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