def matlab2PointCorrespondences(filename):
'''Loads and converts the point correspondences saved
by the matlab camera calibration tool'''
from numpy.lib.io import loadtxt, savetxt
from numpy.lib.function_base import append
points = loadtxt(filename, delimiter=',')
savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0))
def cameraIntrinsicCalibration(path, checkerBoardSize=[6,7], secondPassSearch=False, display=False):
''' Camera calibration searches through all the images (jpg or png) located
in _path_ for matches to a checkerboard pattern of size checkboardSize.
These images should all be of the same camera with the same resolution.
For best results, use an asymetric board and ensure that the image has
very high contrast, including the background. Suitable checkerboard:
http://ftp.isr.ist.utl.pt/pub/roswiki/attachments/camera_calibration(2f)Tutorials(2f)StereoCalibration/check-108.png
The code below is based off of:
https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html
Modified by Paul St-Aubin
'''
from numpy import zeros, mgrid, float32, savetxt
import glob, os
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = zeros((checkerBoardSize[0]*checkerBoardSize[1],3), float32)
objp[:,:2] = mgrid[0:checkerBoardSize[1],0:checkerBoardSize[0]].T.reshape(-1,2)
# 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.
## Loop throuhg all images in _path_
images = glob.glob(os.path.join(path,'*.[jJ][pP][gG]'))+glob.glob(os.path.join(path,'*.[jJ][pP][eE][gG]'))+glob.glob(os.path.join(path,'*.[pP][nN][gG]'))
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (checkerBoardSize[1],checkerBoardSize[0]), None)
# If found, add object points, image points (after refining them)
if ret:
print 'Found pattern in '+fname
if(secondPassSearch):
corners = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
if(display):
img = cv2.drawChessboardCorners(img, (checkerBoardSize[1],checkerBoardSize[0]), corners, ret)
if(img):
cv2.imshow('img',img)
cv2.waitKey(0)
## Close up image loading and calibrate
cv2.destroyAllWindows()
if len(objpoints) == 0 or len(imgpoints) == 0:
return False
try:
ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
except NameError:
return False
savetxt('intrinsic-camera.txt', camera_matrix)
return camera_matrix, dist_coeffs