def import_prediction(pred):
if isinstance(pred, ac.ClassificationPrediction):
scores = softmax(pred.scores)
return (dm.Label(label_id, attributes={'score': float(score)})
for label_id, score in enumerate(scores))
elif isinstance(pred, ac.ArgMaxClassificationPrediction):
return (dm.Label(int(pred.label)), )
elif isinstance(pred, ac.CharacterRecognitionPrediction):
return (dm.Label(int(pred.label)), )
elif isinstance(pred,
(ac.DetectionPrediction, ac.ActionDetectionPrediction)):
return (dm.Bbox(x0,
y0,
x1 - x0,
y1 - y0,
int(label_id),
attributes={'score': float(score)})
for label, score, x0, y0, x1, y1 in zip(
pred.labels, pred.scores, pred.x_mins, pred.y_mins,
pred.x_maxs, pred.y_maxs))
elif isinstance(pred, ac.DepthEstimationPrediction):
return (dm.Mask(pred.depth_map), ) # 2d floating point mask
# elif isinstance(pred, ac.HitRatioPrediction):
# -
elif isinstance(pred, ac.ImageInpaintingPrediction):
return (dm.Mask(pred.value), ) # an image
# elif isinstance(pred, ac.MultiLabelRecognitionPrediction):
# -
# elif isinstance(pred, ac.MachineTranslationPrediction):
# -
# elif isinstance(pred, ac.QuestionAnsweringPrediction):
# -
# elif isinstance(pred, ac.PoseEstimation3dPrediction):
# -
# elif isinstance(pred, ac.PoseEstimationPrediction):
# -
# elif isinstance(pred, ac.RegressionPrediction):
# -
else:
raise NotImplementedError("Can't convert %s" % type(pred))
def _read_cvat_anno(self, cvat_frame_anno, task_data):
item_anno = []
categories = self.categories()
label_cat = categories[datumaro.AnnotationType.label]
def map_label(name):
return label_cat.find(name)[0]
label_attrs = {
label['name']: label['attributes']
for _, label in task_data.meta['task']['labels']
}
def convert_attrs(label, cvat_attrs):
cvat_attrs = {a.name: a.value for a in cvat_attrs}
dm_attr = dict()
for _, a_desc in label_attrs[label]:
a_name = a_desc['name']
a_value = cvat_attrs.get(a_name, a_desc['default_value'])
try:
if a_desc['input_type'] == AttributeType.NUMBER:
a_value = float(a_value)
elif a_desc['input_type'] == AttributeType.CHECKBOX:
a_value = (a_value.lower() == 'true')
dm_attr[a_name] = a_value
except Exception as e:
raise Exception(
"Failed to convert attribute '%s'='%s': %s" %
(a_name, a_value, e))
return dm_attr
for tag_obj in cvat_frame_anno.tags:
anno_group = tag_obj.group
anno_label = map_label(tag_obj.label)
anno_attr = convert_attrs(tag_obj.label, tag_obj.attributes)
anno = datumaro.Label(label=anno_label,
attributes=anno_attr,
group=anno_group)
item_anno.append(anno)
for shape_obj in cvat_frame_anno.labeled_shapes:
anno_group = shape_obj.group
anno_label = map_label(shape_obj.label)
anno_attr = convert_attrs(shape_obj.label, shape_obj.attributes)
anno_attr['occluded'] = shape_obj.occluded
if hasattr(shape_obj, 'track_id'):
anno_attr['track_id'] = shape_obj.track_id
anno_attr['keyframe'] = shape_obj.keyframe
anno_points = shape_obj.points
if shape_obj.type == ShapeType.POINTS:
anno = datumaro.Points(anno_points,
label=anno_label,
attributes=anno_attr,
group=anno_group,
z_order=shape_obj.z_order)
elif shape_obj.type == ShapeType.POLYLINE:
anno = datumaro.PolyLine(anno_points,
label=anno_label,
attributes=anno_attr,
group=anno_group,
z_order=shape_obj.z_order)
elif shape_obj.type == ShapeType.POLYGON:
anno = datumaro.Polygon(anno_points,
label=anno_label,
attributes=anno_attr,
group=anno_group,
z_order=shape_obj.z_order)
elif shape_obj.type == ShapeType.RECTANGLE:
x0, y0, x1, y1 = anno_points
anno = datumaro.Bbox(x0,
y0,
x1 - x0,
y1 - y0,
label=anno_label,
attributes=anno_attr,
group=anno_group,
z_order=shape_obj.z_order)
elif shape_obj.type == ShapeType.CUBOID:
continue # Datumaro does not support cuboids
else:
raise Exception("Unknown shape type '%s'" % shape_obj.type)
item_anno.append(anno)
return item_anno