Add Max_Entropy.cpp

Author: 2209520576

Imge segmentation/Max_Entropy.cpp

@@ -0,0 +1,101 @@
+#include <iostream>
+#include <opencv2/core.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/imgproc.hpp>
+
+int Max_Entropy(cv::Mat& src, cv::Mat& dst, int thresh, int p){
+	const int Grayscale = 256;
+	int Graynum[Grayscale] = { 0 };
+	int r = src.rows;
+	int c = src.cols;
+	for (int i = 0; i < r; ++i){
+		const uchar* ptr = src.ptr<uchar>(i);   
+		for (int j = 0; j < c; ++j){   
+			if (ptr[j] == 0)				//排除掉黑色的像素点
+				continue;
+			Graynum[ptr[j]]++;
+		}
+	}
+
+	float probability = 0.0; //概率
+	float max_Entropy = 0.0; //最大熵
+	int totalpix = r*c;
+	for (int i = 0; i < Grayscale; ++i){
+
+		float HO = 0.0; //前景熵
+		float HB = 0.0; //背景熵
+
+	    //计算前景像素数
+		int frontpix = 0;
+		for (int j = 0; j < i; ++j){
+			frontpix += Graynum[j];
+		}
+		//计算前景熵
+		for (int j = 0; j < i; ++j){
+			if (Graynum[j] != 0){
+				probability = (float)Graynum[j] / frontpix;
+				HO = HO + probability*log(1/probability);
+			}
+		}
+
+		//计算背景熵
+		for (int k = i; k < Grayscale; ++k){
+			if (Graynum[k] != 0){
+				probability = (float)Graynum[k] / (totalpix - frontpix);
+				HB = HB + probability*log(1/probability);
+			}
+		}
+
+		//计算最大熵
+		if(HO + HB > max_Entropy){
+			max_Entropy = HO + HB;
+			thresh = i + p; 
+		}
+	}
+
+	//阈值处理
+	src.copyTo(dst);
+	for (int i = 0; i < r; ++i){
+		uchar* ptr = dst.ptr<uchar>(i);
+		for (int j = 0; j < c; ++j){
+			if (ptr[j]> thresh)
+				ptr[j] = 255;
+			else
+				ptr[j] = 0;
+		}
+	}
+	return thresh;
+}
+
+
+int main(){
+	cv::Mat src = cv::imread("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Img\\Fig0943(a)(dark_blobs_on_light_background).tif");
+	if (src.empty()){
+		return -1;
+	}
+	if (src.channels() > 1)
+		cv::cvtColor(src, src, CV_RGB2GRAY);
+
+	cv::Mat dst, dst2;
+	int thresh = 0;
+	double t2 = (double)cv::getTickCount();
+	thresh = Max_Entropy(src, dst, thresh,-20); //Max_Entropy
+	std::cout << "Mythresh=" << thresh << std::endl;
+	t2 = (double)cv::getTickCount() - t2;
+	double time2 = (t2 *1000.) / ((double)cv::getTickFrequency());
+	std::cout << "my_process=" << time2 << " ms. " << std::endl << std::endl;
+
+	double  Otsu = 0;
+	Otsu = cv::threshold(src, dst2, Otsu, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+	std::cout << "Otsuthresh=" << Otsu << std::endl;
+	
+
+	cv::namedWindow("src", CV_WINDOW_NORMAL);
+	cv::imshow("src", src);
+	cv::namedWindow("dst", CV_WINDOW_NORMAL);
+	cv::imshow("dst", dst);
+	cv::namedWindow("dst2", CV_WINDOW_NORMAL);
+	cv::imshow("dst2", dst2);
+	//cv::imwrite("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Image Filtering\\MeanFilter\\TXT.jpg",dst);
+	cv::waitKey(0);
+}