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calibrate_camera.py
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calibrate_camera.py
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'''
Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
'''
import numpy as np
import cv2
import glob
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)
cv2.imshow('img', img)
cv2.waitKey(200)
cv2.destroyAllWindows()
# Get image shape
img = cv2.imread(images[0])
img_size = (img.shape[1], img.shape[0])
# Compute the camera calibration matrix and distortion coefficients
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
# Output corrected image example
# img = cv2.imread("./camera_cal/calibration1.jpg")
# cv2.imwrite("./output_images/chessboard_input.jpg", img)
# img = cv2.undistort(img, mtx, dist, None, mtx)
# cv2.imwrite("./output_images/chessboard_undistorted.jpg", img)
print ("\nCalibration Matrix:")
print (mtx)
print ("\nDistortion Coefficients:")
print (dist)
calibration_data = {
'mtx': mtx,
'dist': dist
}
print(calibration_data)
pickle.dump(calibration_data, open("./calibration.p", "wb"))
print("\nCalibration data stored.")