本文整理汇总了Python中datasets.json_dataset_evaluator.evaluate_keypoints方法的典型用法代码示例。如果您正苦于以下问题：Python json_dataset_evaluator.evaluate_keypoints方法的具体用法？Python json_dataset_evaluator.evaluate_keypoints怎么用？Python json_dataset_evaluator.evaluate_keypoints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datasets.json_dataset_evaluator的用法示例。
在下文中一共展示了json_dataset_evaluator.evaluate_keypoints方法的6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: evaluate_all
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_all(
dataset, all_boxes, all_segms, all_keyps, output_dir, use_matlab=False
):
"""Evaluate "all" tasks, where "all" includes box detection, instance
segmentation, and keypoint detection.
"""
all_results = evaluate_boxes(
dataset, all_boxes, output_dir, use_matlab=use_matlab
)
logger.info('Evaluating bounding boxes is done!')
if cfg.MODEL.MASK_ON:
results = evaluate_masks(dataset, all_boxes, all_segms, output_dir)
all_results[dataset.name].update(results[dataset.name])
logger.info('Evaluating segmentations is done!')
if cfg.MODEL.KEYPOINTS_ON:
results = evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir)
all_results[dataset.name].update(results[dataset.name])
logger.info('Evaluating keypoints is done!')
return all_results 示例2: evaluate_keypoints
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir):
"""Evaluate human keypoint detection (i.e., 2D pose estimation)."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
assert dataset.name.startswith('keypoints_coco_'), \
'Only COCO keypoints are currently supported'
coco_eval = json_dataset_evaluator.evaluate_keypoints(
dataset,
all_boxes,
all_keyps,
output_dir,
use_salt=not_comp,
cleanup=not_comp
)
keypoint_results = _coco_eval_to_keypoint_results(coco_eval)
return OrderedDict([(dataset.name, keypoint_results)]) 示例3: evaluate_keypoints
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir):
"""Evaluate human keypoint detection (i.e., 2D pose estimation)."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
# assert dataset.name.startswith('keypoints_coco_'), \
# 'Only COCO keypoints are currently supported'
coco_eval = json_dataset_evaluator.evaluate_keypoints(
dataset,
all_boxes,
all_keyps,
output_dir,
use_salt=not_comp,
cleanup=not_comp
)
keypoint_results = _coco_eval_to_keypoint_results(coco_eval)
return OrderedDict([(dataset.name, keypoint_results)]) 示例4: evaluate_all
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_all(
dataset, all_boxes, all_segms, all_keyps, all_hois, all_keyps_vcoco, output_dir, use_matlab=False
):
"""Evaluate "all" tasks, where "all" includes box detection, instance
segmentation, and keypoint detection.
"""
all_results = evaluate_boxes(
dataset, all_boxes, output_dir, use_matlab=use_matlab
)
logger.info('Evaluating bounding boxes is done!')
if cfg.MODEL.MASK_ON:
results = evaluate_masks(dataset, all_boxes, all_segms, output_dir)
all_results[dataset.name].update(results[dataset.name])
logger.info('Evaluating segmentations is done!')
if cfg.MODEL.KEYPOINTS_ON:
results = evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir)
all_results[dataset.name].update(results[dataset.name])
logger.info('Evaluating keypoints is done!')
if cfg.MODEL.VCOCO_ON:
results = evaluate_hoi_vcoco(dataset, all_hois, output_dir)
#all_results[dataset.name].update(results[dataset.name])
# if cfg.VCOCO.KEYPOINTS_ON:
# results = evaluate_keypoints(dataset, all_boxes, all_keyps_vcoco, output_dir)
# all_results[dataset.name].update(results[dataset.name])
logger.info('Evaluating hois is done!')
return all_results 示例5: evaluate_keypoints
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir):
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
if dataset.name.startswith('keypoints_coco_'):
import datasets.json_dataset_evaluator as json_dataset_evaluator
json_dataset_evaluator.evaluate_keypoints(
dataset, all_boxes, all_keyps, output_dir,
use_salt=not_comp, cleanup=not_comp)
else:
raise NotImplementedError(
'No evaluator for dataset: {}'.format(dataset.name)) 示例6: evaluate_all
# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_keypoints [as 别名]
def evaluate_all(
dataset, all_boxes, all_segms, all_keyps, output_dir, use_matlab=False):
evaluate_detections(dataset, all_boxes, output_dir, use_matlab=use_matlab)
logger.info('Evaluating bounding boxes is done!')
if cfg.MODEL.MASK_ON:
evaluate_segmentations(dataset, all_boxes, all_segms, output_dir)
logger.info('Evaluating segmentations is done!')
if cfg.MODEL.KEYPOINTS_ON:
evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir)
logger.info('Evaluating keypoints is done!')