mirror of
https://github.com/AntoineHX/smart_augmentation.git
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476 lines
No EOL
19 KiB
Python
Executable file
476 lines
No EOL
19 KiB
Python
Executable file
""" PyTorch implementation of some PIL image transformations.
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Those implementation are thinked to take advantages of batched computation of PyTorch on GPU.
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Based on Kornia library.
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See: https://github.com/kornia/kornia
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And PIL.
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See:
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https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
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Inspired from AutoAugment.
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See: https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py
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"""
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import torch
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import kornia
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import random
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#TF that don't have use for magnitude parameter.
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TF_no_mag={'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend'}
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#TF which implemetation doesn't allow gradient propagaition.
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TF_no_grad={'Solarize', 'Posterize', '=Solarize', '=Posterize'}
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#TF for which magnitude should be ignored (Magnitude fixed).
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TF_ignore_mag= TF_no_mag | TF_no_grad
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# What is the max 'level' a transform could be predicted
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PARAMETER_MAX = 1
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# What is the min 'level' a transform could be predicted
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PARAMETER_MIN = 0.1
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# Dictionnary mapping tranformations identifiers to their function.
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# Each value of the dict should be a lambda function taking a (batch of data, magnitude of transformations) tuple as input and returns a batch of data.
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TF_dict={ #Dataugv5+
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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'FlipUD' : (lambda x, mag: flipUD(x)),
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'FlipLR' : (lambda x, mag: flipLR(x)),
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'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
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'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
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'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
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'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
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'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
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#Color TF (Common mag scale)
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'+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
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## Bad Tranformations ##
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# Bad Geometric TF #
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'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=0))),
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'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=1))),
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'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=0))),
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'BTranslateX-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=0))),
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'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=1))),
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'BTranslateY-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=1))),
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# Bad Color TF #
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'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9*2, maxval=2*4))),
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'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
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# Random TF #
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'Random':(lambda x, mag: torch.rand_like(x)),
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'RandBlend': (lambda x, mag: blend(x,torch.rand_like(x), alpha=torch.tensor(0.7,device=mag.device).expand(x.shape[0]))),
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#Not ready for use
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#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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## Image type cast ##
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def int_image(float_image):
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"""Convert a float Tensor/Image to an int Tensor/Image.
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Be warry that this transformation isn't bijective, each conversion will result in small loss of information.
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Granularity: 1/256 = 0.0039.
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This will also result in the loss of the gradient associated to input as gradient cannot be tracked on int Tensor.
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Args:
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float_image (FloatTensor): Image tensor.
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Returns:
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(ByteTensor) Converted tensor.
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"""
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return (float_image*255.).type(torch.uint8)
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def float_image(int_image):
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"""Convert a int Tensor/Image to an float Tensor/Image.
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Args:
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int_image (ByteTensor): Image tensor.
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Returns:
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(FloatTensor) Converted tensor.
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"""
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return int_image.type(torch.float)/255.
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## Parameters utils ##
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def rand_floats(size, mag, maxval, minval=None):
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"""Generate a batch of random values.
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Args:
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size (int): Number of value to generate.
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mag (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
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maxval (float): Maximum value that can be generated. This will be scaled to mag/PARAMETER_MAX.
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minval (float): Minimum value that can be generated. (default: -maxval)
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Returns:
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(Tensor) Generated batch of float values between [minval, maxval].
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"""
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real_mag = float_parameter(mag, maxval=maxval)
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if not minval : minval = -real_mag
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#return random.uniform(minval, real_max)
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return minval + (real_mag-minval) * torch.rand(size, device=mag.device) #[min_val, real_mag]
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def invScale_rand_floats(size, mag, maxval, minval):
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"""Generate a batch of random values.
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Similar to rand_floats() except that the mag is used in an inversed scale.
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Mag:[0,PARAMETER_MAX] => [PARAMETER_MAX, 0]
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Args:
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size (int): Number of value to generate.
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mag (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
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maxval (float): Maximum value that can be generated. This will be scaled to mag/PARAMETER_MAX.
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minval (float): Minimum value that can be generated. (default: -maxval)
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Returns:
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(Tensor) Generated batch of float values between [minval, maxval].
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"""
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real_mag = float_parameter(float(PARAMETER_MAX) - mag, maxval=maxval-minval)+minval
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return real_mag + (maxval-real_mag) * torch.rand(size, device=mag.device) #[real_mag, max_val]
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def zero_stack(tensor, zero_pos):
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"""Add a row of zeros to a Tensor.
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This function is intended to be used with single row Tensor, thus returning a 2 dimension Tensor.
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Args:
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tensor (Tensor): Tensor to be stacked with zeros.
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zero_pos (int): Wheter the zeros should be added before or after the Tensor. Either 0 or 1.
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Returns:
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Stacked Tensor.
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"""
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if zero_pos==0:
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return torch.stack((tensor, torch.zeros((tensor.shape[0],), device=tensor.device)), dim=1)
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if zero_pos==1:
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return torch.stack((torch.zeros((tensor.shape[0],), device=tensor.device), tensor), dim=1)
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else:
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raise Exception("Invalid zero_pos : ", zero_pos)
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def float_parameter(level, maxval):
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"""Scale level between 0 and maxval.
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Args:
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level (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
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maxval: Maximum value that the operation can have. This will be scaled to level/PARAMETER_MAX.
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Returns:
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A float that results from scaling `maxval` according to `level`.
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"""
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#return float(level) * maxval / PARAMETER_MAX
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return (level * maxval / PARAMETER_MAX)#.to(torch.float)
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## Tranformations ##
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def flipLR(x):
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"""Flip horizontaly/Left-Right images.
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Args:
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x (Tensor): Batch of images.
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Returns:
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(Tensor): Batch of fliped images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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M =torch.tensor( [[[-1., 0., w-1],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1)
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# warp the original image by the found transform
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return kornia.warp_perspective(x, M, dsize=(h, w))
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def flipUD(x):
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"""Flip vertically/Up-Down images.
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Args:
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x (Tensor): Batch of images.
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Returns:
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(Tensor): Batch of fliped images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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M =torch.tensor( [[[ 1., 0., 0.],
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[ 0., -1., h-1],
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[ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1)
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# warp the original image by the found transform
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return kornia.warp_perspective(x, M, dsize=(h, w))
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def rotate(x, angle):
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"""Rotate images.
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Args:
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x (Tensor): Batch of images.
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angle (Tensor): Angles (degrees) of rotation for each images.
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Returns:
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(Tensor): Batch of rotated images.
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"""
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return kornia.rotate(x, angle=angle.type(torch.float)) #Kornia ne supporte pas les int
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def translate(x, translation):
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"""Translate images.
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Args:
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x (Tensor): Batch of images.
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translation (Tensor): Distance (pixels) of translation for each images.
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Returns:
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(Tensor): Batch of translated images.
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"""
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return kornia.translate(x, translation=translation.type(torch.float)) #Kornia ne supporte pas les int
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def shear(x, shear):
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"""Shear images.
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Args:
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x (Tensor): Batch of images.
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shear (Tensor): Angle of shear for each images.
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Returns:
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(Tensor): Batch of skewed images.
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"""
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return kornia.shear(x, shear=shear)
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def contrast(x, contrast_factor):
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"""Adjust contast of images.
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Args:
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x (FloatTensor): Batch of images.
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contrast_factor (FloatTensor): Contrast adjust factor per element in the batch.
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0 generates a compleatly black image, 1 does not modify the input image while any other non-negative number modify the brightness by this factor.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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return kornia.adjust_contrast(x, contrast_factor=contrast_factor) #Expect image in the range of [0, 1]
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def color(x, color_factor):
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"""Adjust color of images.
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Args:
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x (Tensor): Batch of images.
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color_factor (Tensor): Color factor for each images.
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0.0 gives a black and white image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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(batch_size, channels, h, w) = x.shape
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gray_x = kornia.rgb_to_grayscale(x)
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gray_x = gray_x.repeat_interleave(channels, dim=1)
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return blend(gray_x, x, color_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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def brightness(x, brightness_factor):
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"""Adjust brightness of images.
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Args:
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x (Tensor): Batch of images.
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brightness_factor (Tensor): Brightness factor for each images.
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0.0 gives a black image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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device = x.device
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return blend(torch.zeros(x.size(), device=device), x, brightness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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def sharpeness(x, sharpness_factor):
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"""Adjust sharpness of images.
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Args:
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x (Tensor): Batch of images.
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sharpness_factor (Tensor): Sharpness factor for each images.
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0.0 gives a black image. A factor of 1.0 gives the original image.
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Returns:
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(Tensor): Batch of adjusted images.
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"""
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device = x.device
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(batch_size, channels, h, w) = x.shape
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k = torch.tensor([[[ 1., 1., 1.],
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[ 1., 5., 1.],
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[ 1., 1., 1.]]], device=device) #Smooth Filter : https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py
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smooth_x = kornia.filter2D(x, kernel=k, border_type='reflect', normalized=True) #Peut etre necessaire de s'occuper du channel Alhpa differement
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return blend(smooth_x, x, sharpness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1]
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def posterize(x, bits):
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"""Reduce the number of bits for each color channel.
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Be warry that the cast to integers block the gradient propagation.
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Args:
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x (Tensor): Batch of images.
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bits (Tensor): The number of bits to keep for each channel (1-8).
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Returns:
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(Tensor): Batch of posterized images.
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"""
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bits = bits.type(torch.uint8) #Perte du gradient
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x = int_image(x) #Expect image in the range of [0, 1]
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mask = ~(2 ** (8 - bits) - 1).type(torch.uint8)
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(batch_size, channels, h, w) = x.shape
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mask = mask.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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return float_image(x & mask)
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def solarize(x, thresholds):
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"""Invert all pixel values above a threshold.
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Be warry that the use of the inequality (x>tresholds) block the gradient propagation.
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Args:
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x (Tensor): Batch of images.
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thresholds (Tensor): All pixels above this level are inverted
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Returns:
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(Tensor): Batch of solarized images.
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"""
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batch_size, channels, h, w = x.shape
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#imgs=[]
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#for idx, t in enumerate(thresholds): #Operation par image
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# mask = x[idx] > t #Perte du gradient
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#In place
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# inv_x = 1-x[idx][mask]
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# x[idx][mask]=inv_x
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#
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#Out of place
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# im = x[idx]
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# inv_x = 1-im[mask]
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# imgs.append(im.masked_scatter(mask,inv_x))
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#idxs=torch.tensor(range(x.shape[0]), device=x.device)
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#idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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#x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
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#
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thresholds = thresholds.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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x=torch.where(x>thresholds,1-x, x)
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#x=x.min(thresholds)
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#inv_x = 1-x[mask]
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#x=x.where(x<thresholds,1-x)
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#x[mask]=inv_x
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#x=x.masked_scatter(mask, inv_x)
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return x
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def blend(x,y,alpha):
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"""Creates a new images by interpolating between two input images, using a constant alpha.
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x and y should have the same size.
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alpha should have the same batch size as the images.
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Apply batch wise :
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out = image1 * (1.0 - alpha) + image2 * alpha
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Args:
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x (Tensor): Batch of images.
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y (Tensor): Batch of images.
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alpha (Tensor): The interpolation alpha factor for each images.
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Returns:
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(Tensor): Batch of solarized images.
|
||
"""
|
||
#return kornia.add_weighted(src1=x, alpha=(1-alpha), src2=y, beta=alpha, gamma=0) #out=src1∗alpha+src2∗beta+gamma #Ne fonctionne pas pour des batch de alpha
|
||
|
||
if not isinstance(x, torch.Tensor):
|
||
raise TypeError("x should be a tensor. Got {}".format(type(x)))
|
||
|
||
if not isinstance(y, torch.Tensor):
|
||
raise TypeError("y should be a tensor. Got {}".format(type(y)))
|
||
|
||
assert(x.shape==y.shape and x.shape[0]==alpha.shape[0])
|
||
|
||
(batch_size, channels, h, w) = x.shape
|
||
alpha = alpha.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
|
||
res = x*(1-alpha) + y*alpha
|
||
|
||
return res
|
||
|
||
#Not working
|
||
def auto_contrast(x):
|
||
"""NOT TESTED - EXTRA SLOW
|
||
|
||
"""
|
||
# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
|
||
print("Warning : Pas encore check !")
|
||
(batch_size, channels, h, w) = x.shape
|
||
x = int_image(x) #Expect image in the range of [0, 1]
|
||
#print('Start',x[0])
|
||
for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
|
||
#print(img.shape)
|
||
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
|
||
#print(chan.shape)
|
||
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
|
||
|
||
# find lowest/highest samples after preprocessing
|
||
for lo in range(256):
|
||
if hist[lo]:
|
||
break
|
||
for hi in range(255, -1, -1):
|
||
if hist[hi]:
|
||
break
|
||
if hi <= lo:
|
||
# don't bother
|
||
pass
|
||
else:
|
||
scale = 255.0 / (hi - lo)
|
||
offset = -lo * scale
|
||
for ix in range(256):
|
||
n_ix = int(ix * scale + offset)
|
||
if n_ix < 0: n_ix = 0
|
||
elif n_ix > 255: n_ix = 255
|
||
|
||
chan[chan==ix]=n_ix
|
||
x[im_idx, chan_idx]=chan
|
||
|
||
#print('End',x[0])
|
||
return float_image(x)
|
||
|
||
def equalize(x):
|
||
""" NOT WORKING
|
||
|
||
"""
|
||
raise Exception(self, "not implemented")
|
||
# Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel
|
||
(batch_size, channels, h, w) = x.shape
|
||
x = int_image(x) #Expect image in the range of [0, 1]
|
||
#print('Start',x[0])
|
||
for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
|
||
#print(img.shape)
|
||
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
|
||
#print(chan.shape)
|
||
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
|
||
|
||
return float_image(x) |