150 lines
4.9 KiB
Markdown
150 lines
4.9 KiB
Markdown
Camera Calibration Using OpenCV
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===============================
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Python scripts for camera intrinsic parameters calibration and image undistortion.
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It finds following parameters:
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* focal length
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* principal point
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* radial distortion coefficients
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using video of a moving chessboard pattern or a sequence of images as an input.
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Example input:
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Example output YAML file:
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~~~
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camera_matrix:
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- [1016.5691777733053, 0.0, 632.3505845656954]
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- [0.0, 1013.9401023559311, 351.0453222243043]
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- [0.0, 0.0, 1.0]
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dist_coefs:
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- [-0.3797582960152331, 0.20896823985868346, -0.0003239082442461539, -0.0019027617884934114,
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-0.0668551319250156]
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rms: 1.1814231691868478
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~~~
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Installation
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------------
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Requirements:
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* numpy
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* PyYAML
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* OpenCV 3
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Using pip the numpy and PyYAML can be installed as follows:
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~~~
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$ pip install < requirements.txt
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~~~
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Download and install [OpenCV 3.0 or newer](http://opencv.org/downloads.html). The python bindings have to be installed. If the OpenCV is not installed in the system, the python sys.path has to be set to point to cv2.so or cv2.pyd. It can be achieved by setting PYTHONPATH environment variable.
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Camera Calibration
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------------------
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1. print the pattern.png and glue it to a solid board
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3. fix the camera lens zoom, the calibration values change with the lens zoom changes
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2. record a video with the pattern moving in front of the camera
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* the pattern should be most of the time completely visible
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* try to move the pattern to cover all parts of the camera view, pay attention to the corners
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* the length of the video should be 1 or 2 minutes
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3. run the calibration.py to extract chessboard pattern corners from the video and perform camera calibration
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Example usage (you can actually run the example, the input data is present in the ./example_input):
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~~~
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$ ./calibrate.py --help
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usage: calibrate.py [-h] [--debug-dir DEBUG_DIR] [-c CORNERS] [-fs FRAMESTEP]
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input out
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Calibrate camera using a video of a chessboard or a sequence of images.
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positional arguments:
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input input video file or glob mask
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out output calibration yaml file
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optional arguments:
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-h, --help show this help message and exit
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--debug-dir DEBUG_DIR
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path to directory where images with detected
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chessboard will be written
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-c CORNERS, --corners CORNERS
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output corners file
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-fs FRAMESTEP, --framestep FRAMESTEP
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use every nth frame in the video
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$ mkdir out
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$ ./calibrate.py example_input/chessboard.avi calibration.yaml --debug-dir out
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Searching for chessboard in frame 0... not found
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Searching for chessboard in frame 20... not found
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Searching for chessboard in frame 40... not found
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Searching for chessboard in frame 60... not found
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Searching for chessboard in frame 80... not found
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Searching for chessboard in frame 100... not found
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Searching for chessboard in frame 120... not found
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Searching for chessboard in frame 140... ok
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Searching for chessboard in frame 160... ok
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Searching for chessboard in frame 180... ok
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Searching for chessboard in frame 200... ok
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Searching for chessboard in frame 220... ok
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Searching for chessboard in frame 240... ok
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Searching for chessboard in frame 260... ok
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Searching for chessboard in frame 280... ok
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...
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Searching for chessboard in frame 1980... ok
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Searching for chessboard in frame 2000... ok
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Searching for chessboard in frame 2020... ok
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Searching for chessboard in frame 2040... not found
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Searching for chessboard in frame 2060... not found
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Searching for chessboard in frame 2080... not found
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Searching for chessboard in frame 2100... ok
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Searching for chessboard in frame 2120... not found
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Searching for chessboard in frame 2140... not found
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Searching for chessboard in frame 2160... not found
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Searching for chessboard in frame 2180... not found
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Performing calibration...
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RMS: 1.01973939405
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camera matrix:
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[[ 774.55857698 0. 619.69416634]
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[ 0. 772.96410156 352.49790333]
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[ 0. 0. 1. ]]
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distortion coefficients: [ -3.65385859e-01 1.63224385e-01 -2.67163331e-03 3.38261891e-04
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-3.81711948e-02]
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~~~
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Removing Radial Distortion
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--------------------------
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You can test the found radial distortion coefficients by removing distortion from an image and checking if straight lines are really straight.
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~~~
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$ ./undistort.py --help
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usage: undistort.py [-h] calibration input_mask out
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Undistort images based on camera calibration.
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positional arguments:
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calibration input video file
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input_mask input mask
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out output directory
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optional arguments:
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-h, --help show this help message and exit
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$ ./undistort.py calibration.yaml 'example_input/*.png' out/
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processing example_input/distorted.png... ok
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~~~
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License
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-------
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MIT License, except `pattern.png` from OpenCV (3-clause BSD License). |