def hls_unsharp_mask(image, amount, radius, threshold):
sigma = 0.2
copy = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
(h, l, s) = cv2.split(copy)
blurred = filters.gaussian_blur(l, 2 * radius + 1, sigma)
mask = cv2.absdiff(blurred, l)
print 255.0 / mask.max()
mask = np.array(mask * (255.0 / mask.max()), dtype="uint8")
(dummy, mask) = cv2.threshold(mask, threshold, 255, cv2.THRESH_TOZERO)
cv2.imshow("mask", mask)
cv2.imshow("l before", l)
corrected = filters.simplest_color_balance(l, 10, 10)
# corrected = l * (1 + amount) - blurred * amount
cv2.imshow("corrected", corrected)
l = blend(l, corrected, mask)
cv2.imshow("l after", l)
copy = cv2.merge((h, l, s))
result = cv2.cvtColor(copy, cv2.COLOR_HLS2RGB)
return result
def hls_unsharp_mask(image, amount, radius, threshold):
sigma = 0.2
copy = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
(h, l, s) = cv2.split(copy)
blurred = filters.gaussian_blur(l, 2 * radius + 1, sigma)
mask = cv2.absdiff(blurred, l)
print 255.0 / mask.max()
mask = np.array(mask * (255.0 / mask.max()), dtype='uint8')
(dummy, mask) = cv2.threshold(mask, threshold, 255, cv2.THRESH_TOZERO)
cv2.imshow('mask', mask)
cv2.imshow('l before', l)
corrected = filters.simplest_color_balance(l, 10, 10)
#corrected = l * (1 + amount) - blurred * amount
cv2.imshow('corrected', corrected)
l = blend(l, corrected, mask)
cv2.imshow('l after', l)
copy = cv2.merge((h, l, s))
result = cv2.cvtColor(copy, cv2.COLOR_HLS2RGB)
return result
def unsharp_mask(image, amount, radius, threshold):
sigma = 0.15
copy = image.copy()
cv2.imshow("copy", copy)
converted = cv2.cvtColor(copy, cv2.COLOR_RGB2GRAY)
blurred = filters.gaussian_blur(converted, 2 * radius + 1, sigma)
cv2.imshow("blurred", blurred)
# blurred = cv2.blur(converted, tuple([2*radius + 1]*2))
mask = cv2.absdiff(blurred, converted)
mask = np.array(mask * (255.0 / mask.max()), dtype="uint8")
(dummy, mask) = cv2.threshold(mask, threshold, 255, cv2.THRESH_TOZERO)
cv2.imshow("mask", mask)
corrected = filters.simplest_color_balance(copy, 15, 15, mask)
# corrected = image.copy() * 0
print corrected
# cv2.imshow('corrected', corrected)
result = blend(copy, corrected, mask)
# result = cv2.resize(image, (width, height))
return result
def unsharp_mask(image, amount, radius, threshold):
sigma = 0.15
copy = image.copy()
cv2.imshow('copy', copy)
converted = cv2.cvtColor(copy, cv2.COLOR_RGB2GRAY)
blurred = filters.gaussian_blur(converted, 2 * radius + 1, sigma)
cv2.imshow('blurred', blurred)
#blurred = cv2.blur(converted, tuple([2*radius + 1]*2))
mask = cv2.absdiff(blurred, converted)
mask = np.array(mask * (255.0 / mask.max()), dtype='uint8')
(dummy, mask) = cv2.threshold(mask, threshold, 255, cv2.THRESH_TOZERO)
cv2.imshow('mask', mask)
corrected = filters.simplest_color_balance(copy, 15, 15, mask)
#corrected = image.copy() * 0
print corrected
#cv2.imshow('corrected', corrected)
result = blend(copy, corrected, mask)
#result = cv2.resize(image, (width, height))
return result
