A cpp implementation the weighted average threshold as the criterion for opt

				//原图二值图
				cv::Mat binary;
				//target_rect  通过搜索得到的填涂区域
				binary = ori_binary(target_rect);

				vectr<vector<Point>> contours;
				vector<Vec4i> hierarchy;
				findContours(binary, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
				for (size_t i = 0; i < contours.size(); i++) {
					double area = contourArea(contours[i]);
					if (area < 4) {
						drawContours(binary, contours, i, Scalar(0), -1); 
					}
				}

				//目标区域内 统计黑白像素各自的数量
				int blackPixelCount = 0;
				for (int i = 0; i < binary.rows; i++) {
					for (int j = 0; j < binary.cols; j++) {
						if (binary.at<uchar>(i, j) == 0) {
							blackPixelCount++;
						}
					}
				}

				int whitePixelCount = 0;
				for (int i = 0; i < binary.rows; i++) {
					for (int j = 0; j < binary.cols; j++) {
						if (binary.at<uchar>(i, j) == 255) {
							whitePixelCount++;
						}
					}
				}

　　　　　　　　　　//计算黑白像素占比
				int a_target_area = binary.rows * binary.cols;
				double rf= blackPixelCount / (double)a_target_area; 
				double rb = whitePixelCount / (double)a_target_area;   
				int T= rf_bg_rate * 255;   
				int A= rb_bg_rate * 255;   

				Mat dst1(target_area.size(), target_area.type(), gray_f_value);
				dst1.copyTo(gray(Rect(target_rect.x, target_rect.y, dst1.cols, dst1.rows)));
				show_img(dst1);

				Mat dst2(target_area.size(), target_area.type(), gray_b_value);
				dst2.copyTo(gray(Rect(target_rect.x, target_rect.y, dst2.cols, dst2.rows)));
				show_img(dst2);

				Mat binary_img;
				cv::cvtColor(binary, binary_img, COLOR_GRAY2BGR);
				binary_img.copyTo(orignal_img(Rect(target_rect.x, target_rect.y, binary_img.cols, binary_img.rows)));

				std::string str_rate = std::to_string(rb);
				if (str_rate.length() > 5)
				str_rate = str_rate.substr(0, 5);
				cv::putText(orignal_img, str_rate, cv::Point(target_rect.x, target_rect.y), cv::FONT_HERSHEY_SIMPLEX, 0.4, Scalar(0, 0, 255), 1);
				show_img(orignal_img);

				double sum_gwm_value = 0.0;
				double sum_gwm_mask = 0.0;
				for (int i = 0; i < target_area.rows; i++) {  
					for (int j = 0; j < target_area.cols; j++) {
						int pixelValue = target_area.at<uchar>(i, j);
						double mask_rate = 0;
						if (pixelValue >= T)
							mask_rate = 0;
						else if (pixelValue >= A)
							mask_rate = 1 - (pixelValue - A) / (double)(T- A);
						else
							mask_rate = 1;
						sum_gwm_value += pixelValue * mask_rate;
						sum_gwm_mask += mask_rate;
					}
				}        

				int gmw_pixel_value = sum_gwm_value / sum_gwm_mask;

				std::string a_pixel_value = std::to_string(gmw_pixel_value);
				cv::putText(orignal_img, a_pixel_value, cv::Point(target_rect.x, target_rect.y), cv::FONT_HERSHEY_SIMPLEX, 0.4, Scalar(0, 0, 255), 1);
				
				int gwmPixelCount = 0;
				for (int i = 0; i < target_area.rows; i++) {
					for (int j = 0; j < target_area.cols; j++) {
						if (target_area.at<uchar>(i, j) < gmw_pixel_value) {
							gwmPixelCount++;
						}
					}
				}  
				double gmw_rate = gwmPixelCount / (double)a_target_area;
				int gmw_value = gmw_rate * 255;

				std::string a_gmw_value = std::to_string(gmw_value);
				if (a_gmw_value.length() > 4)
						a_gmw_value = a_gmw_value.substr(0, 4);
				cv::putText(orignal_img, a_gmw_value, cv::Point(target_rect.x, target_rect.y), cv::FONT_HERSHEY_SIMPLEX, 0.4, Scalar(0, 0, 255), 1);

				Mat dst3(target_area.size(), target_area.type(), gmw_pixel_value);
				show_img(dst3);

				Mat dst4(target_area.size(), target_area.type(), gmw_value);
				show_img(dst4);

				dst4.copyTo(gray(Rect(target_rect.x, target_rect.y, dst4.cols, dst4.rows)));