def test_write_excel_tables():
num_sheets = np.random.randint(1, 4)
num_cols = np.random.randint(1, 5, size=num_sheets)
num_rows = np.random.randint(20, 40, size=num_sheets)
tables = []
for m, n in zip(num_rows, num_cols):
columns = [f'column{i}' for i in range(n)]
data = np.random.random((m, n))
tables.append(pd.DataFrame(data=data, columns=columns))
sheet_names = [f'sheet {i}' for i in range(num_sheets)]
kwargs = dict(zip(sheet_names, tables))
kwargs['config'] = {
'image files': ['image1.tif', 'image2.tif'],
'image format': 'fei',
'threshold radius': 5e-8
}
with temporary_file(suffix='.xlsx') as file:
io.write_excel(file, **kwargs)
tables_in = [
pd.read_excel(file, sheet_name=name, index_col=0)
for name in sheet_names
]
config_in_df = pd.read_excel(file, sheet_name='config')
config_in = dict(
zip(config_in_df['parameters'], config_in_df['values']))
for table, table_in in zip(tables, tables_in):
assert list(table.columns) == list(table_in.columns)
np.testing.assert_allclose(table_in.values, table.values)
for key, val in kwargs['config'].items():
str(val) == str(config_in[key])
def test_server_with_id_service(dummy_data):
frag, gt, fman = dummy_data
id_service_port = 5600
config = {'client_url': 'tcp://*:5590',
'id_service_url': 'tcp://localhost:%i' % id_service_port,
'solver_url': 'tcp://localhost:5590'}
with temporary_file('.json') as config_filename:
with open(config_filename, 'w') as fout:
json.dump(config, fout)
solver = serve.Solver(frag, feature_manager=fman,
config_file=config_filename)
starting_id = 23461
id_thread = threading.Thread(target=id_serve, name='id-service',
daemon=True,
kwargs=dict(port=id_service_port,
curr_id=starting_id))
id_thread.start()
thread = threading.Thread(target=solver.listen, name='solver')
thread.start()
host, port = config['solver_url'].rsplit(':', maxsplit=1)
_, dst = serve.proofread(frag, gt, host=host, port=int(port),
num_operations=2, stop_when_finished=True,
random_state=0)
result = np.array(dst)[frag]
# test: resulting segmentation should be improvement over fragments alone
assert (ev.vi(result, gt, ignore_x=[], ignore_y=[]) <
ev.vi(frag, gt, ignore_x=[], ignore_y=[]))
# test 2: make sure ID service worked: starting ID should be as above
# should be equal but boundary ID messes things up
assert np.min(result) > starting_id
thread.join()
def test_imsave_incorrect_dimension():
with temporary_file(suffix='.png') as fname:
with testing.raises(ValueError):
with expected_warnings([fname + ' is a low contrast image']):
imsave(fname, np.zeros((2, 3, 3, 1)))
with testing.raises(ValueError):
with expected_warnings([fname + ' is a low contrast image']):
imsave(fname, np.zeros((2, 3, 2)))
def test_imsave_incorrect_dimension():
with temporary_file(suffix='.png') as fname:
with testing.raises(ValueError):
with expected_warnings([fname + ' is a low contrast image']):
imsave(fname, np.zeros((2, 3, 3, 1)))
with testing.raises(ValueError):
with expected_warnings([fname + ' is a low contrast image']):
imsave(fname, np.zeros((2, 3, 2)))
def test_jpg_quality_arg():
chessboard = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy'))
with temporary_file(suffix='.jpg') as jpg:
imsave(jpg, chessboard, quality=95)
im = imread(jpg)
sim = ssim(chessboard, im,
data_range=chessboard.max() - chessboard.min())
assert sim > 0.99
def test_jpg_quality_arg():
chessboard = np.load(fetch('data/chessboard_GRAY_U8.npy'))
with temporary_file(suffix='.jpg') as jpg:
imsave(jpg, chessboard, quality=95)
im = imread(jpg)
sim = structural_similarity(
chessboard, im,
data_range=chessboard.max() - chessboard.min())
assert sim > 0.99
def test_vtk_roundtrip():
for i in range(4):
ar_shape = np.random.randint(1, 50, size=3)
dtypes = list(imio.numpy_type_to_vtk_string.keys())
cur_dtype = np.random.choice(dtypes)
if np.issubdtype(cur_dtype, np.integer):
ar = np.random.randint(0, 127, size=ar_shape).astype(cur_dtype)
else:
ar = np.random.rand(*ar_shape).astype(cur_dtype)
with temporary_file('.vtk') as fout:
imio.write_vtk(ar, fout)
ar_in = imio.read_vtk(fout)
np.testing.assert_equal(ar, ar_in)
def test_vtk_roundtrip():
for i in range(4):
ar_shape = np.random.randint(1, 50, size=3)
dtypes = list(imio.numpy_type_to_vtk_string.keys())
cur_dtype = np.random.choice(dtypes)
if np.issubdtype(cur_dtype, np.integer):
ar = np.random.randint(0, 127, size=ar_shape).astype(cur_dtype)
else:
ar = np.random.rand(*ar_shape).astype(cur_dtype)
with temporary_file('.vtk') as fout:
imio.write_vtk(ar, fout)
ar_in = imio.read_vtk(fout)
np.testing.assert_equal(ar, ar_in)
def test_cremi_roundtrip():
raw_image = np.random.randint(256, size=(5, 100, 100), dtype=np.uint8)
labels = np.random.randint(4096, size=raw_image.shape, dtype=np.uint64)
for ax in range(labels.ndim):
labels.sort(axis=ax) # try to get something vaguely contiguous. =P
with temporary_file('.hdf') as fout:
imio.write_cremi({'/volumes/raw': raw_image,
'/volumes/labels/neuron_ids': labels}, fout)
raw_in, lab_in = imio.read_cremi(fout)
f = h5py.File(fout)
stored_resolution = f['/volumes/raw'].attrs['resolution']
f.close()
np.testing.assert_equal(stored_resolution, (40, 4, 4))
np.testing.assert_equal(raw_in, raw_image)
np.testing.assert_equal(lab_in, labels)
def test_cremi_roundtrip():
raw_image = np.random.randint(256, size=(5, 100, 100), dtype=np.uint8)
labels = np.random.randint(4096, size=raw_image.shape, dtype=np.uint64)
for ax in range(labels.ndim):
labels.sort(axis=ax) # try to get something vaguely contiguous. =P
with temporary_file('.hdf') as fout:
imio.write_cremi(
{
'/volumes/raw': raw_image,
'/volumes/labels/neuron_ids': labels
}, fout)
raw_in, lab_in = imio.read_cremi(fout)
f = h5py.File(fout)
stored_resolution = f['/volumes/raw'].attrs['resolution']
f.close()
np.testing.assert_equal(stored_resolution, (40, 4, 4))
np.testing.assert_equal(raw_in, raw_image)
np.testing.assert_equal(lab_in, labels)
def test_server_with_periodic_send(dummy_data):
frag, gt, fman = dummy_data
id_service_port = 5601
config = {
'client_url': 'tcp://*:5591',
'id_service_url': 'tcp://localhost:%i' % id_service_port,
'solver_url': 'tcp://localhost:5591'
}
with temporary_file('.json') as config_filename:
with open(config_filename, 'w') as fout:
json.dump(config, fout)
solver = serve.Solver(frag,
feature_manager=fman,
config_file=config_filename)
starting_id = 23461
id_thread = threading.Thread(target=id_serve,
name='id-service',
daemon=True,
kwargs=dict(port=id_service_port,
curr_id=starting_id))
id_thread.start()
thread = threading.Thread(target=solver.listen,
name='solver',
daemon=True,
kwargs=dict(send_every=10))
thread.start()
host, port = config['solver_url'].rsplit(':', maxsplit=1)
_, dst = serve.proofread(frag,
gt,
host=host,
port=int(port),
num_operations=2,
stop_when_finished=True,
request_seg=False,
random_state=0)
result = np.array(dst)[frag]
# test: resulting segmentation should be improvement over fragments alone
assert (ev.vi(result, gt, ignore_x=[], ignore_y=[]) < ev.vi(
frag, gt, ignore_x=[], ignore_y=[]))
# test 2: make sure ID service worked: starting ID should be as above
# should be equal but boundary ID messes things up
assert np.min(result) > starting_id
def roundtrip_file(self, x):
with temporary_file(suffix='.png') as fname:
imsave(fname, x)
y = imread(fname)
return y
def test_imsave_incorrect_dimension():
with temporary_file(suffix='.png') as fname:
with testing.raises(ValueError):
imsave(fname, np.zeros((2, 3, 3, 1)))
with testing.raises(ValueError):
imsave(fname, np.zeros((2, 3, 2)))
def roundtrip_file(self, x):
with temporary_file(suffix='.png') as fname:
imsave(fname, x)
y = imread(fname)
return y
