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