Module keras.api.keras.preprocessing.image

Public API for tf.keras.preprocessing.image namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.preprocessing.image namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.preprocessing.image import DirectoryIterator
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import Iterator
from keras.preprocessing.image import NumpyArrayIterator
from keras.preprocessing.image import array_to_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from keras.preprocessing.image import save_img
from keras_preprocessing.image.affine_transformations import apply_affine_transform
from keras_preprocessing.image.affine_transformations import apply_brightness_shift
from keras_preprocessing.image.affine_transformations import apply_channel_shift
from keras_preprocessing.image.affine_transformations import random_brightness
from keras_preprocessing.image.affine_transformations import random_channel_shift
from keras_preprocessing.image.affine_transformations import random_rotation
from keras_preprocessing.image.affine_transformations import random_shear
from keras_preprocessing.image.affine_transformations import random_shift
from keras_preprocessing.image.affine_transformations import random_zoom

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras.preprocessing.image", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest', cval=0.0, order=1)

Applies an affine transformation specified by the parameters given.

Arguments

x: 2D numpy array, single image.
theta: Rotation angle in degrees.
tx: Width shift.
ty: Heigh shift.
shear: Shear angle in degrees.
zx: Zoom in x direction.
zy: Zoom in y direction
row_axis: Index of axis for rows in the input image.
col_axis: Index of axis for columns in the input image.
channel_axis: Index of axis for channels in the input image.
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'`.
order: int, order of interpolation

Returns

The transformed version of the input.
Expand source code
def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,
                           row_axis=0, col_axis=1, channel_axis=2,
                           fill_mode='nearest', cval=0., order=1):
    """Applies an affine transformation specified by the parameters given.

    # Arguments
        x: 2D numpy array, single image.
        theta: Rotation angle in degrees.
        tx: Width shift.
        ty: Heigh shift.
        shear: Shear angle in degrees.
        zx: Zoom in x direction.
        zy: Zoom in y direction
        row_axis: Index of axis for rows in the input image.
        col_axis: Index of axis for columns in the input image.
        channel_axis: Index of axis for channels in the input image.
        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'`.
        order: int, order of interpolation

    # Returns
        The transformed version of the input.
    """
    if scipy is None:
        raise ImportError('Image transformations require SciPy. '
                          'Install SciPy.')
    transform_matrix = None
    if theta != 0:
        theta = np.deg2rad(theta)
        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]])
        if transform_matrix is None:
            transform_matrix = shift_matrix
        else:
            transform_matrix = np.dot(transform_matrix, shift_matrix)

    if shear != 0:
        shear = np.deg2rad(shear)
        shear_matrix = np.array([[1, -np.sin(shear), 0],
                                 [0, np.cos(shear), 0],
                                 [0, 0, 1]])
        if transform_matrix is None:
            transform_matrix = shear_matrix
        else:
            transform_matrix = 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]])
        if transform_matrix is None:
            transform_matrix = zoom_matrix
        else:
            transform_matrix = np.dot(transform_matrix, zoom_matrix)

    if transform_matrix is not None:
        h, w = x.shape[row_axis], x.shape[col_axis]
        transform_matrix = transform_matrix_offset_center(
            transform_matrix, h, w)
        x = np.rollaxis(x, channel_axis, 0)
        final_affine_matrix = transform_matrix[:2, :2]
        final_offset = transform_matrix[:2, 2]

        channel_images = [ndimage.interpolation.affine_transform(
            x_channel,
            final_affine_matrix,
            final_offset,
            order=order,
            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 apply_brightness_shift(x, brightness)

Performs a brightness shift.

Arguments

x: Input tensor. Must be 3D.
brightness: Float. The new brightness value.
channel_axis: Index of axis for channels in the input tensor.

Returns

Numpy image tensor.

Raises

ValueError if <code>brightness\_range</code> isn't a tuple.
Expand source code
def apply_brightness_shift(x, brightness):
    """Performs a brightness shift.

    # Arguments
        x: Input tensor. Must be 3D.
        brightness: Float. The new brightness value.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    # Raises
        ValueError if `brightness_range` isn't a tuple.
    """
    if ImageEnhance is None:
        raise ImportError('Using brightness shifts requires PIL. '
                          'Install PIL or Pillow.')
    x = array_to_img(x)
    x = imgenhancer_Brightness = ImageEnhance.Brightness(x)
    x = imgenhancer_Brightness.enhance(brightness)
    x = img_to_array(x)
    return x
def apply_channel_shift(x, intensity, channel_axis=0)

Performs a channel shift.

Arguments

x: Input tensor. Must be 3D.
intensity: Transformation intensity.
channel_axis: Index of axis for channels in the input tensor.

Returns

Numpy image tensor.
Expand source code
def apply_channel_shift(x, intensity, channel_axis=0):
    """Performs a channel shift.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    """
    x = np.rollaxis(x, channel_axis, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [
        np.clip(x_channel + 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 array_to_img(x, data_format=None, scale=True, dtype=None)

Converts a 3D Numpy array to a PIL Image instance.

Usage:

from PIL import Image
img = np.random.random(size=(100, 100, 3))
pil_img = tf.keras.preprocessing.image.array_to_img(img)

Args

x
Input data, in any form that can be converted to a Numpy array.
data_format
Image data format, can be either "channels_first" or "channels_last". Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").
scale
Whether to rescale the image such that minimum and maximum values are 0 and 255 respectively. Defaults to True.
dtype
Dtype to use. Default to None, in which case the global setting

tf.keras.backend.floatx() is used (unless you changed it, it defaults to "float32")

Returns

A PIL Image instance.

Raises

ImportError
if PIL is not available.
ValueError
if invalid x or data_format is passed.
Expand source code
@keras_export('keras.utils.array_to_img',
              'keras.preprocessing.image.array_to_img')
def array_to_img(x, data_format=None, scale=True, dtype=None):
  """Converts a 3D Numpy array to a PIL Image instance.

  Usage:

  ```python
  from PIL import Image
  img = np.random.random(size=(100, 100, 3))
  pil_img = tf.keras.preprocessing.image.array_to_img(img)
  ```


  Args:
      x: Input data, in any form that can be converted to a Numpy array.
      data_format: Image data format, can be either "channels_first" or
        "channels_last". Defaults to `None`, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it,
        it defaults to "channels_last").
      scale: Whether to rescale the image such that minimum and maximum values
        are 0 and 255 respectively. Defaults to `True`.
      dtype: Dtype to use. Default to `None`, in which case the global setting
      `tf.keras.backend.floatx()` is used (unless you changed it, it defaults
      to "float32")

  Returns:
      A PIL Image instance.

  Raises:
      ImportError: if PIL is not available.
      ValueError: if invalid `x` or `data_format` is passed.
  """

  if data_format is None:
    data_format = backend.image_data_format()
  kwargs = {}
  if 'dtype' in tf_inspect.getfullargspec(image.array_to_img)[0]:
    if dtype is None:
      dtype = backend.floatx()
    kwargs['dtype'] = dtype
  return image.array_to_img(x, data_format=data_format, scale=scale, **kwargs)
def img_to_array(img, data_format=None, dtype=None)

Converts a PIL Image instance to a Numpy array.

Usage:

from PIL import Image
img_data = np.random.random(size=(100, 100, 3))
img = tf.keras.preprocessing.image.array_to_img(img_data)
array = tf.keras.preprocessing.image.img_to_array(img)

Args

img
Input PIL Image instance.
data_format
Image data format, can be either "channels_first" or "channels_last". Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").
dtype
Dtype to use. Default to None, in which case the global setting

tf.keras.backend.floatx() is used (unless you changed it, it defaults to "float32")

Returns

A 3D Numpy array.

Raises

ValueError
if invalid img or data_format is passed.
Expand source code
@keras_export('keras.utils.img_to_array',
              'keras.preprocessing.image.img_to_array')
def img_to_array(img, data_format=None, dtype=None):
  """Converts a PIL Image instance to a Numpy array.

  Usage:

  ```python
  from PIL import Image
  img_data = np.random.random(size=(100, 100, 3))
  img = tf.keras.preprocessing.image.array_to_img(img_data)
  array = tf.keras.preprocessing.image.img_to_array(img)
  ```


  Args:
      img: Input PIL Image instance.
      data_format: Image data format, can be either "channels_first" or
        "channels_last". Defaults to `None`, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it,
        it defaults to "channels_last").
      dtype: Dtype to use. Default to `None`, in which case the global setting
      `tf.keras.backend.floatx()` is used (unless you changed it, it defaults
      to "float32")

  Returns:
      A 3D Numpy array.

  Raises:
      ValueError: if invalid `img` or `data_format` is passed.
  """

  if data_format is None:
    data_format = backend.image_data_format()
  kwargs = {}
  if 'dtype' in tf_inspect.getfullargspec(image.img_to_array)[0]:
    if dtype is None:
      dtype = backend.floatx()
    kwargs['dtype'] = dtype
  return image.img_to_array(img, data_format=data_format, **kwargs)
def load_img(path, grayscale=False, color_mode='rgb', target_size=None, interpolation='nearest')

Loads an image into PIL format.

Usage:

image = tf.keras.preprocessing.image.load_img(image_path)
input_arr = tf.keras.preprocessing.image.img_to_array(image)
input_arr = np.array([input_arr])  # Convert single image to a batch.
predictions = model.predict(input_arr)

Args

path
Path to image file.
grayscale
DEPRECATED use color_mode="grayscale".
color_mode
One of "grayscale", "rgb", "rgba". Default: "rgb". The desired image format.
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, "nearest" is used.

Returns

A PIL Image instance.

Raises

ImportError
if PIL is not available.
ValueError
if interpolation method is not supported.
Expand source code
@keras_export('keras.utils.load_img',
              'keras.preprocessing.image.load_img')
def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
             interpolation='nearest'):
  """Loads an image into PIL format.

  Usage:

  ```
  image = tf.keras.preprocessing.image.load_img(image_path)
  input_arr = tf.keras.preprocessing.image.img_to_array(image)
  input_arr = np.array([input_arr])  # Convert single image to a batch.
  predictions = model.predict(input_arr)
  ```

  Args:
      path: Path to image file.
      grayscale: DEPRECATED use `color_mode="grayscale"`.
      color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
          The desired image format.
      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, "nearest" is used.

  Returns:
      A PIL Image instance.

  Raises:
      ImportError: if PIL is not available.
      ValueError: if interpolation method is not supported.
  """
  return image.load_img(path, grayscale=grayscale, color_mode=color_mode,
                        target_size=target_size, interpolation=interpolation)
def random_brightness(x, brightness_range)

Performs a random brightness shift.

Arguments

x: Input tensor. Must be 3D.
brightness_range: Tuple of floats; brightness range.
channel_axis: Index of axis for channels in the input tensor.

Returns

Numpy image tensor.

Raises

ValueError if <code>brightness\_range</code> isn't a tuple.
Expand source code
def random_brightness(x, brightness_range):
    """Performs a random brightness shift.

    # Arguments
        x: Input tensor. Must be 3D.
        brightness_range: Tuple of floats; brightness range.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    # Raises
        ValueError if `brightness_range` isn't a tuple.
    """
    if len(brightness_range) != 2:
        raise ValueError(
            '`brightness_range should be tuple or list of two floats. '
            'Received: %s' % (brightness_range,))

    u = np.random.uniform(brightness_range[0], brightness_range[1])
    return apply_brightness_shift(x, u)
def random_channel_shift(x, intensity_range, channel_axis=0)

Performs a random channel shift.

Arguments

x: Input tensor. Must be 3D.
intensity_range: Transformation intensity.
channel_axis: Index of axis for channels in the input tensor.

Returns

Numpy image tensor.
Expand source code
def random_channel_shift(x, intensity_range, channel_axis=0):
    """Performs a random channel shift.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity_range: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.
    """
    intensity = np.random.uniform(-intensity_range, intensity_range)
    return apply_channel_shift(x, intensity, channel_axis=channel_axis)
def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0, fill_mode='nearest', cval=0.0, interpolation_order=1)

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'`.
interpolation_order: int, order of spline interpolation.
    see <code>ndimage.interpolation.affine\_transform</code>

Returns

Rotated Numpy image tensor.
Expand source code
def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,
                    fill_mode='nearest', cval=0., interpolation_order=1):
    """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'`.
        interpolation_order: int, order of spline interpolation.
            see `ndimage.interpolation.affine_transform`

    # Returns
        Rotated Numpy image tensor.
    """
    theta = np.random.uniform(-rg, rg)
    x = apply_affine_transform(x, theta=theta, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval,
                               order=interpolation_order)
    return x
def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0, fill_mode='nearest', cval=0.0, interpolation_order=1)

Performs a random spatial shear of a Numpy image tensor.

Arguments

x: Input tensor. Must be 3D.
intensity: Transformation intensity 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'`.
interpolation_order: int, order of spline interpolation.
    see <code>ndimage.interpolation.affine\_transform</code>

Returns

Sheared Numpy image tensor.
Expand source code
def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0,
                 fill_mode='nearest', cval=0., interpolation_order=1):
    """Performs a random spatial shear of a Numpy image tensor.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity 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'`.
        interpolation_order: int, order of spline interpolation.
            see `ndimage.interpolation.affine_transform`

    # Returns
        Sheared Numpy image tensor.
    """
    shear = np.random.uniform(-intensity, intensity)
    x = apply_affine_transform(x, shear=shear, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval,
                               order=interpolation_order)
    return x
def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0, fill_mode='nearest', cval=0.0, interpolation_order=1)

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'`.
interpolation_order: int, order of spline interpolation.
    see <code>ndimage.interpolation.affine\_transform</code>

Returns

Shifted Numpy image tensor.
Expand source code
def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,
                 fill_mode='nearest', cval=0., interpolation_order=1):
    """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'`.
        interpolation_order: int, order of spline interpolation.
            see `ndimage.interpolation.affine_transform`

    # 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
    x = apply_affine_transform(x, tx=tx, ty=ty, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval,
                               order=interpolation_order)
    return x
def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0, fill_mode='nearest', cval=0.0, interpolation_order=1)

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'`.
interpolation_order: int, order of spline interpolation.
    see <code>ndimage.interpolation.affine\_transform</code>

Returns

Zoomed Numpy image tensor.

Raises

ValueError: if <code>zoom\_range</code> isn't a tuple.
Expand source code
def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,
                fill_mode='nearest', cval=0., interpolation_order=1):
    """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'`.
        interpolation_order: int, order of spline interpolation.
            see `ndimage.interpolation.affine_transform`

    # 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: %s' % (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)
    x = apply_affine_transform(x, zx=zx, zy=zy, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval,
                               order=interpolation_order)
    return x
def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs)

Saves an image stored as a Numpy array to a path or file object.

Args

path
Path or file object.
x
Numpy array.
data_format
Image data format, either "channels_first" or "channels_last".
file_format
Optional file format override. If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used.
scale
Whether to rescale image values to be within [0, 255].
**kwargs
Additional keyword arguments passed to PIL.Image.save().
Expand source code
@keras_export('keras.utils.save_img',
              'keras.preprocessing.image.save_img')
def save_img(path,
             x,
             data_format=None,
             file_format=None,
             scale=True,
             **kwargs):
  """Saves an image stored as a Numpy array to a path or file object.

  Args:
      path: Path or file object.
      x: Numpy array.
      data_format: Image data format,
          either "channels_first" or "channels_last".
      file_format: Optional file format override. If omitted, the
          format to use is determined from the filename extension.
          If a file object was used instead of a filename, this
          parameter should always be used.
      scale: Whether to rescale image values to be within `[0, 255]`.
      **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
  """
  if data_format is None:
    data_format = backend.image_data_format()
  image.save_img(path,
                 x,
                 data_format=data_format,
                 file_format=file_format,
                 scale=scale, **kwargs)

Classes

class DirectoryIterator (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, subset=None, interpolation='nearest', dtype=None)

Iterator capable of reading images from a directory on disk.

Args

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", "rgba", "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).
subset
Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
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.
dtype
Dtype to use for generated arrays.
Expand source code
class DirectoryIterator(image.DirectoryIterator, Iterator):  # pylint: disable=inconsistent-mro
  """Iterator capable of reading images from a directory on disk.

  Args:
      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"`, `"rgba"`, `"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).
      subset: Subset of data (`"training"` or `"validation"`) if
          validation_split is set in ImageDataGenerator.
      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.
      dtype: Dtype to use for generated arrays.
  """

  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,
               subset=None,
               interpolation='nearest',
               dtype=None):
    if data_format is None:
      data_format = backend.image_data_format()
    kwargs = {}
    if 'dtype' in tf_inspect.getfullargspec(
        image.ImageDataGenerator.__init__)[0]:
      if dtype is None:
        dtype = backend.floatx()
      kwargs['dtype'] = dtype
    super(DirectoryIterator, self).__init__(
        directory, image_data_generator,
        target_size=target_size,
        color_mode=color_mode,
        classes=classes,
        class_mode=class_mode,
        batch_size=batch_size,
        shuffle=shuffle,
        seed=seed,
        data_format=data_format,
        save_to_dir=save_to_dir,
        save_prefix=save_prefix,
        save_format=save_format,
        follow_links=follow_links,
        subset=subset,
        interpolation=interpolation,
        **kwargs)

Ancestors

  • keras_preprocessing.image.directory_iterator.DirectoryIterator
  • keras_preprocessing.image.iterator.BatchFromFilesMixin
  • Iterator
  • keras_preprocessing.image.iterator.Iterator
  • Sequence

Inherited members

class ImageDataGenerator (featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)

Generate batches of tensor image data with real-time data augmentation.

The data will be looped over (in batches).

Args

featurewise_center
Boolean. Set input mean to 0 over the dataset, feature-wise.
samplewise_center
Boolean. Set each sample mean to 0.
featurewise_std_normalization
Boolean. Divide inputs by std of the dataset, feature-wise.
samplewise_std_normalization
Boolean. Divide each input by its std.
zca_epsilon
epsilon for ZCA whitening. Default is 1e-6.
zca_whitening
Boolean. Apply ZCA whitening.
rotation_range
Int. Degree range for random rotations.
width_shift_range
Float, 1-D array-like or int - float: fraction of total width, if < 1, or pixels if >= 1. - 1-D array-like: random elements from the array. - int: integer number of pixels from interval (-width_shift_range, +width_shift_range) - With width_shift_range=2 possible values are integers [-1, 0, +1], same as with width_shift_range=[-1, 0, +1], while with width_shift_range=1.0 possible values are floats in the interval [-1.0, +1.0).
height_shift_range
Float, 1-D array-like or int - float: fraction of total height, if < 1, or pixels if >= 1. - 1-D array-like: random elements from the array. - int: integer number of pixels from interval (-height_shift_range, +height_shift_range) - With height_shift_range=2 possible values are integers [-1, 0, +1], same as with height_shift_range=[-1, 0, +1], while with height_shift_range=1.0 possible values are floats in the interval [-1.0, +1.0).
brightness_range
Tuple or list of two floats. Range for picking a brightness shift value from.
shear_range
Float. Shear Intensity (Shear angle in counter-clockwise direction in degrees)
zoom_range
Float or [lower, upper]. Range for random zoom. If a float, [lower, upper] = [1-zoom_range, 1+zoom_range].
channel_shift_range
Float. Range for random channel shifts.
fill_mode
One of {"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
cval
Float or Int. Value used for points outside the boundaries when fill_mode = "constant".
horizontal_flip
Boolean. Randomly flip inputs horizontally.
vertical_flip
Boolean. Randomly flip inputs vertically.
rescale
rescaling factor. Defaults to None. If None or 0, no rescaling is applied, otherwise we multiply the data by the value provided (after applying all other transformations).
preprocessing_function
function that will be applied on each input. The function will run after the image is resized and augmented. 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
Image data format, either "channels_first" or "channels_last". "channels_last" mode means that the images should have shape (samples, height, width, channels), "channels_first" mode means that the images should have shape (samples, channels, height, width). 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".
validation_split
Float. Fraction of images reserved for validation (strictly between 0 and 1).
dtype
Dtype to use for the generated arrays.

Raises

ValueError
If the value of the argument, data_format is other than "channels_last" or "channels_first".
ValueError
If the value of the argument, validation_split > 1 or validation_split < 0.

Examples:

Example of using .flow(x, y):

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = utils.to_categorical(y_train, num_classes)
y_test = utils.to_categorical(y_test, num_classes)
datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2)
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(x_train)
# fits the model on batches with real-time data augmentation:
model.fit(datagen.flow(x_train, y_train, batch_size=32,
         subset='training'),
         validation_data=datagen.flow(x_train, y_train,
         batch_size=8, subset='validation'),
         steps_per_epoch=len(x_train) / 32, epochs=epochs)
# here's a more "manual" example
for e in range(epochs):
    print('Epoch', e)
    batches = 0
    for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
        model.fit(x_batch, y_batch)
        batches += 1
        if batches >= len(x_train) / 32:
            # we need to break the loop by hand because
            # the generator loops indefinitely
            break

Example of using .flow_from_directory(directory):

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        'data/train',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
        'data/validation',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')
model.fit(
        train_generator,
        steps_per_epoch=2000,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=800)

Example of transforming images and masks together.

# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=True,
                     featurewise_std_normalization=True,
                     rotation_range=90,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     zoom_range=0.2)
image_datagen = ImageDataGenerator(**data_gen_args)
mask_datagen = ImageDataGenerator(**data_gen_args)
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
image_datagen.fit(images, augment=True, seed=seed)
mask_datagen.fit(masks, augment=True, seed=seed)
image_generator = image_datagen.flow_from_directory(
    'data/images',
    class_mode=None,
    seed=seed)
mask_generator = mask_datagen.flow_from_directory(
    'data/masks',
    class_mode=None,
    seed=seed)
# combine generators into one which yields image and masks
train_generator = zip(image_generator, mask_generator)
model.fit(
    train_generator,
    steps_per_epoch=2000,
    epochs=50)
Expand source code
class ImageDataGenerator(image.ImageDataGenerator):
  """Generate batches of tensor image data with real-time data augmentation.

   The data will be looped over (in batches).

  Args:
      featurewise_center: Boolean.
          Set input mean to 0 over the dataset, feature-wise.
      samplewise_center: Boolean. Set each sample mean to 0.
      featurewise_std_normalization: Boolean.
          Divide inputs by std of the dataset, feature-wise.
      samplewise_std_normalization: Boolean. Divide each input by its std.
      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
      zca_whitening: Boolean. Apply ZCA whitening.
      rotation_range: Int. Degree range for random rotations.
      width_shift_range: Float, 1-D array-like or int
          - float: fraction of total width, if < 1, or pixels if >= 1.
          - 1-D array-like: random elements from the array.
          - int: integer number of pixels from interval
              `(-width_shift_range, +width_shift_range)`
          - With `width_shift_range=2` possible values
              are integers `[-1, 0, +1]`,
              same as with `width_shift_range=[-1, 0, +1]`,
              while with `width_shift_range=1.0` possible values are floats
              in the interval [-1.0, +1.0).
      height_shift_range: Float, 1-D array-like or int
          - float: fraction of total height, if < 1, or pixels if >= 1.
          - 1-D array-like: random elements from the array.
          - int: integer number of pixels from interval
              `(-height_shift_range, +height_shift_range)`
          - With `height_shift_range=2` possible values
              are integers `[-1, 0, +1]`,
              same as with `height_shift_range=[-1, 0, +1]`,
              while with `height_shift_range=1.0` possible values are floats
              in the interval [-1.0, +1.0).
      brightness_range: Tuple or list of two floats. Range for picking
          a brightness shift value from.
      shear_range: Float. Shear Intensity
          (Shear angle in counter-clockwise direction in degrees)
      zoom_range: Float or [lower, upper]. Range for random zoom.
          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.
      channel_shift_range: Float. Range for random channel shifts.
      fill_mode: One of {"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
      cval: Float or Int.
          Value used for points outside the boundaries
          when `fill_mode = "constant"`.
      horizontal_flip: Boolean. Randomly flip inputs horizontally.
      vertical_flip: Boolean. Randomly flip inputs vertically.
      rescale: rescaling factor. Defaults to None.
          If None or 0, no rescaling is applied,
          otherwise we multiply the data by the value provided
          (after applying all other transformations).
      preprocessing_function: function that will be applied on each input.
          The function will run after the image is resized and augmented.
          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: Image data format,
          either "channels_first" or "channels_last".
          "channels_last" mode means that the images should have shape
          `(samples, height, width, channels)`,
          "channels_first" mode means that the images should have shape
          `(samples, channels, height, width)`.
          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".
      validation_split: Float. Fraction of images reserved for validation
          (strictly between 0 and 1).
      dtype: Dtype to use for the generated arrays.

  Raises:
    ValueError: If the value of the argument, `data_format` is other than
          `"channels_last"` or `"channels_first"`.
    ValueError: If the value of the argument, `validation_split` > 1
          or `validation_split` < 0.

  Examples:

  Example of using `.flow(x, y)`:

  ```python
  (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  y_train = utils.to_categorical(y_train, num_classes)
  y_test = utils.to_categorical(y_test, num_classes)
  datagen = ImageDataGenerator(
      featurewise_center=True,
      featurewise_std_normalization=True,
      rotation_range=20,
      width_shift_range=0.2,
      height_shift_range=0.2,
      horizontal_flip=True,
      validation_split=0.2)
  # compute quantities required for featurewise normalization
  # (std, mean, and principal components if ZCA whitening is applied)
  datagen.fit(x_train)
  # fits the model on batches with real-time data augmentation:
  model.fit(datagen.flow(x_train, y_train, batch_size=32,
           subset='training'),
           validation_data=datagen.flow(x_train, y_train,
           batch_size=8, subset='validation'),
           steps_per_epoch=len(x_train) / 32, epochs=epochs)
  # here's a more "manual" example
  for e in range(epochs):
      print('Epoch', e)
      batches = 0
      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
          model.fit(x_batch, y_batch)
          batches += 1
          if batches >= len(x_train) / 32:
              # we need to break the loop by hand because
              # the generator loops indefinitely
              break
  ```

  Example of using `.flow_from_directory(directory)`:

  ```python
  train_datagen = ImageDataGenerator(
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True)
  test_datagen = ImageDataGenerator(rescale=1./255)
  train_generator = train_datagen.flow_from_directory(
          'data/train',
          target_size=(150, 150),
          batch_size=32,
          class_mode='binary')
  validation_generator = test_datagen.flow_from_directory(
          'data/validation',
          target_size=(150, 150),
          batch_size=32,
          class_mode='binary')
  model.fit(
          train_generator,
          steps_per_epoch=2000,
          epochs=50,
          validation_data=validation_generator,
          validation_steps=800)
  ```

  Example of transforming images and masks together.

  ```python
  # we create two instances with the same arguments
  data_gen_args = dict(featurewise_center=True,
                       featurewise_std_normalization=True,
                       rotation_range=90,
                       width_shift_range=0.1,
                       height_shift_range=0.1,
                       zoom_range=0.2)
  image_datagen = ImageDataGenerator(**data_gen_args)
  mask_datagen = ImageDataGenerator(**data_gen_args)
  # Provide the same seed and keyword arguments to the fit and flow methods
  seed = 1
  image_datagen.fit(images, augment=True, seed=seed)
  mask_datagen.fit(masks, augment=True, seed=seed)
  image_generator = image_datagen.flow_from_directory(
      'data/images',
      class_mode=None,
      seed=seed)
  mask_generator = mask_datagen.flow_from_directory(
      'data/masks',
      class_mode=None,
      seed=seed)
  # combine generators into one which yields image and masks
  train_generator = zip(image_generator, mask_generator)
  model.fit(
      train_generator,
      steps_per_epoch=2000,
      epochs=50)
  ```
  """

  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.,
               brightness_range=None,
               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,
               validation_split=0.0,
               dtype=None):
    if data_format is None:
      data_format = backend.image_data_format()
    kwargs = {}
    if 'dtype' in tf_inspect.getfullargspec(
        image.ImageDataGenerator.__init__)[0]:
      if dtype is None:
        dtype = backend.floatx()
      kwargs['dtype'] = dtype
    super(ImageDataGenerator, self).__init__(
        featurewise_center=featurewise_center,
        samplewise_center=samplewise_center,
        featurewise_std_normalization=featurewise_std_normalization,
        samplewise_std_normalization=samplewise_std_normalization,
        zca_whitening=zca_whitening,
        zca_epsilon=zca_epsilon,
        rotation_range=rotation_range,
        width_shift_range=width_shift_range,
        height_shift_range=height_shift_range,
        brightness_range=brightness_range,
        shear_range=shear_range,
        zoom_range=zoom_range,
        channel_shift_range=channel_shift_range,
        fill_mode=fill_mode,
        cval=cval,
        horizontal_flip=horizontal_flip,
        vertical_flip=vertical_flip,
        rescale=rescale,
        preprocessing_function=preprocessing_function,
        data_format=data_format,
        validation_split=validation_split,
        **kwargs)

  def flow(self,
           x,
           y=None,
           batch_size=32,
           shuffle=True,
           sample_weight=None,
           seed=None,
           save_to_dir=None,
           save_prefix='',
           save_format='png',
           subset=None):
    """Takes data & label arrays, generates batches of augmented data.

    Args:
        x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first
          element should contain the images and the second element another numpy
          array or a list of numpy arrays that gets passed to the output without
          any modifications. Can be used to feed the model miscellaneous data
          along with the images. In case of grayscale data, the channels axis of
          the image array should have value 1, in case of RGB data, it should
          have value 3, and in case of RGBA data, it should have value 4.
        y: Labels.
        batch_size: Int (default: 32).
        shuffle: Boolean (default: True).
        sample_weight: Sample weights.
        seed: Int (default: None).
        save_to_dir: None or str (default: None). This allows you to optionally
          specify a directory to which to save the augmented pictures being
          generated (useful for visualizing what you are doing).
        save_prefix: Str (default: `''`). Prefix to use for filenames of saved
          pictures (only relevant if `save_to_dir` is set).
        save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
            "tif", "jpg"
            (only relevant if `save_to_dir` is set). Default: "png".
        subset: Subset of data (`"training"` or `"validation"`) if
          `validation_split` is set in `ImageDataGenerator`.

    Returns:
        An `Iterator` yielding tuples of `(x, y)`
            where `x` is a numpy array of image data
            (in the case of a single image input) or a list
            of numpy arrays (in the case with
            additional inputs) and `y` is a numpy array
            of corresponding labels. If 'sample_weight' is not None,
            the yielded tuples are of the form `(x, y, sample_weight)`.
            If `y` is None, only the numpy array `x` is returned.
    Raises:
      ValueError: If the Value of the argument, `subset` is other than
            "training" or "validation".

    """
    return NumpyArrayIterator(
        x,
        y,
        self,
        batch_size=batch_size,
        shuffle=shuffle,
        sample_weight=sample_weight,
        seed=seed,
        data_format=self.data_format,
        save_to_dir=save_to_dir,
        save_prefix=save_prefix,
        save_format=save_format,
        subset=subset)

  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,
                          subset=None,
                          interpolation='nearest'):
    """Takes the path to a directory & generates batches of augmented data.

    Args:
        directory: string, path to the target directory. It should contain one
          subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside
          each of the subdirectories directory tree will be included in the
          generator. See [this script](
            https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
              for more details.
        target_size: Tuple of integers `(height, width)`, defaults to `(256,
          256)`. The dimensions to which all images found will be resized.
        color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb". Whether
          the images will be converted to have 1, 3, or 4 channels.
        classes: Optional list of class subdirectories
            (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list
              of classes will be automatically inferred from the subdirectory
              names/structure under `directory`, where each subdirectory will be
              treated as a different class (and the order of the classes, which
              will map to the label indices, will be alphanumeric). The
              dictionary containing the mapping from class names to class
              indices can be obtained via the attribute `class_indices`.
        class_mode: One of "categorical", "binary", "sparse",
            "input", or None. Default: "categorical".
            Determines the type of label arrays that are returned:
            - "categorical" will be 2D one-hot encoded labels,
            - "binary" will be 1D binary labels,
            - "sparse" will be 1D integer labels,
            - "input"  will be images identical to input images (mainly used to
              work with autoencoders).
            - If None, no labels are returned (the generator will only yield
              batches of image data, which is useful to use with
              `model.predict()`).
            Please note that in case of class_mode None, the data still needs to
            reside in a subdirectory of `directory` for it to work correctly.
        batch_size: Size of the batches of data (default: 32).
        shuffle: Whether to shuffle the data (default: True) If set to False,
          sorts the data in alphanumeric order.
        seed: Optional random seed for shuffling and transformations.
        save_to_dir: None or str (default: None). This allows you to optionally
          specify a directory to which to save the augmented pictures being
          generated (useful for visualizing what you are doing).
        save_prefix: Str. Prefix to use for filenames of saved pictures (only
          relevant if `save_to_dir` is set).
        save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
            "tif", "jpg"
            (only relevant if `save_to_dir` is set). Default: "png".
        follow_links: Whether to follow symlinks inside
            class subdirectories (default: False).
        subset: Subset of data (`"training"` or `"validation"`) if
          `validation_split` is set in `ImageDataGenerator`.
        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.

    Returns:
        A `DirectoryIterator` yielding tuples of `(x, y)`
            where `x` is a numpy array containing a batch
            of images with shape `(batch_size, *target_size, channels)`
            and `y` is a numpy array of corresponding labels.
    """
    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,
        subset=subset,
        interpolation=interpolation)

  def flow_from_dataframe(self,
                          dataframe,
                          directory=None,
                          x_col='filename',
                          y_col='class',
                          weight_col=None,
                          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',
                          subset=None,
                          interpolation='nearest',
                          validate_filenames=True,
                          **kwargs):
    """Takes the dataframe and the path to a directory + generates batches.

     The generated batches contain augmented/normalized data.

    **A simple tutorial can be found **[here](
                                http://bit.ly/keras_flow_from_dataframe).

    Args:
        dataframe: Pandas dataframe containing the filepaths relative to
          `directory` (or absolute paths if `directory` is None) of the images
          in a string column. It should include other column/s
            depending on the `class_mode`:
            - if `class_mode` is `"categorical"` (default value) it must include
              the `y_col` column with the class/es of each image. Values in
              column can be string/list/tuple if a single class or list/tuple if
              multiple classes.
            - if `class_mode` is `"binary"` or `"sparse"` it must include the
              given `y_col` column with class values as strings.
            - if `class_mode` is `"raw"` or `"multi_output"` it should contain
              the columns specified in `y_col`.
            - if `class_mode` is `"input"` or `None` no extra column is needed.
        directory: string, path to the directory to read images from. If `None`,
          data in `x_col` column should be absolute paths.
        x_col: string, column in `dataframe` that contains the filenames (or
          absolute paths if `directory` is `None`).
        y_col: string or list, column/s in `dataframe` that has the target data.
        weight_col: string, column in `dataframe` that contains the sample
            weights. Default: `None`.
        target_size: tuple of integers `(height, width)`, default: `(256, 256)`.
          The dimensions to which all images found will be resized.
        color_mode: one of "grayscale", "rgb", "rgba". Default: "rgb". Whether
          the images will be converted to have 1 or 3 color channels.
        classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is
          None. If not provided, the list of classes will be automatically
          inferred from the `y_col`, which will map to the label indices, will
          be alphanumeric). The dictionary containing the mapping from class
          names to class indices can be obtained via the attribute
          `class_indices`.
        class_mode: one of "binary", "categorical", "input", "multi_output",
            "raw", sparse" or None. Default: "categorical".
            Mode for yielding the targets:
            - `"binary"`: 1D numpy array of binary labels,
            - `"categorical"`: 2D numpy array of one-hot encoded labels.
              Supports multi-label output.
            - `"input"`: images identical to input images (mainly used to work
              with autoencoders),
            - `"multi_output"`: list with the values of the different columns,
            - `"raw"`: numpy array of values in `y_col` column(s),
            - `"sparse"`: 1D numpy array of integer labels,
            - `None`, no targets are returned (the generator will only yield
              batches of image data, which is useful to use in
              `model.predict()`).
        batch_size: size of the batches of data (default: 32).
        shuffle: whether to shuffle the data (default: True)
        seed: optional random seed for shuffling and transformations.
        save_to_dir: None or str (default: None). This allows you to optionally
          specify a directory to which to save the augmented pictures being
          generated (useful for visualizing what you are doing).
        save_prefix: str. Prefix to use for filenames of saved pictures (only
          relevant if `save_to_dir` is set).
        save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
            "tif", "jpg"
            (only relevant if `save_to_dir` is set). Default: "png".
        subset: Subset of data (`"training"` or `"validation"`) if
          `validation_split` is set in `ImageDataGenerator`.
        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.
        validate_filenames: Boolean, whether to validate image filenames in
          `x_col`. If `True`, invalid images will be ignored. Disabling this
          option can lead to speed-up in the execution of this function.
          Defaults to `True`.
        **kwargs: legacy arguments for raising deprecation warnings.

    Returns:
        A `DataFrameIterator` yielding tuples of `(x, y)`
        where `x` is a numpy array containing a batch
        of images with shape `(batch_size, *target_size, channels)`
        and `y` is a numpy array of corresponding labels.
    """
    if 'has_ext' in kwargs:
      tf_logging.warning(
          'has_ext is deprecated, filenames in the dataframe have '
          'to match the exact filenames in disk.', DeprecationWarning)
    if 'sort' in kwargs:
      tf_logging.warning(
          'sort is deprecated, batches will be created in the'
          'same order than the filenames provided if shuffle'
          'is set to False.', DeprecationWarning)
    if class_mode == 'other':
      tf_logging.warning(
          '`class_mode` "other" is deprecated, please use '
          '`class_mode` "raw".', DeprecationWarning)
      class_mode = 'raw'
    if 'drop_duplicates' in kwargs:
      tf_logging.warning(
          'drop_duplicates is deprecated, you can drop duplicates '
          'by using the pandas.DataFrame.drop_duplicates method.',
          DeprecationWarning)

    return DataFrameIterator(
        dataframe,
        directory,
        self,
        x_col=x_col,
        y_col=y_col,
        weight_col=weight_col,
        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,
        subset=subset,
        interpolation=interpolation,
        validate_filenames=validate_filenames)

Ancestors

  • keras_preprocessing.image.image_data_generator.ImageDataGenerator

Methods

def flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)

Takes data & label arrays, generates batches of augmented data.

Args

x
Input data. Numpy array of rank 4 or a tuple. If tuple, the first element should contain the images and the second element another numpy array or a list of numpy arrays that gets passed to the output without any modifications. Can be used to feed the model miscellaneous data along with the images. In case of grayscale data, the channels axis of the image array should have value 1, in case of RGB data, it should have value 3, and in case of RGBA data, it should have value 4.
y
Labels.
batch_size
Int (default: 32).
shuffle
Boolean (default: True).
sample_weight
Sample weights.
seed
Int (default: None).
save_to_dir
None or str (default: None). This allows you to optionally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
save_prefix
Str (default: ''). Prefix to use for filenames of saved pictures (only relevant if save_to_dir is set).
save_format
one of "png", "jpeg", "bmp", "pdf", "ppm", "gif", "tif", "jpg" (only relevant if save_to_dir is set). Default: "png".
subset
Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.

Returns

An Iterator yielding tuples of (x, y) where x is a numpy array of image data (in the case of a single image input) or a list of numpy arrays (in the case with additional inputs) and y is a numpy array of corresponding labels. If 'sample_weight' is not None, the yielded tuples are of the form (x, y, sample_weight). If y is None, only the numpy array x is returned.

Raises

ValueError
If the Value of the argument, subset is other than "training" or "validation".
Expand source code
def flow(self,
         x,
         y=None,
         batch_size=32,
         shuffle=True,
         sample_weight=None,
         seed=None,
         save_to_dir=None,
         save_prefix='',
         save_format='png',
         subset=None):
  """Takes data & label arrays, generates batches of augmented data.

  Args:
      x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first
        element should contain the images and the second element another numpy
        array or a list of numpy arrays that gets passed to the output without
        any modifications. Can be used to feed the model miscellaneous data
        along with the images. In case of grayscale data, the channels axis of
        the image array should have value 1, in case of RGB data, it should
        have value 3, and in case of RGBA data, it should have value 4.
      y: Labels.
      batch_size: Int (default: 32).
      shuffle: Boolean (default: True).
      sample_weight: Sample weights.
      seed: Int (default: None).
      save_to_dir: None or str (default: None). This allows you to optionally
        specify a directory to which to save the augmented pictures being
        generated (useful for visualizing what you are doing).
      save_prefix: Str (default: `''`). Prefix to use for filenames of saved
        pictures (only relevant if `save_to_dir` is set).
      save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
          "tif", "jpg"
          (only relevant if `save_to_dir` is set). Default: "png".
      subset: Subset of data (`"training"` or `"validation"`) if
        `validation_split` is set in `ImageDataGenerator`.

  Returns:
      An `Iterator` yielding tuples of `(x, y)`
          where `x` is a numpy array of image data
          (in the case of a single image input) or a list
          of numpy arrays (in the case with
          additional inputs) and `y` is a numpy array
          of corresponding labels. If 'sample_weight' is not None,
          the yielded tuples are of the form `(x, y, sample_weight)`.
          If `y` is None, only the numpy array `x` is returned.
  Raises:
    ValueError: If the Value of the argument, `subset` is other than
          "training" or "validation".

  """
  return NumpyArrayIterator(
      x,
      y,
      self,
      batch_size=batch_size,
      shuffle=shuffle,
      sample_weight=sample_weight,
      seed=seed,
      data_format=self.data_format,
      save_to_dir=save_to_dir,
      save_prefix=save_prefix,
      save_format=save_format,
      subset=subset)
def flow_from_dataframe(self, dataframe, directory=None, x_col='filename', y_col='class', weight_col=None, 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', subset=None, interpolation='nearest', validate_filenames=True, **kwargs)

Takes the dataframe and the path to a directory + generates batches.

The generated batches contain augmented/normalized data.

A simple tutorial can be found here.

Args

dataframe
Pandas dataframe containing the filepaths relative to directory (or absolute paths if directory is None) of the images in a string column. It should include other column/s depending on the class_mode: - if class_mode is "categorical" (default value) it must include the y_col column with the class/es of each image. Values in column can be string/list/tuple if a single class or list/tuple if multiple classes. - if class_mode is "binary" or "sparse" it must include the given y_col column with class values as strings. - if class_mode is "raw" or "multi_output" it should contain the columns specified in y_col. - if class_mode is "input" or None no extra column is needed.
directory
string, path to the directory to read images from. If None, data in x_col column should be absolute paths.
x_col
string, column in dataframe that contains the filenames (or absolute paths if directory is None).
y_col
string or list, column/s in dataframe that has the target data.
weight_col
string, column in dataframe that contains the sample weights. Default: None.
target_size
tuple of integers (height, width), default: (256, 256). The dimensions to which all images found will be resized.
color_mode
one of "grayscale", "rgb", "rgba". Default: "rgb". Whether the images will be converted to have 1 or 3 color channels.
classes
optional list of classes (e.g. ['dogs', 'cats']). Default is None. If not provided, the list of classes will be automatically inferred from the y_col, which will map to the label indices, will be alphanumeric). The dictionary containing the mapping from class names to class indices can be obtained via the attribute class_indices.
class_mode
one of "binary", "categorical", "input", "multi_output", "raw", sparse" or None. Default: "categorical". Mode for yielding the targets: - "binary": 1D numpy array of binary labels, - "categorical": 2D numpy array of one-hot encoded labels. Supports multi-label output. - "input": images identical to input images (mainly used to work with autoencoders), - "multi_output": list with the values of the different columns, - "raw": numpy array of values in y_col column(s), - "sparse": 1D numpy array of integer labels, - None, no targets are returned (the generator will only yield batches of image data, which is useful to use in model.predict()).
batch_size
size of the batches of data (default: 32).
shuffle
whether to shuffle the data (default: True)
seed
optional random seed for shuffling and transformations.
save_to_dir
None or str (default: None). This allows you to optionally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
save_prefix
str. Prefix to use for filenames of saved pictures (only relevant if save_to_dir is set).
save_format
one of "png", "jpeg", "bmp", "pdf", "ppm", "gif", "tif", "jpg" (only relevant if save_to_dir is set). Default: "png".
subset
Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
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.
validate_filenames
Boolean, whether to validate image filenames in x_col. If True, invalid images will be ignored. Disabling this option can lead to speed-up in the execution of this function. Defaults to True.
**kwargs
legacy arguments for raising deprecation warnings.

Returns

A DataFrameIterator yielding tuples of (x, y) where x is a numpy array containing a batch of images with shape (batch_size, *target_size, channels) and y is a numpy array of corresponding labels.

Expand source code
def flow_from_dataframe(self,
                        dataframe,
                        directory=None,
                        x_col='filename',
                        y_col='class',
                        weight_col=None,
                        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',
                        subset=None,
                        interpolation='nearest',
                        validate_filenames=True,
                        **kwargs):
  """Takes the dataframe and the path to a directory + generates batches.

   The generated batches contain augmented/normalized data.

  **A simple tutorial can be found **[here](
                              http://bit.ly/keras_flow_from_dataframe).

  Args:
      dataframe: Pandas dataframe containing the filepaths relative to
        `directory` (or absolute paths if `directory` is None) of the images
        in a string column. It should include other column/s
          depending on the `class_mode`:
          - if `class_mode` is `"categorical"` (default value) it must include
            the `y_col` column with the class/es of each image. Values in
            column can be string/list/tuple if a single class or list/tuple if
            multiple classes.
          - if `class_mode` is `"binary"` or `"sparse"` it must include the
            given `y_col` column with class values as strings.
          - if `class_mode` is `"raw"` or `"multi_output"` it should contain
            the columns specified in `y_col`.
          - if `class_mode` is `"input"` or `None` no extra column is needed.
      directory: string, path to the directory to read images from. If `None`,
        data in `x_col` column should be absolute paths.
      x_col: string, column in `dataframe` that contains the filenames (or
        absolute paths if `directory` is `None`).
      y_col: string or list, column/s in `dataframe` that has the target data.
      weight_col: string, column in `dataframe` that contains the sample
          weights. Default: `None`.
      target_size: tuple of integers `(height, width)`, default: `(256, 256)`.
        The dimensions to which all images found will be resized.
      color_mode: one of "grayscale", "rgb", "rgba". Default: "rgb". Whether
        the images will be converted to have 1 or 3 color channels.
      classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is
        None. If not provided, the list of classes will be automatically
        inferred from the `y_col`, which will map to the label indices, will
        be alphanumeric). The dictionary containing the mapping from class
        names to class indices can be obtained via the attribute
        `class_indices`.
      class_mode: one of "binary", "categorical", "input", "multi_output",
          "raw", sparse" or None. Default: "categorical".
          Mode for yielding the targets:
          - `"binary"`: 1D numpy array of binary labels,
          - `"categorical"`: 2D numpy array of one-hot encoded labels.
            Supports multi-label output.
          - `"input"`: images identical to input images (mainly used to work
            with autoencoders),
          - `"multi_output"`: list with the values of the different columns,
          - `"raw"`: numpy array of values in `y_col` column(s),
          - `"sparse"`: 1D numpy array of integer labels,
          - `None`, no targets are returned (the generator will only yield
            batches of image data, which is useful to use in
            `model.predict()`).
      batch_size: size of the batches of data (default: 32).
      shuffle: whether to shuffle the data (default: True)
      seed: optional random seed for shuffling and transformations.
      save_to_dir: None or str (default: None). This allows you to optionally
        specify a directory to which to save the augmented pictures being
        generated (useful for visualizing what you are doing).
      save_prefix: str. Prefix to use for filenames of saved pictures (only
        relevant if `save_to_dir` is set).
      save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
          "tif", "jpg"
          (only relevant if `save_to_dir` is set). Default: "png".
      subset: Subset of data (`"training"` or `"validation"`) if
        `validation_split` is set in `ImageDataGenerator`.
      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.
      validate_filenames: Boolean, whether to validate image filenames in
        `x_col`. If `True`, invalid images will be ignored. Disabling this
        option can lead to speed-up in the execution of this function.
        Defaults to `True`.
      **kwargs: legacy arguments for raising deprecation warnings.

  Returns:
      A `DataFrameIterator` yielding tuples of `(x, y)`
      where `x` is a numpy array containing a batch
      of images with shape `(batch_size, *target_size, channels)`
      and `y` is a numpy array of corresponding labels.
  """
  if 'has_ext' in kwargs:
    tf_logging.warning(
        'has_ext is deprecated, filenames in the dataframe have '
        'to match the exact filenames in disk.', DeprecationWarning)
  if 'sort' in kwargs:
    tf_logging.warning(
        'sort is deprecated, batches will be created in the'
        'same order than the filenames provided if shuffle'
        'is set to False.', DeprecationWarning)
  if class_mode == 'other':
    tf_logging.warning(
        '`class_mode` "other" is deprecated, please use '
        '`class_mode` "raw".', DeprecationWarning)
    class_mode = 'raw'
  if 'drop_duplicates' in kwargs:
    tf_logging.warning(
        'drop_duplicates is deprecated, you can drop duplicates '
        'by using the pandas.DataFrame.drop_duplicates method.',
        DeprecationWarning)

  return DataFrameIterator(
      dataframe,
      directory,
      self,
      x_col=x_col,
      y_col=y_col,
      weight_col=weight_col,
      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,
      subset=subset,
      interpolation=interpolation,
      validate_filenames=validate_filenames)
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, subset=None, interpolation='nearest')

Takes the path to a directory & generates batches of augmented data.

Args

directory
string, path to the target directory. It should contain one subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside each of the subdirectories directory tree will be included in the generator. See this script for more details.
target_size
Tuple of integers (height, width), defaults to (256, 256). The dimensions to which all images found will be resized.
color_mode
One of "grayscale", "rgb", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.
classes
Optional list of class subdirectories (e.g. ['dogs', 'cats']). Default: None. If not provided, the list of classes will be automatically inferred from the subdirectory names/structure under directory, where each subdirectory will be treated as a different class (and the order of the classes, which will map to the label indices, will be alphanumeric). The dictionary containing the mapping from class names to class indices can be obtained via the attribute class_indices.
class_mode
One of "categorical", "binary", "sparse", "input", or None. Default: "categorical". Determines the type of label arrays that are returned: - "categorical" will be 2D one-hot encoded labels, - "binary" will be 1D binary labels, - "sparse" will be 1D integer labels, - "input" will be images identical to input images (mainly used to work with autoencoders). - If None, no labels are returned (the generator will only yield batches of image data, which is useful to use with model.predict()). Please note that in case of class_mode None, the data still needs to reside in a subdirectory of directory for it to work correctly.
batch_size
Size of the batches of data (default: 32).
shuffle
Whether to shuffle the data (default: True) If set to False, sorts the data in alphanumeric order.
seed
Optional random seed for shuffling and transformations.
save_to_dir
None or str (default: None). This allows you to optionally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
save_prefix
Str. Prefix to use for filenames of saved pictures (only relevant if save_to_dir is set).
save_format
one of "png", "jpeg", "bmp", "pdf", "ppm", "gif", "tif", "jpg" (only relevant if save_to_dir is set). Default: "png".
follow_links
Whether to follow symlinks inside class subdirectories (default: False).
subset
Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
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.

Returns

A DirectoryIterator yielding tuples of (x, y) where x is a numpy array containing a batch of images with shape (batch_size, *target_size, channels) and y is a numpy array of corresponding labels.

Expand source code
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,
                        subset=None,
                        interpolation='nearest'):
  """Takes the path to a directory & generates batches of augmented data.

  Args:
      directory: string, path to the target directory. It should contain one
        subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside
        each of the subdirectories directory tree will be included in the
        generator. See [this script](
          https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
            for more details.
      target_size: Tuple of integers `(height, width)`, defaults to `(256,
        256)`. The dimensions to which all images found will be resized.
      color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb". Whether
        the images will be converted to have 1, 3, or 4 channels.
      classes: Optional list of class subdirectories
          (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list
            of classes will be automatically inferred from the subdirectory
            names/structure under `directory`, where each subdirectory will be
            treated as a different class (and the order of the classes, which
            will map to the label indices, will be alphanumeric). The
            dictionary containing the mapping from class names to class
            indices can be obtained via the attribute `class_indices`.
      class_mode: One of "categorical", "binary", "sparse",
          "input", or None. Default: "categorical".
          Determines the type of label arrays that are returned:
          - "categorical" will be 2D one-hot encoded labels,
          - "binary" will be 1D binary labels,
          - "sparse" will be 1D integer labels,
          - "input"  will be images identical to input images (mainly used to
            work with autoencoders).
          - If None, no labels are returned (the generator will only yield
            batches of image data, which is useful to use with
            `model.predict()`).
          Please note that in case of class_mode None, the data still needs to
          reside in a subdirectory of `directory` for it to work correctly.
      batch_size: Size of the batches of data (default: 32).
      shuffle: Whether to shuffle the data (default: True) If set to False,
        sorts the data in alphanumeric order.
      seed: Optional random seed for shuffling and transformations.
      save_to_dir: None or str (default: None). This allows you to optionally
        specify a directory to which to save the augmented pictures being
        generated (useful for visualizing what you are doing).
      save_prefix: Str. Prefix to use for filenames of saved pictures (only
        relevant if `save_to_dir` is set).
      save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
          "tif", "jpg"
          (only relevant if `save_to_dir` is set). Default: "png".
      follow_links: Whether to follow symlinks inside
          class subdirectories (default: False).
      subset: Subset of data (`"training"` or `"validation"`) if
        `validation_split` is set in `ImageDataGenerator`.
      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.

  Returns:
      A `DirectoryIterator` yielding tuples of `(x, y)`
          where `x` is a numpy array containing a batch
          of images with shape `(batch_size, *target_size, channels)`
          and `y` is a numpy array of corresponding labels.
  """
  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,
      subset=subset,
      interpolation=interpolation)
class Iterator (n, batch_size, shuffle, seed)

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.
Expand source code
class Iterator(image.Iterator, data_utils.Sequence):
  pass

Ancestors

  • keras_preprocessing.image.iterator.Iterator
  • Sequence

Subclasses

Inherited members

class NumpyArrayIterator (x, y, image_data_generator, batch_size=32, shuffle=False, sample_weight=None, seed=None, data_format=None, save_to_dir=None, save_prefix='', save_format='png', subset=None, dtype=None)

Iterator yielding data from a Numpy array.

Args

x
Numpy array of input data or tuple. If tuple, the second elements is either another numpy array or a list of numpy arrays, each of which gets passed through as an output without any modifications.
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.
sample_weight
Numpy array of sample weights.
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).
subset
Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
dtype
Dtype to use for the generated arrays.
Expand source code
class NumpyArrayIterator(image.NumpyArrayIterator, Iterator):
  """Iterator yielding data from a Numpy array.

  Args:
      x: Numpy array of input data or tuple.
          If tuple, the second elements is either
          another numpy array or a list of numpy arrays,
          each of which gets passed
          through as an output without any modifications.
      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.
      sample_weight: Numpy array of sample weights.
      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).
      subset: Subset of data (`"training"` or `"validation"`) if
          validation_split is set in ImageDataGenerator.
      dtype: Dtype to use for the generated arrays.
  """

  def __init__(self, x, y, image_data_generator,
               batch_size=32,
               shuffle=False,
               sample_weight=None,
               seed=None,
               data_format=None,
               save_to_dir=None,
               save_prefix='',
               save_format='png',
               subset=None,
               dtype=None):
    if data_format is None:
      data_format = backend.image_data_format()
    kwargs = {}
    if 'dtype' in tf_inspect.getfullargspec(
        image.NumpyArrayIterator.__init__)[0]:
      if dtype is None:
        dtype = backend.floatx()
      kwargs['dtype'] = dtype
    super(NumpyArrayIterator, self).__init__(
        x, y, image_data_generator,
        batch_size=batch_size,
        shuffle=shuffle,
        sample_weight=sample_weight,
        seed=seed,
        data_format=data_format,
        save_to_dir=save_to_dir,
        save_prefix=save_prefix,
        save_format=save_format,
        subset=subset,
        **kwargs)

Ancestors

  • keras_preprocessing.image.numpy_array_iterator.NumpyArrayIterator
  • Iterator
  • keras_preprocessing.image.iterator.Iterator
  • Sequence

Inherited members