def test_get_methods(self):
tmp_arr = np.ones((5, 5), dtype=np.uint8)
vox = [1.0, 1.0]
orig = [0, 0]
img = SpatialImage(tmp_arr)
metadata = {
'dim': 2,
'extent': [5.0, 5.0],
'shape': (5, 5),
'type': 'uint8',
'voxelsize': [1.0, 1.0],
'origin': [0, 0],
'max': 1,
'mean': 1.0,
'min': 1
}
self.assertEqual(img.get_type(), tmp_arr.dtype)
self.assertEqual(img.get_voxelsize(), vox)
self.assertEqual(img.get_shape(), tmp_arr.shape)
self.assertEqual(img.get_origin(), orig)
self.assertEqual(img.get_extent(),
[tmp_arr.shape[0], tmp_arr.shape[1]])
self.assertEqual(img.get_dim(), 2)
self.assertDictEqual(img.get_metadata(), metadata)
def test_get_methods(self):
tmp_arr = np.ones((5,5),dtype=np.uint8)
vox = [1.0, 1.0]
orig = [0, 0]
img = SpatialImage(tmp_arr)
metadata = {'dim': 2, 'extent': [5.0, 5.0], 'shape': (5, 5), 'type': 'uint8',
'voxelsize': [1.0, 1.0], 'origin': [0, 0], 'max': 1, 'mean': 1.0,
'min': 1}
self.assertEqual(img.get_type(), tmp_arr.dtype)
self.assertEqual(img.get_voxelsize(), vox)
self.assertEqual(img.get_shape(), tmp_arr.shape)
self.assertEqual(img.get_origin(), orig)
self.assertEqual(img.get_extent(), [tmp_arr.shape[0], tmp_arr.shape[1]])
self.assertEqual(img.get_dim(), 2)
self.assertDictEqual(img.get_metadata(), metadata)
def vt_img_to_sp_img(vt_image):
"""
_VT_IMAGE structure to SpatialImage
"""
dt = vt_type_to_c_type(vt_image.type)
x, y, z, v = vt_image.dim.x, vt_image.dim.y, vt_image.dim.z, vt_image.dim.v
size = x * y * z * v
vx, vy, vz = vt_image.siz.x, vt_image.siz.z, vt_image.siz.z
_ct_array = (dt * size).from_address(vt_image.buf)
_np_array = np.ctypeslib.as_array(_ct_array)
# -- This used to be arr = np.array(_np_array.reshape(z,x,y).transpose(2,1,0)).
# but that is wrong. first the shape is x,y,z. Then the transposition
# doesn't fix the byte ordering which for some reason must be read in
# Fortran order --
out_arr = np.array(_np_array.reshape(x, y, z, order="F"))
out_sp_img = SpatialImage(out_arr, voxelsize=[vx, vy, vz])
if 1 in out_sp_img.get_shape(): # 2D management
out_sp_img = out_sp_img.to_2D()
return out_sp_img
def vt_img_to_sp_img(vt_image):
"""
_VT_IMAGE structure to SpatialImage
"""
dt = vt_type_to_c_type(vt_image.type)
x, y, z, v = vt_image.dim.x, vt_image.dim.y, vt_image.dim.z, vt_image.dim.v
size = x * y * z * v
vx, vy, vz = vt_image.siz.x, vt_image.siz.z, vt_image.siz.z
_ct_array = (dt * size).from_address(vt_image.buf)
_np_array = np.ctypeslib.as_array(_ct_array)
# -- This used to be arr = np.array(_np_array.reshape(z,x,y).transpose(2,1,0)).
# but that is wrong. first the shape is x,y,z. Then the transposition
# doesn't fix the byte ordering which for some reason must be read in
# Fortran order --
out_arr = np.array(_np_array.reshape(x, y, z, order="F"))
out_sp_img = SpatialImage(out_arr, voxelsize=[vx, vy, vz])
if 1 in out_sp_img.get_shape(): # 2D management
out_sp_img = out_sp_img.to_2D()
return out_sp_img