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# Code with dataset loader for VOC12 and Cityscapes (adapted from bodokaiser/piwise code)
# Sept 2017
# Eduardo Romera
#######################
import numpy as np
import os
import torch
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(filename):
return filename.endswith("_labelTrainIds.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_masks/')
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]
image = image_path(self.images_root, filename, '.png')
#print(image)
#with open(image_path(self.images_root, filename, '.png'), 'rb') as f:
# image = load_image(f).convert('RGB')
# print(image)
#with open(image_path(self.labels_root, filename, '.png'), 'rb') as f:
# label = load_image(f).convert('P')
label =image_path(self.labels_root, filename, '.png')
if self.input_transform is not None:
image = self.input_transform(image)
if self.target_transform is not None:
label = self.target_transform(label)
#print(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='val'):
self.images_root = os.path.join(root, 'leftImg8bit/' + subset)
self.labels_root = os.path.join(root, 'gtFine/' + subset)
print(self.images_root, self.labels_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(f)]
self.filenamesGt.sort()
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
filenameGt = self.filenamesGt[index]
#print(filename)
with open(image_path_city('', filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city('', 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 ValidationDataset(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, 'label_marks/')
print(self.images_root, self.labels_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(f)]
self.filenamesGt.sort()
self.input_transform = input_transform
self.target_transform = target_transform
def __getitem__(self, index):
filename = self.filenames[index]
filenameGt = self.filenamesGt[index]
#print(filename)
with open(image_path_city('', filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city('', 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 cityscapesTemperature(Dataset):
def __init__(self, root, input_transform=None, target_transform=None, subset='val'):
self.images_root = os.path.join(root, 'leftImg8bit/' + subset)
self.labels_root = os.path.join(root, 'gtFine/' + subset)
print("ROOT: " + root)
print("IMAGES: " + self.images_root)
print("LABELS: " + self.labels_root)
print(self.images_root, self.labels_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(f)]
self.filenamesGt.sort()
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("", filename), 'rb') as f:
image = load_image(f).convert('RGB')
with open(image_path_city("", 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)
# Converti l'etichetta in un array numpy
label_np = np.array(label)
# Assicurati che l'etichetta sia bidimensionale
if len(label_np.shape) > 2:
# Riduci la dimensione dell'etichetta a 2 dimensioni
label_np = label_np.squeeze()
# Converti l'etichetta in un tensore PyTorch
label = torch.from_numpy(label_np)
return image, label
def __len__(self):
return len(self.filenames)