def evaluate(self, preds, output_dir, all_boxes, img_path): res_folder = os.path.join(output_dir, 'results') if not os.path.exists(res_folder): try: os.makedirs(res_folder) except Exception: logger.error('Fail to make {}'.format(res_folder)) # person x (keypoints) _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][-16:-4]) }) # image x person x (keypoints) kpts = defaultdict(list) for kpt in _kpts: kpts[kpt['image']].append(kpt) # rescoring and oks nms num_joints = self.num_joints 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 if self.soft_nms: keep = soft_oks_nms( [img_kpts[i] for i in range(len(img_kpts))], oks_thre) else: keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre) oks_nmsed_kpts.append(img_kpts if len(keep) == 0 else [img_kpts[_keep] for _keep in keep]) return oks_nmsed_kpts
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args, **kwargs): rank = cfg.RANK res_folder = os.path.join(output_dir, "results") if not os.path.exists(res_folder): try: os.makedirs(res_folder) except Exception: logger.error("Fail to make {}".format(res_folder)) res_file = os.path.join( res_folder, "keypoints_{}_results_{}.json".format(self.image_set, rank) ) # person x (keypoints) _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][-16:-4]), } ) # image x person x (keypoints) kpts = defaultdict(list) for kpt in _kpts: kpts[kpt["image"]].append(kpt) # rescoring and oks nms num_joints = self.num_joints 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 if self.soft_nms: keep = soft_oks_nms( [img_kpts[i] for i in range(len(img_kpts))], oks_thre ) else: 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, res_file) if "test" not in self.image_set: info_str = self._do_python_keypoint_eval(res_file, res_folder) name_value = OrderedDict(info_str) return name_value, name_value["AP"] else: return {"Null": 0}, 0
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args, **kwargs): rank = cfg.RANK res_folder = os.path.join(output_dir, 'results') if not os.path.exists(res_folder): try: os.makedirs(res_folder) except Exception: logger.error('Fail to make {}'.format(res_folder)) res_file = os.path.join( res_folder, 'keypoints_{}_results_{}.json'.format(self.image_set, rank)) # person x (keypoints) _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].split('/')[-1].replace('.jpg', '')) }) # image x person x (keypoints) kpts = defaultdict(list) for kpt in _kpts: kpts[kpt['image']].append(kpt) # rescoring and oks nms num_joints = self.num_joints 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 if self.soft_nms: keep = soft_oks_nms( [img_kpts[i] for i in range(len(img_kpts))], oks_thre, sigmas=self.sigmas) else: keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre, sigmas=self.sigmas) 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, res_file) if 'val' in self.image_set: info_str = self._do_python_keypoint_eval(res_file, res_folder) name_value = OrderedDict(info_str) return name_value, name_value['AP'] else: return {'Null': 0}, 0
def rescore_and_save_result(self, output_file, preds, all_boxes, img_path, orig_boxes): assert output_file.endswith('.json') or output_file.endswith( '.npy'), "Only json and numpy output is supported" ensuredir(os.path.dirname(output_file)) # person x (keypoints) _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': img_path[idx], 'origbox': orig_boxes[idx] }) # image x person x (keypoints) kpts = defaultdict(list) for kpt in _kpts: kpts[kpt['image']].append(kpt) # rescoring and oks nms num_joints = self.num_joints in_vis_thre = self.in_vis_thre oks_thre = self.oks_thre oks_nmsed_kpts = [] nmsed_kpts_by_frame = defaultdict(list) 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 if self.soft_nms: keep = soft_oks_nms( [img_kpts[i] for i in range(len(img_kpts))], oks_thre) else: keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre) if len(keep) == 0: selected_kpts = img_kpts else: selected_kpts = [img_kpts[_keep] for _keep in keep] oks_nmsed_kpts.append(selected_kpts) nmsed_kpts_by_frame[img] = selected_kpts self._write_keypoint_results(nmsed_kpts_by_frame, output_file)