This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o
Opencv C++ simple tutorial to use GStreamer to send video to Server that converts RTSP to HLS video stream. The source code and all needed configurations are included. O pencv is a powerful computer vision library. You can use it in production and use it for image and video processing and modern machine learning. In some applications, You may want to stream your processed video results from your C++ OpenCV app outside and not just use a simple OpenCV graphical interface. The video streaming of your results is what you are looking for. Do you want to stream processed video from your IoT device? Yes, This is mainly for Linux. Do you want to stream processed video to the Web player, broadcast the video or just use VLC to play video processed by OpenCV? You may be interested in reading the next lines. Opencv video stream to VLC or WEB There are basically two main options with OpenCV. The first one is to write a streaming application using FFMPEG. This is a little bit more advanced appro
Comments
Post a Comment