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data.py
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data.py
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import os
from PIL import Image
import torch.utils.data as data
from torchvision.transforms import transforms
import numpy as np
import torch
import random
from gridmask import GridMask
class ObjDataset(data.Dataset):
def __init__(self, images, gts, trainsize, mode):
self.trainsize = trainsize
self.images = images
self.mode = mode
self.gts = gts
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.filter_files()
self.size = len(self.images)
self.gridmask = GridMask()
self.img_transform_w = transforms.Compose([
transforms.Resize((self.trainsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform_w = transforms.Compose([
transforms.Resize((self.trainsize)),
transforms.ToTensor()])
self.img_transform_s = transforms.Compose([
transforms.RandomRotation(90),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomAffine(degrees = 90,translate=(0.5,0.5),shear=30),
transforms.ColorJitter(hue = 0.5),
transforms.RandomGrayscale(p=0.2),
transforms.GaussianBlur(3),
transforms.Resize((self.trainsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform_s = transforms.Compose([
transforms.RandomRotation(90),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomAffine(degrees = 90,translate=(0.5,0.5),shear=30),
transforms.Resize((self.trainsize)),
transforms.ToTensor()])
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
gt = self.binary_loader(self.gts[index])
seed = np.random.randint(2147483647)
if self.mode == 'weak':
image = self.img_transform_w(image)
gt = self.gt_transform_w(gt)
if self.mode =='strong':
torch.manual_seed(seed)
image = self.img_transform_s(image)
torch.manual_seed(seed)
gt = self.gt_transform_s(gt)
image = self.gridmask(image)
return image, gt
def filter_files(self):
assert len(self.images) == len(self.gts)
images = []
gts = []
for img_path, gt_path in zip(self.images, self.gts):
img = Image.open(img_path)
gt = Image.open(gt_path)
if img.size == gt.size:
images.append(img_path)
gts.append(gt_path)
self.images = images
self.gts = gts
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
# return img.convert('1')
return img.convert('L')
def resize(self, img, gt):
assert img.size == gt.size
w, h = img.size
if h < self.trainsize or w < self.trainsize:
h = max(h, self.trainsize)
w = max(w, self.trainsize)
return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
else:
return img, gt
def __len__(self):
return self.size
class ValObjDataset(data.Dataset):
def __init__(self, images, gts, trainsize):
self.trainsize = trainsize
self.images = images
self.gts = gts
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.filter_files()
self.size = len(self.images)
self.img_transform = transforms.Compose([
transforms.Resize((self.trainsize)),
transforms.ToTensor()])
self.gt_transform = transforms.Compose([
transforms.Resize((self.trainsize)),
transforms.ToTensor()])
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
gt = self.binary_loader(self.gts[index])
image = self.img_transform(image)
gt = self.gt_transform(gt)
return image, gt
def filter_files(self):
assert len(self.images) == len(self.gts)
images = []
gts = []
for img_path, gt_path in zip(self.images, self.gts):
img = Image.open(img_path)
gt = Image.open(gt_path)
if img.size == gt.size:
images.append(img_path)
gts.append(gt_path)
self.images = images
self.gts = gts
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
# return img.convert('1')
return img.convert('L')
def resize(self, img, gt):
assert img.size == gt.size
w, h = img.size
if h < 256 or w < 256:
h = max(h, 256)
w = max(w, 256)
return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
else:
return img, gt
def __len__(self):
return self.size
def image_loader(image_root, gt_root,val_img_root,val_gt_root, batch_size, image_size, split=1, labeled_ratio=0.05,mode='weak_1'):
images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg') or f.endswith('.png')]
val_img = [val_img_root+ f for f in os.listdir(val_img_root) if f.endswith('.jpg') or f.endswith('.png')]
val_label = [val_gt_root+ f for f in os.listdir(val_gt_root) if f.endswith('.jpg') or f.endswith('.png')]
train_images = images[0:int(len(images) * split)]
val_images = val_img[0:int(len(val_img) * split)]
val_gts = val_label[0:int(len(val_label) * split)]
train_gts = gts[0:int(len(images) * split)]
labeled_train_images = train_images[0:int(len(train_images) * labeled_ratio)]
labeled_train_images_1 = labeled_train_images[0:int(len(labeled_train_images) * 0.5)]
labeled_train_images_2 = labeled_train_images[int(len(labeled_train_images) * 0.5):]
unlabeled_train_images = train_images[int(len(train_images) * labeled_ratio):]
labeled_train_gts = train_gts[0:int(len(train_gts) * labeled_ratio)]
labeled_train_gts_1 = labeled_train_gts[0:int(len(labeled_train_gts) * 0.5)]
labeled_train_gts_2 = labeled_train_gts[int(len(labeled_train_gts) * 0.5):]
unlabeled_train_gts = train_gts[int(len(train_gts) * labeled_ratio):]
labeled_train_dataset_1 = ObjDataset(labeled_train_images_1, labeled_train_gts_1, image_size,mode='weak')
labeled_train_dataset_2 = ObjDataset(labeled_train_images_2, labeled_train_gts_2, image_size,mode='weak')
unlabeled_train_dataset = ObjDataset(unlabeled_train_images, unlabeled_train_gts, image_size,mode='strong')
val_dataset = ValObjDataset(val_images, val_gts, image_size)
labeled_data_loader_1 = data.DataLoader(dataset=labeled_train_dataset_1,
batch_size=batch_size,
num_workers=1,
pin_memory=True,
shuffle=True)
labeled_data_loader_2 = data.DataLoader(dataset=labeled_train_dataset_2,
batch_size=batch_size,
num_workers=1,
pin_memory=True,
shuffle=True)
unlabeled_data_loader = data.DataLoader(dataset=unlabeled_train_dataset,
batch_size=batch_size,
num_workers=1,
pin_memory=True,
shuffle=True)
val_loader = data.DataLoader(dataset=val_dataset,
batch_size=batch_size,
num_workers=1,
pin_memory=True,
shuffle=False)
return labeled_data_loader_1, labeled_data_loader_2, unlabeled_data_loader, val_loader