Update UnevenLightCompensate.cpp

Author: BBuf

README.md

@@ -24,3 +24,4 @@
 - Contrast Image Correction Method.cpp C++复现了《Contrast Image Correction Method》这篇论文，可以自适应矫正图像。算法原理请看：https://blog.csdn.net/just_sort/article/details/85005510
 - MultiScaleDetailBoosting.cpp C++复现了《DARK IMAGE ENHANCEMENT BASED ON PAIRWISE TARGET CONTRAST AND MULTI-SCALE DETAIL BOOSTING》论文，可以用于提升图像不同程度的细节信息。算法原理请看：https://blog.csdn.net/just_sort/article/details/85007555
 - Inrbl.cpp C++复现了《改进非线性亮度提升模型的逆光图像恢复》这篇论文，可以做逆光图像恢复。算法原理请看：https://blog.csdn.net/just_sort/article/details/86681325
+- unevenLightCompensate.cpp C++复现了《一种基于亮度均衡的图像阈值分割技术》这篇论文的光照补偿部分，可以对光照不均匀，曝光，逆光图像做亮度均衡，效果不错。原理请看：https://blog.csdn.net/just_sort/article/details/88551771

UnevenLightCompensate.cpp

@@ -0,0 +1,54 @@
+Mat speed_rgb2gray(Mat src) {
+	Mat dst(src.rows, src.cols, CV_8UC1);
+#pragma omp parallel for num_threads(12)
+	for (int i = 0; i < src.rows; i++) {
+		for (int j = 0; j < src.cols; j++) {
+			dst.at<uchar>(i, j) = ((src.at<Vec3b>(i, j)[0] << 18) + (src.at<Vec3b>(i, j)[0] << 15) + (src.at<Vec3b>(i, j)[0] << 14) +
+				(src.at<Vec3b>(i, j)[0] << 11) + (src.at<Vec3b>(i, j)[0] << 7) + (src.at<Vec3b>(i, j)[0] << 7) + (src.at<Vec3b>(i, j)[0] << 5) +
+				(src.at<Vec3b>(i, j)[0] << 4) + (src.at<Vec3b>(i, j)[0] << 2) +
+				(src.at<Vec3b>(i, j)[1] << 19) + (src.at<Vec3b>(i, j)[1] << 16) + (src.at<Vec3b>(i, j)[1] << 14) + (src.at<Vec3b>(i, j)[1] << 13) +
+				(src.at<Vec3b>(i, j)[1] << 10) + (src.at<Vec3b>(i, j)[1] << 8) + (src.at<Vec3b>(i, j)[1] << 4) + (src.at<Vec3b>(i, j)[1] << 3) + (src.at<Vec3b>(i, j)[1] << 1) +
+				(src.at<Vec3b>(i, j)[2] << 16) + (src.at<Vec3b>(i, j)[2] << 15) + (src.at<Vec3b>(i, j)[2] << 14) + (src.at<Vec3b>(i, j)[2] << 12) +
+				(src.at<Vec3b>(i, j)[2] << 9) + (src.at<Vec3b>(i, j)[2] << 7) + (src.at<Vec3b>(i, j)[2] << 6) + (src.at<Vec3b>(i, j)[2] << 5) + (src.at<Vec3b>(i, j)[2] << 4) + (src.at<Vec3b>(i, j)[2] << 1) >> 20);
+		}
+	}
+	return dst;
+}
+
+
+Mat unevenLightCompensate(Mat src, int block_Size) {
+	int row = src.rows;
+	int col = src.cols;
+	Mat gray(row, col, CV_8UC1);
+	if (src.channels() == 3) {
+		gray = speed_rgb2gray(src);
+	}
+	else {
+		gray = src;
+	}
+	float average = mean(gray)[0];
+	int new_row = ceil(1.0 * row / block_Size);
+	int new_col = ceil(1.0 * col / block_Size);
+	Mat new_img(new_row, new_col, CV_32FC1);
+	for (int i = 0; i < new_row; i++) {
+		for (int j = 0; j < new_col; j++) {
+			int rowx = i * block_Size;
+			int rowy = (i + 1) * block_Size;
+			int colx = j * block_Size;
+			int coly = (j + 1) * block_Size;
+			if (rowy > row) rowy = row;
+			if (coly > col) coly = col;
+			Mat ROI = src(Range(rowx, rowy), Range(colx, coly));
+			float block_average = mean(ROI)[0];
+			new_img.at<float>(i, j) = block_average;
+		}
+	}
+	new_img = new_img - average;
+	Mat new_img2;
+	resize(new_img, new_img2, Size(row, col), (0, 0), (0, 0), INTER_CUBIC);
+	Mat new_src;
+	gray.convertTo(new_src, CV_32FC1);
+	Mat dst = new_src - new_img2;
+	dst.convertTo(dst, CV_8UC1);
+	return dst;
+}