ofDocsexamples computervision opencvExample


About opencvExample

Screenshot of opencvExample

Learning Objectives

OpenCV is a powerful open-source library for image processing and computer vision. This example demonstrates one particularly common workflow in new-media art: performing background subtraction, blob detection and contour tracing. This is immensely useful for locating objects or people that have entered a scene!

After studying this example, you'll understand how to:

  • Obtain video from a camera or stored file
  • Use that video as the basis for image processing operations with OpenCV, including image arithmetic
  • Extract blobs and their contours from background subtraction, a common workflow in computer vision applications.

Expected Behavior

When launching this app, you'll see a window that displays five different stages in processing a video.

  1. In the upper-left is the raw, unmodified video of a hand creating a shadow. Although it's not obvious, this is a color video (that happens to be showing a mostly black-and-white scene). After reading the hand video from its source file (using an ofVideoPlayer), this video is stored in colorImg, an ofxCvColorImage.
  2. In the upper-right of the display is the same video, converted to grayscale. Here it is stored in the grayImage object, which is an instance of an ofxCvGrayscaleImage. It's easy to miss the grayscale conversion; it's done implicitly in the assignment grayImage = colorImg; (line 48 in the ofApp.cpp file) using operator overloading of the = sign. In this example, all of the subsequent image processing is done with grayscale (rather than color) images.
  3. In the middle-left is a view of the background image. This is a grayscale image of the scene captured when the video first started playing, before the hand entered the frame. (See line 48 of the ofApp.cpp file.)
  4. In the middle-right is an image that shows the thresholded absolute difference between the current frame and the background frame. This image has been binarized, meaning that pixel values are either black (0) or white (255). The white pixels represent regions that are significantly different from the background: the hand!
  5. In the bottom right, an ofxCvContourFinder has been tasked to findContours() in the binarized image. It does this by identifying blobs of white pixels that meet certain area requirements -- and then tracing the contours of those blobs into an ofxCvBlob outline of (x,y) points. The app shows the contour of each blob in cyan, and also shows the bounding rectangle of those points in magenta. Note: The contour is a vector-based representation, and can be used for all sorts of further geometric play....

There are a few user-modifiable settings in this app:

  • Pressing the space bar will capture a fresh image of the background.
  • Pressing the + and - keys will adjust the threshold used in the absolute differencing operation. The greater the threshold value, the more different a pixel needs to be from the background in order to be considered part of the "foreground".

One more thing. In line 7 of the ofApp.h file, you'll see the following line commented out:

//#define _USE_LIVE_VIDEO

If you uncomment this line, the app will use your computer's built-in webcam instead of a stored video file! It accomplishes this by swapping out the ofVideoPlayer with an ofVideoGrabber.

Other classes used in this file

This example links against the ofxOpenCv core addon. It uses the following classes from that addon:

In addition, this example uses the following classes to access video from a live camera and/or a pre-stored file: