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_set_methods(self):
tmp_arr = np.ones((5, 5), dtype=np.uint8)
img = SpatialImage(tmp_arr)
new_vox = [0.5, 0.5]
new_orig = [1, 1]
new_ext = [
tmp_arr.shape[0] * new_vox[0], tmp_arr.shape[1] * new_vox[1]
]
new_type = np.uint16
new_met = img.get_metadata()
new_met['name'] = 'img_test'
img.set_voxelsize(new_vox)
self.assertEqual(img.get_voxelsize(), new_vox)
self.assertEqual(img.get_metadata()['voxelsize'], new_vox)
self.assertEqual(img.get_extent(), new_ext)
self.assertEqual(img.get_metadata()['extent'], new_ext)
img = img.set_type(new_type)
self.assertEqual(img.get_type(), 'uint16')
self.assertEqual(img.get_metadata()['type'], 'uint16')
img.set_origin(new_orig)
self.assertEqual(img.get_origin(), new_orig)
self.assertEqual(img.get_metadata()['origin'], new_orig)
img.set_extent([10.0, 5.0])
self.assertEqual(img.get_voxelsize(), [2.0, 1.0])
self.assertEqual(img.get_metadata()['extent'], [10.0, 5.0])
self.assertEqual(img.get_metadata()['voxelsize'], [2.0, 1.0])
img.set_metadata(new_met)
self.assertDictEqual(img.get_metadata(), new_met)
img.set_pixel([2, 2], 10)
self.assertEqual(img.get_pixel([2, 2]), 10)
#--- numpy compatibility (for example transposition)
new_arr = np.ones((10, 5), dtype=np.uint8)
img = SpatialImage(new_arr, voxelsize=[0.5, 1])
img = img.transpose()
self.assertEqual(img.get_metadata()['shape'], (5, 10))
self.assertEqual(img.get_metadata()['voxelsize'], [1.0, 0.5])
self.assertEqual(img.get_metadata()['voxelsize'], img.get_voxelsize())
self.assertEqual(img.get_metadata()['extent'], [5.0, 5.0])
self.assertEqual(img.get_metadata()['extent'], img.get_extent())
def test_set_methods(self):
tmp_arr = np.ones((5,5),dtype=np.uint8)
img = SpatialImage(tmp_arr)
new_vox = [0.5, 0.5]
new_orig = [1, 1]
new_ext = [tmp_arr.shape[0]*new_vox[0], tmp_arr.shape[1]*new_vox[1]]
new_type = np.uint16
new_met = img.get_metadata()
new_met['name'] = 'img_test'
img.set_voxelsize(new_vox)
self.assertEqual(img.get_voxelsize(), new_vox)
self.assertEqual(img.get_metadata()['voxelsize'], new_vox)
self.assertEqual(img.get_extent(), new_ext)
self.assertEqual(img.get_metadata()['extent'], new_ext)
img = img.set_type(new_type)
self.assertEqual(img.get_type(), 'uint16')
self.assertEqual(img.get_metadata()['type'], 'uint16')
img.set_origin(new_orig)
self.assertEqual(img.get_origin(), new_orig)
self.assertEqual(img.get_metadata()['origin'], new_orig)
img.set_extent([10.0, 5.0])
self.assertEqual(img.get_voxelsize(), [2.0,1.0])
self.assertEqual(img.get_metadata()['extent'], [10.0, 5.0])
self.assertEqual(img.get_metadata()['voxelsize'], [2.0,1.0])
img.set_metadata(new_met)
self.assertDictEqual(img.get_metadata(), new_met)
img.set_pixel([2,2], 10)
self.assertEqual(img.get_pixel([2,2]), 10)
#--- numpy compatibility (for example transposition)
new_arr = np.ones((10,5), dtype=np.uint8)
img = SpatialImage(new_arr, voxelsize=[0.5,1])
img = img.transpose()
self.assertEqual(img.get_metadata()['shape'], (5,10))
self.assertEqual(img.get_metadata()['voxelsize'], [1.0,0.5])
self.assertEqual(img.get_metadata()['voxelsize'], img.get_voxelsize())
self.assertEqual(img.get_metadata()['extent'], [5.0,5.0])
self.assertEqual(img.get_metadata()['extent'], img.get_extent())
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)
times = [3,4]
segmentation_list, feature_space_list, back_id_list = [], [], []
for ind, val in enumerate(times):
img = imread(data_path('time_' + str(val) + '_seg.inr'))
# subimage extraction
shape = img.get_shape()
indices = [0,shape[0]-1,0,shape[1]-1,0,5]
img = img.get_region(indices=indices)
# remove small cells
labels = np.unique(img).tolist()
img = labels_post_processing(img, method='labels_erosion', radius=2)
img[img==0] = np.min(labels)
img = SpatialImage(img, voxelsize=img.get_voxelsize())
# save input labeled images
res_name = 'example_track_time_' + str(val) + '_seg.inr'
imsave(out_path+res_name, img)
labels = np.unique(img).tolist() # list of labels
back_id = np.min(labels) # background identifier
back_id_list.append(back_id)
labels.remove(back_id)
# feature space computation
obj_gf = GeometricalFeatures(img, label=labels)
feature_space = obj_gf.compute_feature_space()
segmentation_list.append(img), feature_space_list.append(feature_space)