def load_data(self, data: SampleCollection, dataset: Optional[Any] = None) -> Sequence[Dict[str, Any]]:
self._validate(data)
data.compute_metadata()
classes = self._get_classes(data)
class_map = ClassMap(classes)
dataset.num_classes = len(class_map)
parser = FiftyOneParser(data, class_map, self.label_field, self.iscrowd)
records = parser.parse(data_splitter=SingleSplitSplitter())
return [{DefaultDataKeys.INPUT: record} for record in records[0]]
def load_data(self,
data: SampleCollection,
dataset: Optional[Any] = None) -> Sequence[Dict[str, Any]]:
self._validate(data)
data.compute_metadata()
filepaths = data.values("filepath")
widths = data.values("metadata.width")
heights = data.values("metadata.height")
labels = data.values(self.label_field + ".detections.label")
bboxes = data.values(self.label_field + ".detections.bounding_box")
iscrowds = data.values(self.label_field + ".detections." +
self.iscrowd)
classes = self._get_classes(data)
class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
if dataset is not None:
dataset.num_classes = len(classes)
output_data = []
img_id = 1
for fp, w, h, sample_labs, sample_boxes, sample_iscrowd in zip(
filepaths, widths, heights, labels, bboxes, iscrowds):
output_boxes = []
output_labs = []
output_iscrowd = []
output_areas = []
for lab, box, iscrowd in zip(sample_labs, sample_boxes,
sample_iscrowd):
output_box, output_area = self._reformat_bbox(
box[0], box[1], box[2], box[3], w, h)
output_areas.append(output_area)
output_labs.append(class_to_idx[lab])
output_boxes.append(output_box)
if iscrowd is None:
iscrowd = 0
output_iscrowd.append(iscrowd)
output_data.append(
dict(input=fp,
target=dict(
boxes=output_boxes,
labels=output_labs,
image_id=img_id,
area=output_areas,
iscrowd=output_iscrowd,
)))
img_id += 1
return output_data