merge

app/service/outfit_matcher/service.py

@@ -18,38 +18,43 @@ if __name__ == '__main__':
     all_items = param["query"] + param["database"]
     unextracted_item = []
     prepared_feature = {}
+
+    # 拿到所有需要提取特征的图片
     for item in all_items:
         if f'{item["item_name"]}.npy' not in os.listdir("feature"):
             unextracted_item.append(item)
     if len(unextracted_item) > 0:
+        # 通过backbone模型提取图片特征
         extracted_features = backbone_service.get_result(unextracted_item)
         for i, item in enumerate(unextracted_item):
+            # save  features
+            # 链接milvus
+            # TODO
             np.save(f'feature/{item["item_name"]}.npy', extracted_features[i])
+            # 存入数据库
+            # 关闭链接
+
+    # TODO 读取本次任务需要的图片特征
     for item in all_items:
         if item["item_name"] not in prepared_feature.keys():
             prepared_feature[item["item_name"]] = np.load(f'feature/{item["item_name"]}.npy')
+
+    # 开始服装搭配任务
     for item in tqdm(param["query"] * 10):
+        # 根据一定规则生成outfit
         outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
-<<<<<<< HEAD
+        # 根据模型对生成的outfit打分
         scores = service.get_result(outfits, prepared_feature)
-=======
-        scores, features = service.get_result(outfits)
-        # save  features
+        # 对评分排序，拿到最好的topk个outfit输出
+        sorted_indices = np.argsort(scores)[:param["topk"]]  # type-aware
+        outfits = [outfits[i] for i in sorted_indices]  # 最好的五个
 
-        # 链接milvus
-
-        # 存入数据库
-        # 关闭链接
-
->>>>>>> 1f23781b16e59bfbcbbb4d252e6a61685267e6c7
-        # print(scores)
-        # print(len(scores))
+        # 结果可视化
         # service.visualize(outfits, scores, param["topk"], best=True,
         #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
         #                                            f"{item['item_name']}_best_{param['topk']}.png"))
         # service.visualize(outfits, scores, param["topk"], best=False,
         #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
         #                                            f"{item['item_name']}_worst_{param['topk']}.png"))
-        sorted_indices = np.argsort(scores)[:param["topk"]]  # type-aware
-        outfits = [outfits[i] for i in sorted_indices]  # 最好的五个
+