It is a little side tracked from the previous posts. The example demonstrated the use of Kalman Filter in OpenCV with Processing.
OpenCV and Processing 20
OpenCV and Processing 19
Similar to the last Hough Line detection, the following example illustrates the use of the new LineSegmentDetector class in the Imgproc module. Instead of using the new command, we have to use the Imgproc.createLineSegmentDetector() function to create a new instance of the class.
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OpenCV and Processing 18
In addition to the Hough circle detection, this example works on the Hough line segment detection. It inputs the live webcam image; converts it into greyscale; applies a medianBlur filter; processes the Canny edge detection. The Imgproc.HoughLinesP() function will finally single out the line segments into a Mat – lines in our example codes.
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OpenCV and Processing 17
The example explores the Hough Circle detection in the Imgproc module. It starts with a greyscale copy of the live webcam image with an application of a blur filter, in this case, a medianBlur.
OpenCV and Processing 16
This example continues from the last post to compute the optical flow between 2 greyscale images by using the calcOpticalFlowPyrLK() function in the Video module. The new position of the pixels tracked will be delivered in a MatOfPoint2f object. By using the last and current position of the feature points, we can plot the path of the pixel movements. Furthermore, we can use such information for interactive or generative drawings, found in my artwork, Movement in Time.
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OpenCV and Processing 15
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.
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OpenCV and Processing 14
This example continues to explore the Video module in OpenCV. It uses one of the BackgroundSubtractors, the BackgroundSubtractorKNN. It learns the motion in front of the camera and treats the stationary scene as background.
In the code, the important command is
bkg.apply(frame, fgmask); |
The subtractor object bkg takes in the latest frame and generates a foreground mask, fgmask. We can use the foreground mask to single out the foreground moving object.
Here are a number of screen shots from the testing. The background is the planet Earth image from NASA.
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OpenCV and Processing 13
In this example, we move on to the Video module of OpenCV 3.0.0. The first function we test is the Dense Optical Flow. It demonstrates the use of the calcOpticalFlowFarneback function. Again it makes use of the previous CVImage object to bridge between the Processing PImage and OpenCV Mat. The example also reduces the size of the video (using the variable factor) before sending it for the optical flow processing; otherwise, the process can be lengthy.
The optical flow process will basically compare two consecutive frames (the Mat last and grey) from the live webcam video. It will try to compute where the current pixels move to in the new frame.
Here are a number of screen shots from the sample run.
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OpenCV and Processing 12
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.
