initial commit

samples/python/tutorial_code/Histograms_Matching/histogram_calculation/calcHist_Demo.py

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+from __future__ import print_function
+from __future__ import division
+import cv2 as cv
+import numpy as np
+import argparse
+
+## [Load image]
+parser = argparse.ArgumentParser(description='Code for Histogram Calculation tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+## [Load image]
+
+## [Separate the image in 3 places ( B, G and R )]
+bgr_planes = cv.split(src)
+## [Separate the image in 3 places ( B, G and R )]
+
+## [Establish the number of bins]
+histSize = 256
+## [Establish the number of bins]
+
+## [Set the ranges ( for B,G,R) )]
+histRange = (0, 256) # the upper boundary is exclusive
+## [Set the ranges ( for B,G,R) )]
+
+## [Set histogram param]
+accumulate = False
+## [Set histogram param]
+
+## [Compute the histograms]
+b_hist = cv.calcHist(bgr_planes, [0], None, [histSize], histRange, accumulate=accumulate)
+g_hist = cv.calcHist(bgr_planes, [1], None, [histSize], histRange, accumulate=accumulate)
+r_hist = cv.calcHist(bgr_planes, [2], None, [histSize], histRange, accumulate=accumulate)
+## [Compute the histograms]
+
+## [Draw the histograms for B, G and R]
+hist_w = 512
+hist_h = 400
+bin_w = int(round( hist_w/histSize ))
+
+histImage = np.zeros((hist_h, hist_w, 3), dtype=np.uint8)
+## [Draw the histograms for B, G and R]
+
+## [Normalize the result to ( 0, histImage.rows )]
+cv.normalize(b_hist, b_hist, alpha=0, beta=hist_h, norm_type=cv.NORM_MINMAX)
+cv.normalize(g_hist, g_hist, alpha=0, beta=hist_h, norm_type=cv.NORM_MINMAX)
+cv.normalize(r_hist, r_hist, alpha=0, beta=hist_h, norm_type=cv.NORM_MINMAX)
+## [Normalize the result to ( 0, histImage.rows )]
+
+## [Draw for each channel]
+for i in range(1, histSize):
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(b_hist[i-1])) ),
+            ( bin_w*(i), hist_h - int(round(b_hist[i])) ),
+            ( 255, 0, 0), thickness=2)
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(g_hist[i-1])) ),
+            ( bin_w*(i), hist_h - int(round(g_hist[i])) ),
+            ( 0, 255, 0), thickness=2)
+    cv.line(histImage, ( bin_w*(i-1), hist_h - int(round(r_hist[i-1])) ),
+            ( bin_w*(i), hist_h - int(round(r_hist[i])) ),
+            ( 0, 0, 255), thickness=2)
+## [Draw for each channel]
+
+## [Display]
+cv.imshow('Source image', src)
+cv.imshow('calcHist Demo', histImage)
+cv.waitKey()
+## [Display]