文章目录
- 一、直方图比较
- 计算公式
- 效果演示
- 二、直方图反向投影
一、直方图比较
对输入的两张图像计算得到直方图H1与H2,归一化到相同的尺度空间,然后可以通过计算H1与H2的之间的距离得到两个直方图的相似程度,进而比较图像本身的相似程度。
Opencv提供的比较方法有四种:
- Correlation相关性比较 相关性程度 = (1,-1) ,为1时相关性最强
- Chi-Square卡方比较 (越接近0,两个直方图越相似)
- Intersection十字交叉性 (取两个直方图每个相同位置的值的最小值,然后求和,这个比较方式不是很好,不建议使用)
- Bhattacharyya distance巴氏距离 (比较结果是很准的,计算结果范围为 0-1 ,0表示两个直方图非常相关,1最不相似)
计算公式
其中N是直方图的BIN个数,
- 相关性计算(CV_COMP_CORREL)
- 卡方计算(CV_COMP_CHISQR)
- 十字计算(CV_COMP_INTERSECT)
- 巴氏距离计算(CV_COMP_BHATTACHARYYA )
- 颜色空间转换BGR2HSV:
计算图像的直方图,然后归一化到[0~1]之间(calcHist和normalize;)
InputArray h1, // 直方图数据,下同
InputArray H2,
int method // 比较方法,上述四种方法之一
)
头文件 quick_opencv.h:声明类与公共函数
#pragma once
#include <opencv2\opencv.hpp>
using namespace cv;
class QuickDemo {
public:
...
void compareHist_Demo(Mat& image1, Mat& image2, Mat& image3);
void backProjection_Demo(Mat& image1);
};
主函数调用该类的公共成员函数
#include <opencv2\opencv.hpp>
#include <quick_opencv.h>
#include <iostream>
using namespace cv;
int main(int argc, char** argv) {
Mat src1 = imread("D:\\Desktop\\pandas_small22.png");
Mat src2 = imread("D:\\Desktop\\pandas_small22_test1.png");
Mat src3 = imread("D:\\Desktop\\pandas_small22_test2.png");
if (src1.empty()) {
printf("Could not load images src1...\n");
return -1;
}
if (src2.empty()) {
printf("Could not load images src2...\n");
return -1;
}
if (src3.empty()) {
printf("Could not load images src3...\n");
return -1;
}
QuickDemo qk;
qk.compareHist_Demo(src1, src2, src3);
qk.backProjection_Demo(src1);
waitKey(0);
destroyAllWindows();
return 0;
}
源文件 quick_demo.cpp:实现类与公共函数
效果演示
void QuickDemo::compareHist_Demo(Mat& image, Mat& test1, Mat& test2) {
Mat hsv_dst1, hsv_dst2, hsv_dst3;
cvtColor(image, hsv_dst1, COLOR_BGR2HSV);
cvtColor(test1, hsv_dst2, COLOR_BGR2HSV);
cvtColor(test2, hsv_dst3, COLOR_BGR2HSV);
Mat hsv_src1 = hsv_dst1.clone();
Mat hsv_src2 = hsv_dst2.clone();
Mat hsv_src3 = hsv_dst3.clone();
int h_bins = 50;
int s_bins = 60;
int histSize[] = { h_bins, s_bins };
//h = [0-179] s=[0,255]
float h_ranges[] = { 0,180 };
float s_ranges[] = { 0,256 };
const float* ranges[] = { h_ranges, s_ranges };
// Use the o-th and 1-st channels
int channels[] = { 0,1 };
MatND hist_base;
MatND hist_test1;
MatND hist_test2;
calcHist(&hsv_dst1, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false);
normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());
calcHist(&hsv_dst2, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false);
normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());
calcHist(&hsv_dst3, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false);
normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());
int method[4] = { HISTCMP_CORREL ,HISTCMP_CHISQR, HISTCMP_INTERSECT,HISTCMP_BHATTACHARYYA };
for (int i = 0; i < 4; i++) {
double basebase = compareHist(hist_base, hist_base, method[i]);
double basetest1 = compareHist(hist_base, hist_test1, method[i]);
double basetest2 = compareHist(hist_base, hist_test2, method[i]);
putText(hsv_dst1, to_string(basebase), Point(20, image.rows - 20), FONT_HERSHEY_COMPLEX, 0.8, Scalar(255, 255, 0), 2, 8);
putText(hsv_dst2, to_string(basetest1), Point(20, image.rows - 20), FONT_HERSHEY_COMPLEX, 0.8, Scalar(255, 255, 0), 2, 8);
putText(hsv_dst3, to_string(basetest2), Point(20, image.rows - 20), FONT_HERSHEY_COMPLEX, 0.8, Scalar(255, 255, 0), 2, 8);
imshow("src", hsv_dst1);
imshow("dst1", hsv_dst2);
imshow("dst2", hsv_dst3);
// 清空图片文字
hsv_src1.copyTo(hsv_dst1);
hsv_src2.copyTo(hsv_dst2);
hsv_src3.copyTo(hsv_dst3);
waitKey(0);
}
}
对测试图片进行光影调整后分别保存为test1,test2副本后测试:
比较方法:HISTCMP_CORREL
比较方法:HISTCMP_CHISQR
比较方法:HISTCMP_INTERSECT
比较方法:HISTCMP_BHATTACHARYYA
二、直方图反向投影
OpenCV—python 反向投影 ROI
反向投影是反映直方图模型在目标图像中的分布情况
简单点说就是用直方图模型去目标图像中寻找是否有相似的对象。通常用HSV色彩空间的HS两个通道直方图模型。
一般检查流程
- 加载图片imread
- 将图像从RGB色彩空间转换到HSV色彩空间cvtColor
- 计算直方图和归一化calcHist与normalize
- Mat与MatND其中Mat表示二维数组,MatND表示三维或者多维数据,此处均可以用Mat表示。
- 计算反向投影图像 - calcBackProject
共三个重载函数,我这里只列出一个
void calcBackProject( const Mat* images, 输入图像,图像深度必须位CV_8U,CV_16U或CV_32F中的一种
int
nimages, 输入图像的数量
const int*
channels, 用于计算反向投影的通道列表,通道数必须与直方图维度相匹配
InputArray
hist, 输入的直方图,直方图的bin可以是密集(dense)或稀疏(sparse)
OutputArray
backProject, 目标反向投影输出图像,是一个单通道图像
const float**
ranges, 方图中每个维度bin的取值范围
double
scale = 1, 可选输出反向投影的比例因子
bool
uniform = true 直方图是否均匀分布(uniform)的标识符,有默认值true
)
void QuickDemo::backProjection_Demo(Mat& image, Mat& test1) {
Mat hsv,h_mat;
cvtColor(image, hsv, COLOR_BGR2HSV);
h_mat = Mat::zeros(hsv.size(), hsv.depth());
int nchannels[] = { 0,0 };
mixChannels(&hsv,1, &h_mat, 1, nchannels, 1);
int binSize = 12;
float range[] = { 0,180 };
const float* histRange{ range };
Mat h_hist;
calcHist(&h_mat, 1, 0, Mat(), h_hist, 1, &binSize, &histRange, true, false);
normalize(h_hist, h_hist, 0, 255, NORM_MINMAX, -1, Mat());
Mat backProjectImage;
calcBackProject(&h_mat, 1,0, h_hist, backProjectImage, &histRange, 1, true);
imshow("backPro", backProjectImage);
int hist_h = 400;
int hist_w = 400;
Mat hist_Image = Mat::zeros(hist_w, hist_h, CV_8UC3);
int bin_w = hist_w / binSize;
for (int i = 0; i < binSize; i++) {
rectangle(hist_Image,
Point((i - 1) * bin_w, hist_h - cvRound(h_hist.at<float>(i - 1) * (400 / 255))),
Point(i* bin_w, hist_h),
Scalar(255, 255, 0), -1);
}
imshow("histogram", hist_Image);
}
使用效果:
使用 trackbar 详情
使用trackbar, 代码有问题,请教大佬。
static void on_bin_hist(int binSize_, void* h_mat_) {
Mat h_hist;
Mat h_mat = *((Mat*)h_mat_);
int binSize = MAX(binSize_, 2);
float range[] = { 0,180 };
const float* histRange{ range };
calcHist(&h_mat, 1, 0, Mat(), h_hist, 1, &binSize, &histRange, true, false);
normalize(h_hist, h_hist, 0, 255, NORM_MINMAX, -1, Mat());
Mat backProjectImage;
calcBackProject(&h_mat, 1, 0, h_hist, backProjectImage, &histRange, 1, true);
imshow("backPro", backProjectImage);
int hist_h = 400;
int hist_w = 400;
Mat hist_Image = Mat::zeros(hist_w, hist_h, CV_8UC3);
int bin_w = cvRound((double)hist_w / binSize);
for (int i = 0; i < binSize; i++) {
rectangle(hist_Image,
Point((i - 1) * bin_w, hist_h - cvRound(h_hist.at<float>(i - 1) * (400 / 255))),
Point(i * bin_w, hist_h),
Scalar(255, 255, 0), -1);
}
imshow("histogram", hist_Image);
}
void QuickDemo::backProjection_track_bar_Demo(Mat& image) {
namedWindow("histogram", WINDOW_NORMAL);
namedWindow("backPro", WINDOW_NORMAL);
int binSize = 12;
Mat hsv, h_mat;
cvtColor(image, hsv, COLOR_BGR2HSV);
h_mat = Mat::zeros(hsv.size(), hsv.depth());
int nchannels[] = { 0,0 };
mixChannels(&hsv, 1, &h_mat, 1, nchannels, 1);
createTrackbar("hist_bins", "histogram", &binSize, 180, on_bin_hist, &h_mat);
on_bin_hist(binSize, &h_mat);
}