计算机概述

中值滤波

卷积核大小为7：

Mat medianFilter(Mat imgO) {
	// 创建一个新的Mat对象用于存储滤波后的图像
	Mat img1 = Mat::zeros(imgO.rows - 6, imgO.cols - 6, CV_8U);

	// 对输入图像进行中值滤波
	for (int y = 3; y < imgO.rows - 3; y++) {
		for (int x = 3; x < imgO.cols - 3; x++) {
			vector<uchar> values;
			for (int j = -3; j <= 3; j++) {
				for (int i = -3; i <= 3; i++) {
					values.push_back(imgO.at<uchar>(y + j, x + i));
				}
			}
			// 对values数组中的值进行排序
			sort(values.begin(), values.end());

			// 计算中值并赋值给输出图像
			img1.at<uchar>(y - 3, x - 3) = values[values.size() / 2];
		}
	}

	return img1;
}

效果

sobel算子边缘检测

Mat sobel3(Mat inputImg) {
	int gx, gy;
	Mat tempImg;
	inputImg.copyTo(tempImg); // 为了能够在输出图像中访问原始输入图像的像素值

	Mat outputImg = Mat::zeros(inputImg.size(), inputImg.type());

	for (int y = 1; y < inputImg.rows - 1; y++) {
		for (int x = 1; x < inputImg.cols - 1; x++) {
			gx = (-1 * tempImg.at<uchar>(y - 1, x - 1) + 1 * tempImg.at<uchar>(y - 1, x + 1) +
				-2 * tempImg.at<uchar>(y, x - 1) + 2 * tempImg.at<uchar>(y, x + 1) +
				-1 * tempImg.at<uchar>(y + 1, x - 1) + 1 * tempImg.at<uchar>(y + 1, x + 1)) / 4;
			gy = (1 * tempImg.at<uchar>(y - 1, x - 1) + 2 * tempImg.at<uchar>(y - 1, x) + 1 * tempImg.at<uchar>(y - 1, x + 1) +
				-1 * tempImg.at<uchar>(y + 1, x - 1) - 2 * tempImg.at<uchar>(y + 1, x) - 1 * tempImg.at<uchar>(y + 1, x + 1));

			outputImg.at<uchar>(y, x) = (abs(gx) + abs(gy)) / 2;
		}
	}

	return outputImg.clone(); // 返回outputImg的副本
}

效果