#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
import numpy as np
import time
import numbers
import random
import os
import re
import sys
import threading
# import Queue # <-- donot work for py3
is_py2 = sys.version[0] == '2'
if is_py2:
import Queue as queue
else:
import queue as queue
from six.moves import range
import scipy
from scipy import linalg
import scipy.ndimage as ndi
from skimage import transform
from skimage import exposure
import skimage
# linalg https://docs.scipy.org/doc/scipy/reference/linalg.html
# ndimage https://docs.scipy.org/doc/scipy/reference/ndimage.html
## Threading
[docs]def threading_data(data=None, fn=None, **kwargs):
"""Return a batch of result by given data.
Usually be used for data augmentation.
Parameters
-----------
data : numpy array or zip of numpy array, see Examples below.
fn : the function for data processing.
more args : the args for fn, see Examples below.
Examples
--------
- Single array
>>> X --> [batch_size, row, col, 1] greyscale
>>> results = threading_data(X, zoom, zoom_range=[0.5, 1], is_random=True)
... results --> [batch_size, row, col, channel]
>>> tl.visualize.images2d(images=np.asarray(results), second=0.01, saveable=True, name='after', dtype=None)
>>> tl.visualize.images2d(images=np.asarray(X), second=0.01, saveable=True, name='before', dtype=None)
- List of array (e.g. functions with ``multi``)
>>> X, Y --> [batch_size, row, col, 1] greyscale
>>> data = threading_data([_ for _ in zip(X, Y)], zoom_multi, zoom_range=[0.5, 1], is_random=True)
... data --> [batch_size, 2, row, col, 1]
>>> X_, Y_ = data.transpose((1,0,2,3,4))
... X_, Y_ --> [batch_size, row, col, 1]
>>> tl.visualize.images2d(images=np.asarray(X_), second=0.01, saveable=True, name='after', dtype=None)
>>> tl.visualize.images2d(images=np.asarray(Y_), second=0.01, saveable=True, name='before', dtype=None)
- Customized function for image segmentation
>>> def distort_img(data):
... x, y = data
... x, y = flip_axis_multi([x, y], axis=0, is_random=True)
... x, y = flip_axis_multi([x, y], axis=1, is_random=True)
... x, y = crop_multi([x, y], 100, 100, is_random=True)
... return x, y
>>> X, Y --> [batch_size, row, col, channel]
>>> data = threading_data([_ for _ in zip(X, Y)], distort_img)
>>> X_, Y_ = data.transpose((1,0,2,3,4))
References
----------
- `python queue <https://pymotw.com/2/Queue/index.html#module-Queue>`_
"""
## plot function info
# for name, value in kwargs.items():
# print('{0} = {1}'.format(name, value))
# exit()
## define function for threading
def function(q, data, kwargs):
result = fn(data, **kwargs)
q.put(result)
## start threading
q = queue.Queue()
for i in range(len(data)):
d = threading.Thread(
name='threading_and_return',
target=function,
args=(q, data[i], kwargs)
)
d.start()
## get results
results = []
for i in range(len(data)):
result = q.get()
results.append(result)
return np.asarray(results)
## Image
[docs]def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Rotate an image randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
rg : int or float
Degree to rotate, usually 0 ~ 180.
is_random : boolean, default False
If True, randomly rotate.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
fill_mode : string
Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
cval : scalar, optional
Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
Examples
---------
>>> x --> [row, col, 1] greyscale
>>> x = rotation(x, rg=40, is_random=False)
>>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray')
"""
if is_random:
theta = np.pi / 180 * np.random.uniform(-rg, rg)
else:
theta = np.pi /180 * rg
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
h, w = x.shape[row_index], x.shape[col_index]
transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
return x
[docs]def rotation_multi(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Rotate multiple images with the same arguments, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``rotation``.
Examples
--------
>>> x, y --> [row, col, 1] greyscale
>>> x, y = rotation_multi([x, y], rg=90, is_random=False)
>>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='x',cmap='gray')
>>> tl.visualize.frame(y[:,:,0], second=0.01, saveable=True, name='y',cmap='gray')
"""
if is_random:
theta = np.pi / 180 * np.random.uniform(-rg, rg)
else:
theta = np.pi /180 * rg
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
h, w = x[0].shape[row_index], x[0].shape[col_index]
transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
results = []
for data in x:
results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
return np.asarray(results)
# crop
[docs]def crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1, channel_index=2):
"""Randomly or centrally crop an image.
Parameters
----------
x : numpy array
An image with dimension of [row, col, channel] (default).
wrg : float
Size of weight.
hrg : float
Size of height.
is_random : boolean, default False
If True, randomly crop, else central crop.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
"""
h, w = x.shape[row_index], x.shape[col_index]
assert (h > hrg) and (w > wrg), "The size of cropping should smaller than the original image"
if is_random:
h_offset = int(np.random.uniform(0, h-hrg) -1)
w_offset = int(np.random.uniform(0, w-wrg) -1)
# print(h_offset, w_offset, x[h_offset: hrg+h_offset ,w_offset: wrg+w_offset].shape)
return x[h_offset: hrg+h_offset ,w_offset: wrg+w_offset]
else:
# central crop
h_offset = (h - hrg)/2
w_offset = (w - wrg)/2
# print(x[h_offset: h-h_offset ,w_offset: w-w_offset].shape)
return x[h_offset: h-h_offset ,w_offset: w-w_offset]
[docs]def crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1, channel_index=2):
"""Randomly or centrally crop multiple images.
Parameters
----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``crop``.
"""
h, w = x[0].shape[row_index], x[0].shape[col_index]
assert (h > hrg) and (w > wrg), "The size of cropping should smaller than the original image"
if is_random:
h_offset = int(np.random.uniform(0, h-hrg) -1)
w_offset = int(np.random.uniform(0, w-wrg) -1)
results = []
for data in x:
results.append( data[h_offset: hrg+h_offset ,w_offset: wrg+w_offset])
return np.asarray(results)
else:
# central crop
h_offset = (h - hrg)/2
w_offset = (w - wrg)/2
results = []
for data in x:
results.append( data[h_offset: h-h_offset ,w_offset: w-w_offset] )
return np.asarray(results)
# flip
[docs]def flip_axis(x, axis, is_random=False):
"""Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly,
Parameters
----------
x : numpy array
An image with dimension of [row, col, channel] (default).
axis : int
- 0, flip up and down
- 1, flip left and right
- 2, flip channel
is_random : boolean, default False
If True, randomly zoom.
"""
if is_random:
factor = np.random.uniform(-1, 1)
if factor > 0:
x = np.asarray(x).swapaxes(axis, 0)
x = x[::-1, ...]
x = x.swapaxes(0, axis)
return x
else:
return x
else:
x = np.asarray(x).swapaxes(axis, 0)
x = x[::-1, ...]
x = x.swapaxes(0, axis)
return x
[docs]def flip_axis_multi(x, axis, is_random=False):
"""Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly,
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``flip_axis``.
"""
if is_random:
factor = np.random.uniform(-1, 1)
if factor > 0:
# x = np.asarray(x).swapaxes(axis, 0)
# x = x[::-1, ...]
# x = x.swapaxes(0, axis)
# return x
results = []
for data in x:
data = np.asarray(data).swapaxes(axis, 0)
data = data[::-1, ...]
data = data.swapaxes(0, axis)
results.append( data )
return np.asarray(results)
else:
return np.asarray(x)
else:
# x = np.asarray(x).swapaxes(axis, 0)
# x = x[::-1, ...]
# x = x.swapaxes(0, axis)
# return x
results = []
for data in x:
data = np.asarray(data).swapaxes(axis, 0)
data = data[::-1, ...]
data = data.swapaxes(0, axis)
results.append( data )
return np.asarray(results)
# shift
[docs]def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Shift an image randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
wrg : float
Percentage of shift in axis x, usually -0.25 ~ 0.25.
hrg : float
Percentage of shift in axis y, usually -0.25 ~ 0.25.
is_random : boolean, default False
If True, randomly shift.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
fill_mode : string
Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
cval : scalar, optional
Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
"""
h, w = x.shape[row_index], x.shape[col_index]
if is_random:
tx = np.random.uniform(-hrg, hrg) * h
ty = np.random.uniform(-wrg, wrg) * w
else:
tx, ty = hrg * h, wrg * w
translation_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = translation_matrix # no need to do offset
x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
return x
[docs]def shift_multi(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Shift images with the same arguments, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``shift``.
"""
h, w = x[0].shape[row_index], x[0].shape[col_index]
if is_random:
tx = np.random.uniform(-hrg, hrg) * h
ty = np.random.uniform(-wrg, wrg) * w
else:
tx, ty = hrg * h, wrg * w
translation_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = translation_matrix # no need to do offset
results = []
for data in x:
results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
return np.asarray(results)
# shear
[docs]def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Shear an image randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
intensity : float
Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False),
you can have a quick try by shear(X, 1).
is_random : boolean, default False
If True, randomly shear.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
fill_mode : string
Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
cval : scalar, optional
Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
"""
if is_random:
shear = np.random.uniform(-intensity, intensity)
else:
shear = intensity
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
h, w = x.shape[row_index], x.shape[col_index]
transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
return x
[docs]def shear_multi(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Shear images with the same arguments, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``shear``.
"""
if is_random:
shear = np.random.uniform(-intensity, intensity)
else:
shear = intensity
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
h, w = x[0].shape[row_index], x[0].shape[col_index]
transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
results = []
for data in x:
results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
return np.asarray(results)
# swirl
[docs]def swirl(x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False):
"""Swirl an image randomly or non-randomly, see `scikit-image swirl API <http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.swirl>`_
and `example <http://scikit-image.org/docs/dev/auto_examples/plot_swirl.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
center : (row, column) tuple or (2,) ndarray, optional
Center coordinate of transformation.
strength : float, optional
The amount of swirling applied.
radius : float, optional
The extent of the swirl in pixels. The effect dies out rapidly beyond radius.
rotation : float, (degree) optional
Additional rotation applied to the image, usually [0, 360], relates to center.
output_shape : tuple (rows, cols), optional
Shape of the output image generated. By default the shape of the input image is preserved.
order : int, optional
The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See skimage.transform.warp for detail.
mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional
Points outside the boundaries of the input are filled according to the given mode, with ‘constant’ used as the default. Modes match the behaviour of numpy.pad.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
is_random : boolean, default False
If True, random swirl.
- random center = [(0 ~ x.shape[0]), (0 ~ x.shape[1])]
- random strength = [0, strength]
- random radius = [1e-10, radius]
- random rotation = [-rotation, rotation]
Examples
---------
>>> x --> [row, col, 1] greyscale
>>> x = swirl(x, strength=4, radius=100)
"""
assert radius != 0, Exception("Invalid radius value")
rotation = np.pi / 180 * rotation
if is_random:
center_h = int(np.random.uniform(0, x.shape[0]))
center_w = int(np.random.uniform(0, x.shape[1]))
center = (center_h, center_w)
strength = np.random.uniform(0, strength)
radius = np.random.uniform(1e-10, radius)
rotation = np.random.uniform(-rotation, rotation)
max_v = np.max(x)
if max_v > 1: # Note: the input of this fn should be [-1, 1], rescale is required.
x = x / max_v
swirled = skimage.transform.swirl(x, center=center, strength=strength, radius=radius, rotation=rotation,
output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
if max_v > 1:
swirled = swirled * max_v
return swirled
[docs]def swirl_multi(x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False):
"""Swirl multiple images with the same arguments, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``swirl``.
"""
assert radius != 0, Exception("Invalid radius value")
rotation = np.pi / 180 * rotation
if is_random:
center_h = int(np.random.uniform(0, x[0].shape[0]))
center_w = int(np.random.uniform(0, x[0].shape[1]))
center = (center_h, center_w)
strength = np.random.uniform(0, strength)
radius = np.random.uniform(1e-10, radius)
rotation = np.random.uniform(-rotation, rotation)
results = []
for data in x:
max_v = np.max(data)
if max_v > 1: # Note: the input of this fn should be [-1, 1], rescale is required.
data = data / max_v
swirled = skimage.transform.swirl(data, center=center, strength=strength, radius=radius, rotation=rotation,
output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
if max_v > 1:
swirled = swirled * max_v
results.append( swirled )
return np.asarray(results)
# elastic_transform
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
# zoom
[docs]def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0.):
"""Zoom in and out of a single image, randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
zoom_range : list or tuple
- If is_random=False, (h, w) are the fixed zoom factor for row and column axies, factor small than one is zoom in.
- If is_random=True, (min zoom out, max zoom out) for x and y with different random zoom in/out factor.
e.g (0.5, 1) zoom in 1~2 times.
is_random : boolean, default False
If True, randomly zoom.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
fill_mode : string
Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
cval : scalar, optional
Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
"""
if len(zoom_range) != 2:
raise Exception('zoom_range should be a tuple or list of two floats. '
'Received arg: ', zoom_range)
if is_random:
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
print(" random_zoom : not zoom in/out")
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
else:
zx, zy = zoom_range
# print(zx, zy)
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
h, w = x.shape[row_index], x.shape[col_index]
transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
return x
[docs]def zoom_multi(x, zoom_range=(0.9, 1.1), is_random=False,
row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.):
"""Zoom in and out of images with the same arguments, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``zoom``.
"""
if len(zoom_range) != 2:
raise Exception('zoom_range should be a tuple or list of two floats. '
'Received arg: ', zoom_range)
if is_random:
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
print(" random_zoom : not zoom in/out")
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
else:
zx, zy = zoom_range
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
h, w = x[0].shape[row_index], x[0].shape[col_index]
transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
# x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
# return x
results = []
for data in x:
results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
return np.asarray(results)
# image = tf.image.random_brightness(image, max_delta=32. / 255.)
# image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
# image = tf.image.random_hue(image, max_delta=0.032)
# image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
# brightness
[docs]def brightness(x, gamma=1, gain=1, is_random=False):
"""Change the brightness of a single image, randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
gamma : float, small than 1 means brighter.
Non negative real number. Default value is 1.
- If is_random is True, gamma in a range of (1-gamma, 1+gamma).
gain : float
The constant multiplier. Default value is 1.
is_random : boolean, default False
- If True, randomly change brightness.
References
-----------
- `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_
- `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_
"""
if is_random:
gamma = np.random.uniform(1-gamma, 1+gamma)
x = exposure.adjust_gamma(x, gamma, gain)
return x
[docs]def brightness_multi(x, gamma=1, gain=1, is_random=False):
"""Change the brightness of multiply images, randomly or non-randomly.
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``brightness``.
"""
if is_random:
gamma = np.random.uniform(1-gamma, 1+gamma)
results = []
for data in x:
results.append( exposure.adjust_gamma(data, gamma, gain) )
return np.asarray(results)
# contrast
def constant(x, cutoff=0.5, gain=10, inv=False, is_random=False):
# TODO
x = exposure.adjust_sigmoid(x, cutoff=cutoff, gain=gain, inv=inv)
return x
def constant_multi():
#TODO
pass
# resize
[docs]def imresize(x, size=[100, 100], interp='bilinear', mode=None):
"""Resize an image by given output size and method.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
size : int, float or tuple (h, w)
- int, Percentage of current size.
- float, Fraction of current size.
- tuple, Size of the output image.
interp : str, optional
Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’).
mode : str, optional
The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing.
Returns
--------
imresize : ndarray
The resized array of image.
References
------------
- `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_
"""
if x.shape[-1] == 1:
# greyscale
x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode)
return x[:, :, np.newaxis]
elif x.shape[-1] == 3:
# rgb, bgr ..
return scipy.misc.imresize(x, size, interp=interp, mode=mode)
else:
raise Exception("Unsupported channel %d" % x.shape[-1])
# normailization
[docs]def samplewise_norm(x, rescale=None, samplewise_center=False, samplewise_std_normalization=False,
channel_index=2, epsilon=1e-7):
"""Normalize an image by rescale, samplewise centering and samplewise centering in order.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
rescale : rescaling factor.
If None or 0, no rescaling is applied, otherwise we multiply the data by the value provided (before applying any other transformation)
samplewise_center : set each sample mean to 0.
samplewise_std_normalization : divide each input by its std.
epsilon : small position value for dividing standard deviation.
Examples
--------
>>> x = samplewise_norm(x, samplewise_center=True, samplewise_std_normalization=True)
>>> print(x.shape, np.mean(x), np.std(x))
... (160, 176, 1), 0.0, 1.0
Notes
------
When samplewise_center and samplewise_std_normalization are True.
- For greyscale image, every pixels are subtracted and divided by the mean and std of whole image.
- For RGB image, every pixels are subtracted and divided by the mean and std of this pixel i.e. the mean and std of a pixel is 0 and 1.
"""
if rescale:
x *= rescale
if x.shape[channel_index] == 1:
# greyscale
if samplewise_center:
x = x - np.mean(x)
if samplewise_std_normalization:
x = x / np.std(x)
return x
elif x.shape[channel_index] == 3:
# rgb
if samplewise_center:
x = x - np.mean(x, axis=channel_index, keepdims=True)
if samplewise_std_normalization:
x = x / (np.std(x, axis=channel_index, keepdims=True) + epsilon)
return x
else:
raise Exception("Unsupported channels %d" % x.shape[channel_index])
[docs]def featurewise_norm(x, mean=None, std=None, epsilon=1e-7):
"""Normalize every pixels by the same given mean and std, which are usually
compute from all examples.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
mean : value for subtraction.
std : value for division.
epsilon : small position value for dividing standard deviation.
"""
if mean:
x = x - mean
if std:
x = x / (std + epsilon)
return x
# whitening
def get_zca_whitening_principal_components_img(X):
"""Return the ZCA whitening principal components matrix.
Parameters
-----------
x : numpy array
Batch of image with dimension of [n_example, row, col, channel] (default).
"""
flatX = np.reshape(X, (X.shape[0], X.shape[1] * X.shape[2] * X.shape[3]))
print("zca : computing sigma ..")
sigma = np.dot(flatX.T, flatX) / flatX.shape[0]
print("zca : computing U, S and V ..")
U, S, V = linalg.svd(sigma)
print("zca : computing principal components ..")
principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + 10e-7))), U.T)
return principal_components
def zca_whitening(x, principal_components):
"""Apply ZCA whitening on an image by given principal components matrix.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
principal_components : matrix from ``get_zca_whitening_principal_components_img``.
"""
# flatx = np.reshape(x, (x.size))
print(principal_components.shape, x.shape) # ((28160, 28160), (160, 176, 1))
# flatx = np.reshape(x, (x.shape))
# flatx = np.reshape(x, (x.shape[0], ))
print(flatx.shape) # (160, 176, 1)
whitex = np.dot(flatx, principal_components)
x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))
return x
# developing
# def barrel_transform(x, intensity):
# # https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py
# # TODO
# pass
#
# def barrel_transform_multi(x, intensity):
# # https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py
# # TODO
# pass
# channel shift
[docs]def channel_shift(x, intensity, is_random=False, channel_index=2):
"""Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
intensity : float
Intensity of shifting.
is_random : boolean, default False
If True, randomly shift.
channel_index : int
Index of channel, default 2.
"""
if is_random:
factor = np.random.uniform(-intensity, intensity)
else:
factor = intensity
x = np.rollaxis(x, channel_index, 0)
min_x, max_x = np.min(x), np.max(x)
channel_images = [np.clip(x_channel + factor, min_x, max_x)
for x_channel in x]
x = np.stack(channel_images, axis=0)
x = np.rollaxis(x, 0, channel_index+1)
return x
# x = np.rollaxis(x, channel_index, 0)
# min_x, max_x = np.min(x), np.max(x)
# channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
# for x_channel in x]
# x = np.stack(channel_images, axis=0)
# x = np.rollaxis(x, 0, channel_index+1)
# return x
[docs]def channel_shift_multi(x, intensity, channel_index=2):
"""Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ .
Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
Parameters
-----------
x : list of numpy array
List of images with dimension of [n_images, row, col, channel] (default).
others : see ``channel_shift``.
"""
if is_random:
factor = np.random.uniform(-intensity, intensity)
else:
factor = intensity
results = []
for data in x:
data = np.rollaxis(data, channel_index, 0)
min_x, max_x = np.min(data), np.max(data)
channel_images = [np.clip(x_channel + factor, min_x, max_x)
for x_channel in x]
data = np.stack(channel_images, axis=0)
data = np.rollaxis(x, 0, channel_index+1)
results.append( data )
return np.asarray(results)
# manual transform
# Numpy and PIL
[docs]def array_to_img(x, dim_ordering=(0,1,2), scale=True):
"""Converts a numpy array to PIL image object (uint8 format).
Parameters
----------
x : numpy array
A image with dimension of 3 and channels of 1 or 3.
dim_ordering : list or tuple of 3 int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
scale : boolean, default is True
If True, converts image to [0, 255] from any range of value like [-1, 2].
References
-----------
- `PIL Image.fromarray <http://pillow.readthedocs.io/en/3.1.x/reference/Image.html?highlight=fromarray>`_
"""
from PIL import Image
# if dim_ordering == 'default':
# dim_ordering = K.image_dim_ordering()
# if dim_ordering == 'th': # theano
# x = x.transpose(1, 2, 0)
x = x.transpose(dim_ordering)
if scale:
x += max(-np.min(x), 0)
x_max = np.max(x)
if x_max != 0:
# print(x_max)
# x /= x_max
x = x / x_max
x *= 255
if x.shape[2] == 3:
# RGB
return Image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale
return Image.fromarray(x[:, :, 0].astype('uint8'), 'L')
else:
raise Exception('Unsupported channel number: ', x.shape[2])
## Sequence
[docs]def pad_sequences(sequences, maxlen=None, dtype='int32',
padding='post', truncating='pre', value=0.):
"""Pads each sequence to the same length:
the length of the longest sequence.
If maxlen is provided, any sequence longer
than maxlen is truncated to maxlen.
Truncation happens off either the beginning (default) or
the end of the sequence.
Supports post-padding and pre-padding (default).
Parameters
----------
sequences : list of lists where each element is a sequence
maxlen : int, maximum length
dtype : type to cast the resulting sequence.
padding : 'pre' or 'post', pad either before or after each sequence.
truncating : 'pre' or 'post', remove values from sequences larger than
maxlen either in the beginning or in the end of the sequence
value : float, value to pad the sequences to the desired value.
Returns
----------
x : numpy array with dimensions (number_of_sequences, maxlen)
Examples
----------
>>> sequences = [[1,1,1,1,1],[2,2,2],[3,3]]
>>> sequences = pad_sequences(sequences, maxlen=None, dtype='int32',
... padding='post', truncating='pre', value=0.)
... [[1 1 1 1 1]
... [2 2 2 0 0]
... [3 3 0 0 0]]
"""
lengths = [len(s) for s in sequences]
nb_samples = len(sequences)
if maxlen is None:
maxlen = np.max(lengths)
# take the sample shape from the first non empty sequence
# checking for consistency in the main loop below.
sample_shape = tuple()
for s in sequences:
if len(s) > 0:
sample_shape = np.asarray(s).shape[1:]
break
x = (np.ones((nb_samples, maxlen) + sample_shape) * value).astype(dtype)
for idx, s in enumerate(sequences):
if len(s) == 0:
continue # empty list was found
if truncating == 'pre':
trunc = s[-maxlen:]
elif truncating == 'post':
trunc = s[:maxlen]
else:
raise ValueError('Truncating type "%s" not understood' % truncating)
# check `trunc` has expected shape
trunc = np.asarray(trunc, dtype=dtype)
if trunc.shape[1:] != sample_shape:
raise ValueError('Shape of sample %s of sequence at position %s is different from expected shape %s' %
(trunc.shape[1:], idx, sample_shape))
if padding == 'post':
x[idx, :len(trunc)] = trunc
elif padding == 'pre':
x[idx, -len(trunc):] = trunc
else:
raise ValueError('Padding type "%s" not understood' % padding)
return x
## Text
# see tensorlayer.nlp
## Tensor Opt
[docs]def distorted_images(images=None, height=24, width=24):
"""Distort images for generating more training data.
Features
---------
They are cropped to height * width pixels randomly.
They are approximately whitened to make the model insensitive to dynamic range.
Randomly flip the image from left to right.
Randomly distort the image brightness.
Randomly distort the image contrast.
Whiten (Normalize) the images.
Parameters
----------
images : 4D Tensor
The tensor or placeholder of images
height : int
The height for random crop.
width : int
The width for random crop.
Returns
-------
result : tuple of Tensor
(Tensor for distorted images, Tensor for while loop index)
Examples
--------
>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
>>> sess = tf.InteractiveSession()
>>> batch_size = 128
>>> x = tf.placeholder(tf.float32, shape=[batch_size, 32, 32, 3])
>>> distorted_images_op = tl.preprocess.distorted_images(images=x, height=24, width=24)
>>> sess.run(tf.initialize_all_variables())
>>> feed_dict={x: X_train[0:batch_size,:,:,:]}
>>> distorted_images, idx = sess.run(distorted_images_op, feed_dict=feed_dict)
>>> tl.visualize.images2d(X_train[0:9,:,:,:], second=2, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)
>>> tl.visualize.images2d(distorted_images[1:10,:,:,:], second=10, saveable=False, name='distorted_images', dtype=None, fig_idx=23012)
Notes
------
- The first image in 'distorted_images' should be removed.
References
-----------
- `tensorflow.models.image.cifar10.cifar10_input <https://github.com/tensorflow/tensorflow/blob/r0.9/tensorflow/models/image/cifar10/cifar10_input.py>`_
"""
print(" [Warning] distorted_images will be deprecated due to speed, see TFRecord tutorial for more info...")
try:
batch_size = int(images._shape[0])
except:
raise Exception('unknow batch_size of images')
distorted_x = tf.Variable(tf.constant(0.1, shape=[1, height, width, 3]))
i = tf.Variable(tf.constant(0))
c = lambda distorted_x, i: tf.less(i, batch_size)
def body(distorted_x, i):
# 1. Randomly crop a [height, width] section of the image.
image = tf.random_crop(tf.gather(images, i), [height, width, 3])
# 2. Randomly flip the image horizontally.
image = tf.image.random_flip_left_right(image)
# 3. Randomly change brightness.
image = tf.image.random_brightness(image, max_delta=63)
# 4. Randomly change contrast.
image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
# 5. Subtract off the mean and divide by the variance of the pixels.
image = tf.image.per_image_whitening(image)
# 6. Append the image to a batch.
image = tf.expand_dims(image, 0)
return tf.concat(0, [distorted_x, image]), tf.add(i, 1)
result = tf.while_loop(cond=c, body=body, loop_vars=(distorted_x, i), parallel_iterations=16)
return result
[docs]def crop_central_whiten_images(images=None, height=24, width=24):
"""Crop the central of image, and normailize it for test data.
They are cropped to central of height * width pixels.
Whiten (Normalize) the images.
Parameters
----------
images : 4D Tensor
The tensor or placeholder of images
height : int
The height for central crop.
width : int
The width for central crop.
Returns
-------
result : tuple Tensor
(Tensor for distorted images, Tensor for while loop index)
Examples
--------
>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
>>> sess = tf.InteractiveSession()
>>> batch_size = 128
>>> x = tf.placeholder(tf.float32, shape=[batch_size, 32, 32, 3])
>>> central_images_op = tl.preprocess.crop_central_whiten_images(images=x, height=24, width=24)
>>> sess.run(tf.initialize_all_variables())
>>> feed_dict={x: X_train[0:batch_size,:,:,:]}
>>> central_images, idx = sess.run(central_images_op, feed_dict=feed_dict)
>>> tl.visualize.images2d(X_train[0:9,:,:,:], second=2, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)
>>> tl.visualize.images2d(central_images[1:10,:,:,:], second=10, saveable=False, name='central_images', dtype=None, fig_idx=23012)
Notes
------
The first image in 'central_images' should be removed.
Code References
----------------
- ``tensorflow.models.image.cifar10.cifar10_input``
"""
print(" [Warning] crop_central_whiten_images will be deprecated due to speed, see TFRecord tutorial for more info...")
try:
batch_size = int(images._shape[0])
except:
raise Exception('unknow batch_size of images')
central_x = tf.Variable(tf.constant(0.1, shape=[1, height, width, 3]))
i = tf.Variable(tf.constant(0))
c = lambda central_x, i: tf.less(i, batch_size)
def body(central_x, i):
# 1. Crop the central [height, width] of the image.
image = tf.image.resize_image_with_crop_or_pad(tf.gather(images, i), height, width)
# 2. Subtract off the mean and divide by the variance of the pixels.
image = tf.image.per_image_whitening(image)
# 5. Append the image to a batch.
image = tf.expand_dims(image, 0)
return tf.concat(0, [central_x, image]), tf.add(i, 1)
result = tf.while_loop(cond=c, body=body, loop_vars=(central_x, i), parallel_iterations=16)
return result
#