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#!/usr/bin/env python
#
# Software License Agreement (BSD License)
#
# Copyright (c) 2009, Willow Garage, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of the Willow Garage nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import cv2
import collections
import copy
import numpy
import roslib
import tarfile
import unittest
from camera_calibration.calibrator import MonoCalibrator, StereoCalibrator, \
Patterns, CalibrationException, ChessboardInfo, image_from_archive
board = ChessboardInfo()
board.n_cols = 8
board.n_rows = 6
board.dim = 0.108
class TestDirected(unittest.TestCase):
def setUp(self):
tar_path = roslib.packages.find_resource('camera_calibration', 'camera_calibration.tar.gz')[0]
self.tar = tarfile.open(tar_path, 'r')
self.limages = [image_from_archive(self.tar, "wide/left%04d.pgm" % i) for i in range(3, 15)]
self.rimages = [image_from_archive(self.tar, "wide/right%04d.pgm" % i) for i in range(3, 15)]
self.l = {}
self.r = {}
self.sizes = [(320,240), (640,480), (800,600), (1024,768)]
for dim in self.sizes:
self.l[dim] = []
self.r[dim] = []
for li,ri in zip(self.limages, self.rimages):
rli = cv2.resize(li, (dim[0], dim[1]))
rri = cv2.resize(ri, (dim[0], dim[1]))
self.l[dim].append(rli)
self.r[dim].append(rri)
def assert_good_mono(self, c, dim, max_err):
self.assertTrue(len(c.ost()) > 0)
lin_err = 0
n = 0
for img in self.l[dim]:
lin_err_local = c.linear_error_from_image(img)
if lin_err_local:
lin_err += lin_err_local
n += 1
if n > 0:
lin_err /= n
self.assertTrue(0.0 < lin_err, 'lin_err is %f' % lin_err)
self.assertTrue(lin_err < max_err, 'lin_err is %f' % lin_err)
flat = c.remap(img)
self.assertEqual(img.shape, flat.shape)
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_stereo(self):
epierrors = [0.1, 0.2, 0.45, 1.0]
for i, dim in enumerate(self.sizes):
print("Dim =", dim)
sc = StereoCalibrator([board], cv2.CALIB_FIX_K3)
sc.cal(self.l[dim], self.r[dim])
sc.report()
#print sc.ost()
# NOTE: epipolar error currently increases with resolution.
# At highest res expect error ~0.75
epierror = 0
n = 0
for l_img, r_img in zip(self.l[dim], self.r[dim]):
epierror_local = sc.epipolar_error_from_images(l_img, r_img)
if epierror_local:
epierror += epierror_local
n += 1
epierror /= n
self.assertTrue(epierror < epierrors[i],
'Epipolar error is %f for resolution i = %d' % (epierror, i))
self.assertAlmostEqual(sc.chessboard_size_from_images(self.l[dim][0], self.r[dim][0]), .108, 2)
#print sc.as_message()
img = self.l[dim][0]
flat = sc.l.remap(img)
self.assertEqual(img.shape, flat.shape)
flat = sc.r.remap(img)
self.assertEqual(img.shape, flat.shape)
sc2 = StereoCalibrator([board])
sc2.from_message(sc.as_message())
# sc2.set_alpha(1.0)
#sc2.report()
self.assertTrue(len(sc2.ost()) > 0)
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))
class TestArtificial(unittest.TestCase):
Setup = collections.namedtuple('Setup', ['pattern', 'cols', 'rows', 'lin_err', 'K_err'])
def setUp(self):
# Define some image transforms that will simulate a camera position
M = []
self.k = numpy.array([[500, 0, 250], [0, 500, 250], [0, 0, 1]], numpy.float32)
self.d = numpy.array([])
# physical size of the board
self.board_width_dim = 1
# Generate data for different grid types. For each grid type, define the different sizes of
# grid that are recognized (n row, n col)
# Patterns.Circles, Patterns.ACircles
self.setups = [ self.Setup(pattern=Patterns.Chessboard, cols=7, rows=8, lin_err=0.2, K_err=8.2),
self.Setup(pattern=Patterns.Circles, cols=7, rows=8, lin_err=0.1, K_err=4),
self.Setup(pattern=Patterns.ACircles, cols=3, rows=5, lin_err=0.1, K_err=8) ]
self.limages = []
self.rimages = []
for setup in self.setups:
self.limages.append([])
self.rimages.append([])
# Create the pattern
if setup.pattern == Patterns.Chessboard:
pattern = numpy.zeros((50*(setup.rows+3), 50*(setup.cols+3), 1), numpy.uint8)
pattern.fill(255)
for j in range(1, setup.rows+2):
for i in range(1+(j%2), setup.cols+2, 2):
pattern[50*j:50*(j+1), 50*i:50*(i+1)].fill(0)
elif setup.pattern == Patterns.Circles:
pattern = numpy.zeros((50*(setup.rows+2), 50*(setup.cols+2), 1), numpy.uint8)
pattern.fill(255)
for j in range(1, setup.rows+1):
for i in range(1, setup.cols+1):
cv2.circle(pattern, (int(50*i + 25), int(50*j + 25)), 15, (0,0,0), -1)
elif setup.pattern == Patterns.ACircles:
x = 60
pattern = numpy.zeros((x*(setup.rows+2), x*(setup.cols+5), 1), numpy.uint8)
pattern.fill(255)
for j in range(1, setup.rows+1):
for i in range(0, setup.cols):
cv2.circle(pattern, (int(x*(1 + 2*i + (j%2)) + x/2), int(x*j + x/2)), int(x/3), (0,0,0), -1)
rows, cols, _ = pattern.shape
object_points_2d = numpy.array([[0, 0], [0, cols-1], [rows-1, cols-1], [rows-1, 0]], numpy.float32)
object_points_3d = numpy.array([[0, 0, 0], [0, cols-1, 0], [rows-1, cols-1, 0], [rows-1, 0, 0]], numpy.float32)
object_points_3d *= self.board_width_dim/float(cols)
# create the artificial view points
rvec = [ [0, 0, 0], [0, 0, 0.4], [0, 0.4, 0], [0.4, 0, 0], [0.4, 0.4, 0], [0.4, 0, 0.4], [0, 0.4, 0.4], [0.4, 0.4, 0.4] ]
tvec = [ [-0.5, -0.5, 3], [-0.5, -0.5, 3], [-0.5, -0.1, 3], [-0.1, -0.5, 3], [-0.1, -0.1, 3], [-0.1, -0.5, 3], [-0.5, -0.1, 3], [-0.1, 0.1, 3] ]
dsize = (480, 640)
for i in range(len(rvec)):
R = numpy.array(rvec[i], numpy.float32)
T = numpy.array(tvec[i], numpy.float32)
image_points, _ = cv2.projectPoints(object_points_3d, R, T, self.k, self.d)
# deduce the perspective transform
M.append(cv2.getPerspectiveTransform(object_points_2d, image_points))
# project the pattern according to the different cameras
pattern_warped = cv2.warpPerspective(pattern, M[i], dsize)
self.limages[-1].append(pattern_warped)
def assert_good_mono(self, c, images, max_err):
#c.report()
self.assertTrue(len(c.ost()) > 0)
lin_err = 0
n = 0
for img in images:
lin_err_local = c.linear_error_from_image(img)
if lin_err_local:
lin_err += lin_err_local
n += 1
if n > 0:
lin_err /= n
print("linear error is %f" % lin_err)
self.assertTrue(0.0 < lin_err, 'lin_err is %f' % lin_err)
self.assertTrue(lin_err < max_err, 'lin_err is %f' % lin_err)
flat = c.remap(img)
self.assertEqual(img.shape, flat.shape)
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)
if __name__ == '__main__':
unittest.main(verbosity=2)
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