def json_to_value(mcs, json: collections.Mapping):
key = json.get('__type__', None)
value = json.get('value', None)
if key is None:
raise ValueError(f"Can not convert with missing type key!")
if value is None:
raise ValueError(f"Can not convert with missing value!")
converter_cls = mcs.__key_to_converter__.get(key, None)
if converter_cls is None:
raise ValueError(f"Can not convert object of type key {key}!")
return converter_cls.json_to_value(value)
def extract_labels_from_mask(mask: np.ndarray, color_id_to_obj_class: collections.Mapping) -> list:
"""
Extract multiclass instances from grayscale mask and save it to labels list.
Args:
mask: multiclass grayscale mask
color_id_to_obj_class: dict of objects classes assigned to color id (e.g. {1: ObjClass('cat), ...})
Returns:
list of labels with bitmap geometry
"""
zero_offset = 1 if 0 in color_id_to_obj_class else 0
if zero_offset > 0:
mask = mask + zero_offset
labeled, labels_count = measure.label(mask, connectivity=1, return_num=True)
objects_slices = ndimage.find_objects(labeled)
labels = []
for object_index, slices in enumerate(objects_slices, start=1):
crop = mask[slices]
sub_mask = crop * (labeled[slices] == object_index).astype(np.int)
class_index = np.max(sub_mask) - zero_offset
if class_index in color_id_to_obj_class:
bitmap = Bitmap(data=sub_mask.astype(np.bool), origin=PointLocation(slices[0].start, slices[1].start))
label = Label(geometry=bitmap, obj_class=color_id_to_obj_class.get(class_index))
labels.append(label)
return labels
def _get_nested(value: collections.Mapping, keys: deque, default=None):
if len(keys) > 0:
attr = keys.popleft()
value = value.get(attr)
if len(keys) > 0 and value is not None:
if isinstance(value, collections.Mapping):
return _get_nested(value, keys, default)
if isinstance(value, object):
return _get_nested(vars(value), keys, default)
return value or default
def prepare_field(self, field_name: str, values: collections.Mapping):
return values.get(field_name)
