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utils_image.py
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utils_image.py
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## PIL only support limited amount of image type and don't support mupti-channel image
## This file rewrites useful image utils with skimage instead of PIL
## function can be used to create torchvision.Compose
## Currently all functions are designed for channel_last image
import os
import re
import sys
import cv2
import math
import numbers
import numpy as np
import scipy
import skimage
import skimage.io
import skimage.transform
import skimage.morphology
import skimage.restoration
import skimage.segmentation
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Wedge, Polygon, Rectangle
from matplotlib.collections import PatchCollection
from PIL import Image
from skimage.color import rgb2hsv, hsv2rgb, hed2rgb, rgb2hed, gray2rgb
from collections import defaultdict
# from pycocotools import mask as mask_utils
# IMAGE_NET_MEAN_TF = np.array([123.68, 116.779, 103.939])
# IMAGE_NET_STD_TF = 1.0
# IMAGE_NET_MEAN_TORCH = np.array([0.485, 0.456, 0.406])
# IMAGE_NET_STD_TORCH = np.array([0.225, 0.224, 0.229])
CHANNEL_AXIS = -1
SKIMAGE_VERSION = skimage.__version__
class Processor(object):
""" An image processor class.
Args:
transforms (list of functions): list of functions to process the image/images.
Example:
>>> Processor([
>>> resize(10),
>>> transforms.ToTensor(),
>>> ])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
def resize(img, size, order=1, pkg='skimage', **kwargs):
""" Resize the input numpy array image to the given size.
Notes and bugs about resize:
PIL resize (scipy.misc) don't use affine transformation and is much faster than skimage.
But PIL don't support 3d image, and contains lots of unexpected behaviors and bugs
(see resize_2d_pil for details). So it's deprected though it's very fast.
Current benchmark on resize a 500*500 rgb into 600*800, (100 runs):
resize_2d_pil: ~3s, resize_nd_skimage: ~6s, resize_2d_ndimage: ~6s, resize_nd_ndimage: ~10s
Default use pkg='skimage', scipy.ndimage.zoom don't perserve range, and don't have anti-aliasing.
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
order (int, optional): Desired interpolation. Default is 1
pkg (str, optional): which inner function to call, default is skimage
kwargs: other parameters for skimage.transform.resize (and PIL.resize).
Returns:
numpy array: Resized image.
"""
if pkg == 'skimage':
## resize_nd_skimage will meet problem for some binary masks.
return resize_nd_skimage(img, size, order, **kwargs)
elif pkg == 'scipy':
if len(size) == 2:
## resize_2d_ndimage don't perserve range, and don't have anti-aliasing
return resize_2d_ndimage(img, size, order, **kwargs)
else:
return resize_nd_ndimage(img, size, order, **kwargs)
else:
raise ValueError("resize with package {} is not supported yet! ".format(pkg))
## This function is fast but have lots of unexpected behaviros and bugs.
## 1). Wrong transformation for binary image.
## 2). NLSI0000294_2_6.png, transfer color [ 0, 148, 225] to [ 0, 255, 225]. While all other images are normal.
## 3). resize_2d_pil will rescale normalized image back to 0~1
def resize_2d_pil(img, size, order=1, **kwargs):
""" Resize the input numpy array image to the given size.
scipy.misc.toimage will be deprected in future. PIL Image.fromarray has problem
with binary inputs, see:
https://stackoverflow.com/questions/50134468/convert-boolean-numpy-array-to-pillow-image
scipy.misc.toimage source code:
https://github.com/scipy/scipy/blob/v0.18.1/scipy/misc/pilutil.py#L258-L369
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
order (int, optional): Desired interpolation. Default is 1
kwargs: other parameters for skimage.transform.resize
Returns:
numpy array: Resized image.
"""
import scipy.misc
channel_axis = CHANNEL_AXIS if img.ndim > 2 else None
dtype = img.dtype
## parameters for Image.resize
mode = kwargs.setdefault('pil_mode', None)
## pil takes size = (ncol, nrow) instead of (nrow, ncol)
size = (size[1], size[0])
def f(x):
# x1 = Image.fromarray(x, mode=mode)
# x1 = np.array(x1.resize(size, resample=order))
x = scipy.misc.toimage(x, mode=mode)
x = np.array(x.resize(size, resample=order))
return x
return apply_to_channel(img, f, channel_axis, in_dtype='float', out_dtype='image')
def resize_2d_ndimage(img, size, order=1, **kwargs):
channel_axis = CHANNEL_AXIS if img.ndim > 2 else None
dtype = img.dtype
def f(x):
zoom = [1.0 * o/i for o, i in zip(size, x.shape)]
return scipy.ndimage.zoom(x, zoom, order=order, **kwargs)
return apply_to_channel(img, f, channel_axis, in_dtype='float', out_dtype='image')
def resize_nd_ndimage(img, size, order=1, **kwargs):
size = size + img.shape[len(size):]
zoom = [1.0 * o/i for i, o in zip(img.shape, size)]
return scipy.ndimage.zoom(img, zoom, order=order, **kwargs)
def resize_nd_skimage(img, size, order=1, **kwargs):
""" Resize the input numpy array image to the given size.
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
order (int, optional): Desired interpolation. Default is 1
kwargs: other parameters for skimage.transform.resize
Returns:
numpy array: Resized image.
"""
if not isinstance(img, np.ndarray):
raise TypeError('img should be numpy array. Got {}'.format(type(img)))
mode = kwargs.setdefault('mode', 'reflect')
cval = kwargs.setdefault('cval', 0.)
clip = kwargs.setdefault('clip', True)
preserve_range = kwargs.setdefault('preserve_range', False)
args = ({'anti_aliasing': kwargs.setdefault('anti_aliasing', True),
'anti_aliasing_sigma': kwargs.setdefault('anti_aliasing_sigma', None)}
if SKIMAGE_VERSION > '0.14' else {})
return skimage.transform.resize(img, output_shape=size, order=order, mode=mode, cval=cval,
clip=clip, preserve_range=preserve_range, **args)
class Resize(object):
def __init__(self, size, order=1, **kwargs):
self.size = size
self.order = order
self.kwargs = kwargs
def __call__(self, images, kwargs=None):
return [resize(_, self.size, self.order, pkg='skimage', **self.kwargs)
if _ is not None else None
for _ in images]
## split images into list of channels
## The following if else is duplicated with function resize_pil_2d,
## but this can run around 50% faster, no idea why.
# def f(img):
# channel_axis = CHANNEL_AXIS if img.ndim > 2 else None
# return apply_to_channel(img, resize, channel_axis, in_dtype='image', out_dtype='image',
# args=[self.size, self.order], kwargs=self.kwargs)
# return [f(img) if img is not None else None for img in images]
def __repr__(self):
return self.__class__.__name__ + '(size={0}, order={1})'.format(self.size, self.order)
def get_pad_width(input_size, output_size, pos='center'):
output_size = output_size + input_size[len(output_size):]
output_size = np.maximum(input_size, output_size)
if pos == 'center':
l = np.floor_divide(output_size - input_size, 2)
elif pos == 'random':
l = [np.random.randint(0, _ + 1) for _ in output_size - input_size]
return list(zip(l, output_size - input_size - l))
def pad(img, size=None, pad_width=None, pos='center', mode='reflect', **kwargs):
""" Pad the input numpy array image with pad_width and to given size.
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
pad_width (list of tuples): Desired pad_width.
pos: one of {'center, 'random'}, default is 'center'. if given
size, the parameter will decide whether to put original
image in the center or a random location.
mode: supported mode in skimage.util.pad
kwargs: other parameters in skimage.util.pad
pad_width and size can have same length as img, or 1d less than img.
pad_width and size cannot be both None. If size = None, function will
image with return img_size + pad_width. If pad_width = None, function
will return image with size. If both size and pad_width is not None,
function will pad with pad_width first, then will try to meet size.
Function don't do any resize, rescale, crop process. Return img size
will be max(img.size+pad_width, size).
Returns:
numpy array: Resized image.
"""
if mode == 'constant':
pars = {'constant_values': kwargs.setdefault('cval', 0.0)}
elif mode == 'linear_ramp':
pars = {'end_values': kwargs.setdefault('end_values', 0.0)}
elif mode == 'reflect' or mode == 'symmetric':
pars = {'reflect_type': kwargs.setdefault('reflect_type', 'even')}
else:
pars = {'stat_length': kwargs.setdefault('stat_length', None)}
if pad_width is not None:
pad_width = pad_width + [(0, 0)] * (img.ndim - len(pad_width))
img = skimage.util.pad(img, pad_width[:img.ndim], mode=mode, **pars)
if size is not None:
pad_var = get_pad_width(img.shape, output_size=size, pos=pos)
img = skimage.util.pad(img, pad_var, mode=mode, **pars)
return img
class Pad(object):
def __init__(self, size=None, pad_width=None, pos='center', mode='reflect', **kwargs):
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
if size is not None:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
self.pad_width = pad_width
self.pos = pos
self.mode = mode
self.kwargs = kwargs
def __call__(self, images, kwargs=None):
## Add support to kwargs, let kwargs over-write self.kwargs for different inputs.
## like different cvals for different type of images.
if kwargs is None:
kwargs = [{}] * len(images)
if self.pad_width is not None:
images = [pad(img, size=None, pad_width=self.pad_width,
mode=self.mode, **{**self.kwargs, **args})
if img is not None else None
for img, args in zip(images, kwargs)]
if self.size is not None:
pad_width = get_pad_width(images[0].shape, output_size=self.size, pos=self.pos)
images = [pad(img, size=None, pad_width=pad_width,
mode=self.mode, **{**self.kwargs, **args})
if img is not None else None
for img, args in zip(images, kwargs)]
return images
def __repr__(self):
return self.__class__.__name__ + '(size={0}, pad_width={1}, pos={2}, mode={3})'.\
format(self.size, self.pad_width, self.pos, self.mode)
def get_crop_width(input_size, output_size, pos='center'):
output_size = output_size + input_size[len(output_size):]
output_size = np.minimum(input_size, output_size)
if pos == 'center':
l = np.floor_divide(input_size - output_size, 2)
elif pos == 'random':
l = [np.random.randint(0, _ + 1) for _ in input_size - output_size]
return list(zip(l, input_size - output_size - l))
def crop(img, size=None, crop_width=None, pos='center', **kwargs):
""" Crop the input numpy array image with crop_width and to given size.
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
crop_width (list of tuples): Desired crop_width.
pos: one of {'center, 'random'}, default is 'center'. if given
size, the parameter will decide whether to put original
image in the center or a random location.
kwargs: other parameters in skimage.util.crop, use default just fine.
crop_width and size can have same length as img, or 1d less than img.
crop_width and size cannot be both None. If size = None, function will
return image with img_size - crop_width. If crop_width = None, function
will return image with size. If both size and crop_width is not None,
function will crop with crop_width first, then will try to meet size.
Function don't do any resize, rescale and pad process. Return img size
will be min(img.size-pad_width, size).
Returns:
numpy array: Resized image.
"""
copy = kwargs.setdefault('copy', False)
order = kwargs.setdefault('order', 'K')
if crop_width is not None:
crop_width = crop_width + [(0, 0)] * (img.ndim - len(crop_width))
img = skimage.util.crop(img, crop_width[:img.ndim], copy=copy, order=order)
if size is not None:
crop_var = get_crop_width(img.shape, output_size=size, pos=pos)
img = skimage.util.crop(img, crop_var, copy=copy, order=order)
return img
class Crop(object):
def __init__(self, size=None, crop_width=None, pos='center', **kwargs):
if size is not None:
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
self.crop_width = crop_width
self.pos = pos
self.kwargs = kwargs
def __call__(self, images, kwargs=None):
if kwargs is None:
kwargs = [{}] * len(images)
if self.crop_width is not None:
images = [crop(img, size=None, crop_width=self.crop_width, **{**self.kwargs, **args})
if img is not None else None
for img, args in zip(images, kwargs)]
if self.size is not None:
crop_width = get_crop_width(images[0].shape, output_size=self.size, pos=self.pos)
images = [crop(img, size=None, crop_width=crop_width, **{**self.kwargs, **args})
if img is not None else None
for img, args in zip(images, kwargs)]
return images
def __repr__(self):
return self.__class__.__name__ + '(size={0}, crop_width={1}, pos={2})'.\
format(self.size, self.crop_width, self.pos)
def center_crop(img, size):
return crop(img, size=size, crop_width=None, pos='center')
class CenterCrop(object):
def __init__(self, size):
self.size = size
def __call__(self, images):
return [center_crop(img, size=self.size) for img in images]
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
def five_crop(img, size):
"""Crop the given PIL Image into four corners and the central crop.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center)
Corresponding top left, top right, bottom left, bottom right and center crop.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
tl = crop(img, crop_width=[(0, h - crop_h), (0, w - crop_w)])
tr = crop(img, crop_width=[(0, h - crop_h), (w - crop_w, 0)])
bl = crop(img, crop_width=[(h - crop_h, 0), (0, w - crop_w)])
br = crop(img, crop_width=[(h - crop_h, 0), (w - crop_w, 0)])
center = center_crop(img, size)
return (tl, tr, bl, br, center)
def hflip(img):
return img[:, ::-1, ...]
class RandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, images):
if np.random.random() < self.p:
return [hflip(img) if img is not None else None for img in images]
return images
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
def vflip(img):
return img[::-1, ...]
class RandomVerticalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, images):
if np.random.random() < self.p:
return [vflip(img) if img is not None else None for img in images]
return images
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
def random_transform_pars(N, rotation=0., translate_x=0., translate_y=0.,
scale_x=0., scale_r=0., shear=0.,
projection_g=0., projection_h=0., p=0.5, seed=None):
""" Randomly generate parameters for image transformation.
If a scalar value is provided, the function will
randomly generate N parameters inside the range
If a list/array is provided, the function will use
all combination of these values.
# Returns
A dictionary contains args for random transformation.
Use get_transform_matrix to generate a transform matrix.
And use transform to do affine/projective transformation.
"""
if seed is not None:
np.random.seed(seed)
pars = dict()
# rotation
rotation = (-rotation, rotation) if np.isscalar(rotation) else rotation
r = np.random.uniform(rotation[0], rotation[1], N) * (np.random.random(N) < p)
pars['rotation'] = r.tolist()
# translation
translate_x = (-translate_x, translate_x) if np.isscalar(translate_x) else translate_x
tx = np.random.uniform(translate_x[0], translate_x[1], N) * (np.random.random(N) < p)
translate_y = (-translate_y, translate_y) if np.isscalar(translate_y) else translate_y
ty = np.random.uniform(translate_y[0], translate_y[1], N) * (np.random.random(N) < p)
pars['translate'] = np.stack([tx, ty], axis=-1).tolist()
# shear
shear = (-shear, shear) if np.isscalar(shear) else shear
s = np.random.uniform(shear[0], shear[1], N) * (np.random.random(N) < p)
pars['shear'] = s.tolist()
# scale
scale_x = (1.*(1-scale_x), 1./(1-scale_x)) if np.isscalar(scale_x) else scale_x
zx = np.random.uniform(np.log(scale_x[0]), np.log(scale_x[1]), N) * (np.random.random(N) < p)
scale_r = (1.*(1-scale_r), 1./(1-scale_r)) if np.isscalar(scale_r) else scale_r
zy = zx + (np.random.uniform(np.log(scale_r[0]), np.log(scale_r[1]), N) * (np.random.random(N) < p))
pars['scale'] = np.stack([np.exp(zx), np.exp(zy)], axis=-1).tolist()
# projection
projection_g = (- projection_g, projection_g) if np.isscalar(projection_g) else projection_g
pg = np.random.uniform(projection_g[0], projection_g[1], N) * (np.random.random(N) < p)
projection_h = (- projection_h, projection_h) if np.isscalar(projection_h) else projection_h
ph = np.random.uniform(projection_h[0], projection_h[1], N) * (np.random.random(N) < p)
pars['projection'] = np.stack([pg, ph], axis=-1).tolist()
return unpack_dict(pars, N)
def get_transform_matrix(rotation, translate, scale, shear, projection=(0, 0), center=(0, 0), inverse=True):
""" Compute (inverse) matrix for affine/projective transformation
.. Note::
Affine transformation matrix is calculated as: M = T * C * RSS * C^-1
T is translation matrix after rotation: [[1, 0, tx], [0, 1, ty], [0, 0, 1]]
C is translation matrix to keep center: [[1, 0, cx], [0, 1, cy], [0, 0, 1]]
RSS is rotation with scale and shear matrix
RSS(a, scale, shear) = [[cos(a)*scale_height, -sin(a + shear)*scale_height, 0],
[sin(a)*scale_width, cos(a + shear)*scale_width, 0],
[0, 0, 1]]
The inverse matrix is M^-1 = C * RSS^-1 * C^-1 * T^-1
Projective transformation: [[a, b, c], [d, e, f], [g, h, 1]], where g, h != 0
Args:
rotation (float or int): rotation angle in degrees between -180 and 180.
translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
scale (list or tuple of floats): height_scale and width_scale
shear (float): shear angle value in degrees between -180 to 180.
center (tuple, optional): center offset in translation matrix
projection (list or tuple of floats, optional): the projective transformation
inverse (bool): apply inverse matrix (clockwise) or original matrix (anti-cloakwise)
Returns:
a 3*3 (inverse) matrix for affine/projective transformation
"""
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"Argument translate should be a list or tuple of length 2"
assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
"Argument scale should be a list or tuple of length 2"
assert isinstance(projection, (tuple, list)) and len(projection) == 2, \
"Argument projection should be a list or tuple of length 2"
assert isinstance(center, (tuple, list)) and len(center) == 2, \
"Argument center should be a list or tuple of length 2"
rotation = math.radians(rotation)
shear = math.radians(shear)
if inverse:
# Inverted rotation matrix with scale and shear
d = math.cos(rotation + shear) * math.cos(rotation) + math.sin(rotation + shear) * math.sin(rotation)
matrix = np.array([[math.cos(rotation + shear) / scale[1] / d, math.sin(rotation + shear) / scale[1] / d, 0],
[-math.sin(rotation) / scale[0] / d, math.cos(rotation) / scale[0] / d, 0], [0, 0, 1]])
else:
matrix = np.array([[math.cos(rotation + shear) * scale[0], -math.sin(rotation + shear) * scale[0], 0],
[math.sin(rotation) * scale[1], math.cos(rotation) * scale[1], 0],
[0, 0, 1]])
## Offset center and apply translation: C * RSS^-1 * C^-1 * T^-1
matrix[0, 2] = center[1] + matrix[0, 0] * (-center[1] - translate[1]) + matrix[0, 1] * (-center[0] - translate[0])
matrix[1, 2] = center[0] + matrix[1, 0] * (-center[1] - translate[1]) + matrix[1, 1] * (-center[0] - translate[0])
## Add projection
matrix[2, 0] = projection[0]
matrix[2, 1] = projection[1]
return matrix
def translate_offset_center(translate, input_size, output_size):
# offset matrix to the center of image
center = (input_size[0] * 0.5 + 0.5, input_size[1] * 0.5 + 0.5)
offset = (output_size[0] * 0.5 + 0.5, output_size[1] * 0.5 + 0.5)
translate = (translate[0] + offset[0] - center[0],
translate[1] + offset[1] - center[1])
return center, offset, translate
def transform(img, matrix, size=None, out_dtype='image', **kwargs):
"""Apply affine/projective transformation on the image.
.. Note::
image is centered under new size after affine transformation.
Args:
img (numpy array): input image.
matrix (3*3 numpy array or a dictionary): provide either a transform matrix or pars to generate matrix.
size (tuple, optional): the output image size.
kwargs: parameters for get_transform_matrix and skimage.transform.warp.
get_transform_matrix args: [rotation, translate, scale, shear, projection, inverse]
skimage.transform.warp functions:
order: use order = 0 for mask to keep labels. This will avoid unnecessary post-treatment.
mode and cval: fill area outside the transform with specific padding method/color.
preserve_range: use preserve_range=True for higher order
Return:
images after transformation
"""
out_dtype = img.dtype if out_dtype == 'image' else out_dtype
if size is None:
size = img.shape[:2]
## if no transform matrix is given, use default setting
if matrix is None:
matrix = {}
if isinstance(matrix, dict):
rotation = matrix.setdefault('rotation', 0.)
translate = matrix.setdefault('translate', (0., 0.))
scale = matrix.setdefault('scale', (0., 0.))
shear = matrix.setdefault('shear', 0.)
projection = matrix.setdefault('projection', (0., 0.))
inverse = matrix.setdefault('inverse', True)
# offset matrix to center
center, _, translate = translate_offset_center(translate, input_size=img.shape[:2], output_size=size)
# center = (img.shape[0] * 0.5 + 0.5, img.shape[1] * 0.5 + 0.5)
# offset = (size[0] * 0.5 + 0.5, size[1] * 0.5 + 0.5)
# translate = (translate[0] + offset[0] - center[0], translate[1] + offset[1] - center[1])
matrix = get_transform_matrix(rotation, translate, scale, shear,
projection=projection, center=center, inverse=inverse)
assert isinstance(matrix, np.ndarray) and matrix.shape == (3, 3), \
"Invalid transform matrix"
if not np.allclose(matrix, np.eye(3)):
order = kwargs.setdefault('order', 1)
mode = kwargs.setdefault('mode', 'constant')
cval = kwargs.setdefault('cval', 0.0)
clip = kwargs.setdefault('clip', True)
preserve_range = kwargs.setdefault('preserve_range', False)
if np.any(matrix[-1, :-1]):
tform = skimage.transform.ProjectiveTransform(matrix=matrix)
else:
tform = skimage.transform.AffineTransform(matrix=matrix)
img = skimage.transform.warp(img, tform, output_shape=size, order=order,
mode=mode, cval=cval, clip=clip,
preserve_range=preserve_range)
return img_as(out_dtype)(img)
class RandomTransform(object):
""" Random Transformation.
Argument:
size: output image size
rotation: float (degree)
shear: float(degree)
translate: tuple(x, y)
scale: tuple(zoom, h/w ratio)
projection: tuple(x, y)
inverse: use inverse transform or not
p: probability for each transform
"""
def __init__(self, size=None, rotation=0., translate=(0., 0.),
scale=(0., 0.), shear=0., projection=(0., 0.),
inverse=True, p=0.5, **kwargs):
self.size = size
self.rotation = rotation if rotation is not None else 0.
self.shear = shear if shear is not None else 0.
if translate is not None:
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"translate should be a list or tuple and it must be of length 2."
else:
translate = (0., 0.)
self.translate = translate
if scale is not None:
assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
"scale should be a list or tuple and it must be of length 2."
else:
scale = (0., 0.)
self.scale = scale
if projection is not None:
assert isinstance(projection, (tuple, list)) and len(projection) == 2, \
"scale should be a list or tuple and it must be of length 2."
else:
projection = (0., 0.)
self.projection = projection
self.inverse = inverse
self.p = p
self.kwargs = kwargs
def get_params(self, input_size, output_size):
pars = random_transform_pars(N=1, rotation=self.rotation,
translate_x=self.translate[0], translate_y=self.translate[1],
scale_x=self.scale[0], scale_r=self.scale[1], shear=self.shear,
projection_g=self.projection[0], projection_h=self.projection[1],
p=self.p, seed=None)[0]
center, _, translate = translate_offset_center(pars['translate'], input_size, output_size)
pars.update({'center': center, 'translate': translate, 'inverse': self.inverse})
matrix = get_transform_matrix(**pars)
return matrix, pars
def __call__(self, images, kwargs=None):
input_size = images[0].shape[:2]
output_size = input_size if self.size is None else self.size
matrix, pars = self.get_params(input_size, output_size)
if kwargs is None:
kwargs = [{}] * len(images)
def f(img, args={}):
if img.ndim > 2:
res = np.rollaxis(img, CHANNEL_AXIS)
res = np.stack([transform(res[i], matrix, size=output_size, **{**self.kwargs, **args})
for i in range(len(res))], axis=CHANNEL_AXIS)
else:
res = transform(img, matrix, size=output_size, **{**self.kwargs, **args})
return res
return [f(img, args) if img is not None else None for img, args in zip(images, kwargs)]
# return [transform(x, matrix, size=output_size, **self.kwargs) for x in images]
def __repr__(self):
s = '{name}(rotation={rotation}, translate={translate}, scale={scale}, shear={shear}, projection={projection})'
return s.format(name=self.__class__.__name__, **dict(self.__dict__))
def normalize(img, mean=0., std=1.):
return (img - mean)/std
class Normalize(object):
def __init__(self, mean=0., std=1.):
self.mean = mean
self.std = std
def get_params(self, x):
ndim = x.ndim
mean = np.mean(x, axis=tuple(range(x.ndim-1))) if self.mean == 'sample' else self.mean
std = np.std(x, axis=tuple(range(x.ndim-1))) if self.std == 'sample' else self.std
return {'mean': np.array(mean), 'std': np.array(std)}
def __call__(self, images):
return [normalize(img, **self.get_params(img)) if img is not None else None for img in images]
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
def rescale_intensity(img, rescale_method, out_range='dtype', **kwargs):
""" Rescale image channel with stretch, hist or adaptive.
Args:
img (numpy array): image (will be transfer to float64 between 0~1)
rescale_method: one of {'stretch', 'hist', 'adaptive'}.
out_range (optional): default will use skimage.dtype_limits(img.dtype).
Return:
rescaled image in out_range.
Function will first stretch img into [0, 1]. Than rescale back to
the out_range and keep the input img.dtype
"""
if out_range == 'dtype':
out_range = skimage.dtype_limits(img)
if rescale_method == 'stretch':
# Contrast stretching: (in_range[0], in_range[1]) -> (out_range[0], out_range[1])
in_range = kwargs.setdefault('in_range', 'image') # default in_range = (img.min(), img.max())
return skimage.exposure.rescale_intensity(img, in_range=in_range, out_range=out_range)
elif rescale_method == 'hist':
# Equalization, equalize_hist will balance everythin fullfill "mask" into (0, 1)
nbins = kwargs.setdefault('nbins', 256)
mask = kwargs.setdefault('mask', None)
x = skimage.exposure.equalize_hist(img, nbins=nbins, mask=mask)
# result after equalize_hist is always in range (0. 1.)
return skimage.exposure.rescale_intensity(x, in_range=(0., 1.), out_range=out_range).astype(img.dtype)
elif rescale_method == 'adaptive':
# Adaptive Equalization
kernel_size = kwargs.setdefault('kernel_size', None)
clip_limit = kwargs.setdefault('clip_limit', 0.01)
nbins = kwargs.setdefault('nbins', 256)
x = skimage.exposure.equalize_adapthist(img, kernel_size=kernel_size, clip_limit=clip_limit, nbins=nbins)
# result after equalize_adapthist is always in range (0. 1.)
return skimage.exposure.rescale_intensity(x, in_range=(0., 1.), out_range=out_range).astype(img.dtype)
class RescaleChannelIntensity(object):
def __init__(self, rescale_method, out_range='dtype', **kwargs):
self.rescale_method = rescale_method
self.out_range = out_range
self.kwargs = kwargs
def __call__(self, images):
def f(img):
if img.ndim > 2:
res = np.rollaxis(img, CHANNEL_AXIS)
res = np.stack([rescale_intensity(res[i], self.rescale_method, self.out_range, **self.kwargs)
for i in range(len(res))], axis=CHANNEL_AXIS)
else:
res = rescale_intensity(img, self.rescale_method, self.out_range, **self.kwargs)
return res
return [f(img) if img is not None else None for img in images]
def __repr__(self):
return self.__class__.__name__ + '(size={0}, crop_width={1}, pos={2}, order={3})'.\
format(self.size, self.crop_width, self.pos, self.order)
def get_gaussian_kernel(size, sigma):
if isinstance(size, numbers.Number):
size = (int(size), int(size))
if isinstance(sigma, numbers.Number):
sigma = (sigma, sigma)
x = cv2.getGaussianKernel(size[0]*2+1, sigma[0])
y = cv2.getGaussianKernel(size[1]*2+1, sigma[1])
kernel = np.dot(x, y.T)
return kernel/np.sum(kernel)
def blur_image(x, method='gaussian', out_dtype='image', *args, **kwargs):
assert method in ['gaussian', 'mean', 'median'], "%s bluring is not supported" % method
out_dtype = x.dtype if out_dtype == 'image' else out_dtype
fn = {'gaussian': skimage.filters.gaussian,
'median': skimage.filters.rank.median,
'mean': skimage.filters.rank.mean}[method]
if x.ndim < 3:
res = fn(x, *args, **kwargs)
else:
if method == 'gaussian':
res = fn(x, *args, **kwargs)
else:
## transfer rgb 2 hsv for median and mean filter
x = np.moveaxis(rgb2hsv(x), -1, 0)
x = np.stack([fn(_, *args, **kwargs) for _ in x], axis=-1)
res = hsv2rgb(x)
return img_as(out_dtype)(res)
def random_blur_whole_image(img, kernel=None):
filters = [
{'method': 'gaussian', 'sigma': np.random.uniform(low=1, high=8)},
{'method': 'median', 'selem': skimage.morphology.disk(np.random.randint(4)*2+1)},
{'method': 'mean', 'selem': skimage.morphology.disk(np.random.randint(4)*2+1)}
]
pars = filters[np.random.randint(0, len(filters))]
return blur_image(img, **pars)
def random_blur_local_region(x, kernels=None, masks=None, out_dtype='image'):
""" This function is in beta. Current very flow and only support gaussian kernel. """
out_dtype = x.dtype if out_dtype == 'image' else out_dtype
## build a gaussian kernel
if kernels is None:
kernels = [get_gaussian_kernel(np.random.randint(2, 6), np.random.uniform(low=8, high=16))]
## Calculate max pad_width for kernel
p0, p1 = np.amax([k.shape for k in kernels], axis=0) // 2
padded_x = pad(x, pad_width=[(p0, p0), (p1, p1)])
h, w = x.shape[0], x.shape[1]
## generate a masks
if masks is None:
masks = {'max_shapes': 10, 'min_shapes': 3, 'min_size': 96, 'max_size': 256, 'random_seed': None}
if isinstance(masks, dict):
masks, labels = skimage.draw.random_shapes((h, w), **masks)
masks, = RandomTransform(size=(h, w), rotation=45, translate=(int(h/10), int(w/10)),
scale=(0.2, 0.3), shear=20, projection=(0.0006, 0.0006),
order=0, p=0.7)([masks])
masks, = RandomHorizontalFlip(0.5)([masks])
masks, = RandomVerticalFlip(0.5)([masks])
masks = np.mean(masks, axis=-1) < 255
## Assign blurring in mask region
res = np.copy(x)
for idx, prop in enumerate(skimage.measure.regionprops(skimage.measure.label(masks))):
x0, y0 = prop.coords[:,0], prop.coords[:,1]
kernel = kernels[idx % len(kernels)]
kernel = np.expand_dims(kernel/np.sum(kernel), axis=-1)
i_s, i_e = p0-kernel.shape[0]//2, p0+(kernel.shape[0]+1)//2
j_s, j_e = p1-kernel.shape[1]//2, p1+(kernel.shape[1]+1)//2
res[x0, y0, ...] = np.array([
np.sum(padded_x[i+i_s:i+i_e, j+j_s:j+j_e] * kernel, axis=(0,1)) for i, j in zip(x0, y0)
])
return img_as(out_dtype)(res)
def shift_invariant_denoising(x, max_shifts, func='denoise_wavelet', out_dtype='image', **kwargs):
""" A wrapper of the skimage.restoration module.
Unfinished, only support denoise_wavelet now.
"""
import skimage.restoration
if not callable(func):
if isinstance(func, str):
func = getattr(skimage.restoration, func)
if not kwargs:
kwargs = dict(multichannel=True, convert2ycbcr=True, wavelet='db1')
if SKIMAGE_VERSION >= '0.16':
kwargs['rescale_sigma'] = True
out_dtype = x.dtype if out_dtype == 'image' else out_dtype
x = img_as('float')(x)
res = skimage.restoration.cycle_spin(
x, func=func, max_shifts=max_shifts,
func_kw=kwargs, multichannel=True)
return img_as(out_dtype)(res)
def random_adjust_color(img, global_mean=1e-3, channel_mean=8e-4, channel_sigma=0.2):
""" A simple and effective random color augmentation (from Shidan).
The function treat last dimension as channel.
global_mean: the relative mean add to all channel.
channel_mean: the relative mean add to each channel.
channel_sigma: the relative variace add to each channel.
"""
dtype = img.dtype
img = img_as('float')(img)
n_channel = img.shape[-1]
# add global mean and channel mean
img += np.random.randn() * global_mean + np.random.randn(n_channel) * channel_mean
# add channel variance
img += img * np.clip(np.random.randn(n_channel) * channel_sigma, -channel_sigma, channel_sigma)
return img_as(dtype)(np.clip(img, 0., 1.))
class ColorDodge(object):
""" Randomly color augmentation with mean and std.
Args:
global_mean: the relative mean add to all channel.
channel_mean: the relative mean add to each channel.
channel_sigma: the relative variace add to each channel.
"""
def __init__(self, global_mean=1e-3, channel_mean=8e-4, channel_sigma=0.2, p=0.5):
self.global_mean = global_mean
self.channel_mean = channel_mean
self.channel_sigma = channel_sigma
self.p = p
def __call__(self, images, kwargs=None):
return [random_adjust_color(img, self.global_mean, self.channel_mean, self.channel_sigma)
if img is not None and np.random.random() < self.p else img
for img in images]
def __repr__(self):
format_string = self.__class__.__name__ + '('
format_string += 'global_mean={0}'.format(self.global_mean)
format_string += ', channel_mean={0}'.format(self.channel_mean)
format_string += ', channel_sigma={0}'.format(self.channel_sigma)
return format_string
def adjust_brightness(img, brightness_factor):
""" Adjust brightness of an Image. """
min_val, max_val = skimage.dtype_limits(img)
return np.clip(img * brightness_factor, min_val, max_val).astype(img.dtype)
def adjust_contrast(img, contrast_factor):
""" Adjust contrast of an Image. """
min_val, max_val = skimage.dtype_limits(img)
degenerate = np.mean(rgb2gray(img))
res = degenerate * (1-contrast_factor) + img * contrast_factor
return np.clip(res, min_val, max_val).astype(img.dtype)
def adjust_saturation(img, saturation_factor):
""" Adjust color saturation of an image (PIL ImageEnhance.Color). """
min_val, max_val = skimage.dtype_limits(img)
degenerate = rgb2gray(img, 1)
res = degenerate * (1-saturation_factor) + img * saturation_factor
return np.clip(res, min_val, max_val).astype(img.dtype)
def adjust_hue(img, hue_factor):
""" Adjust hue of an image.
hue_factor is the amount of shift in H channel [-0.5, 0.5].
"""
if not(-0.5 <= hue_factor <= 0.5):
raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))
hsv = rgb2hsv(img)
hsv[..., 0] *= (1 + hue_factor)
res = hsv2rgb(hsv) # float image
return img_as(img.dtype)(res)
def adjust_gamma(img, gamma=1, gain=1):
""" Perform gamma correction (Power Law Transform) on an image. """
return skimage.exposure.adjust_gamma(img, gamma=gamma, gain=gain)
def random_color_jitter(img, factors):
func_list = {'brightness': adjust_brightness, 'contrast': adjust_contrast,
'saturation': adjust_saturation, 'hue': adjust_hue}
for key, val in factors:
img = func_list[key](img, val)
return img
## Copy from torch.vision
class ColorJitter(object):
""" Randomly change the brightness, contrast and saturation of an image.
Args:
brightness (float or tuple of float (min, max)): How much to jitter brightness.
brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
or the given [min, max]. Should be non negative numbers.
contrast (float or tuple of float (min, max)): How much to jitter contrast.
contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
or the given [min, max]. Should be non negative numbers.
saturation (float or tuple of float (min, max)): How much to jitter saturation.
saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
or the given [min, max]. Should be non negative numbers.
hue (float or tuple of float (min, max)): How much to jitter hue.
hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
Should have 0 <= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
"""
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, p=0.5):
self.brightness = self._check_input(brightness, 'brightness')
self.contrast = self._check_input(contrast, 'contrast')
self.saturation = self._check_input(saturation, 'saturation')
self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5), clip_first_on_zero=False)
self.p = p