keras\preprocessing目录文件详解5.1（image.py）-keras学习笔记五
用于图像数据的实时数据增强的（相当）基本的工具集。
功能：生成图片样本（旋转、平移、缩放等），增加训练的数据样本
keras-master\keras\preprocessing\image.py
建立词向量嵌入层，把输入文本转为可以进一步处理的数据格式（例如，矩阵）
Keras开发包文件目录
Keras实例文件目录
代码注释
"""Fairly basic set of tools for real-time data augmentation on image data.
用于图像数据的实时数据增强的（相当）基本的工具集。
Can easily be extended to include new transformations,
new preprocessing methods, etc...
可以很容易地扩展到(包括)新的变换，新的预处理方法等。
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import re
from scipy import linalg
import scipy.ndimage as ndi
from six.moves import range
import os
import threading
import warnings
import multiprocessing.pool
from functools import partial
from .. import backend as K
from ..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).
# 这些方法只在3.4.0（2016）版本中引入。
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).
# 这个方法只在1.1.2（2013）版本中引入。
if hasattr(pil_image, 'LANCZOS'):
_PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS
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.
对数字图像张量进行随机旋转。
# Arguments
参数
x: Input tensor. Must be 3D.
x: 输入张量。必须是3维
rg: Rotation range, in degrees.
rg: 旋转范围，单位角度。
row_axis: Index of axis for rows in the input tensor.
row_axis: 输入张量中的行的轴索引。
col_axis: Index of axis for columns in the input tensor.
col_axis: 输入张量中列的轴索引。
channel_axis: Index of axis for channels in the input tensor.
channel_axis: 输入张量中的通道的轴的索引。
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
fill_mode: 输入的边界之外的点根据给定的模式（{常数”、“最近”、“反射”、“包装”}中的一个填充。
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
cval: 如果mode='constant'，则用于输入边界以外的点的值。
# Returns
返回
Rotated Numpy image tensor.
旋转的数字图像张量。
"""
theta = np.deg2rad(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.
执行数字图像张量的随机空间移位。
# Arguments
参数
x: Input tensor. Must be 3D.
x: 输入张量。必须是3维
wrg: Width shift range, as a float fraction of the width.
wrg: 宽度偏移范围，作为宽度的浮动部分。
hrg: Height shift range, as a float fraction of the height.
hrg: 高度移动范围，作为高度的浮动部分。
row_axis: Index of axis for rows in the input tensor.
row_axis: 输入张量中的行的轴索引。
col_axis: Index of axis for columns in the input tensor.
col_axis: 输入张量中的列的轴索引。
channel_axis: Index of axis for channels in the input tensor.
channel_axis: 输入张量中的通道的轴的索引。
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
fill_mode: 输入的边界之外的点根据给定的模式（{常数”、“最近”、“反射”、“包装”}中的一个填充。
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
cval: 如果mode='constant'，则用于输入边界以外的点的值。
# Returns
返回
Shifted Numpy image tensor.
移动的数字图像张量。
"""
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 # 无需做偏移
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.
执行数字图像张量的随机空间剪切。
# Arguments
参数
x: Input tensor. Must be 3D.
x: 输入张量。必须是3维
intensity: Transformation intensity in degrees.
intensity: 角度转换强度。
row_axis: Index of axis for rows in the input tensor.
row_axis: 输入张量中的行的轴索引。
col_axis: Index of axis for columns in the input tensor.
col_axis: 输入张量中的列的轴索引。
channel_axis: Index of axis for channels in the input tensor.
channel_axis: 输入张量中的通道的轴索引。
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
fill_mode: 输入的边界之外的点根据给定的模式（{常数”、“最近”、“反射”、“包装”}中的一个填充。
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
cval: 如果mode='constant'，则用于输入边界以外的点的值。
# Returns
返回
Sheared Numpy image tensor.
剪切的数字图像张量。
"""
shear = np.deg2rad(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.
执行数字图像张量的随机空间缩放（缩小放大）。
# Arguments
参数
x: Input tensor. Must be 3D.
x: 输入张量。必须是3维
zoom_range: Tuple of floats; zoom range for width and height.
zoom_range: 浮动的元组；缩放范围的宽度和高度。
row_axis: Index of axis for rows in the input tensor.
row_axis: 输入张量中的行的轴索引。
col_axis: Index of axis for columns in the input tensor.
col_axis: 输入张量中列的轴索引。
channel_axis: Index of axis for channels in the input tensor.
channel_axis: 输入张量中通道的轴索引。
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
fill_mode: 输入的边界之外的点根据给定的模式（{常数”、“最近”、“反射”、“包装”}中的一个填充。
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
cval: 如果mode='constant'，则用于输入边界以外的点的值。
# Returns
返回
Zoomed Numpy image tensor.
缩放的数字图像张量。
# Raises
补充
ValueError: if `zoom_range` isn't a tuple.
ValueError: 如果zoom_range不是元组
"""
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.
应用矩阵指定的图像变换。
# Arguments
参数
x: 2D numpy array, single image.
x: 二维数字阵列，单个图像。
transform_matrix: Numpy array specifying the geometric transformation.
transform_matrix: 指定几何变换的Numpy数组。
channel_axis: Index of axis for channels in the input tensor.
channel_axis: 输入张量中的通道的轴的索引。
fill_mode: Points outside the boundaries of the input
are filled according to the given mode
(one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
fill_mode: 输入的边界之外的点根据给定的模式（{常数”、“最近”、“反射”、“包装”}中的一个填充。
cval: Value used for points outside the boundaries
of the input if `mode='constant'`.
cval: 如果mode='constant'，则用于输入边界以外的点的值。
# Returns
返回
The transformed version of the input.
输入的转换版本。
"""
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=1,
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.
将3维 Numpy数组转换为PIL（Python Imaging Library ）图像实例。
# Arguments
参数
x: Input Numpy array.
x: 输入数字数组。
data_format: Image data format.
data_format: 图像数据格式。
scale: Whether to rescale image values
to be within [0, 255].
scale: 是否将图像值重新缩放为[0, 255]以内。
# Returns
返回
A PIL Image instance.
PIL图像实例
# Raises
补充
ImportError: if PIL is not available.
ImportError: 如果PIL不可行
ValueError: if invalid `x` or `data_format` is passed.
ValueError: 如果无效的`x` 或 `data_format` 被传递。
"""
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)
# 原始的Numpy数组x具有格式（高度、宽度、通道）
# or (channel, height, width)
# 或者(通道, 高度、宽度)
# but target PIL image has format (width, height, channel)
# 但是目标PIL图像具有格式（宽度、高度、通道）
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 # 红绿蓝
return pil_image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale # 灰度级
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.
将一个PIL图像实例转换为一个Numpy数组。
# Arguments
参数
img: PIL Image instance.
img: PIL 图像实例。
data_format: Image data format.
data_format: 图像格式
# Returns
返回
A 3D Numpy array.
3维Numpy数组
# Raises
补充
ValueError: if invalid `img` or `data_format` is passed.
ValueError: 如果无效的“IMG”或“data_format”被传递。
"""
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)
# Numpy数组x格式
# or (channel, height, width)
# 或者(通道、高度、宽度）
# but original PIL image has format (width, height, channel)
# 但是原PIL图片格式为(宽度, 高度, 通道)
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='nearest'):
"""Loads an image into PIL format.
以PIL格式加载图像。
# Arguments
参数
path: Path to image file
path: 图像文件的路径
grayscale: Boolean, whether to load the image as grayscale.
grayscale: bool型, 是否将图像加载为灰度级。
target_size: Either `None` (default to original size)
or tuple of ints `(img_height, img_width)`.
target_size: 要么“None”（默认为原始大小），要么是int的元组“（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, "nearest" is used.
interpolation:如果目标尺寸与加载图像不同，则使用插值方法对图像进行重采样。支持的方法是“nearest”、“bilinear”
和“bicubic”。如果安装了pIL版本1.1.3或更新版本，则还支持“lanczos”。如果安装了PIL版本3.4.0或更新版本，也支
持“Box”和“Hamming”。默认情况下，使用“nearest”。
# Returns
返回
A PIL Image instance.
PIL图像实例
# Raises
补充
ImportError: if PIL is not available.
ImportError: 如过PIL不可用.
ValueError: if interpolation method is not supported.
ValueError: 如果不支持插值方法。
"""
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())))
resample = _PIL_INTERPOLATION_METHODS[interpolation]
img = img.resize(width_height_tuple, resample)
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.
通过实时数据增强生成小批量图像数据。
# Arguments
参数
featurewise_center: set input mean to 0 over the dataset.
featurewise_center:在数据集上设置输入平均为0。
samplewise_center: set each sample mean to 0.
samplewise_center: 将每个样本均值设为0。
featurewise_std_normalization: divide inputs by std of the dataset.
featurewise_std_normalization: 通过数据集的std划分输入。
samplewise_std_normalization: divide each input by its std.
samplewise_std_normalization: 用std划分每个输入。
zca_whitening: apply ZCA whitening.
zca_whitening: 应用ZCA白化
# https://blog.csdn.net/hjimce/article/details/50864602
zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
zca_epsilon: ZCA编号参数epsilon。默认1e-6
rotation_range: degrees (0 to 180).
rotation_range: 角度 (0 至 180).
width_shift_range: fraction of total width, if < 1, or pixels if >= 1.
width_shift_range:总宽度的分数，如果＜1，或像素如果＞=1。
height_shift_range: fraction of total height, if < 1, or pixels if >= 1.
height_shift_range: 总高度的分数，如果＜1，或像素如果＞=1。
shear_range: shear intensity (shear angle in degrees).
shear_range: 剪切强度（剪切角为度）。
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.
zoom_range:缩放量。如果标量Z，缩放将随机选择在范围[1-Z，1 +Z]。可以通过两个序列来选择这个范围。
channel_shift_range: shift range for each channel.
channel_shift_range: 每个通道的移位范围。
fill_mode: points outside the boundaries are filled according to the
given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default
is 'nearest'.
Points outside the boundaries of the input are filled according to the given mode:
'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
'nearest': aaaaaaaa|abcd|dddddddd
'reflect': abcddcba|abcd|dcbaabcd
'wrap': abcdabcd|abcd|abcdabcd
fill_mode:边界外的点按照给定的模式（'constant', 'nearest', 'reflect' or 'wrap'）填充。默认是“nearest”。
根据给定的模式填充输入边界之外的点：
'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
'nearest': aaaaaaaa|abcd|dddddddd
'reflect': abcddcba|abcd|dcbaabcd
'wrap': abcdabcd|abcd|abcdabcd
cval: value used for points outside the boundaries when fill_mode is
'constant'. Default is 0.
cval: 当填充模式为“constant”时，用于边界外的点的值。默认值为0。
horizontal_flip: whether to randomly flip images horizontally.
horizontal_flip: 是否随机地水平翻转图像。
vertical_flip: whether to randomly flip images vertically.
vertical_flip: 是否随机地垂直翻转图像。
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.
rescale:尺度因子。如果没有或0，没有尺度缩放应用，否则我们乘以数据提供的值。这是在“预处理函数”（如果有的话）
之后，但在任何其他转换之前应用的。
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.
preprocessing_function:将在每个输入中隐含的函数。
该函数将在对其进行任何其他修改之前运行。
这个函数应该有一个参数：
一个图像（秩为3的Numpy张量），输出一个相同形状的Numpy张量。
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".
data_format: 'channels_first' or 'channels_last'.在'channels_first' 模式，通道维度（深度）是索引1，在
'channels_last' 模式，通道维度是3.默认是``image_data_format` 值，在Keras配置文件`~/.keras/keras.json`。
如果不设置，默认为"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,
data_format=None):
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
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)
if zca_whitening:
if not featurewise_center:
self.featurewise_center = True
warnings.warn('This ImageDataGenerator specifies '
'`zca_whitening`, which overrides '
'setting of `featurewise_center`.')
if featurewise_std_normalization:
self.featurewise_std_normalization = False
warnings.warn('This ImageDataGenerator specifies '
'`zca_whitening` '
'which overrides setting of'
'`featurewise_std_normalization`.')
if featurewise_std_normalization:
if not featurewise_center:
self.featurewise_center = True
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_std_normalization`, '
'which overrides setting of '
'`featurewise_center`.')
if samplewise_std_normalization:
if not samplewise_center:
self.samplewise_center = True
warnings.warn('This ImageDataGenerator specifies '
'`samplewise_std_normalization`, '
'which overrides setting of '
'`samplewise_center`.')
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,
interpolation='nearest'):
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,
interpolation=interpolation)
def standardize(self, x):
"""Apply the normalization configuration to a batch of inputs.
将归一化配置应用于一批输入。
# Arguments
参数
x: batch of inputs to be normalized.
x: 一批输入要归一化。
# Returns
返回
The inputs, normalized.
输入，归一化。
"""
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) + K.epsilon())
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 + K.epsilon())
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.
随机增加单个图像张量。
# Arguments
参数
x: 3D tensor, single image.
x: 三维张量，单图像。
seed: random seed.
seed: 随机种子
# Returns
返回
A randomly transformed version of the input (same shape).
输入（相同形状）的随机变换版本。
"""
# x is a single image, so it doesn't have image number at index 0
# X是单个图像，因此在索引0上没有图像编号。
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
# 使用单应性的组合生成需要应用的最终变换
if self.rotation_range:
theta = np.deg2rad(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)
if self.height_shift_range < 1:
tx *= 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)
if self.width_shift_range < 1:
ty *= x.shape[img_col_axis]
else:
ty = 0
if self.shear_range:
shear = np.deg2rad(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)
return x
def fit(self, x,
augment=False,
rounds=1,
seed=None):
"""Fits internal statistics to some sample data.
适合内部统计的一些样本数据。
Required for featurewise_center, featurewise_std_normalization
and zca_whitening.
要求：featurewise_center, featurewise_std_normalization
and zca_whitening。
# Arguments
参数
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.
x: Numpy数组，适配的数据。需要4级。
对于在灰度数据的情况下，通道轴应该具有值1，并且在RGB数据的情况下，它应该具有值3。
augment: Whether to fit on randomly augmented samples
augment: 是否适合随机增广样本
rounds: If `augment`,
how many augmentation passes to do over the data
rounds: 如果`augment`,在数据上做多少扩充
seed: random seed.
seed: 随机种子
# Raises
补充
ValueError: in case of invalid input `x`.
ValueError: `x`无效输入.
"""
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)
s_inv = 1. / np.sqrt(s[np.newaxis] + self.zca_epsilon)
self.principal_components = (u * s_inv).dot(u.T)
class Iterator(Sequence):
"""Base class for image data iterators.
图像数据迭代的基础类
Every `Iterator` must implement the `_get_batches_of_transformed_samples`
method.
每个迭代器需要执行`_get_batches_of_transformed_samples`方法。
# Arguments
参数
n: Integer, total number of samples in the dataset to loop over.
n:整型，要循环的数据集中的样本总数。
batch_size: Integer, size of a batch.
batch_size: 整型，批次大小
shuffle: Boolean, whether to shuffle the data between epochs.
shuffle: 布尔型，是否要在周期间洗牌。
seed: Random seeding for data shuffling.
seed: 数据清洗的随机种子
"""
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 (self.n + self.batch_size - 1) // self.batch_size # round up
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.
# 确保self.batch_index为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(...):
# 如果想做如下事情
# 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.
获取一批转换后的样本。
# Arguments
参数
index_array: array of sample indices to include in batch.
index_array: 批次包含的样本索引数组。
# Returns
返回
A batch of transformed samples.
一批次转换的样本
"""
raise NotImplementedError
class NumpyArrayIterator(Iterator):
"""Iterator yielding data from a Numpy array.
迭代器通过Numpy数组生成数据
# Arguments
参数
x: Numpy array of input data.
x:输入数据是Numpy数组
y: Numpy array of targets data.
y: 目标数据是Numpy数组
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
image_data_generator: `ImageDataGenerator`实例，用来随机转换和归一化。
batch_size: Integer, size of a batch.
batch_size: 整型，批次大小
shuffle: Boolean, whether to shuffle the data between epochs.
shuffle: 布尔型，是否在周期间清洗数据
seed: Random seed for data shuffling.
seed: 数据清洗的随机种子
data_format: String, one of `channels_first`, `channels_last`.
data_format:字符串, `channels_first`或`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_to_dir:可选目录，以可查看的格式保存正在生成的图片。这对于可视化应用的随机变换是有用的，用于调试目的。
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_prefix: 用于保存示例图像的字符串前缀（如果“save_to_dir”被设置）。
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
save_format: 保存图像所用的格式（如果“save_to_dir”被设置）。
"""
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.
python 2.x.使用
# Returns
返回
The next batch.
下一个批次
"""
# Keeps under lock only the mechanism which advances
# the indexing of each batch.
# 锁定每个批次标引的机制。
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
# 图片转换没有线程所得，他可以并行执行。
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 directory.
对目录中包含的“white_list_formats”的扩展文件进行计数。
# Arguments
参数
directory: absolute path to the directory
containing files to be counted
directory:包含要计数的文件的目录的绝对路径
white_list_formats: set of strings containing allowed extensions for
the files to be counted.
white_list_formats:包含要被计数的文件的允许扩展的字符串集合。
follow_links: boolean.
follow_links: 布尔型
# Returns
返回
the count of files with extension in `white_list_formats` contained in
the directory.
目录中包含的扩展名为“white_list_formats”的文件计数。
"""
def _recursive_list(subpath):
return sorted(os.walk(subpath, followlinks=follow_links), key=lambda x: x[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('.tiff'):
warnings.warn('Using \'.tiff\' files with multiple bands will cause distortion. '
'Please verify your output.')
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` with extensions in `white_list_formats`.
'subdir'中列出文件的路径，在“white_list_formats”中使用扩展名。
# Arguments
参数
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`.
directory: 包含要列出的文件的目录的绝对路径。目录名用作类标签，必须是“class_indices”的关键字。.
white_list_formats: set of strings containing allowed extensions for
the files to be counted.
white_list_formats: 包含要被计数的文件的允许扩展的字符串集合。
class_indices: dictionary mapping a class name to its index.
class_indices: 字典将类名映射到其索引。
follow_links: boolean.
follow_links: 布尔型
# Returns
返回
classes: a list of class indices
classes: 类索引列表
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", ...]).
filenames:“目录”中有效文件的路径，与“目录”的父节点相对应 (例如，如果目录是"dataset/class1",
文件名应该是["class1/file1.jpg", "class1/file2.jpg", ...]).
"""
def _recursive_list(subpath):
return sorted(os.walk(subpath, followlinks=follow_links), key=lambda x: x[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.
能够从磁盘上的目录读取图像的迭代器。
# Arguments
参数
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.
directory: 从该目录读取图像的路径。该目录中的每个子目录将被认为包含来自一个类的图像，或者可以通过“类”参数
指定类子目录。
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
image_data_generator:`ImageDataGenerator`实例，用来随机转换和归一化。
target_size: tuple of integers, dimensions to resize input images to.
target_size: 整数元组，整数元组，将输入图像的大小调整为（的目标大小）
color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images.
color_mode: "rgb"`, `"grayscale"`中的一个，读取图片的色彩模式。
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.
classes: 可选的字符串列表，包含每个类的图像的子目录的名称（例如‘[狗’，“猫”）），如果没有设置，将自动计算。
class_mode: Mode for yielding the targets:
生成目标的模式
`"binary"`: binary targets (if there are only two classes),
`"binary"`: 2个目标(如果是2类),
`"categorical"`: categorical targets,
`"categorical"`: 多目标,
`"sparse"`: integer targets,
`"sparse"`: 整数目标,
`"input"`: targets are images identical to input images (mainly
used to work with autoencoders), `
"input"`:目标是与输入图像相同的图像（主要用于与自动编码器一起工作），
`None`: no targets get yielded (only input images are yielded).
`None`: 没有目标获得（只有输入图像产生）。
batch_size: Integer, size of a batch.
batch_size: 整数，一个批次的大小。
shuffle: Boolean, whether to shuffle the data between epochs.
shuffle: 布尔型，是否要在周期间洗牌。
seed: Random seed for data shuffling.
seed: 用于数据洗牌的随机种子。
data_format: String, one of `channels_first`, `channels_last`.
data_format: 字符串型, `channels_first`或`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_to_dir: 可选目录，以可查看的格式保存正在生成的图片。这对于可视化应用的随机变换是有用的，用于调试目的。
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_prefix: 用于保存示例图像的字符串前缀（如果设置`save_to_dir`）。
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
save_format:用于保存示例图像的格式（如果设置`save_to_dir`）。
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, "nearest" is used.
interpolation: 如果目标尺寸与加载图像不同，则使用插值方法对图像进行重采样。支持的方法是“nearest”、“bilinear”
和“bicubic”。如果安装了pIL版本1.1.3或更新版本，则还支持“lanczos”。如果安装了PIL版本3.4.0或更新版本，也支
持“Box”和“Hamming”。默认情况下，使用“nearest”。
"""
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, interpolation='nearest'):
if data_format is None:
data_format = K.image_data_format()
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
self.interpolation = interpolation
white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff'}
# first, count the number of samples and classes
# 首先，统计样本和类的数量
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
# 第二，在不同的类子文件夹中构建图像索引。
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
# 建立一批图像数据。
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,
interpolation=self.interpolation)
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)
batch_x[i] = x
# optionally save augmented images to disk for debugging purposes
# 可选地将增强图像保存到磁盘以进行调试。
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.
对于python 2.x.
# Returns
返回
The next batch.
下一个批次
"""
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
# 图像的转换不在锁定线程下，所以可以并行完成。
return self._get_batches_of_transformed_samples(index_array)
代码执行
Keras详细介绍
英文：https://keras.io/
中文：http://keras-cn.readthedocs.io/en/latest/
实例下载
https://github.com/keras-team/keras
https://github.com/keras-team/keras/tree/master/examples
完整项目下载
方便没积分童鞋，请加企鹅452205574，共享文件夹。
包括：代码、数据集合（图片）、已生成model、安装库文件等。
