# Copyright (c) 2021 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. import copy import os import json from collections import OrderedDict from collections import defaultdict import numpy as np from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from ..modeling.keypoint_utils import oks_nms __all__ = ['KeyPointTopDownCOCOEval'] class KeyPointTopDownCOCOEval(object): def __init__(self, anno_file, num_samples, num_joints, output_eval, iou_type='keypoints', in_vis_thre=0.2, oks_thre=0.9): super(KeyPointTopDownCOCOEval, self).__init__() self.coco = COCO(anno_file) self.num_samples = num_samples self.num_joints = num_joints self.iou_type = iou_type self.in_vis_thre = in_vis_thre self.oks_thre = oks_thre self.output_eval = output_eval self.res_file = os.path.join(output_eval, "keypoints_results.json") self.reset() def reset(self): self.results = { 'all_preds': np.zeros( (self.num_samples, self.num_joints, 3), dtype=np.float32), 'all_boxes': np.zeros((self.num_samples, 6)), 'image_path': [] } self.eval_results = {} self.idx = 0 def update(self, inputs, outputs): kpts, _ = outputs['keypoint'][0] num_images = inputs['image'].shape[0] self.results['all_preds'][self.idx:self.idx + num_images, :, 0: 3] = kpts[:, :, 0:3] self.results['all_boxes'][self.idx:self.idx + num_images, 0:2] = inputs[ 'center'].numpy()[:, 0:2] self.results['all_boxes'][self.idx:self.idx + num_images, 2:4] = inputs[ 'scale'].numpy()[:, 0:2] self.results['all_boxes'][self.idx:self.idx + num_images, 4] = np.prod( inputs['scale'].numpy() * 200, 1) self.results['all_boxes'][self.idx:self.idx + num_images, 5] = np.squeeze(inputs['score'].numpy()) self.results['image_path'].extend(inputs['im_id'].numpy()) self.idx += num_images def _write_coco_keypoint_results(self, keypoints): data_pack = [{ 'cat_id': 1, 'cls': 'person', 'ann_type': 'keypoints', 'keypoints': keypoints }] results = self._coco_keypoint_results_one_category_kernel(data_pack[0]) if not os.path.exists(self.output_eval): os.makedirs(self.output_eval) with open(self.res_file, 'w') as f: json.dump(results, f, sort_keys=True, indent=4) try: json.load(open(self.res_file)) except Exception: content = [] with open(self.res_file, 'r') as f: for line in f: content.append(line) content[-1] = ']' with open(self.res_file, 'w') as f: for c in content: f.write(c) def _coco_keypoint_results_one_category_kernel(self, data_pack): cat_id = data_pack['cat_id'] keypoints = data_pack['keypoints'] cat_results = [] for img_kpts in keypoints: if len(img_kpts) == 0: continue _key_points = np.array( [img_kpts[k]['keypoints'] for k in range(len(img_kpts))]) _key_points = _key_points.reshape(_key_points.shape[0], -1) result = [{ 'image_id': img_kpts[k]['image'], 'category_id': cat_id, 'keypoints': _key_points[k].tolist(), 'score': img_kpts[k]['score'], 'center': list(img_kpts[k]['center']), 'scale': list(img_kpts[k]['scale']) } for k in range(len(img_kpts))] cat_results.extend(result) return cat_results def get_final_results(self, preds, all_boxes, img_path): _kpts = [] for idx, kpt in enumerate(preds): _kpts.append({ 'keypoints': kpt, 'center': all_boxes[idx][0:2], 'scale': all_boxes[idx][2:4], 'area': all_boxes[idx][4], 'score': all_boxes[idx][5], 'image': int(img_path[idx]) }) # image x person x (keypoints) kpts = defaultdict(list) for kpt in _kpts: kpts[kpt['image']].append(kpt) # rescoring and oks nms num_joints = preds.shape[1] in_vis_thre = self.in_vis_thre oks_thre = self.oks_thre oks_nmsed_kpts = [] for img in kpts.keys(): img_kpts = kpts[img] for n_p in img_kpts: box_score = n_p['score'] kpt_score = 0 valid_num = 0 for n_jt in range(0, num_joints): t_s = n_p['keypoints'][n_jt][2] if t_s > in_vis_thre: kpt_score = kpt_score + t_s valid_num = valid_num + 1 if valid_num != 0: kpt_score = kpt_score / valid_num # rescoring n_p['score'] = kpt_score * box_score keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre) if len(keep) == 0: oks_nmsed_kpts.append(img_kpts) else: oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep]) self._write_coco_keypoint_results(oks_nmsed_kpts) def accumulate(self): self.get_final_results(self.results['all_preds'], self.results['all_boxes'], self.results['image_path']) coco_dt = self.coco.loadRes(self.res_file) coco_eval = COCOeval(self.coco, coco_dt, 'keypoints') coco_eval.params.useSegm = None coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() keypoint_stats = [] for ind in range(len(coco_eval.stats)): keypoint_stats.append((coco_eval.stats[ind])) self.eval_results['keypoint'] = keypoint_stats def log(self): stats_names = [ 'AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)' ] num_values = len(stats_names) print(' '.join(['| {}'.format(name) for name in stats_names]) + ' |') print('|---' * (num_values + 1) + '|') print(' '.join([ '| {:.3f}'.format(value) for value in self.eval_results['keypoint'] ]) + ' |') def get_results(self): return self.eval_results