The coming example will be the sparse optical flow. Before that, we first work on the 2D feature points tracking. The function goodFeaturesToTrack() belongs to the Imgproc module. It takes in a greyscale image and identifies the feature points (corners) as a matrix of point, MatOfPoint. The sample code here uses the feature points to render a live graphics of the webcam image.
The example again adopts the CVImage to demonstrate the use of blur filter. It uses the medianBlur() function from the Imgproc module in OpenCV. Detailed description can also be found in the OpenCV tutorial here.
In this example, I demonstrate the use of the image processing module (Imgproc) with the filter2D function. It is similar to the generic Photoshop filter where you can design the convolution matrix. The example references the OpenCV tutorial and uses a sharpen filter. It also uses the CVImage class in the last post.
Here is a screen shot of the Processing window.
It is about time to write up something about the recent works (post 1, post 2) that I have done with OpenCV and the classic film sequences. I started the recent version of the project first for the submission to the Image Conference in Berlin and then the Microworld exhibition in Hong Kong. The software I have been testing is the OpenCV library, openFrameworks and Processing. For the final show, I may choose openFrameworks for performance and stability reasons.
Here are another testing images with the use of OpenCV optical flow algorithms to render a number of classic film sequences.
