OpenCV之C++經典案例

四個案例實戰

1、刀片缺陷檢測

2、自定義物件檢測

3、實時二維碼檢測

4、影像分割與色彩提取

1、刀片缺陷檢測

問題分析

解決思路

• 嘗試二值影像分析
• 模板匹配技術

程式碼實現

#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);

	//影像二值化
	Mat gray, binary;
	cvtColor(src, gray, COLOR_BGR2GRAY);
	threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);  //全域性閾值
	imshow("binary", binary);

	//定義結構元素，進行開操作去除小的干擾點
	Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	morphologyEx(binary, binary, MORPH_OPEN, se);
	imshow("open-binary", binary);

	//輪廓發現
	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大小的情況下，向rects中放入rect
		//rectangle(src, rect, Scalar(0, 255, 0), 2, 8, 0);  //繪製矩形
		//drawContours(src, contours, t, Scalar(0, 0, 255), 2, 8);  //繪製輪廓
	}
	
	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++) {  //將檢測到的缺陷部分繪製出來
		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++) {
		//構建diff
		Mat roi = binary(rects[i]);
		resize(roi, roi, tpl.size());  //將roi大小統一
		Mat mask;
		subtract(tpl, roi, mask);
		Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));  //開操作去除微小差異
		morphologyEx(mask, mask, MORPH_OPEN, se);
		threshold(mask, mask, 0, 255, THRESH_BINARY);  //將獲取的mask二值化
		imshow("mask", mask);
		waitKey(0);

		//根據diff查詢缺陷，閾值化
		int count = 0;
		for (int row = 0; row < h; row++) {
			for (int col = 0; col < w; col++) {
				int pv = mask.at<uchar>(row, col);  //獲取每一個畫素值，如果等於255則count+1
				if (pv == 255) {
					count++;
				}
			}
		}

		//填充一個畫素塊
		int mh = mask.rows + 2;
		int mw = mask.cols + 2;
		Mat m1 = Mat::zeros(Size(mw, mh), mask.type());
		Rect mroi;  //將mask複製到m1的mroi區域，並使mroi區域四周各有一個畫素值為0
		mroi.x = 1;
		mroi.y = 1;
		mroi.height = mask.rows;
		mroi.width = mask.cols;
		mask.copyTo(m1(mroi));

		//輪廓分析，對每個矩形中的差異進行過濾
		vector<vector<Point>> contours;
		vector<Vec4i> hierarchy;
		findContours(m1, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE);  //查詢每一個矩形中微小的差異輪廓
		bool find = false;
		for (size_t t = 0; t < contours.size(); t++) {  //迴圈判斷矩形中的差異區域有無滿足要求的，如果有則find=true
			Rect rect = boundingRect(contours[t]);
			float ratio = (float)rect.width / ((float)rect.height);  //計算矩形寬高比
			//將寬高比>4的並且位於上下邊緣的差異區域過濾
			if (ratio > 4.0 && (rect.y < 5 || (m1.rows - (rect.height + rect.y)) < 10)) {  //將邊緣的白色區域過濾
				continue;
			}
			double area = contourArea(contours[t]);
			if (area > 10) {
				printf("ratio:%.2f,area:%.2f \n", ratio, area);
				find = true;
			}
		}

		if (count > 50 && find) {  //如果等於255的畫素個數>50並且符合以上判斷要求，就將該矩形放入缺陷容器defect中
			printf("count:%d \n", count);
			defect.push_back(rects[i]);
		}
	}
	//返回結果
}

效果：

1、影像二值化並開操作

2、獲取每個刀片區域並排序

3、根據與模板差異的畫素個數篩選有缺陷的刀片

4、根據每個刀片區域與模板的差異部位寬高比、位置及畫素個數篩選有缺陷的刀片

2、自定義物件檢測

解決思路

• OpenCV中物件檢測類問題
  • 模板匹配
  • 特徵匹配
  • 特徵 + 機器學習
• 選擇HOG特徵 + SVM機器學習生成模型
• 開窗檢測

HOG特徵

• 灰度影像轉換
• 梯度計算
• 分網格的梯度方向直方圖
• 塊描述子
• 塊描述子歸一化
• 特徵資料與檢測視窗
• 匹配方法

• 根據塊的形狀不一樣HOG特徵分為C-HOG和R-HOG

• 基於 L2 實現塊描述子歸一化，歸一化因子計算：

  

SVM簡要介紹

• 線性不可分對映為線性可分離
• 核函式：線性、高斯、多項式等

首先svm演算法，當遇到分佈比較雜亂的函式時，可以進行升維處理，將二維不好處理的問題改為三維，是一個比較好的辦法；

此外，svm分割資料的操作也比較合理，劃分邊界及區域在經過一些複雜的函式計算什麼的，可以算出劃分的邊界的位置，劃分好邊界線，之後便可以劃分邊界區域，這樣區分樣本的時候就會事半功倍了。

對於升維進行計算資料的話，是存在一個核函式的，具體的講解如下：

當樣本在原始空間線性不可分時，可將樣本從原始空間對映到一個更高維的特徵空間，使得樣本在這個特徵空間內線性可分。而引入這樣的對映後，所要求解的對偶問題的求解中，無需求解真正的對映函式，而只需要知道其核函式。

核函式的定義：K(x,y)=<ϕ(x),ϕ(y)>，即在特徵空間的內積等於它們在原始樣本空間中透過核函式 K 計算的結果。一方面資料變成了高維空間中線性可分的資料，另一方面不需要求解具體的對映函式，只需要給定具體的核函式即可，這樣使得求解的難度大大降低。

程式碼實現

#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");  //讀取訓練好的模型
	
	//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);  //重新設定影像大小dsize與（fx、fy）不能同時為0
	imshow("input", test);
	Rect winRect;
	winRect.width = 64;
	winRect.height = 128;
	int sum_x = 0;
	int sum_y = 0;
	int count = 0;

	//開窗檢測...
	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;
			}
		}
	}
	//顯示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)));  //保證寬為64，同時寬高比例與原圖相同
	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);  //掃描目錄，得到所有正樣本
	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，分類模型越複雜
	svm->setType(SVM::C_SVC);  //分類器型別
	svm->setKernel(SVM::LINEAR);  //線性核心，速度快
	svm->setGamma(5.383);  //線性核心可以忽略，其他核心需要
	svm->train(trainData, ROW_SAMPLE, labels);  //按行讀取
	clog << "....[Done]" << endl;
	printf("end train...\n");

	//save xml
	svm->save("D:/images/elec_watchzip/elec_watch/hog_elec.xml");  //儲存路徑

}

效果：

3、二維碼檢測與定位

二維定位檢測知識點：

• 二維碼特徵
• 影像二值化
• 輪廓提取
• 透視變換
• 幾何分析

二維碼特徵

影像二值化與輪廓分析

• 全域性或者區域性閾值選擇
• 全域性閾值分割
• 最外層輪廓與多層輪廓
• 面積與幾何形狀過濾
• 透視變換與單應性矩陣

幾何分析

• 尋找每個正方形
• 尋找X方向1 : 1 : 3 : 1 : 1結構
• 尋找Y方向比率結構
• 得到輸出結果

演算法流程設計

• 面積太小不能識別排除

程式碼層面知識點與執行

• minAreaRect
• findHomography
• warpPerspective

程式碼實現

#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;  //將面積<100的輪廓去掉

		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) {  //根據寬高比進行過濾
			Mat qr_roi = transformCorner(image, rect);
			// 根據矩形特徵進行幾何分析
			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容器中新增白色畫素點座標
			}
		}
	}
	RotatedRect rrt = minAreaRect(pts);  //獲取pts的最小外接矩形
	Point2f vertices[4];
	rrt.points(vertices);
	pts.clear();
	for (int i = 0; i < 4; i++) {  //繪製最小外接矩形的四根線
		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());  //將result繪製成指定形狀
	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);  //透過與操作，獲取二維碼區域

	imshow("detect result", image);
	//imwrite("D:/case03.png", image);
	imshow("result-mask", mask);
	imshow("qrcode-roi", dst);
}
bool isXCorner(Mat &image) {  //對找到的候選輪廓進行分析
	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;  //判斷中心畫素是否為黑色
	// verfiy finder pattern
	bool findleft = false, findright = false;
	int start = 0, end = 0;
	int offset = 0;
	while (true) {  //從中間畫素開始向兩側遍歷查詢
		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) {  //當左右兩側都找到白色畫素時終止迴圈，start和end分別儲存起止座標
			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為了保證獲取四捨五入的值，避免浮點數轉換為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) {  //對中心畫素一側的畫素進行檢測，對黑白畫素個數分別計數，
	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) {  //如果白色畫素個數大於等於黑色畫素個數的兩倍，返回false，黑色畫素個數兩倍正常是白色畫素個數5倍
		return false;
	}
	return true;
}

Mat transformCorner(Mat &image, RotatedRect &rect) {  //單一性矩陣與透視變換
	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;
}

過程分析

效果：

4、KMeans應用

• 資料聚類
• 影像聚類
• 背景替換
• 主色彩提取

KMeans聚類演算法原理

• 聚類中心
• 根據距離分類

​ 聚類和分類最大的不同在於，分類的目標是事先已知的，而聚類則不一樣，聚類事先不知道目標變數是什麼，類別沒有像分類那樣被預先定義出來，也就是聚類分組不需要提前被告知所劃分的組應該是什麼樣的，因為我們甚至可能都不知道我們再尋找什麼，所以聚類是用於知識發現而不是預測，所以，聚類有時也叫無監督學習。

​ KMeans演算法是最常用的聚類演算法，主要思想是:在給定K值和K個初始類簇中心點的情況下，把每個點(亦即資料記錄)分到離其最近的類簇中心點所代表的類簇中，所有點分配完畢之後，根據一個類簇內的所有點重新計算該類簇的中心點(取平均值)，然後再迭代的進行分配點和更新類簇中心點的步驟，直至類簇中心點的變化很小，或者達到指定的迭代次數。

K-means過程：

1. 首先選擇k個類別的中心點
2. 對任意一個樣本，求其到各類中心的距離，將該樣本歸到距離最短的中心所在的類
3. 聚好類後，重新計算每個聚類的中心點位置
4. 重複2,3步驟迭代，直到k個類中心點的位置不變，或者達到一定的迭代次數，則迭代結束，否則繼續迭代

程式碼實現

#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;  //聚類個數
	int sampleCount = rng.uniform(5, 500);  //隨機產生的資料點個數，均勻分佈
	Mat points(sampleCount, 1, CV_32FC2);  //矩陣大小為：資料點個數*1，每個點有兩個維度

	// 生成隨機數
	for (int k = 0; k < numCluster; k++) {
		Point center;
		center.x = rng.uniform(0, img.cols);
		center.y = rng.uniform(0, img.rows);
		//兩次迴圈產生隨機數的縱座標範圍不同
		Mat pointChunk = points.rowRange(k*sampleCount / numCluster,
			k == numCluster - 1 ? sampleCount : (k + 1)*sampleCount / numCluster);
		//使用指定範圍二維隨機數填充矩陣，填充方式為均勻分佈或高斯分佈
		rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
	}
	randShuffle(points, 1, &rng);  //打亂隨機數順序

	// 使用KMeans
	Mat labels;
	Mat centers;
	//將這些點分為2類，每個點有一個標籤，使用不同的初始聚類中心執行演算法的次數，初始中心點選取方式
	kmeans(points, numCluster, labels, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1), 3, KMEANS_PP_CENTERS, centers);

	// 用不同顏色顯示分類
	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);  //對不同標籤的點按不同顏色進行填充
	}

	// 每個聚類的中心來繪製圓
	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();

	// 初始化定義
	int sampleCount = width * height;
	int clusterCount = 3;
	Mat labels;
	Mat centers;

	// RGB 資料轉換到樣本資料
	Mat sample_data = src.reshape(3, sampleCount);  //將輸入影像轉換到特定維數
	Mat data;
	sample_data.convertTo(data, CV_32F);

	// 執行K-Means
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);  //停止迭代判定條件，迭代10次，精度達到0.1
	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);

	// 顯示影像分割結果
	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];  //按不同標籤對結果中的點設定不同顏色
		}
	}

	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();

	// 初始化定義
	int sampleCount = width * height;
	int clusterCount = 3;
	Mat labels;
	Mat centers;

	// RGB 資料轉換到樣本資料
	Mat sample_data = src.reshape(3, sampleCount);
	Mat data;
	sample_data.convertTo(data, CV_32F);

	// 執行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);  //獲取(0,0)點的label，與(0,0)點相同label的部分為背景
	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;  //將與(0,0)點相同label的部分畫素值設為255
			}
		}
	}
	imshow("mask", mask);

	Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	dilate(mask, mask, se);  //背景白色區域膨脹操作

	// 生成高斯權重
	GaussianBlur(mask, mask, Size(5, 5), 0);  //透過高斯模糊，使輪廓邊緣過度自然
	imshow("mask-blur", mask);

	// 基於高斯權重影像融合
	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);  //對bgr三通道進行分別融合
			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();

	// 初始化定義
	int sampleCount = width * height;
	int clusterCount = 4;
	Mat labels;
	Mat centers;

	// RGB 資料轉換到樣本資料
	Mat sample_data = src.reshape(3, sampleCount);
	Mat data;
	sample_data.convertTo(data, CV_32F);

	// 執行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);  //初始化一個 輸入影像寬*50 的色卡
	vector<float> clusters(clusterCount);

	// 生成色卡比率
	for (int i = 0; i < labels.rows; i++) {  //遍歷標籤
		clusters[labels.at<int>(i, 0)]++;
	}

	for (int i = 0; i < clusters.size(); i++) {  //將clusters對應位置儲存其對應比例
		clusters[i] = clusters[i] / sampleCount;
	}
	int x_offset = 0;

	// 繪製色卡
	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聚類示例

2、使用KMeans根據影像顏色分割

3、影像背景平滑置換

4、獲取圖片中佔比最高的前四種顏色色卡