________________________ ImageFeatureTest.test_images34 ________________________
[gw32] linux -- Python 3.13.9 /nix/store/7gl4khq26h625mpqzkiiqsify3hclar1-python3-3.13.9/bin/python3.13
obj = array([[[ 27, 68, 156],
[108, 16, 90],
[ 76, 43, 200],
...,
[189, 72, 101],
... ...,
[159, 21, 92],
[ 48, 35, 23],
[ 40, 150, 248]]], shape=(128, 100, 3), dtype=uint16)
mode = 'I;16'
def fromarray(obj: SupportsArrayInterface, mode: str | None = None) -> Image:
"""
Creates an image memory from an object exporting the array interface
(using the buffer protocol)::
from PIL import Image
import numpy as np
a = np.zeros((5, 5))
im = Image.fromarray(a)
If ``obj`` is not contiguous, then the ``tobytes`` method is called
and :py:func:`~PIL.Image.frombuffer` is used.
In the case of NumPy, be aware that Pillow modes do not always correspond
to NumPy dtypes. Pillow modes only offer 1-bit pixels, 8-bit pixels,
32-bit signed integer pixels, and 32-bit floating point pixels.
Pillow images can also be converted to arrays::
from PIL import Image
import numpy as np
im = Image.open("hopper.jpg")
a = np.asarray(im)
When converting Pillow images to arrays however, only pixel values are
transferred. This means that P and PA mode images will lose their palette.
:param obj: Object with array interface
:param mode: Optional mode to use when reading ``obj``. Since pixel values do not
contain information about palettes or color spaces, this can be used to place
grayscale L mode data within a P mode image, or read RGB data as YCbCr for
example.
See: :ref:`concept-modes` for general information about modes.
:returns: An image object.
.. versionadded:: 1.1.6
"""
arr = obj.__array_interface__
shape = arr["shape"]
ndim = len(shape)
strides = arr.get("strides", None)
try:
typekey = (1, 1) + shape[2:], arr["typestr"]
except KeyError as e:
if mode is not None:
typekey = None
color_modes: list[str] = []
else:
msg = "Cannot handle this data type"
raise TypeError(msg) from e
if typekey is not None:
try:
> typemode, rawmode, color_modes = _fromarray_typemap[typekey]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
E KeyError: ((1, 1, 3), '<u2')
/nix/store/96vld9s60486ifj2pq1v3gdbq19gx145-python3.13-pillow-12.0.0/lib/python3.13/site-packages/PIL/Image.py:3285: KeyError
The above exception was the direct cause of the following exception:
self = <tensorflow_datasets.core.features.image_feature_test.ImageFeatureTest testMethod=test_images34>
make_lib_fail = <function make_pil_fail at 0x7ffb8c17c400>
dtypes = (tf.uint16, <class 'numpy.uint16'>), channels = 3
@parameterized.product(
make_lib_fail=[make_none_fail, make_cv2_fail, make_pil_fail],
dtypes=[
(np.uint8, np.uint8),
(np.uint16, np.uint16),
(tf.uint8, np.uint8),
(tf.uint16, np.uint16),
],
channels=[1, 3, 4],
)
def test_images(self, make_lib_fail, dtypes, channels):
dtype, np_dtype = dtypes
with make_lib_fail() as failing_lib:
if _unsupported_images_for_pil(np_dtype, failing_lib):
return
img = randint(256, size=(128, 100, channels), dtype=np_dtype)
img_other_shape = randint(256, size=(64, 200, channels), dtype=np_dtype)
filename = {
np.uint8: {
1: '6pixels_grayscale.png',
3: '6pixels.png',
4: '6pixels_4chan.png',
},
np.uint16: {
1: '6pixels_grayscale_16bit.png',
3: '6pixels_16bit.png',
4: '6pixels_16bit_4chan.png',
},
}[np_dtype][channels]
img_file_path = os.path.join(
os.path.dirname(__file__), '../../testing/test_data', filename
)
with tf.io.gfile.GFile(img_file_path, 'rb') as f:
img_byte_content = f.read()
img_file_expected_content = np.array(
[
[[0, 255, 0, 255], [255, 0, 0, 255], [255, 0, 255, 255]],
[[0, 0, 255, 255], [255, 255, 0, 255], [126, 127, 128, 255]],
],
dtype=np_dtype,
)[
:, :, :channels
] # Truncate (h, w, 4) -> (h, w, c)
if dtype == np.uint16 or dtype == tf.uint16:
img_file_expected_content *= 257 # Scale int16 images
numpy_array = testing.FeatureExpectationItem(
value=img,
expected=img,
expected_np=img,
)
file_path_as_string = testing.FeatureExpectationItem(
value=img_file_path,
expected=img_file_expected_content,
expected_np=img_file_expected_content,
)
file_path_as_path = testing.FeatureExpectationItem(
value=pathlib.Path(img_file_path),
expected=img_file_expected_content,
expected_np=img_file_expected_content,
)
images_bytes = testing.FeatureExpectationItem(
value=img_byte_content,
expected=img_file_expected_content,
expected_np=img_file_expected_content,
)
img_shape_can_be_dynamic = testing.FeatureExpectationItem(
value=img_other_shape,
expected=img_other_shape,
expected_np=img_other_shape,
)
invalid_type = testing.FeatureExpectationItem(
value=randint(256, size=(128, 128, channels), dtype=np.uint32),
raise_cls=ValueError,
raise_cls_np=ValueError,
raise_msg='dtype should be',
)
tests = [
numpy_array,
file_path_as_string,
file_path_as_path,
images_bytes,
img_shape_can_be_dynamic,
invalid_type,
]
# PIL doesn't support 16-bit images.
if (failing_lib != LibWithImportError.PIL) and np_dtype != np.uint16:
tests.append(
testing.FeatureExpectationItem(
value=PIL.Image.open(img_file_path),
expected=img_file_expected_content,
expected_np=img_file_expected_content,
)
)
> self.assertFeature(
feature=features_lib.Image(shape=(None, None, channels), dtype=dtype),
shape=(None, None, channels),
dtype=dtype,
tests=tests,
test_attributes=dict(
_encoding_format=None,
_use_colormap=False,
),
)
tensorflow_datasets/core/features/image_feature_test.py:177:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tensorflow_datasets/testing/test_utils.py:421: in decorated
f(self, *args, **kwargs)
tensorflow_datasets/testing/feature_test_case.py:186: in assertFeature
self.assertFeatureEagerOnly(
tensorflow_datasets/testing/feature_test_case.py:236: in assertFeatureEagerOnly
run_tests(serialize_fdict=fdict, deserialize_fdict=fdict, feature=feature)
tensorflow_datasets/testing/feature_test_case.py:318: in _run_tests
self.assertFeatureTest(
tensorflow_datasets/testing/feature_test_case.py:463: in assertFeatureTest
self._test_repr(feature, out_numpy)
tensorflow_datasets/testing/feature_test_case.py:483: in _test_repr
text = f.repr_html(ex)
^^^^^^^^^^^^^^^
tensorflow_datasets/core/features/image_feature.py:398: in repr_html
img = utils.create_thumbnail(ex, use_colormap=self._use_colormap)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow_datasets/core/utils/image_utils.py:190: in create_thumbnail
img = PIL_Image.fromarray(ex, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
obj = array([[[ 27, 68, 156],
[108, 16, 90],
[ 76, 43, 200],
...,
[189, 72, 101],
... ...,
[159, 21, 92],
[ 48, 35, 23],
[ 40, 150, 248]]], shape=(128, 100, 3), dtype=uint16)
mode = 'I;16'
def fromarray(obj: SupportsArrayInterface, mode: str | None = None) -> Image:
"""
Creates an image memory from an object exporting the array interface
(using the buffer protocol)::
from PIL import Image
import numpy as np
a = np.zeros((5, 5))
im = Image.fromarray(a)
If ``obj`` is not contiguous, then the ``tobytes`` method is called
and :py:func:`~PIL.Image.frombuffer` is used.
In the case of NumPy, be aware that Pillow modes do not always correspond
to NumPy dtypes. Pillow modes only offer 1-bit pixels, 8-bit pixels,
32-bit signed integer pixels, and 32-bit floating point pixels.
Pillow images can also be converted to arrays::
from PIL import Image
import numpy as np
im = Image.open("hopper.jpg")
a = np.asarray(im)
When converting Pillow images to arrays however, only pixel values are
transferred. This means that P and PA mode images will lose their palette.
:param obj: Object with array interface
:param mode: Optional mode to use when reading ``obj``. Since pixel values do not
contain information about palettes or color spaces, this can be used to place
grayscale L mode data within a P mode image, or read RGB data as YCbCr for
example.
See: :ref:`concept-modes` for general information about modes.
:returns: An image object.
.. versionadded:: 1.1.6
"""
arr = obj.__array_interface__
shape = arr["shape"]
ndim = len(shape)
strides = arr.get("strides", None)
try:
typekey = (1, 1) + shape[2:], arr["typestr"]
except KeyError as e:
if mode is not None:
typekey = None
color_modes: list[str] = []
else:
msg = "Cannot handle this data type"
raise TypeError(msg) from e
if typekey is not None:
try:
typemode, rawmode, color_modes = _fromarray_typemap[typekey]
except KeyError as e:
typekey_shape, typestr = typekey
msg = f"Cannot handle this data type: {typekey_shape}, {typestr}"
> raise TypeError(msg) from e
E TypeError: Cannot handle this data type: (1, 1, 3), <u2
/nix/store/96vld9s60486ifj2pq1v3gdbq19gx145-python3.13-pillow-12.0.0/lib/python3.13/site-packages/PIL/Image.py:3289: TypeError
------------------------------ Captured log call -------------------------------
WARNING absl:feature.py:71 `FeatureConnector.dtype` is deprecated. Please change your code to use NumPy with the field `FeatureConnector.np_dtype` or use TensorFlow with the field `FeatureConnector.tf_dtype`.
Short description
Running the test suite with pillow 12.0.0 gives the following failures:
Environment information
Operating System: NixOS
Python version: 3.13.9
tensorflow-datasets/tfds-nightlyversion:tensorflow-datasets4.9.9tensorflow/tf-nightlyversion:tensorflow2.20Does the issue still exists with the last
tfds-nightlypackage (pip install --upgrade tfds-nightly) ?-> Not tested
Reproduction instructions
Link to logs
Details
Expected behavior
All tests pass
Additional context
I am working as a nixpkgs maintainer.
Relevant PR: NixOS/nixpkgs#463025