Highlight distortion parameter impact in image plane projection

distortion_demo.py

# This gists highlights the impact of various distortion parameters
import numpy as np
import cv2

# Set camera calibration parameters
fx = 8262.158089604814
fy = 8241.805993224913
cx = 2147.360664332489
cy = 897.9466061409038
k1 = -0.33193540220291506
k2 = 5.3113213233505805
k3 = -67.30762636779896
p1 = -0.012499072958136133
p2 = 0.014968462658950266

# Generate a 4K (3840x2160) chessboard image
chessboard_size = (3840, 2160)
chessboard = np.zeros((chessboard_size[1], chessboard_size[0], 3), dtype=np.uint8)
square_size = 100  # Size of each square in pixels

# Create the chessboard pattern to show areas of uneven correction
for i in range(0, chessboard_size[0], square_size):
    for j in range(0, chessboard_size[1], square_size):
        if (i // square_size + j // square_size) % 2 == 0:
            chessboard[j:j+square_size, i:i+square_size, :] = [255, 255, 255]

# Save the chessboard image
cv2.imwrite("chessboard_4K.png", chessboard)

camera_matrix = np.array([[fx, 0, cx],
                          [0, fy, cy],
                          [0, 0, 1]])
distortion_coefficients = np.array([k1, k2, p1, p2, k3])

# Undistort the chessboard image
chessboard = cv2.imread("chessboard_4K.png")
undistorted_chessboard = cv2.undistort(chessboard, camera_matrix, distortion_coefficients)

# Save the undistorted image
cv2.imwrite("undistorted_chessboard_4K.png", undistorted_chessboard)

# Generate an overlay with color highlighting differences
alpha = 0.7  # Adjust the alpha value for transparency
beta = 1 - alpha
difference_overlay = cv2.addWeighted(chessboard, alpha, undistorted_chessboard, beta, 0)
cv2.imwrite("diff_4K.png", difference_overlay)