opencv 分水岭算法区域的分割和合并

使用直方图相似性合并相似区域

#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/core/core.hpp>
#include<vector>
#include<iostream>
#include<algorithm>
using namespace std;
using namespace cv;

Mat display(Mat& markers, int noOfSegment)
{
	vector<Vec3b> colorTab;
	for (int i = 0; i < noOfSegment; i++)
	{
		int b = theRNG().uniform(0, 255);
		int g = theRNG().uniform(0, 255);
		int r = theRNG().uniform(0, 255);
		colorTab.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
	}

	Mat watershedImage(markers.size(), CV_8UC3);
	for (int i = 0; i < markers.rows; i++)
	{
		for (int j = 0; j < markers.cols; j++)
		{
			int index = markers.at<int>(i, j);
			if (index == -1)
				watershedImage.at<Vec3b>(i, j) = Vec3b((uchar)255, (uchar)255, (uchar)255);
			else if (index <= 0 || index > noOfSegment)
				watershedImage.at<Vec3b>(i, j) = Vec3b(0, 0, 0);
			else
				watershedImage.at<Vec3b>(i, j) = colorTab[index - 1];
		}
	}
	
	return watershedImage;
}

//分水岭图像分割
Mat watershedSrgment(Mat src, int& noOfSegment)//noOfSegment表示分割的类别数
{
	Mat grayMat;
	Mat otsuMat;
	cvtColor(src, grayMat, CV_BGR2GRAY);
	threshold(grayMat, otsuMat, 0, 255, CV_THRESH_BINARY_INV + CV_THRESH_OTSU);
	imshow("src", src);
	imshow("otsuMat", otsuMat);
	//形态学
	morphologyEx(otsuMat, otsuMat, MORPH_OPEN, Mat::ones(9, 9, CV_8SC1), Point(4, 4), 2);//2表示迭代的次数，Point(4,4)表示结构元素的原点
	imshow("Mor-open", otsuMat);
	//距离变换
	Mat disTranMat(otsuMat.rows, otsuMat.cols, CV_32FC1);
	distanceTransform(otsuMat, disTranMat, CV_DIST_L2, 3);
	//归一化
	normalize(disTranMat, disTranMat, 0.0, 1, NORM_MINMAX);
	imshow("DisTranMat", disTranMat);
	//阈值分割
	threshold(disTranMat, disTranMat, 0.1, 1, CV_THRESH_BINARY);
	normalize(disTranMat, disTranMat, 0, 255, NORM_MINMAX);
	disTranMat.convertTo(disTranMat, CV_8UC1);
	imshow("TDisTranMat", disTranMat);
	//计算标记的分割块
	int i, j, compCount = 0;
	vector<vector<Point>> contours;
	vector<Vec4i> hierarchy;
	findContours(disTranMat, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
	Mat markers(disTranMat.size(), CV_32S);
	markers = Scalar::all(0);
	int idx = 0;
	//绘制区域块
	for (; idx >= 0; idx = hierarchy[idx][0], compCount++)
	{
		drawContours(markers, contours, idx, Scalar::all(compCount + 1), -1, 8, hierarchy, INT_MAX);
	}
	imshow("markers", markers * 255);
	double t = (double)getTickCount();
	watershed(src, markers);
	t = (double)getTickCount() - t;
	cout << "time: " << t * 1000 / getTickFrequency() << endl;

	Mat wash = display(markers, compCount);
	imshow("watershedSrgment", wash);
	noOfSegment = compCount;
	waitKey(0);
	return markers;
}


//分水岭图像合并
void segMerge(Mat src, Mat& segments, int& numSeg)
{
	vector<Mat> samples;//每个分割区域
	//统计区域更新
	int newNumSeg = numSeg;
	//初始化samples
	for (int i = 0; i <= numSeg; i++)
	{
		Mat sampleImage;
		samples.push_back(sampleImage);
	}
	//计算每个像素的归属
	for (int i = 0; i < segments.rows; i++)
	{
		for (int j = 0; j < segments.cols; j++)
		{
			int index = segments.at<int>(i,j);
			if (index >= 0 && index < numSeg)
				samples[index].push_back(src(Rect(j,i,1,1)));
		}
	}

	//创建直方图
	vector<MatND> hist_bases;
	Mat hsv_base;
	int h_bins = 35;
	int s_bins = 30;
	int hist_size[] = {h_bins,s_bins};
	float h_ranges[] = {0,256};
	float s_ranges[] = {0,180};
	const float* ranges[] = { h_ranges,s_ranges };
	int channels[] = {0,1};
	MatND hist_base;
	for (int i = 0; i <= numSeg; i++)
	{
		if (samples[i].dims > 0)
		{
			cvtColor(samples[i], hsv_base, CV_BGR2HSV);
			calcHist(&hsv_base, 1, channels, Mat(), hist_base, 2, hist_size, ranges);
			normalize(hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat());
			hist_bases.push_back(hist_base);
		}
		else
			hist_bases.push_back(Mat());
		hist_base.release();
	}

	//计算各直方图之间的相似性
	double similarity = 0;
	vector<bool> merged;
	for (int k = 0; k < hist_bases.size(); k++)
	{
		merged.push_back(false);
	}

	for (int c = 0; c < hist_bases.size(); c++)
	{
		for (int q = c + 1; q < hist_bases.size(); q++)
		{
			if (!merged[q])
			{
				if (hist_bases[c].dims > 0 && hist_bases[q].dims>0)
				{
					similarity = compareHist(hist_bases[c],hist_bases[q],CV_COMP_BHATTACHARYYA);
					if (similarity > 0.8)
					{
						merged[q] = true;
						if (q != c)
						{
							newNumSeg--;
							for (int i = 0; i < segments.rows; i++)
							{
								for (int j = 0; j < segments.cols; j++)
								{
									int index = segments.at<int>(i, j);
									if (index == q)
										segments.at<int>(i, j) = c;
								}
							}
						}
					}
				}
			}
		}
	}
	numSeg = newNumSeg;
	Mat wash=display(segments, numSeg);
	imshow("segMerge", wash);
	waitKey(0);

}


int main()
{
	//imread中0表示灰度返回，1表示原图返回
	Mat srcImage = imread("E:\\研究生\\学习材料\\学习书籍\\OpenCV图像处理编程实例-源码-20160801\\《OpenCV图像处理编程实例-源码-20160801\\images\\flower.jpg");
	if (!srcImage.data)
		return -1;
	int noOfSegment = 0;
	Mat segments=watershedSrgment(srcImage, noOfSegment);
	segMerge(srcImage, segments, noOfSegment);
	return 0;
}