def evaluation(ground_truth_file: str,
predictions_file: str,
string_to_match: Optional[str] = None) -> None:
"""Evaluate the predictions of a dataset.
Args:
ground_truth_file: Path to COCO ground truth file.
predictions_file: Path to COCO predictions file.
string_to_match: If not None, only images whose file_name match this parameter
will be evaluated.
"""
with open(ground_truth_file) as gt:
coco_ground_truth = load_coco_ground_truth_from_StringIO(gt)
with open(predictions_file) as pred:
coco_predictions = coco_ground_truth.loadRes(json.load(pred))
coco_eval = COCOeval(coco_ground_truth, coco_predictions, "bbox")
if string_to_match is not None:
filtered_ids = [
k for k, v in coco_ground_truth.imgs.items()
if re.match(string_to_match, v["file_name"])
]
logger.info("Number of filtered_ids: {}".format(len(filtered_ids)))
else:
filtered_ids = [k for k in coco_ground_truth.imgs.keys()]
coco_eval.image_ids = filtered_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
