def __getitem__(self, index):
"""
:param index: The index within the entire dataset.
:type index: int
:return: An example and label from the crowd dataset.
:rtype: torch.Tensor, torch.Tensor
"""
index_ = random.randrange(self.length)
file_name_index = np.searchsorted(self.start_indexes, index_, side='right') - 1
start_index = self.start_indexes[file_name_index]
file_name = self.file_names[file_name_index]
position_index = index_ - start_index
extract_patch_transform = ExtractPatchForPosition(self.image_patch_size, self.label_patch_size,
allow_padded=True) # In case image is smaller than patch.
preprocess_transform = torchvision.transforms.Compose([NegativeOneToOneNormalizeImage(),
NumpyArraysToTorchTensors()])
image = np.load(os.path.join(self.dataset_directory, 'images', file_name), mmap_mode='r')
label = np.load(os.path.join(self.dataset_directory, 'labels', file_name), mmap_mode='r')
map_ = np.load(os.path.join(self.dataset_directory, self.map_directory_name, file_name), mmap_mode='r')
half_patch_size = int(self.image_patch_size // 2)
y_positions = range(half_patch_size, image.shape[0] - half_patch_size + 1)
x_positions = range(half_patch_size, image.shape[1] - half_patch_size + 1)
positions_shape = [len(y_positions), len(x_positions)]
y_index, x_index = np.unravel_index(position_index, positions_shape)
y = y_positions[y_index]
x = x_positions[x_index]
example = CrowdExample(image=image, label=label, map_=map_)
example = extract_patch_transform(example, y, x)
if self.middle_transform:
example = self.middle_transform(example)
example = preprocess_transform(example)
return example.image, example.label, example.map
def __getitem__(self, index):
"""
:param index: The index within the entire dataset.
:type index: int
:return: An example and label from the crowd dataset.
:rtype: torch.Tensor, torch.Tensor
"""
index_ = random.randrange(self.length)
camera_data_index = np.searchsorted(self.start_indexes, index_, side='right') - 1
start_index = self.start_indexes[camera_data_index]
camera_data = self.camera_data_list[camera_data_index]
camera_images = camera_data.images
array_index = index_ - start_index
half_patch_size = int(self.image_patch_size // 2)
y_positions = range(half_patch_size, camera_images.shape[1] - half_patch_size + 1)
x_positions = range(half_patch_size, camera_images.shape[2] - half_patch_size + 1)
image_indexes_length = len(y_positions) * len(x_positions)
image_index = math.floor(array_index / image_indexes_length)
position_index = array_index % image_indexes_length
image = camera_data.images[image_index]
label = camera_data.labels[image_index]
map_ = label
extract_patch_transform = ExtractPatchForPosition(self.image_patch_size, self.label_patch_size,
allow_padded=True) # In case image is smaller than patch.
preprocess_transform = torchvision.transforms.Compose([NegativeOneToOneNormalizeImage(),
NumpyArraysToTorchTensors()])
y_positions = range(half_patch_size, image.shape[0] - half_patch_size + 1)
x_positions = range(half_patch_size, image.shape[1] - half_patch_size + 1)
positions_shape = [len(y_positions), len(x_positions)]
y_index, x_index = np.unravel_index(position_index, positions_shape)
y = y_positions[y_index]
x = x_positions[x_index]
example = CrowdExample(image=image, label=label, map_=map_)
example = extract_patch_transform(example, y, x)
if self.middle_transform:
example = self.middle_transform(example)
example = preprocess_transform(example)
return example.image, example.label, example.map