1--直方图均衡化

① 简述：

对图片的对比度进行调整，输入为灰度图像，对亮度进行归一化处理，提高灰度图的对比度；

② Opencv API：

cv::equalizeHist(gray, dst);

③ 代码实例：

# include <opencv2/opencv.hpp>
# include <cstdio>
# include <iostream>int main(int argc, char** argv){cv::Mat src;src = cv::imread("C:/Users/Liujinfu/Desktop/opencv_bilibili/test1.jpg");if (src.empty()){printf("could not load image..\n");return -1;}cv::imshow("input", src);cv::Mat gray, dst;cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);cv::imshow("gray", gray);cv::equalizeHist(gray, dst);cv::imshow("output", dst);cv::waitKey(0);return 0;
}

2--直方图计算

① Opencv API：

void cv::calcHist(const cv::Mat *images, int nimages, const int *channels, cv::InputArray mask, cv::OutputArray hist, int dims, const int *histSize, const float **ranges, bool uniform = true, bool accumulate = false);

参数简要分析：（具体参数解释见Opencv API）

const cv::Mat *images：输入图像
int nimages：输入图像的数目，第一幅图像的通道标号从 0 到 image[0].channels( ) - 1
const int *channels：需要统计的通道 dim
cv::InputArray mask：掩码，大小和 image 一致，其中把需要处理的部分部分指定为 1，不需要处理的部分指定为0，一般设置为 None，表示处理整幅图像；
cv::OutputArray hist：输出的灰度分布数组
int dims：hist 的维度
const int *histSize：hist 的大小，即直方图横坐标的范围
const float **ranges：hist 的范围，即直方图纵坐标的范围
bool uniform = true：是否归一化

② 代码实例：

# include <opencv2/opencv.hpp>
# include <cstdio>
# include <iostream>int main(int argc, char** argv){cv::Mat src;src = cv::imread("C:/Users/Liujinfu/Desktop/opencv_bilibili/test1.jpg");if (src.empty()){printf("could not load image..\n");return -1;}cv::imshow("input", src);// 分通道显示std::vector<cv::Mat> bgr_planes;cv::split(src, bgr_planes);// 计算直方图int histSize = 256;float range[] = {0, 256};const float *histRanges = {range};cv::Mat b_hist, g_hist, r_hist;cv::calcHist(&bgr_planes[0], 1, 0, cv::Mat(), b_hist, 1, &histSize, &histRanges, true, false);cv::calcHist(&bgr_planes[1], 1, 0, cv::Mat(), g_hist, 1, &histSize, &histRanges, true, false);cv::calcHist(&bgr_planes[2], 1, 0, cv::Mat(), r_hist, 1, &histSize, &histRanges, true, false);// 归一化int hist_h = 400;int hist_w = 600;int bin_w = hist_w / histSize;cv::Mat histImage(hist_w, hist_h, CV_8UC3, cv::Scalar(0, 0, 0));cv::normalize(b_hist, b_hist, 0, hist_h, cv::NORM_MINMAX, -1, cv::Mat()); // 归一化到(0, hist_h)cv::normalize(g_hist, g_hist, 0, hist_h, cv::NORM_MINMAX, -1, cv::Mat());cv::normalize(r_hist, r_hist, 0, hist_h, cv::NORM_MINMAX, -1, cv::Mat());// 绘制直方图（直方图坐标和画图坐标不同，画图坐标从左上角开始，即左上角为零点）for(int i = 1; i < histSize; i++){cv::line(histImage, cv::Point((i - 1) * bin_w, hist_h - cvRound(b_hist.at<float>(i - 1))), cv::Point((i)*bin_w, hist_h - cvRound(b_hist.at<float>(i))), cv::Scalar(255, 0, 0), 2, cv::LINE_AA);cv::line(histImage, cv::Point((i - 1) * bin_w, hist_h - cvRound(g_hist.at<float>(i - 1))), cv::Point((i)*bin_w, hist_h - cvRound(g_hist.at<float>(i))), cv::Scalar(0, 255, 0), 2, cv::LINE_AA);cv::line(histImage, cv::Point((i - 1) * bin_w, hist_h - cvRound(r_hist.at<float>(i - 1))), cv::Point((i)*bin_w, hist_h - cvRound(r_hist.at<float>(i))), cv::Scalar(0, 0, 255), 2, cv::LINE_AA);}cv::imshow("output", histImage);cv::waitKey(0);return 0;
}