Zi 字媒體

fanfuhan OpenCV 教學019 ~ opencv-019-圖像直方圖比較 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/03/30/opencv-019/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV 圖像直方圖比較，就是計算兩幅圖像的直方圖數據，比較兩組數據的相似性，從而得到兩幅圖像之間的相似程度，直方圖比較在早期的CBIR(以圖搜圖)中是應用很常見的技術手段，通常會結合邊緣處理、詞袋等技術一起使用。 C++ #include #include using namespace std; using namespace cv; /* * 图像直方图比较 */ int main() { Mat src1 = imread("../images/left01.jpg"); Mat src2 = imread("../images/left13.jpg"); if (src1.empty() || src2.empty()) { cout << "could not load image.." << endl; } imshow("input1", src1); imshow("input2", src2); // 一般在HSV色彩空间进行计算 Mat hsv1, hsv2; cvtColor(src1, hsv1, COLOR_BGR2HSV); cvtColor(src2, hsv2, COLOR_BGR2HSV); int h_bins = 60, s_bins = 64; int histSize[] = {h_bins, s_bins}; float h_ranges[] = {0, 180}; float s_ranges[] = {0, 256}; const float* ranges[] = {h_ranges, s_ranges}; int channels[] = {0, 1}; Mat hist1, hist2; calcHist(&hsv1, 1, channels, Mat(), hist1, 2, histSize, ranges); calcHist(&hsv2, 1, channels, Mat(), hist2, 2, histSize, ranges); normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat()); normalize(hist2, hist2, 0, 1, NORM_MINMAX, -1, Mat()); // 比较 double src1_src2_1 = compareHist(hist1, hist2, HISTCMP_CORREL); double src1_src2_2 = compareHist(hist1, hist2, HISTCMP_BHATTACHARYYA); printf("HISTCMP_CORREL : %.2f\n", src1_src2_1); printf("HISTCMP_BHATTACHARYYA : %.2f\n", src1_src2_1); waitKey(0); return 0; } Python import cv2 as cv import numpy as np src1 = cv.imread("D:/vcprojects/images/m1.png") src2 = cv.imread("D:/vcprojects/images/m2.png") src3 = cv.imread("D:/vcprojects/images/flower.png") src4 = cv.imread("D:/vcprojects/images/wm_test.png") cv.imshow("input1", src1) cv.imshow("input2", src2) cv.imshow("input3", src3) cv.imshow("input4", src4) hsv1 = cv.cvtColor(src1, cv.COLOR_BGR2HSV) hsv2 = cv.cvtColor(src2, cv.COLOR_BGR2HSV) hsv3 = cv.cvtColor(src3, cv.COLOR_BGR2HSV) hsv4 = cv.cvtColor(src4, cv.COLOR_BGR2HSV) hist1 = cv.calcHist([hsv1], [0, 1], None, [60, 64], [0, 180, 0, 256]) hist2 = cv.calcHist([hsv2], [0, 1], None, [60, 64], [0, 180, 0, 256]) hist3 = cv.calcHist([hsv3], [0, 1], None, [60, 64], [0, 180, 0, 256]) hist4 = cv.calcHist([hsv4], [0, 1], None, [60, 64], [0, 180, 0, 256]) cv.normalize(hist1, hist1, 0, 1.0, cv.NORM_MINMAX, dtype=np.float32) cv.normalize(hist2, hist2, 0, 1.0, cv.NORM_MINMAX) cv.normalize(hist3, hist3, 0, 1.0, cv.NORM_MINMAX) cv.normalize(hist4, hist4, 0, 1.0, cv.NORM_MINMAX) methods = [cv.HISTCMP_CORREL, cv.HISTCMP_CHISQR, cv.HISTCMP_INTERSECT, cv.HISTCMP_BHATTACHARYYA] str_method = "" for method in methods: src1_src2 = cv.compareHist(hist1, hist2, method) src3_src4 = cv.compareHist(hist3, hist4, method) if method == cv.HISTCMP_CORREL: str_method = "Correlation" if method == cv.HISTCMP_CHISQR: str_method = "Chi-square" if method == cv.HISTCMP_INTERSECT: str_method = "Intersection" if method == cv.HISTCMP_BHATTACHARYYA: str_method = "Bhattacharyya" print("%s src1_src2 = %.2f, src3_src4 = %.2f"%(str_method, src1_src2, src3_src4)) cv.waitKey(0) cv.destroyAllWindows()