def load_prediction(result_path: str,
max_boxes_per_sample: int,
verbose: bool = False) -> Tuple[EvalBoxes, Dict]:
""" Loads object predictions from file. """
# Load from file and check that the format is correct.
with open(result_path) as f:
data = json.load(f)
assert 'results' in data, 'Error: No field `results` in result file. Please note that the result format changed.' \
'See https://www.nuscenes.org/object-detection for more information.'
# Deserialize results and get meta data.
all_results = EvalBoxes.deserialize(data['results'])
meta = data['meta']
if verbose:
print(
"Loaded results from {}. Found detections for {} samples.".format(
result_path, len(all_results.sample_tokens)))
# Check that each sample has no more than x predicted boxes.
for sample_token in all_results.sample_tokens:
assert len(all_results.boxes[sample_token]) <= max_boxes_per_sample, \
"Error: Only <= %d boxes per sample allowed!" % max_boxes_per_sample
return all_results, meta
def test_serialization(self):
""" Test that instance serialization protocol works with json encoding. """
boxes = EvalBoxes()
for i in range(10):
boxes.add_boxes(str(i), [EvalBox(), EvalBox(), EvalBox()])
recovered = EvalBoxes.deserialize(
json.loads(json.dumps(boxes.serialize())))
self.assertEqual(boxes, recovered)
def load_prediction(result_path: str,
max_boxes_per_sample: int,
verbose: bool = False) -> EvalBoxes:
""" Loads object predictions from file. """
with open(result_path) as f:
all_results = EvalBoxes.deserialize(json.load(f))
if verbose:
print("=> Loaded results from {}. Found detections for {} samples.".
format(result_path, len(all_results.sample_tokens)))
# Check that each sample has no more than x predicted boxes.
for sample_token in all_results.sample_tokens:
assert len(all_results.boxes[sample_token]) <= max_boxes_per_sample, \
"Error: Only <= %d boxes per sample allowed!" % max_boxes_per_sample
return all_results