OpenCV之C++经典案例

技术分享 3年前 (2022-11-25) 0 999+

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四个案例实战

1、刀片缺陷检测

2、自定义对象检测

3、实时二维码检测

4、图像分割与色彩提取

1、刀片缺陷检测

问题分析

解决思路

尝试二值图像分析
模板匹配技术

代码实现

#include <opencv2/opencv.hpp> #include <iostream>  using namespace cv; using namespace std;  Mat tpl; void sort_box(vector<Rect> &boxes); void detect_defect(Mat &binary, vector<Rect> rects, vector<Rect> &defect); int main(int argc, char** argv) { 	Mat src = imread("D:/images/ce_01.jpg"); 	if (src.empty()) { 		printf("could not load image file..."); 		return -1; 	} 	namedWindow("input", WINDOW_AUTOSIZE); 	imshow("input", src);  	//图像二值化 	Mat gray, binary; 	cvtColor(src, gray, COLOR_BGR2GRAY); 	threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);  //全局阈值 	imshow("binary", binary);  	//定义结构元素，进行开操作去除小的干扰点 	Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1)); 	morphologyEx(binary, binary, MORPH_OPEN, se); 	imshow("open-binary", binary);  	//轮廓发现 	vector<vector<Point>> contours; 	vector<Vec4i> hierarchy; 	vector<Rect> rects; 	findContours(binary, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE);  	int height = src.rows; 	for (size_t t = 0; t < contours.size(); t++) { 		Rect rect = boundingRect(contours[t]); 		double area = contourArea(contours[t]); 		if (rect.height > (height / 2)) { 			continue; 		} 		if (area < 150) { 			continue; 		} 		rects.push_back(rect);  //不知道rects大小的情况下，向rects中放入rect 		//rectangle(src, rect, Scalar(0, 255, 0), 2, 8, 0);  //绘制矩形 		//drawContours(src, contours, t, Scalar(0, 0, 255), 2, 8);  //绘制轮廓 	} 	 	sort_box(rects); 	tpl = binary(rects[1]);  	//for (int i = 0; i < rects.size(); i++) { 	//	  putText(src, format("%d", i), rects[i].tl(), FONT_HERSHEY_PLAIN, 1.0, Scalar(0, 255, 0), 1, 8); 	//} 	vector<Rect> defects; 	detect_defect(binary, rects, defects);  	for (int i = 0; i < defects.size(); i++) {  //将检测到的缺陷部分绘制出来 		rectangle(src, defects[i], Scalar(0, 0, 255), 2, 8, 0); 		putText(src, "bad", defects[i].tl(), FONT_HERSHEY_PLAIN, 1.0, Scalar(0, 255, 0), 1, 8); 	} 	imshow("result", src); 	waitKey(0); 	return 0; }  void sort_box(vector<Rect> &boxes) { 	int size = boxes.size(); 	for (int i = 0; i < size; i++) { 		for (int j = i; j < size; j++) { 			int x = boxes[j].x; 			int y = boxes[j].y; 			if (y < boxes[i].y) { 				Rect temp = boxes[i]; 				boxes[i] = boxes[j]; 				boxes[j] = temp; 			} 		} 	} }  void detect_defect(Mat &binary, vector<Rect> rects, vector<Rect> &defect) { 	int h = tpl.rows; 	int w = tpl.cols; 	int size = rects.size(); 	for (int i = 0; i < size; i++) { 		//构建diff 		Mat roi = binary(rects[i]); 		resize(roi, roi, tpl.size());  //将roi大小统一 		Mat mask; 		subtract(tpl, roi, mask); 		Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));  //开操作去除微小差异 		morphologyEx(mask, mask, MORPH_OPEN, se); 		threshold(mask, mask, 0, 255, THRESH_BINARY);  //将获取的mask二值化 		imshow("mask", mask); 		waitKey(0);  		//根据diff查找缺陷，阈值化 		int count = 0; 		for (int row = 0; row < h; row++) { 			for (int col = 0; col < w; col++) { 				int pv = mask.at<uchar>(row, col);  //获取每一个像素值，如果等于255则count+1 				if (pv == 255) { 					count++; 				} 			} 		}  		//填充一个像素块 		int mh = mask.rows + 2; 		int mw = mask.cols + 2; 		Mat m1 = Mat::zeros(Size(mw, mh), mask.type()); 		Rect mroi;  //将mask复制到m1的mroi区域，并使mroi区域四周各有一个像素值为0 		mroi.x = 1; 		mroi.y = 1; 		mroi.height = mask.rows; 		mroi.width = mask.cols; 		mask.copyTo(m1(mroi));  		//轮廓分析，对每个矩形中的差异进行过滤 		vector<vector<Point>> contours; 		vector<Vec4i> hierarchy; 		findContours(m1, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE);  //查找每一个矩形中微小的差异轮廓 		bool find = false; 		for (size_t t = 0; t < contours.size(); t++) {  //循环判断矩形中的差异区域有无满足要求的，如果有则find=true 			Rect rect = boundingRect(contours[t]); 			float ratio = (float)rect.width / ((float)rect.height);  //计算矩形宽高比 			//将宽高比>4的并且位于上下边缘的差异区域过滤 			if (ratio > 4.0 && (rect.y < 5 || (m1.rows - (rect.height + rect.y)) < 10)) {  //将边缘的白色区域过滤 				continue; 			} 			double area = contourArea(contours[t]); 			if (area > 10) { 				printf("ratio:%.2f,area:%.2f n", ratio, area); 				find = true; 			} 		}  		if (count > 50 && find) {  //如果等于255的像素个数>50并且符合以上判断要求，就将该矩形放入缺陷容器defect中 			printf("count:%d n", count); 			defect.push_back(rects[i]); 		} 	} 	//返回结果 }

效果：

1、图像二值化并开操作

2、获取每个刀片区域并排序

3、根据与模板差异的像素个数筛选有缺陷的刀片

4、根据每个刀片区域与模板的差异部位宽高比、位置及像素个数筛选有缺陷的刀片

2、自定义对象检测

解决思路

OpenCV中对象检测类问题
- 模板匹配
- 特征匹配
- 特征 + 机器学习
选择HOG特征 + SVM机器学习生成模型
开窗检测

HOG特征

灰度图像转换
梯度计算
分网格的梯度方向直方图
块描述子
块描述子归一化
特征数据与检测窗口
匹配方法

根据块的形状不一样HOG特征分为C-HOG和R-HOG
基于 L2 实现块描述子归一化，归一化因子计算：

SVM简要介绍

线性不可分映射为线性可分离
核函数：线性、高斯、多项式等

首先svm算法，当遇到分布比较杂乱的函数时，可以进行升维处理，将二维不好处理的问题改为三维，是一个比较好的办法；

此外，svm分割数据的操作也比较合理，划分边界及区域在经过一些复杂的函数计算什么的，可以算出划分的边界的位置，划分好边界线，之后便可以划分边界区域，这样区分样本的时候就会事半功倍了。

对于升维进行计算数据的话，是存在一个核函数的，具体的讲解如下：

当样本在原始空间线性不可分时，可将样本从原始空间映射到一个更高维的特征空间，使得样本在这个特征空间内线性可分。而引入这样的映射后，所要求解的对偶问题的求解中，无需求解真正的映射函数，而只需要知道其核函数。

核函数的定义：K(x,y)=<ϕ(x),ϕ(y)>，即在特征空间的内积等于它们在原始样本空间中通过核函数 K 计算的结果。一方面数据变成了高维空间中线性可分的数据，另一方面不需要求解具体的映射函数，只需要给定具体的核函数即可，这样使得求解的难度大大降低。

代码实现

#include <opencv2/opencv.hpp> #include <iostream>  using namespace cv; using namespace cv::ml; using namespace std;  string positive_dir = "D:/images/elec_watchzip/elec_watch/positive"; string negative_dir = "D:/images/elec_watchzip/elec_watch/negative"; void get_hog_descriptor(Mat &image, vector<float> &desc); void generate_dataset(Mat &trainData, Mat &labels); void svm_train(Mat &trainData, Mat &labels); int main(int argc, char** argv) { 	//read data and generate dataset 	Mat trainData = Mat::zeros(Size(3780, 26), CV_32FC1); 	Mat labels = Mat::zeros(Size(1, 26), CV_32SC1); 	generate_dataset(trainData, labels);  	//SVM train and save model 	svm_train(trainData, labels);  	//load model 	Ptr<SVM> svm = SVM::load("D:/images/elec_watchzip/elec_watch/hog_elec.xml");  //读取训练好的模型 	 	//detect custom object 	Mat test = imread("D:/images/elec_watchzip/elec_watch/test/scene_01.jpg"); 	resize(test, test, Size(0, 0), 0.2, 0.2);  //重新设置图像大小dsize与（fx、fy）不能同时为0 	imshow("input", test); 	Rect winRect; 	winRect.width = 64; 	winRect.height = 128; 	int sum_x = 0; 	int sum_y = 0; 	int count = 0;  	//开窗检测... 	for (int row = 64; row < test.rows - 64; row += 4) { 		for (int col = 32; col < test.cols - 32; col += 4) { 			winRect.x = col - 32; 			winRect.y = row - 64; 			vector<float> fv; 			Mat img = test(winRect); 			get_hog_descriptor(img, fv); 			Mat one_row = Mat::zeros(Size(fv.size(), 1), CV_32FC1); 			for (int i = 0; i < fv.size(); i++) { 				one_row.at<float>(0, i) = fv[i]; 			} 			float result = svm->predict(one_row); 			if (result > 0) { 				//rectangle(test, winRect, Scalar(0, 0, 255), 1, 8, 0); 				count += 1; 				sum_x += winRect.x; 				sum_y += winRect.y; 			} 		} 	} 	//显示box 	winRect.x = sum_x / count; 	winRect.y = sum_y / count; 	rectangle(test, winRect, Scalar(255, 0, 0), 2, 8, 0); 	imshow("object detection result", test); 	waitKey(0); 	return 0;  }  void get_hog_descriptor(Mat &image, vector<float> &desc) { 	HOGDescriptor hog;  //HOG描述子 	int h = image.rows; 	int w = image.cols; 	float rate = 64.0 / w; 	Mat img, gray; 	resize(image, img, Size(64, int(rate*h)));  //保证宽为64，同时宽高比例与原图相同 	cvtColor(img, gray, COLOR_BGR2GRAY); 	Mat result = Mat::zeros(Size(64, 128), CV_8UC1); 	result = Scalar(127); 	Rect roi; 	roi.x = 0; 	roi.width = 64; 	roi.y = (128 - gray.rows) / 2; 	roi.height = gray.rows; 	gray.copyTo(result(roi)); 	hog.compute(result, desc, Size(8, 8), Size(0, 0)); 	printf("desc len:%dn", desc.size()); } void generate_dataset(Mat &trainData, Mat &labels) { 	vector<String> images; 	glob(positive_dir, images);  //扫描目录，得到所有正样本 	int pos_num = images.size(); 	for (int i = 0; i < images.size(); i++) { 		Mat image = imread(images[i].c_str()); 		vector<float> fv; 		get_hog_descriptor(image, fv); 		for (int j = 0; j < fv.size(); j++) { 			trainData.at<float>(i, j) = fv[j]; 		} 		labels.at<int>(i, 0) = 1; 	} 	images.clear(); 	glob(negative_dir, images); 	for (int i = 0; i < images.size(); i++) { 		Mat image = imread(images[i].c_str()); 		vector<float> fv; 		get_hog_descriptor(image, fv); 		for (int j = 0; j < fv.size(); j++) { 			trainData.at<float>(i + pos_num, j) = fv[j]; 		} 		labels.at<int>(i + pos_num, 0) = -1; 	} } void svm_train(Mat &trainData, Mat &labels) { 	printf("n start SVM training... n"); 	Ptr<SVM> svm = SVM::create(); 	svm->setC(2.67);  //值越大，分类模型越复杂 	svm->setType(SVM::C_SVC);  //分类器类型 	svm->setKernel(SVM::LINEAR);  //线性内核，速度快 	svm->setGamma(5.383);  //线性内核可以忽略，其他内核需要 	svm->train(trainData, ROW_SAMPLE, labels);  //按行读取 	clog << "....[Done]" << endl; 	printf("end train...n");  	//save xml 	svm->save("D:/images/elec_watchzip/elec_watch/hog_elec.xml");  //保存路径  }

效果：

3、二维码检测与定位

二维定位检测知识点：

二维码特征
图像二值化
轮廓提取
透视变换
几何分析

二维码特征

图像二值化与轮廓分析

全局或者局部阈值选择
全局阈值分割
最外层轮廓与多层轮廓
面积与几何形状过滤
透视变换与单应性矩阵

几何分析

寻找每个正方形
寻找X方向1 : 1 : 3 : 1 : 1结构
寻找Y方向比率结构
得到输出结果

算法流程设计

面积太小不能识别排除

代码层面知识点与运行

minAreaRect
findHomography
warpPerspective

代码实现

#include <opencv2/opencv.hpp> #include <iostream>  using namespace cv; using namespace std;  void scanAndDetectQRCode(Mat & image); bool isXCorner(Mat &image); bool isYCorner(Mat &image); Mat transformCorner(Mat &image, RotatedRect &rect); int main(int argc, char** argv) { 	// Mat src = imread("D:/images/qrcode.png"); 	Mat src = imread("D:/images/qrcode_07.png"); 	if (src.empty()) { 		printf("could not load image file..."); 		return -1; 	} 	namedWindow("input", WINDOW_AUTOSIZE); 	imshow("input", src); 	scanAndDetectQRCode(src); 	waitKey(0); 	return 0; }  void scanAndDetectQRCode(Mat & image) { 	Mat gray, binary; 	cvtColor(image, gray, COLOR_BGR2GRAY); 	threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); 	imshow("binary", binary);  	// detect rectangle now 	vector<vector<Point>> contours; 	vector<Vec4i> hireachy; 	Moments monents; 	findContours(binary.clone(), contours, hireachy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point()); 	Mat result = Mat::zeros(image.size(), CV_8UC1); 	for (size_t t = 0; t < contours.size(); t++) { 		double area = contourArea(contours[t]); 		if (area < 100) continue;  //将面积<100的轮廓去掉  		RotatedRect rect = minAreaRect(contours[t]); 		float w = rect.size.width; 		float h = rect.size.height; 		float rate = min(w, h) / max(w, h); 		if (rate > 0.85 && w < image.cols / 4 && h < image.rows / 4) {  //根据宽高比进行过滤 			Mat qr_roi = transformCorner(image, rect); 			// 根据矩形特征进行几何分析 			if (isXCorner(qr_roi)) { 				drawContours(image, contours, static_cast<int>(t), Scalar(255, 0, 0), 2, 8); 				drawContours(result, contours, static_cast<int>(t), Scalar(255), 2, 8); 			} 		} 	}  	// scan all key points 	vector<Point> pts; 	for (int row = 0; row < result.rows; row++) { 		for (int col = 0; col < result.cols; col++) { 			int pv = result.at<uchar>(row, col); 			if (pv == 255) { 				pts.push_back(Point(col, row));  //向pts容器中添加白色像素点坐标 			} 		} 	} 	RotatedRect rrt = minAreaRect(pts);  //获取pts的最小外接矩形 	Point2f vertices[4]; 	rrt.points(vertices); 	pts.clear(); 	for (int i = 0; i < 4; i++) {  //绘制最小外接矩形的四根线 		line(image, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0), 2); 		pts.push_back(vertices[i]); 	} 	Mat mask = Mat::zeros(result.size(), result.type());  //将result绘制成指定形状 	vector<vector<Point>> cpts; 	cpts.push_back(pts); 	drawContours(mask, cpts, 0, Scalar(255), -1, 8);  //填充  	Mat dst; 	bitwise_and(image, image, dst, mask);  //通过与操作，获取二维码区域  	imshow("detect result", image); 	//imwrite("D:/case03.png", image); 	imshow("result-mask", mask); 	imshow("qrcode-roi", dst); } bool isXCorner(Mat &image) {  //对找到的候选轮廓进行分析 	Mat gray, binary; 	cvtColor(image, gray, COLOR_BGR2GRAY); 	threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); 	int xb = 0, yb = 0; 	int w1x = 0, w2x = 0; 	int b1x = 0, b2x = 0;  	int width = binary.cols; 	int height = binary.rows; 	int cy = height / 2; 	int cx = width / 2; 	int pv = binary.at<uchar>(cy, cx); 	if (pv == 255) return false;  //判断中心像素是否为黑色 	// verfiy finder pattern 	bool findleft = false, findright = false; 	int start = 0, end = 0; 	int offset = 0; 	while (true) {  //从中间像素开始向两侧遍历查找 		offset++; 		if ((cx - offset) <= width / 8 || (cx + offset) >= width - 1) { 			start = -1; 			end = -1; 			break; 		} 		pv = binary.at<uchar>(cy, cx - offset); 		if (pv == 255) { 			start = cx - offset; 			findleft = true; 		} 		pv = binary.at<uchar>(cy, cx + offset); 		if (pv == 255) { 			end = cx + offset; 			findright = true; 		} 		if (findleft && findright) {  //当左右两侧都找到白色像素时终止循环，start和end分别保存起止坐标 			break; 		} 	}  	if (start <= 0 || end <= 0) { 		return false; 	} 	xb = end - start; 	for (int col = start; col > 0; col--) { 		pv = binary.at<uchar>(cy, col); 		if (pv == 0) { 			w1x = start - col; 			break; 		} 	} 	for (int col = end; col < width - 1; col++) { 		pv = binary.at<uchar>(cy, col); 		if (pv == 0) { 			w2x = col - end; 			break; 		} 	} 	for (int col = (end + w2x); col < width; col++) { 		pv = binary.at<uchar>(cy, col); 		if (pv == 255) { 			b2x = col - end - w2x; 			break; 		} 		else { 			b2x++; 		} 	} 	for (int col = (start - w1x); col > 0; col--) { 		pv = binary.at<uchar>(cy, col); 		if (pv == 255) { 			b1x = start - col - w1x; 			break; 		} 		else { 			b1x++; 		} 	}  	float sum = xb + b1x + b2x + w1x + w2x; 	//printf("xb : %d, b1x = %d, b2x = %d, w1x = %d, w2x = %dn", xb , b1x , b2x , w1x , w2x); 	xb = static_cast<int>((xb / sum)*7.0 + 0.5);  //+0.5为了保证获取四舍五入的值，避免浮点数转换为0 	b1x = static_cast<int>((b1x / sum)*7.0 + 0.5); 	b2x = static_cast<int>((b2x / sum)*7.0 + 0.5); 	w1x = static_cast<int>((w1x / sum)*7.0 + 0.5); 	w2x = static_cast<int>((w2x / sum)*7.0 + 0.5); 	printf("xb : %d, b1x = %d, b2x = %d, w1x = %d, w2x = %dn", xb, b1x, b2x, w1x, w2x); 	if ((xb == 3 || xb == 4) && b1x == b2x && w1x == w2x && w1x == b1x && b1x == 1) { // 1:1:3:1:1 		return true; 	} 	else { 		return false; 	} } bool isYCorner(Mat &image) {  //对中心像素一侧的像素进行检测，对黑白像素个数分别计数， 	Mat gray, binary; 	cvtColor(image, gray, COLOR_BGR2GRAY); 	threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); 	int width = binary.cols; 	int height = binary.rows; 	int cy = height / 2; 	int cx = width / 2; 	int pv = binary.at<uchar>(cy, cx); 	int bc = 0, wc = 0; 	bool found = true; 	for (int row = cy; row > 0; row--) { 		pv = binary.at<uchar>(row, cx); 		if (pv == 0 && found) { 			bc++; 		} 		else if (pv == 255) { 			found = false; 			wc++; 		} 	} 	bc = bc * 2; 	if (bc <= wc) {  //如果白色像素个数大于等于黑色像素个数的两倍，返回false，黑色像素个数两倍正常是白色像素个数5倍 		return false; 	} 	return true; }  Mat transformCorner(Mat &image, RotatedRect &rect) {  //单一性矩阵与透视变换 	int width = static_cast<int>(rect.size.width); 	int height = static_cast<int>(rect.size.height); 	Mat result = Mat::zeros(height, width, image.type()); 	Point2f vertices[4]; 	rect.points(vertices); 	vector<Point> src_corners; 	vector<Point> dst_corners; 	dst_corners.push_back(Point(0, 0)); 	dst_corners.push_back(Point(width, 0)); 	dst_corners.push_back(Point(width, height)); // big trick 	dst_corners.push_back(Point(0, height)); 	for (int i = 0; i < 4; i++) { 		src_corners.push_back(vertices[i]); 	} 	Mat h = findHomography(src_corners, dst_corners); 	warpPerspective(image, result, h, result.size()); 	return result; }

过程分析

效果：

4、KMeans应用

数据聚类
图像聚类
背景替换
主色彩提取

KMeans聚类算法原理

聚类中心
根据距离分类

聚类和分类最大的不同在于，分类的目标是事先已知的，而聚类则不一样，聚类事先不知道目标变量是什么，类别没有像分类那样被预先定义出来，也就是聚类分组不需要提前被告知所划分的组应该是什么样的，因为我们甚至可能都不知道我们再寻找什么，所以聚类是用于知识发现而不是预测，所以，聚类有时也叫无监督学习。

KMeans算法是最常用的聚类算法，主要思想是:在给定K值和K个初始类簇中心点的情况下，把每个点(亦即数据记录)分到离其最近的类簇中心点所代表的类簇中，所有点分配完毕之后，根据一个类簇内的所有点重新计算该类簇的中心点(取平均值)，然后再迭代的进行分配点和更新类簇中心点的步骤，直至类簇中心点的变化很小，或者达到指定的迭代次数。

K-means过程：

首先选择k个类别的中心点
对任意一个样本，求其到各类中心的距离，将该样本归到距离最短的中心所在的类
聚好类后，重新计算每个聚类的中心点位置
重复2,3步骤迭代，直到k个类中心点的位置不变，或者达到一定的迭代次数，则迭代结束，否则继续迭代

代码实现

#include <opencv2/opencv.hpp> #include <iostream>  using namespace cv; using namespace std;  void kmeans_data_demo(); void kmeans_image_demo(); void kmeans_background_replace(); void kmeans_color_card(); int main(int argc, char** argv) { 	// kmeans_data_demo(); 	// kmeans_image_demo(); 	// kmeans_background_replace(); 	kmeans_color_card(); 	return 0;  	waitKey(0); 	return 0; }  void kmeans_data_demo() { 	Mat img(500, 500, CV_8UC3); 	RNG rng(12345);  	Scalar colorTab[] = { 		Scalar(0, 0, 255), 		Scalar(255, 0, 0), 	};  	int numCluster = 2;  //聚类个数 	int sampleCount = rng.uniform(5, 500);  //随机产生的数据点个数，均匀分布 	Mat points(sampleCount, 1, CV_32FC2);  //矩阵大小为：数据点个数*1，每个点有两个维度  	// 生成随机数 	for (int k = 0; k < numCluster; k++) { 		Point center; 		center.x = rng.uniform(0, img.cols); 		center.y = rng.uniform(0, img.rows); 		//两次循环产生随机数的纵坐标范围不同 		Mat pointChunk = points.rowRange(k*sampleCount / numCluster, 			k == numCluster - 1 ? sampleCount : (k + 1)*sampleCount / numCluster); 		//使用指定范围二维随机数填充矩阵，填充方式为均匀分布或高斯分布 		rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05)); 	} 	randShuffle(points, 1, &rng);  //打乱随机数顺序  	// 使用KMeans 	Mat labels; 	Mat centers; 	//将这些点分为2类，每个点有一个标签，使用不同的初始聚类中心执行算法的次数，初始中心点选取方式 	kmeans(points, numCluster, labels, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1), 3, KMEANS_PP_CENTERS, centers);  	// 用不同颜色显示分类 	img = Scalar::all(255); 	for (int i = 0; i < sampleCount; i++) { 		int index = labels.at<int>(i); 		Point p = points.at<Point2f>(i); 		circle(img, p, 2, colorTab[index], -1, 8);  //对不同标签的点按不同颜色进行填充 	}  	// 每个聚类的中心来绘制圆 	for (int i = 0; i < centers.rows; i++) { 		int x = centers.at<float>(i, 0); 		int y = centers.at<float>(i, 1); 		printf("c.x= %d, c.y=%dn", x, y); 		circle(img, Point(x, y), 40, colorTab[i], 1, LINE_AA); 	}  	imshow("KMeans-Data-Demo", img); 	waitKey(0); } void kmeans_image_demo() { 	Mat src = imread("D:/images/toux.jpg"); 	if (src.empty()) { 		printf("could not load image...n"); 		return; 	} 	namedWindow("input image", WINDOW_AUTOSIZE); 	imshow("input image", src);  	Vec3b colorTab[] = { 		Vec3b(0, 0, 255), 		Vec3b(0, 255, 0), 		Vec3b(255, 0, 0), 		Vec3b(0, 255, 255), 		Vec3b(255, 0, 255) 	};  	int width = src.cols; 	int height = src.rows; 	int dims = src.channels();  	// 初始化定义 	int sampleCount = width * height; 	int clusterCount = 3; 	Mat labels; 	Mat centers;  	// RGB 数据转换到样本数据 	Mat sample_data = src.reshape(3, sampleCount);  //将输入图像转换到特定维数 	Mat data; 	sample_data.convertTo(data, CV_32F);  	// 运行K-Means 	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);  //停止迭代判定条件，迭代10次，精度达到0.1 	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);  	// 显示图像分割结果 	int index = 0; 	Mat result = Mat::zeros(src.size(), src.type()); 	for (int row = 0; row < height; row++) { 		for (int col = 0; col < width; col++) { 			index = row * width + col; 			int label = labels.at<int>(index, 0); 			result.at<Vec3b>(row, col) = colorTab[label];  //按不同标签对结果中的点设置不同颜色 		} 	}  	imshow("KMeans-image-Demo", result); 	waitKey(0); } void kmeans_background_replace() { 	Mat src = imread("D:/images/toux.jpg"); 	if (src.empty()) { 		printf("could not load image...n"); 		return; 	} 	namedWindow("input image", WINDOW_AUTOSIZE); 	imshow("input image", src);  	int width = src.cols; 	int height = src.rows; 	int dims = src.channels();  	// 初始化定义 	int sampleCount = width * height; 	int clusterCount = 3; 	Mat labels; 	Mat centers;  	// RGB 数据转换到样本数据 	Mat sample_data = src.reshape(3, sampleCount); 	Mat data; 	sample_data.convertTo(data, CV_32F);  	// 运行K-Means 	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1); 	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);  	// 生成mask 	Mat mask = Mat::zeros(src.size(), CV_8UC1); 	int index = labels.at<int>(0, 0);  //获取(0,0)点的label，与(0,0)点相同label的部分为背景 	labels = labels.reshape(1, height); 	for (int row = 0; row < height; row++) { 		for (int col = 0; col < width; col++) { 			int c = labels.at<int>(row, col); 			if (c == index) { 				mask.at<uchar>(row, col) = 255;  //将与(0,0)点相同label的部分像素值设为255 			} 		} 	} 	imshow("mask", mask);  	Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1)); 	dilate(mask, mask, se);  //背景白色区域膨胀操作  	// 生成高斯权重 	GaussianBlur(mask, mask, Size(5, 5), 0);  //通过高斯模糊，使轮廓边缘过度自然 	imshow("mask-blur", mask);  	// 基于高斯权重图像融合 	Mat result = Mat::zeros(src.size(), CV_8UC3); 	for (int row = 0; row < height; row++) { 		for (int col = 0; col < width; col++) { 			float w1 = mask.at<uchar>(row, col) / 255.0; 			Vec3b bgr = src.at<Vec3b>(row, col); 			bgr[0] = w1 * 255.0 + bgr[0] * (1.0 - w1);  //对bgr三通道进行分别融合 			bgr[1] = w1 * 0 + bgr[1] * (1.0 - w1); 			bgr[2] = w1 * 255.0 + bgr[2] * (1.0 - w1); 			result.at<Vec3b>(row, col) = bgr; 		} 	} 	imshow("background-replacement-demo", result); 	waitKey(0); } void kmeans_color_card() { 	Mat src = imread("D:/images/test.png"); 	if (src.empty()) { 		printf("could not load image...n"); 		return; 	} 	namedWindow("input image", WINDOW_AUTOSIZE); 	imshow("input image", src);  	int width = src.cols; 	int height = src.rows; 	int dims = src.channels();  	// 初始化定义 	int sampleCount = width * height; 	int clusterCount = 4; 	Mat labels; 	Mat centers;  	// RGB 数据转换到样本数据 	Mat sample_data = src.reshape(3, sampleCount); 	Mat data; 	sample_data.convertTo(data, CV_32F);  	// 运行K-Means 	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1); 	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);  	Mat card = Mat::zeros(Size(width, 50), CV_8UC3);  //初始化一个 输入图像宽*50 的色卡 	vector<float> clusters(clusterCount);  	// 生成色卡比率 	for (int i = 0; i < labels.rows; i++) {  //遍历标签 		clusters[labels.at<int>(i, 0)]++; 	}  	for (int i = 0; i < clusters.size(); i++) {  //将clusters对应位置保存其对应比例 		clusters[i] = clusters[i] / sampleCount; 	} 	int x_offset = 0;  	// 绘制色卡 	for (int x = 0; x < clusterCount; x++) { 		Rect rect; 		rect.x = x_offset; 		rect.y = 0; 		rect.height = 50; 		rect.width = round(clusters[x] * width); 		x_offset += rect.width; 		int b = centers.at<float>(x, 0); 		int g = centers.at<float>(x, 1); 		int r = centers.at<float>(x, 2); 		rectangle(card, rect, Scalar(b, g, r), -1, 8, 0); 	}  	imshow("Image Color Card", card); 	waitKey(0); }