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Fixed data preparation

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-04-19 15:55:48 +02:00
parent 70ff4b2591
commit e98196ec0d
1 changed files with 18 additions and 8 deletions
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26
src/twomartens/masterthesis/data.py
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@ -228,7 +228,7 @@ def _load_images_callback(resized_shape: Sequence[int]) -> Callable[
def prepare_scenenet_val(data_path: str, protobuf_path: str) -> Tuple[List[List[str]], def prepare_scenenet_val(data_path: str, protobuf_path: str) -> Tuple[List[List[str]],
List[List[str]], List[List[str]],
List[Dict[int, dict]]]: List[List[List[dict]]]]:
""" """
Prepares the SceneNet RGB-D data and returns it in Python format. Prepares the SceneNet RGB-D data and returns it in Python format.
@ -252,7 +252,8 @@ def prepare_scenenet_val(data_path: str, protobuf_path: str) -> Tuple[List[List[
path = f"{data_path}/{trajectory.render_path}" path = f"{data_path}/{trajectory.render_path}"
file_names_photos_traj = [] file_names_photos_traj = []
file_names_instances_traj = [] file_names_instances_traj = []
instances_traj = {} instances_traj = []
instances_traj_dict = {}
for instance in trajectory.instances: for instance in trajectory.instances:
instance_type = instance.instance_type instance_type = instance.instance_type
@ -264,13 +265,13 @@ def prepare_scenenet_val(data_path: str, protobuf_path: str) -> Tuple[List[List[
if wnid in definitions.WNID_TO_COCO: if wnid in definitions.WNID_TO_COCO:
instance_dict['coco_id'] = definitions.WNID_TO_COCO[wnid] instance_dict['coco_id'] = definitions.WNID_TO_COCO[wnid]
else: else:
instance_dict['coco_id'] = 0 # if no COCO id is found, the correct COCO class is background continue # only save instances that are positive instances and not background
if instance_type == scenenet_pb2.Instance.LIGHT_OBJECT: if instance_type == scenenet_pb2.Instance.LIGHT_OBJECT:
instance_dict['light_info'] = instance.light_info instance_dict['light_info'] = instance.light_info
if instance_type == scenenet_pb2.Instance.RANDOM_OBJECT: if instance_type == scenenet_pb2.Instance.RANDOM_OBJECT:
instance_dict['object_info'] = instance.object_info instance_dict['object_info'] = instance.object_info
instances_traj[instance_id] = instance_dict instances_traj_dict[instance_id] = instance_dict
# iterate through images/frames # iterate through images/frames
for view in trajectory.views: for view in trajectory.views:
@ -278,20 +279,29 @@ def prepare_scenenet_val(data_path: str, protobuf_path: str) -> Tuple[List[List[
instance_file = f"{path}/instance/{frame_num}.png" instance_file = f"{path}/instance/{frame_num}.png"
file_names_photos_traj.append(f"{path}/photo/{frame_num}.jpg") file_names_photos_traj.append(f"{path}/photo/{frame_num}.jpg")
file_names_instances_traj.append(instance_file) file_names_instances_traj.append(instance_file)
instances_view = []
# load instance file # load instance file
instance_image = scipy.misc.imread(instance_file) instance_image = scipy.misc.imread(instance_file)
for instance_id in instances_traj: for instance_id in instances_traj_dict:
instance_local = np.copy(instance_image) instance_local = np.copy(instance_image)
instance_local[instance_local != instance_id] = 0 instance_local[instance_local != instance_id] = 0
instance_local[instance_local == instance_id] = 1 instance_local[instance_local == instance_id] = 1
print(instance_local) coordinates = ndimage.find_objects(instance_local)
coordinates = ndimage.find_objects(instance_local)[0] if not coordinates: # the current instance was not in this frame
continue
else:
coordinates = coordinates[0] # extract the coords of the one object
x = coordinates[0] x = coordinates[0]
y = coordinates[1] y = coordinates[1]
xmin, xmax = x.start, x.stop xmin, xmax = x.start, x.stop
ymin, ymax = y.start, y.stop ymin, ymax = y.start, y.stop
instances_traj[instance_id]['bbox'] = (xmin, ymin, xmax, ymax) instance = instances_traj_dict[instance_id].copy()
instance['bbox'] = (xmin, ymin, xmax, ymax)
instances_view.append(instance)
instances_traj.append(instances_view)
file_names_photos.append(file_names_photos_traj) file_names_photos.append(file_names_photos_traj)
file_names_instances.append(file_names_instances_traj) file_names_instances.append(file_names_instances_traj)