calibrate the camera and undistort the image.

calibrate_undistort.py

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')

# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (9,6), None)

    # If found, add object points, image points
    if ret == True:
        objpoints.append(objp)
        imgpoints.append(corners)

        # Draw and display the corners
        cv2.drawChessboardCorners(img, (9,6), corners, ret)
        write_name = 'board'+str(idx)+'.jpg'
        cv2.imwrite(write_name, img)


# Test undistortion on an image
img = cv2.imread('camera_cal/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])

# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)

# undistort the image using camera matrix and distortion co-efficients.
dst = cv2.undistort(img, mtx, dist, None, mtx)
# save the undistorted image.
cv2.imwrite('result/test_undist.jpg',dst)