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#! /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
from multiprocessing import Pool
# linalg https://docs.scipy.org/doc/scipy/reference/linalg.html
# ndimage https://docs.scipy.org/doc/scipy/reference/ndimage.html
## Threading
def threading_data(data=None, fn=None, thread_count=None, **kwargs):
"""Return a batch of result by given data.
Usually be used for data augmentation.
Parameters
-----------
data : numpy array, file names and etc, see Examples below.
thread_count : the number of threads to use
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)
- Single array split across ``thread_count`` threads (e.g. functions with ``multi``)
>>> X, Y --> [batch_size, row, col, 1] greyscale
>>> data = threading_data(X, zoom_multi, 8, 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>`_
- `run with limited queue <http://effbot.org/librarybook/queue.htm>`_
"""
## plot function info
# for name, value in kwargs.items():
# print('{0} = {1}'.format(name, value))
# exit()
# define function for threading
def apply_fn(results, i, data, kwargs):
results[i] = fn(data, **kwargs)
## start multi-threaded reading.
if thread_count is None: # by Milo
results = [None] * len(data) ## preallocate result list
threads = []
for i in range(len(data)):
t = threading.Thread(
name='threading_and_return',
target=apply_fn,
args=(results, i, data[i], kwargs)
)
t.start()
threads.append(t)
else: # by geometrikal
divs = np.linspace(0, len(data), thread_count + 1)
divs = np.round(divs).astype(int)
results = [None] * thread_count
threads = []
for i in range(thread_count):
t = threading.Thread(
name='threading_and_return',
target=apply_fn,
args=(results, i, data[divs[i]:divs[i + 1]], kwargs)
)
t.start()
threads.append(t)
## <Milo> wait for all threads to complete
for t in threads:
t.join()
if thread_count is None:
try:
return np.asarray(results)
except: # if dim don't match
return results
else:
return np.concatenate(results)
## Image
def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0., order=1):
"""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
order : int, optional
The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
- `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, order)
return x
def rotation_multi(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0., order=1):
"""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, order))
return np.asarray(results)
# crop
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 = int(np.floor((h - hrg)/2.))
w_offset = int(np.floor((w - wrg)/2.))
h_end = h_offset + hrg
w_end = w_offset + wrg
return x[h_offset: h_end, w_offset: w_end]
# old implementation
# 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]
# central crop
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
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 flip.
"""
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
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
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., order=1):
"""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.
order : int, optional
The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
- `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, order)
return x
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., order=1):
"""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, order))
return np.asarray(results)
# shear
def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0., order=1):
"""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.
order : int, optional
The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
- `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, order)
return x
def shear_multi(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0., order=1):
"""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, order))
return np.asarray(results)
# swirl
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
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
def elastic_transform(x, alpha, sigma, mode="constant", cval=0, is_random=False):
"""Elastic deformation of images as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`_ .
Parameters
-----------
x : numpy array, a greyscale image.
alpha : scalar factor.
sigma : scalar or sequence of scalars, the smaller the sigma, the more transformation.
Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
mode : default constant, see `scipy.ndimage.filters.gaussian_filter <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.filters.gaussian_filter.html>`_.
cval : float, optional. Used in conjunction with mode ‘constant’, the value outside the image boundaries.
is_random : boolean, default False
Examples
---------
>>> x = elastic_transform(x, alpha = x.shape[1] * 3, sigma = x.shape[1] * 0.07)
References
------------
- `Github <https://gist.github.com/chsasank/4d8f68caf01f041a6453e67fb30f8f5a>`_.
- `Kaggle <https://www.kaggle.com/pscion/ultrasound-nerve-segmentation/elastic-transform-for-data-augmentation-0878921a>`_
"""
if is_random is False:
random_state = np.random.RandomState(None)
else:
random_state = np.random.RandomState(int(time.time()))
#
is_3d = False
if len(x.shape) == 3 and x.shape[-1] == 1:
x = x[:,:,0]
is_3d = True
elif len(x.shape) == 3 and x.shape[-1] != 1:
raise Exception("Only support greyscale image")
assert len(x.shape)==2
shape = x.shape
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode=mode, cval=cval) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode=mode, cval=cval) * alpha
x_, y_ = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')
indices = np.reshape(x_ + dx, (-1, 1)), np.reshape(y_ + dy, (-1, 1))
if is_3d:
return map_coordinates(x, indices, order=1).reshape((shape[0], shape[1], 1))
else:
return map_coordinates(x, indices, order=1).reshape(shape)
def elastic_transform_multi(x, alpha, sigma, mode="constant", cval=0, is_random=False):
"""Elastic deformation of images as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`_.
Parameters
-----------
x : list of numpy array
others : see ``elastic_transform``.
"""
if is_random is False:
random_state = np.random.RandomState(None)
else:
random_state = np.random.RandomState(int(time.time()))
shape = x[0].shape
if len(shape) == 3:
shape = (shape[0], shape[1])
new_shape = random_state.rand(*shape)
results = []
for data in x:
is_3d = False
if len(data.shape) == 3 and data.shape[-1] == 1:
data = data[:,:,0]
is_3d = True
elif len(data.shape) == 3 and data.shape[-1] != 1:
raise Exception("Only support greyscale image")
assert len(data.shape)==2
dx = gaussian_filter((new_shape * 2 - 1), sigma, mode=mode, cval=cval) * alpha
dy = gaussian_filter((new_shape * 2 - 1), sigma, mode=mode, cval=cval) * alpha
x_, y_ = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')
indices = np.reshape(x_ + dx, (-1, 1)), np.reshape(y_ + dy, (-1, 1))
# print(data.shape)
if is_3d:
results.append( map_coordinates(data, indices, order=1).reshape((shape[0], shape[1], 1)))
else:
results.append( map_coordinates(data, indices, order=1).reshape(shape) )
return np.asarray(results)
# zoom
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., order=1):
"""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.
order : int, optional
The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
- `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, order)
return x
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., order=1):
"""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, order))
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
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, smaller means brighter.
- 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
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)
# illumination
def illumination(x, gamma=1, contrast=1, saturation=1, is_random=False):
"""Perform illumination augmentation for a single image, randomly or non-randomly.
Parameters
-----------
x : numpy array
an image with dimension of [row, col, channel] (default).
gamma : change brightness
contrast : change contrast
saturation : change saturation
is_random : whether the parameters are randomly set
"""
from PIL import Image, ImageEnhance
if is_random:
## random change brightness # small --> brighter
illum_settings = np.random.randint(0,3) # 0-brighter, 1-darker, 2 keep normal
if illum_settings == 0: # brighter
gamma = np.random.uniform(.5, 1.0)
elif illum_settings == 1: # darker
gamma = np.random.uniform(1.0, 5.0)
else:
gamma = 1
im_ = brightness(x, gamma=gamma, gain=1, is_random=False)
# print("using contrast and saturation")
image = Image.fromarray(im_) # array -> PIL
contrast_adjust = ImageEnhance.Contrast(image)
image = contrast_adjust.enhance(np.random.uniform(0.3,0.9))
saturation_adjust = ImageEnhance.Color(image)
image = saturation_adjust.enhance(np.random.uniform(0.7,1.0))
im_ = np.array(image) # PIL -> array
else:
im_ = brightness(x, gamma=gamma, gain=1, is_random=False)
image = Image.fromarray(im_) # array -> PIL
contrast_adjust = ImageEnhance.Contrast(image)
image = contrast_adjust.enhance(contrast)
saturation_adjust = ImageEnhance.Color(image)
image = saturation_adjust.enhance(saturation)
im_ = np.array(image) # PIL -> array
return np.asarray(im_)
# 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
def imresize(x, size=[100, 100], interp='bicubic', mode=None):
"""Resize an image by given output size and method. Warning, this function
will rescale the value to [0, 255].
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
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])
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
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