cal.py

import numpy as np
import cv2
import glob
import os

# Define the chess board rows and columns
rows = 6
cols = 9

# Set the termination criteria for the corner sub-pixel algorithm
criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 30, 0.001)

# Prepare the object points: (0,0,0), (1,0,0), (2,0,0), ..., (6,5,0). They are the same for all images
objectPoints = np.zeros((rows * cols, 3), np.float32)
objectPoints[:, :2] = np.mgrid[0:rows, 0:cols].T.reshape(-1, 2)

# Create the arrays to store the object points and the image points
objectPointsArray = []
imgPointsArray = []
# gray=0

images = glob.glob('./newcal/*.jpg')
# Loop over the image files
for path in images:
    # Load the image and convert it to gray scale
    img = cv2.imread(path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Find the chess board corners
    ret, corners = cv2.findChessboardCorners(gray, (rows, cols), None)

    # Make sure the chess board pattern was found in the image
    if ret:
        # Refine the corner position
        corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
        
        # Add the object points and the image points to the arrays
        objectPointsArray.append(objectPoints)
        imgPointsArray.append(corners)

        # Draw the corners on the image
        cv2.drawChessboardCorners(img, (rows, cols), corners, ret)
        
        # Display the image
        cv2.imshow('chess board', img)
        cv2.waitKey(300)

# Calibrate the camera and save the results
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objectPointsArray, imgPointsArray, gray.shape[::-1], None, None)
np.savez('./data/calib.npz', mtx=mtx, dist=dist, rvecs=rvecs, tvecs=tvecs)

# Print the camera calibration error
error = 0

for i in range(len(objectPointsArray)):
    imgPoints, _ = cv2.projectPoints(objectPointsArray[i], rvecs[i], tvecs[i], mtx, dist)
    error += cv2.norm(imgPointsArray[i], imgPoints, cv2.NORM_L2) / len(imgPoints)

print("Total error: ", error / len(objectPointsArray))

# Load one of the test images
img = cv2.imread('./newcal/12.jpg')

h, w = img.shape[:2]

# Obtain the new camera matrix and undistort the image
newCameraMtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
undistortedImg = cv2.undistort(img, mtx, dist, None, newCameraMtx)

# Crop the undistorted image
# x, y, w, h = roi
# undistortedImg = undistortedImg[y:y + h, x:x + w]

# Display the final result
cv2.imshow('chess board', np.hstack((img, undistortedImg)))
cv2.waitKey(0)
cv2.destroyAllWindows()