def get_dishes(result, top_k=3, threshold=0.1):
"""Returns list of Dish objects decoded from the inference result."""
assert len(result.tensors) == 2
bboxes = utils.reshape(result.tensors['bounding_boxes'].data, 4)
dish_scores = utils.reshape(result.tensors['dish_scores'].data, len(_CLASSES))
assert len(bboxes) == len(dish_scores)
return [Dish(_get_sorted_scores(scores, top_k, threshold), tuple(bbox))
for scores, bbox in zip(dish_scores, bboxes)]
def get_faces(result):
"""Returns list of Face objects decoded from the inference result."""
assert len(result.tensors) == 3
# TODO(dkovalev): check tensor shapes
bboxes = utils.reshape(result.tensors['bounding_boxes'].data, 4)
face_scores = tuple(result.tensors['face_scores'].data)
joy_scores = tuple(result.tensors['joy_scores'].data)
assert len(bboxes) == len(joy_scores)
assert len(bboxes) == len(face_scores)
return [
Face(face_score, joy_score, tuple(bbox))
for face_score, joy_score, bbox in zip(face_scores, joy_scores, bboxes)
]
def get_faces(result):
"""Returns list of Face objects decoded from the inference result."""
assert len(result.tensors) == 3
# TODO(dkovalev): check tensor shapes
bboxes = utils.reshape(result.tensors['bounding_boxes'].data, 4)
face_scores = tuple(result.tensors['face_scores'].data)
joy_scores = tuple(result.tensors['joy_scores'].data)
assert len(bboxes) == len(joy_scores)
assert len(bboxes) == len(face_scores)
return [
Face(face_score, joy_score, tuple(bbox))
for face_score, joy_score, bbox in zip(face_scores, joy_scores, bboxes)
]