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[资源分享]     OpenCV之C++经典案例

  • By - 楼主

  • 2022-11-25 00:04:27
  • 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:%d\n", 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 = %d\n", 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 = %d\n", 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:

    1. 首先选择k个类别的中心点
    2. 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
    3. 聚好类后,重新计算每个聚类的中心点位置
    4. 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=%d\n", 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);
    }
    

    效果:

    1、KMeans聚类示例

    2、使用KMeans根据图像颜色分割

    3、图像背景平滑置换

    4、获取图片中占比最高的前四种颜色色卡

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