shape = cv2.MORPH_RECT
else:
shape = cv2.MORPH_CROSS
st = cv2.getStructuringElement(shape, (size, size))
image = cv2.morphologyEx(img,
cv2.MORPH_GRADIENT,
kernel=st,
iterations=iter)
cv2.imshow('results', image)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['iterations'] = cvparam.sliderparam("Iterations", 5, 1,
updateImage)
cvparam_dict['shape'] = cvparam.sliderparam("K Ellipse/Rect/Cross", 2, 1,
updateImage)
cvparam_dict['size'] = cvparam.kernelparam("K Size", 21, 5, updateImage)
import argparse
parser = argparse.ArgumentParser(
description=
"Interactively experiment with morphology gradient parameters.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
minLength = cvparam_dict["minLength"].value
maxGapLength = cvparam_dict["maxGapLength"].value
lines = cv2.HoughLinesP(edges, rho, theta, threshold=threshold, minLineLength=minLength, maxLineGap=maxGapLength)
imag = img.copy()
for x1, y1, x2, y2 in lines[0]:
cv2.line(imag, (x1, y1), (x2, y2), (0, 255, 0), 2)
cvparams.annotateImageWithParams(cvparam_dict, imag)
cv2.imshow("results", imag)
if __name__ == "__main__":
print __doc__
cvparam_dict = {}
cvparam_dict["threshold"] = cvparams.sliderparam("Threshold", 500, 50, updateImage)
cvparam_dict["minLength"] = cvparams.sliderparam("Min Length", 500, 100, updateImage)
cvparam_dict["maxGapLength"] = cvparams.sliderparam("Max Gap", 500, 20, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Interactively experiment with HoughLinesP parameters.")
parser.add_argument("file", help="image to threshold", type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
'invert'].value > 0 else cv2.THRESH_BINARY
image = cv2.adaptiveThreshold(img,
maxValue=255,
adaptiveMethod=threshMethod,
thresholdType=threshType,
blockSize=blockSize,
C=c)
cv2.imshow('results', image)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['useGaussian'] = cvparam.sliderparam("Use Gaussian", 1, 1,
updateImage)
cvparam_dict['invert'] = cvparam.sliderparam("Invert", 1, 0, updateImage)
cvparam_dict['c'] = cvparam.sliderparam("C", 30, 5, updateImage)
cvparam_dict['blockSize'] = cvparam.kernelparam("Block Size (3,5,..)", 21,
5, updateImage)
import argparse
parser = argparse.ArgumentParser(
description=
"Interactively experiment with adaptiveThreshold parameters.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
for x1, y1, x2, y2 in lines:
cv2.line(img, (x1, y1), (x2, y2), color, 2)
def updateImage():
copy = img.copy()
draw_lines(copy, lines)
draw_lines(copy, vert_lines, color=(255, 127, 0))
cv2.imshow('results', copy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['maxDegFromVert'] = cvparam.sliderparam(
"Deg From Vert", 90, 10, updateMaxVertAngle)
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('file', help='image(s) to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
img = cv2.imread(filename)
# extract lines
def draw_lines(img, lines, color=(0,255,0)):
for x1,y1,x2,y2 in lines:
cv2.line(img,(x1,y1),(x2,y2),color,2)
def updateImage():
copy = img.copy()
draw_lines(copy, lines)
draw_lines(copy, vert_lines, color=(255,127,0))
cv2.imshow('results', copy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['maxDegFromVert'] = cvparam.sliderparam("Deg From Vert", 90, 10, updateMaxVertAngle)
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('file', help='image(s) to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
img = cv2.imread(filename)
# extract lines
cvparam_dict['threshold2'].value,
apertureSize=cvparam_dict['aperture'].value)
imag = img.copy()
imag /= 2
imag[edges != 0] = (0, 255, 0)
cvparams.annotateImageWithParams(cvparam_dict, imag)
cv2.imshow('results', imag)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['threshold1'] = cvparams.sliderparam("Threshold1", 2000, 500,
updateImage)
cvparam_dict['threshold2'] = cvparams.sliderparam("Threshold2", 2000, 2000,
updateImage)
cvparam_dict['aperture'] = cvparams.kernelparam("Aperture (3, 5, 7)", 7, 5,
updateImage)
import argparse
parser = argparse.ArgumentParser(
description="Interactively experiment with Canny operator.")
parser.add_argument('file',
help='image to threshold (wildcard ok)',
type=str)
parser.add_argument('--blur',
help='blur preprocessing',
action='store_true')
# add a command line parameter for every slider parameter
cvparams.annotateImageWithParams(cvparam_dict, copy)
cv2.imshow('results', copy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['filterSize'] = cvparams.kernelparam("Filter Size", 100, 11,
updateImage)
cvparam_dict['spaceKernelSize'] = cvparams.kernelparam(
"Space Kernel Size", 150, 109, updateImage)
cvparam_dict['colorKernelSize'] = cvparams.kernelparam(
"Color Kernel Size", 150, 109, updateImage)
cvparam_dict['erode'] = cvparams.sliderparam("Erode", 2, 0, updateImage)
cvparam_dict['dilate'] = cvparams.sliderparam("Dilate", 2, 0, updateImage)
cvparam_dict['threshK'] = cvparams.kernelparam("ThreshK", 21, 11,
updateImage)
cvparam_dict['threshC'] = cvparams.sliderparam("ThreshC", 20, 10,
updateImage)
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('file', help='image(s) to process', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
copy = img.copy()
squares = find_contours(copy)
cv2.drawContours(copy, squares, -1, (0, 255, 0), 1 )
cvparams.annotateImageWithParams(cvparam_dict, copy)
cv2.imshow('results', copy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['filterSize'] = cvparams.kernelparam("Filter Size", 100, 11, updateImage)
cvparam_dict['spaceKernelSize'] = cvparams.kernelparam("Space Kernel Size", 150, 109, updateImage)
cvparam_dict['colorKernelSize'] = cvparams.kernelparam("Color Kernel Size", 150, 109, updateImage)
cvparam_dict['erode'] = cvparams.sliderparam("Erode", 2, 0, updateImage)
cvparam_dict['dilate'] = cvparams.sliderparam("Dilate", 2, 0, updateImage)
cvparam_dict['threshK'] = cvparams.kernelparam("ThreshK", 21, 11, updateImage)
cvparam_dict['threshC'] = cvparams.sliderparam("ThreshC", 20, 10, updateImage)
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('file', help='image(s) to process', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
def updateImage():
c = cvparam_dict['c'].value
blockSize = cvparam_dict['blockSize'].value
threshMethod = cv2.ADAPTIVE_THRESH_GAUSSIAN_C if cvparam_dict['useGaussian'].value > 0 else cv2.ADAPTIVE_THRESH_MEAN_C
threshType = cv2.THRESH_BINARY_INV if cvparam_dict['invert'].value > 0 else cv2.THRESH_BINARY
image = cv2.adaptiveThreshold(img, maxValue=255, adaptiveMethod=threshMethod, thresholdType=threshType, blockSize=blockSize, C=c)
cv2.imshow('results', image)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['useGaussian'] = cvparam.sliderparam("Use Gaussian", 1, 1, updateImage)
cvparam_dict['invert'] = cvparam.sliderparam("Invert", 1, 0, updateImage)
cvparam_dict['c'] = cvparam.sliderparam("C", 30, 5, updateImage)
cvparam_dict['blockSize'] = cvparam.kernelparam("Block Size (3,5,..)", 21, 5, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Interactively experiment with adaptiveThreshold parameters.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
import sliderparam as cvparams
def updateImage():
destDepth = cv2.CV_32F; # should be higher than source depth to avoid overflow
imgCopy = cv2.Sobel(img, ddepth=destDepth, dx=cvparam_dict['dx'].value, dy=cvparam_dict['dy'].value, ksize=cvparam_dict['kernelSize'].value,
scale=cvparam_dict['scale'].value, delta=cvparam_dict['delta'].value)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['dx'] = cvparams.sliderparam("dx", 2, 1, updateImage)
cvparam_dict['dy'] = cvparams.sliderparam("dy", 2, 1, updateImage)
cvparam_dict['kernelSize'] = cvparams.kernelparam("Kernel (3, 5,..)", 21, 5, updateImage)
cvparam_dict['delta'] = cvparams.sliderparam("delta", 5, 0, updateImage)
cvparam_dict['scale'] = cvparams.sliderparam("scale", 2, 1, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Apply Sobel operator.")
parser.add_argument('file', help='image to process', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
if cvparam_dict['shape'].value == 0:
shape = cv2.MORPH_ELLIPSE
elif cvparam_dict['shape'].value == 1:
shape = cv2.MORPH_RECT
else:
shape = cv2.MORPH_CROSS
st = cv2.getStructuringElement(shape, (size, size))
image = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel=st, iterations=iter)
cv2.imshow('results', image)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['iterations'] = cvparam.sliderparam("Iterations", 5, 1, updateImage)
cvparam_dict['shape'] = cvparam.sliderparam("K Ellipse/Rect/Cross", 2, 1, updateImage)
cvparam_dict['size'] = cvparam.kernelparam("K Size", 21, 5, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Interactively experiment with morphology gradient parameters.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparam.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparam.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
img = cv2.imread(filename)
cvparam_dict['kernelHeight'].value),
cvparam_dict['sigmaY'].value)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['kernelWidth'] = cvparams.kernelparam("Kernel X (3,5,..)", 51,
5, updateImage)
cvparam_dict['kernelHeight'] = cvparams.kernelparam(
"Kernel Y (3,5,..)", 51, 5, updateImage)
cvparam_dict['sigmaY'] = cvparams.sliderparam("Sigma Y", 7, 0, updateImage)
import argparse
parser = argparse.ArgumentParser(
description="Interactively experiment with blur operator.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
img = cv2.imread(filename)
assert img is not None, "File " + args.file + " was not found."
def updateImage():
edges = cv2.Canny(gray, cvparam_dict['threshold1'].value, cvparam_dict['threshold2'].value, apertureSize=cvparam_dict['aperture'].value)
imag = img.copy()
imag /= 2
imag[edges != 0] = (0, 255, 0)
cvparams.annotateImageWithParams(cvparam_dict, imag)
cv2.imshow('results', imag)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['threshold1'] = cvparams.sliderparam("Threshold1", 2000, 500, updateImage)
cvparam_dict['threshold2'] = cvparams.sliderparam("Threshold2", 2000, 2000, updateImage)
cvparam_dict['aperture'] = cvparams.kernelparam("Aperture (3, 5, 7)", 7, 5, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Interactively experiment with Canny operator.")
parser.add_argument('file', help='image to threshold (wildcard ok)', type=str)
parser.add_argument('--blur', help='blur preprocessing', action='store_true')
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
threshold = cvparam_dict['threshold'].value
minLength = cvparam_dict['minLength'].value
maxGapLength = cvparam_dict['maxGapLength'].value
lines = cv2.HoughLinesP(edges, rho, theta, threshold=threshold, minLineLength=minLength, maxLineGap=maxGapLength)
imag = img.copy()
for x1,y1,x2,y2 in lines[0]:
cv2.line(imag,(x1,y1),(x2,y2),(0,255,0),2)
cvparams.annotateImageWithParams(cvparam_dict, imag)
cv2.imshow('results', imag)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['threshold'] = cvparams.sliderparam("Threshold", 500, 50, updateImage)
cvparam_dict['minLength'] = cvparams.sliderparam("Min Length", 500, 100, updateImage)
cvparam_dict['maxGapLength'] = cvparams.sliderparam("Max Gap", 500, 20, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Interactively experiment with HoughLinesP parameters.")
parser.add_argument('file', help='image to threshold', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)
from glob import glob
for filename in glob(args.file):
img = get_image(filename)
ddepth=destDepth,
dx=cvparam_dict['dx'].value,
dy=cvparam_dict['dy'].value,
ksize=cvparam_dict['kernelSize'].value,
scale=cvparam_dict['scale'].value,
delta=cvparam_dict['delta'].value)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
if __name__ == '__main__':
print __doc__
cvparam_dict = {}
cvparam_dict['dx'] = cvparams.sliderparam("dx", 2, 1, updateImage)
cvparam_dict['dy'] = cvparams.sliderparam("dy", 2, 1, updateImage)
cvparam_dict['kernelSize'] = cvparams.kernelparam("Kernel (3, 5,..)", 21,
5, updateImage)
cvparam_dict['delta'] = cvparams.sliderparam("delta", 5, 0, updateImage)
cvparam_dict['scale'] = cvparams.sliderparam("scale", 2, 1, updateImage)
import argparse
parser = argparse.ArgumentParser(description="Apply Sobel operator.")
parser.add_argument('file', help='image to process', type=str)
# add a command line parameter for every slider parameter
cvparams.addCommandLineArgs(cvparam_dict, parser)
args = parser.parse_args()
cvparams.setValuesFromCommandLine(cvparam_dict, args)