《OpenCV3编程入门》学习笔记九：直方图与匹配

一：内容介绍

本节主要介绍OpenCV的imgproc模块的直方图与模板匹配部分：
1. 直方图的计算与绘制
2. 直方图对比
3. 反向投影
4. 模板匹配

二：学习笔记

1. 关于图像的HSV格式

平常老是用RGB了解这个较少，参见：HSL和HSV色彩空间，如何通俗地解释色彩三要素：色相、明度、纯度？，什么是色像？什么是饱和度？什么是色温？什么是色调呢？。

2. 对比直方图的方法

有多种

3. 累积直方图

有时候根据需要我们也会用到累积直方图，或者水平直方图、竖直直方图等，知道就行。

4. 反向投影

这里主要使用了HSV中的H色相直方图，具体见书吧，写的还比较清楚。或者调试例程时，多用image watch插件看一看。

5. matchTemplate()函数

用的很广泛，最简单的目标匹配方法啦。其度量方式有很多种，官网type of the template matching operation ，这一点书中也有介绍。

6 . 本节函数清单

三：相关源码及解析

本章示例较多，示例列表：
1.绘制H-S直方图
2.绘制RGB三色直方图
3.直方图对比
4. 反向投影
5.模板匹配

1 . 绘制H-S直方图

源码：

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;int main()
{Mat srcImage, hsvImage;srcImage = imread("poster_cup.jpg");cvtColor(srcImage, hsvImage, COLOR_BGR2HSV);int hueBinNum = 30;  //色调的直方图直条数量int saturationBinNum = 32;  //饱和度的直方图直条数量int histSize[] = { hueBinNum, saturationBinNum };float hueRanges[] = {0, 180}; //色相的变化范围为0到179float saturationRanges[] = { 0, 256 }; //定义饱和度的变化范围为0（黑、白、灰）到255（纯光谱颜色）const float* ranges[] = {hueRanges, saturationRanges};  //指向指针的数组，指针数组MatND dstHist;int channels[] = { 0, 1 };//calcHist函数中将计算第0通道和第1通道的直方图calcHist(&hsvImage, //输入的数组1,  //数组个数为1channels,  //通道索引Mat(),  //不使用掩膜dstHist,  //输出的目标直方图2,  //需要计算的直方图的维度为2histSize,  //存放每个维度的直方图尺寸的数组ranges //每一维数值的取值范围数组);double maxValue = 0;  //最大值minMaxLoc(dstHist, 0, &maxValue, 0, 0);  //查找数组和子数组的全局最大值存入maxValue中int scale = 10;Mat histImg = Mat::zeros(saturationBinNum*scale, hueBinNum*scale, CV_8UC3);for (int hue = 0; hue < hueBinNum; hue++)  //双层循环，进行直方图绘制for (int saturation = 0; saturation < saturationBinNum; saturation++) {float binValue = dstHist.at<float>(hue, saturation); //直方图直条的值int intensity = cvRound(binValue*255/maxValue);  //强度,归一化到0-255之间rectangle(histImg, Point(hue*scale, saturation*scale), Point((hue+1)*scale-1, (saturation+1)*scale-1), Scalar::all(intensity), FILLED); //绘制}imshow("【素材图】", srcImage);imshow("【H-S 直方图】", histImg);while (waitKey(8)!=27);return 0;
}

素材：

效果图：

提示：
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2 . 绘制RGB三色直方图

源码：

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;int main() {Mat srcImage = imread("poster_building_1.jpg");
//  Mat srcImage(200, 200, CV_8UC3, Scalar(50, 100, 150));imshow("【素材图】", srcImage);int bins = 256;int hist_size[] = { bins };float range[] = { 0, 256 };const float* ranges[] = { range };  //指针数组MatND redHist, greenHist, blueHist;//红色分量直方图的计算(OpenCV中的存储顺序为BGR)int channels_r[] = { 2 };calcHist(&srcImage, 1, channels_r, Mat(), redHist, 1, hist_size, ranges);//绿色分量直方图的计算int channels_g[] = { 1 };calcHist(&srcImage, 1, channels_g, Mat(), greenHist, 1, hist_size, ranges);//蓝色分量直方图的计算int channels_b[] = { 0 };calcHist(&srcImage, 1, channels_b, Mat(), blueHist, 1, hist_size, ranges);double maxValue_red, maxValue_green, maxValue_blue;minMaxLoc(redHist, 0, &maxValue_red, 0, 0);  //此处不需要最小值minMaxLoc(greenHist, 0, &maxValue_green, 0, 0);minMaxLoc(blueHist, 0, &maxValue_blue, 0, 0);int scale = 1;int histHeight = 256;Mat histImage = Mat::zeros(histHeight, bins*3, CV_8UC3);for (int i = 0; i < bins; i++){float binValue_red = redHist.at<float>(i);float binValue_green = greenHist.at<float>(i);float binValue_blue = blueHist.at<float>(i);int intensity_red = cvRound(binValue_red*histHeight/maxValue_red);  //要绘制的高度int intensity_green = cvRound(binValue_green*histHeight / maxValue_green);int intensity_blue = cvRound(binValue_blue*histHeight / maxValue_blue);rectangle(histImage, Point(i*scale, histHeight-1), Point((i+1)*scale-1, histHeight - intensity_red), Scalar(0, 0, 255));rectangle(histImage, Point((i+bins)*scale, histHeight - 1), Point((i+bins + 1)*scale - 1, histHeight - intensity_green), Scalar(0, 255, 0));rectangle(histImage, Point((i+bins*2)*scale, histHeight - 1), Point((i+bins*2 + 1)*scale - 1, histHeight - intensity_blue), Scalar(255, 0, 0));}imshow("【图像的RGB直方图】", histImage);while (waitKey(9) !=27);return 0;
}

素材：

效果图：

提示：
无

3 . 直方图对比

源码：

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;int main() {Mat srcImage_base, hsvImage_base;Mat srcImage_test1, hsvImage_test1;Mat srcImage_test2, hsvImage_test2;Mat hsvImage_halfDown;srcImage_base = imread("poster_book.jpg");srcImage_test1 = imread("poster_book_1.jpg");srcImage_test2 = imread("poster_book_2.jpg");imshow("【基准图像】", srcImage_base);imshow("【测试图像1】", srcImage_test1);imshow("【测试图像2】", srcImage_test2);cvtColor(srcImage_base, hsvImage_base, COLOR_BGR2HSV);cvtColor(srcImage_test1, hsvImage_test1, COLOR_BGR2HSV);cvtColor(srcImage_test2, hsvImage_test2, COLOR_BGR2HSV);hsvImage_halfDown = hsvImage_base(Range(hsvImage_base.rows/2, hsvImage_base.rows - 1), Range(0, hsvImage_base.cols - 1));  //此处选取的是ROI，引用int h_bins = 30;  //对hue通道分30个等级int s_bins = 32;  //saturation通道分32个等级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 };MatND baseHist, halfDownHist, testHist1, testHist2;calcHist(&hsvImage_base, 1, channels, Mat(), baseHist, 2, histSize, ranges);normalize(baseHist, baseHist, 0, 1, NORM_MINMAX);calcHist(&hsvImage_halfDown, 1, channels, Mat(), halfDownHist, 2, histSize, ranges);normalize(halfDownHist, halfDownHist, 0, 1, NORM_MINMAX);calcHist(&hsvImage_test1, 1, channels, Mat(), testHist1, 2, histSize, ranges);normalize(testHist1, testHist1, 0, 1, NORM_MINMAX);calcHist(&hsvImage_test2, 1, channels, Mat(), testHist2, 2, histSize, ranges);normalize(testHist2, testHist2, 0, 1, NORM_MINMAX);for (int i = 0; i < 4; i++) {int compare_method = i;double base_base = compareHist(baseHist, baseHist, compare_method);double base_half = compareHist(baseHist, halfDownHist, compare_method);double base_test1 = compareHist(baseHist, testHist1, compare_method);double base_test2 = compareHist(baseHist, testHist2, compare_method);printf("方法[%d]的匹配结果如下：\n\n【基准图-基准图】：%f,【基准图-半身图】：%f,【基准图-测试图1】：%f,【基准图-测试图2】：%f \n--------------------------------------------\n", i, base_base, base_half, base_test1, base_test2);}cout << "检测结束。";while (waitKey(5) != 27);return 0;
}

素材：
poster_book.jpg：

poster_book_1.jpg：

poster_book_2.jpg：

效果图：

提示：
无

4 . 反向投影

源码：

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;#define WINDOW_NAME1 "【原始图】"
Mat g_srcImage, g_hsvImage, g_hueImage;
int g_bins = 30;  //直方图组距
void on_BinChange(int, void*);int main() {g_srcImage = imread("poster_hand.jpg");cvtColor(g_srcImage, g_hsvImage, COLOR_BGR2HSV);g_hueImage.create(g_hsvImage.size(), g_hsvImage.depth());int ch[]{ 0, 0 };mixChannels(&g_hsvImage, 1, &g_hueImage, 1, ch, 1);namedWindow(WINDOW_NAME1);createTrackbar("色相组距", WINDOW_NAME1, &g_bins, 180, on_BinChange);on_BinChange(0, 0);  imshow(WINDOW_NAME1, g_srcImage);while (waitKey(9)!=27);return 0;
}void on_BinChange(int, void*) {MatND hist;g_bins = MAX(g_bins, 2);int histSize[] = { g_bins };float hue_range[] = { 0, 180 };const float* ranges[] = { hue_range };int channels[]={0};calcHist(&g_hueImage, 1, channels, Mat(), hist, 1, histSize, ranges);  //计算直方图并归一化normalize(hist, hist, 0, 255, NORM_MINMAX);MatND backproj;  //计算反向投影calcBackProject(&g_hueImage, 1, channels, hist, backproj, ranges);imshow("【反向投影图】", backproj);int w = 400, h = 400;int bin_w = cvRound((double)w/ g_bins);Mat histImg = Mat::zeros(w, h, CV_8UC3);for(int i = 0; i < g_bins; i++ ){rectangle(histImg, Point(i*bin_w, h), Point((i+1)*bin_w, h-cvRound(hist.at<float>(i)*h/255.0)), Scalar(100, 123, 255), CV_FILLED);}imshow("【直方图】", histImg);
}

素材：

效果图：

提示：
无

5 . 模板匹配

源码：

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;#define WINDOW_NAME1 "【原始图片】"
#define WINDOW_NAME2 "【效果窗口】"
Mat g_srcImage, g_templateImage, g_resultImage;
int g_nMatchMethod;
int g_nMaxTrackbarNum=5;
void on_Matching(int, void*);int main() {g_srcImage = imread("poster_girl_4.jpg");g_templateImage = imread("poster_girl_4_ROI.jpg");namedWindow(WINDOW_NAME1);namedWindow(WINDOW_NAME2);createTrackbar("方法", WINDOW_NAME1, &g_nMatchMethod, g_nMaxTrackbarNum, on_Matching);on_Matching(0, 0);while (waitKey(8)!=27);return 0;
}void on_Matching(int, void*) {Mat srcImage;g_srcImage.copyTo(srcImage);g_resultImage.create(g_srcImage.cols-g_templateImage.cols+1, g_srcImage.rows - g_templateImage.rows + 1, CV_32FC1);matchTemplate(g_srcImage, g_templateImage, g_resultImage, g_nMatchMethod);  //第3中方法明显跑偏了normalize(g_resultImage, g_resultImage, 0, 1, NORM_MINMAX);double minValue, maxValue;Point minLocation, maxLocation;minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation);Point matchLocation;if (g_nMatchMethod == TM_SQDIFF || g_nMatchMethod == TM_SQDIFF_NORMED) {matchLocation = minLocation;}else {matchLocation = maxLocation;}rectangle(srcImage, Point(matchLocation),Point(matchLocation.x+g_templateImage.cols, matchLocation.y+g_templateImage.rows), Scalar(0, 0, 255), 2);rectangle(g_resultImage, Point(matchLocation), Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2);imshow(WINDOW_NAME1, srcImage);imshow(WINDOW_NAME2, g_resultImage);
}

素材：

效果图：

提示：
无