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cmirajak created page: estimate_laser_plane authored by Cmíral Jakub's avatar Cmíral Jakub
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find_camera_mtx.md
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...@@ -14,7 +14,7 @@ where ![alt text](http://mathurl.com/zbyrpbe.png) are camera focal lengths, ![al ...@@ -14,7 +14,7 @@ where ![alt text](http://mathurl.com/zbyrpbe.png) are camera focal lengths, ![al
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) as a 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) as a calibration object.
### Calibration pictures ### Calibration pictures
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. We must take pictures with calibration object in the camera's Field of View (FoV) in many different positions to cover most of FoV. Moreover, we must respect that the whole chessboard must be visible.
### Finding chessboards ### Finding chessboards
We use OpenCV function `findChessboarCorners(gray,(rows,column),params=None)->ret,corners` for finding the chessboard in pictures, where `corners` are chessboard's internal corners in pixel (u,v) coordinates. After the chessboard is found, the position of corners is made more precise via OpenCV `cornerSubPixel(gray,corners,winSize,zeroZone,criteria)->None`, which finds them with sub-pixel precision. You can read more about this on the page listed above. We use OpenCV function `findChessboarCorners(gray,(rows,column),params=None)->ret,corners` for finding the chessboard in pictures, where `corners` are chessboard's internal corners in pixel (u,v) coordinates. After the chessboard is found, the position of corners is made more precise via OpenCV `cornerSubPixel(gray,corners,winSize,zeroZone,criteria)->None`, which finds them with sub-pixel precision. You can read more about this on the page listed above.
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