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import os
from PIL import Image, ImageOps, ImageFilter#, PILLOW_VERSION
import torch
import random
import numpy as np
from torch.utils import data
from torchvision import transforms
from torchvision.transforms import functional as F
import numbers
import random
import pandas as pd
class CoData(data.Dataset):
def __init__(self, img_root, gt_root, img_size, transform, max_num, is_train):
class_list = os.listdir(img_root)
self.size = [img_size, img_size]
self.img_dirs = list(
map(lambda x: os.path.join(img_root, x), class_list))
self.gt_dirs = list(
map(lambda x: os.path.join(gt_root, x), class_list))
self.transform = transform
self.max_num = max_num
self.is_train = is_train
def __getitem__(self, item):
names = os.listdir(self.img_dirs[item])
num = len(names)
img_paths = list(
map(lambda x: os.path.join(self.img_dirs[item], x), names))
gt_paths = list(
map(lambda x: os.path.join(self.gt_dirs[item], x[:-4]+'.png'), names))
if self.is_train:
final_num = min(num, self.max_num)
sampled_list = random.sample(range(num), final_num)
# print(sampled_list)
new_img_paths = [img_paths[i] for i in sampled_list]
img_paths = new_img_paths
new_gt_paths = [gt_paths[i] for i in sampled_list]
gt_paths = new_gt_paths
final_num = final_num
else:
final_num = num
imgs = torch.Tensor(final_num, 3, self.size[0], self.size[1])
gts = torch.Tensor(final_num, 1, self.size[0], self.size[1])
subpaths = []
ori_sizes = []
for idx in range(final_num):
# print(idx)
img = Image.open(img_paths[idx]).convert('RGB')
gt = Image.open(gt_paths[idx]).convert('L')
subpaths.append(os.path.join(img_paths[idx].split('/')[-2], img_paths[idx].split('/')[-1][:-4]+'.png'))
ori_sizes.append((img.size[1], img.size[0]))
# ori_sizes += ((img.size[1], img.size[0]))
[img, gt] = self.transform(img, gt)
imgs[idx] = img
gts[idx] = gt
if self.is_train:
cls_ls = [item] * int(final_num)
return imgs, gts, subpaths, ori_sizes, cls_ls
else:
return imgs, gts, subpaths, ori_sizes
def __len__(self):
return len(self.img_dirs)
class FixedResize(object):
def __init__(self, size):
self.size = (size, size) # size: (h, w)
def __call__(self, img, gt):
# assert img.size == gt.size
img = img.resize(self.size, Image.BILINEAR)
gt = gt.resize(self.size, Image.NEAREST)
# gt = gt.resize(self.size, Image.BILINEAR)
return img, gt
class ToTensor(object):
def __call__(self, img, gt):
return F.to_tensor(img), F.to_tensor(gt)
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple): standard deviations for each channel.
"""
def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)):
self.mean = mean
self.std = std
def __call__(self, img, gt):
img = F.normalize(img, self.mean, self.std)
return img, gt
class RandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, gt):
if random.random() < self.p:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
return img, gt
class RandomScaleCrop(object):
def __init__(self, base_size, crop_size, fill=0):
self.base_size = base_size
self.crop_size = crop_size
self.fill = fill
def __call__(self, img, mask):
# random scale (short edge)
# img = img.numpy()
# mask = mask.numpy()
short_size = random.randint(int(self.base_size * 0.8), int(self.base_size * 1.2))
w, h = img.size
if h > w:
ow = short_size
oh = int(1.0 * h * ow / w)
else:
oh = short_size
ow = int(1.0 * w * oh / h)
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# pad crop
if short_size < self.crop_size:
padh = self.crop_size - oh if oh < self.crop_size else 0
padw = self.crop_size - ow if ow < self.crop_size else 0
img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill)
# random crop crop_size
w, h = img.size
x1 = random.randint(0, w - self.crop_size)
y1 = random.randint(0, h - self.crop_size)
img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
return img, mask
class RandomRotation(object):
def __init__(self, degrees, resample=False, expand=False, center=None):
if isinstance(degrees, numbers.Number):
if degrees < 0:
raise ValueError("If degrees is a single number, it must be positive.")
self.degrees = (-degrees, degrees)
else:
if len(degrees) != 2:
raise ValueError("If degrees is a sequence, it must be of len 2.")
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
@staticmethod
def get_params(degrees):
angle = random.uniform(degrees[0], degrees[1])
return angle
def __call__(self, img, gt):
"""
img (PIL Image): Image to be rotated.
Returns:
PIL Image: Rotated image.
"""
angle = self.get_params(self.degrees)
return F.rotate(img, angle, Image.BILINEAR, self.expand, self.center), F.rotate(gt, angle, Image.NEAREST, self.expand, self.center)
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, gt):
for t in self.transforms:
img, gt = t(img, gt)
return img, gt
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
# get the dataloader (Note: without data augmentation)
def get_loader(img_root, gt_root, img_size, batch_size, max_num = float('inf'), istrain=True, shuffle=False, num_workers=0, pin=False):
if istrain:
transform = Compose([
RandomScaleCrop(img_size*2, img_size*2),
FixedResize(img_size),
RandomHorizontalFlip(),
RandomRotation((-90, 90)),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
else:
transform = Compose([
FixedResize(img_size),
# RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
dataset = CoData(img_root, gt_root, img_size, transform, max_num, is_train=istrain)
data_loader = data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers,
pin_memory=pin)
return data_loader
if __name__ == '__main__':
import matplotlib.pyplot as plt
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img_root = './data/testtrain/img/'
gt_root = './data/testtrain/gt/'
loader = get_loader(img_root, gt_root, 20, 1, 16, istrain=False)
for batch in loader:
b, c, h, w = batch[0][0].shape
for i in range(b):
img = batch[0].squeeze(0)[i].permute(1, 2, 0).cpu().numpy() * std + mean
image = img * 255
mask = batch[1].squeeze(0)[i].squeeze().cpu().numpy()
plt.subplot(121)
plt.imshow(np.uint8(image))
plt.subplot(122)
plt.imshow(mask)
plt.show(block=True)
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