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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
import torchvision.transforms as transforms
import random
def cv_random_flip(img, label):
flip_flag = random.randint(0, 1)
if flip_flag == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)
# flip_flag2 = random.randint(0, 1)
# if flip_flag2 == 1:
# img = img.transpose(Image.FLIP_TOP_BOTTOM)
# label = label.transpose(Image.FLIP_TOP_BOTTOM)
# img = img[:,:,::-1].copy()
# label = label[:,:,::-1].copy()
return img, label
class SalObjDataset(data.Dataset):
def __init__(self, image_root, gt_root, trainsize,randomflip = False):
self.trainsize = trainsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
# self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
# or f.endswith('.png')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.png')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.filter_files()
self.size = len(self.images)
self.randomflip = randomflip
self.img_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()]) #[0, 1.0] torch.FloadTensor
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
gt = self.binary_loader(self.gts[index])
if self.randomflip:
image, gt = cv_random_flip(image, gt)
image = self.img_transform(image)
gt = self.gt_transform(gt)
# if self.randomflip:
# image, gt = cv_random_flip(image, 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') #0,255
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
def get_loader(image_root, gt_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True,randomflip = False):
dataset = SalObjDataset(image_root, gt_root, trainsize,randomflip)
data_loader = data.DataLoader(dataset=dataset,
batch_size=batchsize,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory, drop_last=True)
return data_loader
class test_dataset:
def __init__(self, image_root, gt_root, testsize):
self.testsize = testsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
or f.endswith('.png')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.transform = transforms.Compose([
transforms.Resize((self.testsize, self.testsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.ToTensor()
self.size = len(self.images)
self.index = 0
def load_data(self):
image = self.rgb_loader(self.images[self.index])
image = self.transform(image).unsqueeze(0)
gt = self.binary_loader(self.gts[self.index])
name = self.images[self.index].split('/')[-1]
if name.endswith('.jpg'):
name = name.split('.jpg')[0] + '.png'
self.index += 1
return image, gt, name
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('L')
class test_dataset2:
def __init__(self, image_root, gt_root, testsize):
self.testsize = testsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.png')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
print (len(self.images))
print (len(self.gts))
self.transform = transforms.Compose([
transforms.Resize((self.testsize, self.testsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.ToTensor()
self.size = len(self.images)
self.index = 0
def load_data(self):
image = self.rgb_loader(self.images[self.index])
image = self.transform(image).unsqueeze(0)
gt = self.binary_loader(self.gts[self.index])
name = self.images[self.index].split('/')[-1]
if name.endswith('.jpg'):
name = name.split('.jpg')[0] + '.png'
self.index += 1
return image, gt, name
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('L')
class test_dataset3:
def __init__(self, image_root, gt_root, testsize):
self.testsize = testsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.png')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
print (len(self.images))
print (len(self.gts))
self.transform = transforms.Compose([
transforms.Resize((self.testsize, self.testsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.ToTensor()
self.size = len(self.images)
self.index = 0
def load_data(self):
image = self.rgb_loader(self.images[self.index])
image = self.transform(image).unsqueeze(0)
# gt = self.binary_loader(self.gts[self.index])
name = self.images[self.index].split('/')[-1]
if name.endswith('.jpg'):
name = name.split('.jpg')[0] + '.png'
self.index += 1
return image, name
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('L')