OpenCV and Processing 14

Posted by bryan

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

Posted by bryan

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 11

Posted by bryan

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.


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OpenCV and Processing 10

Posted by bryan

I decide to put together the OpenCV and Processing codes into a class to encapsulate the functions. In this example, I extend the original PImage class and create the CVImage class.  Besides the constructor, the following functions are the major interfaces:

  • toCV() – copy the content of the PImage pixels[] array to the internal Mat variable cvImg; the internal format for the Mat is BGRA.
  • fromCV(Mat) – convert a parameter Mat to the internal storage of Mat and pixels[] array; it accepts input of 1, 3, and 4 channels.
  • Mat getBGRA() – output the BGRA Mat from the internal Mat storage.
  • Mat getBGR() – output the BGR Mat from the internal Mat storage.
  • Mat getGrey() – out the greyscale Mat from the internal Mat storage.

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OpenCV and Processing 6

Posted by bryan

To perform the same image loading task in OpenCV, we use the imread() function in the Imgcodecs module. The function was in the Highgui module before. The image loaded in the Mat data structure is in BGR format. We shall use the split and merge commands to align the proper channels. Finally, we use a byte array to transfer the pixels information to the pixels[] array of a PImage object.
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