opticalflow

问题 I need to find the optical flow between every 2 adjacent frames of a video using Lucas Kanade optical flow. I'm using python and openCV for the project. As per my understanding Lucas Kanade is a sparse method to find optical flow. Is there a dense implementation of it? If so, how to use it in python? Using cv2.calcOpticalFlowFarneback(), which is a dense method, we get as output an ndarray ('flow' in below example) which contains the optical flow. cv2.calcOpticalFlowFarneback(prev, next, pyr

问题 I am using OpenCV 2.4.2 on Linux. I am writing in C++. I want to track simple objects (e.g. black rectangle on the white background). Firstly I am using goodFeaturesToTrack and then calcOpticalFlowPyrLK to find those points on another image. The problem is that calcOpticalFlowPyrLK doesn't find those points. I have found code that does it in C, which does not work in my case: http://dasl.mem.drexel.edu/~noahKuntz/openCVTut9.html I have converted it into C++: int main(int, char**) { Mat

问题 How should I fix this error in CentOS 7? [jalal@goku HW4]$ git clone https://github.com/pathak22/pyflow.git Cloning into 'pyflow'... remote: Counting objects: 96, done. remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96 Unpacking objects: 100% (96/96), done. [jalal@goku HW4]$ ls pyflow [jalal@goku HW4]$ cd pyflow/ [jalal@goku pyflow]$ ls LICENSE README.md demo.py examples pyflow.pyx setup.py src [jalal@goku pyflow]$ python demo.py Traceback (most recent call last): File "demo.py",

问题 I'm trying to perform a complex warp of an image using Dense Optical Flow (I am trying to wap the second image into roughly the same shape as the first image). I'm probably getting this all wrong but Ill post up what I've tried: cv::Mat flow; cv::calcOpticalFlowFarneback( mGrayFrame1, mGrayFrame2, flow, 0.5, 3, 15, 3, 5, 1.2, 0 ); cv::Mat newFrame = cv::Mat::zeros( frame.rows, frame.cols, frame.type() ); cv:remap( frame, newFrame, flow, cv::Mat(), CV_INTER_LINEAR ); The idea if that I am

问题 I am trying to calculate and display dense optical flow in OpenCV using the farneback method. I found an example that uses CUDA functions to generate that and display the color map which I used as a base for my own code. Optical flow calculation: calcOpticalFlowFarneback(prevgray, gray, flow, 0.5, 3, 15, 3, 5, 1.2, 0); drawField(flow,cflow); imshow("flows",cflow); Display function: void drawField(const Mat& flow, Mat& imgColor){ Mat imgColorHSV = cv::Mat::zeros(Size(imgColor.cols,imgColor

问题 I'm trying to compute a differential image velocity invariants (e.g. curl, divergence, deformation, etc) from a video using OpenCV in Python. To do that, I need to compute the spatial derivatives in the x,y directions of the optical flow. Unfortunately, OpenCV only seems to supply the APIs for computing optical flow, not its derivative. Are there any Python libraries out there for computing spatial derivatives of optical flow? I found this SO question that was somewhat similar Lucas Kanade

问题 I have been trying to form a small optical flow example with OpenCV for a while now. Everything works except the function call calcOpticalFlowPyrLK, which prints the following failed assertion in the console window: OpenCV Error: Assertion failed (mytype == typ0 || (CV_MAT_CN(mytype) == CV_MAT_CV(type0) && ((1 << type0) & fixedDepthMask) != 0)) in unknown function, file ......\src\opencv\modules\core\src\matrix.cpp, line 1421 The video that I'm parsing is separated into 300 images, labelled

问题 How to draw Optical flow images from ocl::PyrLKOpticalFlow::dense() Which actually calculates both horizontal and vertical component of the Optical flow? So I don't know how to draw them. I'm new to opencv . Can anyone help me? Syntax : ocl::PyrLKOpticalFlow::dense(oclMat &prevImg, oclMat& nextImg, oclMat& u, oclMat &v,oclMat &err) 回答1: A well establische method used in the optical flow community is to display a motion vector field as a color coded image as you can see at one of the various

问题 I am trying to use OpenCV for Android (OpenCV 2.4.3) I am writing a program to track keypoints. I am trying to use FeatureDetector to detect keypoints and then Video.calcOpticalFlowPyrLK to track them. The question that has me stumped is that the FeatureDetector function returns a MatOfKeyPoint while calcOpticalFlowPyrLK takes a MatOfPoint2f. Note that MatOfKeyPoint is different from MatOfPoint (Conversion from MatOfPoint to MatOfPont2f is straightforward). Here is my code so far: //Feature

问题 Hi I would like to segment objects in a video sequence based on optical flow (as suggested in this answer), I am using opencv and have been able to generate a flow field for two successive frames using the calcOpticalFlowFarneback function. Could someone explain how I would go about segmenting an image based on this flow field? 回答1: Since you want to use Optical Flow to segment object so your decision making will be based on the type of motion your objects under go...You can have a look at