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)
def updateImage():
imgCopy = cv2.GaussianBlur(img, (cvparam_dict['kernelWidth'].value,
cvparam_dict['kernelHeight'].value),
cvparam_dict['sigmaY'].value)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
def updateImage():
d = cvparam_dict['filterSize'].value
sigmaColor = cvparam_dict['colorKernelSize'].value
sigmaSpace = cvparam_dict['spaceKernelSize'].value
imgCopy = cv2.bilateralFilter(img, d=d, sigmaColor=sigmaColor, sigmaSpace=sigmaSpace)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
def updateImage():
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)
def updateImage():
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)
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)
def updateImage():
d = cvparam_dict['filterSize'].value
sigmaColor = cvparam_dict['colorKernelSize'].value
sigmaSpace = cvparam_dict['spaceKernelSize'].value
imgCopy = cv2.bilateralFilter(img,
d=d,
sigmaColor=sigmaColor,
sigmaSpace=sigmaSpace)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
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)
def updateImage():
theta = np.pi / 180
rho = 1
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)
def updateImage():
theta = np.pi/180
rho = 1
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)
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)
def updateImage():
w = cvparam_dict['kernelSize'].value
imgCopy = cv2.medianBlur(img, w)
cvparams.annotateImageWithParams(cvparam_dict, imgCopy)
cv2.imshow('results', imgCopy)
