Created
February 7, 2019 12:43
-
-
Save emadeldeen24/736c33ac2af0c00cc48810ad62e1f54a to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
""" | |
From KERAS package | |
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 functools import partial | |
import multiprocessing.pool | |
import numpy as np | |
import os | |
import re | |
from scipy import linalg | |
import scipy.ndimage as ndi | |
from six.moves import range | |
import threading | |
import warnings | |
try: | |
from keras import backend as K | |
except: | |
pass | |
try: | |
from PIL import Image as pil_image | |
except ImportError: | |
pil_image = None | |
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=3, | |
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 random_mutiplication(x, range_mult): | |
x = x * np.random.uniform(range_mult[0], range_mult[1]) | |
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): | |
"""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)`. | |
# Returns | |
A PIL Image instance. | |
# Raises | |
ImportError: if PIL is not available. | |
""" | |
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: | |
hw_tuple = (target_size[1], target_size[0]) | |
if img.size != hw_tuple: | |
img = img.resize(hw_tuple) | |
return img | |
def list_pictures(directory, ext='jpg|jpeg|bmp|png'): | |
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 channels. | |
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, | |
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, | |
expand_dims = True, | |
data_format=None, | |
random_mult_range=0): | |
if data_format is None: | |
data_format = K.image_data_format() | |
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 | |
self.random_mult_range = random_mult_range | |
self.expand_dims = expand_dims | |
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', | |
frames_per_step=4, | |
batch_size=32, shuffle=True, seed=None, | |
save_to_dir=None, | |
save_prefix='', | |
save_format='png', | |
follow_links=False): | |
return DirectoryIterator( | |
directory, self, | |
target_size=target_size, color_mode=color_mode, | |
frames_per_step=frames_per_step, | |
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) | |
# my addition | |
def change_dims(self, x): | |
"""expands dimenstions of a batch of images""" | |
img_channel_axis = self.channel_axis #- 1 | |
x = np.expand_dims(x, axis=0) | |
return x | |
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 | |
# x is a single image, so it doesn't have image number at index 0 | |
img_channel_axis = self.channel_axis - 1 | |
if self.samplewise_center: | |
x -= np.mean(x, axis=img_channel_axis, keepdims=True) | |
if self.samplewise_std_normalization: | |
x /= (np.std(x, axis=img_channel_axis, 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 = np.random.uniform( | |
self.zoom_range[0], self.zoom_range[1], 1)[0] | |
zy = zx.copy() | |
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.random_mult_range != 0: | |
if np.random.random() < 0.5: | |
x = random_mutiplication(x, self.random_mult_range) | |
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(object): | |
"""Abstract base class for image data iterators. | |
# 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, frames_per_step, shuffle, seed): | |
self.n = n | |
self.batch_size = batch_size | |
self.frames_per_step = frames_per_step | |
self.shuffle = shuffle | |
self.batch_index = 0 | |
self.total_batches_seen = 0 | |
self.lock = threading.Lock() | |
self.index_generator = self._flow_index(n, batch_size, frames_per_step, shuffle, seed) | |
def reset(self): | |
self.batch_index = 0 | |
def _flow_index(self, n, batch_size=32, frames_per_step=4, shuffle=False, seed=None): | |
# Ensure self.batch_index is 0. | |
self.reset() | |
while True: | |
if seed is not None: | |
np.random.seed(seed + self.total_batches_seen) | |
if self.batch_index == 0: | |
index_array = np.arange(n) | |
if shuffle: | |
index_array = np.random.permutation(n) | |
current_index = (self.batch_index * batch_size * frames_per_step) % n | |
if n > current_index + batch_size*frames_per_step: | |
current_batch_size = batch_size*frames_per_step | |
self.batch_index += 1 | |
else: | |
current_batch_size = n - current_index | |
self.batch_index = 0 | |
self.total_batches_seen += 1 | |
yield (index_array[current_index: current_index + current_batch_size], | |
current_index, current_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) | |
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 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, current_index, current_batch_size = next( | |
self.index_generator) | |
# The transformation of images is not under thread lock | |
# so it can be done in parallel | |
batch_x = np.zeros( | |
tuple([current_batch_size]+(1,) + list(self.x.shape)[1:]), dtype=K.floatx()) #Added +(1,) + | |
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) | |
x = self.image_data_generator.change_dims(x) # my addition | |
batch_x[i] = x | |
if self.save_to_dir: | |
for i in range(current_batch_size): | |
img = array_to_img(batch_x[i], self.data_format, scale=True) | |
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, | |
index=current_index + i, | |
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 _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 _, _, 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 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 sudirectories | |
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', | |
frames_per_step=4, | |
batch_size=32, shuffle=True, seed=None, | |
data_format=None, | |
save_to_dir=None, save_prefix='', save_format='png', | |
follow_links=False): | |
if data_format is None: | |
data_format = K.image_data_format() | |
self.directory = directory | |
self.frames_per_step = frames_per_step | |
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_class = 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_class)) | |
# 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, frames_per_step, shuffle, seed) | |
def next(self): | |
"""For python 2.x. | |
# Returns | |
The next batch. | |
""" | |
with self.lock: | |
index_array, current_index, current_batch_size = next( | |
self.index_generator) | |
# The transformation of images is not under thread lock | |
# so it can be done in parallel | |
batch_x = np.zeros((current_batch_size,) +(self.frames_per_step,)+ | |
self.image_shape, dtype=K.floatx()) # # my addition of +(1,) | |
grayscale = self.color_mode == 'grayscale' | |
# build batch of image data | |
for kk in range(self.frames_per_step): | |
for i in range(int(len(index_array)/self.frames_per_step)): | |
fname = self.filenames[index_array[i]] | |
img = load_img(os.path.join(self.directory, fname), | |
grayscale=grayscale, | |
target_size=self.target_size) | |
x = img_to_array(img, data_format=self.data_format) | |
x = self.image_data_generator.random_transform(x) | |
x = self.image_data_generator.standardize(x) | |
x = self.image_data_generator.change_dims(x) # my addition | |
batch_x[i,kk] = x | |
# optionally save augmented images to disk for debugging purposes | |
if self.save_to_dir: | |
for i in range(current_batch_size): | |
img = array_to_img(batch_x[i], self.data_format, scale=True) | |
fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix, | |
index=current_index + i, | |
hash=np.random.randint( | |
1e4), | |
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_class), 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 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
I'm sorry, but I stopped working on this for a long time now.
you can follow my updates. I commented with (# my addition) besides the lines I added .. you can follow the same additions.
Best.