[人工智能]【机器视觉学习笔记】二值图像和灰度图像的膨胀、腐蚀、开运算、闭运算算法（C++）

平台：Windows 10 20H2
Visual Studio 2015
OpenCV 4.5.3

二值图像

完整源码

//包含头文件
#include <opencv2/opencv.hpp>

//命名空间
using namespace cv;
using namespace std;

//全局函数声明部分

//我的腐蚀运算
Mat Erode(Mat src, Mat Mask, uint32_t x0, uint32_t y0)
{
	uint32_t x = 0, y = 0;
	Mat dst(src.rows, src.cols, CV_8U, Scalar(0));

	for (x = 0; x < src.cols; ++x)
	{
		for (y = 0; y < src.rows; ++y)
		{
			//确定是否需要腐蚀
			uint8_t dst_P = 1;
			for (uint32_t xm = 0; xm < Mask.cols; ++xm)
			{
				for (uint32_t ym = 0; ym < Mask.rows; ++ym)
				{
					if (dst_P && Mask.at<uchar>(Point(xm, ym)) && (x + xm - x0) < src.cols && (y + ym - y0) < src.rows)
						dst_P &= (Mask.at<uchar>(Point(xm, ym)) & src.at<uchar>(Point(x + xm - x0, y + ym - y0)));
				}
				if (!dst_P)
					break;
			}

			if (dst_P)
				dst.at<uchar>(Point(x, y)) = src.at<uchar>(Point(x, y));
			else
				dst.at<uchar>(Point(x, y)) = 0;	//腐蚀
		}
	}
	return dst;
}

//我的膨胀运算
Mat Dilate(Mat src, Mat Mask, uint32_t x0, uint32_t y0)
{
	uint32_t x = 0, y = 0;
	Mat dst(src.rows, src.cols, CV_8U, Scalar(0));

	for (x = 0; x < src.cols; ++x)
	{
		for (y = 0; y < src.rows; ++y)
		{
			//确定是否需要膨胀
			uint8_t dst_P = 0;
			for (uint32_t xm = 0; xm < Mask.cols; ++xm)
			{
				for (uint32_t ym = 0; ym < Mask.rows; ++ym)
				{
					if (!dst_P && Mask.at<uchar>(Point(xm, ym)) && (x + xm - x0) < src.cols && (y + ym - y0) < src.rows)
						dst_P |= src.at<uchar>(Point(x + xm - x0, y + ym - y0));
				}
				if (dst_P)
					break;
			}

			for (uint32_t xm = 0; xm < Mask.cols; ++xm)
			{
				for (uint32_t ym = 0; ym < Mask.rows; ++ym)
				{
					if ((x + xm - x0) < src.cols && (y + ym - y0) < src.rows)
						if (dst_P)
							dst.at<uchar>(Point(x + xm - x0, y + ym - y0)) = 255;	//膨胀
						else
							dst.at<uchar>(Point(x + xm - x0, y + ym - y0)) = src.at<uchar>(Point(x, y));
				}
			}
		}
	}
	return dst;
}

//主函数
int main(int argc, char * argv[])
{
	//【1】载入图像,灰度化
	Mat image = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\2.jpg", 0);//灰度原图

	//【2】检查是否载入成功
	if (image.empty())
	{
		printf("读取图片错误，请确认目录下是否有imread函数指定图片存在！\n");
		return 0;
	}

	//【3】阈值化生成二值图像
	Mat binaryImage;
	threshold(image, binaryImage, 127, 255, THRESH_BINARY);

	//【4】生成结构元素
	uint8_t element_a[3][3] =
	{	1,	1,	1,
		1,	1,	1,
		1,	1,	1,	};
	Mat element = Mat(sizeof(element_a)/sizeof(element_a[0]), sizeof(element_a[0])/element_a[0][0], CV_8U, element_a);

	//【5】显示图像
	imshow("原二值图像", binaryImage);

	//【6】腐蚀运算
	Mat erodedImage;
	erode(binaryImage, erodedImage, element);
	imshow("Opencv的腐蚀运算", erodedImage);
	imshow("我的腐蚀运算", Erode(binaryImage, element, 1, 1));

	//【7】膨胀运算
	Mat dilatedImage;
	dilate(binaryImage, dilatedImage, element);
	imshow("Opencv的膨胀运算", dilatedImage);
	imshow("我的膨胀运算", Dilate(binaryImage, element, 1, 1));

	//【8】开运算
	Mat OpenImage;
	morphologyEx(binaryImage, OpenImage, MORPH_OPEN, element);
	imshow("Opencv的开运算", OpenImage);
	imshow("我的开运算", Dilate(Erode(binaryImage, element, 1, 1), element, 1, 1));	//开运算即先腐蚀再膨胀

	//【9】闭运算
	Mat CloseImage;
	morphologyEx(binaryImage, CloseImage, MORPH_CLOSE, element);
	imshow("Opencv的闭运算", CloseImage);
	imshow("我的闭运算", Erode(Dilate(binaryImage, element, 1, 1), element, 1, 1));	//闭运算即先膨胀再腐蚀
	
	//【10】保持窗口显示
	waitKey(0);
	return 0;
}

效果

原图

腐蚀运算

膨胀运算

开运算

闭运算

灰度图像

完整源码

//包含头文件
#include <opencv2/opencv.hpp>

//命名空间
using namespace cv;
using namespace std;

//全局函数声明部分

//我的腐蚀运算
Mat Erode(Mat src, Mat Mask, uint32_t x0, uint32_t y0)
{
	uint32_t x = 0, y = 0;
	Mat dst(src.rows, src.cols, CV_8U, Scalar(0));

	for (x = 0; x < src.cols; ++x)
	{
		for (y = 0; y < src.rows; ++y)
		{
			uint8_t dst_P = 255;
			for (uint32_t xm = 0; xm < Mask.cols; ++xm)
			{
				for (uint32_t ym = 0; ym < Mask.rows; ++ym)
				{
					if (dst_P && Mask.at<uchar>(Point(xm, ym)) && (x + xm - x0) < src.cols && (y + ym - y0) < src.rows)
						if (dst_P > src.at<uchar>(Point(x + xm - x0, y + ym - y0)))	//寻找最小值
							dst_P = src.at<uchar>(Point(x + xm - x0, y + ym - y0));
				}
				if (!dst_P)
					break;
			}
			dst.at<uchar>(Point(x, y)) = dst_P;
		}
	}
	return dst;
}

//我的膨胀运算
Mat Dilate(Mat src, Mat Mask, uint32_t x0, uint32_t y0)
{
	uint32_t x = 0, y = 0;
	Mat dst(src.rows, src.cols, CV_8U, Scalar(0));

	for (x = 0; x < src.cols; ++x)
	{
		for (y = 0; y < src.rows; ++y)
		{
			uint8_t dst_P = 0;
			for (uint32_t xm = 0; xm < Mask.cols; ++xm)
			{
				for (uint32_t ym = 0; ym < Mask.rows; ++ym)
				{
					if (dst_P != 255 && Mask.at<uchar>(Point(xm, ym)) && (x + xm - x0) < src.cols && (y + ym - y0) < src.rows)
						if (dst_P < src.at<uchar>(Point(x + xm - x0, y + ym - y0)))	//寻找最大值
							dst_P = src.at<uchar>(Point(x + xm - x0, y + ym - y0));
				}
				if (dst_P == 255)
					break;
			}
			dst.at<uchar>(Point(x, y)) = dst_P;
		}
	}
	return dst;
}

//主函数
int main(int argc, char * argv[])
{
	//【1】载入图像,灰度化
	Mat image = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\8.jpg", 0);//灰度原图

	//【2】检查是否载入成功
	if (image.empty())
	{
		printf("读取图片错误，请确认目录下是否有imread函数指定图片存在！\n");
		return 0;
	}

	//【3】生成结构元素
	uint8_t element_a[7][7] =
	{	1,	1,	1,	1,	1,	1,	1,
		1,	1,	1,	1,	1,	1,	1,
		1,	1,	1,	1,	1,	1,	1,
		1,	1,	1,	1,	1,	1,	1,
		1,	1,	1,	1,	1,	1,	1,
		1,	1,	1,	1,	1,	1,	1, 
		1,	1,	1,	1,	1,	1,	1,	};
	Mat element = Mat(sizeof(element_a)/sizeof(element_a[0]), sizeof(element_a[0])/element_a[0][0], CV_8U, element_a);

	//【4】显示图像
	imshow("原灰度图像", image);

	//【5】腐蚀运算
	Mat erodedImage;
	erode(image, erodedImage, element);
	imshow("Opencv的腐蚀运算", erodedImage);
	imshow("我的腐蚀运算", Erode(image, element, 3, 3));

	//【6】膨胀运算
	Mat dilatedImage;
	dilate(image, dilatedImage, element);
	imshow("Opencv的膨胀运算", dilatedImage);
	imshow("我的膨胀运算", Dilate(image, element, 3, 3));

	//【7】开运算
	Mat OpenImage;
	morphologyEx(image, OpenImage, MORPH_OPEN, element);
	imshow("Opencv的开运算", OpenImage);
	imshow("我的开运算", Dilate(Erode(image, element, 3, 3), element, 3, 3));	//开运算即先腐蚀再膨胀

	//【8】闭运算
	Mat CloseImage;
	morphologyEx(image, CloseImage, MORPH_CLOSE, element);
	imshow("Opencv的闭运算", CloseImage);
	imshow("我的闭运算", Erode(Dilate(image, element, 3, 3), element, 3, 3));	//闭运算即先膨胀再腐蚀
	
	//【10】保持窗口显示
	waitKey(0);
	return 0;
}

效果

原图

腐蚀运算

膨胀运算

开运算

闭运算