Python使用余弦相似度比对特征，numpy余弦相似度比对特征

　　先介绍下背景：我们有个项目是基于深度神经网络的菜品识别类项目，简易流程可以理解为：通过深度学习模型，提取菜品图像的菜品特征。将特征存储到特征库中。获取摄像头下方的菜品图像，提取特征。拿到摄像头下发图像的特征，通过余弦相似度对比，得出相似度。
　　先来一段特征注册的代码逻辑：import numpy as np import pickle  def featRegister(tag):     ＂＂＂     repo 为字典类型, 特征库     {         ＂包子＂: array([[2.9554849,..,0.45501372],[1.6043618,0.,...,0.]], dtype=float32),          ＂鱼头＂: array([[1.6043618,..., 0.]], dtype=float32),          ＂米饭＂: array([[0., 0., 2.9554849 , ..., 0., 0.,0.45501372]], dtype=float32)     }     ＂＂＂     # 1. 组建特征库     repo = {}     for dishFeaId, featureSrc in tag.items():         if not featureSrc:             continue         feat = pickle.loads(featureSrc.encode(＂latin1＂))         repo[dishFeaId] = np.array([feat])              # 2. 计算 active_repo_feats all_repo_feats repo_count     repo_concat = np.concatenate([repo[key] for key in repo], 0)     all_repo_feats = np.array([x / np.linalg.norm(x) for x in repo_concat])     active_repo_ids = []     for id in repo:         active_repo_ids += [id] * len(repo[id])     repo_count = len(repo)          return all_repo_feats, active_repo_ids, repo_count
　　组成了一个repo的dict，并使用了pickle进行序列化，通过numpy进行拼接。
　　接下来，我们来模拟一个需要比对的特征值：feats = [] for dishFeaId, featureSrc in featsN.items(): 	feat_index = pickle.loads(featureSrc.encode(＂latin1＂)) 	feats.append(np.array(feat_index))
　　然后就是通过与余弦的相似度进行特征比对的逻辑：import uuid import numpy as np  def featsCompare(feats, feat_boxes, all_repo_feats, active_repo_ids, repo_count):     if repo_count <= 0:         return None          dishId_feaIds_ids = []     dishId_feaId_scores = []     feats = [x / np.linalg.norm(x) for x in feats]     cos_distance = np.matmul(feats, all_repo_feats.transpose((1, 0)))     ids_score_sorted = np.sort(cos_distance)     ids_score_sorted = [x[::-1] for x in ids_score_sorted]     ids_indices_sorted = np.argsort(cos_distance)     ids_indices_sorted = [x[::-1] for x in ids_indices_sorted]     for i in range(len(ids_indices_sorted)):         food_ids, food_scores = [], []         all_repo_ids_sorted = np.array(active_repo_ids)[list(ids_indices_sorted[i])]         for j in range(len(all_repo_ids_sorted)):             if all_repo_ids_sorted[j] not in food_ids:                 food_ids.append(all_repo_ids_sorted[j])                 food_scores.append(ids_score_sorted[i][j])         for x in food_ids:             dishId_feaIds_ids.append(x)         for x in food_scores:             dishId_feaId_scores.append(round(float(100 * x), 2))                  resultCount = len(feat_boxes)     resultList = []      for i in range(resultCount):         resultN = feat_boxes[i]         resultN[＂dishes＂] = []         idList, scoreList = [], []         for j in range(repo_count):             k = j + i * repo_count             idList.append(dishId_feaIds_ids[k])             scoreList.append(dishId_feaId_scores[k])          # 筛选dishId与feaId、score对应关系         dishId_feaId_score = {}         for m in range(len(idList)):             dishId_feaId = idList[m]             dishId_feaIds = dishId_feaId.split(＂_＂)             if len(dishId_feaIds) == 1:                 dishId = dishId_feaIds[0]                 feaId = dishId             elif len(dishId_feaIds) == 2:                 dishId = dishId_feaIds[0]                 feaId = dishId_feaIds[1]             else:                 uuId = str(uuid.uuid1())                 dishId = uuId.replace(＂-＂, ＂＂)                 uuId = str(uuid.uuid1())                 feaId = uuId.replace(＂-＂, ＂＂)             if dishId not in dishId_feaId_score:                 # 排名最高的dishIds，及其对应的feaId和score 的汇总                 dishId_feaId_score[dishId] = [feaId, scoreList[m]]             if len(dishId_feaId_score) >= 10:                 break                      dishes = []         for dishId in dishId_feaId_score:             feaId = dishId_feaId_score[dishId][0]             score = dishId_feaId_score[dishId][1]             topN = {                 ＂dishId＂: dishId,                 ＂score＂: score,             }             dishes.append(topN)         resultN[＂dishes＂] = dishes         resultList.append(resultN)              return resultList
　　以上有一些是为了测试的业务逻辑，但整体逻辑和主要代码是没什么问题的。
　　以上，仅供参考。