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This article mainly explains "how to achieve histogram normalization in C++". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Next let the editor to take you to learn "C++ how to achieve histogram normalization" bar!

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C++ version:

# include # include / / histogram normalizationcv::Mat histogram_normalization (cv::Mat img, int a, int b) {/ / get height and width int width = img.cols; int height = img.channels ()

Int c, d; int val

/ / prepare output cv::Mat out = cv::Mat::zeros (height, width, CV_8UC3)

/ / get [c, d] for (int y = 0; y < height; yearly +) {for (int x = 0; x < width; x +) {for (int _ c = 0; _ c < channel; _ C++) {val = (float) img.at (y, x) [c]; c = fmin (c, val); d = fmax (d, val);}

/ / histogram transformation for (int y = 0; y < height; yearly +) {for (int x = 0; x < width; xylene +) {for (int _ c = 0; _ c < 3; _ C++) {val = img.at (y, x) [_ c]

If (val < a) {out.at (y, x) [c] = (uchar) a;} else if (val b] = b out = out.astype (np.uint8)

Return out

# Read imageimg = cv2.imread ("imori_dark.jpg") .astype (np.float) H, W, C = img.shape

# histogram normalizationout = hist_normalization (img)

# Display histogramplt.hist (out.ravel (), bins=255, rwidth=0.8, range= (0,255)) plt.savefig ("out_his.png") plt.show ()

# Save resultcv2.imshow ("result", out) cv2.waitKey (0) cv2.imwrite ("out.jpg", out)

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