Opencv分水岭算法实战之分割硬币（C++）

前言：

opencv的分水岭算法的输入是包含一个轮廓的mask。

1、读取照片

	Mat src = imread("C:/opencv/data/coins.jpg");
	// Mat src = imread("D:/kuaidi.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}

2、对图片进行二值化并进行腐蚀

	Mat binaryImg;
	Mat grayImg;
	cvtColor(src, grayImg, CV_BGR2GRAY);
	threshold(grayImg, binaryImg, 40, 255, THRESH_BINARY);
	Mat k1 = Mat::ones(9, 9, CV_8UC1);
	erode(binaryImg, binaryImg, k1, Point(-1, -1), 2);

2、轮廓查找

	Mat dist_8u;
	binaryImg.convertTo(dist_8u, CV_8U);
	vector> contours;
	findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));

	// create makers
	Mat markers = Mat::zeros(src.size(), CV_32SC1);
	for (size_t i = 0; i < contours.size(); i++) {
		drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i) + 1), -1);
	}
	circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);

3、分水岭算法

	watershed(src, markers);
	Mat mark = Mat::zeros(markers.size(), CV_8UC1);
	markers.convertTo(mark, CV_8UC1);
	bitwise_not(mark, mark, Mat());
	imshow("watershed image", mark);

4、给每个区域上色

	vector colors;
	for (size_t i = 0; i < contours.size(); i++) {
		int r = theRNG().uniform(0, 255);
		int g = theRNG().uniform(0, 255);
		int b = theRNG().uniform(0, 255);
		colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
	}

	// fill with color and display final result
	Mat dst = Mat::zeros(markers.size(), CV_8UC3);
	for (int row = 0; row < markers.rows; row++) {
		for (int col = 0; col < markers.cols; col++) {
			int index = markers.at(row, col);
			if (index > 0 && index <= static_cast(contours.size())) {
				dst.at(row, col) = colors[index - 1];
			}
			else {
				dst.at(row, col) = Vec3b(0, 0, 0);
			}
		}
	}

5、总结

虽然9块硬币都分割出来了，但是感觉总体效果不是太好，希望有大神能帮忙指导一下。

我看网上有些大神会加入距离变换进来，我尝试了一下，会丢失一个硬币。

	Mat distImg;
	distanceTransform(binaryImg, distImg, DIST_L1, 3, 5);
	normalize(distImg, distImg, 0, 1, NORM_MINMAX);

附录：

#include 
#include 
#include 

using namespace std;
using namespace cv;

int main(int argc, char** argv) {
	char input_win[] = "input image";
	char watershed_win[] = "watershed segmentation demo";
	Mat src = imread("C:/opencv/data/coins.jpg");
	// Mat src = imread("D:/kuaidi.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow(input_win, CV_WINDOW_AUTOSIZE);
	imshow(input_win, src);
	Mat binaryImg;
	Mat grayImg;
	cvtColor(src, grayImg, CV_BGR2GRAY);
	threshold(grayImg, binaryImg, 40, 255, THRESH_BINARY);
	Mat k1 = Mat::ones(9, 9, CV_8UC1);
	erode(binaryImg, binaryImg, k1, Point(-1, -1), 2);
	imwrite("binaryImg.jpg", binaryImg);
	Mat dist_8u;
	binaryImg.convertTo(dist_8u, CV_8U);
	vector> contours;
	findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));

	// create makers
	Mat markers = Mat::zeros(src.size(), CV_32SC1);
	for (size_t i = 0; i < contours.size(); i++) {
		drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i) + 1), -1);
	}
	circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);
	imshow("my markers", markers * 1000);
	imwrite("my_markers.jpg", markers);

	// perform watershed
	watershed(src, markers);
	Mat mark = Mat::zeros(markers.size(), CV_8UC1);
	markers.convertTo(mark, CV_8UC1);
	bitwise_not(mark, mark, Mat());
	imshow("watershed image", mark);
	imwrite("watershed_image.jpg", mark);

	// generate random color
	vector colors;
	for (size_t i = 0; i < contours.size(); i++) {
		int r = theRNG().uniform(0, 255);
		int g = theRNG().uniform(0, 255);
		int b = theRNG().uniform(0, 255);
		colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
	}

	// fill with color and display final result
	Mat dst = Mat::zeros(markers.size(), CV_8UC3);
	for (int row = 0; row < markers.rows; row++) {
		for (int col = 0; col < markers.cols; col++) {
			int index = markers.at(row, col);
			if (index > 0 && index <= static_cast(contours.size())) {
				dst.at(row, col) = colors[index - 1];
			}
			else {
				dst.at(row, col) = Vec3b(0, 0, 0);
			}
		}
	}
	imshow("Final Result", dst);
	imwrite("Final_Result.jpg", dst);

	waitKey(0);
	return 0;
}