So basically I want to find out what the invert distortion parameters would be for a calibration I do as showed here. NOTE: I know that we can perform undistortion and then use remapping to do what I have done below, but my goal is to be able to find out the actual inverted distortion parameters and use them to distort other images, not to just be able to revert what cv2.undistort() does Overview: I have tried passing in the negation of the distortion parameters: # _, mat, distortion, _, _ = cv2.calibrateCamera(...) # undistorted_image = cv2.undistort(with distortion) # redistorted_image = cv2.undistort(with np.negative(distortion)) In theory, I was thinking that if the redistorted_image is similar to the original image, then the np.negative(distortion) parameters are what I am looking for, but it turned out to be false. Actual method I use: def save_undistorted_images(image, matrix, distortion): test_image_su = cv.imread(image) height, width = test_image_su.shape[:2] new_camera_mtx, roi = cv.getOptimalNewCameraMatrix(matrix, distortion, (width, height), 1, (width, height)) distortion_u = np.negative(distortion) # unsure if this line helps new_camera_mtx_inv, roi = cv.getOptimalNewCameraMatrix(matrix, distortion_u, (width, height), 1, (width, height)) # undistort the image undistorted_image = cv.undistort(test_image_su, matrix, distortion, None, new_camera_mtx) cv.imwrite('undistorted_frame.png', undistorted_image) # redistort trying to get something like original image (image_test_su) distorted_image = cv.undistort(undistorted_image, matrix, distortion_u, None, new_camera_mtx_inv) cv.imwrite('redistorted_frame.png', distorted_image) The results: (left a: original) (right b: undistorted) (left c: distorted using np.negative(distortion)) (right d: undistorted image redistorted using np.negative(distortion))) The image d here is basically c performed on b, which I expected would be similar to a Why is b here overpowering the effect of c? Other way of calculating inverse that I tried: The following is my python implementation of this paper distortion_u = distortion k1 = distortion_u[0][0] k2 = distortion_u[0][1] k3 = distortion_u[0][4] b1 = -1 * k1 b2 = 3 * (k1 * k1) - k2 b3 = (8 * k1 * k2) + (-1 * (12 * (k1 * k1 * k1))) - k3 # radial: distortion_u[0][0] = b1 distortion_u[0][1] = b2 distortion_u[0][4] = b3 # tangential: #distortion_u[0][2] = -1 * distortion_u[0][2] #distortion_u[0][3] = -1 * distortion_u[0][3] The results of applying distortion on undistorted image using above distortion parameters is also not good, looks really similar to results above. So, this brings us to: Why is the effect of normal distortion always overpowering np.negative(distortion) or anything else? Does all distortion work this way? (negative values does not equal to opposite effect) How to get the actually opposite distortion parameters? Continue reading...
