def test_load_from_url():
url = "https://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/multi-channel.ome.tif"
img = intake_io.imload(url)
assert img["image"].shape == (3, 167, 439)
assert img["image"].dtype == np.int8
assert intake_io.get_axes(img) == "cyx"
url = "https://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/multi-channel-4D-series.ome.tif"
img = intake_io.imload(url)
assert img["image"].shape == (7, 3, 5, 167, 439)
assert img["image"].dtype == np.int8
assert intake_io.get_axes(img) == "tczyx"
def test_round_trip_nifti_version_1(tmp_path):
fpath = os.path.join(tmp_path, "compressed.nii.gz")
if os.path.exists(fpath):
os.remove(fpath)
for img0, shape, axes, spacing, units in random_images():
if axes != "zyx":
continue
try:
assert not os.path.exists(fpath)
intake_io.imsave(img0, fpath, nifti_version=1)
assert os.path.exists(fpath)
with intake_io.source.NiftiSource(fpath) as src:
img1 = intake_io.imload(src)["image"]
assert axes == intake_io.get_axes(img1)
assert shape == img1.shape
#assert spacing == intake_io.get_spacing(img1)
np.testing.assert_array_almost_equal(spacing, intake_io.get_spacing(img1))
assert units == intake_io.get_spacing_units(img1)
assert np.mean(img0.data) not in (0, 1)
assert np.mean(img0.data) == np.mean(img1.data)
finally:
if os.path.exists(fpath):
os.remove(fpath)
def test_round_trip_uncompressed(tmp_path):
fpath = os.path.join(tmp_path, "uncompressed.klb")
if os.path.exists(fpath):
os.remove(fpath)
for img0, shape, axes, spacing, units in random_images():
if "i" in axes:
continue
try:
assert not os.path.exists(fpath)
intake_io.imsave(img0, fpath, compress=False)
assert os.path.exists(fpath)
with intake_io.source.KlbSource(fpath) as src:
img1 = intake_io.imload(src)["image"]
assert axes == intake_io.get_axes(img1)
assert shape == img1.shape
_spacing = [1.0 if i is None else i for i in spacing]
np.testing.assert_array_almost_equal(_spacing,
intake_io.get_spacing(img1))
# assert units == intake_io.get_spacing_units(img1)
assert np.mean(img0.data) not in (0, 1)
assert np.mean(img0.data) == np.mean(img1.data)
finally:
if os.path.exists(fpath):
os.remove(fpath)
def test_round_trip_uncompressed(tmp_path):
fpath = os.path.join(tmp_path, "uncompressed.tif")
if os.path.exists(fpath):
os.remove(fpath)
for img0, shape, axes, spacing, units in random_images():
if img0.dtype in (np.int8, np.int32, np.int64, np.uint32, np.uint64, np.float64):
continue
if "i" in axes:
continue
try:
assert not os.path.exists(fpath)
intake_io.imsave(img0, fpath, compress=False)
assert os.path.exists(fpath)
with intake_io.source.TifSource(fpath) as src:
img1 = intake_io.imload(src)["image"]
assert axes == intake_io.get_axes(img1)
assert shape == img1.shape
assert spacing == intake_io.get_spacing(img1)
assert units == intake_io.get_spacing_units(img1)
assert np.mean(img0.data) not in (0, 1)
assert np.mean(img0.data) == np.mean(img1.data)
finally:
if os.path.exists(fpath):
os.remove(fpath)
def test_loading(tmp_path):
url = "https://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/multi-channel.ome.tif"
fpath = os.path.join(tmp_path, url.rsplit("/", 1)[-1])
with open(fpath, "wb") as fh:
fh.write(requests.get(url).content)
img = intake_io.imload(fpath)
assert img["image"].shape == (3, 167, 439)
assert img["image"].dtype == np.int8
assert intake_io.get_axes(img) == "cyx"
url = "https://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/multi-channel-4D-series.ome.tif"
fpath = os.path.join(tmp_path, url.rsplit("/", 1)[-1])
with open(fpath, "wb") as fh:
fh.write(requests.get(url).content)
img = intake_io.imload(fpath)
assert img["image"].shape == (7, 3, 5, 167, 439)
assert img["image"].dtype == np.int8
assert intake_io.get_axes(img) == "tczyx"
def test_load_from_url():
urls = [
"https://nifti.nimh.nih.gov/nifti-1/data/avg152T1_LR_nifti.nii.gz",
"https://nifti.nimh.nih.gov/pub/dist/data/nifti2/avg152T1_LR_nifti2.nii.gz"
]
for url in urls:
img = intake_io.imload(url)
assert img["image"].shape == (91, 109, 91)
if "nifti-1" in url:
assert img["image"].dtype == np.uint8
else:
assert img["image"].dtype == np.float32
assert intake_io.get_axes(img) == "zyx"
assert intake_io.get_spacing(img) == (2.0, 2.0, 2.0)
assert intake_io.get_spacing_units(img) == ("mm", "mm", "mm")
def test_round_trip_uncompressed(tmp_path):
fpath = os.path.join(tmp_path, "uncompressed.nrrd")
if os.path.exists(fpath):
os.remove(fpath)
for img0, shape, axes, spacing, units in random_images():
try:
assert not os.path.exists(fpath)
intake_io.imsave(img0, fpath, compress=False)
assert os.path.exists(fpath)
with intake_io.source.NrrdSource(fpath) as src:
img1 = intake_io.imload(src)["image"]
assert axes == intake_io.get_axes(img1)
assert shape == img1.shape
assert spacing == intake_io.get_spacing(img1)
assert units == intake_io.get_spacing_units(img1)
assert np.mean(img0.data) not in (0, 1)
assert np.mean(img0.data) == np.mean(img1.data)
finally:
if os.path.exists(fpath):
os.remove(fpath)
def test_round_trip_compressed(tmp_path):
fpaths = {
False: os.path.join(tmp_path, "uncompressed.klb"),
True: os.path.join(tmp_path, "compressed.klb")
}
for fpath in fpaths.values():
if os.path.exists(fpath):
os.remove(fpath)
for img0 in ramp_images():
if "i" in intake_io.get_axes(img0):
continue
try:
for compress, fpath in fpaths.items():
if os.path.exists(fpath):
os.remove(fpath)
assert not os.path.exists(fpath)
intake_io.imsave(img0, fpath, compress=compress)
assert os.path.exists(fpath)
with intake_io.source.KlbSource(fpath) as src:
img1 = intake_io.imload(src)["image"]
img2 = intake_io.imload(fpath)["image"]
assert img0.shape == img1.shape == img2.shape
assert img0.dims == img1.dims == img2.dims
assert np.mean(img0.data) not in (0, 1)
assert np.mean(img0.data) == np.mean(img1.data) == np.mean(
img2.data)
assert os.path.getsize(
fpaths[True]) <= 0.9 * os.path.getsize(fpaths[False])
finally:
for fpath in fpaths.values():
if os.path.exists(fpath):
os.remove(fpath)