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fanfuhan OpenCV 教學020 ~ opencv-020-圖像直方圖反向投影 [用區域顏色圖片 進行圖片前景/背景 標記/分割 ~ back Projection(背景投影)] 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/03/30/opencv-020/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV C++ #include #include using namespace std; using namespace cv; void backProjection_demo(Mat &mat, Mat &model); /* * 图像直方图反向投影 */ int main() { Mat src = imread("../images/target.png"); Mat model = imread("../images/sample.png"); if (src.empty() || model.empty()) { cout << "could not load image.." << endl; } namedWindow("model", WINDOW_NORMAL); imshow("input", src); imshow("model", model); backProjection_demo(src, model); waitKey(0); return 0; } void backProjection_demo(Mat &image, Mat &model) { Mat image_hsv, model_hsv; cvtColor(image, image_hsv, COLOR_BGR2HSV); cvtColor(model, model_hsv, COLOR_BGR2HSV); // 定义直方图参数与属性 int h_bins = 32, s_bins = 32; int histSize[] = {h_bins, s_bins}; float h_ranges[] = {0, 180}, s_ranges[] = {0, 256}; const float* ranges[] = {h_ranges, s_ranges}; int channels[] = {0, 1}; Mat roiHist; calcHist(&model_hsv, 1, channels, Mat(), roiHist, 2, histSize, ranges); normalize(roiHist, roiHist, 0, 255, NORM_MINMAX, -1, Mat()); MatND backproj; calcBackProject(&image_hsv, 1, channels, roiHist, backproj, ranges); imshow("BackProj", backproj); } Python import cv2 as cv import numpy as np from matplotlib import pyplot as plt def back_projection_demo(): sample = cv.imread("D:/javaopencv/sample.png") # hist2d_demo(sample) target = cv.imread("D:/javaopencv/target.png") # hist2d_demo(target) roi_hsv = cv.cvtColor(sample, cv.COLOR_BGR2HSV) target_hsv = cv.cvtColor(target, cv.COLOR_BGR2HSV) # show images cv.imshow("sample", sample) cv.imshow("target", target) roiHist = cv.calcHist([roi_hsv], [0, 1], None, [32, 32], [0, 180, 0, 256]) cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX) dst = cv.calcBackProject([target_hsv], [0, 1], roiHist, [0, 180, 0, 256], 1) cv.imshow("backProjectionDemo", dst) def hist2d_demo(image): hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV) hist = cv.calcHist([hsv], [0, 1], None, [32, 32], [0, 180, 0, 256]) dst = cv.resize(hist, (400, 400)) cv.imshow("image", image) cv.imshow("hist", dst) plt.imshow(hist, interpolation='nearest') plt.title("2D Histogram") plt.show() back_projection_demo() cv.waitKey(0) cv.destroyAllWindows()