How to realize histogram normalization in C++
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!
Image processing 100 questions, this project really contains 100 questions that directly hit your weak foundation, which can help you improve a lot of knowledge loopholes and misunderstandings.
Just look at the catalog:
After intercepting three pictures, we should be able to see that his coverage is still very comprehensive. With python and C++ two sets of code, you can choose according to your own conditions.
Let's take a look at a random question:
The question is simple and straightforward, with a little bit of knowledge. The author of the project pursues a handwritten code implementation instead of simply calling an API of opencv. You can see the answer to this question:
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)
At this point, I believe that "C++ how to achieve histogram normalization" have a deeper understanding, you might as well to practice it! Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!
