54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
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# Calculating and displaying 2D Hue-Saturation histogram of a color image
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import sys
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import cv
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def hs_histogram(src):
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# Convert to HSV
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hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
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cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
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# Extract the H and S planes
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h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
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s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
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cv.Split(hsv, h_plane, s_plane, None, None)
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planes = [h_plane, s_plane]
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h_bins = 30
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s_bins = 32
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hist_size = [h_bins, s_bins]
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# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
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h_ranges = [0, 180]
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# saturation varies from 0 (black-gray-white) to
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# 255 (pure spectrum color)
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s_ranges = [0, 255]
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ranges = [h_ranges, s_ranges]
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scale = 10
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hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
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cv.CalcHist([cv.GetImage(i) for i in planes], hist)
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(_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
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hist_img = cv.CreateImage((h_bins*scale, s_bins*scale), 8, 3)
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for h in range(h_bins):
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for s in range(s_bins):
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bin_val = cv.QueryHistValue_2D(hist, h, s)
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intensity = cv.Round(bin_val * 255 / max_value)
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cv.Rectangle(hist_img,
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(h*scale, s*scale),
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((h+1)*scale - 1, (s+1)*scale - 1),
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cv.RGB(intensity, intensity, intensity),
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cv.CV_FILLED)
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return hist_img
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if __name__ == '__main__':
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src = cv.LoadImageM(sys.argv[1])
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cv.NamedWindow("Source", 1)
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cv.ShowImage("Source", src)
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cv.NamedWindow("H-S Histogram", 1)
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cv.ShowImage("H-S Histogram", hs_histogram(src))
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cv.WaitKey(0)
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