mirror of https://github.com/alibaba/EasyCV.git
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import numpy as np
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from easycv.core.evaluation.wholebody_keypoint_eval import \
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WholeBodyKeyPointEvaluator
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from easycv.datasets.pose.data_sources.coco import PoseTopDownSource
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from easycv.datasets.registry import DATASETS
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from easycv.datasets.shared.raw import RawDataset
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@DATASETS.register_module()
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class WholeBodyCocoTopDownDataset(RawDataset):
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"""CocoWholeBodyDataset dataset for top-down pose estimation.
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Args:
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data_source: Data_source config dict
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pipeline: Pipeline config list
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profiling: If set True, will print pipeline time
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"""
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def __init__(self, data_source, pipeline, profiling=False):
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super(WholeBodyCocoTopDownDataset,
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self).__init__(data_source, pipeline, profiling)
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def evaluate(self, outputs, evaluators, **kwargs):
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if len(evaluators) > 1 or not isinstance(evaluators[0],
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WholeBodyKeyPointEvaluator):
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raise ValueError(
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'HandCocoWholeBodyDataset only support one `WholeBodyKeyPointEvaluator` now, '
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'but get %s' % evaluators)
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evaluator = evaluators[0]
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image_ids = outputs['image_ids']
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preds = outputs['preds']
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boxes = outputs['boxes']
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bbox_ids = outputs['bbox_ids']
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kpts = []
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for i, image_id in enumerate(image_ids):
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kpts.append({
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'keypoints': preds[i],
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'center': boxes[i][0:2],
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'scale': boxes[i][2:4],
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'area': boxes[i][4],
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'score': boxes[i][5],
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'image_id': image_id,
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'bbox_id': bbox_ids[i]
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})
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kpts = self._sort_and_unique_bboxes(kpts)
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eval_res = evaluator.evaluate(kpts, self.data_source.db)
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return eval_res
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