opencv各种小例子

图像腐蚀

#include <opencv2/highgui/highgui.hpp>//OpenCV highgui 模块头文件 ~

#include <opencv2/imgproc/imgproc.hpp>//OpenCV 图像处理头文件

using namespace cv; // 包含 cv 命名空间

int main() //控制台应用程序的入 口 函数，我们的程序从这里开始

{

Mat srclmage = imread("G:\\QQ图片20190428194331.jpg");

imshow("[ 原图 ] ",srclmage);

//进行腐蚀操作

Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));//getStructuringElement函数的返回值为指定形状和尺寸的结构元素(内核矩阵〉

Mat dstlmage;

erode(srclmage, dstlmage, element);

//显示效果图

imshow ("[ 效果图 ] ", dstlmage);

	waitKey(0);

	return 0;

}

图像模糊

#include <opencv2/highgui/highgui.hpp>//OpenCV highgui 模块头文件 ~

#include <opencv2/imgproc/imgproc.hpp>//OpenCV 图像处理头文件

using namespace cv; // 包含 cv 命名空间

int main() //控制台应用程序的入 口 函数，我们的程序从这里开始

{

Mat srclmage = imread("G:\\QQ图片20190428194331.jpg");

imshow("[ 原图 ] ",srclmage);

//进行均值滤波操作

Mat dstlmage;

blur(srclmage, dstlmage, Size(7, 7));

imshow ("[ 效果图 ] ", dstlmage);

	waitKey(0);

	return 0;

}

canny边缘检测

#include<opencv2/opencv.hpp>

#include <opencv2/imgproc/imgproc.hpp>//OpenCV 图像处理头文件

using namespace cv; // 包含 cv 命名空间

int main() //控制台应用程序的入 口 函数，我们的程序从这里开始

{

Mat srcImage = imread("G:\\QQ图片20190428194331.jpg");

imshow("[ 原图 ] ",srcImage);

Mat dstImage, edge, grayImage;

//创建与src同类型和大小的矩阵（dst）

dstImage.create(srcImage.size(), srcImage.type());

//将原图像转换为灰度图像,Opencv2

cvtColor(srcImage, grayImage, CV_BGR2GRAY);

/*Opencv3

cvtColor(srcImage,grayImage,COLOR_BGR2GRAY);

*/

//先使用3x3内核来降噪

blur(grayImage, edge, Size(3, 3));

//运行Canny算子

Canny(edge, edge, 3, 9, 3);

//显示

imshow ("[ 效果图 ] ", edge);

	waitKey(0);

	return 0;

}

读取视频

#include<opencv2\opencv.hpp>

using namespace cv;

int main()

{

	//读入视频

	VideoCapture capture("G:\\视觉资料\\【OpenCV3版】《OpenCV3编程入门》书本配套源代码\\【1】书本正篇程序源代码\\【1】第一章\\【6】播放视频\\6_播放视频\\1.avi ");

		//循环显示每一帧

		while(1)

	{

		Mat frame;//定义一个Mat变量，用于储存每一帧的图像

		capture >> frame;//读取当前帧

		imshow("读取视频",frame);//显示当前帧

		waitKey(30);//延时30ms

	}

	return 0;

}

canny从摄像头得到的视频

#include<opencv2\opencv.hpp>

using namespace cv;

int main()

{

	VideoCapture capture(0);

	Mat edges;

		//循环显示每一帧

		while(1)

	{

		Mat frame;//定义一个Mat变量，用于储存每一帧的图像

		capture >> frame;//读取当前帧

		cvtColor(frame, edges, CV_BGR2GRAY);

		blur(edges, edges, Size(7, 7));

		Canny(edges, edges, 0, 30, 3);

		imshow("canny后的视频",edges);//显示当前帧

		if(waitKey(30)>=0)break;//延时30ms

	}

	return 0;

}

2.1.1 彩色目标跟踪:Camshift

注意：本代码仅供学习交流所用，所有权归《OpenCV3编程入门》OpenCV3版书，请勿商用

#include "opencv2/video/tracking.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/highgui/highgui.hpp"

#include <iostream>

#include <ctype.h>

using namespace cv;

using namespace std;

//-----------------------------------【全局变量声明】-----------------------------------------

//		描述：声明全局变量

//-------------------------------------------------------------------------------------------------

Mat image;

bool backprojMode = false;

bool selectObject = false;

int trackObject = 0;

bool showHist = true;

Point origin;

Rect selection;

int vmin = 10, vmax = 256, smin = 30;

//--------------------------------【onMouse( )回调函数】------------------------------------

//		描述：鼠标操作回调

//-------------------------------------------------------------------------------------------------

static void onMouse(int event, int x, int y, int, void*)

{

	if (selectObject)

	{

		selection.x = MIN(x, origin.x);

		selection.y = MIN(y, origin.y);

		selection.width = std::abs(x - origin.x);

		selection.height = std::abs(y - origin.y);

		selection &= Rect(0, 0, image.cols, image.rows);

	}

	switch (event)

	{

		//此句代码的OpenCV2版为：

		//case CV_EVENT_LBUTTONDOWN:

		//此句代码的OpenCV3版为：

	case EVENT_LBUTTONDOWN:

		origin = Point(x, y);

		selection = Rect(x, y, 0, 0);

		selectObject = true;

		break;

		//此句代码的OpenCV2版为：

		//case CV_EVENT_LBUTTONUP:

		//此句代码的OpenCV3版为：

	case EVENT_LBUTTONUP:

		selectObject = false;

		if (selection.width > 0 && selection.height > 0)

			trackObject = -1;

		break;

	}

}

//--------------------------------【help( )函数】----------------------------------------------

//		描述：输出帮助信息

//-------------------------------------------------------------------------------------------------

static void ShowHelpText()

{

	cout << "\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"

		<< "\n\n\t\t\t此为本书OpenCV3版的第8个配套示例程序\n"

		<< "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION

		<< "\n\n  ----------------------------------------------------------------------------";

	cout << "\n\n\t此Demo显示了基于均值漂移的追踪（tracking）技术\n"

		"\t请用鼠标框选一个有颜色的物体，对它进行追踪操作\n";

	cout << "\n\n\t操作说明： \n"

		"\t\t用鼠标框选对象来初始化跟踪\n"

		"\t\tESC - 退出程序\n"

		"\t\tc - 停止追踪\n"

		"\t\tb - 开/关-投影视图\n"

		"\t\th - 显示/隐藏-对象直方图\n"

		"\t\tp - 暂停视频\n";

}

const char* keys =

{

	"{1|  | 0 | camera number}"

};

//-----------------------------------【main( )函数】--------------------------------------------

//		描述：控制台应用程序的入口函数，我们的程序从这里开始

//-------------------------------------------------------------------------------------------------

int main(int argc, const char** argv)

{

	ShowHelpText();

	VideoCapture cap;

	Rect trackWindow;

	int hsize = 16;

	float hranges[] = { 0,180 };

	const float* phranges = hranges;

	cap.open(0);

	if (!cap.isOpened())

	{

		cout << "不能初始化摄像头\n";

	}

	namedWindow("Histogram", 0);

	namedWindow("CamShift Demo", 0);

	setMouseCallback("CamShift Demo", onMouse, 0);

	createTrackbar("Vmin", "CamShift Demo", &vmin, 256, 0);

	createTrackbar("Vmax", "CamShift Demo", &vmax, 256, 0);

	createTrackbar("Smin", "CamShift Demo", &smin, 256, 0);

	Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj;

	bool paused = false;

	for (;;)

	{

		if (!paused)

		{

			cap >> frame;

			if (frame.empty())

				break;

		}

		frame.copyTo(image);

		if (!paused)

		{

			cvtColor(image, hsv, COLOR_BGR2HSV);

			if (trackObject)

			{

				int _vmin = vmin, _vmax = vmax;

				inRange(hsv, Scalar(0, smin, MIN(_vmin, _vmax)),

					Scalar(180, 256, MAX(_vmin, _vmax)), mask);

				int ch[] = { 0, 0 };

				hue.create(hsv.size(), hsv.depth());

				mixChannels(&hsv, 1, &hue, 1, ch, 1);

				if (trackObject < 0)

				{

					Mat roi(hue, selection), maskroi(mask, selection);

					calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);

					//此句代码的OpenCV3版为：

					normalize(hist, hist, 0, 255, NORM_MINMAX);

					//此句代码的OpenCV2版为：

					//normalize(hist, hist, 0, 255, CV_MINMAX);

					trackWindow = selection;

					trackObject = 1;

					histimg = Scalar::all(0);

					int binW = histimg.cols / hsize;

					Mat buf(1, hsize, CV_8UC3);

					for (int i = 0; i < hsize; i++)

						buf.at<Vec3b>(i) = Vec3b(saturate_cast<uchar>(i*180. / hsize), 255, 255);

					//此句代码的OpenCV3版为：

					cvtColor(buf, buf, COLOR_HSV2BGR);

					//此句代码的OpenCV2版为：

					//cvtColor(buf, buf, CV_HSV2BGR);

					for (int i = 0; i < hsize; i++)

					{

						int val = saturate_cast<int>(hist.at<float>(i)*histimg.rows / 255);

						rectangle(histimg, Point(i*binW, histimg.rows),

							Point((i + 1)*binW, histimg.rows - val),

							Scalar(buf.at<Vec3b>(i)), -1, 8);

					}

				}

				calcBackProject(&hue, 1, 0, hist, backproj, &phranges);

				backproj &= mask;

				RotatedRect trackBox = CamShift(backproj, trackWindow,

					//此句代码的OpenCV3版为：

					TermCriteria(TermCriteria::EPS | TermCriteria::COUNT, 10, 1));

				//此句代码的OpenCV2版为：

				//TermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ));

				if (trackWindow.area() <= 1)

				{

					int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5) / 6;

					trackWindow = Rect(trackWindow.x - r, trackWindow.y - r,

						trackWindow.x + r, trackWindow.y + r) &

						Rect(0, 0, cols, rows);

				}

				if (backprojMode)

					cvtColor(backproj, image, COLOR_GRAY2BGR);

				//此句代码的OpenCV3版为：

				ellipse(image, trackBox, Scalar(0, 0, 255), 3, LINE_AA);

				//此句代码的OpenCV2版为：

				//ellipse( image, trackBox, Scalar(0,0,255), 3, CV_AA );

			}

		}

		else if (trackObject < 0)

			paused = false;

		if (selectObject && selection.width > 0 && selection.height > 0)

		{

			Mat roi(image, selection);

			bitwise_not(roi, roi);

		}

		imshow("CamShift Demo", image);

		imshow("Histogram", histimg);

		char c = (char)waitKey(10);

		if (c == 27)

			break;

		switch (c)

		{

		case 'b':

			backprojMode = !backprojMode;

			break;

		case 'c':

			trackObject = 0;

			histimg = Scalar::all(0);

			break;

		case 'h':

			showHist = !showHist;

			if (!showHist)

				destroyWindow("Histogram");

			else

				namedWindow("Histogram", 1);

			break;

		case 'p':

			paused = !paused;

			break;

		default:

			;

		}

	}

	return 0;

}

9_用光流法进行运动目标检测

//---------------------------------【头文件、命名空间包含部分】----------------------------

//		描述：包含程序所使用的头文件和命名空间

//-------------------------------------------------------------------------------------------------

#include <opencv2/video/video.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#include <opencv2/core/core.hpp>

#include <iostream>

#include <cstdio>

using namespace std;

using namespace cv;

//-----------------------------------【全局函数声明】-----------------------------------------

//		描述：声明全局函数

//-------------------------------------------------------------------------------------------------

void tracking(Mat &frame, Mat &output);

bool addNewPoints();

bool acceptTrackedPoint(int i);

//-----------------------------------【全局变量声明】-----------------------------------------

//		描述：声明全局变量

//-------------------------------------------------------------------------------------------------

string window_name = "optical flow tracking";

Mat gray;	// 当前图片

Mat gray_prev;	// 预测图片

vector<Point2f> points[2];	// point0为特征点的原来位置，point1为特征点的新位置

vector<Point2f> initial;	// 初始化跟踪点的位置

vector<Point2f> features;	// 检测的特征

int maxCount = 500;	// 检测的最大特征数

double qLevel = 0.01;	// 特征检测的等级

double minDist = 10.0;	// 两特征点之间的最小距离

vector<uchar> status;	// 跟踪特征的状态，特征的流发现为1，否则为0

vector<float> err;

//--------------------------------【help( )函数】----------------------------------------------

//		描述：输出帮助信息

//-------------------------------------------------------------------------------------------------

static void help()

{

	//输出欢迎信息和OpenCV版本

	cout <<"\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"

		<<"\n\n\t\t\t此为本书OpenCV3版的第9个配套示例程序\n"

		<<	"\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION

		<<"\n\n  ----------------------------------------------------------------------------" ;

}

//-----------------------------------【main( )函数】--------------------------------------------

//		描述：控制台应用程序的入口函数，我们的程序从这里开始

//-------------------------------------------------------------------------------------------------

int main()

{

	Mat frame;

	Mat result;

	VideoCapture capture("1.avi");

	help();

	if(capture.isOpened())	// 摄像头读取文件开关

	{

		while(true)

		{

			capture >> frame;

			if(!frame.empty())

			{

				tracking(frame, result);

			}

			else

			{

				printf(" --(!) No captured frame -- Break!");

				break;

			}

			int c = waitKey(50);

			if( (char)c == 27 )

			{

				break;

			}

		}

	}

	return 0;

}

//-------------------------------------------------------------------------------------------------

// function: tracking

// brief: 跟踪

// parameter: frame	输入的视频帧

//			  output 有跟踪结果的视频帧

// return: void

//-------------------------------------------------------------------------------------------------

void tracking(Mat &frame, Mat &output)

{

	//此句代码的OpenCV3版为：

	cvtColor(frame, gray, COLOR_BGR2GRAY);

	//此句代码的OpenCV2版为：

	//cvtColor(frame, gray, CV_BGR2GRAY);

	frame.copyTo(output);

	// 添加特征点

	if (addNewPoints())

	{

		goodFeaturesToTrack(gray, features, maxCount, qLevel, minDist);

		points[0].insert(points[0].end(), features.begin(), features.end());

		initial.insert(initial.end(), features.begin(), features.end());

	}

	if (gray_prev.empty())

	{

		gray.copyTo(gray_prev);

	}

	// l-k光流法运动估计

	calcOpticalFlowPyrLK(gray_prev, gray, points[0], points[1], status, err);

	// 去掉一些不好的特征点

	int k = 0;

	for (size_t i=0; i<points[1].size(); i++)

	{

		if (acceptTrackedPoint(i))

		{

			initial[k] = initial[i];

			points[1][k++] = points[1][i];

		}

	}

	points[1].resize(k);

	initial.resize(k);

	// 显示特征点和运动轨迹

	for (size_t i=0; i<points[1].size(); i++)

	{

		line(output, initial[i], points[1][i], Scalar(0, 0, 255));

		circle(output, points[1][i], 3, Scalar(0, 255, 0), -1);

	}

	// 把当前跟踪结果作为下一此参考

	swap(points[1], points[0]);

	swap(gray_prev, gray);

	imshow(window_name, output);

}

//-------------------------------------------------------------------------------------------------

// function: addNewPoints

// brief: 检测新点是否应该被添加

// parameter:

// return: 是否被添加标志

//-------------------------------------------------------------------------------------------------

bool addNewPoints()

{

	return points[0].size() <= 10;

}

//-------------------------------------------------------------------------------------------------

// function: acceptTrackedPoint

// brief: 决定哪些跟踪点被接受

// parameter:

// return:

//-------------------------------------------------------------------------------------------------

bool acceptTrackedPoint(int i)

{

	return status[i] && ((abs(points[0][i].x - points[1][i].x) + abs(points[0][i].y - points[1][i].y)) > 2);

}

点追踪

#include "opencv2/video/tracking.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/highgui/highgui.hpp"

#include <iostream>

#include <ctype.h>

using namespace cv;

using namespace std;

//--------------------------------【help( )函数】----------------------------------------------

//		描述：输出帮助信息

//-------------------------------------------------------------------------------------------------

static void help()

{

	//输出欢迎信息和OpenCV版本

	cout << "\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"

		<< "\n\n\t\t\t此为本书OpenCV3版的第10个配套示例程序\n"

		<< "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION

		<< "\n\n  ----------------------------------------------------------------------------";

	cout << "\n\n\t该Demo演示了 Lukas-Kanade基于光流的lkdemo\n";

	cout << "\n\t程序默认从摄像头读入视频，可以按需改为从视频文件读入图像\n";

	cout << "\n\t操作说明: \n"

		"\t\t通过点击在图像中添加/删除特征点\n"

		"\t\tESC - 退出程序\n"

		"\t\tr -自动进行追踪\n"

		"\t\tc - 删除所有点\n"

		"\t\tn - 开/光-夜晚模式\n" << endl;

}

Point2f point;

bool addRemovePt = false;

//--------------------------------【onMouse( )回调函数】------------------------------------

//		描述：鼠标操作回调

//-------------------------------------------------------------------------------------------------

static void onMouse(int event, int x, int y, int /*flags*/, void* /*param*/)

{

	//此句代码的OpenCV2版为：

	//if( event == CV_EVENT_LBUTTONDOWN )

	//此句代码的OpenCV3版为：

	if (event == EVENT_LBUTTONDOWN)

	{

		point = Point2f((float)x, (float)y);

		addRemovePt = true;

	}

}

//-----------------------------------【main( )函数】--------------------------------------------

//		描述：控制台应用程序的入口函数，我们的程序从这里开始

//-------------------------------------------------------------------------------------------------

int main(int argc, char** argv)

{

	help();

	VideoCapture cap;

	//此句代码的OpenCV2版为：

	//TermCriteria termcrit(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 20, 0.03);

	//此句代码的OpenCV3版为：

	TermCriteria termcrit(TermCriteria::MAX_ITER | TermCriteria::EPS, 20, 0.03);

	Size subPixWinSize(10, 10), winSize(31, 31);

	const int MAX_COUNT = 500;

	bool needToInit = false;

	bool nightMode = false;

	cap.open(0);

	if (!cap.isOpened())

	{

		cout << "Could not initialize capturing...\n";

		return 0;

	}

	namedWindow("LK Demo", 1);

	setMouseCallback("LK Demo", onMouse, 0);

	Mat gray, prevGray, image;

	vector<Point2f> points[2];

	for (;;)

	{

		Mat frame;

		cap >> frame;

		if (frame.empty())

			break;

		frame.copyTo(image);

		cvtColor(image, gray, COLOR_BGR2GRAY);

		if (nightMode)

			image = Scalar::all(0);

		if (needToInit)

		{

			// 自动初始化

			goodFeaturesToTrack(gray, points[1], MAX_COUNT, 0.01, (double)10, Mat(), 3,(bool) 0, 0.04);

			cornerSubPix(gray, points[1], subPixWinSize, Size(-1, -1), termcrit);

			addRemovePt = false;

		}

		else if (!points[0].empty())

		{

			vector<uchar> status;

			vector<float> err;

			if (prevGray.empty())

				gray.copyTo(prevGray);

			calcOpticalFlowPyrLK(prevGray, gray, points[0], points[1], status, err, winSize,

				3, termcrit, 0, 0.001);

			size_t i, k;

			for (i = k = 0; i < points[1].size(); i++)

			{

				if (addRemovePt)

				{

					if (norm(point - points[1][i]) <= 5)

					{

						addRemovePt = false;

						continue;

					}

				}

				if (!status[i])

					continue;

				points[1][k++] = points[1][i];

				circle(image, points[1][i], 3, Scalar(0, 255, 0), -1, 8);

			}

			points[1].resize(k);

		}

		if (addRemovePt && points[1].size() < (size_t)MAX_COUNT)

		{

			vector<Point2f> tmp;

			tmp.push_back(point);

			//此句代码的OpenCV2版为：

			//cornerSubPix( gray, tmp, winSize, cvSize(-1,-1), termcrit);

			//此句代码的OpenCV3版为：

			cornerSubPix(gray, tmp, winSize, Size(-1, -1), termcrit);

			points[1].push_back(tmp[0]);

			addRemovePt = false;

		}

		needToInit = false;

		imshow("LK Demo", image);

		char c = (char)waitKey(10);

		if (c == 27)

			break;

		switch (c)

		{

		case 'r':

			needToInit = true;

			break;

		case 'c':

			points[0].clear();

			points[1].clear();

			break;

		case 'n':

			nightMode = !nightMode;

			break;

		}

		std::swap(points[1], points[0]);

		cv::swap(prevGray, gray);

	}

	return 0;

}