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I want to calculate 3D histogram of my Cielab image in python. I am using openCV to calculate my histogram. I want to compare images using compareHist function of openCV, thats why I am using openCV to compute 3D histogram of my image.

I tried with the following variables:

i_lab = image.copy()
i_lab = i_lab.astype(np.uint8)
Range_hist = [[0, 100], [-100, 100], [-100, 100]]    
hist_1 = cv2.calcHist([i_lab], [[0], [1], [2]], None, [[20], [20], [20]], Range_hist)

But it gives error SystemError: error return without exception set Please tell me what am I doing wrong and if it is possible to compute 3D histogram using openCV in python

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  • I used the compareHist function on the histogram generated by using numpy library, so I don't need the above function to calculate my histogram anymore. Commented Apr 10, 2013 at 12:21

2 Answers 2

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I came across this question while trying to make a 3D histogram of an HSV image, and encountered the same error. It turns out that the OpenCV documentation is leading us astray here. The docs are written for the C++ API and as such can only be used as a vague guide to the Python cv2 API (although I have found that the docs are misleading for C++ as well at times).

The function signature is as follows:

cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> hist

The key point is that the channels, histSize and ranges parameters should be flat lists, not nested lists as in your example. Try the following, assuming i_lab is a three-channel image:

range_hist = [0, 100, -100, 100, -100, 100]
hist_1 = cv2.calcHist([i_lab], [0, 1, 2], None, [20, 20, 20], range_hist)

For a more complete example, try this code listing from the opencvpython blog.

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For computing a histogram in cielab, this worked for me:

function signature of calcHist: images, channels, mask, number of bins, array of the dims arrays of the histogram bin boundaries in each dimension.

img = cv2.imread(file) # in bgr 
hist = cv2.calcHist([img],[0, 1, 2],None,[256, 256, 256],[0, 255, 0, 255, 0, 255]) # in bgr
img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # in lab 
hist = cv2.calcHist([img],[0, 1, 2],None,[100, 2*128, 2*128],[0, 100, -128, 127, -128, 127])`# in lab 

correlation = cv2.HISTCMP_CORREL # compare histograms using correlation
corr = cv2.compareHist(img, img2, correlation)
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