-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathrunCalibration.py
100 lines (73 loc) · 3.63 KB
/
runCalibration.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
"""
Python script to perform camera calibration based on a bunch of checkerboard images.
This code has been adapted from official OpenCV documentation (https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html)
"""
import numpy as np
import cv2 as cv
import glob
import pickle
from pathlib import Path
################ FIND CHESSBOARD CORNERS - OBJECT POINTS AND IMAGE POINTS #############################
# Change the chessboard size as per the size of your chessboard pattern
chessboardSize = (10,7)
frameSize = (640,480)
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((chessboardSize[0] * chessboardSize[1], 3), np.float32)
objp[:,:2] = np.mgrid[0:chessboardSize[0],0:chessboardSize[1]].T.reshape(-1,2)
size_of_chessboard_squares_mm = 20
objp = objp * size_of_chessboard_squares_mm
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('images/*.png')
for image in images:
img = cv.imread(image)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, chessboardSize, None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
imgpoints.append(corners)
# Draw and display the corners in the 2D images
cv.drawChessboardCorners(img, chessboardSize, corners2, ret)
cv.imshow('img.png', img)
cv.waitKey(1000)
cv.destroyAllWindows()
############## CALIBRATION #######################################################
ret, cameraMatrix, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, frameSize, None, None)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
Path("camera_calib_pkl").mkdir(parents=True, exist_ok=True)
pickle.dump((cameraMatrix, dist), open( "camera_calib_pkl/calibration.pkl", "wb" ))
pickle.dump(cameraMatrix, open( "camera_calib_pkl/cameraMatrix.pkl", "wb" ))
pickle.dump(dist, open( "camera_calib_pkl/dist.pkl", "wb" ))
print("Camera caliibration parameters saved in pickle files in directory : camera_calib_pkl/")
############## UNDISTORTION #####################################################
Path("cali_result").mkdir(parents=True, exist_ok=True)
img = cv.imread('cali_result/test_img.png')
assert img is not None, "Test image is missing. Please capture a test image and save in cali_result/"
h, w = img.shape[:2]
newCameraMatrix, roi = cv.getOptimalNewCameraMatrix(cameraMatrix, dist, (w,h), 1, (w,h))
# Undistort
dst = cv.undistort(img, cameraMatrix, dist, None, newCameraMatrix)
# crop the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv.imwrite('cali_result/result1_test_img.png', dst)
# Undistort with Remapping
mapx, mapy = cv.initUndistortRectifyMap(cameraMatrix, dist, None, newCameraMatrix, (w,h), 5)
dst = cv.remap(img, mapx, mapy, cv.INTER_LINEAR)
# crop the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv.imwrite('cali_result/result2_test_img.png', dst)
# Reprojection Error
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], cameraMatrix, dist)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2)/len(imgpoints2)
mean_error += error
print( "total error: {}".format(mean_error/len(objpoints)) )