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#include <vector>
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#include <iostream>
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#include <exception>
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#include <opencv2/opencv.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/video.hpp>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/video/background_segm.hpp>
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#include <sys/time.h>
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#include "ImageDetectConfig.h"
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#include "log_manager.h"
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using namespace std;
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using namespace cv;
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// cast 计算出的偏差值，小于1.0表示比较正常，大于1.0表示存在亮度异常；
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// 当cast异常时，da大于0表示过亮，da小于0表示过暗。
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void detect_brightness(cv::Mat input_img, float& cast, float& da)
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{
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cv::Mat gray_img;
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cv::cvtColor(input_img, gray_img, COLOR_BGR2GRAY);
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float a = 0, Ma = 0;
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int hist[256] = { 0 };
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for (int i = 0; i < gray_img.rows; i++)
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{
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for (int j = 0; j < gray_img.cols; j++) {
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a += float(gray_img.at<uchar>(i, j) - 128); // 在计算过程中，考虑128为亮度均值点
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hist[gray_img.at<uchar>(i, j)]++;
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}
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}
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da = a / float(gray_img.total());
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for (int i = 0; i < 256; i++) {
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Ma += abs(i - 128 - da) * hist[i];
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}
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Ma /= float(gray_img.total());
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cast = abs(da) / abs(Ma);
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}
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void detect_saturation(cv::Mat input_img, float& satu)
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{
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Mat dstImage1;
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cvtColor(input_img, dstImage1, COLOR_RGB2HSV);
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vector<Mat> channels;
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split(dstImage1, channels);//分离HSV
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Scalar mean; //均值
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Scalar stddev; //标准差
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meanStdDev( channels[1], mean, stddev ); //计算均值和标准差
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satu = mean.val[0];
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}
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int main(int argc, char *argv[])
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{
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Mat img = imread(argv[1]);
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float cast, da, satu;
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detect_brightness(img, cast, da);
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detect_saturation(img, satu);
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cout<<cast<<"||"<<da<<"||"<<satu;
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return 0;
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}
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