本文整理汇总了Python中object_detection.utils.vrd_evaluation.VRDRelationDetectionEvaluator方法的典型用法代码示例。如果您正苦于以下问题：Python vrd_evaluation.VRDRelationDetectionEvaluator方法的具体用法？Python vrd_evaluation.VRDRelationDetectionEvaluator怎么用？Python vrd_evaluation.VRDRelationDetectionEvaluator使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.utils.vrd_evaluation的用法示例。
在下文中一共展示了vrd_evaluation.VRDRelationDetectionEvaluator方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from object_detection.utils import vrd_evaluation [as 别名]
# 或者: from object_detection.utils.vrd_evaluation import VRDRelationDetectionEvaluator [as 别名]
def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map = _load_labelmap(parsed_args.input_class_labelmap)
relationship_label_map = _load_labelmap(
parsed_args.input_relationship_labelmap)
relation_evaluator = vrd_evaluation.VRDRelationDetectionEvaluator()
phrase_evaluator = vrd_evaluation.VRDPhraseDetectionEvaluator()
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
image_groundtruth, class_label_map, relationship_label_map)
relation_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
phrase_evaluator.add_single_ground_truth_image_info(image_id,
groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_vrd_dictionary(
image_predictions, class_label_map, relationship_label_map)
relation_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
phrase_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
relation_metrics = relation_evaluator.evaluate(
relationships=_swap_labelmap_dict(relationship_label_map))
phrase_metrics = phrase_evaluator.evaluate(
relationships=_swap_labelmap_dict(relationship_label_map))
with open(parsed_args.output_metrics, 'w') as fid:
io_utils.write_csv(fid, relation_metrics)
io_utils.write_csv(fid, phrase_metrics) 示例2: main
# 需要导入模块: from object_detection.utils import vrd_evaluation [as 别名]
# 或者: from object_detection.utils.vrd_evaluation import VRDRelationDetectionEvaluator [as 别名]
def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map = _load_labelmap(parsed_args.input_class_labelmap)
relationship_label_map = _load_labelmap(
parsed_args.input_relationship_labelmap)
relation_evaluator = vrd_evaluation.VRDRelationDetectionEvaluator()
phrase_evaluator = vrd_evaluation.VRDPhraseDetectionEvaluator()
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
image_groundtruth, class_label_map, relationship_label_map)
relation_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
phrase_evaluator.add_single_ground_truth_image_info(image_id,
groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_vrd_dictionary(
image_predictions, class_label_map, relationship_label_map)
relation_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
phrase_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
relation_metrics = relation_evaluator.evaluate()
phrase_metrics = phrase_evaluator.evaluate()
with open(parsed_args.output_metrics, 'w') as fid:
utils.write_csv(fid, relation_metrics)
utils.write_csv(fid, phrase_metrics)