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Extra libraries and examples for libfreenect, especially high level processing, vision, and visualizations

branch: master
README.markdown

This is a grand unifying demo of the python scientific computing environments. It lets you tinker with kinect data in OpenCV, OpenGL, and Matplotlib, all at the same time!

Installation

  1. You need to have installed: IPython, Matplotlib, OpenCV 2.1, PyOpengl, wxPython
  2. Build the latest version of libfreenect. https://github.com/openkinect/libfreenect

  3. Build and install the python wrappers for libfreenect cd libfreenect/wrappers/python python setup.py install

  4. Download the latest version of this project git clone https://github.com/amiller/libfreenect-goodies.git cd pykinect

  5. Test that python can find libfreenect by running: python demo_freenect.py

Usage instructions

  1. Please run this script using: ipython -pylab -wthread demo_pykinect.py

  2. You should see an opengl window pop up with a preview of a point cloud. You can pan and zoom with the mouse. Run the following commands: update() # Grabs one frame from kinect and updates the point cloud update_on() # Grabs frames from kinect on a thread - 3d Video! (might be slow!) update_off() # Stops the update thread

  3. You can also use opencv: loopcv() # Grab frames and display them as a cv image (ctrl+c to break)

  4. You can also use matplotlib: imshow(depth)

  5. Most importantly, you can reload any of the code without pausing or destroying your python instance: %run -i demo_pykinect.py

    Try changing some of the code, like the downsampling factor (search: downsampling) or the point size (search: GL_POINT_SIZE) and update the code without quitting python.

This is an ideal environment for developing 3D point cloud algorithms and visualizations. All of your tools are right at hand.

Note to MATLAB users: Yes, MATLAB already does most of this... there are plenty of reasons to prefer python, one of which is access to OpenGL drawing commands - scatter3 isn't adequate!

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