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dataset_loader.py
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dataset_loader.py
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import os
import numpy as np
import PIL.Image
import scipy.io as sio
import torch
from torch.utils import data
class MyData(data.Dataset): # inherit
"""
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])
def __init__(self, root, transform=False):
super(MyData, self).__init__()
self.root = root
self._transform = transform
img_root = os.path.join(self.root, 'train_images')
lbl_root = os.path.join(self.root, 'train_masks')
depth_root = os.path.join(self.root, 'train_depth')
file_names = os.listdir(img_root)
self.img_names = []
self.lbl_names = []
self.depth_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.depth_names.append(
os.path.join(depth_root, name[:-4]+'.png')
)
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)
# img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
# load label
lbl_file = self.lbl_names[index]
lbl = PIL.Image.open(lbl_file)
# lbl = lbl.resize((256, 256))
lbl = np.array(lbl, dtype=np.int32)
lbl[lbl != 0] = 1
# load depth
depth_file = self.depth_names[index]
depth = PIL.Image.open(depth_file)
# depth = depth.resize(256, 256)
depth = np.array(depth, dtype=np.uint8)
if self._transform:
return self.transform(img, lbl, depth)
else:
return img, lbl, depth
# Translating numpy_array into format that pytorch can use on Code.
def transform(self, img, lbl, depth):
img = img.astype(np.float64)/255.0
img -= self.mean_rgb
img /= self.std_rgb
img = img.transpose(2, 0, 1) # to verify
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).long()
depth = depth.astype(np.float64)/255.0
depth = torch.from_numpy(depth).float()
return img, lbl, depth
class MyTestData(data.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])
def __init__(self, root, transform=False):
super(MyTestData, self).__init__()
self.root = root
self._transform = transform
img_root = os.path.join(self.root, 'test_images')
depth_root = os.path.join(self.root, 'test_depth')
file_names = os.listdir(img_root)
self.img_names = []
self.names = []
self.depth_names = []
for i, name in enumerate(file_names):
if not name.endswith('.jpg'):
continue
self.img_names.append(
os.path.join(img_root, name)
)
self.names.append(name[:-4])
self.depth_names.append(
# os.path.join(depth_root, name[:-4]+'_depth.png') # Test RGBD135 dataset
os.path.join(depth_root, name[:-4] + '.png')
)
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)
img_size = img.size
# img = img.resize((256, 256))
img = np.array(img, dtype=np.uint8)
# load focal
depth_file = self.depth_names[index]
depth = PIL.Image.open(depth_file)
# depth = depth.resize(256, 256)
depth = np.array(depth, dtype=np.uint8)
if self._transform:
img, focal = self.transform(img, depth)
return img, focal, self.names[index], img_size
else:
return img, depth, self.names[index], img_size
def transform(self, img, depth):
img = img.astype(np.float64)/255.0
img -= self.mean_rgb
img /= self.std_rgb
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).float()
depth = depth.astype(np.float64)/255.0
depth = torch.from_numpy(depth).float()
return img, depth