def test_monocular(self):
# Run the calibrator, produce a calibration, check it
for i, setup in enumerate(self.setups):
board = ChessboardInfo()
board.n_cols = setup.cols
board.n_rows = setup.rows
board.dim = self.board_width_dim
mc = MonoCalibrator([board],
flags=cv2.CALIB_FIX_K3,
pattern=setup.pattern)
if 0:
# display the patterns viewed by the camera
for pattern_warped in self.limages[i]:
cv2.imshow("toto", pattern_warped)
cv2.waitKey(0)
mc.cal(self.limages[i])
self.assert_good_mono(mc, self.limages[i], setup.lin_err)
# Make sure the intrinsics are similar
err_intrinsics = numpy.linalg.norm(mc.intrinsics - self.k,
ord=numpy.inf)
self.assertTrue(
err_intrinsics < setup.K_err,
'intrinsics error is %f for resolution i = %d' %
(err_intrinsics, i))
print('intrinsics error is %f' %
numpy.linalg.norm(mc.intrinsics - self.k, ord=numpy.inf))
def test_rational_polynomial_model(self):
"""Test that the distortion coefficients returned for a rational_polynomial model are not empty."""
for i, setup in enumerate(self.setups):
board = ChessboardInfo()
board.n_cols = setup.cols
board.n_rows = setup.rows
board.dim = self.board_width_dim
mc = MonoCalibrator([board],
flags=cv2.CALIB_RATIONAL_MODEL,
pattern=setup.pattern)
mc.cal(self.limages[i])
self.assertEqual(
len(mc.distortion.flat), 8,
'length of distortion coefficients is %d' %
len(mc.distortion.flat))
self.assertTrue(
all(mc.distortion.flat != 0),
'some distortion coefficients are zero: %s' %
str(mc.distortion.flatten()))
self.assertEqual(mc.as_message().distortion_model,
'rational_polynomial')
self.assert_good_mono(mc, self.limages[i], setup.lin_err)
yaml = mc.yaml()
# Issue #278
self.assertIn('cols: 8', yaml)
def main(args):
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-s", "--size", default="8x6", help="specify chessboard size as nxm [default: %default]")
parser.add_option("-q", "--square", default=".108", help="specify chessboard square size in meters [default: %default]")
options, args = parser.parse_args()
size = tuple([int(c) for c in options.size.split('x')])
dim = float(options.square)
images = []
for fname in args:
if os.path.isfile(fname):
img = cv.LoadImage(fname)
if img is None:
print("[WARN] Couldn't open image " + fname + "!", file=sys.stderr)
sys.exit(1)
else:
print("[INFO] Loaded " + fname + " (" + str(img.width) + "x" + str(img.height) + ")")
images.append(img)
cboard = ChessboardInfo()
cboard.dim = dim
cboard.n_cols = size[0]
cboard.n_rows = size[1]
mc = MonoCalibrator([cboard])
mc.cal(images)
print(mc.as_message())
def test_monocular(self):
# Run the calibrator, produce a calibration, check it
mc = MonoCalibrator([board], flags=cv2.CALIB_FIX_K3)
for dim in self.sizes:
mc.cal(self.l[dim])
self.assert_good_mono(mc, dim)
# Make another calibration, import previous calibration as a message,
# and assert that the new one is good.
mc2 = MonoCalibrator([board])
mc2.from_message(mc.as_message())
self.assert_good_mono(mc2, dim)
def test_monocular(self):
# Run the calibrator, produce a calibration, check it
mc = MonoCalibrator([ board ], cv2.CALIB_FIX_K3)
for dim in self.sizes:
mc.cal(self.l[dim])
self.assert_good_mono(mc, dim)
# Make another calibration, import previous calibration as a message,
# and assert that the new one is good.
mc2 = MonoCalibrator([board])
mc2.from_message(mc.as_message())
self.assert_good_mono(mc2, dim)
def test_monocular(self):
# Run the calibrator, produce a calibration, check it
mc = MonoCalibrator([board], cv2.CALIB_FIX_K3)
max_errs = [0.1, 0.2, 0.4, 0.7]
for i, dim in enumerate(self.sizes):
mc.cal(self.l[dim])
self.assert_good_mono(mc, dim, max_errs[i])
# Make another calibration, import previous calibration as a message,
# and assert that the new one is good.
mc2 = MonoCalibrator([board])
mc2.from_message(mc.as_message())
self.assert_good_mono(mc2, dim, max_errs[i])
def test_monocular(self):
# Run the calibrator, produce a calibration, check it
mc = MonoCalibrator([ board ], cv2.CALIB_FIX_K3)
max_errs = [0.1, 0.2, 0.4, 0.7]
for i, dim in enumerate(self.sizes):
mc.cal(self.l[dim])
self.assert_good_mono(mc, dim, max_errs[i])
# Make another calibration, import previous calibration as a message,
# and assert that the new one is good.
mc2 = MonoCalibrator([board])
mc2.from_message(mc.as_message())
self.assert_good_mono(mc2, dim, max_errs[i])
def test_nochecker(self):
# Run with same images, but looking for an incorrect chessboard size (8, 7).
# Should raise an exception because of lack of input points.
new_board = copy.deepcopy(board)
new_board.n_cols = 8
new_board.n_rows = 7
sc = StereoCalibrator([new_board])
self.assertRaises(CalibrationException, lambda: sc.cal(self.limages, self.rimages))
mc = MonoCalibrator([new_board])
self.assertRaises(CalibrationException, lambda: mc.cal(self.limages))
def main(args):
from optparse import OptionParser
parser = OptionParser()
parser.add_option(
"-s",
"--size",
default="8x6",
help="specify chessboard size as nxm [default: %default]")
parser.add_option(
"-q",
"--square",
default=".108",
help="specify chessboard square size in meters [default: %default]")
options, args = parser.parse_args()
size = tuple([int(c) for c in options.size.split('x')])
dim = float(options.square)
images = []
for fname in args:
if os.path.isfile(fname):
img = cv.LoadImage(fname)
if img is None:
print >> sys.stderr, "[WARN] Couldn't open image " + fname + "!"
sys.exit(1)
else:
print "[INFO] Loaded " + fname + " (" + str(
img.width) + "x" + str(img.height) + ")"
images.append(img)
cboard = ChessboardInfo()
cboard.dim = dim
cboard.n_cols = size[0]
cboard.n_rows = size[1]
mc = MonoCalibrator([cboard])
mc.cal(images)
print mc.as_message()
def test_monocular(self):
# Run the calibrator, produce a calibration, check it
for i, setup in enumerate(self.setups):
board = ChessboardInfo()
board.n_cols = setup.cols
board.n_rows = setup.rows
board.dim = self.board_width_dim
mc = MonoCalibrator([ board ], flags=cv2.CALIB_FIX_K3, pattern=setup.pattern)
if 0:
# display the patterns viewed by the camera
for pattern_warped in self.limages[i]:
cv2.imshow("toto", pattern_warped)
cv2.waitKey(0)
mc.cal(self.limages[i])
self.assert_good_mono(mc, self.limages[i], setup.lin_err)
# Make sure the intrinsics are similar
err_intrinsics = numpy.linalg.norm(mc.intrinsics - self.K, ord=numpy.inf)
self.assert_(err_intrinsics < setup.K_err, 'intrinsics error is %f' % err_intrinsics)
print('intrinsics error is %f' % numpy.linalg.norm(mc.intrinsics - self.K, ord=numpy.inf))
import cv2
from camera_calibration.calibrator import MonoCalibrator, ChessboardInfo
numImages = 30
images = [
cv2.imread('../Images/frame{:04d}.jpg'.format(i)) for i in range(numImages)
]
board = ChessboardInfo()
board.n_cols = 7
board.n_rows = 5
board.dim = 0.050
mc = MonoCalibrator([board], cv2.CALIB_FIX_K3)
mc.cal(images)
print(mc.as_message())
mc.do_save()
