULTILINE
)
return re.sub(pattern, replacer, s)
def getTokens(s):
return re.findall(r'[a-z_A-Z0-9_]+|[^[a-z_A-Z0-9_ \n\r\t]', s)
def getParameter(pos, tokens):
deep = 0
p = []
while True:
if pos >= len(tokens):
break
if (tokens[pos] == ')' or tokens[pos] == ',') and deep == 0:
if tokens[pos] == ')':
pos = len(tokens)
else:
pos += 1
break
if tokens[pos] == '(':
deep += 1
if tokens[pos] == ')':
deep -= 1
p.append(tokens[pos])
pos += 1
return (' '.join(p), pos)
def getParameters(i, tokens):
assert tokens[i] == '('
i += 1
params = []
while True:
if i >= len(tokens) or tokens[i] == ')':
break
(param, i) = getParameter(i, tokens)
if len(param) > 0:
params.append(param)
else:
assert False
break
if len(params) > 0 and params[0] == 'void':
del params[0]
return params
def postProcessParameters(fns):
fns.sort(key=lambda x: x['name'])
for fn in fns:
fn['params_full'] = list(fn['params'])
for i in range(len(fn['params'])):
p = fn['params'][i]
if p.find('(') != -1:
p = re.sub(r'\* *([a-zA-Z0-9_]*) ?\)', '*)', p, 1)
fn['params'][i] = p
continue
parts = re.findall(r'[a-z_A-Z0-9]+|\*', p)
if len(parts) > 1:
if parts[-1].find('*') == -1:
del parts[-1]
fn['params'][i] = ' '.join(parts)
def readFunctionFilter(fns, fileName):
try:
f = open(fileName, "r")
except:
print("ERROR: Can't open filter file: %s" % fileName)
return 0
count = 0
while f:
line = f.readline()
if not line:
break
assert isinstance(line, str)
if line.startswith('#') or line.startswith('//'):
continue
line = line.replace('\n', '')
if len(line) == 0:
continue
found = False
for fn in fns:
if fn['name'] == line:
found = True
fn['enabled'] = True
if not found:
sys.exit("FATAL ERROR: Unknown function: %s" % line)
count = count + 1
f.close()
return count
#
# Generator helpers
#
def outputToString(f):
def wrapped(*args, **kwargs):
from cStringIO import StringIO
old_stdout = sys.stdout
sys.stdout = str_stdout = StringIO()
res = f(*args, **kwargs)
assert res is None
sys.stdout = old_stdout
result = str_stdout.getvalue()
result = re.sub(r'([^\n /]) [ ]+', r'\1 ', result) # don't remove spaces at start of line
result = re.sub(r' ,', ',', result)
result = re.sub(r' \*', '*', result)
result = re.sub(r'\( ', '(', result)
result = re.sub(r' \)', ')', result)
return result
return wrapped
@outputToString
def generateFilterNames(fns):
for fn in fns:
print('%s%s' % ('' if 'enabled' in fn else '//', fn['name']))
print('#total %d' % len(fns))
callback_check = re.compile(r'([^\(]*\(.*)(\* *)(\).*\(.*\))')
def getTypeWithParam(t, p):
if callback_check.match(t):
return callback_check.sub(r'\1 *' + p + r'\3', t)
return t + ' ' + p
@outputToString
def generateStructDefinitions(fns, lprefix='opencl_fn', enumprefix='OPENCL_FN'):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
commentStr = '' if 'enabled' in fn else '//'
decl_args = []
for (i, t) in enumerate(fn['params']):
decl_args.append(getTypeWithParam(t, 'p%d' % (i+1)))
decl_args_str = '(' + (', '.join(decl_args)) + ')'
print('%s%s%d(%s_%s, %s, %s)' % \
(commentStr, lprefix, len(fn['params']), enumprefix, fn['name'], \
' '.join(fn['ret']), decl_args_str))
print(commentStr + ('%s%s (%s *%s)(%s) =\n%s %s_%s_switch_fn;' % \
((' '.join(fn['modifiers'] + ' ') if len(fn['modifiers']) > 0 else ''),
' '.join(fn['ret']), ' '.join(fn['calling']), fn['name'], ', '.join(fn['params']), \
commentStr, enumprefix, fn['name'])))
print(commentStr + ('static const struct DynamicFnEntry %s_definition = { "%s", (void**)&%s};' % (fn['name'], fn['name'], fn['name'])))
print()
@outputToString
def generateStaticDefinitions(fns):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
commentStr = '' if 'enabled' in fn else '//'
decl_args = []
for (i, t) in enumerate(fn['params']):
decl_args.append(getTypeWithParam(t, 'p%d' % (i+1)))
decl_args_str = '(' + (', '.join(decl_args)) + ')'
print(commentStr + ('CL_RUNTIME_EXPORT %s%s (%s *%s_pfn)(%s) = %s;' % \
((' '.join(fn['modifiers'] + ' ') if len(fn['modifiers']) > 0 else ''),
' '.join(fn['ret']), ' '.join(fn['calling']), fn['name'], ', '.join(fn['params']), \
fn['name'])))
@outputToString
def generateListOfDefinitions(fns, name='opencl_fn_list'):
print('// generated by %s' % os.path.basename(sys.argv[0]))
print('static const struct DynamicFnEntry* %s[] = {' % (name))
for fn in fns:
commentStr = '' if 'enabled' in fn else '//'
if 'enabled' in fn:
print(' &%s_definition,' % (fn['name']))
else:
print(' NULL/*&%s_definition*/,' % (fn['name']))
first = False
print('};')
@outputToString
def generateEnums(fns, prefix='OPENCL_FN'):
print('// generated by %s' % os.path.basename(sys.argv[0]))
print('enum %s_ID {' % prefix)
for (i, fn) in enumerate(fns):
commentStr = '' if 'enabled' in fn else '//'
print(commentStr + (' %s_%s = %d,' % (prefix, fn['name'], i)))
print('};')
@outputToString
def generateRemapOrigin(fns):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
print('#define %s %s_' % (fn['name'], fn['name']))
@outputToString
def generateRemapDynamic(fns):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
print('#undef %s' % (fn['name']))
commentStr = '' if 'enabled' in fn else '//'
print(commentStr + ('#define %s %s_pfn' % (fn['name'], fn['name'])))
@outputToString
def generateFnDeclaration(fns):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
commentStr = '' if 'enabled' in fn else '//'
print(commentStr + ('extern CL_RUNTIME_EXPORT %s %s (%s *%s)(%s);' % (' '.join(fn['modifiers']), ' '.join(fn['ret']), ' '.join(fn['calling']),
fn['name'], ', '.join(fn['params'] if 'params_full' not in fn else fn['params_full']))))
@outputToString
def generateTemplates(total, lprefix, switch_name, calling_convention=''):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for sz in range(total):
template_params = ['ID', '_R', 'decl_args']
params = ['p%d' % (i + 1) for i in range(0, sz)]
print('#define %s%d(%s) \\' % (lprefix, sz, ', '.join(template_params)))
print(' typedef _R (%s *ID##FN)decl_args; \\' % (calling_convention))
print(' static _R %s ID##_switch_fn decl_args \\' % (calling_convention))
print(' { return ((ID##FN)%s(ID))(%s); } \\' % (switch_name, ', '.join(params)))
print('')
@outputToString
def generateInlineWrappers(fns):
print('// generated by %s' % os.path.basename(sys.argv[0]))
for fn in fns:
commentStr = '' if 'enabled' in fn else '//'
print('#undef %s' % (fn['name']))
print(commentStr + ('#define %s %s_fn' % (fn['name'], fn['name'])))
params = []
call_params = []
for i in range(0, len(fn['params'])):
t = fn['params'][i]
if t.find('*)') >= 0:
p = re.sub(r'\*\)', (' *p%d)' % i), t, 1)
params.append(p)
else:
params.append('%s p%d' % (t, i))
call_params.append('p%d' % (i))
if len(fn['ret']) == 1 and fn['ret'][0] == 'void':
print(commentStr + ('inline void %s(%s) { %s_pfn(%s); }' \
% (fn['name'], ', '.join(params), fn['name'], ', '.join(call_params))))
else:
print(commentStr + ('inline %s %s(%s) { return %s_pfn(%s); }' \
% (' '.join(fn['ret']), fn['name'], ', '.join(params), fn['name'], ', '.join(call_params))))
def ProcessTemplate(inputFile, ctx, noteLine='//\n// AUTOGENERATED, DO NOT EDIT\n//'):
f = open(inputFile, "r")
if noteLine:
print(noteLine)
for line in f:
if line.startswith('@'):
assert line[-1] == '\n'
line = line[:-1] # remove '\n'
assert line[-1] == '@'
name = line[1:-1]
assert name in ctx, name
line = ctx[name] + ('\n' if len(ctx[name]) > 0 and ctx[name][-1] != '\n' else '')
sys.stdout.write(line)
f.close()
|
#!/bin/python
# usage:
# cat opencl11/cl.h | $0 cl_runtime_opencl11
# cat opencl12/cl.h | $0 cl_runtime_opencl12
from __future__ import print_function
import sys, re;
from common import remove_comments, getTokens, getParameters, postProcessParameters
try:
if len(sys.argv) > 1:
module_name = sys.argv[1]
outfile = open('../../../../include/opencv2/core/opencl/runtime/autogenerated/%s.hpp' % module_name, 'wb')
outfile_impl = open('../autogenerated/%s_impl.hpp' % module_name, 'wb')
outfile_static_impl = open('../autogenerated/%s_static_impl.hpp' % module_name, 'wb')
outfile_wrappers = open('../../../../include/opencv2/core/opencl/runtime/autogenerated/%s_wrappers.hpp' % module_name, 'wb')
if len(sys.argv) > 2:
f = open(sys.argv[2], "r")
else:
f = sys.stdin
else:
sys.exit("ERROR. Specify output file")
except:
sys.exit("ERROR. Can't open input/output file, check parameters")
fns = []
while True:
line = f.readline()
if len(line) == 0:
break
assert isinstance(line, str)
parts = line.split();
if line.startswith('extern') and line.find('CL_API_CALL') != -1:
# read block of lines
while True:
nl = f.readline()
nl = nl.strip()
nl = re.sub(r'\n', r'', nl)
if len(nl) == 0:
break;
line += ' ' + nl
line = remove_comments(line)
parts = getTokens(line)
fn = {}
modifiers = []
ret = []
calling = []
i = 1
while (i < len(parts)):
if parts[i].startswith('CL_'):
modifiers.append(parts[i])
else:
break
i += 1
while (i < len(parts)):
if not parts[i].startswith('CL_'):
ret.append(parts[i])
else:
break
i += 1
while (i < len(parts)):
calling.append(parts[i])
i += 1
if parts[i - 1] == 'CL_API_CALL':
break
fn['modifiers'] = [] # modifiers
fn['ret'] = ret
fn['calling'] = calling
# print 'modifiers='+' '.join(modifiers)
# print 'ret='+' '.join(type)
# print 'calling='+' '.join(calling)
name = parts[i]; i += 1;
fn['name'] = name
print('name=' + name)
params = getParameters(i, parts)
fn['params'] = params
# print 'params="'+','.join(params)+'"'
fns.append(fn)
f.close()
print('Found %d functions' % len(fns))
postProcessParameters(fns)
from pprint import pprint
pprint(fns)
from common import *
filterFileName = './filter/%s_functions.list' % module_name
numEnabled = readFunctionFilter(fns, filterFileName)
functionsFilter = generateFilterNames(fns)
filter_file = open(filterFileName, 'wb')
filter_file.write(functionsFilter)
ctx = {}
ctx['CL_REMAP_ORIGIN'] = generateRemapOrigin(fns)
ctx['CL_REMAP_DYNAMIC'] = generateRemapDynamic(fns)
ctx['CL_FN_DECLARATIONS'] = generateFnDeclaration(fns)
sys.stdout = outfile
ProcessTemplate('template/%s.hpp.in' % module_name, ctx)
ctx['CL_FN_INLINE_WRAPPERS'] = generateInlineWrappers(fns)
sys.stdout = outfile_wrappers
ProcessTemplate('template/%s_wrappers.hpp.in' % module_name, ctx)
if module_name == 'opencl_core':
ctx['CL_FN_ENTRY_DEFINITIONS'] = generateStructDefinitions(fns)
ctx['CL_FN_ENTRY_LIST'] = generateListOfDefinitions(fns)
ctx['CL_FN_ENUMS'] = generateEnums(fns)
ctx['CL_FN_SWITCH'] = generateTemplates(15, 'opencl_fn', 'opencl_check_fn', 'CL_API_CALL')
else:
lprefix = module_name + '_fn'
enumprefix = module_name.upper() + '_FN'
fn_list_name = module_name + '_fn_list'
ctx['CL_FN_ENTRY_DEFINITIONS'] = generateStructDefinitions(fns, lprefix=lprefix, enumprefix=enumprefix)
ctx['CL_FN_ENTRY_LIST'] = generateListOfDefinitions(fns, fn_list_name)
ctx['CL_FN_ENUMS'] = generateEnums(fns, prefix=enumprefix)
ctx['CL_FN_SWITCH'] = generateTemplates(15, lprefix, '%s_check_fn' % module_name, 'CL_API_CALL')
ctx['CL_NUMBER_OF_ENABLED_FUNCTIONS'] = '// number of enabled functions: %d' % (numEnabled)
sys.stdout = outfile_impl
ProcessTemplate('template/%s_impl.hpp.in' % module_name, ctx)
sys.stdout = outfile_static_impl
ProcessTemplate('template/static_impl.hpp.in', dict(CL_STATIC_DEFINITIONS=generateStaticDefinitions(fns)))
|
#!/bin/python
# usage:
# cat clAmdFft.h | $0
from __future__ import print_function
import sys, re;
from common import remove_comments, getTokens, getParameters, postProcessParameters
try:
if len(sys.argv) > 1:
f = open(sys.argv[1], "r")
else:
f = sys.stdin
except:
sys.exit("ERROR. Can't open input file")
fns = []
while True:
line = f.readline()
if len(line) == 0:
break
assert isinstance(line, str)
line = line.strip()
if line.startswith('CLAMDFFTAPI'):
line = re.sub(r'\n', r'', line)
while True:
nl = f.readline()
nl = nl.strip()
nl = re.sub(r'\n', r'', nl)
if len(nl) == 0:
break;
line += ' ' + nl
line = remove_comments(line)
parts = getTokens(line)
fn = {}
modifiers = []
ret = []
calling = []
i = 0
while True:
if parts[i] == "CLAMDFFTAPI":
modifiers.append(parts[i])
else:
break
i += 1
while (i < len(parts)):
if not parts[i] == '(':
ret.append(parts[i])
else:
del ret[-1]
i -= 1
break
i += 1
fn['modifiers'] = [] # modifiers
fn['ret'] = ret
fn['calling'] = calling
name = parts[i]; i += 1;
fn['name'] = name
print('name=' + name)
params = getParameters(i, parts)
if len(params) > 0 and params[0] == 'void':
del params[0]
fn['params'] = params
# print 'params="'+','.join(params)+'"'
fns.append(fn)
f.close()
print('Found %d functions' % len(fns))
postProcessParameters(fns)
from pprint import pprint
pprint(fns)
from common import *
filterFileName='./filter/opencl_clamdfft_functions.list'
numEnabled = readFunctionFilter(fns, filterFileName)
functionsFilter = generateFilterNames(fns)
filter_file = open(filterFileName, 'wb')
filter_file.write(functionsFilter)
ctx = {}
ctx['CLAMDFFT_REMAP_ORIGIN'] = generateRemapOrigin(fns)
ctx['CLAMDFFT_REMAP_DYNAMIC'] = generateRemapDynamic(fns)
ctx['CLAMDFFT_FN_DECLARATIONS'] = generateFnDeclaration(fns)
sys.stdout = open('../../../../include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp', 'wb')
ProcessTemplate('template/opencl_clamdfft.hpp.in', ctx)
ctx['CL_FN_ENUMS'] = generateEnums(fns, 'OPENCLAMDFFT_FN')
ctx['CL_FN_SWITCH'] = generateTemplates(23, 'openclamdfft_fn', 'openclamdfft_check_fn', '')
ctx['CL_FN_ENTRY_DEFINITIONS'] = generateStructDefinitions(fns, 'openclamdfft_fn', 'OPENCLAMDFFT_FN')
ctx['CL_FN_ENTRY_LIST'] = generateListOfDefinitions(fns, 'openclamdfft_fn')
ctx['CL_NUMBER_OF_ENABLED_FUNCTIONS'] = '// number of enabled functions: %d' % (numEnabled)
sys.stdout = open('../autogenerated/opencl_clamdfft_impl.hpp', 'wb')
ProcessTemplate('template/opencl_clamdfft_impl.hpp.in', ctx)
|
#!/usr/bin/env python
import cv2 as cv
from tests_common import NewOpenCVTests
class stitching_test(NewOpenCVTests):
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA)
(_result, pano) = stitcher.stitch((img1, img2))
#cv.imshow("pano", pano)
#cv.waitKey()
self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape))
self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape))
class stitching_detail_test(NewOpenCVTests):
def test_simple(self):
img = self.get_sample('stitching/a1.png')
finder= cv.ORB.create()
imgFea = cv.detail.computeImageFeatures2(finder,img)
self.assertIsNotNone(imgFea)
matcher = cv.detail_BestOf2NearestMatcher(False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_AffineBestOf2NearestMatcher(False, False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_BestOf2NearestRangeMatcher(2, False, 0.3)
self.assertIsNotNone(matcher)
estimator = cv.detail_AffineBasedEstimator()
self.assertIsNotNone(estimator)
estimator = cv.detail_HomographyBasedEstimator()
self.assertIsNotNone(estimator)
adjuster = cv.detail_BundleAdjusterReproj()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterRay()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterAffinePartial()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_NoBundleAdjuster()
self.assertIsNotNone(adjuster)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_NO)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN_BLOCKS)
self.assertIsNotNone(compensator)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
self.assertIsNotNone(seam_finder)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTI_BAND)
self.assertIsNotNone(blender)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_AS_IS);
self.assertIsNotNone(timelapser)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_CROP);
self.assertIsNotNone(timelapser)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Robust line fitting.
==================
Example of using cv.fitLine function for fitting line
to points in presence of outliers.
Switch through different M-estimator functions and see,
how well the robust functions fit the line even
in case of ~50% of outliers.
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
w, h = 512, 256
def toint(p):
return tuple(map(int, p))
def sample_line(p1, p2, n, noise=0.0):
np.random.seed(10)
p1 = np.float32(p1)
t = np.random.rand(n,1)
return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
dist_func_names = ['DIST_L2', 'DIST_L1', 'DIST_L12', 'DIST_FAIR', 'DIST_WELSCH', 'DIST_HUBER']
class fitline_test(NewOpenCVTests):
def test_fitline(self):
noise = 5
n = 200
r = 5 / 100.0
outn = int(n*r)
p0, p1 = (90, 80), (w-90, h-80)
line_points = sample_line(p0, p1, n-outn, noise)
outliers = np.random.rand(outn, 2) * (w, h)
points = np.vstack([line_points, outliers])
lines = []
for name in dist_func_names:
func = getattr(cv, name)
vx, vy, cx, cy = cv.fitLine(np.float32(points), func, 0, 0.01, 0.01)
line = [float(vx), float(vy), float(cx), float(cy)]
lines.append(line)
eps = 0.05
refVec = (np.float32(p1) - p0) / cv.norm(np.float32(p1) - p0)
for i in range(len(lines)):
self.assertLessEqual(cv.norm(refVec - lines[i][0:2], cv.NORM_L2), eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
"""Algorithm serialization test."""
import tempfile
import os
import cv2 as cv
from tests_common import NewOpenCVTests
class algorithm_rw_test(NewOpenCVTests):
def test_algorithm_rw(self):
fd, fname = tempfile.mkstemp(prefix="opencv_python_algorithm_", suffix=".yml")
os.close(fd)
# some arbitrary non-default parameters
gold = cv.AKAZE_create(descriptor_size=1, descriptor_channels=2, nOctaves=3, threshold=4.0)
gold.write(cv.FileStorage(fname, cv.FILE_STORAGE_WRITE), "AKAZE")
fs = cv.FileStorage(fname, cv.FILE_STORAGE_READ)
algorithm = cv.AKAZE_create()
algorithm.read(fs.getNode("AKAZE"))
self.assertEqual(algorithm.getDescriptorSize(), 1)
self.assertEqual(algorithm.getDescriptorChannels(), 2)
self.assertEqual(algorithm.getNOctaves(), 3)
self.assertEqual(algorithm.getThreshold(), 4.0)
os.remove(fname)
|
#!/usr/bin/env python
from __future__ import print_function
import ctypes
from functools import partial
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests, unittest
def is_numeric(dtype):
return np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)
def get_limits(dtype):
if not is_numeric(dtype):
return None, None
if np.issubdtype(dtype, np.integer):
info = np.iinfo(dtype)
else:
info = np.finfo(dtype)
return info.min, info.max
def get_conversion_error_msg(value, expected, actual):
return 'Conversion "{}" of type "{}" failed\nExpected: "{}" vs Actual "{}"'.format(
value, type(value).__name__, expected, actual
)
def get_no_exception_msg(value):
return 'Exception is not risen for {} of type {}'.format(value, type(value).__name__)
class Bindings(NewOpenCVTests):
def test_inheritance(self):
bm = cv.StereoBM_create()
bm.getPreFilterCap() # from StereoBM
bm.getBlockSize() # from SteroMatcher
boost = cv.ml.Boost_create()
boost.getBoostType() # from ml::Boost
boost.getMaxDepth() # from ml::DTrees
boost.isClassifier() # from ml::StatModel
def test_redirectError(self):
try:
cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0)
except cv.error as _e:
pass
handler_called = [False]
def test_error_handler(status, func_name, err_msg, file_name, line):
handler_called[0] = True
cv.redirectError(test_error_handler)
try:
cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0)
except cv.error as _e:
self.assertEqual(handler_called[0], True)
pass
cv.redirectError(None)
try:
cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0)
except cv.error as _e:
pass
class Arguments(NewOpenCVTests):
def _try_to_convert(self, conversion, value):
try:
result = conversion(value).lower()
except Exception as e:
self.fail(
'{} "{}" is risen for conversion {} of type {}'.format(
type(e).__name__, e, value, type(value).__name__
)
)
else:
return result
def test_InputArray(self):
res1 = cv.utils.dumpInputArray(None)
# self.assertEqual(res1, "InputArray: noArray()") # not supported
self.assertEqual(res1, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1")
res2_1 = cv.utils.dumpInputArray((1, 2))
self.assertEqual(res2_1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=2 dims(-1)=2 size(-1)=1x2 type(-1)=CV_64FC1")
res2_2 = cv.utils.dumpInputArray(1.5) # Scalar(1.5, 1.5, 1.5, 1.5)
self.assertEqual(res2_2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=4 dims(-1)=2 size(-1)=1x4 type(-1)=CV_64FC1")
a = np.array([[1, 2], [3, 4], [5, 6]])
res3 = cv.utils.dumpInputArray(a) # 32SC1
self.assertEqual(res3, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=2x3 type(-1)=CV_32SC1")
a = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
res4 = cv.utils.dumpInputArray(a) # 32FC2
self.assertEqual(res4, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=3x1 type(-1)=CV_32FC2")
a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]], dtype=float)
res5 = cv.utils.dumpInputArray(a) # 64FC2
self.assertEqual(res5, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=1x3 type(-1)=CV_64FC2")
def test_InputArrayOfArrays(self):
res1 = cv.utils.dumpInputArrayOfArrays(None)
# self.assertEqual(res1, "InputArray: noArray()") # not supported
self.assertEqual(res1, "InputArrayOfArrays: empty()=true kind=0x00050000 flags=0x01050000 total(-1)=0 dims(-1)=1 size(-1)=0x0")
res2_1 = cv.utils.dumpInputArrayOfArrays((1, 2)) # { Scalar:all(1), Scalar::all(2) }
self.assertEqual(res2_1, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
res2_2 = cv.utils.dumpInputArrayOfArrays([1.5])
self.assertEqual(res2_2, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=1 dims(-1)=1 size(-1)=1x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
a = np.array([[1, 2], [3, 4], [5, 6]])
b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
res3 = cv.utils.dumpInputArrayOfArrays([a, b])
self.assertEqual(res3, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32SC1 dims(0)=2 size(0)=2x3 type(0)=CV_32SC1")
c = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
res4 = cv.utils.dumpInputArrayOfArrays([c, a, b])
self.assertEqual(res4, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=3 dims(-1)=1 size(-1)=3x1 type(0)=CV_32FC2 dims(0)=2 size(0)=3x1 type(0)=CV_32FC2")
def test_parse_to_bool_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
for convertible_true in (True, 1, 64, np.bool(1), np.int8(123), np.int16(11), np.int32(2),
np.int64(1), np.bool_(3), np.bool8(12)):
actual = try_to_convert(convertible_true)
self.assertEqual('bool: true', actual,
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
for convertible_false in (False, 0, np.uint8(0), np.bool_(0), np.int_(0)):
actual = try_to_convert(convertible_false)
self.assertEqual('bool: false', actual,
msg=get_conversion_error_msg(convertible_false, 'bool: false', actual))
def test_parse_to_bool_not_convertible(self):
for not_convertible in (1.2, np.float(2.3), 's', 'str', (1, 2), [1, 2], complex(1, 1),
complex(imag=2), complex(1.1), np.array([1, 0], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpBool(not_convertible)
def test_parse_to_bool_convertible_extra(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
_, max_size_t = get_limits(ctypes.c_size_t)
for convertible_true in (-1, max_size_t):
actual = try_to_convert(convertible_true)
self.assertEqual('bool: true', actual,
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
def test_parse_to_bool_not_convertible_extra(self):
for not_convertible in (np.array([False]), np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpBool(not_convertible)
def test_parse_to_int_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpInt)
min_int, max_int = get_limits(ctypes.c_int)
for convertible in (-10, -1, 2, int(43.2), np.uint8(15), np.int8(33), np.int16(-13),
np.int32(4), np.int64(345), (23), min_int, max_int, np.int_(33)):
expected = 'int: {0:d}'.format(convertible)
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_int_not_convertible(self):
min_int, max_int = get_limits(ctypes.c_int)
for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
np.array([1, 2]), (1,), [1, 2], min_int - 1, max_int + 1,
complex(1, 1), complex(imag=2), complex(1.1)):
with self.assertRaises((TypeError, OverflowError, ValueError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpInt(not_convertible)
def test_parse_to_int_not_convertible_extra(self):
for not_convertible in (np.bool_(True), True, False, np.float32(2.3),
np.array([3, ], dtype=int), np.array([-2, ], dtype=np.int32),
np.array([1, ], dtype=np.int), np.array([11, ], dtype=np.uint8)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpInt(not_convertible)
def test_parse_to_size_t_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
_, max_uint = get_limits(ctypes.c_uint)
for convertible in (2, max_uint, (12), np.uint8(34), np.int8(12), np.int16(23),
np.int32(123), np.int64(344), np.uint64(3), np.uint16(2), np.uint32(5),
np.uint(44)):
expected = 'size_t: {0:d}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_size_t_not_convertible(self):
min_long, _ = get_limits(ctypes.c_long)
for not_convertible in (1.2, True, False, np.bool_(True), np.float(4), float(3),
np.double(45), 's', 'str', np.array([1, 2]), (1,), [1, 2],
np.float64(6), complex(1, 1), complex(imag=2), complex(1.1),
-1, min_long, np.int8(-35)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpSizeT(not_convertible)
def test_parse_to_size_t_convertible_extra(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
_, max_size_t = get_limits(ctypes.c_size_t)
for convertible in (max_size_t,):
expected = 'size_t: {0:d}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_size_t_not_convertible_extra(self):
for not_convertible in (np.bool_(True), True, False, np.array([123, ], dtype=np.uint8),):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpSizeT(not_convertible)
def test_parse_to_float_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpFloat)
min_float, max_float = get_limits(ctypes.c_float)
for convertible in (2, -13, 1.24, float(32), np.float(32.45), np.double(12.23),
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
max_float, np.inf, -np.inf, float('Inf'), -float('Inf'),
np.double(np.inf), np.double(-np.inf), np.double(float('Inf')),
np.double(-float('Inf'))):
expected = 'Float: {0:.2f}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
# Workaround for Windows NaN tests due to Visual C runtime
# special floating point values (indefinite NaN)
for nan in (float('NaN'), np.nan, np.float32(np.nan), np.double(np.nan),
np.double(float('NaN'))):
actual = try_to_convert(nan)
self.assertIn('nan', actual, msg="Can't convert nan of type {} to float. "
"Actual: {}".format(type(nan).__name__, actual))
min_double, max_double = get_limits(ctypes.c_double)
for inf in (min_float * 10, max_float * 10, min_double, max_double):
expected = 'float: {}inf'.format('-' if inf < 0 else '')
actual = try_to_convert(inf)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(inf, expected, actual))
def test_parse_to_float_not_convertible(self):
for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
complex(1.1)):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpFloat(not_convertible)
def test_parse_to_float_not_convertible_extra(self):
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
np.array([1., ]), np.array([False]),
np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpFloat(not_convertible)
def test_parse_to_double_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpDouble)
min_float, max_float = get_limits(ctypes.c_float)
min_double, max_double = get_limits(ctypes.c_double)
for convertible in (2, -13, 1.24, np.float(32.45), float(2), np.double(12.23),
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
max_float, min_double, max_double, np.inf, -np.inf, float('Inf'),
-float('Inf'), np.double(np.inf), np.double(-np.inf),
np.double(float('Inf')), np.double(-float('Inf'))):
expected = 'Double: {0:.2f}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
# Workaround for Windows NaN tests due to Visual C runtime
# special floating point values (indefinite NaN)
for nan in (float('NaN'), np.nan, np.double(np.nan),
np.double(float('NaN'))):
actual = try_to_convert(nan)
self.assertIn('nan', actual, msg="Can't convert nan of type {} to double. "
"Actual: {}".format(type(nan).__name__, actual))
def test_parse_to_double_not_convertible(self):
for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
complex(1.1)):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpDouble(not_convertible)
def test_parse_to_double_not_convertible_extra(self):
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
np.array([1., ]), np.array([False]),
np.array([12.4], dtype=np.double), np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpDouble(not_convertible)
def test_parse_to_cstring_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpCString)
for convertible in ('s', 'str', str(123), ('char'), np.str('test1'), np.str_('test2')):
expected = 'string: ' + convertible
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_cstring_not_convertible(self):
for not_convertible in ((12,), ('t', 'e', 's', 't'), np.array(['123', ]),
np.array(['t', 'e', 's', 't']), 1, -1.4, True, False, None):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpCString(not_convertible)
class SamplesFindFile(NewOpenCVTests):
def test_ExistedFile(self):
res = cv.samples.findFile('lena.jpg', False)
self.assertNotEqual(res, '')
def test_MissingFile(self):
res = cv.samples.findFile('non_existed.file', False)
self.assertEqual(res, '')
def test_MissingFileException(self):
try:
_res = cv.samples.findFile('non_existed.file', True)
self.assertEqual("Dead code", 0)
except cv.error as _e:
pass
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Camshift tracker
================
This is a demo that shows mean-shift based tracking
You select a color objects such as your face and it tracks it.
This reads from video camera (0 by default, or the camera number the user enters)
http://www.robinhewitt.com/research/track/camshift.html
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
import cv2 as cv
from tst_scene_render import TestSceneRender
from tests_common import NewOpenCVTests, intersectionRate
class camshift_test(NewOpenCVTests):
framesNum = 300
frame = None
selection = None
drag_start = None
show_backproj = False
track_window = None
render = None
errors = 0
def prepareRender(self):
self.render = TestSceneRender(self.get_sample('samples/data/pca_test1.jpg'), deformation = True)
def runTracker(self):
framesCounter = 0
self.selection = True
xmin, ymin, xmax, ymax = self.render.getCurrentRect()
self.track_window = (xmin, ymin, xmax - xmin, ymax - ymin)
while True:
framesCounter += 1
self.frame = self.render.getNextFrame()
hsv = cv.cvtColor(self.frame, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.render.getCurrentRect() + 50
x0 -= 100
y0 -= 100
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX)
self.hist = hist.reshape(-1)
self.selection = False
if self.track_window and self.track_window[2] > 0 and self.track_window[3] > 0:
self.selection = None
prob = cv.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
_track_box, self.track_window = cv.CamShift(prob, self.track_window, term_crit)
trackingRect = np.array(self.track_window)
trackingRect[2] += trackingRect[0]
trackingRect[3] += trackingRect[1]
if intersectionRate(self.render.getCurrentRect(), trackingRect) < 0.4:
self.errors += 1
if framesCounter > self.framesNum:
break
self.assertLess(float(self.errors) / self.framesNum, 0.4)
def test_camshift(self):
self.prepareRender()
self.runTracker()
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/python
'''
This example illustrates how to use Hough Transform to find lines
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
import math
from tests_common import NewOpenCVTests
def linesDiff(line1, line2):
norm1 = cv.norm(line1 - line2, cv.NORM_L2)
line3 = line1[2:4] + line1[0:2]
norm2 = cv.norm(line3 - line2, cv.NORM_L2)
return min(norm1, norm2)
class houghlines_test(NewOpenCVTests):
def test_houghlines(self):
fn = "/samples/data/pic1.png"
src = self.get_sample(fn)
dst = cv.Canny(src, 50, 200)
lines = cv.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)[:,0,:]
eps = 5
testLines = [
#rect1
[ 232, 25, 43, 25],
[ 43, 129, 232, 129],
[ 43, 129, 43, 25],
[232, 129, 232, 25],
#rect2
[251, 86, 314, 183],
[252, 86, 323, 40],
[315, 183, 386, 137],
[324, 40, 386, 136],
#triangle
[245, 205, 377, 205],
[244, 206, 305, 278],
[306, 279, 377, 205],
#rect3
[153, 177, 196, 177],
[153, 277, 153, 179],
[153, 277, 196, 277],
[196, 177, 196, 277]]
matches_counter = 0
for i in range(len(testLines)):
for j in range(len(lines)):
if linesDiff(testLines[i], lines[j]) < eps:
matches_counter += 1
self.assertGreater(float(matches_counter) / len(testLines), .7)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Watershed segmentation test
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class watershed_test(NewOpenCVTests):
def test_watershed(self):
img = self.get_sample('cv/inpaint/orig.png')
markers = self.get_sample('cv/watershed/wshed_exp.png', 0)
refSegments = self.get_sample('cv/watershed/wshed_segments.png')
if img is None or markers is None:
self.assertEqual(0, 1, 'Missing test data')
colors = np.int32( list(np.ndindex(3, 3, 3)) ) * 122
cv.watershed(img, np.int32(markers))
segments = colors[np.maximum(markers, 0)]
if refSegments is None:
refSegments = segments.copy()
cv.imwrite(self.extraTestDataPath + '/cv/watershed/wshed_segments.png', refSegments)
self.assertLess(cv.norm(segments - refSegments, cv.NORM_L1) / 255.0, 50)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class AsyncTest(NewOpenCVTests):
def test_async_simple(self):
m = np.array([[1,2],[3,4],[5,6]])
async_result = cv.utils.testAsyncArray(m)
self.assertTrue(async_result.valid())
ret, result = async_result.get(timeoutNs=10**6) # 1ms
self.assertTrue(ret)
self.assertFalse(async_result.valid())
self.assertEqual(cv.norm(m, result, cv.NORM_INF), 0)
def test_async_exception(self):
async_result = cv.utils.testAsyncException()
self.assertTrue(async_result.valid())
try:
_ret, _result = async_result.get(timeoutNs=10**6) # 1ms
self.fail("Exception expected")
except cv.error as e:
self.assertEqual(cv.Error.StsOk, e.code)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Test for disctrete fourier transform (dft)
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
from tests_common import NewOpenCVTests
class dft_test(NewOpenCVTests):
def test_dft(self):
img = self.get_sample('samples/data/rubberwhale1.png', 0)
eps = 0.001
#test direct transform
refDft = np.fft.fft2(img)
refDftShift = np.fft.fftshift(refDft)
refMagnitide = np.log(1.0 + np.abs(refDftShift))
testDft = cv.dft(np.float32(img),flags = cv.DFT_COMPLEX_OUTPUT)
testDftShift = np.fft.fftshift(testDft)
testMagnitude = np.log(1.0 + cv.magnitude(testDftShift[:,:,0], testDftShift[:,:,1]))
refMagnitide = cv.normalize(refMagnitide, 0.0, 1.0, cv.NORM_MINMAX)
testMagnitude = cv.normalize(testMagnitude, 0.0, 1.0, cv.NORM_MINMAX)
self.assertLess(cv.norm(refMagnitide - testMagnitude), eps)
#test inverse transform
img_back = np.fft.ifft2(refDft)
img_back = np.abs(img_back)
img_backTest = cv.idft(testDft)
img_backTest = cv.magnitude(img_backTest[:,:,0], img_backTest[:,:,1])
img_backTest = cv.normalize(img_backTest, 0.0, 1.0, cv.NORM_MINMAX)
img_back = cv.normalize(img_back, 0.0, 1.0, cv.NORM_MINMAX)
self.assertLess(cv.norm(img_back - img_backTest), eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Morphology operations.
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class morphology_test(NewOpenCVTests):
def test_morphology(self):
fn = 'samples/data/rubberwhale1.png'
img = self.get_sample(fn)
modes = ['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient']
str_modes = ['ellipse', 'rect', 'cross']
referenceHashes = { modes[0]: '071a526425b79e45b4d0d71ef51b0562', modes[1] : '071a526425b79e45b4d0d71ef51b0562',
modes[2] : '427e89f581b7df1b60a831b1ed4c8618', modes[3] : '0dd8ad251088a63d0dd022bcdc57361c'}
def update(cur_mode):
cur_str_mode = str_modes[0]
sz = 10
iters = 1
opers = cur_mode.split('/')
if len(opers) > 1:
sz = sz - 10
op = opers[sz > 0]
sz = abs(sz)
else:
op = opers[0]
sz = sz*2+1
str_name = 'MORPH_' + cur_str_mode.upper()
oper_name = 'MORPH_' + op.upper()
st = cv.getStructuringElement(getattr(cv, str_name), (sz, sz))
return cv.morphologyEx(img, getattr(cv, oper_name), st, iterations=iters)
for mode in modes:
res = update(mode)
self.assertEqual(self.hashimg(res), referenceHashes[mode])
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Test for copyto with mask
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
from tests_common import NewOpenCVTests
class copytomask_test(NewOpenCVTests):
def test_copytomask(self):
img = self.get_sample('python/images/baboon.png', cv.IMREAD_COLOR)
eps = 0.
#Create mask using inRange
valeurBGRinf = np.array([0,0,100])
valeurBGRSup = np.array([70, 70,255])
maskRed = cv.inRange(img, valeurBGRinf, valeurBGRSup)
#New binding
dstcv = np.full(np.array((2, 2, 1))*img.shape, 255, dtype=img.dtype)
cv.copyTo(img, maskRed, dstcv[:img.shape[0],:img.shape[1],:])
#using numpy
dstnp = np.full(np.array((2, 2, 1))*img.shape, 255, dtype=img.dtype)
mask2=maskRed.astype(bool)
_, mask_b = np.broadcast_arrays(img, mask2[..., None])
np.copyto(dstnp[:img.shape[0],:img.shape[1],:], img, where=mask_b)
self.assertEqual(cv.norm(dstnp ,dstcv), eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
""""Core serialization tests."""
import tempfile
import os
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests
class persistence_test(NewOpenCVTests):
def test_yml_rw(self):
fd, fname = tempfile.mkstemp(prefix="opencv_python_persistence_", suffix=".yml")
os.close(fd)
# Writing ...
expected = np.array([[[0, 1, 2, 3, 4]]])
expected_str = ("Hello", "World", "!")
fs = cv.FileStorage(fname, cv.FILE_STORAGE_WRITE)
fs.write("test", expected)
fs.write("strings", expected_str)
fs.release()
# Reading ...
fs = cv.FileStorage(fname, cv.FILE_STORAGE_READ)
root = fs.getFirstTopLevelNode()
self.assertEqual(root.name(), "test")
test = fs.getNode("test")
self.assertEqual(test.empty(), False)
self.assertEqual(test.name(), "test")
self.assertEqual(test.type(), cv.FILE_NODE_MAP)
self.assertEqual(test.isMap(), True)
actual = test.mat()
self.assertEqual(actual.shape, expected.shape)
self.assertEqual(np.array_equal(expected, actual), True)
strings = fs.getNode("strings")
self.assertEqual(strings.isSeq(), True)
self.assertEqual(strings.size(), len(expected_str))
self.assertEqual(all(strings.at(i).isString() for i in range(strings.size())), True)
self.assertSequenceEqual([strings.at(i).string() for i in range(strings.size())], expected_str)
fs.release()
os.remove(fname)
|
#!/usr/bin/env python
from itertools import product
from functools import reduce
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
def norm_inf(x, y=None):
def norm(vec):
return np.linalg.norm(vec.flatten(), np.inf)
x = x.astype(np.float64)
return norm(x) if y is None else norm(x - y.astype(np.float64))
def norm_l1(x, y=None):
def norm(vec):
return np.linalg.norm(vec.flatten(), 1)
x = x.astype(np.float64)
return norm(x) if y is None else norm(x - y.astype(np.float64))
def norm_l2(x, y=None):
def norm(vec):
return np.linalg.norm(vec.flatten())
x = x.astype(np.float64)
return norm(x) if y is None else norm(x - y.astype(np.float64))
def norm_l2sqr(x, y=None):
def norm(vec):
return np.square(vec).sum()
x = x.astype(np.float64)
return norm(x) if y is None else norm(x - y.astype(np.float64))
def norm_hamming(x, y=None):
def norm(vec):
return sum(bin(i).count('1') for i in vec.flatten())
return norm(x) if y is None else norm(np.bitwise_xor(x, y))
def norm_hamming2(x, y=None):
def norm(vec):
def element_norm(element):
binary_str = bin(element).split('b')[-1]
if len(binary_str) % 2 == 1:
binary_str = '0' + binary_str
gen = filter(lambda p: p != '00',
(binary_str[i:i+2]
for i in range(0, len(binary_str), 2)))
return sum(1 for _ in gen)
return sum(element_norm(element) for element in vec.flatten())
return norm(x) if y is None else norm(np.bitwise_xor(x, y))
norm_type_under_test = {
cv.NORM_INF: norm_inf,
cv.NORM_L1: norm_l1,
cv.NORM_L2: norm_l2,
cv.NORM_L2SQR: norm_l2sqr,
cv.NORM_HAMMING: norm_hamming,
cv.NORM_HAMMING2: norm_hamming2
}
norm_name = {
cv.NORM_INF: 'inf',
cv.NORM_L1: 'L1',
cv.NORM_L2: 'L2',
cv.NORM_L2SQR: 'L2SQR',
cv.NORM_HAMMING: 'Hamming',
cv.NORM_HAMMING2: 'Hamming2'
}
def get_element_types(norm_type):
if norm_type in (cv.NORM_HAMMING, cv.NORM_HAMMING2):
return (np.uint8,)
else:
return (np.uint8, np.int8, np.uint16, np.int16, np.int32, np.float32,
np.float64)
def generate_vector(shape, dtype):
if np.issubdtype(dtype, np.integer):
return np.random.randint(0, 100, shape).astype(dtype)
else:
return np.random.normal(10., 12.5, shape).astype(dtype)
shapes = (1, 2, 3, 5, 7, 16, (1, 1), (2, 2), (3, 5), (1, 7))
class norm_test(NewOpenCVTests):
def test_norm_for_one_array(self):
np.random.seed(123)
for norm_type, norm in norm_type_under_test.items():
element_types = get_element_types(norm_type)
for shape, element_type in product(shapes, element_types):
array = generate_vector(shape, element_type)
expected = norm(array)
actual = cv.norm(array, norm_type)
self.assertAlmostEqual(
expected, actual, places=2,
msg='Array {0} of {1} and norm {2}'.format(
array, element_type.__name__, norm_name[norm_type]
)
)
def test_norm_for_two_arrays(self):
np.random.seed(456)
for norm_type, norm in norm_type_under_test.items():
element_types = get_element_types(norm_type)
for shape, element_type in product(shapes, element_types):
first = generate_vector(shape, element_type)
second = generate_vector(shape, element_type)
expected = norm(first, second)
actual = cv.norm(first, second, norm_type)
self.assertAlmostEqual(
expected, actual, places=2,
msg='Arrays {0} {1} of type {2} and norm {3}'.format(
first, second, element_type.__name__,
norm_name[norm_type]
)
)
def test_norm_fails_for_wrong_type(self):
for norm_type in (cv.NORM_HAMMING, cv.NORM_HAMMING2):
with self.assertRaises(Exception,
msg='Type is not checked {0}'.format(
norm_name[norm_type]
)):
cv.norm(np.array([1, 2], dtype=np.int32), norm_type)
def test_norm_fails_for_array_and_scalar(self):
for norm_type in norm_type_under_test:
with self.assertRaises(Exception,
msg='Exception is not thrown for {0}'.format(
norm_name[norm_type]
)):
cv.norm(np.array([1, 2], dtype=np.uint8), 123, norm_type)
def test_norm_fails_for_scalar_and_array(self):
for norm_type in norm_type_under_test:
with self.assertRaises(Exception,
msg='Exception is not thrown for {0}'.format(
norm_name[norm_type]
)):
cv.norm(4, np.array([1, 2], dtype=np.uint8), norm_type)
def test_norm_fails_for_array_and_norm_type_as_scalar(self):
for norm_type in norm_type_under_test:
with self.assertRaises(Exception,
msg='Exception is not thrown for {0}'.format(
norm_name[norm_type]
)):
cv.norm(np.array([3, 4, 5], dtype=np.uint8),
norm_type, normType=norm_type)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
MSER detector test
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class mser_test(NewOpenCVTests):
def test_mser(self):
img = self.get_sample('cv/mser/puzzle.png', 0)
smallImg = [
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]
]
thresharr = [ 0, 70, 120, 180, 255 ]
kDelta = 5
mserExtractor = cv.MSER_create()
mserExtractor.setDelta(kDelta)
np.random.seed(10)
for _i in range(100):
use_big_image = int(np.random.rand(1,1)*7) != 0
invert = int(np.random.rand(1,1)*2) != 0
binarize = int(np.random.rand(1,1)*5) != 0 if use_big_image else False
blur = int(np.random.rand(1,1)*2) != 0
thresh = thresharr[int(np.random.rand(1,1)*5)]
src0 = img if use_big_image else np.array(smallImg).astype('uint8')
src = src0.copy()
kMinArea = 256 if use_big_image else 10
kMaxArea = int(src.shape[0]*src.shape[1]/4)
mserExtractor.setMinArea(kMinArea)
mserExtractor.setMaxArea(kMaxArea)
if invert:
cv.bitwise_not(src, src)
if binarize:
_, src = cv.threshold(src, thresh, 255, cv.THRESH_BINARY)
if blur:
src = cv.GaussianBlur(src, (5, 5), 1.5, 1.5)
minRegs = 7 if use_big_image else 2
maxRegs = 1000 if use_big_image else 20
if binarize and (thresh == 0 or thresh == 255):
minRegs = maxRegs = 0
msers, boxes = mserExtractor.detectRegions(src)
nmsers = len(msers)
self.assertEqual(nmsers, len(boxes))
self.assertLessEqual(minRegs, nmsers)
self.assertGreaterEqual(maxRegs, nmsers)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Location of tests:
- <opencv_src>/modules/python/test
- <opencv_src>/modules/<module>/misc/python/test/
'''
from __future__ import print_function
import sys
sys.dont_write_bytecode = True # Don't generate .pyc files / __pycache__ directories
import os
import unittest
# Python 3 moved urlopen to urllib.requests
try:
from urllib.request import urlopen
except ImportError:
from urllib import urlopen
from tests_common import NewOpenCVTests
basedir = os.path.abspath(os.path.dirname(__file__))
def load_tests(loader, tests, pattern):
cwd = os.getcwd()
config_file = 'opencv_python_tests.cfg'
locations = [cwd, basedir]
if os.path.exists(config_file):
with open(config_file, 'r') as f:
locations += [str(s).strip() for s in f.readlines()]
else:
print('WARNING: OpenCV tests config file ({}) is missing, running subset of tests'.format(config_file))
tests_pattern = os.environ.get('OPENCV_PYTEST_FILTER', 'test_') + '*.py'
if tests_pattern != 'test_*py':
print('Tests filter: {}'.format(tests_pattern))
processed = set()
for l in locations:
if not os.path.isabs(l):
l = os.path.normpath(os.path.join(cwd, l))
if l in processed:
continue
processed.add(l)
print('Discovering python tests from: {}'.format(l))
sys_path_modify = l not in sys.path
if sys_path_modify:
sys.path.append(l) # Hack python loader
discovered_tests = loader.discover(l, pattern=tests_pattern, top_level_dir=l)
print(' found {} tests'.format(discovered_tests.countTestCases()))
tests.addTests(loader.discover(l, pattern=tests_pattern))
if sys_path_modify:
sys.path.remove(l)
return tests
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Simple "Square Detector" program.
Loads several images sequentially and tries to find squares in each image.
'''
# Python 2/3 compatibility
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
import cv2 as cv
def angle_cos(p0, p1, p2):
d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
def find_squares(img):
img = cv.GaussianBlur(img, (5, 5), 0)
squares = []
for gray in cv.split(img):
for thrs in xrange(0, 255, 26):
if thrs == 0:
bin = cv.Canny(gray, 0, 50, apertureSize=5)
bin = cv.dilate(bin, None)
else:
_retval, bin = cv.threshold(gray, thrs, 255, cv.THRESH_BINARY)
contours, _hierarchy = cv.findContours(bin, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
for cnt in contours:
cnt_len = cv.arcLength(cnt, True)
cnt = cv.approxPolyDP(cnt, 0.02*cnt_len, True)
if len(cnt) == 4 and cv.contourArea(cnt) > 1000 and cv.isContourConvex(cnt):
cnt = cnt.reshape(-1, 2)
max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
if max_cos < 0.1 and filterSquares(squares, cnt):
squares.append(cnt)
return squares
def intersectionRate(s1, s2):
area, _intersection = cv.intersectConvexConvex(np.array(s1), np.array(s2))
return 2 * area / (cv.contourArea(np.array(s1)) + cv.contourArea(np.array(s2)))
def filterSquares(squares, square):
for i in range(len(squares)):
if intersectionRate(squares[i], square) > 0.95:
return False
return True
from tests_common import NewOpenCVTests
class squares_test(NewOpenCVTests):
def test_squares(self):
img = self.get_sample('samples/data/pic1.png')
squares = find_squares(img)
testSquares = [
[[43, 25],
[43, 129],
[232, 129],
[232, 25]],
[[252, 87],
[324, 40],
[387, 137],
[315, 184]],
[[154, 178],
[196, 180],
[198, 278],
[154, 278]],
[[0, 0],
[400, 0],
[400, 300],
[0, 300]]
]
matches_counter = 0
for i in range(len(squares)):
for j in range(len(testSquares)):
if intersectionRate(squares[i], testSquares[j]) > 0.9:
matches_counter += 1
self.assertGreater(matches_counter / len(testSquares), 0.9)
self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
CUDA-accelerated Computer Vision functions
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import os
from tests_common import NewOpenCVTests, unittest
class cuda_test(NewOpenCVTests):
def setUp(self):
super(cuda_test, self).setUp()
if not cv.cuda.getCudaEnabledDeviceCount():
self.skipTest("No CUDA-capable device is detected")
def test_cuda_upload_download(self):
npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8)
cuMat = cv.cuda_GpuMat()
cuMat.upload(npMat)
self.assertTrue(np.allclose(cuMat.download(), npMat))
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class UMat(NewOpenCVTests):
def test_umat_construct(self):
data = np.random.random([512, 512])
# UMat constructors
data_um = cv.UMat(data) # from ndarray
data_sub_um = cv.UMat(data_um, (128, 256), (128, 256)) # from UMat
data_dst_um = cv.UMat(128, 128, cv.CV_64F) # from size/type
# test continuous and submatrix flags
assert data_um.isContinuous() and not data_um.isSubmatrix()
assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
# test operation on submatrix
cv.multiply(data_sub_um, 2., dst=data_dst_um)
assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())
def test_umat_handle(self):
a_um = cv.UMat(256, 256, cv.CV_32F)
_ctx_handle = cv.UMat.context() # obtain context handle
_queue_handle = cv.UMat.queue() # obtain queue handle
_a_handle = a_um.handle(cv.ACCESS_READ) # obtain buffer handle
_offset = a_um.offset # obtain buffer offset
def test_umat_matching(self):
img1 = self.get_sample("samples/data/right01.jpg")
img2 = self.get_sample("samples/data/right02.jpg")
orb = cv.ORB_create()
img1, img2 = cv.UMat(img1), cv.UMat(img2)
ps1, descs_umat1 = orb.detectAndCompute(img1, None)
ps2, descs_umat2 = orb.detectAndCompute(img2, None)
self.assertIsInstance(descs_umat1, cv.UMat)
self.assertIsInstance(descs_umat2, cv.UMat)
self.assertGreater(len(ps1), 0)
self.assertGreater(len(ps2), 0)
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
res_umats = bf.match(descs_umat1, descs_umat2)
res = bf.match(descs_umat1.get(), descs_umat2.get())
self.assertGreater(len(res), 0)
self.assertEqual(len(res_umats), len(res))
def test_umat_optical_flow(self):
img1 = self.get_sample("samples/data/right01.jpg", cv.IMREAD_GRAYSCALE)
img2 = self.get_sample("samples/data/right02.jpg", cv.IMREAD_GRAYSCALE)
# Note, that if you want to see performance boost by OCL implementation - you need enough data
# For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way:
# img = np.hstack([np.vstack([img] * 6)] * 6)
feature_params = dict(maxCorners=239,
qualityLevel=0.3,
minDistance=7,
blockSize=7)
p0 = cv.goodFeaturesToTrack(img1, mask=None, **feature_params)
p0_umat = cv.goodFeaturesToTrack(cv.UMat(img1), mask=None, **feature_params)
self.assertEqual(p0_umat.get().shape, p0.shape)
p0 = np.array(sorted(p0, key=lambda p: tuple(p[0])))
p0_umat = cv.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0]))))
self.assertTrue(np.allclose(p0_umat.get(), p0))
_p1_mask_err = cv.calcOpticalFlowPyrLK(img1, img2, p0, None)
_p1_mask_err_umat0 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(img1, img2, p0_umat, None)))
_p1_mask_err_umat1 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(cv.UMat(img1), img2, p0_umat, None)))
_p1_mask_err_umat2 = list(map(lambda umat: umat.get(), cv.calcOpticalFlowPyrLK(img1, cv.UMat(img2), p0_umat, None)))
for _p1_mask_err_umat in [_p1_mask_err_umat0, _p1_mask_err_umat1, _p1_mask_err_umat2]:
for data, data_umat in zip(_p1_mask_err, _p1_mask_err_umat):
self.assertEqual(data.shape, data_umat.shape)
self.assertEqual(data.dtype, data_umat.dtype)
for _p1_mask_err_umat in [_p1_mask_err_umat1, _p1_mask_err_umat2]:
for data_umat0, data_umat in zip(_p1_mask_err_umat0[:2], _p1_mask_err_umat[:2]):
self.assertTrue(np.allclose(data_umat0, data_umat))
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class Hackathon244Tests(NewOpenCVTests):
def test_int_array(self):
a = np.array([-1, 2, -3, 4, -5])
absa0 = np.abs(a)
self.assertTrue(cv.norm(a, cv.NORM_L1) == 15)
absa1 = cv.absdiff(a, 0)
self.assertEqual(cv.norm(absa1, absa0, cv.NORM_INF), 0)
def test_imencode(self):
a = np.zeros((480, 640), dtype=np.uint8)
flag, ajpg = cv.imencode("img_q90.jpg", a, [cv.IMWRITE_JPEG_QUALITY, 90])
self.assertEqual(flag, True)
self.assertEqual(ajpg.dtype, np.uint8)
self.assertGreater(ajpg.shape[0], 1)
self.assertEqual(ajpg.shape[1], 1)
def test_projectPoints(self):
objpt = np.float64([[1,2,3]])
imgpt0, jac0 = cv.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([]))
imgpt1, jac1 = cv.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None)
self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2))
self.assertEqual(imgpt1.shape, imgpt0.shape)
self.assertEqual(jac0.shape, jac1.shape)
self.assertEqual(jac0.shape[0], 2*objpt.shape[0])
def test_estimateAffine3D(self):
pattern_size = (11, 8)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= 10
(retval, out, inliers) = cv.estimateAffine3D(pattern_points, pattern_points)
self.assertEqual(retval, 1)
if cv.norm(out[2,:]) < 1e-3:
out[2,2]=1
self.assertLess(cv.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3)
self.assertEqual(cv.countNonZero(inliers), pattern_size[0]*pattern_size[1])
def test_fast(self):
fd = cv.FastFeatureDetector_create(30, True)
img = self.get_sample("samples/data/right02.jpg", 0)
img = cv.medianBlur(img, 3)
keypoints = fd.detect(img)
self.assertTrue(600 <= len(keypoints) <= 700)
for kpt in keypoints:
self.assertNotEqual(kpt.response, 0)
def check_close_angles(self, a, b, angle_delta):
self.assertTrue(abs(a - b) <= angle_delta or
abs(360 - abs(a - b)) <= angle_delta)
def check_close_pairs(self, a, b, delta):
self.assertLessEqual(abs(a[0] - b[0]), delta)
self.assertLessEqual(abs(a[1] - b[1]), delta)
def check_close_boxes(self, a, b, delta, angle_delta):
self.check_close_pairs(a[0], b[0], delta)
self.check_close_pairs(a[1], b[1], delta)
self.check_close_angles(a[2], b[2], angle_delta)
def test_geometry(self):
npt = 100
np.random.seed(244)
a = np.random.randn(npt,2).astype('float32')*50 + 150
be = cv.fitEllipse(a)
br = cv.minAreaRect(a)
mc, mr = cv.minEnclosingCircle(a)
be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582)
mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
self.check_close_boxes(be, be0, 5, 15)
self.check_close_boxes(br, br0, 5, 15)
self.check_close_pairs(mc, mc0, 5)
self.assertLessEqual(abs(mr - mr0), 5)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
from __future__ import print_function
import os
import sys
import unittest
import hashlib
import random
import argparse
import numpy as np
import cv2 as cv
# Python 3 moved urlopen to urllib.requests
try:
from urllib.request import urlopen
except ImportError:
from urllib import urlopen
class NewOpenCVTests(unittest.TestCase):
# path to local repository folder containing 'samples' folder
repoPath = None
extraTestDataPath = None
# github repository url
repoUrl = 'https://raw.github.com/opencv/opencv/master'
def find_file(self, filename, searchPaths=[], required=True):
searchPaths = searchPaths if searchPaths else [self.repoPath, self.extraTestDataPath]
for path in searchPaths:
if path is not None:
candidate = path + '/' + filename
if os.path.isfile(candidate):
return candidate
if required:
self.fail('File ' + filename + ' not found')
return None
def get_sample(self, filename, iscolor = None):
if iscolor is None:
iscolor = cv.IMREAD_COLOR
if not filename in self.image_cache:
filepath = self.find_file(filename)
with open(filepath, 'rb') as f:
filedata = f.read()
self.image_cache[filename] = cv.imdecode(np.fromstring(filedata, dtype=np.uint8), iscolor)
return self.image_cache[filename]
def setUp(self):
cv.setRNGSeed(10)
self.image_cache = {}
def hashimg(self, im):
""" Compute a hash for an image, useful for image comparisons """
return hashlib.md5(im.tostring()).hexdigest()
if sys.version_info[:2] == (2, 6):
def assertLess(self, a, b, msg=None):
if not a < b:
self.fail('%s not less than %s' % (repr(a), repr(b)))
def assertLessEqual(self, a, b, msg=None):
if not a <= b:
self.fail('%s not less than or equal to %s' % (repr(a), repr(b)))
def assertGreater(self, a, b, msg=None):
if not a > b:
self.fail('%s not greater than %s' % (repr(a), repr(b)))
@staticmethod
def bootstrap():
parser = argparse.ArgumentParser(description='run OpenCV python tests')
parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), '
'if not set, samples will be downloaded from github.com')
parser.add_argument('--data', help='<not used> use data files from local folder (path to folder), '
'if not set, data files will be downloaded from docs.opencv.org')
args, other = parser.parse_known_args()
print("Testing OpenCV", cv.__version__)
print("Local repo path:", args.repo)
NewOpenCVTests.repoPath = args.repo
try:
NewOpenCVTests.extraTestDataPath = os.environ['OPENCV_TEST_DATA_PATH']
except KeyError:
print('Missing opencv extra repository. Some of tests may fail.')
random.seed(0)
unit_argv = [sys.argv[0]] + other
unittest.main(argv=unit_argv)
def intersectionRate(s1, s2):
x1, y1, x2, y2 = s1
s1 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
x1, y1, x2, y2 = s2
s2 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
area, _intersection = cv.intersectConvexConvex(s1, s2)
return 2 * area / (cv.contourArea(s1) + cv.contourArea(s2))
def isPointInRect(p, rect):
if rect[0] <= p[0] and rect[1] <=p[1] and p[0] <= rect[2] and p[1] <= rect[3]:
return True
else:
return False
|
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
from numpy import pi, sin, cos
import cv2 as cv
defaultSize = 512
class TestSceneRender():
def __init__(self, bgImg = None, fgImg = None, deformation = False, noise = 0.0, speed = 0.25, **params):
self.time = 0.0
self.timeStep = 1.0 / 30.0
self.foreground = fgImg
self.deformation = deformation
self.noise = noise
self.speed = speed
if bgImg is not None:
self.sceneBg = bgImg.copy()
else:
self.sceneBg = np.zeros(defaultSize, defaultSize, np.uint8)
self.w = self.sceneBg.shape[0]
self.h = self.sceneBg.shape[1]
if fgImg is not None:
self.foreground = fgImg.copy()
self.center = self.currentCenter = (int(self.w/2 - fgImg.shape[0]/2), int(self.h/2 - fgImg.shape[1]/2))
self.xAmpl = self.sceneBg.shape[0] - (self.center[0] + fgImg.shape[0])
self.yAmpl = self.sceneBg.shape[1] - (self.center[1] + fgImg.shape[1])
self.initialRect = np.array([ (self.h/2, self.w/2), (self.h/2, self.w/2 + self.w/10),
(self.h/2 + self.h/10, self.w/2 + self.w/10), (self.h/2 + self.h/10, self.w/2)]).astype(int)
self.currentRect = self.initialRect
np.random.seed(10)
def getXOffset(self, time):
return int(self.xAmpl*cos(time*self.speed))
def getYOffset(self, time):
return int(self.yAmpl*sin(time*self.speed))
def setInitialRect(self, rect):
self.initialRect = rect
def getRectInTime(self, time):
if self.foreground is not None:
tmp = np.array(self.center) + np.array((self.getXOffset(time), self.getYOffset(time)))
x0, y0 = tmp
x1, y1 = tmp + self.foreground.shape[0:2]
return np.array([y0, x0, y1, x1])
else:
x0, y0 = self.initialRect[0] + np.array((self.getXOffset(time), self.getYOffset(time)))
x1, y1 = self.initialRect[2] + np.array((self.getXOffset(time), self.getYOffset(time)))
return np.array([y0, x0, y1, x1])
def getCurrentRect(self):
if self.foreground is not None:
x0 = self.currentCenter[0]
y0 = self.currentCenter[1]
x1 = self.currentCenter[0] + self.foreground.shape[0]
y1 = self.currentCenter[1] + self.foreground.shape[1]
return np.array([y0, x0, y1, x1])
else:
x0, y0 = self.currentRect[0]
x1, y1 = self.currentRect[2]
return np.array([x0, y0, x1, y1])
def getNextFrame(self):
img = self.sceneBg.copy()
if self.foreground is not None:
self.currentCenter = (self.center[0] + self.getXOffset(self.time), self.center[1] + self.getYOffset(self.time))
img[self.currentCenter[0]:self.currentCenter[0]+self.foreground.shape[0],
self.currentCenter[1]:self.currentCenter[1]+self.foreground.shape[1]] = self.foreground
else:
self.currentRect = self.initialRect + np.int( 30*cos(self.time) + 50*sin(self.time/3))
if self.deformation:
self.currentRect[1:3] += int(self.h/20*cos(self.time))
cv.fillConvexPoly(img, self.currentRect, (0, 0, 255))
self.time += self.timeStep
if self.noise:
noise = np.zeros(self.sceneBg.shape, np.int8)
cv.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
img = cv.add(img, noise, dtype=cv.CV_8UC3)
return img
def resetTime(self):
self.time = 0.0
if __name__ == '__main__':
backGr = cv.imread('../../../samples/data/lena.jpg')
render = TestSceneRender(backGr, noise = 0.5)
while True:
img = render.getNextFrame()
cv.imshow('img', img)
ch = cv.waitKey(3)
if ch == 27:
break
cv.destroyAllWindows()
|
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class Features2D_Tests(NewOpenCVTests):
def test_issue_13406(self):
self.assertEqual(True, hasattr(cv, 'drawKeypoints'))
self.assertEqual(True, hasattr(cv, 'DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS'))
self.assertEqual(True, hasattr(cv, 'DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS'))
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
===============================================================================
Interactive Image Segmentation using GrabCut algorithm.
===============================================================================
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import sys
from tests_common import NewOpenCVTests
class grabcut_test(NewOpenCVTests):
def verify(self, mask, exp):
maxDiffRatio = 0.02
expArea = np.count_nonzero(exp)
nonIntersectArea = np.count_nonzero(mask != exp)
curRatio = float(nonIntersectArea) / expArea
return curRatio < maxDiffRatio
def scaleMask(self, mask):
return np.where((mask==cv.GC_FGD) + (mask==cv.GC_PR_FGD),255,0).astype('uint8')
def test_grabcut(self):
img = self.get_sample('cv/shared/airplane.png')
mask_prob = self.get_sample("cv/grabcut/mask_probpy.png", 0)
exp_mask1 = self.get_sample("cv/grabcut/exp_mask1py.png", 0)
exp_mask2 = self.get_sample("cv/grabcut/exp_mask2py.png", 0)
if img is None:
self.assertTrue(False, 'Missing test data')
rect = (24, 126, 459, 168)
mask = np.zeros(img.shape[:2], dtype = np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
cv.grabCut(img, mask, rect, bgdModel, fgdModel, 0, cv.GC_INIT_WITH_RECT)
cv.grabCut(img, mask, rect, bgdModel, fgdModel, 2, cv.GC_EVAL)
if mask_prob is None:
mask_prob = mask.copy()
cv.imwrite(self.extraTestDataPath + '/cv/grabcut/mask_probpy.png', mask_prob)
if exp_mask1 is None:
exp_mask1 = self.scaleMask(mask)
cv.imwrite(self.extraTestDataPath + '/cv/grabcut/exp_mask1py.png', exp_mask1)
self.assertEqual(self.verify(self.scaleMask(mask), exp_mask1), True)
mask = mask_prob
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
cv.grabCut(img, mask, rect, bgdModel, fgdModel, 0, cv.GC_INIT_WITH_MASK)
cv.grabCut(img, mask, rect, bgdModel, fgdModel, 1, cv.GC_EVAL)
if exp_mask2 is None:
exp_mask2 = self.scaleMask(mask)
cv.imwrite(self.extraTestDataPath + '/cv/grabcut/exp_mask2py.png', exp_mask2)
self.assertEqual(self.verify(self.scaleMask(mask), exp_mask2), True)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/python
'''
This example illustrates how to use cv.HoughCircles() function.
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
from numpy import pi, sin, cos
from tests_common import NewOpenCVTests
def circleApproximation(circle):
nPoints = 30
dPhi = 2*pi / nPoints
contour = []
for i in range(nPoints):
contour.append(([circle[0] + circle[2]*cos(i*dPhi),
circle[1] + circle[2]*sin(i*dPhi)]))
return np.array(contour).astype(int)
def convContoursIntersectiponRate(c1, c2):
s1 = cv.contourArea(c1)
s2 = cv.contourArea(c2)
s, _ = cv.intersectConvexConvex(c1, c2)
return 2*s/(s1+s2)
class houghcircles_test(NewOpenCVTests):
def test_houghcircles(self):
fn = "samples/data/board.jpg"
src = self.get_sample(fn, 1)
img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
img = cv.medianBlur(img, 5)
circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)[0]
testCircles = [[38, 181, 17.6],
[99.7, 166, 13.12],
[142.7, 160, 13.52],
[223.6, 110, 8.62],
[79.1, 206.7, 8.62],
[47.5, 351.6, 11.64],
[189.5, 354.4, 11.64],
[189.8, 298.9, 10.64],
[189.5, 252.4, 14.62],
[252.5, 393.4, 15.62],
[602.9, 467.5, 11.42],
[222, 210.4, 9.12],
[263.1, 216.7, 9.12],
[359.8, 222.6, 9.12],
[518.9, 120.9, 9.12],
[413.8, 113.4, 9.12],
[489, 127.2, 9.12],
[448.4, 121.3, 9.12],
[384.6, 128.9, 8.62]]
matches_counter = 0
for i in range(len(testCircles)):
for j in range(len(circles)):
tstCircle = circleApproximation(testCircles[i])
circle = circleApproximation(circles[j])
if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
matches_counter += 1
self.assertGreater(float(matches_counter) / len(testCircles), .5)
self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
Texture flow direction estimation.
Sample shows how cv.cornerEigenValsAndVecs function can be used
to estimate image texture flow direction.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import sys
from tests_common import NewOpenCVTests
class texture_flow_test(NewOpenCVTests):
def test_texture_flow(self):
img = self.get_sample('samples/data/chessboard.png')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
h, w = img.shape[:2]
eigen = cv.cornerEigenValsAndVecs(gray, 5, 3)
eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2]
flow = eigen[:,:,2]
d = 300
eps = d / 30
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
textureVectors = []
for x, y in np.int32(points):
textureVectors.append(np.int32(flow[y, x]*d))
for i in range(len(textureVectors)):
self.assertTrue(cv.norm(textureVectors[i], cv.NORM_L2) < eps
or abs(cv.norm(textureVectors[i], cv.NORM_L2) - d) < eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
'''
K-means clusterization test
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from numpy import random
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
from tests_common import NewOpenCVTests
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
sizes = []
for _ in xrange(cluster_n):
mean = (0.1 + 0.8*random.rand(2)) * img_size
a = (random.rand(2, 2)-0.5)*img_size*0.1
cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
n = 100 + random.randint(900)
pts = random.multivariate_normal(mean, cov, n)
points.append( pts )
ref_distrs.append( (mean, cov) )
sizes.append(n)
points = np.float32( np.vstack(points) )
return points, ref_distrs, sizes
def getMainLabelConfidence(labels, nLabels):
n = len(labels)
labelsDict = dict.fromkeys(range(nLabels), 0)
labelsConfDict = dict.fromkeys(range(nLabels))
for i in range(n):
labelsDict[labels[i][0]] += 1
for i in range(nLabels):
labelsConfDict[i] = float(labelsDict[i]) / n
return max(labelsConfDict.values())
class kmeans_test(NewOpenCVTests):
def test_kmeans(self):
np.random.seed(10)
cluster_n = 5
img_size = 512
points, _, clusterSizes = make_gaussians(cluster_n, img_size)
term_crit = (cv.TERM_CRITERIA_EPS, 30, 0.1)
_ret, labels, centers = cv.kmeans(points, cluster_n, None, term_crit, 10, 0)
self.assertEqual(len(centers), cluster_n)
offset = 0
for i in range(cluster_n):
confidence = getMainLabelConfidence(labels[offset : (offset + clusterSizes[i])], cluster_n)
offset += clusterSizes[i]
self.assertGreater(confidence, 0.9)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
from numpy import random
import cv2 as cv
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
for _ in xrange(cluster_n):
mean = (0.1 + 0.8*random.rand(2)) * img_size
a = (random.rand(2, 2)-0.5)*img_size*0.1
cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
n = 100 + random.randint(900)
pts = random.multivariate_normal(mean, cov, n)
points.append( pts )
ref_distrs.append( (mean, cov) )
points = np.float32( np.vstack(points) )
return points, ref_distrs
from tests_common import NewOpenCVTests
class gaussian_mix_test(NewOpenCVTests):
def test_gaussian_mix(self):
np.random.seed(10)
cluster_n = 5
img_size = 512
points, ref_distrs = make_gaussians(cluster_n, img_size)
em = cv.ml.EM_create()
em.setClustersNumber(cluster_n)
em.setCovarianceMatrixType(cv.ml.EM_COV_MAT_GENERIC)
em.trainEM(points)
means = em.getMeans()
covs = em.getCovs() # Known bug: https://github.com/opencv/opencv/pull/4232
#found_distrs = zip(means, covs)
matches_count = 0
meanEps = 0.05
covEps = 0.1
for i in range(cluster_n):
for j in range(cluster_n):
if (cv.norm(means[i] - ref_distrs[j][0], cv.NORM_L2) / cv.norm(ref_distrs[j][0], cv.NORM_L2) < meanEps and
cv.norm(covs[i] - ref_distrs[j][1], cv.NORM_L2) / cv.norm(ref_distrs[j][1], cv.NORM_L2) < covEps):
matches_count += 1
self.assertEqual(matches_count, cluster_n)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
|
#!/usr/bin/env python
from __future__ import print_function
import os, sys, re, string, io
# the list only for debugging. The real list, used in the real OpenCV build, is specified in CMakeLists.txt
opencv_hdr_list = [
"../../core/include/opencv2/core.hpp",
"../../core/include/opencv2/core/mat.hpp",
"../../core/include/opencv2/core/ocl.hpp",
"../../flann/include/opencv2/flann/miniflann.hpp",
"../../ml/include/opencv2/ml.hpp",
"../../imgproc/include/opencv2/imgproc.hpp",
"../../calib3d/include/opencv2/calib3d.hpp",
"../../features2d/include/opencv2/features2d.hpp",
"../../video/include/opencv2/video/tracking.hpp",
"../../video/include/opencv2/video/background_segm.hpp",
"../../objdetect/include/opencv2/objdetect.hpp",
"../../imgcodecs/include/opencv2/imgcodecs.hpp",
"../../videoio/include/opencv2/videoio.hpp",
"../../highgui/include/opencv2/highgui.hpp",
]
"""
Each declaration is [funcname, return_value_type /* in C, not in Python */, <list_of_modifiers>, <list_of_arguments>, original_return_type, docstring],
where each element of <list_of_arguments> is 4-element list itself:
[argtype, argname, default_value /* or "" if none */, <list_of_modifiers>]
where the list of modifiers is yet another nested list of strings
(currently recognized are "/O" for output argument, "/S" for static (i.e. class) methods
and "/A value" for the plain C arrays with counters)
original_return_type is None if the original_return_type is the same as return_value_type
"""
class CppHeaderParser(object):
def __init__(self, generate_umat_decls=False, generate_gpumat_decls=False):
self._generate_umat_decls = generate_umat_decls
self._generate_gpumat_decls = generate_gpumat_decls
self.BLOCK_TYPE = 0
self.BLOCK_NAME = 1
self.PROCESS_FLAG = 2
self.PUBLIC_SECTION = 3
self.CLASS_DECL = 4
self.namespaces = set()
def batch_replace(self, s, pairs):
for before, after in pairs:
s = s.replace(before, after)
return s
def get_macro_arg(self, arg_str, npos):
npos2 = npos3 = arg_str.find("(", npos)
if npos2 < 0:
print("Error: no arguments for the macro at %d" % (self.lineno,))
sys.exit(-1)
balance = 1
while 1:
t, npos3 = self.find_next_token(arg_str, ['(', ')'], npos3+1)
if npos3 < 0:
print("Error: no matching ')' in the macro call at %d" % (self.lineno,))
sys.exit(-1)
if t == '(':
balance += 1
if t == ')':
balance -= 1
if balance == 0:
break
return arg_str[npos2+1:npos3].strip(), npos3
def parse_arg(self, arg_str, argno):
"""
Parses <arg_type> [arg_name]
Returns arg_type, arg_name, modlist, argno, where
modlist is the list of wrapper-related modifiers (such as "output argument", "has counter", ...)
and argno is the new index of an anonymous argument.
That is, if no arg_str is just an argument type without argument name, the argument name is set to
"arg" + str(argno), and then argno is incremented.
"""
modlist = []
# pass 0: extracts the modifiers
if "CV_OUT" in arg_str:
modlist.append("/O")
arg_str = arg_str.replace("CV_OUT", "")
if "CV_IN_OUT" in arg_str:
modlist.append("/IO")
arg_str = arg_str.replace("CV_IN_OUT", "")
isarray = False
npos = arg_str.find("CV_CARRAY")
if npos >= 0:
isarray = True
macro_arg, npos3 = self.get_macro_arg(arg_str, npos)
modlist.append("/A " + macro_arg)
arg_str = arg_str[:npos] + arg_str[npos3+1:]
npos = arg_str.find("CV_CUSTOM_CARRAY")
if npos >= 0:
isarray = True
macro_arg, npos3 = self.get_macro_arg(arg_str, npos)
modlist.append("/CA " + macro_arg)
arg_str = arg_str[:npos] + arg_str[npos3+1:]
npos = arg_str.find("const")
if npos >= 0:
modlist.append("/C")
npos = arg_str.find("&")
if npos >= 0:
modlist.append("/Ref")
arg_str = arg_str.strip()
word_start = 0
word_list = []
npos = -1
#print self.lineno, ":\t", arg_str
# pass 1: split argument type into tokens
while 1:
npos += 1
t, npos = self.find_next_token(arg_str, [" ", "&", "*", "<", ">", ","], npos)
w = arg_str[word_start:npos].strip()
if w == "operator":
word_list.append("operator " + arg_str[npos:].strip())
break
if w not in ["", "const"]:
word_list.append(w)
if t not in ["", " ", "&"]:
word_list.append(t)
if not t:
break
word_start = npos+1
npos = word_start - 1
arg_type = ""
arg_name = ""
angle_stack = []
#print self.lineno, ":\t", word_list
# pass 2: decrypt the list
wi = -1
prev_w = ""
for w in word_list:
wi += 1
if w == "*":
if prev_w == "char" and not isarray:
arg_type = arg_type[:-len("char")] + "c_string"
else:
arg_type += w
continue
elif w == "<":
arg_type += "_"
angle_stack.append(0)
elif w == "," or w == '>':
if not angle_stack:
print("Error at %d: argument contains ',' or '>' not within template arguments" % (self.lineno,))
sys.exit(-1)
if w == ",":
arg_type += "_and_"
elif w == ">":
if angle_stack[0] == 0:
print("Error at %s:%d: template has no arguments" % (self.hname, self.lineno))
sys.exit(-1)
if angle_stack[0] > 1:
arg_type += "_end_"
angle_stack[-1:] = []
elif angle_stack:
arg_type += w
angle_stack[-1] += 1
elif arg_type == "struct":
arg_type += " " + w
elif arg_type and arg_type != "~":
arg_name = " ".join(word_list[wi:])
break
else:
arg_type += w
prev_w = w
counter_str = ""
add_star = False
if ("[" in arg_name) and not ("operator" in arg_str):
#print arg_str
p1 = arg_name.find("[")
p2 = arg_name.find("]",p1+1)
if p2 < 0:
print("Error at %d: no closing ]" % (self.lineno,))
sys.exit(-1)
counter_str = arg_name[p1+1:p2].strip()
if counter_str == "":
counter_str = "?"
if not isarray:
modlist.append("/A " + counter_str.strip())
arg_name = arg_name[:p1]
add_star = True
if not arg_name:
if arg_type.startswith("operator"):
arg_type, arg_name = "", arg_type
else:
arg_name = "arg" + str(argno)
argno += 1
while arg_type.endswith("_end_"):
arg_type = arg_type[:-len("_end_")]
if add_star:
arg_type += "*"
arg_type = self.batch_replace(arg_type, [("std::", ""), ("cv::", ""), ("::", "_")])
return arg_type, arg_name, modlist, argno
def parse_enum(self, decl_str):
l = decl_str
ll = l.split(",")
if ll[-1].strip() == "":
ll = ll[:-1]
prev_val = ""
prev_val_delta = -1
decl = []
for pair in ll:
pv = pair.split("=")
if len(pv) == 1:
prev_val_delta += 1
val = ""
if prev_val:
val = prev_val + "+"
val += str(prev_val_delta)
else:
prev_val_delta = 0
prev_val = val = pv[1].strip()
decl.append(["const " + self.get_dotted_name(pv[0].strip()), val, [], [], None, ""])
return decl
def parse_class_decl(self, decl_str):
"""
Parses class/struct declaration start in the form:
{class|struct} [CV_EXPORTS] <class_name> [: public <base_class1> [, ...]]
Returns class_name1, <list of base_classes>
"""
l = decl_str
modlist = []
if "CV_EXPORTS_W_MAP" in l:
l = l.replace("CV_EXPORTS_W_MAP", "")
modlist.append("/Map")
if "CV_EXPORTS_W_SIMPLE" in l:
l = l.replace("CV_EXPORTS_W_SIMPLE", "")
modlist.append("/Simple")
npos = l.find("CV_EXPORTS_AS")
if npos >= 0:
macro_arg, npos3 = self.get_macro_arg(l, npos)
modlist.append("=" + macro_arg)
l = l[:npos] + l[npos3+1:]
l = self.batch_replace(l, [("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("public virtual ", " "), ("public ", " "), ("::", ".")]).strip()
ll = re.split(r'\s+|\s*[,:]\s*', l)
ll = [le for le in ll if le]
classname = ll[1]
bases = ll[2:]
return classname, bases, modlist
def parse_func_decl_no_wrap(self, decl_str, static_method=False, docstring=""):
decl_str = (decl_str or "").strip()
virtual_method = False
explicit_method = False
if decl_str.startswith("explicit"):
decl_str = decl_str[len("explicit"):].lstrip()
explicit_method = True
if decl_str.startswith("virtual"):
decl_str = decl_str[len("virtual"):].lstrip()
virtual_method = True
if decl_str.startswith("static"):
decl_str = decl_str[len("static"):].lstrip()
static_method = True
fdecl = decl_str.replace("CV_OUT", "").replace("CV_IN_OUT", "")
fdecl = fdecl.strip().replace("\t", " ")
while " " in fdecl:
fdecl = fdecl.replace(" ", " ")
fname = fdecl[:fdecl.find("(")].strip()
fnpos = fname.rfind(" ")
if fnpos < 0:
fnpos = 0
fname = fname[fnpos:].strip()
rettype = fdecl[:fnpos].strip()
if rettype.endswith("operator"):
fname = ("operator " + fname).strip()
rettype = rettype[:rettype.rfind("operator")].strip()
if rettype.endswith("::"):
rpos = rettype.rfind(" ")
if rpos >= 0:
fname = rettype[rpos+1:].strip() + fname
rettype = rettype[:rpos].strip()
else:
fname = rettype + fname
rettype = ""
apos = fdecl.find("(")
if fname.endswith("operator"):
fname += " ()"
apos = fdecl.find("(", apos+1)
fname = "cv." + fname.replace("::", ".")
decl = [fname, rettype, [], [], None, docstring]
# inline constructor implementation
implmatch = re.match(r"(\(.*?\))\s*:\s*(\w+\(.*?\),?\s*)+", fdecl[apos:])
if bool(implmatch):
fdecl = fdecl[:apos] + implmatch.group(1)
args0str = fdecl[apos+1:fdecl.rfind(")")].strip()
if args0str != "" and args0str != "void":
args0str = re.sub(r"\([^)]*\)", lambda m: m.group(0).replace(',', "@comma@"), args0str)
args0 = args0str.split(",")
args = []
narg = ""
for arg in args0:
narg += arg.strip()
balance_paren = narg.count("(") - narg.count(")")
balance_angle = narg.count("<") - narg.count(">")
if balance_paren == 0 and balance_angle == 0:
args.append(narg.strip())
narg = ""
for arg in args:
dfpos = arg.find("=")
defval = ""
if dfpos >= 0:
defval = arg[dfpos+1:].strip()
else:
dfpos = arg.find("CV_DEFAULT")
if dfpos >= 0:
defval, pos3 = self.get_macro_arg(arg, dfpos)
else:
dfpos = arg.find("CV_WRAP_DEFAULT")
if dfpos >= 0:
defval, pos3 = self.get_macro_arg(arg, dfpos)
if dfpos >= 0:
defval = defval.replace("@comma@", ",")
arg = arg[:dfpos].strip()
pos = len(arg)-1
while pos >= 0 and (arg[pos] in "_[]" or arg[pos].isalpha() or arg[pos].isdigit()):
pos -= 1
if pos >= 0:
aname = arg[pos+1:].strip()
atype = arg[:pos+1].strip()
if aname.endswith("&") or aname.endswith("*") or (aname in ["int", "String", "Mat"]):
atype = (atype + " " + aname).strip()
aname = ""
else:
atype = arg
aname = ""
if aname.endswith("]"):
bidx = aname.find('[')
atype += aname[bidx:]
aname = aname[:bidx]
decl[3].append([atype, aname, defval, []])
if static_method:
decl[2].append("/S")
if virtual_method:
decl[2].append("/V")
if explicit_method:
decl[2].append("/E")
if bool(re.match(r".*\)\s*(const)?\s*=\s*0", decl_str)):
decl[2].append("/A")
if bool(re.match(r".*\)\s*const(\s*=\s*0)?", decl_str)):
decl[2].append("/C")
return decl
def parse_func_decl(self, decl_str, mat="Mat", docstring=""):
"""
Parses the function or method declaration in the form:
[([CV_EXPORTS] <rettype>) | CVAPI(rettype)]
[~]<function_name>
(<arg_type1> <arg_name1>[=<default_value1>] [, <arg_type2> <arg_name2>[=<default_value2>] ...])
[const] {; | <function_body>}
Returns the function declaration entry:
[<func name>, <return value C-type>, <list of modifiers>, <list of arguments>, <original return type>, <docstring>] (see above)
"""
if self.wrap_mode:
if not (("CV_EXPORTS_AS" in decl_str) or ("CV_EXPORTS_W" in decl_str) or ("CV_WRAP" in decl_str)):
return []
# ignore old API in the documentation check (for now)
if "CVAPI(" in decl_str and self.wrap_mode:
return []
top = self.block_stack[-1]
func_modlist = []
npos = decl_str.find("CV_EXPORTS_AS")
if npos >= 0:
arg, npos3 = self.get_macro_arg(decl_str, npos)
func_modlist.append("="+arg)
decl_str = decl_str[:npos] + decl_str[npos3+1:]
npos = decl_str.find("CV_WRAP_AS")
if npos >= 0:
arg, npos3 = self.get_macro_arg(decl_str, npos)
func_modlist.append("="+arg)
decl_str = decl_str[:npos] + decl_str[npos3+1:]
npos = decl_str.find("CV_WRAP_PHANTOM")
if npos >= 0:
decl_str, _ = self.get_macro_arg(decl_str, npos)
func_modlist.append("/phantom")
npos = decl_str.find("CV_WRAP_MAPPABLE")
if npos >= 0:
mappable, npos3 = self.get_macro_arg(decl_str, npos)
func_modlist.append("/mappable="+mappable)
classname = top[1]
return ['.'.join([classname, classname]), None, func_modlist, [], None, None]
virtual_method = False
pure_virtual_method = False
const_method = False
# filter off some common prefixes, which are meaningless for Python wrappers.
# note that we do not strip "static" prefix, which does matter;
# it means class methods, not instance methods
decl_str = self.batch_replace(decl_str, [("static inline", ""), ("inline", ""),\
("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""), ("CV_WRAP ", " "), ("CV_INLINE", ""),
("CV_DEPRECATED", ""), ("CV_DEPRECATED_EXTERNAL", "")]).strip()
if decl_str.strip().startswith('virtual'):
virtual_method = True
decl_str = decl_str.replace('virtual' , '')
end_tokens = decl_str[decl_str.rfind(')'):].split()
const_method = 'const' in end_tokens
pure_virtual_method = '=' in end_tokens and '0' in end_tokens
static_method = False
context = top[0]
if decl_str.startswith("static") and (context == "class" or context == "struct"):
decl_str = decl_str[len("static"):].lstrip()
static_method = True
args_begin = decl_str.find("(")
if decl_str.startswith("CVAPI"):
rtype_end = decl_str.find(")", args_begin+1)
if rtype_end < 0:
print("Error at %d. no terminating ) in CVAPI() macro: %s" % (self.lineno, decl_str))
sys.exit(-1)
decl_str = decl_str[args_begin+1:rtype_end] + " " + decl_str[rtype_end+1:]
args_begin = decl_str.find("(")
if args_begin < 0:
print("Error at %d: no args in '%s'" % (self.lineno, decl_str))
sys.exit(-1)
decl_start = decl_str[:args_begin].strip()
# handle operator () case
if decl_start.endswith("operator"):
args_begin = decl_str.find("(", args_begin+1)
if args_begin < 0:
print("Error at %d: no args in '%s'" % (self.lineno, decl_str))
sys.exit(-1)
decl_start = decl_str[:args_begin].strip()
# TODO: normalize all type of operators
if decl_start.endswith("()"):
decl_start = decl_start[0:-2].rstrip() + " ()"
# constructor/destructor case
if bool(re.match(r'^(\w+::)*(?P<x>\w+)::~?(?P=x)$', decl_start)):
decl_start = "void " + decl_start
rettype, funcname, modlist, argno = self.parse_arg(decl_start, -1)
# determine original return type, hack for return types with underscore
original_type = None
i = decl_start.rfind(funcname)
if i > 0:
original_type = decl_start[:i].replace("&", "").replace("const", "").strip()
if argno >= 0:
classname = top[1]
if rettype == classname or rettype == "~" + classname:
rettype, funcname = "", rettype
else:
if bool(re.match('\w+\s+\(\*\w+\)\s*\(.*\)', decl_str)):
return [] # function typedef
elif bool(re.match('\w+\s+\(\w+::\*\w+\)\s*\(.*\)', decl_str)):
return [] # class method typedef
elif bool(re.match('[A-Z_]+', decl_start)):
return [] # it seems to be a macro instantiation
elif "__declspec" == decl_start:
return []
elif bool(re.match(r'\w+\s+\(\*\w+\)\[\d+\]', decl_str)):
return [] # exotic - dynamic 2d array
else:
#print rettype, funcname, modlist, argno
print("Error at %s:%d the function/method name is missing: '%s'" % (self.hname, self.lineno, decl_start))
sys.exit(-1)
if self.wrap_mode and (("::" in funcname) or funcname.startswith("~")):
# if there is :: in function name (and this is in the header file),
# it means, this is inline implementation of a class method.
# Thus the function has been already declared within the class and we skip this repeated
# declaration.
# Also, skip the destructors, as they are always wrapped
return []
funcname = self.get_dotted_name(funcname)
if not self.wrap_mode:
decl = self.parse_func_decl_no_wrap(decl_str, static_method, docstring)
decl[0] = funcname
return decl
arg_start = args_begin+1
npos = arg_start-1
balance = 1
angle_balance = 0
# scan the argument list; handle nested parentheses
args_decls = []
args = []
argno = 1
while balance > 0:
npos += 1
t, npos = self.find_next_token(decl_str, ["(", ")", ",", "<", ">"], npos)
if not t:
print("Error: no closing ')' at %d" % (self.lineno,))
sys.exit(-1)
if t == "<":
angle_balance += 1
if t == ">":
angle_balance -= 1
if t == "(":
balance += 1
if t == ")":
balance -= 1
if (t == "," and balance == 1 and angle_balance == 0) or balance == 0:
# process next function argument
a = decl_str[arg_start:npos].strip()
#print "arg = ", a
arg_start = npos+1
if a:
eqpos = a.find("=")
defval = ""
modlist = []
if eqpos >= 0:
defval = a[eqpos+1:].strip()
else:
eqpos = a.find("CV_DEFAULT")
if eqpos >= 0:
defval, pos3 = self.get_macro_arg(a, eqpos)
else:
eqpos = a.find("CV_WRAP_DEFAULT")
if eqpos >= 0:
defval, pos3 = self.get_macro_arg(a, eqpos)
if defval == "NULL":
defval = "0"
if eqpos >= 0:
a = a[:eqpos].strip()
arg_type, arg_name, modlist, argno = self.parse_arg(a, argno)
if self.wrap_mode:
# TODO: Vectors should contain UMat, but this is not very easy to support and not very needed
vector_mat = "vector_{}".format("Mat")
vector_mat_template = "vector<{}>".format("Mat")
if arg_type == "InputArray":
arg_type = mat
elif arg_type == "InputOutputArray":
arg_type = mat
modlist.append("/IO")
elif arg_type == "OutputArray":
arg_type = mat
modlist.append("/O")
elif arg_type == "InputArrayOfArrays":
arg_type = vector_mat
elif arg_type == "InputOutputArrayOfArrays":
arg_type = vector_mat
modlist.append("/IO")
elif arg_type == "OutputArrayOfArrays":
arg_type = vector_mat
modlist.append("/O")
defval = self.batch_replace(defval, [("InputArrayOfArrays", vector_mat_template),
("InputOutputArrayOfArrays", vector_mat_template),
("OutputArrayOfArrays", vector_mat_template),
("InputArray", mat),
("InputOutputArray", mat),
("OutputArray", mat),
("noArray", arg_type)]).strip()
args.append([arg_type, arg_name, defval, modlist])
npos = arg_start-1
if static_method:
func_modlist.append("/S")
if const_method:
func_modlist.append("/C")
if virtual_method:
func_modlist.append("/V")
if pure_virtual_method:
func_modlist.append("/PV")
return [funcname, rettype, func_modlist, args, original_type, docstring]
def get_dotted_name(self, name):
"""
adds the dot-separated container class/namespace names to the bare function/class name, e.g. when we have
namespace cv {
class A {
public:
f(int);
};
}
the function will convert "A" to "cv.A" and "f" to "cv.A.f".
"""
if not self.block_stack:
return name
if name.startswith("cv."):
return name
qualified_name = (("." in name) or ("::" in name))
n = ""
for b in self.block_stack:
block_type, block_name = b[self.BLOCK_TYPE], b[self.BLOCK_NAME]
if block_type in ["file", "enum"]:
continue
if block_type in ["enum struct", "enum class"] and block_name == name:
continue
if block_type not in ["struct", "class", "namespace", "enum struct", "enum class"]:
print("Error at %d: there are non-valid entries in the current block stack %s" % (self.lineno, self.block_stack))
sys.exit(-1)
if block_name and (block_type == "namespace" or not qualified_name):
n += block_name + "."
n += name.replace("::", ".")
if n.endswith(".Algorithm"):
n = "cv.Algorithm"
return n
def parse_stmt(self, stmt, end_token, mat="Mat", docstring=""):
"""
parses the statement (ending with ';' or '}') or a block head (ending with '{')
The function calls parse_class_decl or parse_func_decl when necessary. It returns
<block_type>, <block_name>, <parse_flag>, <declaration>
where the first 3 values only make sense for blocks (i.e. code blocks, namespaces, classes, enums and such)
"""
stack_top = self.block_stack[-1]
context = stack_top[self.BLOCK_TYPE]
stmt_type = ""
if end_token == "{":
stmt_type = "block"
if context == "block":
print("Error at %d: should not call parse_stmt inside blocks" % (self.lineno,))
sys.exit(-1)
if context == "class" or context == "struct":
while 1:
colon_pos = stmt.find(":")
if colon_pos < 0:
break
w = stmt[:colon_pos].strip()
if w in ["public", "protected", "private"]:
if w == "public" or (not self.wrap_mode and w == "protected"):
stack_top[self.PUBLIC_SECTION] = True
else:
stack_top[self.PUBLIC_SECTION] = False
stmt = stmt[colon_pos+1:].strip()
break
# do not process hidden class members and template classes/functions
if not stack_top[self.PUBLIC_SECTION] or stmt.startswith("template"):
return stmt_type, "", False, None
if end_token == "{":
if not self.wrap_mode and stmt.startswith("typedef struct"):
stmt_type = "struct"
try:
classname, bases, modlist = self.parse_class_decl(stmt[len("typedef "):])
except:
print("Error at %s:%d" % (self.hname, self.lineno))
exit(1)
if classname.startswith("_Ipl"):
classname = classname[1:]
decl = [stmt_type + " " + self.get_dotted_name(classname), "", modlist, [], None, docstring]
if bases:
decl[1] = ": " + ", ".join([self.get_dotted_name(b).replace(".","::") for b in bases])
return stmt_type, classname, True, decl
if stmt.startswith("class") or stmt.startswith("struct"):
stmt_type = stmt.split()[0]
if stmt.strip() != stmt_type:
try:
classname, bases, modlist = self.parse_class_decl(stmt)
except:
print("Error at %s:%d" % (self.hname, self.lineno))
exit(1)
decl = []
if ("CV_EXPORTS_W" in stmt) or ("CV_EXPORTS_AS" in stmt) or (not self.wrap_mode):# and ("CV_EXPORTS" in stmt)):
decl = [stmt_type + " " + self.get_dotted_name(classname), "", modlist, [], None, docstring]
if bases:
decl[1] = ": " + ", ".join([self.get_dotted_name(b).replace(".","::") for b in bases])
return stmt_type, classname, True, decl
if stmt.startswith("enum") or stmt.startswith("namespace"):
stmt_list = stmt.rsplit(" ", 1)
if len(stmt_list) < 2:
stmt_list.append("<unnamed>")
return stmt_list[0], stmt_list[1], True, None
if stmt.startswith("extern") and "\"C\"" in stmt:
return "namespace", "", True, None
if end_token == "}" and context.startswith("enum"):
decl = self.parse_enum(stmt)
name = stack_top[self.BLOCK_NAME]
return context, name, False, decl
if end_token == ";" and stmt.startswith("typedef"):
# TODO: handle typedef's more intelligently
return stmt_type, "", False, None
paren_pos = stmt.find("(")
if paren_pos >= 0:
# assume it's function or method declaration,
# since we filtered off the other places where '(' can normally occur:
# - code blocks
# - function pointer typedef's
decl = self.parse_func_decl(stmt, mat=mat, docstring=docstring)
# we return parse_flag == False to prevent the parser to look inside function/method bodies
# (except for tracking the nested blocks)
return stmt_type, "", False, decl
if (context == "struct" or context == "class") and end_token == ";" and stmt:
# looks like it's member declaration; append the members to the class declaration
class_decl = stack_top[self.CLASS_DECL]
if ("CV_PROP" in stmt): # or (class_decl and ("/Map" in class_decl[2])):
var_modlist = []
if "CV_PROP_RW" in stmt:
var_modlist.append("/RW")
stmt = self.batch_replace(stmt, [("CV_PROP_RW", ""), ("CV_PROP", "")]).strip()
var_list = stmt.split(",")
var_type, var_name1, modlist, argno = self.parse_arg(var_list[0], -1)
var_list = [var_name1] + [i.strip() for i in var_list[1:]]
for v in var_list:
class_decl[3].append([var_type, v, "", var_modlist])
return stmt_type, "", False, None
# something unknown
return stmt_type, "", False, None
def find_next_token(self, s, tlist, p=0):
"""
Finds the next token from the 'tlist' in the input 's', starting from position 'p'.
Returns the first occurred token and its position, or ("", len(s)) when no token is found
"""
token = ""
tpos = len(s)
for t in tlist:
pos = s.find(t, p)
if pos < 0:
continue
if pos < tpos:
tpos = pos
token = t
return token, tpos
def parse(self, hname, wmode=True):
"""
The main method. Parses the input file.
Returns the list of declarations (that can be print using print_decls)
"""
self.hname = hname
decls = []
f = io.open(hname, 'rt', encoding='utf-8')
linelist = list(f.readlines())
f.close()
# states:
SCAN = 0 # outside of a comment or preprocessor directive
COMMENT = 1 # inside a multi-line comment
DIRECTIVE = 2 # inside a multi-line preprocessor directive
DOCSTRING = 3 # inside a multi-line docstring
DIRECTIVE_IF_0 = 4 # inside a '#if 0' directive
state = SCAN
self.block_stack = [["file", hname, True, True, None]]
block_head = ""
docstring = ""
self.lineno = 0
self.wrap_mode = wmode
depth_if_0 = 0
for l0 in linelist:
self.lineno += 1
#print(state, self.lineno, l0)
l = l0.strip()
if state == SCAN and l.startswith("#"):
state = DIRECTIVE
# fall through to the if state == DIRECTIVE check
if state == DIRECTIVE:
if l.endswith("\\"):
continue
state = SCAN
l = re.sub(r'//(.+)?', '', l).strip() # drop // comment
if l == '#if 0' or l == '#if defined(__OPENCV_BUILD)' or l == '#ifdef __OPENCV_BUILD':
state = DIRECTIVE_IF_0
depth_if_0 = 1
continue
if state == DIRECTIVE_IF_0:
if l.startswith('#'):
l = l[1:].strip()
if l.startswith("if"):
depth_if_0 += 1
continue
if l.startswith("endif"):
depth_if_0 -= 1
if depth_if_0 == 0:
state = SCAN
else:
# print('---- {:30s}:{:5d}: {}'.format(hname[-30:], self.lineno, l))
pass
continue
if state == COMMENT:
pos = l.find("*/")
if pos < 0:
continue
l = l[pos+2:]
state = SCAN
if state == DOCSTRING:
pos = l.find("*/")
if pos < 0:
docstring += l0
continue
docstring += l[:pos] + "\n"
l = l[pos+2:]
state = SCAN
if l.startswith('CV__') or l.startswith('__CV_'): # just ignore these lines
#print('IGNORE: ' + l)
state = SCAN
continue
if state != SCAN:
print("Error at %d: invalid state = %d" % (self.lineno, state))
sys.exit(-1)
while 1:
token, pos = self.find_next_token(l, [";", "\"", "{", "}", "//", "/*"])
if not token:
block_head += " " + l
block_head = block_head.strip()
if len(block_head) > 0 and block_head[-1] == ')' and block_head.startswith('CV_ENUM_FLAGS('):
l = ''
token = ';'
else:
break
if token == "//":
block_head += " " + l[:pos]
l = ''
continue
if token == "/*":
block_head += " " + l[:pos]
end_pos = l.find("*/", pos+2)
if len(l) > pos + 2 and l[pos+2] == "*":
# '/**', it's a docstring
if end_pos < 0:
state = DOCSTRING
docstring = l[pos+3:] + "\n"
break
else:
docstring = l[pos+3:end_pos]
elif end_pos < 0:
state = COMMENT
break
l = l[end_pos+2:]
continue
if token == "\"":
pos2 = pos + 1
while 1:
t2, pos2 = self.find_next_token(l, ["\\", "\""], pos2)
if t2 == "":
print("Error at %d: no terminating '\"'" % (self.lineno,))
sys.exit(-1)
if t2 == "\"":
break
pos2 += 2
block_head += " " + l[:pos2+1]
l = l[pos2+1:]
continue
stmt = (block_head + " " + l[:pos]).strip()
stmt = " ".join(stmt.split()) # normalize the statement
#print(stmt)
stack_top = self.block_stack[-1]
if stmt.startswith("@"):
# Objective C ?
break
decl = None
if stack_top[self.PROCESS_FLAG]:
# even if stack_top[PUBLIC_SECTION] is False, we still try to process the statement,
# since it can start with "public:"
docstring = docstring.strip()
stmt_type, name, parse_flag, decl = self.parse_stmt(stmt, token, docstring=docstring)
if decl:
if stmt_type.startswith("enum"):
decls.append([stmt_type + " " + self.get_dotted_name(name), "", [], decl, None, ""])
else:
decls.append(decl)
if self._generate_gpumat_decls and "cv.cuda" in decl[0]:
# If function takes as one of arguments Mat or vector<Mat> - we want to create the
# same declaration working with GpuMat
args = decl[3]
has_mat = len(list(filter(lambda x: x[0] in {"Mat", "vector_Mat"}, args))) > 0
if has_mat:
_, _, _, gpumat_decl = self.parse_stmt(stmt, token, mat="cuda::GpuMat", docstring=docstring)
decls.append(gpumat_decl)
if self._generate_umat_decls:
# If function takes as one of arguments Mat or vector<Mat> - we want to create the
# same declaration working with UMat (this is important for T-Api access)
args = decl[3]
has_mat = len(list(filter(lambda x: x[0] in {"Mat", "vector_Mat"}, args))) > 0
if has_mat:
_, _, _, umat_decl = self.parse_stmt(stmt, token, mat="UMat", docstring=docstring)
decls.append(umat_decl)
docstring = ""
if stmt_type == "namespace":
chunks = [block[1] for block in self.block_stack if block[0] == 'namespace'] + [name]
self.namespaces.add('.'.join(chunks))
else:
stmt_type, name, parse_flag = "block", "", False
if token == "{":
if stmt_type == "class":
public_section = False
else:
public_section = True
self.block_stack.append([stmt_type, name, parse_flag, public_section, decl])
if token == "}":
if not self.block_stack:
print("Error at %d: the block stack is empty" % (self.lineno,))
self.block_stack[-1:] = []
if pos+1 < len(l) and l[pos+1] == ';':
pos += 1
block_head = ""
l = l[pos+1:]
return decls
def print_decls(self, decls):
"""
Prints the list of declarations, retrieived by the parse() method
"""
for d in decls:
print(d[0], d[1], ";".join(d[2]))
# Uncomment below line to see docstrings
# print('"""\n' + d[5] + '\n"""')
for a in d[3]:
print(" ", a[0], a[1], a[2], end="")
if a[3]:
print("; ".join(a[3]))
else:
print()
if __name__ == '__main__':
parser = CppHeaderParser(generate_umat_decls=True, generate_gpumat_decls=True)
decls = []
for hname in opencv_hdr_list:
decls += parser.parse(hname)
#for hname in sys.argv[1:]:
#decls += parser.parse(hname, wmode=False)
parser.print_decls(decls)
print(len(decls))
print("namespaces:", " ".join(sorted(parser.namespaces)))
|
#!/usr/bin/env python
from __future__ import print_function
import hdr_parser, sys, re, os
from string import Template
from pprint import pprint
from collections import namedtuple
if sys.version_info[0] >= 3:
from io import StringIO
else:
from cStringIO import StringIO
forbidden_arg_types = ["void*"]
ignored_arg_types = ["RNG*"]
pass_by_val_types = ["Point*", "Point2f*", "Rect*", "String*", "double*", "float*", "int*"]
gen_template_check_self = Template("""
${cname} * self1 = 0;
if (!pyopencv_${name}_getp(self, self1))
return failmsgp("Incorrect type of self (must be '${name}' or its derivative)");
${pname} _self_ = ${cvt}(self1);
""")
gen_template_call_constructor_prelude = Template("""new (&(self->v)) Ptr<$cname>(); // init Ptr with placement new
if(self) """)
gen_template_call_constructor = Template("""self->v.reset(new ${cname}${args})""")
gen_template_simple_call_constructor_prelude = Template("""if(self) """)
gen_template_simple_call_constructor = Template("""new (&(self->v)) ${cname}${args}""")
gen_template_parse_args = Template("""const char* keywords[] = { $kw_list, NULL };
if( PyArg_ParseTupleAndKeywords(args, kw, "$fmtspec", (char**)keywords, $parse_arglist)$code_cvt )""")
gen_template_func_body = Template("""$code_decl
$code_parse
{
${code_prelude}ERRWRAP2($code_fcall);
$code_ret;
}
""")
gen_template_mappable = Template("""
{
${mappable} _src;
if (pyopencv_to(src, _src, info))
{
return cv_mappable_to(_src, dst);
}
}
""")
gen_template_type_decl = Template("""
// Converter (${name})
template<>
struct PyOpenCV_Converter< ${cname} >
{
static PyObject* from(const ${cname}& r)
{
return pyopencv_${name}_Instance(r);
}
static bool to(PyObject* src, ${cname}& dst, const ArgInfo& info)
{
if(!src || src == Py_None)
return true;
${cname} * dst_;
if (pyopencv_${name}_getp(src, dst_))
{
dst = *dst_;
return true;
}
${mappable_code}
failmsg("Expected ${cname} for argument '%s'", info.name);
return false;
}
};
""")
gen_template_map_type_cvt = Template("""
template<> bool pyopencv_to(PyObject* src, ${cname}& dst, const ArgInfo& info);
""")
gen_template_set_prop_from_map = Template("""
if( PyMapping_HasKeyString(src, (char*)"$propname") )
{
tmp = PyMapping_GetItemString(src, (char*)"$propname");
ok = tmp && pyopencv_to(tmp, dst.$propname, ArgInfo("$propname", false));
Py_DECREF(tmp);
if(!ok) return false;
}""")
gen_template_type_impl = Template("""
// GetSet (${name})
${getset_code}
// Methods (${name})
${methods_code}
// Tables (${name})
static PyGetSetDef pyopencv_${name}_getseters[] =
{${getset_inits}
{NULL} /* Sentinel */
};
static PyMethodDef pyopencv_${name}_methods[] =
{
${methods_inits}
{NULL, NULL}
};
""")
gen_template_get_prop = Template("""
static PyObject* pyopencv_${name}_get_${member}(pyopencv_${name}_t* p, void *closure)
{
return pyopencv_from(p->v${access}${member});
}
""")
gen_template_get_prop_algo = Template("""
static PyObject* pyopencv_${name}_get_${member}(pyopencv_${name}_t* p, void *closure)
{
$cname* _self_ = dynamic_cast<$cname*>(p->v.get());
if (!_self_)
return failmsgp("Incorrect type of object (must be '${name}' or its derivative)");
return pyopencv_from(_self_${access}${member});
}
""")
gen_template_set_prop = Template("""
static int pyopencv_${name}_set_${member}(pyopencv_${name}_t* p, PyObject *value, void *closure)
{
if (!value)
{
PyErr_SetString(PyExc_TypeError, "Cannot delete the ${member} attribute");
return -1;
}
return pyopencv_to(value, p->v${access}${member}, ArgInfo("value", false)) ? 0 : -1;
}
""")
gen_template_set_prop_algo = Template("""
static int pyopencv_${name}_set_${member}(pyopencv_${name}_t* p, PyObject *value, void *closure)
{
if (!value)
{
PyErr_SetString(PyExc_TypeError, "Cannot delete the ${member} attribute");
return -1;
}
$cname* _self_ = dynamic_cast<$cname*>(p->v.get());
if (!_self_)
{
failmsgp("Incorrect type of object (must be '${name}' or its derivative)");
return -1;
}
return pyopencv_to(value, _self_${access}${member}, ArgInfo("value", false)) ? 0 : -1;
}
""")
gen_template_prop_init = Template("""
{(char*)"${member}", (getter)pyopencv_${name}_get_${member}, NULL, (char*)"${member}", NULL},""")
gen_template_rw_prop_init = Template("""
{(char*)"${member}", (getter)pyopencv_${name}_get_${member}, (setter)pyopencv_${name}_set_${member}, (char*)"${member}", NULL},""")
class FormatStrings:
string = 's'
unsigned_char = 'b'
short_int = 'h'
int = 'i'
unsigned_int = 'I'
long = 'l'
unsigned_long = 'k'
long_long = 'L'
unsigned_long_long = 'K'
size_t = 'n'
float = 'f'
double = 'd'
object = 'O'
ArgTypeInfo = namedtuple('ArgTypeInfo',
['atype', 'format_str', 'default_value',
'strict_conversion'])
# strict_conversion is False by default
ArgTypeInfo.__new__.__defaults__ = (False,)
simple_argtype_mapping = {
"bool": ArgTypeInfo("bool", FormatStrings.unsigned_char, "0", True),
"size_t": ArgTypeInfo("size_t", FormatStrings.unsigned_long_long, "0", True),
"int": ArgTypeInfo("int", FormatStrings.int, "0", True),
"float": ArgTypeInfo("float", FormatStrings.float, "0.f", True),
"double": ArgTypeInfo("double", FormatStrings.double, "0", True),
"c_string": ArgTypeInfo("char*", FormatStrings.string, '(char*)""')
}
def normalize_class_name(name):
return re.sub(r"^cv\.", "", name).replace(".", "_")
def get_type_format_string(arg_type_info):
if arg_type_info.strict_conversion:
return FormatStrings.object
else:
return arg_type_info.format_str
class ClassProp(object):
def __init__(self, decl):
self.tp = decl[0].replace("*", "_ptr")
self.name = decl[1]
self.readonly = True
if "/RW" in decl[3]:
self.readonly = False
class ClassInfo(object):
def __init__(self, name, decl=None):
self.cname = name.replace(".", "::")
self.name = self.wname = normalize_class_name(name)
self.sname = name[name.rfind('.') + 1:]
self.ismap = False
self.issimple = False
self.isalgorithm = False
self.methods = {}
self.props = []
self.mappables = []
self.consts = {}
self.base = None
self.constructor = None
customname = False
if decl:
bases = decl[1].split()[1:]
if len(bases) > 1:
print("Note: Class %s has more than 1 base class (not supported by Python C extensions)" % (self.name,))
print(" Bases: ", " ".join(bases))
print(" Only the first base class will be used")
#return sys.exit(-1)
elif len(bases) == 1:
self.base = bases[0].strip(",")
if self.base.startswith("cv::"):
self.base = self.base[4:]
if self.base == "Algorithm":
self.isalgorithm = True
self.base = self.base.replace("::", "_")
for m in decl[2]:
if m.startswith("="):
self.wname = m[1:]
customname = True
elif m == "/Map":
self.ismap = True
elif m == "/Simple":
self.issimple = True
self.props = [ClassProp(p) for p in decl[3]]
if not customname and self.wname.startswith("Cv"):
self.wname = self.wname[2:]
def gen_map_code(self, codegen):
all_classes = codegen.classes
code = "static bool pyopencv_to(PyObject* src, %s& dst, const ArgInfo& info)\n{\n PyObject* tmp;\n bool ok;\n" % (self.cname)
code += "".join([gen_template_set_prop_from_map.substitute(propname=p.name,proptype=p.tp) for p in self.props])
if self.base:
code += "\n return pyopencv_to(src, (%s&)dst, info);\n}\n" % all_classes[self.base].cname
else:
code += "\n return true;\n}\n"
return code
def gen_code(self, codegen):
all_classes = codegen.classes
if self.ismap:
return self.gen_map_code(codegen)
getset_code = StringIO()
getset_inits = StringIO()
sorted_props = [(p.name, p) for p in self.props]
sorted_props.sort()
access_op = "->"
if self.issimple:
access_op = "."
for pname, p in sorted_props:
if self.isalgorithm:
getset_code.write(gen_template_get_prop_algo.substitute(name=self.name, cname=self.cname, member=pname, membertype=p.tp, access=access_op))
else:
getset_code.write(gen_template_get_prop.substitute(name=self.name, member=pname, membertype=p.tp, access=access_op))
if p.readonly:
getset_inits.write(gen_template_prop_init.substitute(name=self.name, member=pname))
else:
if self.isalgorithm:
getset_code.write(gen_template_set_prop_algo.substitute(name=self.name, cname=self.cname, member=pname, membertype=p.tp, access=access_op))
else:
getset_code.write(gen_template_set_prop.substitute(name=self.name, member=pname, membertype=p.tp, access=access_op))
getset_inits.write(gen_template_rw_prop_init.substitute(name=self.name, member=pname))
methods_code = StringIO()
methods_inits = StringIO()
sorted_methods = list(self.methods.items())
sorted_methods.sort()
if self.constructor is not None:
methods_code.write(self.constructor.gen_code(codegen))
for mname, m in sorted_methods:
methods_code.write(m.gen_code(codegen))
methods_inits.write(m.get_tab_entry())
code = gen_template_type_impl.substitute(name=self.name, wname=self.wname, cname=self.cname,
getset_code=getset_code.getvalue(), getset_inits=getset_inits.getvalue(),
methods_code=methods_code.getvalue(), methods_inits=methods_inits.getvalue())
return code
def gen_def(self, codegen):
all_classes = codegen.classes
baseptr = "NoBase"
if self.base and self.base in all_classes:
baseptr = all_classes[self.base].name
constructor_name = "0"
if self.constructor is not None:
constructor_name = self.constructor.get_wrapper_name()
return "CVPY_TYPE({}, {}, {}, {}, {});\n".format(
self.name,
self.cname if self.issimple else "Ptr<{}>".format(self.cname),
self.sname if self.issimple else "Ptr",
baseptr,
constructor_name
)
def handle_ptr(tp):
if tp.startswith('Ptr_'):
tp = 'Ptr<' + "::".join(tp.split('_')[1:]) + '>'
return tp
class ArgInfo(object):
def __init__(self, arg_tuple):
self.tp = handle_ptr(arg_tuple[0])
self.name = arg_tuple[1]
self.defval = arg_tuple[2]
self.isarray = False
self.arraylen = 0
self.arraycvt = None
self.inputarg = True
self.outputarg = False
self.returnarg = False
for m in arg_tuple[3]:
if m == "/O":
self.inputarg = False
self.outputarg = True
self.returnarg = True
elif m == "/IO":
self.inputarg = True
self.outputarg = True
self.returnarg = True
elif m.startswith("/A"):
self.isarray = True
self.arraylen = m[2:].strip()
elif m.startswith("/CA"):
self.isarray = True
self.arraycvt = m[2:].strip()
self.py_inputarg = False
self.py_outputarg = False
def isbig(self):
return self.tp in ["Mat", "vector_Mat", "cuda::GpuMat", "GpuMat", "vector_GpuMat", "UMat", "vector_UMat"] # or self.tp.startswith("vector")
def crepr(self):
return "ArgInfo(\"%s\", %d)" % (self.name, self.outputarg)
class FuncVariant(object):
def __init__(self, classname, name, decl, isconstructor, isphantom=False):
self.classname = classname
self.name = self.wname = name
self.isconstructor = isconstructor
self.isphantom = isphantom
self.docstring = decl[5]
self.rettype = decl[4] or handle_ptr(decl[1])
if self.rettype == "void":
self.rettype = ""
self.args = []
self.array_counters = {}
for a in decl[3]:
ainfo = ArgInfo(a)
if ainfo.isarray and not ainfo.arraycvt:
c = ainfo.arraylen
c_arrlist = self.array_counters.get(c, [])
if c_arrlist:
c_arrlist.append(ainfo.name)
else:
self.array_counters[c] = [ainfo.name]
self.args.append(ainfo)
self.init_pyproto()
def init_pyproto(self):
# string representation of argument list, with '[', ']' symbols denoting optional arguments, e.g.
# "src1, src2[, dst[, mask]]" for cv.add
argstr = ""
# list of all input arguments of the Python function, with the argument numbers:
# [("src1", 0), ("src2", 1), ("dst", 2), ("mask", 3)]
# we keep an argument number to find the respective argument quickly, because
# some of the arguments of C function may not present in the Python function (such as array counters)
# or even go in a different order ("heavy" output parameters of the C function
# become the first optional input parameters of the Python function, and thus they are placed right after
# non-optional input parameters)
arglist = []
# the list of "heavy" output parameters. Heavy parameters are the parameters
# that can be expensive to allocate each time, such as vectors and matrices (see isbig).
outarr_list = []
# the list of output parameters. Also includes input/output parameters.
outlist = []
firstoptarg = 1000000
argno = -1
for a in self.args:
argno += 1
if a.name in self.array_counters:
continue
assert not a.tp in forbidden_arg_types, 'Forbidden type "{}" for argument "{}" in "{}" ("{}")'.format(a.tp, a.name, self.name, self.classname)
if a.tp in ignored_arg_types:
continue
if a.returnarg:
outlist.append((a.name, argno))
if (not a.inputarg) and a.isbig():
outarr_list.append((a.name, argno))
continue
if not a.inputarg:
continue
if not a.defval:
arglist.append((a.name, argno))
else:
firstoptarg = min(firstoptarg, len(arglist))
# if there are some array output parameters before the first default parameter, they
# are added as optional parameters before the first optional parameter
if outarr_list:
arglist += outarr_list
outarr_list = []
arglist.append((a.name, argno))
if outarr_list:
firstoptarg = min(firstoptarg, len(arglist))
arglist += outarr_list
firstoptarg = min(firstoptarg, len(arglist))
noptargs = len(arglist) - firstoptarg
argnamelist = [aname for aname, argno in arglist]
argstr = ", ".join(argnamelist[:firstoptarg])
argstr = "[, ".join([argstr] + argnamelist[firstoptarg:])
argstr += "]" * noptargs
if self.rettype:
outlist = [("retval", -1)] + outlist
elif self.isconstructor:
assert outlist == []
outlist = [("self", -1)]
if self.isconstructor:
classname = self.classname
if classname.startswith("Cv"):
classname=classname[2:]
outstr = "<%s object>" % (classname,)
elif outlist:
outstr = ", ".join([o[0] for o in outlist])
else:
outstr = "None"
self.py_arg_str = argstr
self.py_return_str = outstr
self.py_prototype = "%s(%s) -> %s" % (self.wname, argstr, outstr)
self.py_noptargs = noptargs
self.py_arglist = arglist
for aname, argno in arglist:
self.args[argno].py_inputarg = True
for aname, argno in outlist:
if argno >= 0:
self.args[argno].py_outputarg = True
self.py_outlist = outlist
class FuncInfo(object):
def __init__(self, classname, name, cname, isconstructor, namespace, is_static):
self.classname = classname
self.name = name
self.cname = cname
self.isconstructor = isconstructor
self.namespace = namespace
self.is_static = is_static
self.variants = []
def add_variant(self, decl, isphantom=False):
self.variants.append(FuncVariant(self.classname, self.name, decl, self.isconstructor, isphantom))
def get_wrapper_name(self):
name = self.name
if self.classname:
classname = self.classname + "_"
if "[" in name:
name = "getelem"
else:
classname = ""
if self.is_static:
name += "_static"
return "pyopencv_" + self.namespace.replace('.','_') + '_' + classname + name
def get_wrapper_prototype(self, codegen):
full_fname = self.get_wrapper_name()
if self.isconstructor:
return "static int {fn_name}(pyopencv_{type_name}_t* self, PyObject* args, PyObject* kw)".format(
fn_name=full_fname, type_name=codegen.classes[self.classname].name)
if self.classname:
self_arg = "self"
else:
self_arg = ""
return "static PyObject* %s(PyObject* %s, PyObject* args, PyObject* kw)" % (full_fname, self_arg)
def get_tab_entry(self):
prototype_list = []
docstring_list = []
have_empty_constructor = False
for v in self.variants:
s = v.py_prototype
if (not v.py_arglist) and self.isconstructor:
have_empty_constructor = True
if s not in prototype_list:
prototype_list.append(s)
docstring_list.append(v.docstring)
# if there are just 2 constructors: default one and some other,
# we simplify the notation.
# Instead of ClassName(args ...) -> object or ClassName() -> object
# we write ClassName([args ...]) -> object
if have_empty_constructor and len(self.variants) == 2:
idx = self.variants[1].py_arglist != []
s = self.variants[idx].py_prototype
p1 = s.find("(")
p2 = s.rfind(")")
prototype_list = [s[:p1+1] + "[" + s[p1+1:p2] + "]" + s[p2:]]
# The final docstring will be: Each prototype, followed by
# their relevant doxygen comment
full_docstring = ""
for prototype, body in zip(prototype_list, docstring_list):
full_docstring += Template("$prototype\n$docstring\n\n\n\n").substitute(
prototype=prototype,
docstring='\n'.join(
['. ' + line
for line in body.split('\n')]
)
)
# Escape backslashes, newlines, and double quotes
full_docstring = full_docstring.strip().replace("\\", "\\\\").replace('\n', '\\n').replace("\"", "\\\"")
# Convert unicode chars to xml representation, but keep as string instead of bytes
full_docstring = full_docstring.encode('ascii', errors='xmlcharrefreplace').decode()
return Template(' {"$py_funcname", CV_PY_FN_WITH_KW_($wrap_funcname, $flags), "$py_docstring"},\n'
).substitute(py_funcname = self.variants[0].wname, wrap_funcname=self.get_wrapper_name(),
flags = 'METH_STATIC' if self.is_static else '0', py_docstring = full_docstring)
def gen_code(self, codegen):
all_classes = codegen.classes
proto = self.get_wrapper_prototype(codegen)
code = "%s\n{\n" % (proto,)
code += " using namespace %s;\n\n" % self.namespace.replace('.', '::')
selfinfo = None
ismethod = self.classname != "" and not self.isconstructor
# full name is needed for error diagnostic in PyArg_ParseTupleAndKeywords
fullname = self.name
if self.classname:
selfinfo = all_classes[self.classname]
if not self.isconstructor:
if not self.is_static:
code += gen_template_check_self.substitute(
name=selfinfo.name,
cname=selfinfo.cname if selfinfo.issimple else "Ptr<{}>".format(selfinfo.cname),
pname=(selfinfo.cname + '*') if selfinfo.issimple else "Ptr<{}>".format(selfinfo.cname),
cvt='' if selfinfo.issimple else '*'
)
fullname = selfinfo.wname + "." + fullname
all_code_variants = []
for v in self.variants:
code_decl = ""
code_ret = ""
code_cvt_list = []
code_args = "("
all_cargs = []
if v.isphantom and ismethod and not self.is_static:
code_args += "_self_"
# declare all the C function arguments,
# add necessary conversions from Python objects to code_cvt_list,
# form the function/method call,
# for the list of type mappings
for a in v.args:
if a.tp in ignored_arg_types:
defval = a.defval
if not defval and a.tp.endswith("*"):
defval = "0"
assert defval
if not code_args.endswith("("):
code_args += ", "
code_args += defval
all_cargs.append([[None, ""], ""])
continue
tp1 = tp = a.tp
amp = ""
defval0 = ""
if tp in pass_by_val_types:
tp = tp1 = tp[:-1]
amp = "&"
if tp.endswith("*"):
defval0 = "0"
tp1 = tp.replace("*", "_ptr")
tp_candidates = [a.tp, normalize_class_name(self.namespace + "." + a.tp)]
if any(tp in codegen.enums.keys() for tp in tp_candidates):
defval0 = "static_cast<%s>(%d)" % (a.tp, 0)
arg_type_info = simple_argtype_mapping.get(tp, ArgTypeInfo(tp, FormatStrings.object, defval0, True))
parse_name = a.name
if a.py_inputarg:
if arg_type_info.strict_conversion:
code_decl += " PyObject* pyobj_%s = NULL;\n" % (a.name,)
parse_name = "pyobj_" + a.name
if a.tp == 'char':
code_cvt_list.append("convert_to_char(pyobj_%s, &%s, %s)" % (a.name, a.name, a.crepr()))
else:
code_cvt_list.append("pyopencv_to(pyobj_%s, %s, %s)" % (a.name, a.name, a.crepr()))
all_cargs.append([arg_type_info, parse_name])
defval = a.defval
if not defval:
defval = arg_type_info.default_value
else:
if "UMat" in tp:
if "Mat" in defval and "UMat" not in defval:
defval = defval.replace("Mat", "UMat")
if "cuda::GpuMat" in tp:
if "Mat" in defval and "GpuMat" not in defval:
defval = defval.replace("Mat", "cuda::GpuMat")
# "tp arg = tp();" is equivalent to "tp arg;" in the case of complex types
if defval == tp + "()" and arg_type_info.format_str == FormatStrings.object:
defval = ""
if a.outputarg and not a.inputarg:
defval = ""
if defval:
code_decl += " %s %s=%s;\n" % (arg_type_info.atype, a.name, defval)
else:
code_decl += " %s %s;\n" % (arg_type_info.atype, a.name)
if not code_args.endswith("("):
code_args += ", "
code_args += amp + a.name
code_args += ")"
if self.isconstructor:
if selfinfo.issimple:
templ_prelude = gen_template_simple_call_constructor_prelude
templ = gen_template_simple_call_constructor
else:
templ_prelude = gen_template_call_constructor_prelude
templ = gen_template_call_constructor
code_prelude = templ_prelude.substitute(name=selfinfo.name, cname=selfinfo.cname)
code_fcall = templ.substitute(name=selfinfo.name, cname=selfinfo.cname, args=code_args)
if v.isphantom:
code_fcall = code_fcall.replace("new " + selfinfo.cname, self.cname.replace("::", "_"))
else:
code_prelude = ""
code_fcall = ""
if v.rettype:
code_decl += " " + v.rettype + " retval;\n"
code_fcall += "retval = "
if not v.isphantom and ismethod and not self.is_static:
code_fcall += "_self_->" + self.cname
else:
code_fcall += self.cname
code_fcall += code_args
if code_cvt_list:
code_cvt_list = [""] + code_cvt_list
# add info about return value, if any, to all_cargs. if there non-void return value,
# it is encoded in v.py_outlist as ("retval", -1) pair.
# As [-1] in Python accesses the last element of a list, we automatically handle the return value by
# adding the necessary info to the end of all_cargs list.
if v.rettype:
tp = v.rettype
tp1 = tp.replace("*", "_ptr")
default_info = ArgTypeInfo(tp, FormatStrings.object, "0")
arg_type_info = simple_argtype_mapping.get(tp, default_info)
all_cargs.append(arg_type_info)
if v.args and v.py_arglist:
# form the format spec for PyArg_ParseTupleAndKeywords
fmtspec = "".join([
get_type_format_string(all_cargs[argno][0])
for aname, argno in v.py_arglist
])
if v.py_noptargs > 0:
fmtspec = fmtspec[:-v.py_noptargs] + "|" + fmtspec[-v.py_noptargs:]
fmtspec += ":" + fullname
# form the argument parse code that:
# - declares the list of keyword parameters
# - calls PyArg_ParseTupleAndKeywords
# - converts complex arguments from PyObject's to native OpenCV types
code_parse = gen_template_parse_args.substitute(
kw_list = ", ".join(['"' + aname + '"' for aname, argno in v.py_arglist]),
fmtspec = fmtspec,
parse_arglist = ", ".join(["&" + all_cargs[argno][1] for aname, argno in v.py_arglist]),
code_cvt = " &&\n ".join(code_cvt_list))
else:
code_parse = "if(PyObject_Size(args) == 0 && (!kw || PyObject_Size(kw) == 0))"
if len(v.py_outlist) == 0:
code_ret = "Py_RETURN_NONE"
elif len(v.py_outlist) == 1:
if self.isconstructor:
code_ret = "return 0"
else:
aname, argno = v.py_outlist[0]
code_ret = "return pyopencv_from(%s)" % (aname,)
else:
# there is more than 1 return parameter; form the tuple out of them
fmtspec = "N"*len(v.py_outlist)
code_ret = "return Py_BuildValue(\"(%s)\", %s)" % \
(fmtspec, ", ".join(["pyopencv_from(" + aname + ")" for aname, argno in v.py_outlist]))
all_code_variants.append(gen_template_func_body.substitute(code_decl=code_decl,
code_parse=code_parse, code_prelude=code_prelude, code_fcall=code_fcall, code_ret=code_ret))
if len(all_code_variants)==1:
# if the function/method has only 1 signature, then just put it
code += all_code_variants[0]
else:
# try to execute each signature
code += " PyErr_Clear();\n\n".join([" {\n" + v + " }\n" for v in all_code_variants])
def_ret = "NULL"
if self.isconstructor:
def_ret = "-1"
code += "\n return %s;\n}\n\n" % def_ret
cname = self.cname
classinfo = None
#dump = False
#if dump: pprint(vars(self))
#if dump: pprint(vars(self.variants[0]))
if self.classname:
classinfo = all_classes[self.classname]
#if dump: pprint(vars(classinfo))
if self.isconstructor:
py_name = 'cv.' + classinfo.wname
elif self.is_static:
py_name = '.'.join([self.namespace, classinfo.sname + '_' + self.variants[0].wname])
else:
cname = classinfo.cname + '::' + cname
py_name = 'cv.' + classinfo.wname + '.' + self.variants[0].wname
else:
py_name = '.'.join([self.namespace, self.variants[0].wname])
#if dump: print(cname + " => " + py_name)
py_signatures = codegen.py_signatures.setdefault(cname, [])
for v in self.variants:
s = dict(name=py_name, arg=v.py_arg_str, ret=v.py_return_str)
for old in py_signatures:
if s == old:
break
else:
py_signatures.append(s)
return code
class Namespace(object):
def __init__(self):
self.funcs = {}
self.consts = {}
class PythonWrapperGenerator(object):
def __init__(self):
self.clear()
def clear(self):
self.classes = {}
self.namespaces = {}
self.consts = {}
self.enums = {}
self.code_include = StringIO()
self.code_enums = StringIO()
self.code_types = StringIO()
self.code_funcs = StringIO()
self.code_ns_reg = StringIO()
self.code_ns_init = StringIO()
self.code_type_publish = StringIO()
self.py_signatures = dict()
self.class_idx = 0
def add_class(self, stype, name, decl):
classinfo = ClassInfo(name, decl)
classinfo.decl_idx = self.class_idx
self.class_idx += 1
if classinfo.name in self.classes:
print("Generator error: class %s (cname=%s) already exists" \
% (classinfo.name, classinfo.cname))
sys.exit(-1)
self.classes[classinfo.name] = classinfo
# Add Class to json file.
namespace, classes, name = self.split_decl_name(name)
namespace = '.'.join(namespace)
name = '_'.join(classes+[name])
py_name = 'cv.' + classinfo.wname # use wrapper name
py_signatures = self.py_signatures.setdefault(classinfo.cname, [])
py_signatures.append(dict(name=py_name))
#print('class: ' + classinfo.cname + " => " + py_name)
def split_decl_name(self, name):
chunks = name.split('.')
namespace = chunks[:-1]
classes = []
while namespace and '.'.join(namespace) not in self.parser.namespaces:
classes.insert(0, namespace.pop())
return namespace, classes, chunks[-1]
def add_const(self, name, decl):
cname = name.replace('.','::')
namespace, classes, name = self.split_decl_name(name)
namespace = '.'.join(namespace)
name = '_'.join(classes+[name])
ns = self.namespaces.setdefault(namespace, Namespace())
if name in ns.consts:
print("Generator error: constant %s (cname=%s) already exists" \
% (name, cname))
sys.exit(-1)
ns.consts[name] = cname
value = decl[1]
py_name = '.'.join([namespace, name])
py_signatures = self.py_signatures.setdefault(cname, [])
py_signatures.append(dict(name=py_name, value=value))
#print(cname + ' => ' + str(py_name) + ' (value=' + value + ')')
def add_enum(self, name, decl):
wname = normalize_class_name(name)
if wname.endswith("<unnamed>"):
wname = None
else:
self.enums[wname] = name
const_decls = decl[3]
for decl in const_decls:
name = decl[0]
self.add_const(name.replace("const ", "").strip(), decl)
def add_func(self, decl):
namespace, classes, barename = self.split_decl_name(decl[0])
cname = "::".join(namespace+classes+[barename])
name = barename
classname = ''
bareclassname = ''
if classes:
classname = normalize_class_name('.'.join(namespace+classes))
bareclassname = classes[-1]
namespace = '.'.join(namespace)
isconstructor = name == bareclassname
is_static = False
isphantom = False
mappable = None
for m in decl[2]:
if m == "/S":
is_static = True
elif m == "/phantom":
isphantom = True
cname = cname.replace("::", "_")
elif m.startswith("="):
name = m[1:]
elif m.startswith("/mappable="):
mappable = m[10:]
self.classes[classname].mappables.append(mappable)
return
if isconstructor:
name = "_".join(classes[:-1]+[name])
if is_static:
# Add it as a method to the class
func_map = self.classes[classname].methods
func = func_map.setdefault(name, FuncInfo(classname, name, cname, isconstructor, namespace, is_static))
func.add_variant(decl, isphantom)
# Add it as global function
g_name = "_".join(classes+[name])
func_map = self.namespaces.setdefault(namespace, Namespace()).funcs
func = func_map.setdefault(g_name, FuncInfo("", g_name, cname, isconstructor, namespace, False))
func.add_variant(decl, isphantom)
else:
if classname and not isconstructor:
if not isphantom:
cname = barename
func_map = self.classes[classname].methods
else:
func_map = self.namespaces.setdefault(namespace, Namespace()).funcs
func = func_map.setdefault(name, FuncInfo(classname, name, cname, isconstructor, namespace, is_static))
func.add_variant(decl, isphantom)
if classname and isconstructor:
self.classes[classname].constructor = func
def gen_namespace(self, ns_name):
ns = self.namespaces[ns_name]
wname = normalize_class_name(ns_name)
self.code_ns_reg.write('static PyMethodDef methods_%s[] = {\n'%wname)
for name, func in sorted(ns.funcs.items()):
if func.isconstructor:
continue
self.code_ns_reg.write(func.get_tab_entry())
self.code_ns_reg.write(' {NULL, NULL}\n};\n\n')
self.code_ns_reg.write('static ConstDef consts_%s[] = {\n'%wname)
for name, cname in sorted(ns.consts.items()):
self.code_ns_reg.write(' {"%s", static_cast<long>(%s)},\n'%(name, cname))
compat_name = re.sub(r"([a-z])([A-Z])", r"\1_\2", name).upper()
if name != compat_name:
self.code_ns_reg.write(' {"%s", static_cast<long>(%s)},\n'%(compat_name, cname))
self.code_ns_reg.write(' {NULL, 0}\n};\n\n')
def gen_enum_reg(self, enum_name):
name_seg = enum_name.split(".")
is_enum_class = False
if len(name_seg) >= 2 and name_seg[-1] == name_seg[-2]:
enum_name = ".".join(name_seg[:-1])
is_enum_class = True
wname = normalize_class_name(enum_name)
cname = enum_name.replace(".", "::")
code = ""
if re.sub(r"^cv\.", "", enum_name) != wname:
code += "typedef {0} {1};\n".format(cname, wname)
code += "CV_PY_FROM_ENUM({0});\nCV_PY_TO_ENUM({0});\n\n".format(wname)
self.code_enums.write(code)
def save(self, path, name, buf):
with open(path + "/" + name, "wt") as f:
f.write(buf.getvalue())
def save_json(self, path, name, value):
import json
with open(path + "/" + name, "wt") as f:
json.dump(value, f)
def gen(self, srcfiles, output_path):
self.clear()
self.parser = hdr_parser.CppHeaderParser(generate_umat_decls=True, generate_gpumat_decls=True)
# step 1: scan the headers and build more descriptive maps of classes, consts, functions
for hdr in srcfiles:
decls = self.parser.parse(hdr)
if len(decls) == 0:
continue
if hdr.find('opencv2/') >= 0: #Avoid including the shadow files
self.code_include.write( '#include "{0}"\n'.format(hdr[hdr.rindex('opencv2/'):]) )
for decl in decls:
name = decl[0]
if name.startswith("struct") or name.startswith("class"):
# class/struct
p = name.find(" ")
stype = name[:p]
name = name[p+1:].strip()
self.add_class(stype, name, decl)
elif name.startswith("const"):
# constant
self.add_const(name.replace("const ", "").strip(), decl)
elif name.startswith("enum"):
# enum
self.add_enum(name.rsplit(" ", 1)[1], decl)
else:
# function
self.add_func(decl)
# step 1.5 check if all base classes exist
for name, classinfo in self.classes.items():
if classinfo.base:
chunks = classinfo.base.split('_')
base = '_'.join(chunks)
while base not in self.classes and len(chunks)>1:
del chunks[-2]
base = '_'.join(chunks)
if base not in self.classes:
print("Generator error: unable to resolve base %s for %s"
% (classinfo.base, classinfo.name))
sys.exit(-1)
base_instance = self.classes[base]
classinfo.base = base
classinfo.isalgorithm |= base_instance.isalgorithm # wrong processing of 'isalgorithm' flag:
# doesn't work for trees(graphs) with depth > 2
self.classes[name] = classinfo
# tree-based propagation of 'isalgorithm'
processed = dict()
def process_isalgorithm(classinfo):
if classinfo.isalgorithm or classinfo in processed:
return classinfo.isalgorithm
res = False
if classinfo.base:
res = process_isalgorithm(self.classes[classinfo.base])
#assert not (res == True or classinfo.isalgorithm is False), "Internal error: " + classinfo.name + " => " + classinfo.base
classinfo.isalgorithm |= res
res = classinfo.isalgorithm
processed[classinfo] = True
return res
for name, classinfo in self.classes.items():
process_isalgorithm(classinfo)
# step 2: generate code for the classes and their methods
classlist = list(self.classes.items())
classlist.sort()
for name, classinfo in classlist:
self.code_types.write("//{}\n".format(80*"="))
self.code_types.write("// {} ({})\n".format(name, 'Map' if classinfo.ismap else 'Generic'))
self.code_types.write("//{}\n".format(80*"="))
self.code_types.write(classinfo.gen_code(self))
if classinfo.ismap:
self.code_types.write(gen_template_map_type_cvt.substitute(name=classinfo.name, cname=classinfo.cname))
else:
mappable_code = "\n".join([
gen_template_mappable.substitute(cname=classinfo.cname, mappable=mappable)
for mappable in classinfo.mappables])
code = gen_template_type_decl.substitute(
name=classinfo.name,
cname=classinfo.cname if classinfo.issimple else "Ptr<{}>".format(classinfo.cname),
mappable_code=mappable_code
)
self.code_types.write(code)
# register classes in the same order as they have been declared.
# this way, base classes will be registered in Python before their derivatives.
classlist1 = [(classinfo.decl_idx, name, classinfo) for name, classinfo in classlist]
classlist1.sort()
for decl_idx, name, classinfo in classlist1:
if classinfo.ismap:
continue
self.code_type_publish.write(classinfo.gen_def(self))
# step 3: generate the code for all the global functions
for ns_name, ns in sorted(self.namespaces.items()):
if ns_name.split('.')[0] != 'cv':
continue
for name, func in sorted(ns.funcs.items()):
if func.isconstructor:
continue
code = func.gen_code(self)
self.code_funcs.write(code)
self.gen_namespace(ns_name)
self.code_ns_init.write('CVPY_MODULE("{}", {});\n'.format(ns_name[2:], normalize_class_name(ns_name)))
# step 4: generate the code for enum types
enumlist = list(self.enums.values())
enumlist.sort()
for name in enumlist:
self.gen_enum_reg(name)
# step 5: generate the code for constants
constlist = list(self.consts.items())
constlist.sort()
for name, constinfo in constlist:
self.gen_const_reg(constinfo)
# That's it. Now save all the files
self.save(output_path, "pyopencv_generated_include.h", self.code_include)
self.save(output_path, "pyopencv_generated_funcs.h", self.code_funcs)
self.save(output_path, "pyopencv_generated_enums.h", self.code_enums)
self.save(output_path, "pyopencv_generated_types.h", self.code_type_publish)
self.save(output_path, "pyopencv_generated_types_content.h", self.code_types)
self.save(output_path, "pyopencv_generated_modules.h", self.code_ns_init)
self.save(output_path, "pyopencv_generated_modules_content.h", self.code_ns_reg)
self.save_json(output_path, "pyopencv_signatures.json", self.py_signatures)
if __name__ == "__main__":
srcfiles = hdr_parser.opencv_hdr_list
dstdir = "/Users/vp/tmp"
if len(sys.argv) > 1:
dstdir = sys.argv[1]
if len(sys.argv) > 2:
with open(sys.argv[2], 'r') as f:
srcfiles = [l.strip() for l in f.readlines()]
generator = PythonWrapperGenerator()
generator.gen(srcfiles, dstdir)
|
import os
import sys
import platform
import setuptools
SCRIPT_DIR=os.path.dirname(os.path.abspath(__file__))
def main():
os.chdir(SCRIPT_DIR)
package_name = 'opencv'
package_version = os.environ.get('OPENCV_VERSION', '4.2.0') # TODO
long_description = 'Open Source Computer Vision Library Python bindings' # TODO
setuptools.setup(
name=package_name,
version=package_version,
url='https://github.com/opencv/opencv',
license='BSD',
description='OpenCV python bindings',
long_description=long_description,
long_description_content_type="text/markdown",
packages=setuptools.find_packages(),
maintainer="OpenCV Team",
install_requires="numpy",
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Information Technology',
'Intended Audience :: Science/Research',
'License :: BSD License',
'Operating System :: MacOS',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Operating System :: Unix',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: C++',
'Programming Language :: Python :: Implementation :: CPython',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Image Recognition',
'Topic :: Software Development',
'Topic :: Software Development :: Libraries',
],
)
if __name__ == '__main__':
main()
|
'''
OpenCV Python binary extension loader
'''
import os
import sys
try:
import numpy
import numpy.core.multiarray
except ImportError:
print('OpenCV bindings requires "numpy" package.')
print('Install it via command:')
print(' pip install numpy')
raise
# TODO
# is_x64 = sys.maxsize > 2**32
def bootstrap():
import sys
if hasattr(sys, 'OpenCV_LOADER'):
print(sys.path)
raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.')
sys.OpenCV_LOADER = True
DEBUG = False
if hasattr(sys, 'OpenCV_LOADER_DEBUG'):
DEBUG = True
import platform
if DEBUG: print('OpenCV loader: os.name="{}" platform.system()="{}"'.format(os.name, str(platform.system())))
LOADER_DIR=os.path.dirname(os.path.abspath(__file__))
PYTHON_EXTENSIONS_PATHS = []
BINARIES_PATHS = []
g_vars = globals()
l_vars = locals()
if sys.version_info[:2] < (3, 0):
from . load_config_py2 import exec_file_wrapper
else:
from . load_config_py3 import exec_file_wrapper
def load_first_config(fnames, required=True):
for fname in fnames:
fpath = os.path.join(LOADER_DIR, fname)
if not os.path.exists(fpath):
if DEBUG: print('OpenCV loader: config not found, skip: {}'.format(fpath))
continue
if DEBUG: print('OpenCV loader: loading config: {}'.format(fpath))
exec_file_wrapper(fpath, g_vars, l_vars)
return True
if required:
raise ImportError('OpenCV loader: missing configuration file: {}. Check OpenCV installation.'.format(fnames))
load_first_config(['config.py'], True)
load_first_config([
'config-{}.{}.py'.format(sys.version_info[0], sys.version_info[1]),
'config-{}.py'.format(sys.version_info[0])
], True)
if DEBUG: print('OpenCV loader: PYTHON_EXTENSIONS_PATHS={}'.format(str(l_vars['PYTHON_EXTENSIONS_PATHS'])))
if DEBUG: print('OpenCV loader: BINARIES_PATHS={}'.format(str(l_vars['BINARIES_PATHS'])))
for p in reversed(l_vars['PYTHON_EXTENSIONS_PATHS']):
sys.path.insert(1, p)
if os.name == 'nt':
if sys.version_info[:2] >= (3, 8): # https://github.com/python/cpython/pull/12302
for p in l_vars['BINARIES_PATHS']:
try:
os.add_dll_directory(p)
except Exception as e:
if DEBUG: print('Failed os.add_dll_directory(): '+ str(e))
pass
os.environ['PATH'] = ';'.join(l_vars['BINARIES_PATHS']) + ';' + os.environ.get('PATH', '')
if DEBUG: print('OpenCV loader: PATH={}'.format(str(os.environ['PATH'])))
else:
# amending of LD_LIBRARY_PATH works for sub-processes only
os.environ['LD_LIBRARY_PATH'] = ':'.join(l_vars['BINARIES_PATHS']) + ':' + os.environ.get('LD_LIBRARY_PATH', '')
if DEBUG: print('OpenCV loader: replacing cv2 module')
del sys.modules['cv2']
import cv2
try:
import sys
del sys.OpenCV_LOADER
except:
pass
if DEBUG: print('OpenCV loader: DONE')
bootstrap()
|
# flake8: noqa
import os
import sys
if sys.version_info[:2] >= (3, 0):
def exec_file_wrapper(fpath, g_vars, l_vars):
with open(fpath) as f:
code = compile(f.read(), os.path.basename(fpath), 'exec')
exec(code, g_vars, l_vars)
|
# flake8: noqa
import sys
if sys.version_info[:2] < (3, 0):
def exec_file_wrapper(fpath, g_vars, l_vars):
execfile(fpath, g_vars, l_vars)
|
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