def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
edges = image.auto_edges(im)
hue, sat, val = tuple(
map(image.equalize_hist, image.split(image.rgb2hsv(im))))
l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
values = []
if debug:
image.show(l, "L")
image.show(val, "Value")
sat = image.threshold(val, 255 - 32) #image.And(val, sat)
if debug:
image.show(sat, "Thresh")
#cv.And(val, l, val)
cv.Sub(l, sat, l)
cv.Set(l, 0, image.dilate(edges, iterations=3))
if debug:
image.show(l, "L - Value")
val = l
g = Grid(cv.GetSize(val))
images = g.split_into(val, 16)
arr = image.cv2array(val)
avgmean, avgstd = arr.mean(), arr.std()
for i in images:
a = image.cv2array(i)
mean, std = abs(a.mean() - avgmean), max(a.std(), 0)
values.append((mean + std))
if debug:
print values
print "AVG", avgmean, avgstd
image.show(val, "Result")
return val, (avgmean, avgstd, len([v for v in values if v > avgstd * 2]))
def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
edges = image.auto_edges(im)
_, sat, val = image.split(image.rgb2hsv(im))
edges = image.auto_edges(im)
l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
u, v = tuple(map(image.gaussian, (u, v)))
if debug:
image.show(l, "1. L")
image.show(u, "1. U")
image.show(v, "1. V")
la, ua, va, uva = tuple(map(image.cv2array, (l, u, v, image.And(l, u, v))))
test = image.new_from(gray)
test2 = image.new_from(gray)
cv.Xor(u, v, test)
#cv.AbsDiff(u,v, test2)
if debug:
#cv.Threshold(test, test, 32, 255, cv.CV_THRESH_BINARY)
image.show(test, "2. U Xor V")
#image.show(test2, "TEST 2")
#test = image.dilate(test)
cv.Set(test, 0, image.dilate(edges))
#cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
uv_score = cv.CountNonZero(test) / total
if debug:
image.show(
test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")
arr = image.cv2array(test)
avg_mean, avg_std = arr.mean(), arr.std()
score = uv_score, avg_std
return test, score
def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
edges = image.auto_edges(im)
hue, sat, val = tuple(map(image.equalize_hist, image.split(image.rgb2hsv(im)) ))
l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
values = []
if debug:
image.show(l, "L")
image.show(val, "Value")
sat = image.threshold(val,255-32)#image.And(val, sat)
if debug:
image.show(sat, "Thresh")
#cv.And(val, l, val)
cv.Sub(l, sat, l)
cv.Set(l, 0, image.dilate(edges, iterations=3))
if debug:
image.show(l, "L - Value")
val = l
g = Grid(cv.GetSize(val))
images = g.split_into(val, 16)
arr = image.cv2array(val)
avgmean, avgstd = arr.mean(), arr.std()
for i in images:
a = image.cv2array(i)
mean, std = abs(a.mean() - avgmean), max(a.std(), 0)
values.append((mean+std))
if debug:
print values
print "AVG", avgmean, avgstd
image.show(val, "Result")
return val, (avgmean, avgstd, len([v for v in values if v > avgstd*2]))
def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
edges = image.auto_edges(im)
_, sat, val = image.split(image.rgb2hsv(im))
edges = image.auto_edges(im)
l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
u,v = tuple(map(image.gaussian, (u,v)))
if debug:
image.show(l, "1. L")
image.show(u, "1. U")
image.show(v, "1. V")
la,ua,va,uva = tuple(map(image.cv2array, (l,u,v, image.And(l,u,v))))
test = image.new_from(gray)
test2 = image.new_from(gray)
cv.Xor(u,v,test)
#cv.AbsDiff(u,v, test2)
if debug:
#cv.Threshold(test, test, 32, 255, cv.CV_THRESH_BINARY)
image.show(test, "2. U Xor V")
#image.show(test2, "TEST 2")
#test = image.dilate(test)
cv.Set(test, 0, image.dilate(edges))
#cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
uv_score = cv.CountNonZero(test) / total
if debug:
image.show(test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")
arr = image.cv2array(test)
avg_mean, avg_std = arr.mean(), arr.std()
score = uv_score, avg_std
return test, score
def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
l = image.sub(gray, image.gaussian(gray, 5))
l2 = image.sub(gray, image.gaussian(gray, 9))
edges = image.dilate(image.auto_edges(im, percentage=0.2))
if debug:
image.show(image.threshold(l, threshold=1),
"Before Edge Removal (kernel=5)")
image.show(image.threshold(l2, threshold=1),
"Before Edge Removal (kernel=9)")
cv.Set(l, 0, image.threshold(edges, threshold=1))
cv.Set(l2, 0, image.threshold(edges, threshold=1))
l = image.threshold(l, threshold=1)
l2 = image.threshold(l2, threshold=1)
if debug:
image.show(image.threshold(edges, threshold=1), "Edges")
image.show(l, "After Edge Removal (kernel=5)")
image.show(l2, "After Edge Removal (kernel=9)")
noise2 = image.new_from(gray)
cv.EqualizeHist(gray, noise2)
cv.AbsDiff(noise2, gray, noise2)
cv.Set(noise2, 0,
image.threshold(image.sobel(im, xorder=2, yorder=2), threshold=4))
diff = image.cv2array(noise2)
if debug:
image.show(noise2, "DIFF")
print "M", diff.mean(), "S", diff.std()
diff_stat = (diff.mean(), diff.std())
percent_noise = cv.CountNonZero(noise2) / total
if debug:
image.show(noise2, "NOISE2")
# magical, I don't understand how this works
_, sat, _ = image.split(image.rgb2hsv(im))
edges = image.auto_edges(im)
l, u, v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
u, v = tuple(map(image.gaussian, (u, v)))
if debug:
image.show(l, "1. L")
image.show(u, "1. U")
image.show(v, "1. V")
la, ua, va, uva = tuple(map(image.cv2array, (l, u, v, image.And(l, u, v))))
test = image.new_from(gray)
test2 = image.new_from(gray)
cv.Xor(u, v, test)
if debug:
image.show(test, "2. U Xor V")
cv.Set(test, 0, image.dilate(edges))
#cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
uv_score = cv.CountNonZero(test) / total
if debug:
image.show(
test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")
g = Grid(size)
images = map(image.cv2array, g.split_into(test, 6))
arr = image.cv2array(test)
avg_mean, avg_std = arr.mean(), arr.std()
#ms = [(a.mean(), a.std()) for a in images]
#min_mean = min_std = 255
#max_mean = max_std = 0
#for m,s in ms:
# min_mean = min(min_mean, m)
# min_std = min(min_std, s)
# max_mean = max(max_mean, m)
# max_std = max(max_std, s)
#if debug:
# print min_mean, min_std
# print avg_mean, avg_std
# print max_mean, max_std
#
#score = uv_score, min_mean, avg_mean, avg_std, max_mean
uv_score = uv_score, avg_std
score = cv.CountNonZero(l) / total, cv.CountNonZero(l2) / total, \
diff_stat[0], diff_stat[1], uv_score
return l, score
def measure(im, debug=False):
gray = image.rgb2gray(im)
size = cv.GetSize(im)
total = float(size[0] * size[1])
l = image.sub(gray, image.gaussian(gray, 5))
l2 = image.sub(gray, image.gaussian(gray, 9))
edges = image.dilate(image.auto_edges(im, percentage=0.2))
if debug:
image.show(image.threshold(l, threshold=1), "Before Edge Removal (kernel=5)")
image.show(image.threshold(l2, threshold=1), "Before Edge Removal (kernel=9)")
cv.Set(l, 0, image.threshold(edges, threshold=1))
cv.Set(l2, 0, image.threshold(edges, threshold=1))
l = image.threshold(l, threshold=1)
l2 = image.threshold(l2, threshold=1)
if debug:
image.show(image.threshold(edges, threshold=1), "Edges")
image.show(l, "After Edge Removal (kernel=5)")
image.show(l2, "After Edge Removal (kernel=9)")
noise2 = image.new_from(gray)
cv.EqualizeHist(gray, noise2)
cv.AbsDiff(noise2, gray, noise2)
cv.Set(noise2, 0, image.threshold(image.sobel(im, xorder=2, yorder=2), threshold=4))
diff = image.cv2array(noise2)
if debug:
image.show(noise2, "DIFF")
print "M", diff.mean(), "S", diff.std()
diff_stat = (diff.mean(), diff.std())
percent_noise = cv.CountNonZero(noise2) / total
if debug:
image.show(noise2, "NOISE2")
# magical, I don't understand how this works
_, sat, _ = image.split(image.rgb2hsv(im))
edges = image.auto_edges(im)
l,u,v = tuple(map(image.equalize_hist, image.split(image.rgb2luv(im))))
u,v = tuple(map(image.gaussian, (u,v)))
if debug:
image.show(l, "1. L")
image.show(u, "1. U")
image.show(v, "1. V")
la,ua,va,uva = tuple(map(image.cv2array, (l,u,v, image.And(l,u,v))))
test = image.new_from(gray)
test2 = image.new_from(gray)
cv.Xor(u,v,test)
if debug:
image.show(test, "2. U Xor V")
cv.Set(test, 0, image.dilate(edges))
#cv.Set(test, 0, image.invert(image.threshold(sat, threshold=8)))
uv_score = cv.CountNonZero(test) / total
if debug:
image.show(test, "3. U Xor V - dilate(Edges) - invert(threshold(Saturation))")
g = Grid(size)
images = map(image.cv2array, g.split_into(test, 6))
arr = image.cv2array(test)
avg_mean, avg_std = arr.mean(), arr.std()
#ms = [(a.mean(), a.std()) for a in images]
#min_mean = min_std = 255
#max_mean = max_std = 0
#for m,s in ms:
# min_mean = min(min_mean, m)
# min_std = min(min_std, s)
# max_mean = max(max_mean, m)
# max_std = max(max_std, s)
#if debug:
# print min_mean, min_std
# print avg_mean, avg_std
# print max_mean, max_std
#
#score = uv_score, min_mean, avg_mean, avg_std, max_mean
uv_score = uv_score, avg_std
score = cv.CountNonZero(l) / total, cv.CountNonZero(l2) / total, \
diff_stat[0], diff_stat[1], uv_score
return l, score
