def test_no_video_single_tag():
"""
raises a value error when no video and no image passed.
works on static images
reqs: 03, 04 ,05
"""
config = {'video source': 'none'}
tracker = ArUcoTracker(config)
assert not tracker.has_capture()
tracker.start_tracking()
with pytest.raises(ValueError):
tracker.get_frame()
capture = VideoCapture('data/output.avi')
for frame in range(2):
_, image = capture.read()
(port_handles, timestamps, framenumbers, tracking,
quality) = tracker.get_frame(image)
assert len(port_handles) == len(timestamps)
assert len(port_handles) == len(framenumbers)
assert len(port_handles) == len(tracking)
assert len(port_handles) == len(quality)
assert len(port_handles) == 1
assert port_handles[0] == 'DICT_4X4_50:0'
assert framenumbers[0] == frame
assert quality[0] == 1.0
tracker.stop_tracking()
tracker.close()
def test_with_tool_desc_and_calib():
"""
connect track and close with multitags, defined
rigid bodies, and camera calibration
reqs: 03, 04 ,05, 07
"""
config = {
'video source':
'data/multipattern.avi',
'calibration':
'data/calibration.txt',
'rigid bodies': [{
'name': 'reference',
'filename': 'data/reference.txt',
'aruco dictionary': 'DICT_ARUCO_ORIGINAL',
'tag width': 49.50
}, {
'name': 'pointer',
'filename': 'data/pointer.txt',
'aruco dictionary': 'DICT_ARUCO_ORIGINAL'
}]
}
tracker = ArUcoTracker(config)
tracker.start_tracking()
assert tracker.has_capture()
(port_handles, timestamps, framenumbers, tracking,
quality) = tracker.get_frame()
assert len(port_handles) == len(timestamps)
assert len(port_handles) == len(framenumbers)
assert len(port_handles) == len(tracking)
assert len(port_handles) == len(quality)
assert len(port_handles) == 4 #there is an extraneous marker (1000)
assert 'reference' in port_handles
assert 'pointer' in port_handles
assert 'DICT_4X4_50:0' in port_handles
reference_index = port_handles.index('reference')
pointer_index = port_handles.index('pointer')
assert np.isclose(quality[reference_index], 0.91666666)
assert np.isclose(quality[pointer_index], 0.83333333)
ref_regression = np.array(
[[-0.84701057, 0.51884094, -0.11565978, -8.22903442e+01],
[-0.48733129, -0.67100208, 0.55880625, 4.85032501e+01],
[0.21232361, 0.52967943, 0.82119327, 2.43992401e+02],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]],
dtype=np.float32)
assert np.allclose(tracking[reference_index], ref_regression)
tracker.stop_tracking()
tracker.close()