# coding=utf-8
"""Fairly basic set of tools for real-time data augmentation on image data.
Can easily be extended to include new transformations,
new preprocessing methods, etc...
"""
from __future__ import absolute_import
from __future__ import print_function
import re
from scipy import linalg
import scipy.ndimage as ndi
from six.moves import range
import os
from im_utils import *
import threading
import warnings
import multiprocessing.pool
from functools import partial
from keras import backend as K
from skimage import data, img_as_float
from skimage import exposure
from keras.utils.data_utils import Sequence
try:
from PIL import Image as pil_image
except ImportError:
pil_image = None
if pil_image is not None:
_PIL_INTERPOLATION_METHODS = {
'nearest': pil_image.NEAREST,
'bilinear': pil_image.BILINEAR,
'bicubic': pil_image.BICUBIC,
}
# These methods were only introduced in version 3.4.0 (2016).
if hasattr(pil_image, 'HAMMING'):
_PIL_INTERPOLATION_METHODS['hamming'] = pil_image.HAMMING
if hasattr(pil_image, 'BOX'):
_PIL_INTERPOLATION_METHODS['box'] = pil_image.BOX
# This method is new in version 1.1.3 (2013).
if hasattr(pil_image, 'LANCZOS'):
_PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS
CROP_MODE = ['center', 'random']
def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,
fill_mode='nearest', cval=0.):
"""Performs a random rotation of a Numpy image tensor.
# Arguments
x: Input tensor. Must be 3D.
rg: Rotation range, in degrees.
row_axis: Index of axis for rows in the input tensor.
col_axis: Index of axis for columns in the input tensor.
channel_axis: Index of axis for channels in the input tensor.
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
# Returns
Rotated Numpy image tensor.
"""
theta = np.pi / 180 * np.random.uniform(-rg, rg)
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
h, w = x.shape[row_axis], x.shape[col_axis]
transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)
return x
def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,
fill_mode='nearest', cval=0.):
"""Performs a random spatial shift of a Numpy image tensor.
# Arguments
x: Input tensor. Must be 3D.
wrg: Width shift range, as a float fraction of the width.
hrg: Height shift range, as a float fraction of the height.
row_axis: Index of axis for rows in the input tensor.
col_axis: Index of axis for columns in the input tensor.
channel_axis: Index of axis for channels in the input tensor.
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
# Returns
Shifted Numpy image tensor.
"""
h, w = x.shape[row_axis], x.shape[col_axis]
tx = np.random.uniform(-hrg, hrg) * h
ty = np.random.uniform(-wrg, wrg) * w
translation_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = translation_matrix # no need to do offset
x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)
return x
def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0,
fill_mode='nearest', cval=0.):
"""Performs a random spatial shear of a Numpy image tensor.
# Arguments
x: Input tensor. Must be 3D.
intensity: Transformation intensity.
row_axis: Index of axis for rows in the input tensor.
col_axis: Index of axis for columns in the input tensor.
channel_axis: Index of axis for channels in the input tensor.
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
# Returns
Sheared Numpy image tensor.
"""
shear = np.random.uniform(-intensity, intensity)
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
h, w = x.shape[row_axis], x.shape[col_axis]
transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)
return x
def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,
fill_mode='nearest', cval=0.):
"""Performs a random spatial zoom of a Numpy image tensor.
# Arguments
x: Input tensor. Must be 3D.
zoom_range: Tuple of floats; zoom range for width and height.
row_axis: Index of axis for rows in the input tensor.
col_axis: Index of axis for columns in the input tensor.
channel_axis: Index of axis for channels in the input tensor.
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
# Returns
Zoomed Numpy image tensor.
# Raises
ValueError: if `zoom_range` isn't a tuple.
"""
if len(zoom_range) != 2:
raise ValueError('`zoom_range` should be a tuple or list of two floats. '
'Received arg: ', zoom_range)
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
h, w = x.shape[row_axis], x.shape[col_axis]
transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)
return x
def random_channel_shift(x, intensity, channel_axis=0):
x = np.rollaxis(x, channel_axis, 0)
min_x, max_x = np.min(x), np.max(x)
channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
for x_channel in x]
x = np.stack(channel_images, axis=0)
x = np.rollaxis(x, 0, channel_axis + 1)
return x
def transform_matrix_offset_center(matrix, x, y):
o_x = float(x) / 2 + 0.5
o_y = float(y) / 2 + 0.5
offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])
reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])
transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)
return transform_matrix
def apply_transform(x,
transform_matrix,
channel_axis=0,
fill_mode='nearest',
cval=0.):
"""Apply the image transformation specified by a matrix.
# Arguments
x: 2D numpy array, single image.
transform_matrix: Numpy array specifying the geometric transformation.
channel_axis: Index of axis for channels in the input tensor.
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
# Returns
The transformed version of the input.
"""
x = np.rollaxis(x, channel_axis, 0)
final_affine_matrix = transform_matrix[:2, :2]
final_offset = transform_matrix[:2, 2]
channel_images = [ndi.interpolation.affine_transform(
x_channel,
final_affine_matrix,
final_offset,
order=0,
mode=fill_mode,
cval=cval) for x_channel in x]
x = np.stack(channel_images, axis=0)
x = np.rollaxis(x, 0, channel_axis + 1)
return x
def flip_axis(x, axis):
x = np.asarray(x).swapaxes(axis, 0)
x = x[::-1, ...]
x = x.swapaxes(0, axis)
return x
def array_to_img(x, data_format=None, scale=True):
"""Converts a 3D Numpy array to a PIL Image instance.
# Arguments
x: Input Numpy array.
data_format: Image data format.
scale: Whether to rescale image values
to be within [0, 255].
# Returns
A PIL Image instance.
# Raises
ImportError: if PIL is not available.
ValueError: if invalid `x` or `data_format` is passed.
"""
if pil_image is None:
raise ImportError('Could not import PIL.Image. '
'The use of `array_to_img` requires PIL.')
x = np.asarray(x, dtype=K.floatx())
if x.ndim != 3:
raise ValueError('Expected image array to have rank 3 (single image). '
'Got array with shape:', x.shape)
if data_format is None:
data_format = K.image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Invalid data_format:', data_format)
# Original Numpy array x has format (height, width, channel)
# or (channel, height, width)
# but target PIL image has format (width, height, channel)
if data_format == 'channels_first':
x = x.transpose(1, 2, 0)
if scale:
x = x + max(-np.min(x), 0)
x_max = np.max(x)
if x_max != 0:
x /= x_max
x *= 255
if x.shape[2] == 3:
# RGB
return pil_image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale
return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
else:
raise ValueError('Unsupported channel number: ', x.shape[2])
def img_to_array(img, data_format=None):
"""Converts a PIL Image instance to a Numpy array.
# Arguments
img: PIL Image instance.
data_format: Image data format.
# Returns
A 3D Numpy array.
# Raises
ValueError: if invalid `img` or `data_format` is passed.
"""
if data_format is None:
data_format = K.image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ', data_format)
# Numpy array x has format (height, width, channel)
# or (channel, height, width)
# but original PIL image has format (width, height, channel)
x = np.asarray(img, dtype=K.floatx())
if len(x.shape) == 3:
if data_format == 'channels_first':
x = x.transpose(2, 0, 1)
elif len(x.shape) == 2:
if data_format == 'channels_first':
x = x.reshape((1, x.shape[0], x.shape[1]))
else:
x = x.reshape((x.shape[0], x.shape[1], 1))
else:
raise ValueError('Unsupported image shape: ', x.shape)
return x
def load_img(path, grayscale=False, target_size=None,
interpolation='bilinear', crop_mode=None):
"""Loads an image into PIL format.
# Arguments
path: Path to image file
grayscale: Boolean, whether to load the image as grayscale.
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
target size is different from that of the loaded image.
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, "bilinear" is used.
# Returns
A PIL Image instance.
# Raises
ImportError: if PIL is not available.
ValueError: if interpolation method is not supported.
"""
if pil_image is None:
raise ImportError('Could not import PIL.Image. '
'The use of `array_to_img` requires PIL.')
img = pil_image.open(path)
if grayscale:
if img.mode != 'L':
img = img.convert('L')
else:
if img.mode != 'RGB':
img = img.convert('RGB')
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())))
# 此处改写Keras自带的resize逻辑，支持等比裁剪
# img = img.resize(width_height_tuple, resample)
if crop_mode is None:
img = img.resize(width_height_tuple)
else:
if width_height_tuple[0] != width_height_tuple[1]:
raise Exception('The width_height_tuple size must be equal.')
if crop_mode not in CROP_MODE:
raise Exception('The crop mode "%s" not support.' % crop_mode)
if crop_mode == 'random':
img = random_crop(img, width_height_tuple[0])
elif crop_mode == 'center':
img = center_crop(img, width_height_tuple[0])
return img
def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'):
return [os.path.join(root, f)
for root, _, files in os.walk(directory) for f in files
if re.match(r'([\w]+\.(?:' + ext + '))', f)]
class ImageDataGenerator(object):
"""Generate minibatches of image data with real-time data augmentation.
# Arguments
featurewise_center: set input mean to 0 over the dataset.
samplewise_center: set each sample mean to 0.
featurewise_std_normalization: divide inputs by std of the dataset.
samplewise_std_normalization: divide each input by its std.
zca_whitening: apply ZCA whitening.
zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
rotation_range: degrees (0 to 180).
width_shift_range: fraction of total width.
height_shift_range: fraction of total height.
shear_range: shear intensity (shear angle in radians).
zoom_range: amount of zoom. if scalar z, zoom will be randomly picked
in the range [1-z, 1+z]. A sequence of two can be passed instead
to select this range.
channel_shift_range: shift range for each channel.
fill_mode: points outside the boundaries are filled according to the
given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default
is 'nearest'.
cval: value used for points outside the boundaries when fill_mode is
'constant'. Default is 0.
horizontal_flip: whether to randomly flip images horizontally.
vertical_flip: whether to randomly flip images vertically.
rescale: rescaling factor. If None or 0, no rescaling is applied,
otherwise we multiply the data by the value provided. This is
applied after the `preprocessing_function` (if any provided)
but before any other transformation.
preprocessing_function: function that will be implied on each input.
The function will run before any other modification on it.
The function should take one argument:
one image (Numpy tensor with rank 3),
and should output a Numpy tensor with the same shape.
data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension
(the depth) is at index 1, in 'channels_last' mode it is at index 3.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
"""
def __init__(self,
contrast_stretching=False, #####
histogram_equalization=False, #####
adaptive_equalization=False, #####
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-6,
rotation_range=0.,
width_shift_range=0.,
height_shift_range=0.,
shear_range=0.,
zoom_range=0.,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None):
if data_format is None:
data_format = K.image_data_format()
self.contrast_stretching = contrast_stretching #####
self.adaptive_equalization = adaptive_equalization #####
self.histogram_equalization = histogram_equalization #####
self.featurewise_center = featurewise_center
self.samplewise_center = samplewise_center
self.featurewise_std_normalization = featurewise_std_normalization
self.samplewise_std_normalization = samplewise_std_normalization
self.zca_whitening = zca_whitening
self.zca_epsilon = zca_epsilon
self.rotation_range = rotation_range
self.width_shift_range = width_shift_range
self.height_shift_range = height_shift_range
self.shear_range = shear_range
self.zoom_range = zoom_range
self.channel_shift_range = channel_shift_range
self.fill_mode = fill_mode
self.cval = cval
self.horizontal_flip = horizontal_flip
self.vertical_flip = vertical_flip
self.rescale = rescale
self.preprocessing_function = preprocessing_function
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('`data_format` should be `"channels_last"` (channel after row and '
'column) or `"channels_first"` (channel before row and column). '
'Received arg: ', data_format)
self.data_format = data_format
if data_format == 'channels_first':
self.channel_axis = 1
self.row_axis = 2
self.col_axis = 3
if data_format == 'channels_last':
self.channel_axis = 3
self.row_axis = 1
self.col_axis = 2
self.mean = None
self.std = None
self.principal_components = None
if np.isscalar(zoom_range):
self.zoom_range = [1 - zoom_range, 1 + zoom_range]
elif len(zoom_range) == 2:
self.zoom_range = [zoom_range[0], zoom_range[1]]
else:
raise ValueError('`zoom_range` should be a float or '
'a tuple or list of two floats. '
'Received arg: ', zoom_range)
def flow(self, x, y=None, batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='png'):
return NumpyArrayIterator(
x, y, self,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
data_format=self.data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format)
def flow_from_directory(self, directory,
target_size=(256, 256), color_mode='rgb',
classes=None, class_mode='categorical',
batch_size=32, shuffle=True, seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
follow_links=False,
crop_mode=None,
batch_handler=None):
return DirectoryIterator(
directory, self,
target_size=target_size, color_mode=color_mode,
classes=classes, class_mode=class_mode,
data_format=self.data_format,
batch_size=batch_size, shuffle=shuffle, seed=seed,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
follow_links=follow_links,
crop_mode=crop_mode,
batch_handler=batch_handler
)
def standardize(self, x):
"""Apply the normalization configuration to a batch of inputs.
# Arguments
x: batch of inputs to be normalized.
# Returns
The inputs, normalized.
"""
if self.preprocessing_function:
x = self.preprocessing_function(x)
if self.rescale:
x *= self.rescale
if self.samplewise_center:
x -= np.mean(x, keepdims=True)
if self.samplewise_std_normalization:
x /= np.std(x, keepdims=True) + 1e-7
if self.featurewise_center:
if self.mean is not None:
x -= self.mean
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_center`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.featurewise_std_normalization:
if self.std is not None:
x /= (self.std + 1e-7)
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_std_normalization`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.zca_whitening:
if self.principal_components is not None:
flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
whitex = np.dot(flatx, self.principal_components)
x = np.reshape(whitex, x.shape)
else:
warnings.warn('This ImageDataGenerator specifies '
'`zca_whitening`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
return x
def random_transform(self, x, seed=None):
"""Randomly augment a single image tensor.
# Arguments
x: 3D tensor, single image.
seed: random seed.
# Returns
A randomly transformed version of the input (same shape).
"""
# x is a single image, so it doesn't have image number at index 0
img_row_axis = self.row_axis - 1
img_col_axis = self.col_axis - 1
img_channel_axis = self.channel_axis - 1
if seed is not None:
np.random.seed(seed)
# use composition of homographies
# to generate final transform that needs to be applied
if self.rotation_range:
theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range)
else:
theta = 0
if self.height_shift_range:
tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_axis]
else:
tx = 0
if self.width_shift_range:
ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_axis]
else:
ty = 0
if self.shear_range:
shear = np.random.uniform(-self.shear_range, self.shear_range)
else:
shear = 0
if self.zoom_range[0] == 1 and self.zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2)
transform_matrix = None
if theta != 0:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
transform_matrix = rotation_matrix
if tx != 0 or ty != 0:
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)
if shear != 0:
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix)
if zx != 1 or zy != 1:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)
if transform_matrix is not None:
h, w = x.shape[img_row_axis], x.shape[img_col_axis]
transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
x = apply_transform(x, transform_matrix, img_channel_axis,
fill_mode=self.fill_mode, cval=self.cval)
if self.channel_shift_range != 0:
x = random_channel_shift(x,
self.channel_shift_range,
img_channel_axis)
if self.horizontal_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_col_axis)
if self.vertical_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_row_axis)
if self.contrast_stretching: #####
if np.random.random() < 0.5: #####
p2, p98 = np.percentile(x, (2, 98)) #####
x = exposure.rescale_intensity(x.astype(np.uint8), in_range=(p2, p98)) #####
if self.adaptive_equalization: #####
if np.random.random() < 0.5: #####
x = exposure.equalize_adapthist(x.astype(np.uint8), clip_limit=0.03) #####
x = exposure.equalize_adapthist(x, clip_limit=0.03) #####
x *= 255
if self.histogram_equalization: #####
if np.random.random() < 0.5: #####
x = exposure.equalize_hist(x).astype(np.uint8) #####
x *= 255
if x.dtype == np.uint8:
x = x.astype(np.float32)
return x
def fit(self, x,
augment=False,
rounds=1,
seed=None):
"""Fits internal statistics to some sample data.
Required for featurewise_center, featurewise_std_normalization
and zca_whitening.
# Arguments
x: Numpy array, the data to fit on. Should have rank 4.
In case of grayscale data,
the channels axis should have value 1, and in case
of RGB data, it should have value 3.
augment: Whether to fit on randomly augmented samples
rounds: If `augment`,
how many augmentation passes to do over the data
seed: random seed.
# Raises
ValueError: in case of invalid input `x`.
"""
x = np.asarray(x, dtype=K.floatx())
if x.ndim != 4:
raise ValueError('Input to `.fit()` should have rank 4. '
'Got array with shape: ' + str(x.shape))
if x.shape[self.channel_axis] not in {1, 3, 4}:
warnings.warn(
'Expected input to be images (as Numpy array) '
'following the data format convention "' + self.data_format + '" '
'(channels on axis ' + str(
self.channel_axis) + '), i.e. expected '
'either 1, 3 or 4 channels on axis ' + str(self.channel_axis) + '. '
'However, it was passed an array with shape ' + str(
x.shape) +
' (' + str(x.shape[self.channel_axis]) + ' channels).')
if seed is not None:
np.random.seed(seed)
x = np.copy(x)
if augment:
ax = np.zeros(tuple([rounds * x.shape[0]] + list(x.shape)[1:]), dtype=K.floatx())
for r in range(rounds):
for i in range(x.shape[0]):
ax[i + r * x.shape[0]] = self.random_transform(x[i])
x = ax
if self.featurewise_center:
self.mean = np.mean(x, axis=(0, self.row_axis, self.col_axis))
broadcast_shape = [1, 1, 1]
broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]
self.mean = np.reshape(self.mean, broadcast_shape)
x -= self.mean
if self.featurewise_std_normalization:
self.std = np.std(x, axis=(0, self.row_axis, self.col_axis))
broadcast_shape = [1, 1, 1]
broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]
self.std = np.reshape(self.std, broadcast_shape)
x /= (self.std + K.epsilon())
if self.zca_whitening:
flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]
u, s, _ = linalg.svd(sigma)
self.principal_components = np.dot(np.dot(u, np.diag(1. / np.sqrt(s + self.zca_epsilon))), u.T)
class Iterator(Sequence):
"""Base class for image data iterators.
Every `Iterator` must implement the `_get_batches_of_transformed_samples`
method.
# Arguments
n: Integer, total number of samples in the dataset to loop over.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seeding for data shuffling.
"""
def __init__(self, n, batch_size, shuffle, seed):
self.n = n
self.batch_size = batch_size
self.seed = seed
self.shuffle = shuffle
self.batch_index = 0
self.total_batches_seen = 0
self.lock = threading.Lock()
self.index_array = None
self.index_generator = self._flow_index()
def _set_index_array(self):
self.index_array = np.arange(self.n)
if self.shuffle:
self.index_array = np.random.permutation(self.n)
def __getitem__(self, idx):
if idx >= len(self):
raise ValueError('Asked to retrieve element {idx}, '
'but the Sequence '
'has length {length}'.format(idx=idx,
length=len(self)))
if self.seed is not None:
np.random.seed(self.seed + self.total_batches_seen)
self.total_batches_seen += 1
if self.index_array is None:
self._set_index_array()
index_array = self.index_array[self.batch_size * idx:
self.batch_size * (idx + 1)]
return self._get_batches_of_transformed_samples(index_array)
def __len__(self):
return int(np.ceil(self.n / float(self.batch_size)))
def on_epoch_end(self):
self._set_index_array()
def reset(self):
self.batch_index = 0
def _flow_index(self):
# Ensure self.batch_index is 0.
self.reset()
while 1:
if self.seed is not None:
np.random.seed(self.seed + self.total_batches_seen)
if self.batch_index == 0:
self._set_index_array()
current_index = (self.batch_index * self.batch_size) % self.n
if self.n > current_index + self.batch_size:
self.batch_index += 1
else:
self.batch_index = 0
self.total_batches_seen += 1
yield self.index_array[current_index:
current_index + self.batch_size]
def __iter__(self):
# Needed if we want to do something like:
# for x, y in data_gen.flow(...):
return self
def __next__(self, *args, **kwargs):
return self.next(*args, **kwargs)
def _get_batches_of_transformed_samples(self, index_array):
"""Gets a batch of transformed samples.
# Arguments
index_array: array of sample indices to include in batch.
# Returns
A batch of transformed samples.
"""
raise NotImplementedError
class NumpyArrayIterator(Iterator):
"""Iterator yielding data from a Numpy array.
# Arguments
x: Numpy array of input data.
y: Numpy array of targets data.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
"""
def __init__(self, x, y, image_data_generator,
batch_size=32, shuffle=False, seed=None,
data_format=None,
save_to_dir=None, save_prefix='', save_format='png'):
if y is not None and len(x) != len(y):
raise ValueError('X (images tensor) and y (labels) '
'should have the same length. '
'Found: X.shape = %s, y.shape = %s' %
(np.asarray(x).shape, np.asarray(y).shape))
if data_format is None:
data_format = K.image_data_format()
self.x = np.asarray(x, dtype=K.floatx())
if self.x.ndim != 4:
raise ValueError('Input data in `NumpyArrayIterator` '
'should have rank 4. You passed an array '
'with shape', self.x.shape)
channels_axis = 3 if data_format == 'channels_last' else 1
if self.x.shape[channels_axis] not in {1, 3, 4}:
warnings.warn('NumpyArrayIterator is set to use the '
'data format convention "' + data_format + '" '
'(channels on axis ' + str(
channels_axis) + '), i.e. expected '
'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. '
'However, it was passed an array with shape ' + str(
self.x.shape) +
' (' + str(self.x.shape[channels_axis]) + ' channels).')
if y is not None:
self.y = np.asarray(y)
else:
self.y = None
self.image_data_generator = image_data_generator
self.data_format = data_format
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
super(NumpyArrayIterator, self).__init__(x.shape[0], batch_size, shuffle, seed)
def _get_batches_of_transformed_samples(self, index_array):
batch_x = np.zeros(tuple([len(index_array)] + list(self.x.shape)[1:]),
dtype=K.floatx())
for i, j in enumerate(index_array):
x = self.x[j]
x = self.image_data_generator.random_transform(x.astype(K.floatx()))
x = self.image_data_generator.standardize(x)
batch_x[i] = x
if self.save_to_dir:
for i, j in enumerate(index_array):
img = array_to_img(batch_x[i], self.data_format, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
index=j,
hash=np.random.randint(1e4),
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
if self.y is None:
return batch_x
batch_y = self.y[index_array]
return batch_x, batch_y
def next(self):
"""For python 2.x.
# Returns
The next batch.
"""
# Keeps under lock only the mechanism which advances
# the indexing of each batch.
with self.lock:
index_array = next(self.index_generator)
# The transformation of images is not under thread lock
# so it can be done in parallel
return self._get_batches_of_transformed_samples(index_array)
def _count_valid_files_in_directory(directory, white_list_formats, follow_links):
"""Count files with extension in `white_list_formats` contained in a directory.
# Arguments
directory: absolute path to the directory containing files to be counted
white_list_formats: set of strings containing allowed extensions for
the files to be counted.
# Returns
the count of files with extension in `white_list_formats` contained in
the directory.
"""
def _recursive_list(subpath):
return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
samples = 0
for root, _, files in _recursive_list(directory):
for fname in files:
is_valid = False
for extension in white_list_formats:
if fname.lower().endswith('.' + extension):
is_valid = True
break
if is_valid:
samples += 1
return samples
def _list_valid_filenames_in_directory(directory, white_list_formats,
class_indices, follow_links):
"""List paths of files in `subdir` relative from `directory` whose extensions are in `white_list_formats`.
# Arguments
directory: absolute path to a directory containing the files to list.
The directory name is used as class label and must be a key of `class_indices`.
white_list_formats: set of strings containing allowed extensions for
the files to be counted.
class_indices: dictionary mapping a class name to its index.
# Returns
classes: a list of class indices
filenames: the path of valid files in `directory`, relative from
`directory`'s parent (e.g., if `directory` is "dataset/class1",
the filenames will be ["class1/file1.jpg", "class1/file2.jpg", ...]).
"""
def _recursive_list(subpath):
return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
classes = []
filenames = []
subdir = os.path.basename(directory)
basedir = os.path.dirname(directory)
for root, _, files in _recursive_list(directory):
for fname in sorted(files):
is_valid = False
for extension in white_list_formats:
if fname.lower().endswith('.' + extension):
is_valid = True
break
if is_valid:
classes.append(class_indices[subdir])
# add filename relative to directory
absolute_path = os.path.join(root, fname)
filenames.append(os.path.relpath(absolute_path, basedir))
return classes, filenames
class DirectoryIterator(Iterator):
"""Iterator capable of reading images from a directory on disk.
# Arguments
directory: Path to the directory to read images from.
Each subdirectory in this directory will be
considered to contain images from one class,
or alternatively you could specify class subdirectories
via the `classes` argument.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images.
classes: Optional list of strings, names of subdirectories
containing images from each class (e.g. `["dogs", "cats"]`).
It will be computed automatically if not set.
class_mode: Mode for yielding the targets:
`"binary"`: binary targets (if there are only two classes),
`"categorical"`: categorical targets,
`"sparse"`: integer targets,
`"input"`: targets are images identical to input images (mainly
used to work with autoencoders),
`None`: no targets get yielded (only input images are yielded).
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
"""
def __init__(self, directory, image_data_generator,
target_size=(256, 256), color_mode='rgb',
classes=None, class_mode='categorical',
batch_size=32, shuffle=True, seed=None,
data_format=None,
save_to_dir=None, save_prefix='', save_format='png',
follow_links=False, crop_mode=None, batch_handler=None):
if data_format is None:
data_format = K.image_data_format()
print('The crop mode is %s.' % crop_mode)
self.crop_mode = crop_mode
self.batch_handler = batch_handler
self.directory = directory
self.image_data_generator = image_data_generator
self.target_size = tuple(target_size)
if color_mode not in {'rgb', 'grayscale'}:
raise ValueError('Invalid color mode:', color_mode,
'; expected "rgb" or "grayscale".')
self.color_mode = color_mode
self.data_format = data_format
if self.color_mode == 'rgb':
if self.data_format == 'channels_last':
self.image_shape = self.target_size + (3,)
else:
self.image_shape = (3,) + self.target_size
else:
if self.data_format == 'channels_last':
self.image_shape = self.target_size + (1,)
else:
self.image_shape = (1,) + self.target_size
self.classes = classes
if class_mode not in {'categorical', 'binary', 'sparse',
'input', None}:
raise ValueError('Invalid class_mode:', class_mode,
'; expected one of "categorical", '
'"binary", "sparse", "input"'
' or None.')
self.class_mode = class_mode
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm'}
# first, count the number of samples and classes
self.samples = 0
if not classes:
classes = []
for subdir in sorted(os.listdir(directory)):
if os.path.isdir(os.path.join(directory, subdir)):
classes.append(subdir)
self.num_classes = len(classes)
self.class_indices = dict(zip(classes, range(len(classes))))
pool = multiprocessing.pool.ThreadPool()
function_partial = partial(_count_valid_files_in_directory,
white_list_formats=white_list_formats,
follow_links=follow_links)
self.samples = sum(pool.map(function_partial,
(os.path.join(directory, subdir)
for subdir in classes)))
print('Found %d images belonging to %d classes.' % (self.samples, self.num_classes))
# second, build an index of the images in the different class subfolders
results = []
self.filenames = []
self.classes = np.zeros((self.samples,), dtype='int32')
i = 0
for dirpath in (os.path.join(directory, subdir) for subdir in classes):
results.append(pool.apply_async(_list_valid_filenames_in_directory,
(dirpath, white_list_formats,
self.class_indices, follow_links)))
for res in results:
classes, filenames = res.get()
self.classes[i:i + len(classes)] = classes
self.filenames += filenames
i += len(classes)
pool.close()
pool.join()
super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed)
def _get_batches_of_transformed_samples(self, index_array):
batch_x = np.zeros((len(index_array),) + self.image_shape, dtype=K.floatx())
grayscale = self.color_mode == 'grayscale'
# build batch of image data
for i, j in enumerate(index_array):
fname = self.filenames[j]
img = load_img(os.path.join(self.directory, fname),
grayscale=grayscale,
target_size=self.target_size,
crop_mode=self.crop_mode)
x = img_to_array(img, data_format=self.data_format)
if self.image_data_generator:
x = self.image_data_generator.random_transform(x)
x = self.image_data_generator.standardize(x)
batch_x[i] = x
if self.batch_handler:
batch_x = self.batch_handler(batch_x)
# optionally save augmented images to disk for debugging purposes
if self.save_to_dir:
for i, j in enumerate(index_array):
img = array_to_img(batch_x[i], self.data_format, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
index=j,
hash=np.random.randint(1e7),
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
# build batch of labels
if self.class_mode == 'input':
batch_y = batch_x.copy()
elif self.class_mode == 'sparse':
batch_y = self.classes[index_array]
elif self.class_mode == 'binary':
batch_y = self.classes[index_array].astype(K.floatx())
elif self.class_mode == 'categorical':
batch_y = np.zeros((len(batch_x), self.num_classes), dtype=K.floatx())
for i, label in enumerate(self.classes[index_array]):
batch_y[i, label] = 1.
else:
return batch_x
return batch_x, batch_y
def next(self):
"""For python 2.x.
# Returns
The next batch.
"""
with self.lock:
index_array = next(self.index_generator)
# The transformation of images is not under thread lock
# so it can be done in parallel
return self._get_batches_of_transformed_samples(index_array)