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Image transforms functions for data augmentation
Credit to Dr. Jo Schlemper
"""
try:
from collections import Sequence
except:
from collections.abc import Sequence
import cv2
import numpy as np
import scipy
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import map_coordinates
from numpy.lib.stride_tricks import as_strided
###### UTILITIES ######
def random_num_generator(config, random_state=np.random):
if config[0] == 'uniform':
ret = random_state.uniform(config[1], config[2], 1)[0]
elif config[0] == 'lognormal':
ret = random_state.lognormal(config[1], config[2], 1)[0]
else:
#print(config)
raise Exception('unsupported format')
return ret
def get_translation_matrix(translation):
""" translation: [tx, ty] """
tx, ty = translation
translation_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
return translation_matrix
def get_rotation_matrix(rotation, input_shape, centred=True):
theta = np.pi / 180 * np.array(rotation)
if centred:
rotation_matrix = cv2.getRotationMatrix2D((input_shape[0]/2, input_shape[1]//2), rotation, 1)
rotation_matrix = np.vstack([rotation_matrix, [0, 0, 1]])
else:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
return rotation_matrix
def get_zoom_matrix(zoom, input_shape, centred=True):
zx, zy = zoom
if centred:
zoom_matrix = cv2.getRotationMatrix2D((input_shape[0]/2, input_shape[1]//2), 0, zoom[0])
zoom_matrix = np.vstack([zoom_matrix, [0, 0, 1]])
else:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
return zoom_matrix
def get_shear_matrix(shear_angle):
theta = (np.pi * shear_angle) / 180
shear_matrix = np.array([[1, -np.sin(theta), 0],
[0, np.cos(theta), 0],
[0, 0, 1]])
return shear_matrix
###### AFFINE TRANSFORM ######
class RandomAffine(object):
"""Apply random affine transformation on a numpy.ndarray (H x W x C)
Comment by co1818: this is still doing affine on 2d (H x W plane).
A same transform is applied to all C channels
Parameter:
----------
alpha: Range [0, 4] seems good for small images
order: interpolation method (c.f. opencv)
"""
def __init__(self,
rotation_range=None,
translation_range=None,
shear_range=None,
zoom_range=None,
zoom_keep_aspect=False,
interp='bilinear',
order=3):
"""
Perform an affine transforms.
Arguments
---------
rotation_range : one integer or float
image will be rotated randomly between (-degrees, degrees)
translation_range : (x_shift, y_shift)
shifts in pixels
*NOT TESTED* shear_range : float
image will be sheared randomly between (-degrees, degrees)
zoom_range : (zoom_min, zoom_max)
list/tuple with two floats between [0, infinity).
first float should be less than the second
lower and upper bounds on percent zoom.
Anything less than 1.0 will zoom in on the image,
anything greater than 1.0 will zoom out on the image.
e.g. (0.7, 1.0) will only zoom in,
(1.0, 1.4) will only zoom out,
(0.7, 1.4) will randomly zoom in or out
"""
self.rotation_range = rotation_range
self.translation_range = translation_range
self.shear_range = shear_range
self.zoom_range = zoom_range
self.zoom_keep_aspect = zoom_keep_aspect
self.interp = interp
self.order = order
def build_M(self, input_shape):
tfx = []
final_tfx = np.eye(3)
if self.rotation_range:
rot = np.random.uniform(-self.rotation_range, self.rotation_range)
tfx.append(get_rotation_matrix(rot, input_shape))
if self.translation_range:
tx = np.random.uniform(-self.translation_range[0], self.translation_range[0])
ty = np.random.uniform(-self.translation_range[1], self.translation_range[1])
tfx.append(get_translation_matrix((tx,ty)))
if self.shear_range:
rot = np.random.uniform(-self.shear_range, self.shear_range)
tfx.append(get_shear_matrix(rot))
if self.zoom_range:
sx = np.random.uniform(self.zoom_range[0], self.zoom_range[1])
if self.zoom_keep_aspect:
sy = sx
else:
sy = np.random.uniform(self.zoom_range[0], self.zoom_range[1])
tfx.append(get_zoom_matrix((sx, sy), input_shape))
for tfx_mat in tfx:
final_tfx = np.dot(tfx_mat, final_tfx)
return final_tfx.astype(np.float32)
def __call__(self, image):
# build matrix
input_shape = image.shape[:2]
M = self.build_M(input_shape)
res = np.zeros_like(image)
#if isinstance(self.interp, Sequence):
if type(self.order) is list or type(self.order) is tuple:
for i, intp in enumerate(self.order):
res[..., i] = affine_transform_via_M(image[..., i], M[:2], interp=intp)
else:
# squeeze if needed
orig_shape = image.shape
image_s = np.squeeze(image)
res = affine_transform_via_M(image_s, M[:2], interp=self.order)
res = res.reshape(orig_shape)
#res = affine_transform_via_M(image, M[:2], interp=self.order)
return res
def affine_transform_via_M(image, M, borderMode=cv2.BORDER_CONSTANT, interp=cv2.INTER_NEAREST):
imshape = image.shape
shape_size = imshape[:2]
# Random affine
warped = cv2.warpAffine(image.reshape(shape_size + (-1,)), M, shape_size[::-1],
flags=interp, borderMode=borderMode)
#print(imshape, warped.shape)
warped = warped[..., np.newaxis].reshape(imshape)
return warped
###### ELASTIC TRANSFORM ######
def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):
"""Elastic deformation of image as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
assert image.ndim == 3
shape = image.shape[:2]
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),
sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),
sigma, mode="constant", cval=0) * 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))]
result = np.empty_like(image)
for i in range(image.shape[2]):
result[:, :, i] = map_coordinates(
image[:, :, i], indices, order=spline_order, mode=mode).reshape(shape)
return result
def elastic_transform_nd(image, alpha, sigma, random_state=None, order=1, lazy=False):
"""Expects data to be (nx, ny, n1 ,..., nm)
params:
------
alpha:
the scaling parameter.
E.g.: alpha=2 => distorts images up to 2x scaling
sigma:
standard deviation of gaussian filter.
E.g.
low (sig~=1e-3) => no smoothing, pixelated.
high (1/5 * imsize) => smooth, more like affine.
very high (1/2*im_size) => translation
"""
if random_state is None:
random_state = np.random.RandomState(None)
shape = image.shape
imsize = shape[:2]
dim = shape[2:]
# Random affine
blur_size = int(4*sigma) | 1
dx = cv2.GaussianBlur(random_state.rand(*imsize)*2-1,
ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
dy = cv2.GaussianBlur(random_state.rand(*imsize)*2-1,
ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
# use as_strided to copy things over across n1...nn channels
dx = as_strided(dx.astype(np.float32),
strides=(0,) * len(dim) + (4*shape[1], 4),
shape=dim+(shape[0], shape[1]))
dx = np.transpose(dx, axes=(-2, -1) + tuple(range(len(dim))))
dy = as_strided(dy.astype(np.float32),
strides=(0,) * len(dim) + (4*shape[1], 4),
shape=dim+(shape[0], shape[1]))
dy = np.transpose(dy, axes=(-2, -1) + tuple(range(len(dim))))
coord = np.meshgrid(*[np.arange(shape_i) for shape_i in (shape[1], shape[0]) + dim])
indices = [np.reshape(e+de, (-1, 1)) for e, de in zip([coord[1], coord[0]] + coord[2:],
[dy, dx] + [0] * len(dim))]
if lazy:
return indices
return map_coordinates(image, indices, order=order, mode='reflect').reshape(shape)
class ElasticTransform(object):
"""Apply elastic transformation on a numpy.ndarray (H x W x C)
"""
def __init__(self, alpha, sigma, order=1):
self.alpha = alpha
self.sigma = sigma
self.order = order
def __call__(self, image):
if isinstance(self.alpha, Sequence):
alpha = random_num_generator(self.alpha)
else:
alpha = self.alpha
if isinstance(self.sigma, Sequence):
sigma = random_num_generator(self.sigma)
else:
sigma = self.sigma
return elastic_transform_nd(image, alpha=alpha, sigma=sigma, order=self.order)
class RandomFlip3D(object):
def __init__(self, h=True, v=True, t=True, p=0.5):
"""
Randomly flip an image horizontally and/or vertically with
some probability.
Arguments
---------
h : boolean
whether to horizontally flip w/ probability p
v : boolean
whether to vertically flip w/ probability p
p : float between [0,1]
probability with which to apply allowed flipping operations
"""
self.horizontal = h
self.vertical = v
self.depth = t
self.p = p
def __call__(self, x, y=None):
# horizontal flip with p = self.p
if self.horizontal:
if np.random.random() < self.p:
x = x[::-1, ...]
# vertical flip with p = self.p
if self.vertical:
if np.random.random() < self.p:
x = x[:, ::-1, ...]
if self.depth:
if np.random.random() < self.p:
x = x[..., ::-1]
return x
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