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from __future__ import division
import torch
import random
import numpy as np
import numbers
import types
import scipy.ndimage as ndimage
import cv2
import matplotlib.pyplot as plt
from PIL import Image
# import torchvision.transforms.functional as FF
'''
Data argumentation file
modifed from
https://github.com/ClementPinard/FlowNetPytorch
'''
'''Set of tranform random routines that takes both input and target as arguments,
in order to have random but coherent transformations.
inputs are PIL Image pairs and targets are ndarrays'''
_pil_interpolation_to_str = {
Image.NEAREST: 'PIL.Image.NEAREST',
Image.BILINEAR: 'PIL.Image.BILINEAR',
Image.BICUBIC: 'PIL.Image.BICUBIC',
Image.LANCZOS: 'PIL.Image.LANCZOS',
Image.HAMMING: 'PIL.Image.HAMMING',
Image.BOX: 'PIL.Image.BOX',
}
class Compose(object):
""" Composes several co_transforms together.
For example:
>>> co_transforms.Compose([
>>> co_transforms.CenterCrop(10),
>>> co_transforms.ToTensor(),
>>> ])
"""
def __init__(self, co_transforms):
self.co_transforms = co_transforms
def __call__(self, input, target):
for t in self.co_transforms:
input,target = t(input,target)
return input,target
class ArrayToTensor(object):
"""Converts a numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W)."""
def __call__(self, array):
assert(isinstance(array, np.ndarray))
array = np.transpose(array, (2, 0, 1))
# handle numpy array
tensor = torch.from_numpy(array)
# put it from HWC to CHW format
return tensor.float()
class ArrayToPILImage(object):
"""Converts a numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W)."""
def __call__(self, array):
assert(isinstance(array, np.ndarray))
img = Image.fromarray(array.astype(np.uint8))
return img
class PILImageToTensor(object):
"""Converts a numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W)."""
def __call__(self, img):
assert(isinstance(img, Image.Image))
array = np.asarray(img)
array = np.transpose(array, (2, 0, 1))
tensor = torch.from_numpy(array)
return tensor.float()
class Lambda(object):
"""Applies a lambda as a transform"""
def __init__(self, lambd):
assert isinstance(lambd, types.LambdaType)
self.lambd = lambd
def __call__(self, input,target):
return self.lambd(input,target)
class CenterCrop(object):
"""Crops the given inputs and target arrays at the center to have a region of
the given size. size can be a tuple (target_height, target_width)
or an integer, in which case the target will be of a square shape (size, size)
Careful, img1 and img2 may not be the same size
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, inputs, target):
h1, w1, _ = inputs[0].shape
# h2, w2, _ = inputs[1].shape
th, tw = self.size
x1 = int(round((w1 - tw) / 2.))
y1 = int(round((h1 - th) / 2.))
# x2 = int(round((w2 - tw) / 2.))
# y2 = int(round((h2 - th) / 2.))
for i in range(len(inputs)):
inputs[i] = inputs[i][y1: y1 + th, x1: x1 + tw]
# inputs[0] = inputs[0][y1: y1 + th, x1: x1 + tw]
# inputs[1] = inputs[1][y2: y2 + th, x2: x2 + tw]
target = target[y1: y1 + th, x1: x1 + tw]
return inputs,target
class myRandomResized(object):
"""
based on RandomResizedCrop in
https://pytorch.org/docs/stable/_modules/torchvision/transforms/transforms.html#RandomResizedCrop
"""
def __init__(self, expect_min_size, scale=(0.8, 1.5), interpolation=cv2.INTER_NEAREST):
# assert (min(input_size) * min(scale) > max(expect_size))
# one consider one decimal !!
assert (isinstance(scale,tuple) and len(scale)==2)
self.interpolation = interpolation
self.scale = [ x*0.1 for x in range(int(scale[0]*10),int(scale[1])*10 )]
self.min_size = expect_min_size
@staticmethod
def get_params(img, scale, min_size):
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image): Image to be cropped.
scale (tuple): range of size of the origin size cropped
ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
# area = img.size[0] * img.size[1]
h, w, _ = img.shape
for attempt in range(10):
rand_scale_ = random.choice(scale)
if random.random() < 0.5:
rand_scale = rand_scale_
else:
rand_scale = -1.
if min_size[0] <= rand_scale * h and min_size[1] <= rand_scale * w\
and rand_scale * h % 16 == 0 and rand_scale * w %16 ==0 :
# the 16*n condition is for network architecture
return (int(rand_scale * h),int(rand_scale * w ))
# Fallback
return (h, w)
def __call__(self, inputs, tgt):
"""
Args:
img (PIL Image): Image to be cropped and resized.
Returns:
PIL Image: Randomly cropped and resized image.
"""
h,w = self.get_params(inputs[0], self.scale, self.min_size)
for i in range(len(inputs)):
inputs[i] = cv2.resize(inputs[i], (w,h), self.interpolation)
tgt = cv2.resize(tgt, (w,h), self.interpolation) #for input as h*w*1 the output is h*w
return inputs, np.expand_dims(tgt,-1)
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
format_string = self.__class__.__name__ + '(min_size={0}'.format(self.min_size)
format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
format_string += ', interpolation={0})'.format(interpolate_str)
return format_string
class Scale(object):
""" Rescales the inputs and target arrays to the given 'size'.
'size' will be the size of the smaller edge.
For example, if height > width, then image will be
rescaled to (size * height / width, size)
size: size of the smaller edge
interpolation order: Default: 2 (bilinear)
"""
def __init__(self, size, order=2):
self.size = size
self.order = order
def __call__(self, inputs, target):
h, w, _ = inputs[0].shape
if (w <= h and w == self.size) or (h <= w and h == self.size):
return inputs,target
if w < h:
ratio = self.size/w
else:
ratio = self.size/h
for i in range(len(inputs)):
inputs[i] = ndimage.interpolation.zoom(inputs[i], ratio, order=self.order)[:, :, :3]
target = ndimage.interpolation.zoom(target, ratio, order=self.order)[:, :, :1]
#target *= ratio
return inputs, target
class RandomCrop(object):
"""Crops the given PIL.Image at a random location to have a region of
the given size. size can be a tuple (target_height, target_width)
or an integer, in which case the target will be of a square shape (size, size)
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, inputs,target):
h, w, _ = inputs[0].shape
th, tw = self.size
if w == tw and h == th:
return inputs,target
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
for i in range(len(inputs)):
inputs[i] = inputs[i][y1: y1 + th,x1: x1 + tw]
# inputs[1] = inputs[1][y1: y1 + th,x1: x1 + tw]
# inputs[2] = inputs[2][y1: y1 + th, x1: x1 + tw]
return inputs, target[y1: y1 + th,x1: x1 + tw]
class MyScale(object):
def __init__(self, size, order=2):
self.size = size
self.order = order
def __call__(self, inputs, target):
h, w, _ = inputs[0].shape
if (w <= h and w == self.size) or (h <= w and h == self.size):
return inputs,target
if w < h:
for i in range(len(inputs)):
inputs[i] = cv2.resize(inputs[i], (self.size, int(h * self.size / w)))
target = cv2.resize(target.squeeze(), (self.size, int(h * self.size / w)), cv2.INTER_NEAREST)
else:
for i in range(len(inputs)):
inputs[i] = cv2.resize(inputs[i], (int(w * self.size / h), self.size))
target = cv2.resize(target.squeeze(), (int(w * self.size / h), self.size), cv2.INTER_NEAREST)
target = np.expand_dims(target, axis=2)
return inputs, target
class RandomHorizontalFlip(object):
"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
"""
def __call__(self, inputs, target):
if random.random() < 0.5:
for i in range(len(inputs)):
inputs[i] = np.copy(np.fliplr(inputs[i]))
# inputs[1] = np.copy(np.fliplr(inputs[1]))
# inputs[2] = np.copy(np.fliplr(inputs[2]))
target = np.copy(np.fliplr(target))
# target[:,:,0] *= -1
return inputs,target
class RandomVerticalFlip(object):
"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
"""
def __call__(self, inputs, target):
if random.random() < 0.5:
for i in range(len(inputs)):
inputs[i] = np.copy(np.flipud(inputs[i]))
# inputs[1] = np.copy(np.flipud(inputs[1]))
# inputs[2] = np.copy(np.flipud(inputs[2]))
target = np.copy(np.flipud(target))
# target[:,:,1] *= -1 #for disp there is no y dim
return inputs,target
class RandomRotate(object):
"""Random rotation of the image from -angle to angle (in degrees)
This is useful for dataAugmentation, especially for geometric problems such as FlowEstimation
angle: max angle of the rotation
interpolation order: Default: 2 (bilinear)
reshape: Default: false. If set to true, image size will be set to keep every pixel in the image.
diff_angle: Default: 0. Must stay less than 10 degrees, or linear approximation of flowmap will be off.
"""
def __init__(self, angle, diff_angle=0, order=2, reshape=False):
self.angle = angle
self.reshape = reshape
self.order = order
self.diff_angle = diff_angle
def __call__(self, inputs,target):
applied_angle = random.uniform(-self.angle,self.angle)
diff = random.uniform(-self.diff_angle,self.diff_angle)
angle1 = applied_angle - diff/2
angle2 = applied_angle + diff/2
angle1_rad = angle1*np.pi/180
h, w, _ = target.shape
def rotate_flow(i,j,k):
return -k*(j-w/2)*(diff*np.pi/180) + (1-k)*(i-h/2)*(diff*np.pi/180)
rotate_flow_map = np.fromfunction(rotate_flow, target.shape)
target += rotate_flow_map
inputs[0] = ndimage.interpolation.rotate(inputs[0], angle1, reshape=self.reshape, order=self.order)
inputs[1] = ndimage.interpolation.rotate(inputs[1], angle2, reshape=self.reshape, order=self.order)
target = ndimage.interpolation.rotate(target, angle1, reshape=self.reshape, order=self.order)
# flow vectors must be rotated too! careful about Y flow which is upside down
target_ = np.copy(target)
target[:,:,0] = np.cos(angle1_rad)*target_[:,:,0] + np.sin(angle1_rad)*target_[:,:,1]
target[:,:,1] = -np.sin(angle1_rad)*target_[:,:,0] + np.cos(angle1_rad)*target_[:,:,1]
return inputs,target
class RandomTranslate(object):
def __init__(self, translation):
if isinstance(translation, numbers.Number):
self.translation = (int(translation), int(translation))
else:
self.translation = translation
def __call__(self, inputs,target):
h, w, _ = inputs[0].shape
th, tw = self.translation
tw = random.randint(-tw, tw)
th = random.randint(-th, th)
if tw == 0 and th == 0:
return inputs, target
# compute x1,x2,y1,y2 for img1 and target, and x3,x4,y3,y4 for img2
x1,x2,x3,x4 = max(0,tw), min(w+tw,w), max(0,-tw), min(w-tw,w)
y1,y2,y3,y4 = max(0,th), min(h+th,h), max(0,-th), min(h-th,h)
inputs[0] = inputs[0][y1:y2,x1:x2]
inputs[1] = inputs[1][y3:y4,x3:x4]
target = target[y1:y2,x1:x2]
target[:,:,0] += tw
target[:,:,1] += th
return inputs, target
class RandomColorWarp(object):
def __init__(self, mean_range=0, std_range=0):
self.mean_range = mean_range
self.std_range = std_range
def __call__(self, inputs, target):
random_std = np.random.uniform(-self.std_range, self.std_range, 3)
random_mean = np.random.uniform(-self.mean_range, self.mean_range, 3)
random_order = np.random.permutation(3)
inputs[0] *= (1 + random_std)
inputs[0] += random_mean
inputs[1] *= (1 + random_std)
inputs[1] += random_mean
inputs[0] = inputs[0][:,:,random_order]
inputs[1] = inputs[1][:,:,random_order]
return inputs, target
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