Pincushion distortion In pincushion distortion, image magnification increases with the distance from the optical axis. Ĭoncave (minus) spherical lenses tend to have barrel distortion. In a zoom lens, barrel distortion appears in the middle of the lens's focal length range and is worst at the wide-angle end of the range.
Fisheye lenses, which take hemispherical views, utilize this type of distortion as a way to map an infinitely wide object plane into a finite image area. The apparent effect is that of an image which has been mapped around a sphere (or barrel). Barrel distortion In barrel distortion, image magnification decreases with distance from the optical axis. These radial distortions can usually be classified as either barrel distortions or pincushion distortions. We can also draw the pattern using cv.drawChessboardCorners().Although distortion can be irregular or follow many patterns, the most commonly encountered distortions are radially symmetric, or approximately so, arising from the symmetry of a photographic lens. Once we find the corners, we can increase their accuracy using cv.cornerSubPix(). Fewer images are sufficient to perform camera calibration using a circular grid. In this case, we must use the function cv.findCirclesGrid() to find the pattern. Instead of chess board, we can alternatively use a circular grid. Thus, we must read all the images and take only the good ones. Even in the example provided here, we are not sure how many images out of the 14 given are good. Continue this process until the required number of good patterns are obtained. Also, provide some interval before reading next frame so that we can adjust our chess board in different direction. Once the pattern is obtained, find the corners and store it in a list.
Camera lens distortion calibration code#
So, one good option is to write the code such that, it starts the camera and check each frame for required pattern.
These corners will be placed in an order (from left-to-right, top-to-bottom) Note This function may not be able to find the required pattern in all the images. It returns the corner points and retval which will be True if pattern is obtained. (Normally a chess board has 8x8 squares and 7x7 internal corners). We also need to pass what kind of pattern we are looking for, like 8x8 grid, 5x5 grid etc. So to find pattern in chess board, we can use the function, cv.findChessboardCorners(). (In this case, we don't know square size since we didn't take those images, so we pass in terms of square size).ģD points are called object points and 2D image points are called image points. But if we know the square size, (say 30 mm), we can pass the values as (0,0), (30,0), (60,0). In this case, the results we get will be in the scale of size of chess board square. Now for X,Y values, we can simply pass the points as (0,0), (1,0), (2,0). This consideration helps us to find only X,Y values. But for simplicity, we can say chess board was kept stationary at XY plane, (so Z=0 always) and camera was moved accordingly. What about the 3D points from real world space? Those images are taken from a static camera and chess boards are placed at different locations and orientations. (These image points are locations where two black squares touch each other in chess boards) 2D image points are OK which we can easily find from the image. The important input data needed for calibration of the camera is the set of 3D real world points and the corresponding 2D coordinates of these points in the image. OpenCV comes with some images of a chess board (see samples/data/left01.jpg – left14.jpg), so we will utilize these. CodeĪs mentioned above, we need at least 10 test patterns for camera calibration. For better results, we need at least 10 test patterns. We know the coordinates of these points in real world space and we know the coordinates in the image, so we can solve for the distortion coefficients.
We find some specific points of which we already know the relative positions (e.g. To find these parameters, we must provide some sample images of a well defined pattern (e.g. \\]Įxtrinsic parameters corresponds to rotation and translation vectors which translates a coordinates of a 3D point to a coordinate system.įor stereo applications, these distortions need to be corrected first. Radial distortion can be represented as follows:
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