def test_set_angle(self):
f = Feature(ctype='d')
nose.tools.assert_equal(f.angle, 0) # default value
expected = random.random()
f.angle = expected
nose.tools.assert_almost_equal(f.angle, expected, 16)
f = Feature(ctype='f')
nose.tools.assert_equal(f.angle, 0) # default value
expected = random.random()
f.angle = expected
nose.tools.assert_almost_equal(f.angle, expected, 6)
def test_set_magnitude(self):
f = Feature(ctype=ctypes.c_double)
nose.tools.assert_equal(f.magnitude, 0) # default value
expected = random.random()
f.magnitude = expected
nose.tools.assert_almost_equal(f.magnitude, expected, 16)
f = Feature(ctype=ctypes.c_float)
nose.tools.assert_equal(f.magnitude, 0) # default value
expected = random.random()
f.magnitude = expected
nose.tools.assert_almost_equal(f.magnitude, expected, 6)
def test_set_color(self):
expected = RGBColor(4, 20, 0)
f = Feature(ctype='d')
nose.tools.assert_equal(f.color, RGBColor())
f.color = expected
nose.tools.assert_equal(f.color, expected)
f = Feature(ctype='f')
nose.tools.assert_equal(f.color, RGBColor())
f.color = expected
nose.tools.assert_equal(f.color, expected)
def test_set_location(self):
f = Feature(ctype='d')
expected = [random.random(), random.random()]
f.location = expected
# making sure that we went through the setter, and not just setting the
# exact value to the property
numpy.testing.assert_almost_equal(f.location, expected, 16)
f = Feature(ctype='f')
expected = [random.random(), random.random()]
f.location = expected
numpy.testing.assert_almost_equal(f.location, expected, 6)
def test_set_location(self):
f = Feature(ctype=ctypes.c_double)
expected = [[random.random()], [random.random()]]
f.location = expected
# making sure that we went through the setter, and not just setting the
# exact value to the property
nose.tools.assert_is_instance(f.location, EigenArray)
numpy.testing.assert_almost_equal(f.location, expected, 16)
f = Feature(ctype=ctypes.c_float)
expected = [[random.random()], [random.random()]]
f.location = expected
nose.tools.assert_is_instance(f.location, EigenArray)
numpy.testing.assert_almost_equal(f.location, expected, 6)
def feature(self):
f_ptr = self._call_cfunc("vital_track_state_feature",
[self.C_TYPE_PTR], [self],
Feature.c_ptr_type())
# f_ptr may be null
if f_ptr:
return Feature(from_cptr=f_ptr)
else:
return None
def test_set_covar(self):
f = Feature(ctype='d')
#nose.tools.assert_equal(f.covariance, Covariance.new_covar())
expected = [[1, 2], [3, 4]]
c = Covariance.from_matrix(2, 'd', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
#nose.tools.assert_equal(f.covariance, c)
# And for floats...
f = Feature(ctype='f')
#nose.tools.assert_equal(f.covariance, Covariance())
expected = [[1, 2], [3, 4]]
c = Covariance.from_matrix(2, 'f', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
def test_set_covar(self):
f = Feature(ctype=ctypes.c_double)
nose.tools.assert_equal(f.covariance, Covariance())
expected = [[1, 2], [3, 4]]
c = Covariance(2, ctypes.c_double, expected)
f.covariance = c
nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
nose.tools.assert_equal(f.covariance, c)
# And for floats...
f = Feature(ctype=ctypes.c_float)
nose.tools.assert_equal(f.covariance, Covariance())
expected = [[1, 2], [3, 4]]
c = Covariance(2, ctypes.c_float, expected)
f.covariance = c
nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
nose.tools.assert_equal(f.covariance, c)
def test_new_ts(self):
TrackState(0)
TrackState(23456)
# With feat, desc, feat/desc
f = Feature()
d = Descriptor()
TrackState(0, feature=f)
TrackState(0, descriptor=d)
TrackState(0, f, d)
# Only integers
nose.tools.assert_raises(ctypes.ArgumentError, TrackState, 1.2)
nose.tools.assert_raises(ctypes.ArgumentError, TrackState, 'foo')
def projected_tracks(lmap, cmap):
"""
Use the cameras to project the landmarks back into their images.
:type lmap: LandmarkMap
:type cmap: CameraMap
"""
tracks = []
cam_d = cmap.as_dict()
landmark_d = lmap.as_dict()
for lid, l in landmark_d.iteritems():
t = Track(lid)
tracks.append(t)
# Sort camera iteration to make sure that we go in order of frame IDs
for fid in sorted(cam_d):
cam = cam_d[fid]
f = Feature(cam.project(l.loc))
t.append(TrackState(fid, f))
assert t.size == len(cam_d)
return TrackSet(tracks)
def test_get_angle(self):
f = Feature()
nose.tools.assert_equal(f.angle, 0)
f = Feature(angle=1.1)
nose.tools.assert_equal(f.angle, 1.1)
def test_get_covar(self):
dflt_covar = Covariance(2)
f = Feature()
nose.tools.assert_equal(f.covariance, dflt_covar)
def test_get_scale(self):
f = Feature()
nose.tools.assert_equal(f.scale, 1)
f = Feature(scale=2.1)
nose.tools.assert_equal(f.scale, 2.1)
def test_get_mag(self):
f = Feature()
nose.tools.assert_equal(f.magnitude, 0)
f = Feature(mag=1.1)
nose.tools.assert_equal(f.magnitude, 1.1)
def test_new(self):
f1 = Feature()
f2 = Feature([1, 1], 1, 2, 1)
def test_get_covar(self):
dflt_covar = Covariance.new_covar(2)
f = Feature()
def test_feat(self):
f = Feature()
ts = TrackState(0, f)
nose.tools.assert_equal(ts.feature, f)
