# 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()