def test_transforms_regress(self):
"""Transformed values are as before."""
input = np.full((3, 2), 100.)
output = np.zeros((3, 2))
correct_output_pixel_to_metric = [[-8181., 6657.92], [-8181., 6657.92],
[-8181., 6657.92]]
correct_output_metric_to_pixel = [[646.60066007, 505.81188119],
[646.60066007, 505.81188119],
[646.60066007, 505.81188119]]
# Test when passing an array for output
convert_arr_pixel_to_metric(input, self.control, out=output)
np.testing.assert_array_almost_equal(output,
correct_output_pixel_to_metric,
decimal=7)
output = np.zeros((3, 2))
convert_arr_metric_to_pixel(input, self.control, out=output)
np.testing.assert_array_almost_equal(output,
correct_output_metric_to_pixel,
decimal=7)
# Test when NOT passing an array for output
output = convert_arr_pixel_to_metric(input, self.control, out=None)
np.testing.assert_array_almost_equal(output,
correct_output_pixel_to_metric,
decimal=7)
output = np.zeros((3, 2))
output = convert_arr_metric_to_pixel(input, self.control, out=None)
np.testing.assert_array_almost_equal(output,
correct_output_metric_to_pixel,
decimal=7)
def test_transforms_typecheck(self):
"""Transform bindings check types"""
# Assert TypeError is raised when passing a non (n,2) shaped numpy ndarray
with self.assertRaises(TypeError):
list = [[0 for x in range(2)] for x in range(10)] # initialize a 10x2 list (but not numpy matrix)
convert_arr_pixel_to_metric(list, self.control, out=None)
with self.assertRaises(TypeError):
convert_arr_pixel_to_metric(np.empty((10, 3)), self.control, out=None)
with self.assertRaises(TypeError):
convert_arr_metric_to_pixel(np.empty((2, 1)), self.control, out=None)
with self.assertRaises(TypeError):
convert_arr_metric_to_pixel(np.zeros((11, 2)), self.control, out=np.zeros((12, 2)))
def test_transforms(self):
"""Transform in well-known setup gives precalculates results."""
cpar = ControlParams(1)
cpar.set_image_size((1280, 1000))
cpar.set_pixel_size((0.1, 0.1))
metric_pos = np.array([[1., 1.], [-10., 15.], [20., -30.]])
pixel_pos = np.array([[650., 490.], [540., 350.], [840., 800.]])
np.testing.assert_array_almost_equal(
pixel_pos, convert_arr_metric_to_pixel(metric_pos, cpar))
np.testing.assert_array_almost_equal(
metric_pos, convert_arr_pixel_to_metric(pixel_pos, cpar))
def test_transforms_regress(self):
"""Transformed values are as before."""
input = np.full((3, 2), 100.)
output = np.zeros((3, 2))
correct_output_pixel_to_metric = [[-8181. , 6657.92],
[-8181. , 6657.92],
[-8181. , 6657.92]]
correct_output_metric_to_pixel= [[ 646.60066007, 505.81188119],
[ 646.60066007, 505.81188119],
[ 646.60066007, 505.81188119]]
# Test when passing an array for output
convert_arr_pixel_to_metric(input, self.control, out=output)
np.testing.assert_array_almost_equal(output, correct_output_pixel_to_metric,decimal=7)
output = np.zeros((3, 2))
convert_arr_metric_to_pixel(input, self.control, out=output)
np.testing.assert_array_almost_equal(output, correct_output_metric_to_pixel, decimal=7)
# Test when NOT passing an array for output
output=convert_arr_pixel_to_metric(input, self.control, out=None)
np.testing.assert_array_almost_equal(output, correct_output_pixel_to_metric,decimal=7)
output = np.zeros((3, 2))
output=convert_arr_metric_to_pixel(input, self.control, out=None)
np.testing.assert_array_almost_equal(output, correct_output_metric_to_pixel, decimal=7)
def test_transforms(self):
"""Transform in well-known setup gives precalculates results."""
cpar = ControlParams(1)
cpar.set_image_size((1280, 1000))
cpar.set_pixel_size((0.1, 0.1))
metric_pos = np.array([
[1., 1.],
[-10., 15.],
[20., -30.]
])
pixel_pos = np.array([
[650., 490.],
[540., 350.],
[840., 800.]
])
np.testing.assert_array_almost_equal(pixel_pos,
convert_arr_metric_to_pixel(metric_pos, cpar))
np.testing.assert_array_almost_equal(metric_pos,
convert_arr_pixel_to_metric(pixel_pos, cpar))
