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 batches_of_patches_with_position(self,
full_example,
window_step_size=32):
"""
A generator for extracting patches from an image in batches.
:param full_example: The full example to be patched.
:type full_example: CrowdExample
:param window_step_size: The sliding window size.
:type window_step_size: int
:return: A batch of patches.
:rtype: list[list[CrowdExample]]
"""
extract_patch_transform = ExtractPatchForPosition()
test_transform = torchvision.transforms.Compose([
data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()
])
batch = []
for y in range(0, full_example.label.shape[0], window_step_size):
for x in range(0, full_example.label.shape[1], window_step_size):
patch = extract_patch_transform(full_example, y, x)
example = test_transform(patch)
example_with_position = CrowdExample(image=example.image,
label=example.label,
patch_center_x=x,
patch_center_y=y)
batch.append(example_with_position)
if len(batch) == self.settings.batch_size:
yield batch
batch = []
yield batch
def batches_of_patches_with_position(self, full_example, window_step_size=32):
extract_patch_transform = ExtractPatchForPosition()
test_transform = torchvision.transforms.Compose([data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()])
batch = []
for y in range(0, full_example.label.shape[0], window_step_size):
for x in range(0, full_example.label.shape[1], window_step_size):
patch = extract_patch_transform(full_example, y, x)
example = test_transform(patch)
example_with_position = CrowdExampleWithPosition(example.image, example.label, x, y)
batch.append(example_with_position)
if len(batch) == self.settings.batch_size:
yield batch
batch = []
yield batch
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