# Copyright 2022-present NAVER Corp.
# CC BY-NC-SA 4.0
# Available only for non-commercial use
from pdb import set_trace as bb
import warnings
import numpy as np
from PIL import Image, ImageOps
import torch
import torch.nn as nn
from torchvision import transforms as tvf
from . import transforms_tools as F
from .utils import DatasetWithRng
'''
Example command to try out some transformation chain:
python -m pytools.transforms --trfs "Scale(384), ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1), RandomRotation(10), RandomTilting(0.5, 'all'), RandomScale(240,320), RandomCrop(224)"
'''
def instanciate_transforms(transforms, use_gpu=False, rng=None, compose=True):
''' Instanciate a sequence of transformations.
transforms: (str, list)
Comma-separated list of transformations.
Ex: "Rotate(10), Scale(256)"
'''
try:
transforms = transforms or '[]'
if isinstance(transforms, str):
if transforms.lstrip()[0] not in '[(': transforms = f'[{transforms}]'
if compose: transforms = f'Compose({transforms})'
transforms = eval(transforms)
if isinstance(transforms, list) and transforms and isinstance(transforms[0], str):
transforms = [eval(trf) for trf in transforms]
if compose: transforms = Compose(transforms)
if use_gpu and not isinstance(transforms, nn.Module):
while hasattr(transforms,'transforms') or hasattr(transforms,'transform'):
transforms = getattr(transforms,'transforms',getattr(transforms,'transform',None))
transforms = [trf for trf in transforms if isinstance(trf, nn.Module)]
transforms = nn.Sequential(*transforms) if compose else nn.ModuleList(transforms)
if transforms and rng:
for trf in transforms.transforms:
assert hasattr(trf, 'rng'), f"Transformation {trf} has no self.rng"
trf.rng = rng
if isinstance(transforms, Compose) and len(transforms.transforms) == 1:
transforms = transforms.transforms[0]
return transforms
except Exception as e:
print("\nError: Cannot interpret this transform list: %s\n" % transforms)
raise e
class Compose (DatasetWithRng):
def __init__(self, transforms, **rng_seed):
super().__init__(**rng_seed)
self.transforms = [self.with_same_rng(trf) for trf in transforms]
def __call__(self, data):
for trf in self.transforms:
data = trf(data)
return data
class Scale (DatasetWithRng):
""" Rescale the input PIL.Image to a given size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
The smallest dimension of the resulting image will be = size.
if largest == True: same behaviour for the largest dimension.
if not can_upscale: don't upscale
if not can_downscale: don't downscale
"""
def __init__(self, size, interpolation=Image.BILINEAR, largest=False,
can_upscale=True, can_downscale=True, **rng_seed):
super().__init__(**rng_seed)
assert isinstance(size, int) or (len(size) == 2)
self.size = size
self.interpolation = interpolation
self.largest = largest
self.can_upscale = can_upscale
self.can_downscale = can_downscale
def __repr__(self):
fmt_str = "RandomScale(%s" % str(self.size)
if self.largest: fmt_str += ', largest=True'
if not self.can_upscale: fmt_str += ', can_upscale=False'
if not self.can_downscale: fmt_str += ', can_downscale=False'
return fmt_str+')'
def get_params(self, imsize):
w,h = imsize
if isinstance(self.size, int):
cmp = lambda a,b: (a>=b) if self.largest else (a<=b)
if (cmp(w, h) and w == self.size) or (cmp(h, w) and h == self.size):
ow, oh = w, h
elif cmp(w, h):
ow = self.size
oh = int(self.size * h / w)
else:
oh = self.size
ow = int(self.size * w / h)
else:
ow, oh = self.size
return ow, oh
def __call__(self, inp):
img = F.grab(inp,'img')
w, h = img.size
size2 = ow, oh = self.get_params(img.size)
if size2 != img.size:
a1, a2 = img.size, size2
if (self.can_upscale and min(a1) < min(a2)) or (self.can_downscale and min(a1) > min(a2)):
img = img.resize(size2, self.interpolation)
return F.update(inp, img=img, homography=np.diag((ow/w,oh/h,1)))
class RandomScale (Scale):
"""Rescale the input PIL.Image to a random size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
Args:
min_size (int): min size of the smaller edge of the picture.
max_size (int): max size of the smaller edge of the picture.
ar (float or tuple):
max change of aspect ratio (width/height).
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def __init__(self, min_size, max_size, ar=1, larger=False,
can_upscale=False, can_downscale=True, interpolation=Image.BILINEAR):
Scale.__init__(self, (min_size,max_size), can_upscale=can_upscale, can_downscale=can_downscale, interpolation=interpolation)
assert type(min_size) == type(max_size), 'min_size and max_size can only be 2 ints or 2 floats'
assert isinstance(min_size, int) and min_size >= 1 or isinstance(min_size, float) and min_size>0
assert isinstance(max_size, (int,float)) and min_size <= max_size
self.min_size = min_size
self.max_size = max_size
if type(ar) in (float,int): ar = (min(1/ar,ar),max(1/ar,ar))
assert 0.2 < ar[0] <= ar[1] < 5
self.ar = ar
self.larger = larger
def get_params(self, imsize):
w,h = imsize
if isinstance(self.min_size, float): min_size = int(self.min_size*min(w,h) + 0.5)
if isinstance(self.max_size, float): max_size = int(self.max_size*min(w,h) + 0.5)
if isinstance(self.min_size, int): min_size = self.min_size
if isinstance(self.max_size, int): max_size = self.max_size
if not(self.can_upscale) and not(self.larger):
max_size = min(max_size,min(w,h))
size = int(0.5 + F.rand_log_uniform(self.rng, min_size, max_size))
if not(self.can_upscale) and self.larger:
size = min(size, min(w,h))
ar = F.rand_log_uniform(self.rng, *self.ar) # change of aspect ratio
if w < h: # image is taller
ow = size
oh = int(0.5 + size * h / w / ar)
if oh < min_size:
ow,oh = int(0.5 + ow*float(min_size)/oh),min_size
else: # image is wider
oh = size
ow = int(0.5 + size * w / h * ar)
if ow < min_size:
ow,oh = min_size,int(0.5 + oh*float(min_size)/ow)
assert ow >= min_size, 'image too small (width=%d < min_size=%d)' % (ow, min_size)
assert oh >= min_size, 'image too small (height=%d < min_size=%d)' % (oh, min_size)
return ow, oh
class RandomCrop (DatasetWithRng):
"""Crop the given PIL Image at a random location.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
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.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
"""
def __init__(self, size, padding=0, **rng_seed):
super().__init__(**rng_seed)
if isinstance(size, int):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
def __repr__(self):
return "RandomCrop(%s)" % str(self.size)
def get_params(self, img, output_size):
w, h = img.size
th, tw = output_size
assert h >= th and w >= tw, "Image of %dx%d is too small for crop %dx%d" % (w,h,tw,th)
y = self.rng.integers(0, h - th) if h > th else 0
x = self.rng.integers(0, w - tw) if w > tw else 0
return x, y, tw, th
def __call__(self, inp):
img = F.grab(inp,'img')
padl = padt = 0
if self.padding:
if F.is_pil_image(img):
img = ImageOps.expand(img, border=self.padding, fill=0)
else:
assert isinstance(img, F.DummyImg)
img = img.expand(border=self.padding)
if isinstance(self.padding, int):
padl = padt = self.padding
else:
padl, padt = self.padding[0:2]
i, j, tw, th = self.get_params(img, self.size)
img = img.crop((i, j, i+tw, j+th))
return F.update(inp, img=img, homography=np.float32(((1,0,padl-i),(0,1,padt-j),(0,0,1))))
class CenterCrop (RandomCrop):
"""Crops the given PIL Image at the center.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
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.
"""
@staticmethod
def get_params(img, output_size):
w, h = img.size
th, tw = output_size
y = int(0.5 +((h - th) / 2.))
x = int(0.5 +((w - tw) / 2.))
return x, y, tw, th
class RandomRotation (DatasetWithRng):
"""Rescale the input PIL.Image to a random size.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
Args:
degrees (float):
rotation angle.
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def __init__(self, degrees, interpolation=Image.BILINEAR, **rng_seed):
super().__init__(**rng_seed)
self.degrees = degrees
self.interpolation = interpolation
def __repr__(self):
return f"RandomRotation({self.degrees})"
def __call__(self, inp):
img = F.grab(inp,'img')
w, h = img.size
angle = self.rng.uniform(-self.degrees, self.degrees)
img = img.rotate(angle, resample=self.interpolation)
w2, h2 = img.size
trf = F.translate(w2/2,h2/2) @ F.rotate(-angle * np.pi/180) @ F.translate(-w/2,-h/2)
return F.update(inp, img=img, homography=trf)
class RandomTilting (DatasetWithRng):
"""Apply a random tilting (left, right, up, down) to the input PIL.Image
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
Args:
maginitude (float):
maximum magnitude of the random skew (value between 0 and 1)
directions (string):
tilting directions allowed (all, left, right, up, down)
examples: "all", "left,right", "up-down-right"
"""
def __init__(self, magnitude, directions='all', **rng_seed):
super().__init__(**rng_seed)
self.magnitude = magnitude
self.directions = directions.lower().replace(',',' ').replace('-',' ')
def __repr__(self):
return "RandomTilt(%g, '%s')" % (self.magnitude,self.directions)
def __call__(self, inp):
img = F.grab(inp,'img')
w, h = img.size
x1,y1,x2,y2 = 0,0,h,w
original_plane = [(y1, x1), (y2, x1), (y2, x2), (y1, x2)]
max_skew_amount = max(w, h)
max_skew_amount = int(np.ceil(max_skew_amount * self.magnitude))
skew_amount = self.rng.integers(1, max_skew_amount)
if self.directions == 'all':
choices = [0,1,2,3]
else:
dirs = ['left', 'right', 'up', 'down']
choices = []
for d in self.directions.split():
try:
choices.append(dirs.index(d))
except:
raise ValueError('Tilting direction %s not recognized' % d)
skew_direction = self.rng.choice(choices)
# print('randomtitlting: ', skew_amount, skew_direction) # to debug random
if skew_direction == 0:
# Left Tilt
new_plane = [(y1, x1 - skew_amount), # Top Left
(y2, x1), # Top Right
(y2, x2), # Bottom Right
(y1, x2 + skew_amount)] # Bottom Left
elif skew_direction == 1:
# Right Tilt
new_plane = [(y1, x1), # Top Left
(y2, x1 - skew_amount), # Top Right
(y2, x2 + skew_amount), # Bottom Right
(y1, x2)] # Bottom Left
elif skew_direction == 2:
# Forward Tilt
new_plane = [(y1 - skew_amount, x1), # Top Left
(y2 + skew_amount, x1), # Top Right
(y2, x2), # Bottom Right
(y1, x2)] # Bottom Left
elif skew_direction == 3:
# Backward Tilt
new_plane = [(y1, x1), # Top Left
(y2, x1), # Top Right
(y2 + skew_amount, x2), # Bottom Right
(y1 - skew_amount, x2)] # Bottom Left
# To calculate the coefficients required by PIL for the perspective skew,
# see the following Stack Overflow discussion: https://goo.gl/sSgJdj
homography = F.homography_from_4pts(original_plane, new_plane)
img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC)
homography = np.linalg.pinv(np.float32(homography+(1,)).reshape(3,3))
return F.update(inp, img=img, homography=homography)
RandomHomography = RandomTilt = RandomTilting # redefinition
class Homography(object):
"""Apply a known tilting to an image
"""
def __init__(self, *homography):
assert len(homography) == 8
self.homography = homography
def __call__(self, inp):
img = F.grab(inp, 'img')
homography = self.homography
img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC)
homography = np.linalg.pinv(np.float32(list(homography)+[1]).reshape(3,3))
return F.update(inp, img=img, homography=homography)
class StillTransform (DatasetWithRng):
""" Takes and return an image, without changing its shape or geometry.
"""
def _transform(self, img):
raise NotImplementedError()
def __call__(self, inp):
img = F.grab(inp,'img')
# transform the image (size should not change)
try:
img = self._transform(img)
except TypeError:
pass
return F.update(inp, img=img)
class PixelNoise (StillTransform):
""" Takes an image, and add random white noise.
"""
def __init__(self, ampl=20, **rng_seed):
super().__init__(**rng_seed)
assert 0 <= ampl < 255
self.ampl = ampl
def __repr__(self):
return "PixelNoise(%g)" % self.ampl
def _transform(self, img):
img = np.float32(img)
img += self.rng.uniform(0.5-self.ampl/2, 0.5+self.ampl/2, size=img.shape)
return Image.fromarray(np.uint8(img.clip(0,255)))
class ColorJitter (StillTransform):
"""Randomly change the brightness, contrast and saturation of an image.
Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
Args:
brightness (float): How much to jitter brightness. brightness_factor
is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
contrast (float): How much to jitter contrast. contrast_factor
is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
saturation (float): How much to jitter saturation. saturation_factor
is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
hue(float): How much to jitter hue. hue_factor is chosen uniformly from
[-hue, hue]. Should be >=0 and <= 0.5.
"""
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
self.hue = hue
def __repr__(self):
return "ColorJitter(%g,%g,%g,%g)" % (
self.brightness, self.contrast, self.saturation, self.hue)
def get_params(self, brightness, contrast, saturation, hue):
"""Get a randomized transform to be applied on image.
Arguments are same as that of __init__.
Returns:
Transform which randomly adjusts brightness, contrast and
saturation in a random order.
"""
transforms = []
if brightness > 0:
brightness_factor = self.rng.uniform(max(0, 1 - brightness), 1 + brightness)
transforms.append(tvf.Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))
if contrast > 0:
contrast_factor = self.rng.uniform(max(0, 1 - contrast), 1 + contrast)
transforms.append(tvf.Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))
if saturation > 0:
saturation_factor = self.rng.uniform(max(0, 1 - saturation), 1 + saturation)
transforms.append(tvf.Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))
if hue > 0:
hue_factor = self.rng.uniform(-hue, hue)
transforms.append(tvf.Lambda(lambda img: F.adjust_hue(img, hue_factor)))
# print('colorjitter: ', brightness_factor, contrast_factor, saturation_factor, hue_factor) # to debug random seed
self.rng.shuffle(transforms)
transform = tvf.Compose(transforms)
return transform
def _transform(self, img):
transform = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
return transform(img)
def pil_loader(path, mode='RGB'):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with (path if hasattr(path,'read') else open(path, 'rb')) as f:
img = Image.open(f)
return img.convert(mode)
def torchvision_loader(path, mode='RGB'):
from torchvision.io import read_file, decode_image, read_image, image
return read_image(getattr(path,'name',path), mode=getattr(image.ImageReadMode,mode))
if __name__ == '__main__':
from matplotlib import pyplot as pl
import argparse
parser = argparse.ArgumentParser("Script to try out and visualize transformations")
parser.add_argument('--img', type=str, default='imgs/test.png', help='input image')
parser.add_argument('--trfs', type=str, required=True, help='list of transformations')
parser.add_argument('--layout', type=int, nargs=2, default=(3,3), help='nb of rows,cols')
args = parser.parse_args()
img = dict(img=pil_loader(args.img))
trfs = instanciate_transforms(args.trfs)
pl.subplots_adjust(0,0,1,1)
nr,nc = args.layout
while True:
t0 = now()
imgs2 = [trfs(img) for _ in range(nr*nc)]
for j in range(nr):
for i in range(nc):
pl.subplot(nr,nc,i+j*nc+1)
img2 = img if i==j==0 else imgs2.pop() #trfs(img)
img2 = img2['img']
pl.imshow(img2)
pl.xlabel("%d x %d" % img2.size)
print(f'Took {now() - t0:.2f} seconds')
pl.show()