• OpenCV4经典案例实战教程 笔记

    OpenCV4经典案例实战教程 笔记

    这几天在看OpenCV4经典的案例实战教程，这里记录一下学习的过程。

    案例一 刀片1的缺陷检测

    这里的目的是检测出有缺陷的刀片，如下图。

    在这里插入图片描述
    先总结一下思路，这里首先需要将图像进行二值化，通过轮廓的查找，找到刀片所有的刀片，然后进入缺陷的识别。缺陷识别主要还是选取一个没有缺陷的模板，然后对相应的二值图像进行相减操作，得出缺陷，通过形态学开操作，去掉一部分的噪声，并通过面积，位置信息等排除掉干扰项，就可以完成检测了。

    下面附上实现的代码：

    void sort_box(vector<Rect> &boxes);
void detect_defect(Mat &src, Mat &binary, vector<Rect> rects, vector<Rect> &defect);
Mat tpl;

int Advance::blade() {
	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);
	imshow("binary", binary);
	// 定义结构元素
	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> hirarchy;
	vector<Rect> rects;
	findContours(binary, contours, hirarchy, 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) | area < 150) {
			continue;
		}
		rects.push_back(rect);
		//rectangle(src, rect, Scalar(0, 0, 255), 2);
		//drawContours(src, contours, t, Scalar(0, 0, 255), 2);
	}
	
	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));
	//}

	vector<Rect> defects;
	detect_defect(src, binary, rects, defects);

	for (int i = 0; i < defects.size(); i++) {
		rectangle(src, defects[i], Scalar(0, 0, 255), 2);
		putText(src, "bad", defects[i].tl(), FONT_HERSHEY_PLAIN, 1.0, Scalar(0, 255, 0));

	}
	imshow("result", src);
	waitKey(0);
}


void sort_box(vector<Rect> &boxes) {
	int size = boxes.size();
	for (int i = 0; i < size - 1; ++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 &src, 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());
		Mat mask;
		subtract(tpl, roi, mask);
		Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
		morphologyEx(mask, mask, MORPH_OPEN, se);
		threshold(mask, mask, 0, 255, THRESH_BINARY);

		//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);
				if (pv == 255) {
					count++;
				}
			}
		}
		// 填充一个像素宽
		int mh = mask.rows + 2;
		int mw = mask.cols + 2;
		Mat m1 = Mat::zeros(Size(mw, mh), mask.type());
		Rect mroi;
		mroi.x = 1;
		mroi.y = 1;
		mroi.height = mask.rows;
		mroi.width = mask.cols;
		mask.copyTo(m1(mroi));

		// 轮廓分析
		vector<vector<Point>> contours;
		vector<Vec4i> hierarchy;
		findContours(m1, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE);
		bool find = false;
		for (size_t t = 0; t < contours.size(); ++t) {
			Rect rect = boundingRect(contours[t]);
			float ratio = (float)rect.width / ((float)rect.height);
			if (ratio > 4.0 && (rect.y < 5 || (m1.rows - (rect.height + rect.y)) < 10)) {
				continue;
			}
			double area = contourArea(contours[t]);
			if (area > 10) {
				printf("index: %d, ratio: %.2f, area: %.2f\n", i, ratio, area);
				find = true;

				// 绘制缺陷
				Mat sroi = src(rects[i]);
				drawContours(sroi, contours, t, Scalar(255, 0, 255), 0.5);
				imshow("sroi", sroi);
			}
		}

		if (count > 50 && find == true) {
			printf("index: %d, count: %d\n", i, count);
			defect.push_back(rects[i]);
		}
		imshow("mask", mask);
		waitKey(0);
	}
	// 返回结果
	destroyAllWindows();
}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60
    • 61
    • 62
    • 63
    • 64
    • 65
    • 66
    • 67
    • 68
    • 69
    • 70
    • 71
    • 72
    • 73
    • 74
    • 75
    • 76
    • 77
    • 78
    • 79
    • 80
    • 81
    • 82
    • 83
    • 84
    • 85
    • 86
    • 87
    • 88
    • 89
    • 90
    • 91
    • 92
    • 93
    • 94
    • 95
    • 96
    • 97
    • 98
    • 99
    • 100
    • 101
    • 102
    • 103
    • 104
    • 105
    • 106
    • 107
    • 108
    • 109
    • 110
    • 111
    • 112
    • 113
    • 114
    • 115
    • 116
    • 117
    • 118
    • 119
    • 120
    • 121
    • 122
    • 123
    • 124
    • 125
    • 126
    • 127
    • 128
    • 129
    • 130
    • 131
    • 132
    • 133
    • 134
    • 135
    • 136
    • 137
    • 138
    • 139
    • 140
    • 141
    • 142

    执行过程
    在这里插入图片描述
    执行结果：
    在这里插入图片描述

    案例二：使用HOG特征描述加SVM进行表计的识别

    本案例的目的是使用HOG对图片进行特征提取，然后使用SVM判断检测窗口是否有表计，属于传统的目标检测范畴。实验的数据分为positive，即有表计的图片，negative，没有表计的图片。以及test，测试样例图片。
    在这里插入图片描述
    下面是其中一张示例的图片。
    在这里插入图片描述
    在这里插入图片描述

    对于训练图片，我们统一resize成(128, 64) (宽， 高)大小，64 * 128 = 8 * 16 cells （高，宽），所以经过特征提取后，HOG特征数为36，总计数目 7*15*36=3780个特征。所以输出的维度应为（1, 3780）。1是batch_size，文字的表述和图上有些一致，以文字为准即可。

    string positive_dir = "D:/images/elec_watch/positive";
string negative_dir = "D:/images/elec_watch/negative";
void get_hog_descriptor(Mat &image, vector<float> &desc);
void generate_dataset(Mat &trainData, Mat &label);
void svm_train(Mat &trainData, Mat &labels);

int Advance::instrument() {
	// 读取和生成数据集
	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_watch/test.xml");
	// detect object
	Mat test = imread("D:/images/elec_watch/test/scene_01.jpg");
	resize(test, test, Size(0, 0), 0.2, 0.2);
	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 test_win = test(winRect);
			get_hog_descriptor(test_win, 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));
				sum_x += winRect.x;
				sum_y += winRect.y;
				count++;
			}
		}
	}
	winRect.x = sum_x / count;
	winRect.y = sum_y / count;
	rectangle(test, winRect, Scalar(255, 0, 0));

	imshow("object detection result", test);
	waitKey(0);

	return 0;
}


void get_hog_descriptor(Mat &image, vector<float> &desc) {
	HOGDescriptor hog;
	int h = image.rows;
	int w = image.cols;
	float rate = 64.0 / w;
	Mat img, gray;
	resize(image, img, Size(64, int(rate*h)));
	cvtColor(img, gray, COLOR_BGR2GRAY);
	// 图像统一resize成(128, 64)
	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));
	// cell = 8 * 8像素块
	// 64 * 128 = 8 * 16 cells
	// 总计数目 7*15*36=3780
	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);
	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);
	svm->setType(SVM::C_SVC);
	svm->setKernel(SVM::LINEAR);
	svm->setGamma(5.383);
	svm->train(trainData, ROW_SAMPLE, labels);
	clog << "...[Done]" << endl;
	printf("end train...\n");
	svm->save("D:/images/elec_watch/test.xml");

}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60
    • 61
    • 62
    • 63
    • 64
    • 65
    • 66
    • 67
    • 68
    • 69
    • 70
    • 71
    • 72
    • 73
    • 74
    • 75
    • 76
    • 77
    • 78
    • 79
    • 80
    • 81
    • 82
    • 83
    • 84
    • 85
    • 86
    • 87
    • 88
    • 89
    • 90
    • 91
    • 92
    • 93
    • 94
    • 95
    • 96
    • 97
    • 98
    • 99
    • 100
    • 101
    • 102
    • 103
    • 104
    • 105
    • 106
    • 107
    • 108
    • 109
    • 110
    • 111
    • 112
    • 113
    • 114
    • 115
    • 116
    • 117
    • 118
    • 119
    • 120
    • 121
    • 122

    案例三 二维码检测

    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
    请添加图片描述
    核心重点：主要使用图像的二值化，然后findcontour找到轮廓，利用透视摆正。利用外接矩形的宽高比过滤一部分不合适的选项，然后使用二维码固有特征。找到左上，右上，左下的三个正方形。并且如上图b1x:w1x:xb:w2x:b2x=1:1:3:1:1。这样就可以过滤其他的轮廓，得到正确值。
    代码部分：

    
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;

		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) {
			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));
			}
		}
	}
	RotatedRect rrt = minAreaRect(pts);
	Point2f vertices[4];
	rrt.points(vertices);
	pts.clear();
	for (int i = 0; i < 4; i++) {
		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());
	vector<vector<Point>> cpts;
	cpts.push_back(pts);
	drawContours(mask, cpts, 0, Scalar(255), -1, 8);

	Mat dst;
	bitwise_and(image, image, dst, mask);

	imshow("detect result", image);
	imshow("result-mask", mask);
	imshow("qrcode-roi", dst);


	//imshow("contour-image", image);
	//imshow("result", result);
}


bool isXCorner(Mat &image) {
	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;
	// verify finder pattern
	bool findleft = false, findright = false;
	int start = 0, end = 0;
	int offset = 0;
	while (true) {
		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) {
			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 - w1x - col;
			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);
	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) {
	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) {
		return false;
	}
	return true;
}


Mat transformCorner(Mat &image, RotatedRect &rect) {
	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));
	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;
}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60
    • 61
    • 62
    • 63
    • 64
    • 65
    • 66
    • 67
    • 68
    • 69
    • 70
    • 71
    • 72
    • 73
    • 74
    • 75
    • 76
    • 77
    • 78
    • 79
    • 80
    • 81
    • 82
    • 83
    • 84
    • 85
    • 86
    • 87
    • 88
    • 89
    • 90
    • 91
    • 92
    • 93
    • 94
    • 95
    • 96
    • 97
    • 98
    • 99
    • 100
    • 101
    • 102
    • 103
    • 104
    • 105
    • 106
    • 107
    • 108
    • 109
    • 110
    • 111
    • 112
    • 113
    • 114
    • 115
    • 116
    • 117
    • 118
    • 119
    • 120
    • 121
    • 122
    • 123
    • 124
    • 125
    • 126
    • 127
    • 128
    • 129
    • 130
    • 131
    • 132
    • 133
    • 134
    • 135
    • 136
    • 137
    • 138
    • 139
    • 140
    • 141
    • 142
    • 143
    • 144
    • 145
    • 146
    • 147
    • 148
    • 149
    • 150
    • 151
    • 152
    • 153
    • 154
    • 155
    • 156
    • 157
    • 158
    • 159
    • 160
    • 161
    • 162
    • 163
    • 164
    • 165
    • 166
    • 167
    • 168
    • 169
    • 170
    • 171
    • 172
    • 173
    • 174
    • 175
    • 176
    • 177
    • 178
    • 179
    • 180
    • 181
    • 182
    • 183
    • 184
    • 185
    • 186
    • 187
    • 188
    • 189
    • 190
    • 191
    • 192
    • 193
    • 194
    • 195
    • 196
    • 197
    • 198
    • 199
    • 200
    • 201
    • 202
    • 203
    • 204
    • 205
    • 206
    • 207
    • 208
    • 209
    • 210
    • 211
    • 212
    • 213
    • 214
    • 215

    程序运行的结果输出如下图所示：
    在这里插入图片描述
    值得注意的是，我们调用透视变换api后的输出结果如下面图所示：
    在这里插入图片描述
    在这里插入图片描述
    在这里插入图片描述
    我们可以看到，二维码上面的三个定位矩形，经过透视变换以后，均已经摆正了，就可以接下来做我们的1:1:3:1:1的特征检测了。

    案例四 kmean聚类

    4.1 kmeans的原理

    下面案例是在图片上随机生成点，然后再进行了kmeans的聚类。

    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;
	int sampleCount = rng.uniform(5, 500);
	Mat points(sampleCount, 1, CV_32FC2);

	for (int k = 0; k<numCluster; ++k)
	{
		Point center;
		center.x = rng.uniform(0, img.cols);
		center.y = rng.uniform(0, img.rows);
		Mat pointChunk = points.rowRange(k*sampleCount / numCluster,
			k == numCluster - 1 ? sampleCount : (k + 1)*sampleCount / numCluster);
		rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
	};
	randShuffle(points, 1, &rng);

	// 使用KMeans
	Mat labels;
	Mat centers;
	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);
	}

	// 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);

}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49

    在这里插入图片描述

    4.2 kmean图片分割

    下面代码进行了图片的分割，是基于像素级别的kmeans的聚类。

    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();

	int sampleCount = width * height;
	int clusterCount = 3;
	Mat labels;
	Mat centers;
	
	Mat sample_data = src.reshape(3, sampleCount);
	Mat data;
	sample_data.convertTo(data, CV_32F);
	
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);

	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];
		}
	}
	imshow("KMeans-image-Demo", result);
	waitKey(0);

}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46

    在这里插入图片描述

    4.3 kmeans图片背景的替换

    使用kmean进行图片分割然后替换背景

    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 simpleCount = width * height;
	int clusterCount = 3;
	Mat labels;
	Mat centers;

	Mat sample_data = src.reshape(3, simpleCount);
	Mat data;
	sample_data.convertTo(data, CV_32F);
	
	// 运行kmeans
	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);
	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;
			}
		}
	}
	imshow("mask", mask);

	Mat se = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	dilate(mask, mask, se);

	// mask边缘进行高斯模糊
	GaussianBlur(mask, mask, Size(5, 5), 0);
	imshow("mask-blur", mask);

	// 生成高斯权重图像融合
	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);
			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);

}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60
    • 61
    • 62
    • 63
    • 64
    • 65

    在这里插入图片描述

    4.4 kmeans生成图像色卡

    有别4.1中使用位置信息聚类，这里使用的是像素值信息进行聚类。聚类以后通过label信息，在像素级别上面统计不同颜色的数量，然后进行色卡的绘制。

    void kmeans_color_card() {
	Mat src = imread("D:/images/master.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 = 4;
	Mat labels;
	Mat centers;

	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);
	vector<float> clusters(clusterCount);

	for (int i = 0; i<labels.rows; i++){
		clusters[labels.at<int>(i, 0)]++;
	}

	for (int i = 0; i < clusters.size(); i++) {
		clusters[i] = clusters[i] / sampleCount;
	}
	int x_offset = 0;

	cout << centers << endl;

	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;
		float b = centers.at<float>(x, 0);
		float g = centers.at<float>(x, 1);
		float r = centers.at<float>(x, 2);


		rectangle(card, rect, Scalar(b, g, r), -1, 8, 0);
	}
	imshow("Image Color Card", card);
	waitKey(0);

}

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60

    在这里插入图片描述

  • 相关阅读:
    华为机试 - 信道分配
    ElasticSearch深浅分页查询及原理
    第01篇:系统化学习, 搞定Spring容器管理
    微信小程序自动化测试pytest版工具使用方法
    python pycharm 下载 安装 自(1)
    double类型数相减有小数误差问题
    【论文笔记】Enabling technologies and tools for digital twin
    RabbitMQ安装
    人工智能介绍
    Python灰帽编程——网页信息爬取
  • 原文地址：https://blog.csdn.net/HELLOWORLD2424/article/details/127247135