def evaluate(self):
"""Evaluates with detections from all images with COCO API.
Returns:
coco_metric: float numpy array with shape [24] representing the
coco-style evaluation metrics (box and mask).
"""
if not self._annotation_file:
logging.info('There is no annotation_file in COCOEvaluator.')
gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
self._groundtruths)
coco_gt = coco_utils.COCOWrapper(
eval_type=('mask' if self._include_mask else 'box'),
gt_dataset=gt_dataset)
else:
logging.info('Using annotation file: %s', self._annotation_file)
coco_gt = self._coco_gt
coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
self._predictions)
coco_dt = coco_gt.loadRes(predictions=coco_predictions)
image_ids = [ann['image_id'] for ann in coco_predictions]
coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_metrics = coco_eval.stats
if self._include_mask:
mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
mcoco_eval.params.imgIds = image_ids
mcoco_eval.evaluate()
mcoco_eval.accumulate()
mcoco_eval.summarize()
mask_coco_metrics = mcoco_eval.stats
if self._include_mask:
metrics = np.hstack((coco_metrics, mask_coco_metrics))
else:
metrics = coco_metrics
metrics_dict = {}
for i, name in enumerate(self._metric_names):
metrics_dict[name] = metrics[i].astype(np.float32)
# Adds metrics per category.
if self._per_category_metrics:
metrics_dict.update(self._retrieve_per_category_metrics(coco_eval))
if self._include_mask:
metrics_dict.update(
self._retrieve_per_category_metrics(mcoco_eval,
prefix='mask'))
return metrics_dict
def evaluate(self):
"""Evaluates with detections from all images with COCO API.
Returns:
coco_metric: float numpy array with shape [24] representing the
coco-style evaluation metrics (box and mask).
"""
if not self._annotation_file:
logging.info('There is no annotation_file in COCOEvaluator.')
gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
self._groundtruths)
coco_gt = coco_utils.COCOWrapper(
eval_type=('mask' if self._include_mask else 'box'),
gt_dataset=gt_dataset)
else:
logging.info('Using annotation file: %s', self._annotation_file)
coco_gt = self._coco_gt
coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
self._predictions)
coco_dt = coco_gt.loadRes(predictions=coco_predictions)
image_ids = [ann['image_id'] for ann in coco_predictions]
coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_metrics = coco_eval.stats
if self._include_mask:
mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
mcoco_eval.params.imgIds = image_ids
mcoco_eval.evaluate()
mcoco_eval.accumulate()
mcoco_eval.summarize()
mask_coco_metrics = mcoco_eval.stats
if self._include_mask:
metrics = np.hstack((coco_metrics, mask_coco_metrics))
else:
metrics = coco_metrics
metrics_dict = {}
for i, name in enumerate(self._metric_names):
metrics_dict[name] = metrics[i].astype(np.float32)
# Adds metrics per category.
if self._per_category_metrics and hasattr(coco_eval, 'category_stats'):
for category_index, category_id in enumerate(
coco_eval.params.catIds):
metrics_dict['Precision mAP ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[0][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory@50IoU/{}'.format(
category_id
)] = coco_eval.category_stats[1][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory@75IoU/{}'.format(
category_id
)] = coco_eval.category_stats[2][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (small) /{}'.format(
category_id
)] = coco_eval.category_stats[3][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (medium) /{}'.format(
category_id
)] = coco_eval.category_stats[4][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (large) /{}'.format(
category_id
)] = coco_eval.category_stats[5][category_index].astype(
np.float32)
metrics_dict['Recall AR@1 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[6][category_index].astype(
np.float32)
metrics_dict['Recall AR@10 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[7][category_index].astype(
np.float32)
metrics_dict['Recall AR@100 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[8][category_index].astype(
np.float32)
metrics_dict['Recall AR (small) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[9][category_index].astype(
np.float32)
metrics_dict['Recall AR (medium) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[10][category_index].astype(
np.float32)
metrics_dict['Recall AR (large) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[11][category_index].astype(
np.float32)
return metrics_dict