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As the calibration object we chose classic black-white chessboard pattern with 8 rows, 7 columns and square's size approximately 35x35mm (Exact dimensions are specified in source code).
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As the calibration object we chose classic black-white chessboard pattern with 8 rows, 7 columns and square's size approximately 35x35mm (Exact dimensions are specified in source code).
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### Calibration pictures
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### Calibration pictures
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We must take pictures with calibration object in the camera's Field of View in many different positions and cover entire picture with as many points as we can. However, We must respect that, the whole chessboard must be visible.
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We must take pictures with calibration object in the camera's Field of View in many different positions and cover entire picture with as many points as we can. Moreover, We must respect that, the whole chessboard must be visible.
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### Finding chessboards
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### Finding chessboards
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We use OpenCV function `findChessboarCorners(gray,(rows,column),params=None)->ret,corners` for finding the chessboard in pictures, where gray is intensity image and corners are chessboard's corners in pixel coordinate system (u,v). After the chessboard is found the position is made more precise and the corners are found with sub-pixel precision `cornerSubPixel(gray,corners,winSize,zeroZone,criteria)->None`. You can read more about this on the page listed above.
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We use OpenCV function `findChessboarCorners(gray,(rows,column),params=None)->ret,corners` for finding the chessboard in pictures, where gray is intensity image and corners are chessboard's corners in pixel coordinate system (u,v). After the chessboard is found the position is made more precise and the corners are found with sub-pixel precision `cornerSubPixel(gray,corners,winSize,zeroZone,criteria)->None`. You can read more about this on the page listed above.
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