reid 行人跟踪import torch import numpy as np def extract_person_features(frame): # 在实际情况下，你应该使用预训练的模型（如ResNet）来提取特征 return torch.randn(1, 512) # 假设每个特征是 512 维 def aqe(query_feat: torch.tensor, gallery_feat: torch.tensor, qe_times: int = 1, qe_k: int = 10, alpha: float = 3.0): """ 之前你提供的 aqe 查询扩展算法代码 """ num_query = query_feat.shape[0] all_feat = torch.cat((query_feat, gallery_feat), dim=0) norm_feat = torch.nn.functional.normalize(all_feat, p=2, dim=1) all_feat = all_feat.numpy() for i in range(qe_times): all_feat_list = [] sims = torch.mm(norm_feat, norm_feat.t()) sims = sims.data.cpu().numpy() for sim in sims: init_rank = np.argpartition(-sim, range(