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here is the log from the commit of package python-Keras-Preprocessing for
openSUSE:Factory checked in at 2021-04-12 15:48:46
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-Keras-Preprocessing (Old)
and /work/SRC/openSUSE:Factory/.python-Keras-Preprocessing.new.2401 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-Keras-Preprocessing"
Mon Apr 12 15:48:46 2021 rev:4 rq:883622 version:1.1.2
Changes:
--------
---
/work/SRC/openSUSE:Factory/python-Keras-Preprocessing/python-Keras-Preprocessing.changes
2020-05-29 21:39:07.530962145 +0200
+++
/work/SRC/openSUSE:Factory/.python-Keras-Preprocessing.new.2401/python-Keras-Preprocessing.changes
2021-04-12 15:48:47.417194092 +0200
@@ -1,0 +2,13 @@
+Tue Apr 6 15:24:28 UTC 2021 - Ben Greiner <[email protected]>
+
+- Update to 1.1.2
+ * Allow zoom_range to be int or int tuple
+- Fix Requirements
+
+-------------------------------------------------------------------
+Mon Feb 15 16:44:28 UTC 2021 - Ben Greiner <[email protected]>
+
+- NEP 29: Tumbleweed does not have python36-numpy and depending
+ packages anymore. Skip python36 build.
+
+-------------------------------------------------------------------
Old:
----
Keras_Preprocessing-1.1.0.tar.gz
New:
----
Keras_Preprocessing-1.1.2.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-Keras-Preprocessing.spec ++++++
--- /var/tmp/diff_new_pack.hL65qV/_old 2021-04-12 15:48:47.825194586 +0200
+++ /var/tmp/diff_new_pack.hL65qV/_new 2021-04-12 15:48:47.825194586 +0200
@@ -1,7 +1,7 @@
#
-# spec file for package python-Keras-Preprocessing
+# spec file for package python-Keras-Preprocessing-test
#
-# Copyright (c) 2020 SUSE LLC
+# Copyright (c) 2021 SUSE LLC
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
@@ -25,9 +25,10 @@
%define psuffix %{nil}
%bcond_with test
%endif
-%define skip_python2 1
+# tensorflow2 is python3 single flavor only
+%define pythons python3
Name: python-Keras-Preprocessing%{psuffix}
-Version: 1.1.0
+Version: 1.1.2
Release: 0
Summary: Data preprocessing and augmentation package for deep learning
models
License: MIT
@@ -37,18 +38,18 @@
BuildRequires: fdupes
BuildRequires: python-rpm-macros
Requires: python-numpy >= 1.9.1
-Requires: python-six >= 1.9.0
# It won't work without it but we don't want a build loop in OBS
-Recommends: python-Keras
+Recommends: tensorflow2
# match up with tensorflow
ExcludeArch: %{ix86}
%if %{with test}
-BuildRequires: %{python_module Keras}
BuildRequires: %{python_module Pillow}
BuildRequires: %{python_module numpy >= 1.9.1}
BuildRequires: %{python_module pandas}
+BuildRequires: %{python_module pytest-xdist}
BuildRequires: %{python_module pytest}
-BuildRequires: %{python_module six >= 1.9.0}
+BuildRequires: %{python_module scipy}
+BuildRequires: tensorflow2
%endif
%python_subpackages
@@ -59,8 +60,6 @@
%prep
%setup -q -n Keras_Preprocessing-%{version}
-# do not add extra opts for pytest
-rm setup.cfg
%build
%python_build
++++++ Keras_Preprocessing-1.1.0.tar.gz -> Keras_Preprocessing-1.1.2.tar.gz
++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/PKG-INFO
new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/PKG-INFO
--- old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/PKG-INFO
2019-05-31 18:39:25.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/PKG-INFO
2020-05-14 05:53:12.000000000 +0200
@@ -1,12 +1,12 @@
Metadata-Version: 1.1
Name: Keras-Preprocessing
-Version: 1.1.0
+Version: 1.1.2
Summary: Easy data preprocessing and data augmentation for deep learning models
Home-page: https://github.com/keras-team/keras-preprocessing
Author: Keras Team
Author-email: UNKNOWN
License: MIT
-Download-URL: https://github.com/keras-team/keras-preprocessing/tarball/1.1.0
+Download-URL: https://github.com/keras-team/keras-preprocessing/tarball/1.1.2
Description:
Keras Preprocessing is the data preprocessing
and data augmentation module of the Keras deep learning library.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/SOURCES.txt
new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/SOURCES.txt
--- old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/SOURCES.txt
2019-05-31 18:39:25.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/SOURCES.txt
2020-05-14 05:53:12.000000000 +0200
@@ -24,6 +24,14 @@
tests/test_api.py
tests/test_documentation.py
tests/text_test.py
+tests/__pycache__/sequence_test.cpython-36-PYTEST.pyc
+tests/__pycache__/sequence_test.cpython-37-pytest-5.1.2.pyc
+tests/__pycache__/test_api.cpython-36-PYTEST.pyc
+tests/__pycache__/test_api.cpython-37-pytest-5.1.2.pyc
+tests/__pycache__/test_documentation.cpython-36-PYTEST.pyc
+tests/__pycache__/test_documentation.cpython-37-pytest-5.1.2.pyc
+tests/__pycache__/text_test.cpython-36-PYTEST.pyc
+tests/__pycache__/text_test.cpython-37-pytest-5.1.2.pyc
tests/image/affine_transformations_test.py
tests/image/dataframe_iterator_test.py
tests/image/directory_iterator_test.py
@@ -31,4 +39,20 @@
tests/image/iterator_test.py
tests/image/numpy_array_iterator_test.py
tests/image/test_image_api.py
-tests/image/utils_test.py
\ No newline at end of file
+tests/image/utils_test.py
+tests/image/__pycache__/affine_transformations_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/affine_transformations_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/dataframe_iterator_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/dataframe_iterator_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/directory_iterator_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/directory_iterator_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/image_data_generator_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/image_data_generator_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/iterator_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/iterator_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/numpy_array_iterator_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/numpy_array_iterator_test.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/test_image_api.cpython-36-PYTEST.pyc
+tests/image/__pycache__/test_image_api.cpython-37-pytest-5.1.2.pyc
+tests/image/__pycache__/utils_test.cpython-36-PYTEST.pyc
+tests/image/__pycache__/utils_test.cpython-37-pytest-5.1.2.pyc
\ No newline at end of file
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/requires.txt
new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/requires.txt
--- old/Keras_Preprocessing-1.1.0/Keras_Preprocessing.egg-info/requires.txt
2019-05-31 18:39:25.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/Keras_Preprocessing.egg-info/requires.txt
2020-05-14 05:53:12.000000000 +0200
@@ -11,7 +11,7 @@
[tests]
pandas
pillow
-tensorflow==1.7
+tensorflow
keras
pytest
pytest-xdist
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/Keras_Preprocessing-1.1.0/PKG-INFO
new/Keras_Preprocessing-1.1.2/PKG-INFO
--- old/Keras_Preprocessing-1.1.0/PKG-INFO 2019-05-31 18:39:25.000000000
+0200
+++ new/Keras_Preprocessing-1.1.2/PKG-INFO 2020-05-14 05:53:12.000000000
+0200
@@ -1,12 +1,12 @@
Metadata-Version: 1.1
Name: Keras_Preprocessing
-Version: 1.1.0
+Version: 1.1.2
Summary: Easy data preprocessing and data augmentation for deep learning models
Home-page: https://github.com/keras-team/keras-preprocessing
Author: Keras Team
Author-email: UNKNOWN
License: MIT
-Download-URL: https://github.com/keras-team/keras-preprocessing/tarball/1.1.0
+Download-URL: https://github.com/keras-team/keras-preprocessing/tarball/1.1.2
Description:
Keras Preprocessing is the data preprocessing
and data augmentation module of the Keras deep learning library.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/__init__.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/__init__.py
--- old/Keras_Preprocessing-1.1.0/keras_preprocessing/__init__.py
2019-05-31 18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/keras_preprocessing/__init__.py
2020-05-14 05:34:00.000000000 +0200
@@ -40,4 +40,4 @@
return _KERAS_UTILS
-__version__ = '1.1.0'
+__version__ = '1.1.2'
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/dataframe_iterator.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/dataframe_iterator.py
---
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/dataframe_iterator.py
2019-05-31 18:30:58.000000000 +0200
+++
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/dataframe_iterator.py
2020-05-12 01:08:38.000000000 +0200
@@ -89,6 +89,15 @@
'binary', 'categorical', 'input', 'multi_output', 'raw', 'sparse', None
}
+ def __new__(cls, *args, **kwargs):
+ try:
+ from tensorflow.keras.utils import Sequence as TFSequence
+ if TFSequence not in cls.__bases__:
+ cls.__bases__ = cls.__bases__ + (TFSequence,)
+ except ImportError:
+ pass
+ return super(DataFrameIterator, cls).__new__(cls)
+
def __init__(self,
dataframe,
directory=None,
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/directory_iterator.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/directory_iterator.py
---
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/directory_iterator.py
2019-05-31 18:30:58.000000000 +0200
+++
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/directory_iterator.py
2020-05-12 01:08:34.000000000 +0200
@@ -64,6 +64,15 @@
"""
allowed_class_modes = {'categorical', 'binary', 'sparse', 'input', None}
+ def __new__(cls, *args, **kwargs):
+ try:
+ from tensorflow.keras.utils import Sequence as TFSequence
+ if TFSequence not in cls.__bases__:
+ cls.__bases__ = cls.__bases__ + (TFSequence,)
+ except ImportError:
+ pass
+ return super(DirectoryIterator, cls).__new__(cls)
+
def __init__(self,
directory,
image_data_generator,
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/image_data_generator.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/image_data_generator.py
---
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/image_data_generator.py
2019-05-31 18:30:58.000000000 +0200
+++
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/image_data_generator.py
2020-05-14 05:29:56.000000000 +0200
@@ -49,7 +49,7 @@
are integers `[-1, 0, +1]`,
same as with `width_shift_range=[-1, 0, +1]`,
while with `width_shift_range=1.0` possible values are floats
- in the interval [-1.0, +1.0).
+ in the interval `[-1.0, +1.0)`.
height_shift_range: Float, 1-D array-like or int
- float: fraction of total height, if < 1, or pixels if >= 1.
- 1-D array-like: random elements from the array.
@@ -59,7 +59,7 @@
are integers `[-1, 0, +1]`,
same as with `height_shift_range=[-1, 0, +1]`,
while with `height_shift_range=1.0` possible values are floats
- in the interval [-1.0, +1.0).
+ in the interval `[-1.0, +1.0)`.
brightness_range: Tuple or list of two floats. Range for picking
a brightness shift value from.
shear_range: Float. Shear Intensity
@@ -87,8 +87,8 @@
preprocessing_function: function that will be applied on each input.
The function will run after the image is resized and augmented.
The function should take one argument:
- one image (Numpy tensor with rank 3),
- and should output a Numpy tensor with the same shape.
+ one image (NumPy tensor with rank 3),
+ and should output a NumPy tensor with the same shape.
data_format: Image data format,
either "channels_first" or "channels_last".
"channels_last" mode means that the images should have shape
@@ -322,9 +322,10 @@
self.std = None
self.principal_components = None
- if np.isscalar(zoom_range):
+ if isinstance(zoom_range, (float, int)):
self.zoom_range = [1 - zoom_range, 1 + zoom_range]
- elif len(zoom_range) == 2:
+ elif (len(zoom_range) == 2 and
+ all(isinstance(val, (float, int)) for val in zoom_range)):
self.zoom_range = [zoom_range[0], zoom_range[1]]
else:
raise ValueError('`zoom_range` should be a float or '
@@ -378,10 +379,10 @@
"""Takes data & label arrays, generates batches of augmented data.
# Arguments
- x: Input data. Numpy array of rank 4 or a tuple.
+ x: Input data. NumPy array of rank 4 or a tuple.
If tuple, the first element
should contain the images and the second element
- another numpy array or a list of numpy arrays
+ another NumPy array or a list of NumPy arrays
that gets passed to the output
without any modifications.
Can be used to feed the model miscellaneous data
@@ -409,13 +410,13 @@
# Returns
An `Iterator` yielding tuples of `(x, y)`
- where `x` is a numpy array of image data
+ where `x` is a NumPy array of image data
(in the case of a single image input) or a list
- of numpy arrays (in the case with
- additional inputs) and `y` is a numpy array
+ of NumPy arrays (in the case with
+ additional inputs) and `y` is a NumPy array
of corresponding labels. If 'sample_weight' is not None,
the yielded tuples are of the form `(x, y, sample_weight)`.
- If `y` is None, only the numpy array `x` is returned.
+ If `y` is None, only the NumPy array `x` is returned.
"""
return NumpyArrayIterator(
x,
@@ -429,7 +430,8 @@
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
- subset=subset
+ subset=subset,
+ dtype=self.dtype
)
def flow_from_directory(self,
@@ -517,9 +519,9 @@
# Returns
A `DirectoryIterator` yielding tuples of `(x, y)`
- where `x` is a numpy array containing a batch
+ where `x` is a NumPy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
- and `y` is a numpy array of corresponding labels.
+ and `y` is a NumPy array of corresponding labels.
"""
return DirectoryIterator(
directory,
@@ -537,7 +539,8 @@
save_format=save_format,
follow_links=follow_links,
subset=subset,
- interpolation=interpolation
+ interpolation=interpolation,
+ dtype=self.dtype
)
def flow_from_dataframe(self,
@@ -600,14 +603,14 @@
class_mode: one of "binary", "categorical", "input",
"multi_output",
"raw", sparse" or None. Default: "categorical".
Mode for yielding the targets:
- - `"binary"`: 1D numpy array of binary labels,
- - `"categorical"`: 2D numpy array of one-hot encoded labels.
+ - `"binary"`: 1D NumPy array of binary labels,
+ - `"categorical"`: 2D NumPy array of one-hot encoded labels.
Supports multi-label output.
- `"input"`: images identical to input images (mainly used to
work with autoencoders),
- `"multi_output"`: list with the values of the different
columns,
- - `"raw"`: numpy array of values in `y_col` column(s),
- - `"sparse"`: 1D numpy array of integer labels,
+ - `"raw"`: NumPy array of values in `y_col` column(s),
+ - `"sparse"`: 1D NumPy array of integer labels,
- `None`, no targets are returned (the generator will only
yield
batches of image data, which is useful to use in
`model.predict_generator()`).
@@ -639,9 +642,9 @@
# Returns
A `DataFrameIterator` yielding tuples of `(x, y)`
- where `x` is a numpy array containing a batch
+ where `x` is a NumPy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
- and `y` is a numpy array of corresponding labels.
+ and `y` is a NumPy array of corresponding labels.
"""
if 'has_ext' in kwargs:
warnings.warn('has_ext is deprecated, filenames in the dataframe
have '
@@ -680,19 +683,20 @@
save_format=save_format,
subset=subset,
interpolation=interpolation,
- validate_filenames=validate_filenames
+ validate_filenames=validate_filenames,
+ dtype=self.dtype
)
def standardize(self, x):
"""Applies the normalization configuration in-place to a batch of
inputs.
`x` is changed in-place since the function is mainly used internally
- to standarize images and feed them to your network. If a copy of `x`
+ to standardize images and feed them to your network. If a copy of `x`
would be created instead it would have a significant performance cost.
If you want to apply this method without changing the input in-place
you can call the method creating a copy before:
- standarize(np.copy(x))
+ standardize(np.copy(x))
# Arguments
x: Batch of inputs to be normalized.
@@ -845,7 +849,7 @@
- `'zy'`: Float. Zoom in the y direction.
- `'flip_horizontal'`: Boolean. Horizontal flip.
- `'flip_vertical'`: Boolean. Vertical flip.
- - `'channel_shift_intencity'`: Float. Channel shift intensity.
+ - `'channel_shift_intensity'`: Float. Channel shift intensity.
- `'brightness'`: Float. Brightness shift intensity.
# Returns
@@ -910,6 +914,9 @@
Only required if `featurewise_center` or
`featurewise_std_normalization` or `zca_whitening` are set to True.
+ When `rescale` is set to a value, rescaling is applied to
+ sample data before computing the internal data stats.
+
# Arguments
x: Sample data. Should have rank 4.
In case of grayscale data,
@@ -943,6 +950,9 @@
np.random.seed(seed)
x = np.copy(x)
+ if self.rescale:
+ x *= self.rescale
+
if augment:
ax = np.zeros(
tuple([rounds * x.shape[0]] + list(x.shape)[1:]),
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/numpy_array_iterator.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/numpy_array_iterator.py
---
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/numpy_array_iterator.py
2019-05-31 18:30:58.000000000 +0200
+++
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/numpy_array_iterator.py
2020-05-12 01:08:42.000000000 +0200
@@ -42,6 +42,15 @@
dtype: Dtype to use for the generated arrays.
"""
+ def __new__(cls, *args, **kwargs):
+ try:
+ from tensorflow.keras.utils import Sequence as TFSequence
+ if TFSequence not in cls.__bases__:
+ cls.__bases__ = cls.__bases__ + (TFSequence,)
+ except ImportError:
+ pass
+ return super(NumpyArrayIterator, cls).__new__(cls)
+
def __init__(self,
x,
y,
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/utils.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/utils.py
--- old/Keras_Preprocessing-1.1.0/keras_preprocessing/image/utils.py
2019-05-31 18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/keras_preprocessing/image/utils.py
2020-05-11 23:54:09.000000000 +0200
@@ -4,6 +4,7 @@
from __future__ import division
from __future__ import print_function
+import io
import os
import warnings
@@ -82,8 +83,9 @@
# Arguments
path: Path to image file.
grayscale: DEPRECATED use `color_mode="grayscale"`.
- color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
- The desired image format.
+ color_mode: The desired image format. One of "grayscale", "rgb",
"rgba".
+ "grayscale" supports 8-bit images and 32-bit signed integer images.
+ Default: "rgb".
target_size: Either `None` (default to original size)
or tuple of ints `(img_height, img_width)`.
interpolation: Interpolation method used to resample the image if the
@@ -91,7 +93,8 @@
Supported methods are "nearest", "bilinear", and "bicubic".
If PIL version 1.1.3 or newer is installed, "lanczos" is also
supported. If PIL version 3.4.0 or newer is installed, "box" and
- "hamming" are also supported. By default, "nearest" is used.
+ "hamming" are also supported.
+ Default: "nearest".
# Returns
A PIL Image instance.
@@ -107,30 +110,33 @@
if pil_image is None:
raise ImportError('Could not import PIL.Image. '
'The use of `load_img` requires PIL.')
- img = pil_image.open(path)
- if color_mode == 'grayscale':
- if img.mode != 'L':
- img = img.convert('L')
- elif color_mode == 'rgba':
- if img.mode != 'RGBA':
- img = img.convert('RGBA')
- elif color_mode == 'rgb':
- if img.mode != 'RGB':
- img = img.convert('RGB')
- else:
- raise ValueError('color_mode must be "grayscale", "rgb", or "rgba"')
- if target_size is not None:
- width_height_tuple = (target_size[1], target_size[0])
- if img.size != width_height_tuple:
- if interpolation not in _PIL_INTERPOLATION_METHODS:
- raise ValueError(
- 'Invalid interpolation method {} specified. Supported '
- 'methods are {}'.format(
- interpolation,
- ", ".join(_PIL_INTERPOLATION_METHODS.keys())))
- resample = _PIL_INTERPOLATION_METHODS[interpolation]
- img = img.resize(width_height_tuple, resample)
- return img
+ with open(path, 'rb') as f:
+ img = pil_image.open(io.BytesIO(f.read()))
+ if color_mode == 'grayscale':
+ # if image is not already an 8-bit, 16-bit or 32-bit grayscale
image
+ # convert it to an 8-bit grayscale image.
+ if img.mode not in ('L', 'I;16', 'I'):
+ img = img.convert('L')
+ elif color_mode == 'rgba':
+ if img.mode != 'RGBA':
+ img = img.convert('RGBA')
+ elif color_mode == 'rgb':
+ if img.mode != 'RGB':
+ img = img.convert('RGB')
+ else:
+ raise ValueError('color_mode must be "grayscale", "rgb", or
"rgba"')
+ if target_size is not None:
+ width_height_tuple = (target_size[1], target_size[0])
+ if img.size != width_height_tuple:
+ if interpolation not in _PIL_INTERPOLATION_METHODS:
+ raise ValueError(
+ 'Invalid interpolation method {} specified. Supported '
+ 'methods are {}'.format(
+ interpolation,
+ ", ".join(_PIL_INTERPOLATION_METHODS.keys())))
+ resample = _PIL_INTERPOLATION_METHODS[interpolation]
+ img = img.resize(width_height_tuple, resample)
+ return img
def list_pictures(directory, ext=('jpg', 'jpeg', 'bmp', 'png', 'ppm', 'tif',
@@ -202,12 +208,11 @@
"""
dirname = os.path.basename(directory)
if split:
- num_files = len(list(
- _iter_valid_files(directory, white_list_formats, follow_links)))
+ all_files = list(_iter_valid_files(directory, white_list_formats,
+ follow_links))
+ num_files = len(all_files)
start, stop = int(split[0] * num_files), int(split[1] * num_files)
- valid_files = list(
- _iter_valid_files(
- directory, white_list_formats, follow_links))[start: stop]
+ valid_files = all_files[start: stop]
else:
valid_files = _iter_valid_files(
directory, white_list_formats, follow_links)
@@ -228,11 +233,13 @@
# Arguments
x: Input Numpy array.
- data_format: Image data format.
- either "channels_first" or "channels_last".
- scale: Whether to rescale image values
- to be within `[0, 255]`.
+ data_format: Image data format, either "channels_first" or
"channels_last".
+ Default: "channels_last".
+ scale: Whether to rescale the image such that minimum and maximum
values
+ are 0 and 255 respectively.
+ Default: True.
dtype: Dtype to use.
+ Default: "float32".
# Returns
A PIL Image instance.
@@ -258,7 +265,7 @@
if data_format == 'channels_first':
x = x.transpose(1, 2, 0)
if scale:
- x = x + max(-np.min(x), 0)
+ x = x - np.min(x)
x_max = np.max(x)
if x_max != 0:
x /= x_max
@@ -271,6 +278,9 @@
return pil_image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale
+ if np.max(x) > 255:
+ # 32-bit signed integer grayscale image. PIL mode "I"
+ return pil_image.fromarray(x[:, :, 0].astype('int32'), 'I')
return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
else:
raise ValueError('Unsupported channel number: %s' % (x.shape[2],))
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/sequence.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/sequence.py
--- old/Keras_Preprocessing-1.1.0/keras_preprocessing/sequence.py
2019-05-31 18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/keras_preprocessing/sequence.py
2020-05-11 23:54:09.000000000 +0200
@@ -23,7 +23,8 @@
or the length of the longest sequence otherwise.
Sequences that are shorter than `num_timesteps`
- are padded with `value` at the end.
+ are padded with `value` at the beginning or the end
+ if padding='post.
Sequences longer than `num_timesteps` are truncated
so that they fit the desired length.
@@ -56,9 +57,18 @@
num_samples = len(sequences)
lengths = []
+ sample_shape = ()
+ flag = True
+
+ # take the sample shape from the first non empty sequence
+ # checking for consistency in the main loop below.
+
for x in sequences:
try:
lengths.append(len(x))
+ if flag and len(x):
+ sample_shape = np.asarray(x).shape[1:]
+ flag = False
except TypeError:
raise ValueError('`sequences` must be a list of iterables. '
'Found non-iterable: ' + str(x))
@@ -66,14 +76,6 @@
if maxlen is None:
maxlen = np.max(lengths)
- # take the sample shape from the first non empty sequence
- # checking for consistency in the main loop below.
- sample_shape = tuple()
- for s in sequences:
- if len(s) > 0:
- sample_shape = np.asarray(s).shape[1:]
- break
-
is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype,
np.unicode_)
if isinstance(value, six.string_types) and dtype != object and not
is_dtype_str:
raise ValueError("`dtype` {} is not compatible with `value`'s type:
{}\n"
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/keras_preprocessing/text.py
new/Keras_Preprocessing-1.1.2/keras_preprocessing/text.py
--- old/Keras_Preprocessing-1.1.0/keras_preprocessing/text.py 2019-05-31
18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/keras_preprocessing/text.py 2020-05-11
23:54:09.000000000 +0200
@@ -43,8 +43,10 @@
text = text.lower()
if sys.version_info < (3,):
- if isinstance(text, unicode):
- translate_map = dict((ord(c), unicode(split)) for c in filters)
+ if isinstance(text, unicode): # noqa: F821
+ translate_map = {
+ ord(c): unicode(split) for c in filters # noqa: F821
+ }
text = text.translate(translate_map)
elif len(split) == 1:
translate_map = maketrans(filters, split * len(filters))
@@ -53,7 +55,7 @@
for c in filters:
text = text.replace(c, split)
else:
- translate_dict = dict((c, split) for c in filters)
+ translate_dict = {c: split for c in filters}
translate_map = maketrans(translate_dict)
text = text.translate(translate_map)
@@ -191,8 +193,8 @@
self.char_level = char_level
self.oov_token = oov_token
self.index_docs = defaultdict(int)
- self.word_index = dict()
- self.index_word = dict()
+ self.word_index = {}
+ self.index_word = {}
def fit_on_texts(self, texts):
"""Updates internal vocabulary based on a list of texts.
@@ -241,9 +243,9 @@
# note that index 0 is reserved, never assigned to an existing word
self.word_index = dict(
- list(zip(sorted_voc, list(range(1, len(sorted_voc) + 1)))))
+ zip(sorted_voc, list(range(1, len(sorted_voc) + 1))))
- self.index_word = dict((c, w) for w, c in self.word_index.items())
+ self.index_word = {c: w for w, c in self.word_index.items()}
for w, c in list(self.word_docs.items()):
self.index_docs[self.word_index[w]] = c
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/Keras_Preprocessing-1.1.0/setup.py
new/Keras_Preprocessing-1.1.2/setup.py
--- old/Keras_Preprocessing-1.1.0/setup.py 2019-05-31 18:30:58.000000000
+0200
+++ new/Keras_Preprocessing-1.1.2/setup.py 2020-05-14 05:33:44.000000000
+0200
@@ -23,21 +23,21 @@
'''
setup(name='Keras_Preprocessing',
- version='1.1.0',
+ version='1.1.2',
description='Easy data preprocessing and data augmentation '
'for deep learning models',
long_description=long_description,
author='Keras Team',
url='https://github.com/keras-team/keras-preprocessing',
download_url='https://github.com/keras-team/'
- 'keras-preprocessing/tarball/1.1.0',
+ 'keras-preprocessing/tarball/1.1.2',
license='MIT',
install_requires=['numpy>=1.9.1',
'six>=1.9.0'],
extras_require={
'tests': ['pandas',
'Pillow' if sys.version_info >= (3, 0) else 'pillow',
- 'tensorflow==1.7', # CPU version
+ 'tensorflow', # CPU version
'keras',
'pytest',
'pytest-xdist',
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diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/tests/image/directory_iterator_test.py
new/Keras_Preprocessing-1.1.2/tests/image/directory_iterator_test.py
--- old/Keras_Preprocessing-1.1.0/tests/image/directory_iterator_test.py
2019-05-31 18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/tests/image/directory_iterator_test.py
2020-05-11 23:54:09.000000000 +0200
@@ -16,23 +16,35 @@
rgb_images = []
rgba_images = []
gray_images = []
+ gray_images_16bit = []
+ gray_images_32bit = []
for n in range(8):
bias = np.random.rand(img_w, img_h, 1) * 64
variance = np.random.rand(img_w, img_h, 1) * (255 - 64)
+ # RGB
imarray = np.random.rand(img_w, img_h, 3) * variance + bias
im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
rgb_images.append(im)
-
+ # RGBA
imarray = np.random.rand(img_w, img_h, 4) * variance + bias
im = Image.fromarray(imarray.astype('uint8')).convert('RGBA')
rgba_images.append(im)
-
+ # 8-bit grayscale
imarray = np.random.rand(img_w, img_h, 1) * variance + bias
- im = Image.fromarray(
- imarray.astype('uint8').squeeze()).convert('L')
+ im = Image.fromarray(imarray.astype('uint8').squeeze()).convert('L')
gray_images.append(im)
+ # 16-bit grayscale
+ imarray = np.array(
+ np.random.randint(-2147483648, 2147483647, (img_w, img_h))
+ )
+ im = Image.fromarray(imarray.astype('uint16'))
+ gray_images_16bit.append(im)
+ # 32-bit grayscale
+ im = Image.fromarray(imarray.astype('uint32'))
+ gray_images_32bit.append(im)
- return [rgb_images, rgba_images, gray_images]
+ return [rgb_images, rgba_images,
+ gray_images, gray_images_16bit, gray_images_32bit]
def test_directory_iterator(all_test_images, tmpdir):
@@ -101,7 +113,7 @@
color_mode='rgb',
batch_size=3,
class_mode='categorical')
- assert len(dir_seq) == np.ceil(count / 3)
+ assert len(dir_seq) == np.ceil(count / 3.)
x1, y1 = dir_seq[1]
assert x1.shape == (3, 26, 26, 3)
assert y1.shape == (3, num_classes)
@@ -109,7 +121,7 @@
assert (x1 == 0).all()
with pytest.raises(ValueError):
- x1, y1 = dir_seq[9]
+ x1, y1 = dir_seq[14] # there are 40 images and batch size is 3
def test_directory_iterator_class_mode_input(all_test_images, tmpdir):
@@ -140,9 +152,9 @@
@pytest.mark.parametrize('validation_split,num_training', [
- (0.25, 18),
- (0.50, 12),
- (0.75, 6),
+ (0.25, 30),
+ (0.50, 20),
+ (0.75, 10),
])
def test_directory_iterator_with_validation_split(all_test_images,
validation_split,
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore'
old/Keras_Preprocessing-1.1.0/tests/image/image_data_generator_test.py
new/Keras_Preprocessing-1.1.2/tests/image/image_data_generator_test.py
--- old/Keras_Preprocessing-1.1.0/tests/image/image_data_generator_test.py
2019-05-31 18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/tests/image/image_data_generator_test.py
2020-05-11 23:54:09.000000000 +0200
@@ -442,5 +442,51 @@
assert transform_dict['brightness'] is None
+def test_fit_rescale(all_test_images):
+ rescale = 1. / 255
+
+ for test_images in all_test_images:
+ img_list = []
+ for im in test_images:
+ img_list.append(utils.img_to_array(im)[None, ...])
+ images = np.vstack(img_list)
+
+ # featurewise_center test
+ generator = image_data_generator.ImageDataGenerator(
+ rescale=rescale,
+ featurewise_center=True,
+ dtype='float64')
+ generator.fit(images)
+ batch = generator.flow(images, batch_size=8).next()
+ assert abs(np.mean(batch)) < 1e-6
+
+ # featurewise_std_normalization test
+ generator = image_data_generator.ImageDataGenerator(
+ rescale=rescale,
+ featurewise_center=True,
+ featurewise_std_normalization=True,
+ dtype='float64')
+ generator.fit(images)
+ batch = generator.flow(images, batch_size=8).next()
+ assert abs(np.mean(batch)) < 1e-6
+ assert abs(1 - np.std(batch)) < 1e-5
+
+ # zca_whitening test
+ generator = image_data_generator.ImageDataGenerator(
+ rescale=rescale,
+ featurewise_center=True,
+ zca_whitening=True,
+ dtype='float64')
+ generator.fit(images)
+ batch = generator.flow(images, batch_size=8).next()
+ batch = np.reshape(batch,
+ (batch.shape[0],
+ batch.shape[1] * batch.shape[2] * batch.shape[3]))
+ # Y * Y_T = n * I, where Y = W * X
+ identity = np.dot(batch, batch.T) / batch.shape[0]
+ assert ((np.abs(identity) - np.identity(identity.shape[0]))
+ < 1e-6).all()
+
+
if __name__ == '__main__':
pytest.main([__file__])
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/Keras_Preprocessing-1.1.0/tests/image/utils_test.py
new/Keras_Preprocessing-1.1.2/tests/image/utils_test.py
--- old/Keras_Preprocessing-1.1.0/tests/image/utils_test.py 2019-05-31
18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/tests/image/utils_test.py 2020-05-11
23:54:09.000000000 +0200
@@ -1,5 +1,7 @@
import numpy as np
import pytest
+import resource
+import PIL
from keras_preprocessing.image import utils
@@ -20,6 +22,9 @@
def test_load_img(tmpdir):
filename_rgb = str(tmpdir / 'rgb_utils.png')
filename_rgba = str(tmpdir / 'rgba_utils.png')
+ filename_grayscale_8bit = str(tmpdir / 'grayscale_8bit_utils.png')
+ filename_grayscale_16bit = str(tmpdir / 'grayscale_16bit_utils.tiff')
+ filename_grayscale_32bit = str(tmpdir / 'grayscale_32bit_utils.tiff')
original_rgb_array = np.array(255 * np.random.rand(100, 100, 3),
dtype=np.uint8)
@@ -31,6 +36,26 @@
original_rgba = utils.array_to_img(original_rgba_array, scale=False)
original_rgba.save(filename_rgba)
+ original_grayscale_8bit_array = np.array(255 * np.random.rand(100, 100, 1),
+ dtype=np.uint8)
+ original_grayscale_8bit = utils.array_to_img(original_grayscale_8bit_array,
+ scale=False)
+ original_grayscale_8bit.save(filename_grayscale_8bit)
+
+ original_grayscale_16bit_array = np.array(
+ np.random.randint(-2147483648, 2147483647, (100, 100, 1)),
dtype=np.int16
+ )
+ original_grayscale_16bit =
utils.array_to_img(original_grayscale_16bit_array,
+ scale=False, dtype='int16')
+ original_grayscale_16bit.save(filename_grayscale_16bit)
+
+ original_grayscale_32bit_array = np.array(
+ np.random.randint(-2147483648, 2147483647, (100, 100, 1)),
dtype=np.int32
+ )
+ original_grayscale_32bit =
utils.array_to_img(original_grayscale_32bit_array,
+ scale=False, dtype='int32')
+ original_grayscale_32bit.save(filename_grayscale_32bit)
+
# Test that loaded image is exactly equal to original.
loaded_im = utils.load_img(filename_rgb)
@@ -48,6 +73,27 @@
assert loaded_im_array.shape == (original_rgb_array.shape[0],
original_rgb_array.shape[1], 1)
+ loaded_im = utils.load_img(filename_grayscale_8bit, color_mode='grayscale')
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert loaded_im_array.shape == original_grayscale_8bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_8bit_array)
+
+ loaded_im = utils.load_img(filename_grayscale_16bit,
color_mode='grayscale')
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int16')
+ assert loaded_im_array.shape == original_grayscale_16bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_16bit_array)
+ # test casting int16 image to float32
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert np.allclose(loaded_im_array, original_grayscale_16bit_array)
+
+ loaded_im = utils.load_img(filename_grayscale_32bit,
color_mode='grayscale')
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
+ assert loaded_im_array.shape == original_grayscale_32bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_32bit_array)
+ # test casting int32 image to float32
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert np.allclose(loaded_im_array, original_grayscale_32bit_array)
+
# Test that nothing is changed when target size is equal to original.
loaded_im = utils.load_img(filename_rgb, target_size=(100, 100))
@@ -67,6 +113,24 @@
assert loaded_im_array.shape == (original_rgba_array.shape[0],
original_rgba_array.shape[1], 1)
+ loaded_im = utils.load_img(filename_grayscale_8bit, color_mode='grayscale',
+ target_size=(100, 100))
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert loaded_im_array.shape == original_grayscale_8bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_8bit_array)
+
+ loaded_im = utils.load_img(filename_grayscale_16bit,
color_mode='grayscale',
+ target_size=(100, 100))
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int16')
+ assert loaded_im_array.shape == original_grayscale_16bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_16bit_array)
+
+ loaded_im = utils.load_img(filename_grayscale_32bit,
color_mode='grayscale',
+ target_size=(100, 100))
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
+ assert loaded_im_array.shape == original_grayscale_32bit_array.shape
+ assert np.all(loaded_im_array == original_grayscale_32bit_array)
+
# Test down-sampling with bilinear interpolation.
loaded_im = utils.load_img(filename_rgb, target_size=(25, 25))
@@ -83,6 +147,21 @@
loaded_im_array = utils.img_to_array(loaded_im)
assert loaded_im_array.shape == (25, 25, 1)
+ loaded_im = utils.load_img(filename_grayscale_8bit, color_mode='grayscale',
+ target_size=(25, 25))
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert loaded_im_array.shape == (25, 25, 1)
+
+ loaded_im = utils.load_img(filename_grayscale_16bit,
color_mode='grayscale',
+ target_size=(25, 25))
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int16')
+ assert loaded_im_array.shape == (25, 25, 1)
+
+ loaded_im = utils.load_img(filename_grayscale_32bit,
color_mode='grayscale',
+ target_size=(25, 25))
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
+ assert loaded_im_array.shape == (25, 25, 1)
+
# Test down-sampling with nearest neighbor interpolation.
loaded_im_nearest = utils.load_img(filename_rgb, target_size=(25, 25),
@@ -98,6 +177,21 @@
assert loaded_im_array_nearest.shape == (25, 25, 4)
assert np.any(loaded_im_array_nearest != loaded_im_array)
+ loaded_im = utils.load_img(filename_grayscale_8bit, color_mode='grayscale',
+ target_size=(25, 25), interpolation="nearest")
+ loaded_im_array = utils.img_to_array(loaded_im)
+ assert loaded_im_array.shape == (25, 25, 1)
+
+ loaded_im = utils.load_img(filename_grayscale_16bit,
color_mode='grayscale',
+ target_size=(25, 25), interpolation="nearest")
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int16')
+ assert loaded_im_array.shape == (25, 25, 1)
+
+ loaded_im = utils.load_img(filename_grayscale_32bit,
color_mode='grayscale',
+ target_size=(25, 25), interpolation="nearest")
+ loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
+ assert loaded_im_array.shape == (25, 25, 1)
+
# Check that exception is raised if interpolation not supported.
loaded_im = utils.load_img(filename_rgb, interpolation="unsupported")
@@ -150,6 +244,17 @@
x = utils.img_to_array(img, data_format='channels_first')
assert x.shape == (1, height, width)
+ # grayscale 32-bit signed integer
+ x = np.array(
+ np.random.randint(-2147483648, 2147483647, (1, height, width)),
+ dtype=np.int32
+ )
+ img = utils.array_to_img(x, data_format='channels_first')
+ assert img.size == (width, height)
+
+ x = utils.img_to_array(img, data_format='channels_first')
+ assert x.shape == (1, height, width)
+
# Test tf data format
# Test RGB 3D
x = np.random.random((height, width, 3))
@@ -175,6 +280,28 @@
x = utils.img_to_array(img, data_format='channels_last')
assert x.shape == (height, width, 1)
+ # grayscale 16-bit signed integer
+ x = np.array(
+ np.random.randint(-2147483648, 2147483647, (height, width, 1)),
+ dtype=np.int16
+ )
+ img = utils.array_to_img(x, data_format='channels_last')
+ assert img.size == (width, height)
+
+ x = utils.img_to_array(img, data_format='channels_last')
+ assert x.shape == (height, width, 1)
+
+ # grayscale 32-bit signed integer
+ x = np.array(
+ np.random.randint(-2147483648, 2147483647, (height, width, 1)),
+ dtype=np.int32
+ )
+ img = utils.array_to_img(x, data_format='channels_last')
+ assert img.size == (width, height)
+
+ x = utils.img_to_array(img, data_format='channels_last')
+ assert x.shape == (height, width, 1)
+
# Test invalid use case
with pytest.raises(ValueError):
x = np.random.random((height, width)) # not 3D
@@ -201,5 +328,25 @@
img = utils.img_to_array(x, data_format='channels_last')
+def write_sample_image(tmpdir):
+ im = utils.array_to_img(np.random.rand(1, 1, 3))
+ path = str(tmpdir / 'sample_image.png')
+ utils.save_img(path, im)
+ return path
+
+
+def test_image_file_handlers_close(tmpdir):
+ path = write_sample_image(tmpdir)
+ max_open_files, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
+ for i in range(max_open_files+1):
+ utils.load_img(path)
+
+
+def test_load_img_returns_image(tmpdir):
+ path = write_sample_image(tmpdir)
+ im = utils.load_img(path)
+ assert isinstance(im, PIL.Image.Image)
+
+
if __name__ == '__main__':
pytest.main([__file__])
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/Keras_Preprocessing-1.1.0/tests/text_test.py
new/Keras_Preprocessing-1.1.2/tests/text_test.py
--- old/Keras_Preprocessing-1.1.0/tests/text_test.py 2019-05-31
18:30:58.000000000 +0200
+++ new/Keras_Preprocessing-1.1.2/tests/text_test.py 2020-05-14
05:30:57.000000000 +0200
@@ -2,7 +2,7 @@
import numpy as np
import pytest
-import keras
+from tensorflow import keras
from keras_preprocessing import text
from collections import OrderedDict
