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pre_process.py
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pre_process.py
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import numpy as np
from torchvision import transforms
import os
from PIL import Image, ImageOps
import numbers
import torch
def image_visda(resize_size=256, crop_size=224, alexnet=False):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
return transforms.Compose([
transforms.Resize((resize_size, resize_size)),
transforms.CenterCrop((crop_size, crop_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize])
def image_train(resize_size=256, crop_size=224, alexnet=False):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
return transforms.Compose([
transforms.Resize((resize_size, resize_size)),
transforms.RandomCrop((crop_size, crop_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize])
def image_test(resize_size=256, crop_size=224, alexnet=False):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
return transforms.Compose([
transforms.Resize((resize_size, resize_size)),
transforms.CenterCrop((crop_size, crop_size)),
transforms.ToTensor(),
normalize])
class ResizeImage():
def __init__(self, size):
if isinstance(size, int):
self.size = (int(size), int(size))
# print('hah')
else:
self.size = size
def __call__(self, img):
th, tw = self.size
return img.resize((th, tw))
class RandomSizedCrop(object):
"""Crop the given PIL.Image to random size and aspect ratio.
A crop of random size of (0.08 to 1.0) of the original size and a random
aspect ratio of 3/4 to 4/3 of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.
Args:
size: size of the smaller edge
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, interpolation=Image.BILINEAR):
self.size = size
self.interpolation = interpolation
def __call__(self, img):
h_off = random.randint(0, img.shape[1]-self.size)
w_off = random.randint(0, img.shape[2]-self.size)
img = img[:, h_off:h_off+self.size, w_off:w_off+self.size]
return img
class Normalize(object):
"""Normalize an tensor image with mean and standard deviation.
Given mean: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = channel - mean
Args:
mean (sequence): Sequence of means for R, G, B channels respecitvely.
"""
def __init__(self, mean=None, meanfile=None):
if mean:
self.mean = mean
else:
arr = np.load(meanfile)
self.mean = torch.from_numpy(arr.astype('float32')/255.0)[[2,1,0],:,:]
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
# TODO: make efficient
for t, m in zip(tensor, self.mean):
t.sub_(m)
return tensor
class PlaceCrop(object):
"""Crops the given PIL.Image at the particular index.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (w, h), a square crop (size, size) is
made.
"""
def __init__(self, size, start_x, start_y):
if isinstance(size, int):
self.size = (int(size), int(size))
else:
self.size = size
self.start_x = start_x
self.start_y = start_y
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be cropped.
Returns:
PIL.Image: Cropped image.
"""
th, tw = self.size
return img.crop((self.start_x, self.start_y, self.start_x + tw, self.start_y + th))
class ForceFlip(object):
"""Horizontally flip the given PIL.Image randomly with a probability of 0.5."""
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be flipped.
Returns:
PIL.Image: Randomly flipped image.
"""
return img.transpose(Image.FLIP_LEFT_RIGHT)
class CenterCrop(object):
"""Crops the given PIL.Image at the center.
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.
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be cropped.
Returns:
PIL.Image: Cropped image.
"""
w, h = (img.shape[1], img.shape[2])
th, tw = self.size
w_off = int((w - tw) / 2.)
h_off = int((h - th) / 2.)
img = img[:, h_off:h_off+th, w_off:w_off+tw]
return img
def image_test_10crop(resize_size=256, crop_size=224, alexnet=False):
if not alexnet:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
normalize = Normalize(meanfile='./ilsvrc_2012_mean.npy')
start_first = 0
start_center = (resize_size - crop_size - 1) / 2
start_last = resize_size - crop_size - 1
data_transforms = [
transforms.Compose([
ResizeImage(resize_size),ForceFlip(),
PlaceCrop(crop_size, start_first, start_first),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),ForceFlip(),
PlaceCrop(crop_size, start_last, start_last),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),ForceFlip(),
PlaceCrop(crop_size, start_last, start_first),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),ForceFlip(),
PlaceCrop(crop_size, start_first, start_last),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),ForceFlip(),
PlaceCrop(crop_size, start_center, start_center),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),
PlaceCrop(crop_size, start_first, start_first),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),
PlaceCrop(crop_size, start_last, start_last),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),
PlaceCrop(crop_size, start_last, start_first),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),
PlaceCrop(crop_size, start_first, start_last),
transforms.ToTensor(),
normalize
]),
transforms.Compose([
ResizeImage(resize_size),
PlaceCrop(crop_size, start_center, start_center),
transforms.ToTensor(),
normalize
])
]
return data_transforms