def __getitem__(self, idx):
#print("Get item: ",str(idx) + self.image_paths[idx])
#print("Label Length",len(self.label))
#print("Test Length",len(self.test))
self.printedItems = self.printedItems + 1
if self.loadLink:
image_name = self.image_paths[idx]
image = imageio.imread(os.path.join(image_name))
image = to_normalized_range(image)
image = transform.resize(image, (self.IMAGE_SIZE, self.IMAGE_SIZE),
mode='symmetric',
preserve_range=True)
#Uncomment and comment the reshape,
#,if every picture should be done in color mode: Remember to change the dimension in the network from 1 to 3!
#if len(image.shape) == 2:
# image = color.gray2rgb(image)
#image = image.transpose((2, 0, 1))
image = image.reshape((1, self.IMAGE_SIZE, self.IMAGE_SIZE))
image = torch.tensor(image.astype(np.float32))
if self.label:
label_image = self.label[idx]
label_image = torch.tensor(label_image, dtype=torch.float32)
return image, label_image
else:
label_image = idx
#label_image = torch.tensor(label_image, dtype=torch.float32)
return image, label_image
else:
# Code for CIL Project
if (self.category == 'query'):
return dataset_img[idx]
else:
return dataset_img[idx], label[idx]
"""
def __getitem__(self, idx):
image_name = self.image_names[idx]
image = imageio.imread(os.path.join(self.dataset_path, image_name))
image = image.transpose((2, 0, 1))
image = torch.tensor(image.astype(np.float32))
image = to_normalized_range(image)
age = self.ages[idx]
age = torch.tensor(age, dtype=torch.float32)
return image, age
def __getitem__(self, index):
image_name = self.image_names[index]
image = np.load(
os.path.join(self.dataset_path, image_name.replace('.jpg',
'.npy')))
image = torch.tensor(image.astype(np.float32))
image = to_normalized_range(image)
angle = self.angles[index]
angle = torch.tensor(angle, dtype=torch.float32)
return image, angle