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# Arguments
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directory: Path to the directory to read images from.
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Each subdirectory in this directory will be
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considered to contain images from one class,
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or alternatively you could specify class subdirectories
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via the `classes` argument.
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image_data_generator: Instance of `ImageDataGenerator`
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to use for random transformations and normalization.
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target_size: tuple of integers, dimensions to resize input images to.
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color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images.
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classes: Optional list of strings, names of sudirectories
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containing images from each class (e.g. `["dogs", "cats"]`).
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It will be computed automatically if not set.
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class_mode: Mode for yielding the targets:
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`"binary"`: binary targets (if there are only two classes),
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`"categorical"`: categorical targets,
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`"sparse"`: integer targets,
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`None`: no targets get yielded (only input images are yielded).
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batch_size: Integer, size of a batch.
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shuffle: Boolean, whether to shuffle the data between epochs.
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seed: Random seed for data shuffling.
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data_format: String, one of `channels_first`, `channels_last`.
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save_to_dir: Optional directory where to save the pictures
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being yielded, in a viewable format. This is useful
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for visualizing the random transformations being
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applied, for debugging purposes.
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save_prefix: String prefix to use for saving sample
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images (if `save_to_dir` is set).
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save_format: Format to use for saving sample images
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(if `save_to_dir` is set).
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"""
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def __init__(self, directory, image_data_generator,
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target_size=(256, 256), color_mode='rgb',
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classes=None, class_mode='categorical',
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batch_size=32, shuffle=True, seed=None,
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data_format=None,
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save_to_dir=None, save_prefix='', save_format='jpeg',
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follow_links=False, file_name_to_y=None, output_function=None):
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if data_format is None:
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data_format = K.image_data_format()
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self.directory = directory
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self.image_data_generator = image_data_generator
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self.target_size = tuple(target_size)
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self.output_function = output_function
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self.file_name_to_y = file_name_to_y
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if color_mode not in {'rgb', 'grayscale'}:
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raise ValueError('Invalid color mode:', color_mode,
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'; expected "rgb" or "grayscale".')
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self.color_mode = color_mode
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self.data_format = data_format
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if self.color_mode == 'rgb':
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if self.data_format == 'channels_last':
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self.image_shape = self.target_size + (3,)
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else:
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self.image_shape = (3,) + self.target_size
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else:
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if self.data_format == 'channels_last':
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self.image_shape = self.target_size + (1,)
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else:
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self.image_shape = (1,) + self.target_size
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self.classes = classes
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if class_mode not in {'categorical', 'binary', 'sparse', 'regression', None}:
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raise ValueError('Invalid class_mode:', class_mode,
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'; expected one of "categorical", '
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'"binary", "sparse", "regression", or None.')
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self.class_mode = class_mode
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self.save_to_dir = save_to_dir
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self.save_prefix = save_prefix
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self.save_format = save_format
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white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'}
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# first, count the number of samples and classes
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self.samples = 0
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if not classes:
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classes = []
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for subdir in sorted(os.listdir(directory)):
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if os.path.isdir(os.path.join(directory, subdir)):
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classes.append(subdir)
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self.num_class = len(classes)
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self.class_indices = dict(zip(classes, range(len(classes))))
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def _recursive_list(subpath):
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return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
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for subdir in classes:
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subpath = os.path.join(directory, subdir)
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for root, _, files in _recursive_list(subpath):
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for fname in files:
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is_valid = False
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for extension in white_list_formats:
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if fname.lower().endswith('.' + extension):
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is_valid = True
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break
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if is_valid:
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self.samples += 1
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print('Found %d images belonging to %d classes.' % (self.samples, self.num_class))
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