【图像处理 直方图 OpenCV实现】
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直方图与直方图匹配
最后一个代码比较实用，对于小角度的模板匹配，还是可以用此函数的。
文章目录
-
前言
-
一、直方图
-
二、使用步骤
-
- 1.引入库
- 2.绘制直方图
-
总结
前言
直方图统计的原理、直方图代表的图像特征、直方图的应用。
直方图作为图像处理的基础，是分析图像的基础工具。不可不了解
直方图表示用于统计像素数据的一种技术手段。它能够展示数据分布情况，并能表现图像中各个亮度等级对应的像素数量。
直方图的作用如下：
- 统计像素数据以掌握图像像素分布情况，并进一步分析其明暗对比程度。
- 利用直方图完成图像二值化处理。
直方图的操作流程包括以下几个关键步骤：首先对图像进行归一化处理以提高分析效果；其次通过统计完成后的直方图来观察图像的灰度分布特征；如果发现像素值在特定区间内分布不均，则需采用归一化方法进行重新计算以更好地反映灰度分布情况；在此过程中提供了五种不同的归一化方法供选择：1）无穷范数处理 2）L1范数（绝对值之和） 3）L2范数及模长归一化 4）L2范数平方处理 5）偏移归一化操作
2：直方图比较
3：直方图均衡
4：直方图匹配
5：直方图反向投影
6：图像的模板匹配
在Opencv中，提供了图像直方图的统计函数calcHist()
一、直方图
二、使用步骤
1.引入库
绘制直方图（示例）：
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;2.绘制直方图
绘制直方图（示例）：
int main1()
{
Mat img;
img = imread("apple.jpg");
if (img.empty())
{
cout << "图像数据有问题" << endl;
return -1;
}
Mat gray;
cvtColor(img,gray,COLOR_BGR2GRAY);
//以下为提取直方图的必要变量
Mat hist;
const int channels[1] = { 0 };//通道索引
float inRanges[2] = { 0,255 };//每个通道灰度值的取值范围
const float*ranges[1] = { inRanges };//像素的灰度值范围
const int bins[1] = { 256 };//每个直方图数组的尺寸
calcHist(&gray,1,channels,Mat(),hist,1,bins,ranges); //统计函数
//准备绘制直方图
int hist_w = 512;//对应直方图的列，因绘制的宽度是2，即需要256*2
int hist_h = 400;//对应行，若不进行归一化，则需要大于255
int width = 2;//绘制的间隙
Mat histImage = Mat::zeros(hist_h,hist_w,CV_8UC3);
for (int i = 1; i <= hist.rows; i++)
{
//简介：从图像的左下角开始绘制，即第一个点的坐标是0，hist_h-1。后面每个增width。 取整然后除以15是为了缩小范围，类似归一。
rectangle(histImage,Point(width*(i-1),hist_h-1),Point(width*i-1,hist_h-cvRound(hist.at<float>(i-1)/15)),Scalar(255,255,255),-1);
cout << hist.at<float>(i - 1) << endl;
}
namedWindow("直方图",WINDOW_NORMAL);
imshow("直方图",histImage);
waitKey(0);
return 0;
} //归一化
int main2()
{
system("color F0");
vector<double> positiveData = { 2.0,8.0,10.0 };//为了只管观察归一化，定义了一个数组
vector<double> normalized_l1, normalized_L2, normalized_Inf, normalized_L2SQR;
//测试不同的归一方法
normalize(positiveData,normalized_l1,1.0,0.0,NORM_L1);
cout << "normalized_L1" << normalized_l1[0] << ":" << normalized_l1[1] << ":" << normalized_l1[2] << ";;;;"<<endl;
normalize(positiveData, normalized_L2, 1.0, 0.0, NORM_L2);
cout << "normalized_L2" << normalized_L2[0] << "+" << normalized_L2[1] << "+" << normalized_L2[2] << endl;
normalize(positiveData,normalized_Inf,1.0,0.0,NORM_INF);
cout << "normalized_inf" << normalized_Inf[0] << "+" << normalized_Inf[1] << "+" << normalized_Inf[2] << endl;
normalize(positiveData, normalized_L2SQR, 1.0, 0.0, NORM_MINMAX);
cout << "MINMAX" << normalized_L2SQR[0] << normalized_L2SQR[1] << normalized_L2SQR[2] << endl;
//将图像直方图归一化
//Mat img = imread("apple.jpg");
Mat img(512, 512, CV_32FC3, Scalar(100, 100, 100));
if (img.empty())
{
cout << "图像数据有问题" << endl;
return -1;
}
imshow("apple",img);
Mat gray, hist;
cvtColor(img, gray, COLOR_BGR2GRAY);
const int channels[1] = { 0 };
float inRanges[2] = {0,255};
const float*ranges[1] = { inRanges };
const int bins[1] = { 256 };
calcHist(&gray,1,channels,Mat(),hist,1,bins,ranges);
int hist_w = 512;
int hist_h = 400;
int width = 2;
Mat hisImage_L1 = Mat::zeros(hist_h,hist_w,CV_8UC3);
Mat hisImage_Inf = Mat::zeros(hist_h, hist_w, CV_8UC3);
Mat hist_L1, hist_Inf;
normalize(hist,hist_L1,1,0,NORM_L1,-1,Mat());
//hist_L1是一个行为256，列为1的矩阵。所以遍历了rows。每个矩阵的没一个点，代表了这个像素归一化的值。
for (int i = 1; i < hist_L1.rows; i++)
{
rectangle(hisImage_L1,Point(width*(i-1),hist_h-1),Point(width*i-1,hist_h-cvRound(30*hist_h*hist_L1.at<float>(i-1))-1),Scalar(255,255,255),-1);
}
normalize(hist,hist_Inf,1,0,NORM_INF,-1,Mat());
for (int i = 1; i < hist_Inf.rows; i++)
{
rectangle(hisImage_Inf,Point(width*(i-1),hist_h-1),Point(width*i-1,hist_h-cvRound(hist_h*hist_Inf.at<float>(i-1))-1),Scalar(255,255,255),-1);
cout << hist_Inf.at<float>(i - 1) << endl;
}
namedWindow("L1归一化",WINDOW_NORMAL);
imshow("L1归一化",hisImage_L1);
namedWindow("INF归一化",WINDOW_NORMAL);
imshow("INF归一化",hisImage_Inf);
waitKey(0);
return 0;
} //直方图的反向投影
void drawHist3(Mat &hist,int type,string name)
{
int hist_w = 512;
int hist_h = 400;
int width = 2;
Mat histImage = Mat::zeros(hist_h,hist_w,CV_8UC3);
normalize(hist,hist,255,0,type,-1,Mat());
namedWindow(name,WINDOW_NORMAL);
imshow(name,hist);
}
int main66()
{
Mat img0 = imread("apple.jpg");
Mat img1 = imread("sub.jpg");
Mat img0_HSV, img1_HSV, hist0, hist1;
if (img0.empty()||img1.empty())
{
cout << "图像数据有问题" << endl;
waitKey(3000);
return - 1;
}
cvtColor(img1,img1_HSV,COLOR_BGR2HSV);
cvtColor(img0, img0_HSV, COLOR_BGR2HSV);
int h_bins = 32; int s_bins = 32;
int histSize[] = { h_bins,s_bins };
//H通道为0-179
float h_ranges[] = { 0,180 };
//S通道
float s_ranges[] = { 0,256 };
//每个通道的范围
const float*ranges[] = { h_ranges,s_ranges };
//统计通道的索引
int channels[] = { 0,1 };
//绘制H-S 二维直方图
//calcHist(&img0_HSV,1,channels,Mat(),hist0,2,histSize,ranges,true,false);
calcHist(&img1_HSV, 1, channels, Mat(), hist0, 2, histSize, ranges, true, false);
drawHist3(img0_HSV,NORM_INF,"hist00");
//drawHist3(img1_HSV,NORM_INF,"hist1");
double dur;
clock_t start, end;
start = clock();
imwrite("111.png",img0);
end = clock();
dur = (double)(end - start);
printf("Use Time:%f\n", (dur / CLOCKS_PER_SEC));
Mat backproj;
calcBackProject(&img0_HSV,1,channels,hist0,backproj,ranges,1.0);
imshow("反向投影的结果",backproj);
waitKey(0);
return 0;
} //比较直方图
void drawingHist(Mat &hist,int type ,string name)
{
int width = 2;
int hist_h = 400;
int hist_w = 512;
Mat histImage = Mat::zeros(hist_h,hist_w,CV_8UC3);
normalize(hist,hist,1,0,type,-1,Mat());
for (int i = 1; i < hist.rows; i++)
{
rectangle(histImage,Point(width*(i-1),hist_h-1),Point(width*(i-1),hist_h-cvRound(hist_h*hist.at<float>(i-1))-1),Scalar(255,255,255),-1);
}
imshow(name, histImage);
}
int main55()
{
Mat img = imread("apple.jpg");
if (img.empty())
{
cout << "图像数据有问题" << endl;
return -1;
}
Mat gray, gray2, gray3, hist, hist2, hist3;
cvtColor(img, gray,COLOR_BGR2GRAY);
resize(gray,gray2,Size(),0.5,0.5);
gray3 = imread("lena.png",IMREAD_GRAYSCALE);
const int channels[1] = { 0 };
float inRanges[2] = { 0,255 };
const float*ranges[1] = { inRanges };
const int bins[1] = { 256 };
calcHist(&gray, 1, channels, Mat(), hist, 1, bins, ranges);
calcHist(&gray2,1, channels, Mat(), hist2,1,bins,ranges);
calcHist(&gray3,1, channels, Mat(), hist3,1,bins,ranges);
drawingHist(hist,NORM_INF,"hist");
drawingHist(hist2,NORM_INF,"hist2");
drawingHist(hist3,NORM_INF,"hist3");
//将原来的直方图和现在的直方图进行比较
double histNumer = compareHist(hist,hist2,HISTCMP_CORREL);
cout << histNumer << endl;
double histN2 = compareHist(hist,hist3,HISTCMP_CORREL);
cout << histN2 << endl;
waitKey(0);
return 0;
} //直方图均衡
void drawHist(Mat &hist,int type,string name) //归一化并绘制直方图函数
{
int hist_w = 600;
int hist_h = 400;
int width = 2;
Mat histImage = Mat::zeros(hist_h,hist_w,CV_8UC3);
normalize(hist,hist,1,0,type,-1,Mat());
for (int i = 1; i < hist.rows; i++)
{
rectangle(histImage,Point(width*(i-1),hist_h-1),Point(width*i-1,hist_h-cvRound(hist_h*hist.at<float>(i-1))-1),Scalar(255,255,255),-1);
}
imshow(name,histImage);
}
int main4()
{
Mat img = imread("gearwHeel.jpg");
if (img.empty())
{
cout << "数据有问题" << endl;
return -1;
}
Mat gray, hist, hist2;
cvtColor(img,gray,COLOR_BGR2GRAY);
Mat equalImg;
equalizeHist(gray,equalImg);//将直方图均值化
const int channels[1] = { 0 };
float inRanges[2] = { 0,255 };
const float*ranges[1] = {inRanges};
const int bins[1] = { 256 };
calcHist(&gray,1,channels,Mat(),hist,1,bins,ranges);
calcHist(&equalImg,1,channels,Mat(),hist2,1,bins,ranges);
drawHist(hist,NORM_INF,"hist");
drawHist(hist2,NORM_INF,"hist2");
namedWindow("原图",WINDOW_NORMAL);
imshow("原图",gray);
imshow("均值化图",equalImg);
waitKey(0);
return 0;
} //图像的模板匹配
int main()
{
Mat img = imread("4.Bmp");
Mat temp = imread("6.Bmp");//待匹配的图，可以是从原图截取出来的一张图
if (img.empty() || temp.empty())
{
cout << "图像数据有问题" << endl;
waitKey(3000);
return -1;
}
Mat result;
matchTemplate(img,temp,result,TM_CCOEFF_NORMED);
double maxVal, minVal;
Point minLoc, maxLoc;
//寻找匹配结果最大的值
minMaxLoc(result,&minVal,&maxVal,&minLoc,&maxLoc);
//绘制最佳的矩形
rectangle(img,cv::Rect(maxLoc.x,maxLoc.y,temp.cols,temp.rows),Scalar(0,0,0),8);
namedWindow("原图",WINDOW_NORMAL);
imshow("原图",img);
namedWindow("匹配图",WINDOW_NORMAL);
imshow("匹配图",temp);
namedWindow("result",WINDOW_AUTOSIZE);
imshow("result",result);
waitKey(0);
return 0;
}该处使用的url网络请求的数据。
总结
代码参考 《OpenCV 4快速入门》 冯振 郭延宁 吕跃勇
本门意在总结在本书学到的知识、复习。
亲测对于简单的，角度差异小的模板匹配，灰度匹配的效果还是可以的。
后期会抽时间进一步讲解此文的代码。以上代码欢迎交流
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