直方图定义：

直方图均衡化-提高对比度-cv::equalizeHist

void equalizeHist( InputArray src, OutputArray dst ); //输入为八位灰度图像

从图片建立直方图-split,calcHist

api:

void split(const Mat& src, Mat* mvbegin);//三Mat图像转化为三个图像 void calcHist( const Mat* images, //输入图像 int nimages, //图像数目 const int* channels, //通道数 InputArray mask, //可选择图像中哪些像素参与直方图计算，不使用填noArray() OutputArray hist, //输出直方图数据 int dims, //维数 const int* histSize, //直方图级数 const float** ranges, //值域范围 bool uniform = true, bool accumulate = false );

实战：

vector<Mat> bgr_plances; split(src, bgr_plances); int histSize = 256; float range[] = { 0,256 }; const float *histRanges = { range }; Mat b_hist, g_hist, r_hist; calcHist(&bgr_plances[0], 1, 0, noArray(), b_hist, 1, &histSize, &histRanges, true, false); calcHist(&bgr_plances[1], 1, 0, noArray(), g_hist, 1, &histSize, &histRanges, true, false); calcHist(&bgr_plances[2], 1, 0, noArray(), r_hist, 1, &histSize, &histRanges, true, false); int hist_h = 400; int hist_w = 512; int bin_w = hist_w / histSize; Mat histImage(hist_w, hist_h, CV_8UC3, Scalar(0, 0)); normalize(b_hist, b_hist, 0, hist_h, NORM_MINMAX, -1, noArray()); normalize(g_hist, g_hist, 0, hist_h, NORM_MINMAX, -1, noArray()); normalize(r_hist, r_hist, 0, hist_h, NORM_MINMAX, -1, noArray()); for (size_t i = 1; i < histSize; i++) { line(histImage, Point((i - 1)*bin_w, hist_h - int(b_hist.at<float>(i - 1))), Point((i)*bin_w, hist_h - int(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, LINE_AA); line(histImage, Point((i - 1)*bin_w, hist_h - int(g_hist.at<float>(i - 1))), Point((i)*bin_w, hist_h - int(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, LINE_AA); line(histImage, Point((i - 1)*bin_w, hist_h - int(r_hist.at<float>(i - 1))), Point((i)*bin_w, hist_h - int(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, LINE_AA); } imshow("output", histImage);

直方图比较

对两个图像直方图求相关性，相关性高则可能是相同或相似的图片

double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); //输入 输出 比较方法 CV_COMP_CORREL相关性方法1CV_COMP_CHISQR_ALT卡方方法1CV_COMP_INTERSECT交集法1CV_COMP_BHATTACHARYYA巴氏距离0

反向投影

暂时没看懂

模板匹配

源图像每个小块都与模板图像进行相关度计算，相关度最高的地方匹配

void matchTemplate( InputArray image, InputArray templ, //源图像与模板图像 OutputArray result, //输出结果，单通道32位浮点数， //width=W-w+1,height=H-h+1 int method, //匹配方法 InputArray mask = noArray() );

 

匹配方法输入完全匹配方差匹配TM_SQDIFF0归一化方差匹配TM_SQDIFF_NORMED0相关性匹配TM_CCORR很大的数归一化的互相关匹配TM_CCORR_NORMED1相关系数匹配TM_CCOEFF1（误匹配-1）归一化相关系数匹配TM_CCOEFF_NORMED1

尝试匹配二维码的三个定位点

int width = src.cols - temp.cols; int height = src.rows - temp.rows; Mat result(width, height, CV_32FC1); matchTemplate(src, temp, result, CV_TM_SQDIFF_NORMED,Mat()); Point minLoc, maxLoc; double min, max; src.copyTo(dst); for (size_t i = 0; i < 3; i++) { minMaxLoc(result, &min, &max, &minLoc, &maxLoc, Mat()); rectangle(dst, Rect(minLoc.x, minLoc.y, temp.cols, temp.rows), Scalar(0, 0, 255), 2, 8); rectangle(result, Rect(minLoc.x, minLoc.y, temp.cols, temp.rows), Scalar(255, 255, 255), 8, 8); } imshow("result", result); imshow("Output_Image", dst);

输出结果：

是不是可以用作二维码识别？

失败了，原因 模板图像大小与源图像上的定位点大小并不同。