OpenCV学习笔记（七）
一、直方图的计算与绘制
1.计算直方图：calcHist()函数
calcHist()函数用于计算一个或多个阵列的直方图
void calcHist(const Mat* images, //输入的数组需为相同的深度（CV_8U或CV_32F）和相同的尺寸
int nimages,//输入数组的个数，也就是第一个参数中存放了多少张“图像”，有几个原数组
const int* channels,//需要统计的通道(dim)索引
InputArray mask,//可选的操作掩码，若不为空需为8位，且与images[i]尺寸相同
OutputArray hist,//输出的模板直方图，一个二维数组
int dims, //需要计算的直方图的维度，一必须为正数，且不大于CV_MAX_DIMS
const int* histSize,//存放每个维度的直方图的取值范围
const float**ranges,//表示每一个数组的每一维的边界阵列，可以理解为每一维数值的取值范围
bool uniform = true,//指示直方图是否均匀的标识符，默认true
bool accumulate = false)//累计标识符，默认false
2.寻找最值：minMaxLoc()函数
void minMaxLoc(InputArray src,//输入的单通道阵列
double* minVal,//返回最小值的指针，若无返回，此值为NULL
double*maxVal = 0,//返回的最大值的指针，若无返回，为NULL
Point* minLoc = 0,//返回最小值的指针（二维情况），若无返回，此值为NULL
Point*maxLoc = 0,//返回的最大值的指针（二维情况），若无返回，为NULL
InputArray mask = noArray())//用于选择子阵列的可选掩膜
绘制H-S直方图
#include
#include
#include
using namespace cv;
using namespace std;
int main()
{
Mat srcImage, hasvImage;
srcImage = imread("1.jpg");
if (!srcImage.data) {
printf("读取图片失败");
return -1;
}
cvtColor(srcImage, hasvImage, COLOR_BGR2HSV);
//将色调量化为30个等级，将饱和度量化为32个等级
int hueBinNum = 30;//色调的直方图条数量
int saturationBinNum = 32;//饱和度的直方图直条数量
int histSize[] = { hueBinNum, saturationBinNum };
//定义色调的变化范围为0到179
float hueRanges[] = { 0,180 };
//定义饱和度的变化范围0（黑白灰）到255（纯光谱颜色）
float saturationRanges[] = { 0,256 };
const float* ranges[] = { hueRanges,saturationRanges };
MatND dstHist;
//参数准备，calcHist函数中将计算第0通道和第1通道的直方图
int channels[] = { 0,1 };
//正式调用calcHist函数，进行直方图计算
calcHist(&hasvImage,//输入的数组
1,//数组个数为1
channels,//通道索引
Mat(),//不使用掩膜
dstHist,//输出目标直方图
2,//需要计算的直方图维度为2
histSize,//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
true,//指示直方图是否均匀的标识符，true表示均匀的直方图
false);//累计标识符，false表示直方图在配置阶段会被置零
//绘制直方图准备参数
double maxValue = 0;//最大值
minMaxLoc(dstHist, 0, &maxValue, 0, 0);//查找数组和子数组的全局最小值
int scale = 10;
Mat histImg = Mat::zeros(saturationBinNum*scale, hueBinNum * 10, CV_8UC3);
//双层循环进行直方图绘制
for(int hue = 0; hue < hueBinNum;hue++)
for (int saturation = 0; saturation < saturationBinNum; saturation++) {
//直方图直条的值
float binValue = dstHist.at(hue, saturation);
int intensity = cvRound(binValue * 255 / maxValue);//强度
rectangle(histImg, Point(hue*scale, saturation*scale),
Point((hue + 1)*scale - 1, (saturation + 1)*scale - 1), Scalar::all(intensity), FILLED);
}
imshow("原图", srcImage);
imshow("H-S直方图", histImg);
waitKey(0);
return 0;
}
计算并绘制图像一维直方图
#include
#include
using namespace cv;
using namespace std;
int main()
{
Mat srcImage = imread("1.jpg", 0);
imshow("原图", srcImage);
if (!srcImage.data) {
printf( "fail to load image" );
return -1;
}
//定义变量
MatND dstHist;//在cv中用cvHistogram * hist = cvCreateHist
int dims = 1;
float hranges[] = { 0,255 };
const float* ranges[] = { hranges };
int size = 256;
int channels = 0;
//计算图像的直方图
calcHist(&srcImage, 1, &channels, Mat(), dstHist, dims, &size, ranges);
//cv中是cvCalcHist
int scale = 1;
Mat dstImage(size * scale, size, CV_8U, Scalar(0));
//获取最大值和最小值
double minValue = 0;
double maxValue = 0;
minMaxLoc(dstHist, &minValue, 0, 0);//在cv中用cvGetMinMaxHistValue
//绘制直方图
int hpt = saturate_cast(0.9 * size);
for (int i = 0; i < 256; i++) {
float binValue = dstHist.at(i);
int realValue = saturate_cast(binValue * hpt / maxValue);
rectangle(dstImage, Point(i * scale, size - 1),
Point((i + 1) * scale - 1,size - realValue), Scalar(255));
}
imshow("一维直方图", dstImage);
waitKey(0);
return 0;
}
绘制RGB三色直方图
#include
#include
using namespace cv;
using namespace std;
int main()
{
Mat srcImage = imread("1.jpg");
imshow("原图", srcImage);
if (!srcImage.data) {
printf( "fail to load image" );
return -1;
}
//参数准备
int bins = 256;
int hist_size[] = { bins };
float range[] = { 0,256 };
const float* ranges[] = { range };
MatND redHist, grayHist, blueHist;
int channels_r[] = { 0 };
//计算图像的直方图红色部分
calcHist(&srcImage, 1, channels_r, Mat(), //不使用掩膜
redHist, 1, hist_size, ranges,true,false);
//计算图像的直方图绿色部分
int channels_g[] = { 1 };
calcHist(&srcImage, 1, channels_g, Mat(), //不使用掩膜
grayHist, 1, hist_size, ranges, true, false);
//计算图像的直方图蓝色部分
int channels_b[] = { 2 };
calcHist(&srcImage, 1, channels_b, Mat(), //不使用掩膜
blueHist, 1, hist_size, ranges, true, false);
//绘制三色直方图
double maxValue_red, maxValue_green, maxValue_blue;
minMaxLoc(redHist, 0, &maxValue_red, 0, 0);
minMaxLoc(grayHist, 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);
//开始绘制
for (int i = 0; i < bins; i++) {
//参数准备
float binValue_red = redHist.at(i);
float binValue_green = grayHist.at(i);
float binValue_blue = blueHist.at(i);
int intensity_red = cvRound(binValue_red * histHeight / maxValue_red);
int intensity_green= cvRound(binValue_green * histHeight / maxValue_green);
int intensity_blue = cvRound(binValue_blue * histHeight / maxValue_blue);
//绘制红色分量
rectangle(histImage,Point(i*scale,histHeight-1),
Point((i+1)*scale-1,histHeight-intensity_red),Scalar(255,0,0));
//绘制绿色分量
rectangle(histImage, Point((i * +bins) * scale, histHeight - 1),
Point((i + bins + 1) * scale - 1, histHeight - intensity_green), Scalar(0, 255, 0));
//绘制蓝色分量
rectangle(histImage, Point((i + scale*2)*scale, histHeight - 1),
Point((i +bins*2+ 1) * scale - 1, histHeight - intensity_blue), Scalar(0, 0, 255));
}
imshow("图像的RGB直方图", histImage);
waitKey(0);
return 0;
}
直方图的对比：compareHist()函数
函数原型
//版本一
double compareHist(InputArray H1,InputArray H2,int method)
//版本二
double compareHist(const SparseMat&H1,const SparseMat&H2,int method)
直方图对比
#include
#include
using namespace cv;
using namespace std;
int main()
{
//声明存储基准图像和另外两张对比图像的矩阵(RGB,HSV)
Mat srcImage_base, hsvImage_base;
Mat srcImage_test1, hsvImage_test1;
Mat srcImage_test2, hsvImage_test2;
Mat hsvImage_halfDown;
//载入基准图像(srcImage_base)和两张测试图像srcImage_test1,srcImage_test2，并显示
srcImage_base = imread("1.jpg", 1);
srcImage_test1 = imread("2.jpg", 1);
srcImage_test2 = imread("3.jpg", 1);
//显示载入的3张图像
imshow("基准图像", srcImage_base);
imshow("测试图像1", srcImage_test1);
imshow("测试图像2", srcImage_test2);
//将图像由BGR色彩空间转换到HSV色彩空间
cvtColor(srcImage_base, hsvImage_base, COLOR_BGR2HSV);
cvtColor(srcImage_test1, hsvImage_test1, COLOR_BGR2HSV);
cvtColor(srcImage_test2, hsvImage_test2, COLOR_BGR2HSV);
//创建包含基准图像下半部分的半身图像（HSV格式）
hsvImage_halfDown = hsvImage_base(Range(hsvImage_base.rows / 2,
hsvImage_base.rows - 1), Range(0, hsvImage_base.cols - 1));
//初始化计算直方图需要的实参
//对hue通道使用30个bin，对saturation通道使用32个bin
int h_bins = 50, s_bins = 60;
int histSize[] = { h_bins,s_bins };
//hue的取值范围从0到256，saturation取值范围0-180
float h_ranges[] = { 0,256 };
float s_ranges[] = { 0,180 };
const float* ranges[] = { h_ranges,s_ranges };
//使用第0和第1通道
int channels[] = { 0,1 };
//创建存储直方图的MatND类的实例
MatND baseHist;
MatND halfDownHist;
MatND testHist1;
MatND testHist2;
//计算基准图像，两张测试图像，半身基准图像的HSV直方图
calcHist(&hsvImage_base, 1, channels, Mat(), baseHist, 2, histSize, ranges, true, false);
normalize(baseHist, baseHist, 0, 1, NORM_MINMAX, -1, Mat());
calcHist(&hsvImage_halfDown, 1, channels, Mat(), halfDownHist, 2, histSize, ranges, true, false);
normalize(halfDownHist, halfDownHist, 0, 1, NORM_MINMAX, -1, Mat());
calcHist(&hsvImage_test1, 1, channels, Mat(), testHist1, 2, histSize, ranges, true, false);
normalize(testHist1, testHist1, 0, 1, NORM_MINMAX, -1, Mat());
calcHist(&hsvImage_test2, 1, channels, Mat(), testHist2, 2, histSize, ranges, true, false);
normalize(testHist2, testHist2, 0, 1, NORM_MINMAX, -1, Mat());
//按顺序使用4种对比标准将基准图像的直方图与其余各直方图进行对比
for (int i = 0; i < 4; i++) {
//进行图像直方图的对比
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);
//输出结果
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);
}
printf("检测结束");
waitKey(0);
return 0;
}
