73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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__all__ = ['DetMetric']
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from .eval_det_iou import DetectionIoUEvaluator
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class DetMetric(object):
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def __init__(self, main_indicator='hmean', **kwargs):
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self.evaluator = DetectionIoUEvaluator()
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self.main_indicator = main_indicator
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self.reset()
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def __call__(self, preds, batch, **kwargs):
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'''
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batch: a list produced by dataloaders.
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image: np.ndarray of shape (N, C, H, W).
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ratio_list: np.ndarray of shape(N,2)
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polygons: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
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ignore_tags: np.ndarray of shape (N, K), indicates whether a region is ignorable or not.
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preds: a list of dict produced by post process
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points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
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'''
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gt_polyons_batch = batch[2]
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ignore_tags_batch = batch[3]
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for pred, gt_polyons, ignore_tags in zip(preds, gt_polyons_batch,
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ignore_tags_batch):
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# prepare gt
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gt_info_list = [{
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'points': gt_polyon,
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'text': '',
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'ignore': ignore_tag
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} for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)]
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# prepare det
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det_info_list = [{
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'points': det_polyon,
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'text': ''
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} for det_polyon in pred['points']]
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result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
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self.results.append(result)
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def get_metric(self):
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"""
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return metrics {
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'precision': 0,
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'recall': 0,
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'hmean': 0
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}
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"""
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metircs = self.evaluator.combine_results(self.results)
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self.reset()
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return metircs
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def reset(self):
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self.results = [] # clear results
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