Add IntegralGraphforMeanFiltering.cpp

Author: BBuf

Algorithm optimization/IntegralGraphforMeanFiltering.cpp

@@ -0,0 +1,101 @@
+Mat speed_rgb2gray(Mat src) {
+	Mat dst(src.rows, src.cols, CV_8UC1);
+#pragma omp parallel for num_threads(4)
+	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;
+}
+
+//积分图常规方法，求2维前缀和
+Mat Normal(Mat src) {
+	int row = src.rows;
+	int col = src.cols;
+	Mat dst = Mat::zeros(row+1, col+1, CV_64F);
+	for (int i = 1; i < dst.rows; i++) {
+		for (int j = 1; j < dst.cols; j++) {
+			double up_left = dst.at<double>(i - 1, j - 1);
+			double up_right = dst.at<double>(i - 1, j);
+			double bot_left = dst.at<double>(i, j - 1);
+			int bot_right = src.at<uchar>(i - 1, j - 1);
+			dst.at<double>(i, j) = (bot_right + bot_left + up_right - up_left);
+		}
+	}
+	return dst;
+}
+
+
+//积分图优化方法，由上方src(i-1,j)加上当前行的和即可
+Mat Fast(Mat src) {
+	int row = src.rows;
+	int col = src.cols;
+	Mat dst = Mat::zeros(row + 1, col + 1, CV_64F);
+	int sum = 0;
+	for (int i = 1; i < row + 1; i++) {
+		sum = 0;
+		for (int j = 1; j < col + 1; j++) {
+			sum += src.at<uchar>(i - 1, j - 1);
+			dst.at<double>(i, j) = (dst.at<double>(i - 1, j) + 1.0 * sum);
+		}
+	}
+	return dst;
+}
+
+//均值滤波
+Mat speed_MeanFilter(Mat src, int ksize) {
+	Mat src_bordered;
+	int row = src.rows;
+	int col = src.cols;
+	int radius = (ksize - 1) / 2;
+	copyMakeBorder(src, src_bordered, radius, radius, radius, radius, BORDER_REFLECT101);
+	Mat dst = Mat::zeros(row, col, CV_8UC1);
+	Mat Table = Normal(src_bordered);
+	for (int i = radius + 1; i < src.rows + radius + 1; i++) {
+		for (int j = radius + 1; j < src.cols + radius + 1; j++) {
+			double up_left = Table.at<double>(i - radius - 1, j - radius - 1);
+			double up_right = Table.at<double>(i - radius - 1, j + radius);
+			double bot_left = Table.at<double>(i + radius, j - radius - 1);
+			double bot_right = Table.at<double>(i + radius, j + radius);
+			double sum = (bot_right - up_right - bot_left + up_left);
+			double mean = sum / (ksize * ksize);
+			if (mean < 0) {
+				mean = 0;
+			}
+			else if (mean > 255) {
+				mean = 255;
+			}
+			dst.at<uchar>(i - radius - 1, j - radius - 1) = (int)mean;
+		}
+	}
+	return dst;
+}
+
+Mat SerialMedianFiltering(Mat src) {
+	int row = src.rows;
+	int col = src.cols;
+	Mat dst(row, col, CV_8UC1);
+	for (int i = 1; i < row - 1; i++) {
+		for (int j = 1; j < col - 1; j++) {
+			dst.at<uchar>(i, j) = ((int)src.at<uchar>(i, j) + src.at<uchar>(i, j + 1) + src.at<uchar>(i, j - 1) +
+				src.at<uchar>(i - 1, j) + src.at<uchar>(i - 1, j - 1) + src.at<uchar>(i - 1, j + 1) +
+				src.at<uchar>(i + 1, j) + src.at<uchar>(i + 1, j - 1) + src.at<uchar>(i + 1, j + 1)) / 9;
+		}
+	}
+	for (int i = 0; i < row; i++) {
+		dst.at<uchar>(i, 0) = src.at<uchar>(i, 0);
+		dst.at<uchar>(i, col - 1) = src.at<uchar>(i, col - 1);
+	}
+	for (int i = 0; i < col; i++) {
+		dst.at<uchar>(0, i) = src.at<uchar>(0, i);
+		dst.at<uchar>(row - 1, i) = src.at<uchar>(row - 1, i);
+	}
+	return dst;
+}

Algorithm optimization/README.md

@@ -7,4 +7,5 @@
 - speed_twoVector_distance.cpp AVX和x86循环展开，优化计算两个向量距离算法
 - Huang_Fast_MedianBlur.cpp 利用直方图实现快速中值滤波算法，算法原理：https://blog.csdn.net/just_sort/article/details/87994573
 - speed_exp.cpp 在神经网络权值较小的情况下的快速exp算法，算法原理：https://blog.csdn.net/just_sort/article/details/88128200
-- fast_meanFilter.cpp 积分图实现o(1)均值滤波，代码来自ImageShop，算法原理：http://www.cnblogs.com/Imageshop/p/6219990.html
+- fast_meanFilter.cpp 积分图实现o(1)均值滤波，代码来自ImageShop，算法原理：http://www.cnblogs.com/Imageshop/p/6219990.html
+- IntegralGraphforMeanFiltering.cpp 积分图实现O(1)均值滤波，算法原理：http://www.cnblogs.com/Imageshop/p/6219990.html