Opencv(七)基于距离变换与分水岭的图像分割

图像分割：

图像分割(Image Segmentation)是图像处理最重要的处理手段之一
图像分割的目标是将图像中像素根据一定的规则分为若干(N)个cluster集合，每个集合包含一类像素。
根据算法分为监督学习方法和无监督学习方法，图像分割的算法多数都是无监督学习方法 - KMeans

距离变换与分水岭介绍

距离变换常见算法有两种

• 不断膨胀/ 腐蚀得到
• 基于倒角距离

分水岭变换常见的算法

• 基于浸泡理论实现

cv::distanceTransform(InputArray src, OutputArray dst, OutputArray labels, int distanceType, int maskSize, int labelType=DIST_LABEL_CCOMP)
distanceType = DIST_L1/DIST_L2,
maskSize = 3x3,最新的支持5x5，推荐3x3、
labels离散维诺图输出
dst输出8位或者32位的浮点数，单一通道，大小与输入图像一致

cv::watershed(InputArray image, InputOutputArray markers)

处理流程：
1.将白色背景变成黑色-目的是为后面的变换做准备
2. 使用filter2D与拉普拉斯算子实现图像对比度提高，sharp
3. 转为二值图像通过threshold
4. 距离变换
5. 对距离变换结果进行归一化到[0~1]之间
6. 使用阈值，再次二值化，得到标记
7. 腐蚀得到每个Peak - erode
8.发现轮廓 – findContours
9. 绘制轮廓- drawContours
10.分水岭变换 watershed
11. 对每个分割区域着色输出结果

#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("D:/vcprojects/images/cards.png");
	// 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);
	// 1. change background
	for (int row = 0; row < src.rows; row++) {
     
		for (int col = 0; col < src.cols; col++) {
     
			if (src.at<Vec3b>(row, col) == Vec3b(255, 255, 255)) {
     
				src.at<Vec3b>(row, col)[0] = 0;
				src.at<Vec3b>(row, col)[1] = 0;
				src.at<Vec3b>(row, col)[2] = 0;
			}
		}
	}
	namedWindow("black background", CV_WINDOW_AUTOSIZE);
	imshow("black background", src);

	// sharpen
	Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);
	Mat imgLaplance;
	Mat sharpenImg = src;
	filter2D(src, imgLaplance, CV_32F, kernel, Point(-1, -1), 0, BORDER_DEFAULT);
	src.convertTo(sharpenImg, CV_32F);
	Mat resultImg = sharpenImg - imgLaplance;

	resultImg.convertTo(resultImg, CV_8UC3);
	imgLaplance.convertTo(imgLaplance, CV_8UC3);
	imshow("sharpen image", resultImg);
	// src = resultImg; // copy back

	// convert to binary
	Mat binaryImg;
	cvtColor(src, resultImg, CV_BGR2GRAY);
	threshold(resultImg, binaryImg, 40, 255, THRESH_BINARY | THRESH_OTSU);
	imshow("binary image", binaryImg);

	Mat distImg;
	distanceTransform(binaryImg, distImg, DIST_L1, 3, 5);
	normalize(distImg, distImg, 0, 1, NORM_MINMAX);
	imshow("distance result", distImg);
	
	// binary again
	threshold(distImg, distImg, .4, 1, THRESH_BINARY);
	Mat k1 = Mat::ones(13, 13, CV_8UC1);
	erode(distImg, distImg, k1, Point(-1, -1));
	imshow("distance binary image", distImg);

	// markers 
	Mat dist_8u;
	distImg.convertTo(dist_8u, CV_8U);
	vector<vector<Point>> 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<int>(i), Scalar::all(static_cast<int>(i) + 1), -1);
	}
	circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);
	imshow("my markers", markers*1000);

	// 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);

	// generate random color
	vector<Vec3b> 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<int>(row, col);
			if (index > 0 && index <= static_cast<int>(contours.size())) {
     
				dst.at<Vec3b>(row, col) = colors[index - 1];
			}
			else {
     
				dst.at<Vec3b>(row, col) = Vec3b(0, 0, 0);
			}
		}
	}
	imshow("Final Result", dst);

	waitKey(0);
	return 0;
}