【OpenCV】直方图

直方图均衡化

  直方图均衡化作用：提高图像对比度，拉伸灰度值范围。

代码示例

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;int main(int, char** argv){    Mat src, dst;    char* source_window = "Source Image";    char* equal_window = "Equalization Image";    src = imread("../data/source.jpg");    if (src.empty()) { return -1; };    cvtColor(src, src, COLOR_BGR2GRAY);     // 灰度图像    equalizeHist(src, dst);                 // 直方图均衡化    imshow(source_window, src);    imshow(equal_window, dst);    waitKey(0);    return 0;}

运行结果

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直方图计算

void cv::calcHist   (   const Mat * images,   int   nimages,                        const int * channels, InputArray    mask,                        OutputArray hist,     int   dims,                        const int * histSize, const float **    ranges,                        bool uniform = true,  bool  accumulate = false )   说明：    src：原图像    nimages:原图像数量          channels:通道    mask:掩模     hist:输出直方图             dims:直方图维度    histSize:直方图每一维的个数                ranges:直方图每一维的范围    uniform:true则直方图范围尺寸均匀分布        accumulate:false则调用该函数前直方图先清零

代码示例

#include "opencv2/highgui.hpp"#include "opencv2/imgcodecs.hpp"#include "opencv2/imgproc.hpp"#include <iostream>using namespace std;using namespace cv;int main( ){    Mat src, dst;    src = imread("../data/lena.jpg");    if (src.empty()) { return -1; }    vector<Mat> bgr_planes;                 // 矩阵向量    split(src, bgr_planes);                 // 将多通道图像分为多个单通道图像    int histSize = 256;    float range[] = { 0,256 };    const float* histRange = { range };     // 必须加const，否则报错    bool uniform = true;                    // 直方图是否分布均匀    bool accumulate = false;                // 如果为true，在开始分配时直方图不会被清除    Mat b_hist, g_hist, r_hist;    calcHist(&bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate);    calcHist(&bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate);    calcHist(&bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate);    int hist_w = 512, hist_h = 400;                     // 直方图的横纵范围    int bin_w = cvRound((double)hist_w / histSize);     // 每个柱状的宽度    Mat histImage(hist_h, hist_w, CV_8UC3, Scalar(0, 0, 0));    normalize(b_hist, b_hist, 9, histImage.rows, NORM_MINMAX, -1, Mat());           // 归一化    normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());    normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());    for (int i = 1; i < histSize; i++)                                              // 绘制直线    {        line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))),                Point(bin_w*(i), hist_h - cvRound(b_hist.at<float>(i))),Scalar(255, 0, 0), 2, 8, 0);        line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))),            Point(bin_w*(i), hist_h - cvRound(g_hist.at<float>(i))),Scalar(0, 255, 0), 2, 8, 0);        line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))),            Point(bin_w*(i), hist_h - cvRound(r_hist.at<float>(i))),Scalar(0, 0, 255), 2, 8, 0);    }    imshow("calcHist Demo", histImage);    waitKey(0);    return 0;}

运行结果

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直方图比较

double cv::compareHist  (   InputArray  H1,                            InputArray  H2,                            int         method )   说明：    method:        相关Correlation  ( CV_COMP_CORREL )        卡方Correlation  ( CV_COMP_CORREL )        交集Intersection ( CV_COMP_INTERSECT )        巴特查里亚距离Bhattacharyya distance ( CV_COMP_BHATTACHARYYA )

  引入了函数cv::compareHist( )，代码与上面类似，在此不赘述，具体查看链接：

  https://docs.opencv.org/master/d8/dc8/tutorial_histogram_comparison.html

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