camera_cal.py

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import pickle
# 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 fname in 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
        img = cv2.drawChessboardCorners(img, (9,6), corners, ret)

        plt.imshow(img)
        plt.plot()
        plt.show()

#cv2.destroyAllWindows()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
calibrated_camera_info ={"mtx":mtx,"dist":dist}
pickle.dump(calibrated_camera_info,open("calibrated_camera_info.p","wb"))

# test undistorted

# for fname in images:
#     img = cv2.imread(fname)
#     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#     undistorted = cv2.undistort(img, mtx, dist, None, mtx)
#     f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
#     f.tight_layout()
#     ax1.imshow(img)
#     ax1.set_title('Original Image', fontsize=50)
#     ax2.imshow(undistorted)
#     ax2.set_title('Undistorted Image', fontsize=50)
#     plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#     plt.show()