def test_image_coord_regress(self):
self.calibration.set_pos(np.array([0, 0, 40]))
self.calibration.set_angles(np.array([0, 0, 0]))
self.calibration.set_primary_point(np.array([0, 0, 10]))
self.calibration.set_glass_vec(np.array([0, 0, 20]))
self.calibration.set_radial_distortion(np.array([0, 0, 0]))
self.calibration.set_decentering(np.array([0, 0]))
self.calibration.set_affine_trans(np.array([1, 0]))
self.mult = MultimediaParams(n1=1,
n2=np.array([1]),
n3=1,
d=np.array([1]))
input = np.array([[10., 5., -20.], [10., 5., -20.]]) # vec3d
output = np.zeros((2, 2))
x = 10. / 6.
y = x / 2.
correct_output = np.array([[x, y], [x, y]])
flat_image_coordinates(input=input,
cal=self.calibration,
mult_params=self.mult,
output=output)
np.testing.assert_array_equal(output, correct_output)
output = np.full((2, 2), 999.)
image_coordinates(input=input,
cal=self.calibration,
mult_params=self.mult,
output=output)
np.testing.assert_array_equal(output, correct_output)
def test_image_coord_regress(self):
self.calibration.set_pos(np.array([0, 0, 40]))
self.calibration.set_angles(np.array([0, 0, 0]))
self.calibration.set_primary_point(np.array([0, 0, 10]))
self.calibration.set_glass_vec(np.array([0, 0, 20]))
self.calibration.set_radial_distortion(np.array([0, 0, 0]))
self.calibration.set_decentering(np.array([0, 0]))
self.calibration.set_affine_trans(np.array([1, 0]))
self.mult = MultimediaParams(n1=1,
n2=np.array([1]),
n3=1,
d=np.array([1]))
input = np.array([[10., 5., -20.],
[10., 5., -20.]]) # vec3d
output = np.zeros((2, 2))
x = 10. / 6.
y = x / 2.
correct_output = np.array([[x, y],
[x, y]])
flat_image_coordinates(input=input, cal=self.calibration, mult_params=self.mult, output=output)
np.testing.assert_array_equal(output, correct_output)
output=np.full((2,2), 999.)
image_coordinates(input=input, cal=self.calibration, mult_params=self.mult, output=output)
np.testing.assert_array_equal(output, correct_output)
def test_img_coord_typecheck(self):
with self.assertRaises(TypeError):
list = [[0 for x in range(3)] for x in range(10)] # initialize a 10x3 list (but not numpy matrix)
flat_image_coordinates(list, self.control, out=None)
with self.assertRaises(TypeError):
flat_image_coordinates(np.empty((10, 2)), self.calibration, self.control.get_multimedia_params(), output=None)
with self.assertRaises(TypeError):
image_coordinates(np.empty((10, 3)), self.calibration, self.control.get_multimedia_params(), output=np.zeros((10, 3)))
with self.assertRaises(TypeError):
image_coordinates(np.zeros((10, 2)), self.calibration, self.control.get_multimedia_params(), output=np.zeros((10, 2)))
def test_img_coord_typecheck(self):
with self.assertRaises(TypeError):
list = [[0 for x in range(3)] for x in range(10)
] # initialize a 10x3 list (but not numpy matrix)
flat_image_coordinates(list, self.control, out=None)
with self.assertRaises(TypeError):
flat_image_coordinates(np.empty((10, 2)),
self.calibration,
self.control.get_multimedia_params(),
output=None)
with self.assertRaises(TypeError):
image_coordinates(np.empty((10, 3)),
self.calibration,
self.control.get_multimedia_params(),
output=np.zeros((10, 3)))
with self.assertRaises(TypeError):
image_coordinates(np.zeros((10, 2)),
self.calibration,
self.control.get_multimedia_params(),
output=np.zeros((10, 2)))
def test_dumbbell(self):
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17.5, 42, 0],
[-17.5, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = r'testing_fodder/dumbbell/cam{cam_num}.tif.ori'
add_file = r'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1)
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_file)
calibs.append(new_cal)
for cam_num, cam_cal in enumerate(calibs):
new_plain_targ = flat_image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
targs_plain = np.array(targs_plain).transpose(1,0,2)
# The cameras are not actually fully calibrated, so the result is not
# an exact 0. The test is that changing the expected distance changes
# the measure.
tf = dumbbell_target_func(targs_plain, self.control, calibs, 35., 0.)
self.assertAlmostEqual(tf, 7.14860, 5) # just a regression test
# As we check the db length, the measure increases...
tf_len = dumbbell_target_func(
targs_plain, self.control, calibs, 35., 1.)
self.assertTrue(tf_len > tf)
# ...but not as much as when giving the wrong length.
tf_too_long = dumbbell_target_func(
targs_plain, self.control, calibs, 25., 1.)
self.assertTrue(tf_too_long > tf_len > tf)
def test_dumbbell(self):
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17.5, 42, 0], [-17.5, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = 'testing_fodder/dumbbell/cam{cam_num}.tif.ori'
add_file = 'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1)
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name.encode(),
add_file=add_file.encode())
calibs.append(new_cal)
for cam_cal in calibs:
new_plain_targ = flat_image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
targs_plain = np.array(targs_plain).transpose(1, 0, 2)
# The cameras are not actually fully calibrated, so the result is not
# an exact 0. The test is that changing the expected distance changes
# the measure.
tf = dumbbell_target_func(targs_plain, self.control, calibs, 35., 0.)
self.assertAlmostEqual(tf, 7.14860, 5) # just a regression test
# As we check the db length, the measure increases...
tf_len = dumbbell_target_func(targs_plain, self.control, calibs, 35.,
1.)
self.assertTrue(tf_len > tf)
# ...but not as much as when giving the wrong length.
tf_too_long = dumbbell_target_func(targs_plain, self.control, calibs,
25., 1.)
self.assertTrue(tf_too_long > tf_len > tf)
