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dataset_custom.py
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import numpy as np
import os
from PIL import Image
from torch.utils.data import Dataset
EXTENSIONS = ['.jpg', '.png']
def load_image(file):
return Image.open(file)
def is_image(filename):
return any(filename.endswith(ext) for ext in EXTENSIONS)
def is_label_city(filename):
return filename.endswith("_labelTrainIds.png")
def is_label_IDD(filename):
return filename.endswith("_labellevel3Ids.png")
def is_label_BDD(filename):
return filename.endswith("_train_id.png")
def image_path(root, basename, extension):
return os.path.join(root, f'{basename}{extension}')
def image_path_city(root, name):
return os.path.join(root, f'{name}')
def image_basename(filename):
return os.path.basename(os.path.splitext(filename)[0])
class VOC12(Dataset):
def __init__(self, root, input_transform=None, target_transform=None):
self.images_root = os.path.join(root, 'images')
self.labels_root = os.path.join(root, 'labels')
self.filenames = [image_basename(f)
for f in os.listdir(self.labels_root) if is_image(f)]
self.filenames.sort()
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
with open(image_path(self.images_root, filename, '.jpg'), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path(self.labels_root, filename, '.png'), 'rb') as f:
label = load_image(f).convert('P')
if self.input_transform is not None:
image = self.input_transform(image)
if self.target_transform is not None:
label = self.target_transform(label)
return image, label
def __len__(self):
return len(self.filenames)
class cityscapes(Dataset):
def __init__(self, root, input_transform=None, target_transform=None, subset='train'):
self.images_root = os.path.join(root, 'leftImg8bit/')
self.labels_root = os.path.join(root, 'gtFine/')
self.images_root += subset
self.labels_root += subset
print(self.images_root)
self.filenames = [os.path.join(dp, f) for dp, dn, fn in os.walk(
os.path.expanduser(self.images_root)) for f in fn if is_image(f)]
self.filenames.sort()
# [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(".")) for f in fn]
# self.filenamesGt = [image_basename(f) for f in os.listdir(self.labels_root) if is_image(f)]
self.filenamesGt = [os.path.join(dp, f) for dp, dn, fn in os.walk(
os.path.expanduser(self.labels_root)) for f in fn if is_label_city(f)]
self.filenamesGt.sort()
# self.filenames = self.filenames[:1]
# self.filenamesGt = self.filenamesGt[:1] # trying to plot the t-sne
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
filenameGt = self.filenamesGt[index]
with open(image_path_city(self.images_root, filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city(self.labels_root, filenameGt), 'rb') as f:
label = load_image(f).convert('P')
if self.input_transform is not None:
image = self.input_transform(image)
if self.target_transform is not None:
label = self.target_transform(label)
return image, label, filename, filenameGt
def __len__(self):
return len(self.filenames)
# added
class IDD(Dataset):
def __init__(self, root, input_transform=None, target_transform=None, subset='train'):
self.images_root = os.path.join(root, 'leftImg8bit/')
self.labels_root = os.path.join(root, 'gtFine/')
self.images_root += subset
self.labels_root += subset
print(self.images_root)
self.filenames = [os.path.join(dp, f) for dp, dn, fn in os.walk(
os.path.expanduser(self.images_root)) for f in fn if is_image(f)]
self.filenames.sort()
self.filenamesGt = [os.path.join(dp, f) for dp, dn, fn in os.walk(
os.path.expanduser(self.labels_root)) for f in fn if is_label_IDD(f)]
self.filenamesGt.sort()
# self.filenames = self.filenames[:20]
# self.filenamesGt = self.filenamesGt[:20]
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
filenameGt = self.filenamesGt[index]
# image_path_city will work for IDD also as the images already have a .png extension
with open(image_path_city(self.images_root, filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city(self.labels_root, filenameGt), 'rb') as f:
label = load_image(f).convert('P')
if self.input_transform is not None:
image = self.input_transform(image)
if self.target_transform is not None:
label = self.target_transform(label)
return image, label, filename, filenameGt
def __len__(self):
return len(self.filenames)
class BDD(Dataset):
def __init__(self, root, input_transform=None, target_transform=None, subset='train'):
self.images_root = os.path.join(root, 'images/')
self.labels_root = os.path.join(root, 'labels/')
self.images_root += subset
self.labels_root += subset
print(self.images_root)
# self.filenames = [image_basename(f) for f in os.listdir(self.images_root) if is_image(f)]
self.filenames = [f for f in os.listdir(self.images_root) if is_image(f)]
self.filenames.sort()
# [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(".")) for f in fn]
# self.filenamesGt = [image_basename(f) for f in os.listdir(self.labels_root) if is_image(f)]
self.filenamesGt = [fn for fn in os.listdir(self.labels_root) if is_label_BDD(fn)]
self.filenamesGt.sort()
# self.filenames = self.filenames[:20]
# self.filenamesGt = self.filenamesGt[:20]
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
filenameGt = self.filenamesGt[index]
with open(image_path_city(self.images_root, filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city(self.labels_root, filenameGt), 'rb') as f:
label = load_image(f).convert('P')
if self.input_transform is not None:
image = self.input_transform(image)
if self.target_transform is not None:
label = self.target_transform(label)
return image, label, filename, filenameGt
def __len__(self):
return len(self.filenames)