RandomFixedSizeCrop.transform_source_batch() - Code Metrics - Inspection of "Merge pull request #345 from dmitriy-serdyuk/fix-w..." - mila-udem/fuel - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 3e1d4c...f31f72 )

by Bart

created 2016-04-13 21:38 UTC

RandomFixedSizeCrop.transform_source_batch() F

↳ Parent: RandomFixedSizeCrop

Complexity

Conditions

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Total Lines

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Lines	0
Ratio	0 %

Metric	Value
cc	9
dl	0
loc	27
rs	3

from __future__ import division

from io import BytesIO
import math

import numpy
from PIL import Image
from six import PY3

try:
    from ._image import window_batch_bchw
    window_batch_bchw_available = True

except ImportError:
    window_batch_bchw_available = False

from . import ExpectsAxisLabels, SourcewiseTransformer
from .. import config


class ImagesFromBytes(SourcewiseTransformer):
    """Load from a stream of bytes objects representing encoded images.

    Parameters
    ----------
    data_stream : instance of :class:`AbstractDataStream`
        The wrapped data stream. The individual examples returned by this
        should be the bytes (in a `bytes` container, or a `str` on legacy
        Python) comprising an image in a format readable by PIL, such as
        PNG, JPEG, etc.
    color_mode : str, optional
        Mode to pass to PIL for color space conversion. Default is RGB.
        If `None`, no coercion is performed.

    Notes
    -----
    Images are returned as NumPy arrays converted from PIL objects.
    If there is more than one color channel, then the array is transposed
    from the `(height, width, channel)` dimension layout native to PIL to
    the `(channel, height, width)` layout that is pervasive in the world
    of convolutional networks. If there is only one color channel, as for
    monochrome or binary images, a leading axis with length 1 is added for
    the sake of uniformity/predictability.

    This SourcewiseTransformer supports streams returning single examples
    as `bytes` objects (`str` on legacy Python) as well as streams that
    return iterables containing such objects. In the case of an iterable, a
    list of loaded images is returned.

    """
    def __init__(self, data_stream, color_mode='RGB', **kwargs):
        kwargs.setdefault('produces_examples', data_stream.produces_examples)
        # Acrobatics currently required to correctly set axis labels.
        which_sources = kwargs.get('which_sources', data_stream.sources)
        axis_labels = self._make_axis_labels(data_stream, which_sources,
                                             kwargs['produces_examples'])
        kwargs.setdefault('axis_labels', axis_labels)
        super(ImagesFromBytes, self).__init__(data_stream, **kwargs)
        self.color_mode = color_mode

    def transform_source_example(self, example, source_name):
        if PY3:
            bytes_type = bytes
        else:
            bytes_type = str
        if not isinstance(example, bytes_type):
            raise TypeError("expected {} object".format(bytes_type.__name__))
        pil_image = Image.open(BytesIO(example))
        if self.color_mode is not None:
            pil_image = pil_image.convert(self.color_mode)
        image = numpy.array(pil_image)
        if image.ndim == 3:
            # Transpose to `(channels, height, width)` layout.
            return image.transpose(2, 0, 1)
        elif image.ndim == 2:
            # Add a channels axis of length 1.
            image = image[numpy.newaxis]
        else:
            raise ValueError('unexpected number of axes')
        return image

    def transform_source_batch(self, batch, source_name):
        return [self.transform_source_example(im, source_name) for im in batch]

    def _make_axis_labels(self, data_stream, which_sources, produces_examples):
        # This is ugly and probably deserves a refactoring of how we handle
        # axis labels. It would be simpler to use memoized read-only
        # properties, but the AbstractDataStream constructor tries to set
        # self.axis_labels currently. We can't use self.which_sources or
        # self.produces_examples here, because this *computes* things that
        # need to be passed into the superclass constructor, necessarily
        # meaning that the superclass constructor hasn't been called.
        # Cooperative inheritance is hard, etc.
        labels = {}
        for source in data_stream.sources:
            if source in which_sources:
                if produces_examples:
                    labels[source] = ('channel', 'height', 'width')
                else:
                    labels[source] = ('batch', 'channel', 'height', 'width')
            else:
                labels[source] = (data_stream.axis_labels[source]
                                  if source in data_stream.axis_labels
                                  else None)
        return labels


class MinimumImageDimensions(SourcewiseTransformer, ExpectsAxisLabels):
    """Resize (lists of) images to minimum dimensions.

    Parameters
    ----------
    data_stream : instance of :class:`AbstractDataStream`
        The data stream to wrap.
    minimum_shape : 2-tuple
        The minimum `(height, width)` dimensions every image must have.
        Images whose height and width are larger than these dimensions
        are passed through as-is.
    resample : str, optional
        Resampling filter for PIL to use to upsample any images requiring
        it. Options include 'nearest' (default), 'bilinear', and 'bicubic'.
        See the PIL documentation for more detailed information.

    Notes
    -----
    This transformer expects stream sources returning individual images,
    represented as 2- or 3-dimensional arrays, or lists of the same.
    The format of the stream is unaltered.

    """
    def __init__(self, data_stream, minimum_shape, resample='nearest',
                 **kwargs):
        self.minimum_shape = minimum_shape
        try:
            self.resample = getattr(Image, resample.upper())
        except AttributeError:
            raise ValueError("unknown resampling filter '{}'".format(resample))
        kwargs.setdefault('produces_examples', data_stream.produces_examples)
        kwargs.setdefault('axis_labels', data_stream.axis_labels)
        super(MinimumImageDimensions, self).__init__(data_stream, **kwargs)

    def transform_source_batch(self, batch, source_name):
        self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        return [self._example_transform(im, source_name) for im in batch]

    def transform_source_example(self, example, source_name):
        self.verify_axis_labels(('channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        return self._example_transform(example, source_name)

    def _example_transform(self, example, _):
        if example.ndim > 3 or example.ndim < 2:
            raise NotImplementedError
        min_height, min_width = self.minimum_shape
        original_height, original_width = example.shape[-2:]
        if original_height < min_height or original_width < min_width:
            dt = example.dtype
            # If we're dealing with a colour image, swap around the axes
            # to be in the format that PIL needs.
            if example.ndim == 3:
                im = example.transpose(1, 2, 0)
            else:
                im = example
            im = Image.fromarray(im)
            width, height = im.size
            multiplier = max(1, min_width / width, min_height / height)
            width = int(math.ceil(width * multiplier))
            height = int(math.ceil(height * multiplier))
            im = numpy.array(im.resize((width, height))).astype(dt)
            # If necessary, undo the axis swap from earlier.
            if im.ndim == 3:
                example = im.transpose(2, 0, 1)
            else:
                example = im
        return example


class RandomFixedSizeCrop(SourcewiseTransformer, ExpectsAxisLabels):
    """Randomly crop images to a fixed window size.

    Parameters
    ----------
    data_stream : :class:`AbstractDataStream`
        The data stream to wrap.
    window_shape : tuple
        The `(height, width)` tuple representing the size of the output
        window.

    Notes
    -----
    This transformer expects to act on stream sources which provide one of

     * Single images represented as 3-dimensional ndarrays, with layout
       `(channel, height, width)`.
     * Batches of images represented as lists of 3-dimensional ndarrays,
       possibly of different shapes (i.e. images of differing
       heights/widths).
     * Batches of images represented as 4-dimensional ndarrays, with
       layout `(batch, channel, height, width)`.

    The format of the stream will be un-altered, i.e. if lists are
    yielded by `data_stream` then lists will be yielded by this
    transformer.

    """
    def __init__(self, data_stream, window_shape, **kwargs):
        if not window_batch_bchw_available:
            raise ImportError('window_batch_bchw not compiled')
        self.window_shape = window_shape
        self.rng = kwargs.pop('rng', None)
        self.warned_axis_labels = False
        if self.rng is None:
            self.rng = numpy.random.RandomState(config.default_seed)
        kwargs.setdefault('produces_examples', data_stream.produces_examples)
        kwargs.setdefault('axis_labels', data_stream.axis_labels)
        super(RandomFixedSizeCrop, self).__init__(data_stream, **kwargs)

    def transform_source_batch(self, source, source_name):
        self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        windowed_height, windowed_width = self.window_shape
        if isinstance(source, numpy.ndarray) and source.ndim == 4:
            # Hardcoded assumption of (batch, channels, height, width).
            # This is what the fast Cython code supports.
            out = numpy.empty(source.shape[:2] + self.window_shape,
                              dtype=source.dtype)
            batch_size = source.shape[0]
            image_height, image_width = source.shape[2:]
            max_h_off = image_height - windowed_height
            max_w_off = image_width - windowed_width
            if max_h_off < 0 or max_w_off < 0:
                raise ValueError("Got ndarray batch with image dimensions {} "
                                 "but requested window shape of {}".format(
                                     source.shape[2:], self.window_shape))
            offsets_w = self.rng.random_integers(0, max_w_off, size=batch_size)
            offsets_h = self.rng.random_integers(0, max_h_off, size=batch_size)
            window_batch_bchw(source, offsets_h, offsets_w, out)
            return out
        elif all(isinstance(b, numpy.ndarray) and b.ndim == 3 for b in source):
            return [self.transform_source_example(im, source_name)
                    for im in source]
        else:
            raise ValueError("uninterpretable batch format; expected a list "
                             "of arrays with ndim = 3, or an array with "
                             "ndim = 4")

    def transform_source_example(self, example, source_name):
        self.verify_axis_labels(('channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        windowed_height, windowed_width = self.window_shape
        if not isinstance(example, numpy.ndarray) or example.ndim != 3:
            raise ValueError("uninterpretable example format; expected "
                             "ndarray with ndim = 3")
        image_height, image_width = example.shape[1:]
        if image_height < windowed_height or image_width < windowed_width:
            raise ValueError("can't obtain ({}, {}) window from image "
                             "dimensions ({}, {})".format(
                                 windowed_height, windowed_width,
                                 image_height, image_width))
        if image_height - windowed_height > 0:
            off_h = self.rng.random_integers(0, image_height - windowed_height)
        else:
            off_h = 0
        if image_width - windowed_width > 0:
            off_w = self.rng.random_integers(0, image_width - windowed_width)
        else:
            off_w = 0
        return example[:, off_h:off_h + windowed_height,
                       off_w:off_w + windowed_width]


class Random2DRotation(SourcewiseTransformer, ExpectsAxisLabels):
    """Randomly rotate 2D images in the spatial plane.

    Parameters
    ----------
    data_stream : :class:`AbstractDataStream`
        The data stream to wrap.
    maximum_rotation : float, default `math.pi`
        Maximum amount of rotation in radians. The image will be rotated by
        an angle in the range [-maximum_rotation, maximum_rotation].
    resample : str, optional
        Resampling filter for PIL to use to upsample any images requiring
        it. Options include 'nearest' (default), 'bilinear', and 'bicubic'.
        See the PIL documentation for more detailed information.

    Notes
    -----
    This transformer expects to act on stream sources which provide one of

     * Single images represented as 3-dimensional ndarrays, with layout
       `(channel, height, width)`.
     * Batches of images represented as lists of 3-dimensional ndarrays,
       possibly of different shapes (i.e. images of differing
       heights/widths).
     * Batches of images represented as 4-dimensional ndarrays, with
       layout `(batch, channel, height, width)`.

    The format of the stream will be un-altered, i.e. if lists are
    yielded by `data_stream` then lists will be yielded by this
    transformer.

    """
    def __init__(self, data_stream, maximum_rotation=math.pi,
                 resample='nearest', **kwargs):
        if maximum_rotation <= 0 or maximum_rotation > math.pi:
            raise ValueError('maximum_rotation ({:.5f}) must be in the range '
                             '(0, math.pi]'.format(maximum_rotation))
        self.maximum_rotation = numpy.rad2deg(maximum_rotation)
        try:
            self.resample = getattr(Image, resample.upper())
        except AttributeError:
            raise ValueError("unknown resampling filter '{}'".format(resample))

        self.rng = kwargs.pop('rng', None)
        self.warned_axis_labels = False
        if self.rng is None:
            self.rng = numpy.random.RandomState(config.default_seed)
        kwargs.setdefault('produces_examples', data_stream.produces_examples)
        kwargs.setdefault('axis_labels', data_stream.axis_labels)
        super(Random2DRotation, self).__init__(data_stream, **kwargs)

    def transform_source_batch(self, source, source_name):
        self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        rotation_angles = self.rng.uniform(-self.maximum_rotation,
                                           self.maximum_rotation,
                                           len(source))
        if isinstance(source, list) and all(isinstance(b, numpy.ndarray) and
                                            b.ndim == 3 for b in source):
            return [self._example_transform(im, angle)
                    for im, angle in zip(source, rotation_angles)]
        elif isinstance(source, numpy.ndarray) and source.dtype == object and \
                all(isinstance(b, numpy.ndarray) and b.ndim == 3 for b in
                    source):
            out = numpy.empty(len(source), dtype=object)
            for im_idx, (im, angle) in enumerate(zip(source, rotation_angles)):
                out[im_idx] = self._example_transform(im, angle)
            return out
        elif isinstance(source, numpy.ndarray) and source.ndim == 4:
            return numpy.array([self._example_transform(im, angle)
                                for im, angle in zip(source, rotation_angles)],
                               dtype=source.dtype)
        else:
            raise ValueError("uninterpretable batch format; expected a list "
                             "of arrays with ndim = 3, or an array with "
                             "ndim = 4")

    def transform_source_example(self, example, source_name):
        self.verify_axis_labels(('channel', 'height', 'width'),
                                self.data_stream.axis_labels[source_name],
                                source_name)
        if not isinstance(example, numpy.ndarray) or example.ndim != 3:
            raise ValueError("uninterpretable example format; expected "
                             "ndarray with ndim = 3")
        rotation_angle = self.rng.uniform(-self.maximum_rotation,
                                          self.maximum_rotation)
        return self._example_transform(example, rotation_angle)

    def _example_transform(self, example, rotation_angle):
        dt = example.dtype
        im = Image.fromarray(example.transpose(1, 2, 0))
        example = numpy.array(im.rotate(rotation_angle,
                                        resample=self.resample)).astype(dt)
        return example.transpose(2, 0, 1)


1			from __future__ import division
			0 ignored issues – show Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.dogs_vs_cats). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.svhn). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.mnist). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.celeba). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.cifar100). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.hdf5). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.text). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.billion). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.cifar10). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.caltech101_silhouettes). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.adult). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.binarized_mnist). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history... Bug introduced 2016-03-09 14:03 UTC by Report Bug Copy Issue Report There seems to be a cyclic import (fuel.datasets -> fuel.datasets.iris). Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug. Loading history...
2			from io import BytesIO
3			import math
4
5			import numpy
6			from PIL import Image
7			from six import PY3
8
9			try:
10			from ._image import window_batch_bchw
11			window_batch_bchw_available = True
			0 ignored issues – show Coding Style Naming introduced 2015-12-02 16:30 UTC by Report Bug Copy Issue Report The name `window_batch_bchw_available` does not conform to the constant naming conventions (`(([A-Z_][A-Z0-9_])\|(__.__))$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
12			except ImportError:
13			window_batch_bchw_available = False
			0 ignored issues – show Coding Style Naming introduced 2015-12-02 16:30 UTC by Report Bug Copy Issue Report The name `window_batch_bchw_available` does not conform to the constant naming conventions (`(([A-Z_][A-Z0-9_])\|(__.__))$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
14			from . import ExpectsAxisLabels, SourcewiseTransformer
15			from .. import config
16
17
18			class ImagesFromBytes(SourcewiseTransformer):
19			"""Load from a stream of bytes objects representing encoded images.
20
21			Parameters
22			----------
23			data_stream : instance of :class:`AbstractDataStream`
24			The wrapped data stream. The individual examples returned by this
25			should be the bytes (in a `bytes` container, or a `str` on legacy
26			Python) comprising an image in a format readable by PIL, such as
27			PNG, JPEG, etc.
28			color_mode : str, optional
29			Mode to pass to PIL for color space conversion. Default is RGB.
30			If `None`, no coercion is performed.
31
32			Notes
33			-----
34			Images are returned as NumPy arrays converted from PIL objects.
35			If there is more than one color channel, then the array is transposed
36			from the `(height, width, channel)` dimension layout native to PIL to
37			the `(channel, height, width)` layout that is pervasive in the world
38			of convolutional networks. If there is only one color channel, as for
39			monochrome or binary images, a leading axis with length 1 is added for
40			the sake of uniformity/predictability.
41
42			This SourcewiseTransformer supports streams returning single examples
43			as `bytes` objects (`str` on legacy Python) as well as streams that
44			return iterables containing such objects. In the case of an iterable, a
45			list of loaded images is returned.
46
47			"""
48			def __init__(self, data_stream, color_mode='RGB', **kwargs):
49			kwargs.setdefault('produces_examples', data_stream.produces_examples)
50			# Acrobatics currently required to correctly set axis labels.
51			which_sources = kwargs.get('which_sources', data_stream.sources)
52			axis_labels = self._make_axis_labels(data_stream, which_sources,
53			kwargs['produces_examples'])
54			kwargs.setdefault('axis_labels', axis_labels)
55			super(ImagesFromBytes, self).__init__(data_stream, **kwargs)
56			self.color_mode = color_mode
57
58			def transform_source_example(self, example, source_name):
59			if PY3:
60			bytes_type = bytes
61			else:
62			bytes_type = str
63			if not isinstance(example, bytes_type):
64			raise TypeError("expected {} object".format(bytes_type.__name__))
65			pil_image = Image.open(BytesIO(example))
66			if self.color_mode is not None:
67			pil_image = pil_image.convert(self.color_mode)
68			image = numpy.array(pil_image)
69			if image.ndim == 3:
70			# Transpose to `(channels, height, width)` layout.
71			return image.transpose(2, 0, 1)
72			elif image.ndim == 2:
73			# Add a channels axis of length 1.
74			image = image[numpy.newaxis]
75			else:
76			raise ValueError('unexpected number of axes')
77			return image
78
79			def transform_source_batch(self, batch, source_name):
80			return [self.transform_source_example(im, source_name) for im in batch]
81
82			def _make_axis_labels(self, data_stream, which_sources, produces_examples):
83			# This is ugly and probably deserves a refactoring of how we handle
84			# axis labels. It would be simpler to use memoized read-only
85			# properties, but the AbstractDataStream constructor tries to set
86			# self.axis_labels currently. We can't use self.which_sources or
87			# self.produces_examples here, because this computes things that
88			# need to be passed into the superclass constructor, necessarily
89			# meaning that the superclass constructor hasn't been called.
90			# Cooperative inheritance is hard, etc.
91			labels = {}
92			for source in data_stream.sources:
93			if source in which_sources:
94			if produces_examples:
95			labels[source] = ('channel', 'height', 'width')
96			else:
97			labels[source] = ('batch', 'channel', 'height', 'width')
98			else:
99			labels[source] = (data_stream.axis_labels[source]
100			if source in data_stream.axis_labels
101			else None)
102			return labels
103
104
105			class MinimumImageDimensions(SourcewiseTransformer, ExpectsAxisLabels):
106			"""Resize (lists of) images to minimum dimensions.
107
108			Parameters
109			----------
110			data_stream : instance of :class:`AbstractDataStream`
111			The data stream to wrap.
112			minimum_shape : 2-tuple
113			The minimum `(height, width)` dimensions every image must have.
114			Images whose height and width are larger than these dimensions
115			are passed through as-is.
116			resample : str, optional
117			Resampling filter for PIL to use to upsample any images requiring
118			it. Options include 'nearest' (default), 'bilinear', and 'bicubic'.
119			See the PIL documentation for more detailed information.
120
121			Notes
122			-----
123			This transformer expects stream sources returning individual images,
124			represented as 2- or 3-dimensional arrays, or lists of the same.
125			The format of the stream is unaltered.
126
127			"""
128			def __init__(self, data_stream, minimum_shape, resample='nearest',
129			**kwargs):
130			self.minimum_shape = minimum_shape
131			try:
132			self.resample = getattr(Image, resample.upper())
133			except AttributeError:
134			raise ValueError("unknown resampling filter '{}'".format(resample))
135			kwargs.setdefault('produces_examples', data_stream.produces_examples)
136			kwargs.setdefault('axis_labels', data_stream.axis_labels)
137			super(MinimumImageDimensions, self).__init__(data_stream, **kwargs)
138
139			def transform_source_batch(self, batch, source_name):
140			self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
141			self.data_stream.axis_labels[source_name],
142			source_name)
143			return [self._example_transform(im, source_name) for im in batch]
144
145			def transform_source_example(self, example, source_name):
146			self.verify_axis_labels(('channel', 'height', 'width'),
147			self.data_stream.axis_labels[source_name],
148			source_name)
149			return self._example_transform(example, source_name)
150
151			def _example_transform(self, example, _):
152			if example.ndim > 3 or example.ndim < 2:
153			raise NotImplementedError
154			min_height, min_width = self.minimum_shape
155			original_height, original_width = example.shape[-2:]
156			if original_height < min_height or original_width < min_width:
157			dt = example.dtype
158			# If we're dealing with a colour image, swap around the axes
159			# to be in the format that PIL needs.
160			if example.ndim == 3:
161			im = example.transpose(1, 2, 0)
162			else:
163			im = example
164			im = Image.fromarray(im)
165			width, height = im.size
166			multiplier = max(1, min_width / width, min_height / height)
167			width = int(math.ceil(width * multiplier))
168			height = int(math.ceil(height * multiplier))
169			im = numpy.array(im.resize((width, height))).astype(dt)
170			# If necessary, undo the axis swap from earlier.
171			if im.ndim == 3:
172			example = im.transpose(2, 0, 1)
173			else:
174			example = im
175			return example
176
177
178			class RandomFixedSizeCrop(SourcewiseTransformer, ExpectsAxisLabels):
179			"""Randomly crop images to a fixed window size.
180
181			Parameters
182			----------
183			data_stream : :class:`AbstractDataStream`
184			The data stream to wrap.
185			window_shape : tuple
186			The `(height, width)` tuple representing the size of the output
187			window.
188
189			Notes
190			-----
191			This transformer expects to act on stream sources which provide one of
192
193			* Single images represented as 3-dimensional ndarrays, with layout
194			`(channel, height, width)`.
195			* Batches of images represented as lists of 3-dimensional ndarrays,
196			possibly of different shapes (i.e. images of differing
197			heights/widths).
198			* Batches of images represented as 4-dimensional ndarrays, with
199			layout `(batch, channel, height, width)`.
200
201			The format of the stream will be un-altered, i.e. if lists are
202			yielded by `data_stream` then lists will be yielded by this
203			transformer.
204
205			"""
206			def __init__(self, data_stream, window_shape, **kwargs):
207			if not window_batch_bchw_available:
208			raise ImportError('window_batch_bchw not compiled')
209			self.window_shape = window_shape
210			self.rng = kwargs.pop('rng', None)
211			self.warned_axis_labels = False
212			if self.rng is None:
213			self.rng = numpy.random.RandomState(config.default_seed)
214			kwargs.setdefault('produces_examples', data_stream.produces_examples)
215			kwargs.setdefault('axis_labels', data_stream.axis_labels)
216			super(RandomFixedSizeCrop, self).__init__(data_stream, **kwargs)
217
218			def transform_source_batch(self, source, source_name):
219			self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
220			self.data_stream.axis_labels[source_name],
221			source_name)
222			windowed_height, windowed_width = self.window_shape
223			if isinstance(source, numpy.ndarray) and source.ndim == 4:
224			# Hardcoded assumption of (batch, channels, height, width).
225			# This is what the fast Cython code supports.
226			out = numpy.empty(source.shape[:2] + self.window_shape,
227			dtype=source.dtype)
228			batch_size = source.shape[0]
229			image_height, image_width = source.shape[2:]
230			max_h_off = image_height - windowed_height
231			max_w_off = image_width - windowed_width
232			if max_h_off < 0 or max_w_off < 0:
233			raise ValueError("Got ndarray batch with image dimensions {} "
234			"but requested window shape of {}".format(
235			source.shape[2:], self.window_shape))
236			offsets_w = self.rng.random_integers(0, max_w_off, size=batch_size)
237			offsets_h = self.rng.random_integers(0, max_h_off, size=batch_size)
238			window_batch_bchw(source, offsets_h, offsets_w, out)
239			return out
240			elif all(isinstance(b, numpy.ndarray) and b.ndim == 3 for b in source):
241			return [self.transform_source_example(im, source_name)
242			for im in source]
243			else:
244			raise ValueError("uninterpretable batch format; expected a list "
245			"of arrays with ndim = 3, or an array with "
246			"ndim = 4")
247
248			def transform_source_example(self, example, source_name):
249			self.verify_axis_labels(('channel', 'height', 'width'),
250			self.data_stream.axis_labels[source_name],
251			source_name)
252			windowed_height, windowed_width = self.window_shape
253			if not isinstance(example, numpy.ndarray) or example.ndim != 3:
254			raise ValueError("uninterpretable example format; expected "
255			"ndarray with ndim = 3")
256			image_height, image_width = example.shape[1:]
257			if image_height < windowed_height or image_width < windowed_width:
258			raise ValueError("can't obtain ({}, {}) window from image "
259			"dimensions ({}, {})".format(
260			windowed_height, windowed_width,
261			image_height, image_width))
262			if image_height - windowed_height > 0:
263			off_h = self.rng.random_integers(0, image_height - windowed_height)
264			else:
265			off_h = 0
266			if image_width - windowed_width > 0:
267			off_w = self.rng.random_integers(0, image_width - windowed_width)
268			else:
269			off_w = 0
270			return example[:, off_h:off_h + windowed_height,
271			off_w:off_w + windowed_width]
272
273
274			class Random2DRotation(SourcewiseTransformer, ExpectsAxisLabels):
275			"""Randomly rotate 2D images in the spatial plane.
276
277			Parameters
278			----------
279			data_stream : :class:`AbstractDataStream`
280			The data stream to wrap.
281			maximum_rotation : float, default `math.pi`
282			Maximum amount of rotation in radians. The image will be rotated by
283			an angle in the range [-maximum_rotation, maximum_rotation].
284			resample : str, optional
285			Resampling filter for PIL to use to upsample any images requiring
286			it. Options include 'nearest' (default), 'bilinear', and 'bicubic'.
287			See the PIL documentation for more detailed information.
288
289			Notes
290			-----
291			This transformer expects to act on stream sources which provide one of
292
293			* Single images represented as 3-dimensional ndarrays, with layout
294			`(channel, height, width)`.
295			* Batches of images represented as lists of 3-dimensional ndarrays,
296			possibly of different shapes (i.e. images of differing
297			heights/widths).
298			* Batches of images represented as 4-dimensional ndarrays, with
299			layout `(batch, channel, height, width)`.
300
301			The format of the stream will be un-altered, i.e. if lists are
302			yielded by `data_stream` then lists will be yielded by this
303			transformer.
304
305			"""
306			def __init__(self, data_stream, maximum_rotation=math.pi,
307			resample='nearest', **kwargs):
308			if maximum_rotation <= 0 or maximum_rotation > math.pi:
309			raise ValueError('maximum_rotation ({:.5f}) must be in the range '
310			'(0, math.pi]'.format(maximum_rotation))
311			self.maximum_rotation = numpy.rad2deg(maximum_rotation)
312			try:
313			self.resample = getattr(Image, resample.upper())
314			except AttributeError:
315			raise ValueError("unknown resampling filter '{}'".format(resample))
316
317			self.rng = kwargs.pop('rng', None)
318			self.warned_axis_labels = False
319			if self.rng is None:
320			self.rng = numpy.random.RandomState(config.default_seed)
321			kwargs.setdefault('produces_examples', data_stream.produces_examples)
322			kwargs.setdefault('axis_labels', data_stream.axis_labels)
323			super(Random2DRotation, self).__init__(data_stream, **kwargs)
324
325			def transform_source_batch(self, source, source_name):
326			self.verify_axis_labels(('batch', 'channel', 'height', 'width'),
327			self.data_stream.axis_labels[source_name],
328			source_name)
329			rotation_angles = self.rng.uniform(-self.maximum_rotation,
330			self.maximum_rotation,
331			len(source))
332			if isinstance(source, list) and all(isinstance(b, numpy.ndarray) and
333			b.ndim == 3 for b in source):
334			return [self._example_transform(im, angle)
335			for im, angle in zip(source, rotation_angles)]
336			elif isinstance(source, numpy.ndarray) and source.dtype == object and \
337			all(isinstance(b, numpy.ndarray) and b.ndim == 3 for b in
338			source):
339			out = numpy.empty(len(source), dtype=object)
340			for im_idx, (im, angle) in enumerate(zip(source, rotation_angles)):
341			out[im_idx] = self._example_transform(im, angle)
342			return out
343			elif isinstance(source, numpy.ndarray) and source.ndim == 4:
344			return numpy.array([self._example_transform(im, angle)
345			for im, angle in zip(source, rotation_angles)],
346			dtype=source.dtype)
347			else:
348			raise ValueError("uninterpretable batch format; expected a list "
349			"of arrays with ndim = 3, or an array with "
350			"ndim = 4")
351
352			def transform_source_example(self, example, source_name):
353			self.verify_axis_labels(('channel', 'height', 'width'),
354			self.data_stream.axis_labels[source_name],
355			source_name)
356			if not isinstance(example, numpy.ndarray) or example.ndim != 3:
357			raise ValueError("uninterpretable example format; expected "
358			"ndarray with ndim = 3")
359			rotation_angle = self.rng.uniform(-self.maximum_rotation,
360			self.maximum_rotation)
361			return self._example_transform(example, rotation_angle)
362
363			def _example_transform(self, example, rotation_angle):
364			dt = example.dtype
365			im = Image.fromarray(example.transpose(1, 2, 0))
366			example = numpy.array(im.rotate(rotation_angle,
367			resample=self.resample)).astype(dt)
368			return example.transpose(2, 0, 1)
369

mila-udem / fuel

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