def rotate(self, theta):
"""
Rotate all data elements and return a new ``Data`` object with rotated data elements.
Parameters
----------
theta : :obj:`float`
Rotation angle in degrees.
Returns
-------
data : :class:`colicoords.data_models.Data`
Rotated ``Data``
"""
data = Data()
for v in self.data_dict.values():
if v.dclass == 'storm':
rotated = _rotate_storm(v, -theta, shape=self.shape)
else:
rotated = scipy_rotate(v,
-theta,
mode='nearest',
axes=(-2, -1)) #todo check dis
data.add_data(rotated, v.dclass, name=v.name, metadata=v.metadata)
return data
def test_rotation(self):
data_rotated = self.data[:2].rotate(60)
rotated = scipy_rotate(self.data.binary_img[:2],
-60,
mode='nearest',
axes=(-1, -2))
self.assertArrayEqual(rotated, data_rotated.binary_img)
self.assertEqual(len(data_rotated), 2)
def rotate_image(image, angle, reshape=False):
"""
Rotate a tensor with a certain angle.
If true, expands the output image to make it large enough to hold the entire rotated image.
Else it keeps the same size
Depreciated...
"""
#image = image.squeeze()
if len(image.size()) == 3: # Case of a single image.
axes = ((1, 2))
elif len(image.size()) == 4: # Case of a batch of images
axes = ((2, 3))
else:
print("Dimension of images must be 4 or 5.")
return
im = scipy_rotate(image.numpy(), angle=angle, reshape=reshape, axes=axes)
im_t = torch.FloatTensor(im)
return (im_t, 360 - angle)
def rotate_mask(mask, angle, reshape=False):
"""
This function take a prediction from the model [batch_size,21,513,513]
and rotate, by an angle add as a parameters, the prediction.
To make sure there is no error it is preferable to use new_angle returned by the function 'rotate_image'.
"""
with torch.no_grad():
if len(mask.size()) == 3: # Case of a single mask.
axes = ((1, 2))
elif len(mask.size()) == 4: # Case of a batch of masks
axes = ((2, 3))
else:
print("Size must be 4 or 5.")
return
m = scipy_rotate(mask.numpy(),
angle=angle,
reshape=reshape,
axes=axes,
mode='nearest')
mask_t = torch.FloatTensor(m)
return mask_t
def rotate_radar_images(radar_image_matrix, ccw_rotation_angle_deg):
"""Rotates each radar image in the xy-plane.
:param radar_image_matrix: See doc for
`deep_learning_utils.check_radar_images`.
:param ccw_rotation_angle_deg: Each image will be rotated counterclockwise
by this angle (degrees).
:return: rotated_image_matrix: Same as `radar_image_matrix`, but after
rotation. The shapes of the two numpy arrays are the same.
"""
dl_utils.check_radar_images(radar_image_matrix=radar_image_matrix,
min_num_dimensions=3,
max_num_dimensions=5)
error_checking.assert_is_geq(ccw_rotation_angle_deg, -180.)
error_checking.assert_is_leq(ccw_rotation_angle_deg, 180.)
return scipy_rotate(input=radar_image_matrix,
angle=-ccw_rotation_angle_deg,
axes=(1, 2),
reshape=False,
order=1,
mode='constant',
cval=PADDING_VALUE)
def getModeMatrix(self, npola=1, shift=None, angle=None):
'''
Returns the matrix containing the mode profiles.
Note that while you can set two polarizations, the modes profiles are obtained under a scalar apporoximation.
Parameters
----------
npola : int (1 or 2)
number of polarizations considered. For npola = 2, the mode matrix will be a block diagonal matrix.
shift : list or None
(slow) value of a coordinate offset, allows to return the mode matrix for a fiber with the center shifted with regard
to the center of the observation window.
defaults to None
rotation: float or None
(slow) angle in radians, allows to rotate the mode matrix with an arbitrary angle.
Note that the rotation is applied BEFORE the transverse shift.
defaults to None
Returns
-------
M : numpy array
the matrix representing the basis of the propagating modes.
'''
assert (self.profiles)
if shift is not None:
assert (len(shift) == 2)
N = self.profiles[0].shape[0]
M = np.zeros((N * npola, npola * self.number), dtype=np.complex128)
angle = angle / np.pi * 180. if (angle is not None) else None
for pol in range(npola):
for ind, modeProfile in enumerate(self.profiles):
if (shift is None and angle is None):
M[pol * N:(pol + 1) * N, pol * self.number +
ind] = modeProfile #.reshape(1,self._npoints**2)
else:
mode2D = modeProfile.reshape([self.indexProfile.npoints] *
2)
if angle is not None:
mode2D = \
scipy_rotate(mode2D.real,angle,reshape=False) + \
complex(0,1)*scipy_rotate(mode2D.imag,angle,reshape=False)
if shift is not None:
mode2D = \
scipy_shift(input=mode2D.real,shift=shift) \
+ complex(0,1)*scipy_shift(input=mode2D.imag,shift=shift)
M[pol * N:(pol + 1) * N,
pol * self.number + ind] = mode2D.flatten()
self.modeMatrix = M
return M
def rotate_custom(image, phi, reshape=True, **kwargs):
# need to fill in holes from scipy
return scipy_rotate(image, phi, reshape=reshape, **kwargs)