find_camera_mtx.md

@@ -17,7 +17,7 @@ As the calibration object we chose classic black-white chessboard pattern with 8
 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.
 
 ### Finding chessboards
-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.
+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 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.
 
 ### Camera Matrix and Distortion Coefficients
 When we know the position of chessboards in pictures (imagePoints) and correct size of squares and its positions (objectPoints), we can now estimate the Camera Matrix and Distortion Coefficients with OpenCV function `calibrateCamera(objectPoints,imagePoints,imageShape,None,None)->ret,cam_mtx,dist,rvect,tvect`, where `cam_mtx` is the wanted Camera Matrix and `dist` are Distortion coefficients.