光流法OPenCV

opencv 光流法特征点摘要：，然后按照详细具体地写一篇文章。

1.简介- OpenCV- 光流法- 特征点2.光流法原理- 计算相邻帧之间像素变化- 估计物体运动3.特征点的作用- 图像匹配- 物体识别4.OpenCV中的光流法与特征点结合- 计算光流场- 提取特征点- 匹配特征点5.应用案例- 运动目标检测- 场景识别正文：OpenCV是一个功能强大的计算机视觉和机器学习软件库，广泛应用于图像处理、视频分析和计算机视觉领域。

其中，光流法是OpenCV提供的一种运动估计方法，通过计算相邻帧之间像素之间的变化来估计物体的运动。

而特征点则是图像中具有独特性质的点，如角点、边缘点等，常用于图像匹配和物体识别。

光流法原理是通过计算相邻帧之间像素的变化来估计物体的运动。

具体来说，光流法首先对图像进行预处理，如滤波、去噪等操作，然后计算相邻帧之间像素之间的变化，得到一个光流场。

光流场包含了物体在相邻帧之间的运动信息，可以用来估计物体的运动速度和方向。

特征点在图像处理中具有重要作用。

特征点具有独特的性质，如角点、边缘点等，可以用于图像匹配和物体识别。

在OpenCV中，特征点被广泛应用于SIFT、SURF、ORB等特征提取算法。

这些算法可以提取图像中的特征点，并计算特征点之间的描述符，用于匹配不同图像之间的特征点。

在OpenCV中，光流法和特征点可以相互结合，共同用于图像处理和计算机视觉任务。

首先，通过光流法计算相邻帧之间的光流场，然后提取光流场中的特征点。

接着，利用特征点之间的匹配关系，可以实现运动目标检测、场景识别等任务。

总之，OpenCV中的光流法和特征点是图像处理和计算机视觉领域的两个重要工具。

通过将光流法和特征点相互结合，可以实现更准确、高效的图像处理和计算机视觉任务。

C++的OpenCV使用方法总结在计算机视觉和图像处理领域，OpenCV是一个非常强大的开源库，它提供了丰富的功能和工具，用于处理图像和视频。

作为C++程序员，了解并熟练使用OpenCV库是非常重要的。

本文将对C++中使用OpenCV的方法进行总结，并探讨一些常见的应用和技巧。

一、安装和配置OpenCV在开始使用OpenCV之前，首先需要安装和配置这个库。

在Windows评台上，可以通过下载预编译的二进制文件进行安装；在Linux评台上，可以通过包管理器进行安装。

安装完毕后，还需进行一些环境配置，确保编译器能够正确信息OpenCV库文件。

二、基本图像处理1. 读取和显示图像在C++中使用OpenCV读取和显示图像非常简单，只需几行代码即可完成。

首先需要使用imread函数读取图像文件，然后使用imshow 函数显示图像。

在进行图像显示后，需要使用waitKey函数等待用户按下某个键，以便关闭显示窗口。

2. 图像的基本操作OpenCV提供了丰富的图像处理函数，包括图像缩放、旋转、平移、通道拆分与合并等。

这些函数可以帮助我们对图像进行各种基本操作，从而满足不同的需求。

三、特征提取与描述1. Harris角点检测Harris角点检测是一种经典的特征点检测方法，它可以用来识别图像中的角点。

在OpenCV中，我们可以使用cornerHarris函数来实现Harris角点检测，然后对检测结果进行筛选和标记。

2. SIFT特征提取SIFT是一种广泛应用的特征提取算法，它具有旋转不变性和尺度不变性。

在OpenCV中，我们可以使用SIFT算法来提取图像的关键点和特征描述子，从而实现图像匹配和目标识别等功能。

四、图像分类与识别1. 使用支持向量机（SVM）进行图像分类OpenCV提供了对机器学习算法的支持，包括SVM分类器。

我们可以使用SVM对图像进行分类，从而实现图像识别和目标检测等功能。

2. 使用深度学习模型进行图像识别近年来，深度学习在图像识别领域取得了显著的成就。

The OpenCV Library: Computing Optical FlowDavid StavensStanford Artificial Intelligence LabI received some requests……on what to cover tonight:“Perhaps you could do one of the 14 projects in the course? In front of us.In one hour.”--AnonymousA fully functional sparse optical flow algorithm!Tell Visual Studio where the includes are.Tell Visual Studio to link against cxcore.lib, cv.lib, and highgui.lib.Tell Visual Studio to disable managed extensions.Optical Flow: OverviewGiven a set of points in an image, find those same points in another image. Or, given point [u x , u y ]T in image I 1find the point [u x + δx , u y + δy ]Tin image I 2that minimizes ε:(the Σ/w’s are needed due to the aperture problem)()∑∑+−=+−=++−=x x x x yy yy w u w u x w uw u y y x y x y x I y x I ),(),(),(21δδδδεOptical Flow: UtilityTracking points (“features”) across multiple images is a fundamental operation in many computer vision applications:To find an object from one image in another. To determine how an object/camera moved. To resolve depth from a single camera. …or stereo.~ 75% of this year’s CS 223b projects. But what are good features to track?Finding Features: Overview Intuitively, a good feature needs at least: Texture (or ambiguity in tracking)Corner (or aperture problem)But what does this mean formally?Shi/Tomasi. Intuitive result really part of motion equation. High eigenvalues imply reliable solvability. Nice!⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎣⎡⎟⎟⎠⎞⎜⎜⎝⎛∂∂∂∂∂∂∂∂⎟⎠⎞⎜⎝⎛∂∂∑∑∑∑od neighborho od neighborho od neighborho od neighborho y y x y x x I I I I 2222A good feature has big eigenvalues, implies:TextureCornerPlan9OpenCV Basics9What is it?9How do we get started?9Feature Finding and Optical Flow 9A brief mathematical discussion.OpenCV Implementation of Optical Flow Step by step.So now let’s code it!Beauty of OpenCV:All of the Above = Two Function CallsPlus some support code :-)Let’s step through the pieces.These slides provide the high-level.Full implementation with extensive comments: /~dstavens/cs223bStep 1: Open Input Video CvCapture*input_video=cvCaptureFromFile(“filename.avi”);Failure modes:The file doesn’t exist.The AVI uses a codec OpenCV can’t read.Codecs like MJPEG and Cinepak are good.DV, in particular, is bad.Step 2: Get A Video Frame cvQueryFrame( input_video);This is a hack so that we can look at the internals of the AVI. OpenCVdoesn’t allow us to do that correctly unless we get a video frame first.Step 3: Read AVI Properties CvSize frame_size;frame_size.height=cvGetCaptureProperty( input_video,CV_CAP_PROP_FRAME_HEIGHT );Similar construction for getting the width and the number of frames.See the handout.Step 4: Create a Window cvNamedWindow(“Optical Flow”,CV_WINDOW_AUTOSIZE);We will put our output here for visualization and debugging.Step 5: Loop Through FramesGo to frame N:cvSetCaptureProperty( input_video,CV_CAP_PROP_POS_FRAMES, N );Get frame N:IplImage*frame = cvQueryFrame(input_video);Important: cvQueryFrame always returns a pointer to the same location in memory.Step 6: Convert/AllocateConvert input frame to 8-bit mono: IplImage*frame1 =cvCreateImage( cvSize(width, height),IPL_DEPTH_8U, 1);cvConvertImage( frame, frame1 );Actually need third argument to conversion: CV_CVTIMG_FLIP.Step 7: Run Shi and Tomasi CvPoint2D32f frame1_features[N]; cvGoodFeaturesToTrack(frame1, eig_image, temp_image,frame1_features, &N, .01, .01, NULL);Allocate eig,temp as in handout.On return frame1_features is full and N is the number of features found.Step 8: Run Optical Flowchar optical_flow_found_feature[];float optical_flow_feature_error[]; CvTermCriteria term =cvTermCriteria( CV_TERMCRIT_ITER |CV_TERMCRIT_EPS, 20, .3 ); cvCalcOpticalFlowPyrLK( …);13 arguments total. All of the above.Both frames, both feature arrays, etc.See full implementation in handout.Step 9: Visualize the Output CvPoint p, q;p.x= 1; p.y= 1; q.x= 2; q.y= 2;CvScalar line_color;line_color= CV_RGB(255, 0, 0);int line_thickness= 1;cvLine(frame1, p,q, line_color, line_thickness, CV_AA, 0); cvShowImage(“Optical Flow”, frame1);CV_AA means draw the line antialiased.0 means there are no fractional bits.(The Stanford Roadrunner Robot.)Stavens, Lookingbill, Lieb, Thrun; CS223b 2004; ICRA 2005 A few closing thoughts…/* --Sparse Optical Flow Demo Program--*WrittenbyDavidStavens(**************************.edu)*/#include <stdio.h>#include <cv.h>#include <highgui.h>#include <math.h>static const double pi = 3.14159265358979323846;inline static double square(int a){return a * a;}/* This is just an inline that allocates images. I did this to reduce clutter in the* actual computer vision algorithmic code. Basically it allocates the requested image* unless that image is already non-NULL. It always leaves a non-NULL image as-is even* if that image's size, depth, and/or channels are different than the request.*/inline static void allocateOnDemand( IplImage **img, CvSize size, int depth, int channels ){if ( *img != NULL ) return;*img = cvCreateImage( size, depth, channels );if ( *img == NULL ){fprintf(stderr, "Error: Couldn't allocate image. Out of memory?\n");exit(-1);}}/* This is a hack. If we don't call this first then getting capture*properties (below) won't work right. This is an OpenCV bug. Weignore the return value here. But it's actually a video frame.**/cvQueryFrame( input_video );/* Read the video's frame size out of the AVI. */frame_size;CvSizeframe_size.height =(int) cvGetCaptureProperty( input_video, CV_CAP_PROP_FRAME_HEIGHT );frame_size.width =(int) cvGetCaptureProperty( input_video, CV_CAP_PROP_FRAME_WIDTH );/* Determine the number of frames in the AVI. */long number_of_frames;/* Go to the end of the AVI (ie: the fraction is "1") */cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_AVI_RATIO, 1. );/* Now that we're at the end, read the AVI position in frames */number_of_frames = (int) cvGetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES );/* Return to the beginning */cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES, 0. );/* Create three windows called "Frame N", "Frame N+1", and "Optical Flow"for visualizing the output. Have those windows automatically change their *size to match the output.**/cvNamedWindow("Optical Flow", CV_WINDOW_AUTOSIZE);long current_frame = 0;while(true){static IplImage *frame = NULL, *frame1 = NULL, *frame1_1C = NULL, *frame2_1C = NULL, *eig_image = NULL, *temp_image = NULL, *pyramid1 = NULL, *pyramid2 = NULL;/* Go to the frame we want. Important if multiple frames are queried in the loop which they of course are for optical flow. Note that the very *first call to this is actually not needed. (Because the correct position *is set outsite the for() loop.)**/cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES, current_frame );/* Get the next frame of the video.IMPORTANT! cvQueryFrame() always returns a pointer to the _same_ *memory location. So successive calls:*frame1 = cvQueryFrame();*frame2 = cvQueryFrame();**frame3 = cvQueryFrame();will result in (frame1 == frame2 && frame2 == frame3) being true.*The solution is to make a copy of the cvQueryFrame() output.**/= cvQueryFrame( input_video );frameif (frame == NULL){/* Why did we get a NULL frame? We shouldn't be at the end. */fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");return -1;}/* Allocate another image if not already allocated.*Image has ONE challenge of color (ie: monochrome) with 8-bit "color" depth.This is the image format OpenCV algorithms actually operate on (mostly).**/allocateOnDemand( &frame1_1C, frame_size, IPL_DEPTH_8U, 1 );/* Convert whatever the AVI image format is into OpenCV's preferred format.AND flip the image vertically. Flip is a shameless hack. OpenCV reads *in AVIs upside-down by default. (No comment :-))**/cvConvertImage(frame, frame1_1C, CV_CVTIMG_FLIP);/* We'll make a full color backup of this frame so that we can draw on it.(It's not the best idea to draw on the static memory space of cvQueryFrame().) **/allocateOnDemand( &frame1, frame_size, IPL_DEPTH_8U, 3 );cvConvertImage(frame, frame1, CV_CVTIMG_FLIP);/* Get the second frame of video. Sample principles as the first. */= cvQueryFrame( input_video );frameif (frame == NULL){fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");return -1;}allocateOnDemand( &frame2_1C, frame_size, IPL_DEPTH_8U, 1 );cvConvertImage(frame, frame2_1C, CV_CVTIMG_FLIP);/* Shi and Tomasi Feature Tracking! *//* Preparation: Allocate the necessary storage. */allocateOnDemand( &eig_image, frame_size, IPL_DEPTH_32F, 1 );allocateOnDemand( &temp_image, frame_size, IPL_DEPTH_32F, 1 );/* Preparation: This array will contain the features found in frame 1. */CvPoint2D32f frame1_features[400];/* Preparation: BEFORE the function call this variable is the array size (or the maximum number of features to find). AFTER the function call **this variable is the number of features actually found.*/int number_of_features;/* I'm hardcoding this at 400. But you should make this a #define so that you can change the number of features you use for an accuracy/speed tradeoff analysis.**/number_of_features = 400;/* Actually run the Shi and Tomasi algorithm!!"frame1_1C" is the input image.*"eig_image" and "temp_image" are just workspace for the algorithm.*The first ".01" specifies the minimum quality of the features (based on the *eigenvalues).The second ".01" specifies the minimum Euclidean distance between features.*"NULL" means use the entire input image. You could point to a part of the *image.WHEN THE ALGORITHM RETURNS:*"frame1_features" will contain the feature points.*"number_of_features" will be set to a value <= 400 indicating the number of *feature points found.*/cvGoodFeaturesToTrack(frame1_1C, eig_image, temp_image, frame1_features, & number_of_features, .01, .01, NULL);/* Pyramidal Lucas Kanade Optical Flow! *//* This array will contain the locations of the points from frame 1 in frame 2. */ CvPoint2D32f frame2_features[400];/* The i-th element of this array is the error in the optical flow for the i-th featureof frame1 as found in frame 2. If the i-th feature was not found (see the *array above)I think the i-th entry in this array is undefined.**/float optical_flow_feature_error[400];/* This is the window size to use to avoid the aperture problem (see slide "Optical Flow: Overview"). */optical_flow_window = cvSize(3,3);CvSize/* This termination criteria tells the algorithm to stop when it has either done20 iterations or when*epsilon is better than .3. You can play with these parameters for speed vs.accuracy but these valueswork pretty well in many situations.**/optical_flow_termination_criteriaCvTermCriteria= cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3 );line_thickness = 1;int line_thickness;/* CV_RGB(red, green, blue) is the red, green, and blue components* of the color you want, each out of 255.*/line_color; line_color = CV_RGB(255,0,0);CvScalar/* Let's make the flow field look nice with arrows. *//* Here we lengthen the arrow by a factor of three. */q.x = (int) (p.x - 3 * hypotenuse * cos(angle));q.y = (int) (p.y - 3 * hypotenuse * sin(angle));/* Now we draw the main line of the arrow. *//* "frame1" is the frame to draw on.* "p" is the point where the line begins.* "q" is the point where the line stops.* "CV_AA" means antialiased drawing.* "0" means no fractional bits in the center cooridinate or radius.*/cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );/* Now draw the tips of the arrow. I do some scaling so that the* tips look proportional to the main line of the arrow.*/p.x = (int) (q.x + 9 * cos(angle + pi / 4));p.y = (int) (q.y + 9 * sin(angle + pi / 4));cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );p.x = (int) (q.x + 9 * cos(angle - pi / 4));p.y = (int) (q.y + 9 * sin(angle - pi / 4));cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );}/* Now display the image we drew on. Recall that "Optical Flow" is the name of the window we created above.**/cvShowImage("Optical Flow", frame1);/* And wait for the user to press a key (so the user has time to look at the image).If the argument is 0 then it waits forever otherwise it waits that number of *milliseconds.The return value is the key the user pressed.**/int key_pressed;key_pressed = cvWaitKey(0);/* If the users pushes "b" or "B" go back one frame.Otherwise go forward one frame.**/if (key_pressed == 'b' || key_pressed == 'B') current_frame--;else current_frame++;/* Don't run past the front/end of the AVI. */if (current_frame < 0) current_frame = 0;if (current_frame >= number_of_frames - 1) current_frame = number_of_frames - 2;}}。

《基于OPENCV的运动目标检测与跟踪技术研究》篇一一、引言随着计算机视觉技术的飞速发展，运动目标检测与跟踪技术已经成为计算机视觉领域研究的热点。

该技术广泛应用于智能监控、交通流量管理、人机交互等众多领域。

OpenCV作为一个强大的计算机视觉库，为运动目标检测与跟踪提供了有效的工具。

本文旨在研究基于OpenCV的运动目标检测与跟踪技术，探讨其原理、方法及实际应用。

二、运动目标检测技术研究1. 背景及原理运动目标检测是计算机视觉中的一项基本任务，其目的是从视频序列中提取出运动的目标。

OpenCV提供了多种运动目标检测方法，如背景减除法、光流法、帧间差分法等。

其中，背景减除法是一种常用的方法，其原理是将当前帧与背景模型进行比较，从而检测出运动目标。

2. 关键技术与方法（1）背景建模：背景建模是运动目标检测的关键步骤。

OpenCV提供了多种背景建模方法，如单高斯模型、混合高斯模型等。

其中，混合高斯模型能够更好地适应背景的动态变化。

（2）阈值设定：设定合适的阈值是运动目标检测的重要环节。

阈值过低可能导致误检，阈值过高则可能导致漏检。

OpenCV通过统计像素值分布，自动设定阈值，从而提高检测的准确性。

3. 实验与分析本文通过实验对比了不同背景建模方法和阈值设定对运动目标检测效果的影响。

实验结果表明，混合高斯模型结合合适的阈值设定能够获得较好的检测效果。

此外，本文还对不同场景下的运动目标检测进行了实验，验证了该方法的稳定性和泛化能力。

三、运动目标跟踪技术研究1. 背景及原理运动目标跟踪是指在视频序列中，对检测到的运动目标进行持续跟踪。

OpenCV提供了多种跟踪方法，如光流法、Meanshift 算法、KCF算法等。

这些方法各有优缺点，适用于不同的场景和需求。

2. 关键技术与方法（1）特征提取：特征提取是运动目标跟踪的关键步骤。

OpenCV可以通过提取目标的颜色、形状、纹理等特征，实现稳定的目标跟踪。

此外，还可以采用深度学习等方法，提取更高级的特征，提高跟踪的准确性。