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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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void detect_graydiff(cv::Mat img, float& CON, float& ASM, float& ENT, float& MEAN)
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{
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Mat mi;
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int row = img.rows;
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int col = img.cols;
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//CON=i*i*P(I)
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//计算i
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mi.create(row, col, img.type());
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for (int i = 0; i < row-1; i++)
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for (int j = 0; j < col; j++)
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{
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//cout << "BEF" << int(img.ptr<uchar>(i)[j]) << endl;
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mi.at<Vec3b>(i, j) = img.at<Vec3b>(i + 1, j) - img.at<Vec3b>(i, j);
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//cout << "IMF:" << int(mi.ptr<uchar>(i)[j]) << endl;
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}
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//计算p(I) 差值的概率
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int new_cols = mi.cols;
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int new_rows = mi.rows;
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int gray[256] = { 0 };
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double gray_prob[256] = { 0 };
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int num = 0;// 像素的总个数
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//统计直方图各个像素灰度值
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for (int i = 0; i < mi.rows; i++)
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{
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uchar *p = mi.ptr<uchar>(i);
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for (int j = 0; j < mi.cols; j++)
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{
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int value = p[j];
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gray[value]++;
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num++;
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}
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}
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//计算直方图概率分布
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for (int i = 0; i < 256; i++)
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{
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gray_prob[i] = ((double)gray[i] / num);
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}
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CON = 0.0;
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ASM = 0.0;
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ENT = 0.0;
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MEAN = 0.0;
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double v = 1.0/ 255.0;
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for (int i = 0; i <= 255; i++)
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{
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CON += i*i*gray_prob[i];//计算CON
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ASM += gray_prob[i] * gray_prob[i];//计算角度方向二阶矩
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if (gray_prob[i]!=0)
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ENT += (0-gray_prob[i] * log10(gray_prob[i]));//计算熵
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MEAN += v*i*gray_prob[i];//计算平均值
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}
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return;
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}
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void detect_definition(cv::Mat input_img, float& sb, float& lap, float& dev)
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{
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Mat imgGray, imgSobel, imgLap, imgDev, meanValueImage;
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cvtColor(input_img, imgGray, CV_BGR2GRAY);
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Sobel(imgGray, imgSobel, CV_16U, 1, 1);
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Laplacian(imgGray, imgLap, CV_16U);
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meanStdDev(imgGray,meanValueImage,imgDev);
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sb = mean(imgSobel)[0];
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lap = mean(imgLap)[0];
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dev = mean(imgDev)[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, CON, ASM, ENT, MEAN, sb, lap, dev;
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Mat img_brightness = img.clone();
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detect_brightness(img_brightness, cast, da);
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Mat img_saturation = img.clone();
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detect_saturation(img_saturation, satu);
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Mat img_graydiff = img.clone();
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detect_graydiff(img_graydiff, CON, ASM, ENT, MEAN);
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Mat img_definition = img.clone();
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detect_definition(img_definition, sb, lap, dev);
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cout<<cast<<"||"<<da<<"||"<<satu<<"||"<<CON<<"||"<<ASM<<"||"<<ENT<<"||"<<MEAN<<"||"<<sb<<"||"<<lap<<"||"<<dev;
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return 0;
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}
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