稀疏光流法跟中移动物体、监督学习聚类、K均值聚类、加载深度神经网络模型、深度神经网络模型的使用-Toy模板网

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1、稀疏光流法跟中移动物体

2、监督学习聚类

3、K均值聚类

4、加载深度神经网络模型

5、深度神经网络模型的使用

1、稀疏光流法跟中移动物体

稀疏光流法跟中移动物体、监督学习聚类、K均值聚类、加载深度神经网络模型、深度神经网络模型的使用,OpenCV学习笔记,opencv,图像处理

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//稀疏光流法跟中移动物体
vector<Scalar> color_lut;//颜色查找表
void draw_lines(Mat &image, vector<Point2f> pt1, vector<Point2f> pt2)
{
	RNG rng(5000);
	if (color_lut.size() < pt1.size())
	{
		for (size_t t = 0; t < pt1.size(); t++)
		{
			color_lut.push_back(Scalar(rng.uniform(0, 255), rng.uniform(0, 255),
				rng.uniform(0, 255)));
		}
	}
	for (size_t t = 0; t < pt1.size(); t++)
	{
		line(image, pt1[t], pt2[t], color_lut[t], 2, 8, 0);
	}
}
int test6()
{
	VideoCapture capture("F:/testMap/lolTFT.mp4");
	Mat prevframe, prevImg;
	if (!capture.read(prevframe))
	{
		cout << "请确认输入视频文件是否正确" << endl;
		return -1;
	}
	cvtColor(prevframe, prevImg, COLOR_BGR2GRAY);
	//角点检测相关参数设置
	vector<Point2f> Points;
	double qualityLevel = 0.01;
	int minDistance = 10;
	int blockSize = 3;
	bool useHarrisDetector = false; double k = 0.04;
	int Corners = 5000;//角点检测
	goodFeaturesToTrack(prevImg, Points, Corners, qualityLevel, minDistance,
		Mat(), blockSize, useHarrisDetector, k);

	//稀疏光流检测相关参数设置
	vector<Point2f> prevPts;//前一帧图像角点坐标
	vector<Point2f> nextPts;//当前帧图像角点坐标
	vector<uchar> status;//检点检测到的状态
	vector<float> err;
	TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, 0.01);
	double derivlambda = 0.5;
	int flags = 0;

	//初始状态的角点
	vector<Point2f> initPoints;
	initPoints.insert(initPoints.end(), Points.begin(), Points.end());
	//前一帧图像中的角点坐标
	prevPts.insert(prevPts.end(), Points.begin(), Points.end());
	while (true)
	{
		Mat nextframe, nextImg;
		if (!capture.read(nextframe))
		{
			break;
		}
		imshow("nextframe", nextframe);

		//光流跟踪
		cvtColor(nextframe, nextImg, COLOR_BGR2GRAY);
		calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err,
			Size(31, 31), 3, criteria, derivlambda, flags);

		//判断角点是否移动,如果不移动就删除
		size_t i, k;
		for (i = k = 0; i < nextPts.size(); i++)
		{
			//距离与状态测量
			double dist = abs(prevPts[i].x - nextPts[i].x) + abs(prevPts[i].y - nextPts[i].y);
			if (status[i] && dist > 2)
			{
				prevPts[k] = prevPts[i];
				initPoints[k] = initPoints[i]; nextPts[k++] = nextPts[i];
				circle(nextframe, nextPts[i], 3, Scalar(0, 255, 0), -1, 8);
			}
		}

		//更新移动角点数目
		nextPts.resize(k);
		prevPts.resize(k);
		initPoints.resize(k);

		//绘制跟踪轨迹
		draw_lines(nextframe, initPoints, nextPts);
		imshow("result", nextframe);
		char c = waitKey(50);
		if (c == 27)
		{
			break;
		}
		//更新角点坐标和前一帧图像
		std::swap(nextPts, prevPts);
		nextImg.copyTo(prevImg);
		//如果角点数目少于30,就重新检测角点
		if (initPoints.size() < 30)
		{
			goodFeaturesToTrack(prevImg, Points, Corners, qualityLevel,
				minDistance, Mat(), blockSize, useHarrisDetector, k);
			initPoints.insert(initPoints.end(), Points.begin(), Points.end());
			prevPts.insert(prevPts.end(), Points.begin(), Points.end());
			printf("total feature points : %d\n", prevPts.size());
		}
	}
	waitKey(0);
	return 0;
}

2、监督学习聚类

稀疏光流法跟中移动物体、监督学习聚类、K均值聚类、加载深度神经网络模型、深度神经网络模型的使用,OpenCV学习笔记,opencv,图像处理

//监督学习聚类
int test7()
{
	Mat img = imread("F:/testMap/digits.png"); Mat gray;
	cvtColor(img, gray, COLOR_BGR2GRAY);
	//分割为160个cells
	Mat images = Mat::zeros(5000, 400, CV_8UC1);
	Mat labels = Mat::zeros(5000, 1, CV_8UC1);

	int index = 0;
	Rect numberImg;
	numberImg.x = 0;
	numberImg.height = 1;
	numberImg.width = 400;
	for (int row = 0; row < 50; row++)
	{
		//从图像中分割出20×20的图像作为独立数字图像
		int label = row / 5;
		int datay = row * 20;
		for (int col = 0; col < 100; col++)
		{
			int datax = col * 20;
			Mat number = Mat::zeros(Size(20, 20), CV_8UC1);
			for (int x = 0; x < 20; x++)
			{
				for (int y = 0; y < 20; y++)
				{
					number.at<uchar>(x, y) = gray.at<uchar>(x + datay, y + datax);
				}
			}
			//将二维图像数据转成行数据
			Mat row = number.reshape(1, 1);
			cout << "提取第"<< index + 1 <<"个数据" << endl; 
			numberImg.y = index;
			//添加到总数据中
			row.copyTo(images(numberImg));
			//记录每个图像对应的数字标签
			labels.at<uchar>(index, 0) = label;
			index++;
		}
	}

	imwrite("所有数据按行排列结果.png", images); 
	imwrite("标签. png", labels);
	//加载训练数据集
	images.convertTo(images,CV_32FC1); 
	labels.convertTo(labels,CV_32SC1);
	Ptr<ml::TrainData> tdata = ml::TrainData::create(images,ml::ROW_SAMPLE, labels);
	//创建K近邻类
	Ptr<KNearest> knn = KNearest::create();
	knn->setDefaultK(5);//每个类别拿出5个数据
	knn->setIsClassifier(true);//进行分类
	//训练数据
	knn->train(tdata);
	//保存训练结果
	knn->save("knn_model.ym1");
	//输出运行结果提示
	cout << "己使用K近邻完成数据训练和保存" << endl;

	waitKey(0);
	return 0;
}