pythonopencvimage-processinghistogramscikit-image
How to calculate skimage-like histogram of image using OpenCV?
In a previously-created code, histogram was calculated using the skimage module, in the following way:
h = skimage.exposure.histogram(img, nbins=256)
But, now, for some reasons, I cannot use skimage but only OpenCV.
So, when I tried
h = cv2.calcHist(img, [0], None, [256], [0,256])
the output of h of skimage and h of OpenCV are different and the code after this breaks.
So, what am I supposed to do to make the OpenCV's histogram output the same as before?
Example image:
(Please click on the image to view it clearly. It is just a green colour with noise added.)
Then I convert it to black-and-white using:
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
img = img/255 #to convert values from [0, 255] to [0, 1]
img = img[:, :, 0]
Histogram of skimage:
(array([ 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 94, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 152, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 183, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 207, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 200, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 187, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 165, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 141, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 78, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 58, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=int64), array([0.08249847, 0.08278952, 0.08308058, 0.08337163, 0.08366268,
0.08395374, 0.08424479, 0.08453585, 0.0848269 , 0.08511795,
0.08540901, 0.08570006, 0.08599112, 0.08628217, 0.08657322,
0.08686428, 0.08715533, 0.08744638, 0.08773744, 0.08802849,
0.08831955, 0.0886106 , 0.08890165, 0.08919271, 0.08948376,
0.08977482, 0.09006587, 0.09035692, 0.09064798, 0.09093903,
0.09123009, 0.09152114, 0.09181219, 0.09210325, 0.0923943 ,
0.09268536, 0.09297641, 0.09326746, 0.09355852, 0.09384957,
0.09414063, 0.09443168, 0.09472273, 0.09501379, 0.09530484,
0.09559589, 0.09588695, 0.096178 , 0.09646906, 0.09676011,
0.09705116, 0.09734222, 0.09763327, 0.09792433, 0.09821538,
0.09850643, 0.09879749, 0.09908854, 0.0993796 , 0.09967065,
0.0999617 , 0.10025276, 0.10054381, 0.10083487, 0.10112592,
0.10141697, 0.10170803, 0.10199908, 0.10229013, 0.10258119,
0.10287224, 0.1031633 , 0.10345435, 0.1037454 , 0.10403646,
0.10432751, 0.10461857, 0.10490962, 0.10520067, 0.10549173,
0.10578278, 0.10607384, 0.10636489, 0.10665594, 0.106947 ,
0.10723805, 0.10752911, 0.10782016, 0.10811121, 0.10840227,
0.10869332, 0.10898437, 0.10927543, 0.10956648, 0.10985754,
0.11014859, 0.11043964, 0.1107307 , 0.11102175, 0.11131281,
0.11160386, 0.11189491, 0.11218597, 0.11247702, 0.11276808,
0.11305913, 0.11335018, 0.11364124, 0.11393229, 0.11422335,
0.1145144 , 0.11480545, 0.11509651, 0.11538756, 0.11567862,
0.11596967, 0.11626072, 0.11655178, 0.11684283, 0.11713388,
0.11742494, 0.11771599, 0.11800705, 0.1182981 , 0.11858915,
0.11888021, 0.11917126, 0.11946232, 0.11975337, 0.12004442,
0.12033548, 0.12062653, 0.12091759, 0.12120864, 0.12149969,
0.12179075, 0.1220818 , 0.12237286, 0.12266391, 0.12295496,
0.12324602, 0.12353707, 0.12382812, 0.12411918, 0.12441023,
0.12470129, 0.12499234, 0.12528339, 0.12557445, 0.1258655 ,
0.12615656, 0.12644761, 0.12673866, 0.12702972, 0.12732077,
0.12761183, 0.12790288, 0.12819393, 0.12848499, 0.12877604,
0.1290671 , 0.12935815, 0.1296492 , 0.12994026, 0.13023131,
0.13052237, 0.13081342, 0.13110447, 0.13139553, 0.13168658,
0.13197763, 0.13226869, 0.13255974, 0.1328508 , 0.13314185,
0.1334329 , 0.13372396, 0.13401501, 0.13430607, 0.13459712,
0.13488817, 0.13517923, 0.13547028, 0.13576134, 0.13605239,
0.13634344, 0.1366345 , 0.13692555, 0.13721661, 0.13750766,
0.13779871, 0.13808977, 0.13838082, 0.13867188, 0.13896293,
0.13925398, 0.13954504, 0.13983609, 0.14012714, 0.1404182 ,
0.14070925, 0.14100031, 0.14129136, 0.14158241, 0.14187347,
0.14216452, 0.14245558, 0.14274663, 0.14303768, 0.14332874,
0.14361979, 0.14391085, 0.1442019 , 0.14449295, 0.14478401,
0.14507506, 0.14536612, 0.14565717, 0.14594822, 0.14623928,
0.14653033, 0.14682138, 0.14711244, 0.14740349, 0.14769455,
0.1479856 , 0.14827665, 0.14856771, 0.14885876, 0.14914982,
0.14944087, 0.14973192, 0.15002298, 0.15031403, 0.15060509,
0.15089614, 0.15118719, 0.15147825, 0.1517693 , 0.15206036,
0.15235141, 0.15264246, 0.15293352, 0.15322457, 0.15351562,
0.15380668, 0.15409773, 0.15438879, 0.15467984, 0.15497089,
0.15526195, 0.155553 , 0.15584406, 0.15613511, 0.15642616,
0.15671722]))
Histogram of OpenCV:
array([[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 1.],
[ 0.],
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[ 0.],
[ 7.],
[ 8.],
[13.],
[12.],
[20.],
[20.],
[17.],
[ 5.],
[ 8.],
[ 4.],
[10.],
[ 3.],
[20.],
[ 2.],
[18.],
[11.],
[39.],
[12.],
[44.],
[13.],
[23.],
[ 8.],
[26.],
[ 8.],
[12.],
[ 1.],
[ 2.],
[ 0.],
[ 2.],
[ 0.],
[ 1.],
[ 0.],
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Solution
Note that skimage.exposure.histogram(img, nbins=256) returns a tuple with two arrays, the first one being the total number of pixels of that specific value in the given img, and the second array is normalized to 1.
So, if you just want to find the total number of pixels of a given value, then you can use this :
def histogram(img):
img = (img*255).astype(int)
a = [0 for x in range(256)]
for x in range(256):
a[x] = np.count_nonzero(img == x)
return a