OpenCV边缘提取算法流程的实现(附DEMO)

int main() {
	Mat srcImage, srcImage2, srcImage3;
	for (int j = 1; j <= 172; j++)
	{
		//白天总共172张图片。1-172；验证：1，162，146;
		//(挑选最好的效果作为模板。对比108与117进行分析)。
		//if (j == 108 || j == 117)
		//{//对比分析之后选择第108张图片作为模板图片
		char ch[4096] = { 0 };
		sprintf(ch, "..\\findriveredge\\day\\2 (%d).jpg", j);
		srcImage = imread(ch, IMREAD_ANYCOLOR);
		srcImage2 = srcImage.clone();
		srcImage3 = srcImage.clone();
		if (srcImage.empty())
		{
			return -1;
		}
		if (srcImage.channels() == 3)
		{
			cvtColor(srcImage, srcImage, COLOR_BGR2GRAY);
		}
 
		Mat outImage;
		medianBlur(srcImage, outImage, g_nKrenel);//首先对灰度图进行中值滤波操作，去除一些杂质。
 
		Mat grad_x, abs_grad_x, grayImage_x;
		Sobel(outImage, grad_x, CV_16S, 1, 0, 3, 1, 1, BORDER_DEFAULT);
		convertScaleAbs(grad_x, abs_grad_x);
		abs_grad_x.convertTo(grayImage_x, CV_8U);
 
 
		Mat grad_y, abs_grad_y, grayImage_y;
		Sobel(outImage, grad_y, CV_16S, 0, 1, 3, 1, 1, BORDER_DEFAULT);
		convertScaleAbs(grad_y, abs_grad_y);
		abs_grad_y.convertTo(grayImage_y, CV_8U);
 
 
		Mat XBin, YBin;
		int n_thresh_x = myOtsu(grayImage_x);
		threshold(grayImage_x, XBin, n_thresh_x, 255, THRESH_BINARY);
 
 
		int n_thresh_y = myOtsu(grayImage_y);
		threshold(grayImage_y, YBin, n_thresh_y, 255, THRESH_BINARY);
 
 
		Mat Bin;
		bitwise_and(XBin, YBin, Bin);
 
 
		Mat outBin;
		medianBlur(Bin, outBin, 3);//去除一些杂质点。
 
 
		Mat outBin2 = outBin.clone();
		int n_iterations = 5;
		Mat element = getStructuringElement(MORPH_ELLIPSE, Size(15, 15));//膨胀变亮形成连通域。
		Mat element2 = getStructuringElement(MORPH_ELLIPSE, Size(5, 5));//腐蚀操作断开一些连通域。
		dilate(outBin2, outBin2, element, Point(-1, -1), n_iterations);
		erode(outBin2, outBin2, element2, Point(-1, -1), 3);//腐蚀操作，断开河流区域内部的连接区域，方便后续的填充处理。
 
 
		//将河流ROI区域小块连通域填黑。
		Mat outBin3 = outBin2.clone();
		vector<vector<Point>> contours;
		vector<Vec4i> hie;
		findContours(outBin3, contours, hie, RETR_LIST, CHAIN_APPROX_SIMPLE);
		float f_area = 0.0;
		for (int i = 0; i < contours.size(); i++)
		{
			f_area = contourArea(contours[i]);
			if (f_area < 250000)
			{
				drawContours(outBin3, contours, i, Scalar(0), -1);
			}
		}
 
 
		//由于final bin2在腐蚀过程中存在部分背景区域为黑色空洞，需要将其填白。
		Mat outBin4_tmp = ~outBin3;
		Mat outBin4;
		contours.clear();
		hie.clear();
		findContours(outBin4_tmp, contours, hie, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
		for (unsigned int i = 0; i < contours.size(); i++)
		{
			f_area = contourArea(contours[i]);
			if (f_area < 250000)
			{
				drawContours(outBin4_tmp, contours, i, Scalar(0), -1);
			}
		}
		outBin4 = ~outBin4_tmp;
 
		//迭代腐蚀突出河流边界区域。
		erode(outBin4, outBin4, element, Point(-1, -1), 10);
 
 
		//(可以对腐蚀图在进行一次外轮廓填充)。
		Mat outBin5_tmp = ~outBin4.clone();
		Mat outBin5;
		contours.clear();
		hie.clear();
		findContours(outBin5_tmp, contours, hie, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
		for (unsigned int i = 0; i < contours.size(); i++)
		{
			f_area = contourArea(contours[i]);
			if (f_area < 100000)
			{
				drawContours(outBin5_tmp, contours, i, Scalar(0), -1);
			}
		}
		outBin5 = ~outBin5_tmp;
 
 
		//根据outBin4，对原rgb图取感兴趣区域（即河流区域）
		for (int i = 0; i < outBin5.rows; i++)
		{
			for (int j = 0; j < outBin5.cols; j++)
			{
				if (outBin5.at<uchar>(i, j) == 255)
				{
					srcImage2.at<Vec3b>(i, j)[0] = 0;
					srcImage2.at<Vec3b>(i, j)[1] = 0;
					srcImage2.at<Vec3b>(i, j)[2] = 0;
				}
			}
		}
		namedWindow("finalImage", 0);
		imshow("finalImage", srcImage2);//这里的srcImage2表示最后所需效果图
 
 
		cout << j << endl;
		waiTKEy(30);
	}
	return 0;
}