forked from zhangyukuin/Light-Field-for-Depth-Estimation
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loaddata.py
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loaddata.py
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import PIL.Image
import scipy.io as sio
from torch.utils.data import Dataset, DataLoader
from transform import *
import os
import PIL.Image
class depthDataset(Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
mean_focal = np.tile(mean_rgb, 12)
std_focal = np.tile(std_rgb, 12)
def __init__(self, root, transform=None):
self.root = root
self.transform = transform
img_root = os.path.join(self.root, 'image')
lbl_root = os.path.join(self.root, 'depth')
focal_root = os.path.join(self.root, 'focal')
file_names = os.listdir(img_root)
self.img_names = []
self.lbl_names = []
self.focal_names = []
for i, name in enumerate(file_names):
if not name.endswith('.png'):
continue
self.lbl_names.append(
os.path.join(lbl_root, name[:-4]+'.png')
)
self.img_names.append(
os.path.join(img_root, name)
)
self.focal_names.append(
os.path.join(focal_root, name[:-4]+'.mat')
)
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file)
image = img.resize((256, 256))
# load label
lbl_file = self.lbl_names[index]
lbl = PIL.Image.open(lbl_file)
depth = lbl.resize((256,256))
depth1 = lbl.resize((64, 64))
# load focal
focal_file = self.focal_names[index]
focal = sio.loadmat(focal_file)
focal = focal['img']
sample = {'image': image, 'depth': depth,'focal':focal,'depth1': depth1}
if self.transform:
sample = self.transform(sample)
return sample
class testdepthDataset(Dataset):
"""
load data in a folder
"""
mean_rgb = np.array([0.447, 0.407, 0.386])
std_rgb = np.array([0.244, 0.250, 0.253])
mean_focal = np.tile(mean_rgb, 12)
std_focal = np.tile(std_rgb, 12)
def __init__(self, root, transform=None):
self.root = root
self.transform = transform
img_root = os.path.join(self.root, 'image')
lbl_root = os.path.join(self.root, 'depth')
focal_root = os.path.join(self.root, 'focal')
file_names = os.listdir(img_root)
self.img_names = []
self.lbl_names = []
self.names = []
self.focal_names = []
for i, name in enumerate(file_names):
if not name.endswith('jpg'):
continue
self.lbl_names.append(
os.path.join(lbl_root, name[:-4] + '.png')
)
self.img_names.append(
os.path.join(img_root, name)
)
self.focal_names.append(
os.path.join(focal_root, name[:-4] + '.mat')
)
self.names.append(name[:-4])
def __len__(self):
return len(self.img_names)
def __getitem__(self, index):
# load image
img_file = self.img_names[index]
img = PIL.Image.open(img_file)
image = img.resize((256, 256))
img_size = image .size
# load label
lbl_file = self.lbl_names[index]
lbl = PIL.Image.open(lbl_file)
depth = lbl.resize((256, 256))
depth1 = lbl.resize((64, 64))
# load focal
focal_file = self.focal_names[index]
focal = sio.loadmat(focal_file)
focal = focal['img']
sample = {'image': image, 'depth': depth, 'focal': focal,'depth1': depth1}
if self.transform:
sample = self.transform(sample)
return sample,self.names[index], img_size
def getTrainingData(batch_size=64):
train_dataRoot = 'D:\\dataset\\train'
__imagenet_pca = {
'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]),
'eigvec': torch.Tensor([
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
])
}
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
transformed_training = depthDataset(train_dataRoot,
transform=transforms.Compose([
RandomRotate(5),
ToTensor(),
Lighting(0.1, __imagenet_pca[
'eigval'], __imagenet_pca['eigvec']),
ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std'])
]))
dataloader_training = DataLoader(transformed_training, batch_size,
shuffle=True, num_workers=0, pin_memory=False)
return dataloader_training
def getTestingData(batch_size=64):
test_dataRoot ='D:\\dataset\\test'
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
transformed_testing = testdepthDataset(test_dataRoot,
transform=transforms.Compose([
ToTensor(is_test=True),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std'])
]))
dataloader_testing= DataLoader(transformed_testing, 1,
shuffle=False, num_workers=0, pin_memory=False)
return dataloader_testing