fix 代码整理

app/api/api_outfit_matcher.py

@@ -16,9 +16,9 @@ logger = logging.getLogger()
 router = APIRouter()
 
 
-@RunTime
 @router.post("outfit_matcher")
 def outfit_matcher(request_item: OutfitMatcher):
+    start_time = time.time()
     request_item = dict(request_item)
     for i in range(len(request_item['query'])):
         request_item['query'][i] = dict(request_item['query'][i])
@@ -69,7 +69,6 @@ def outfit_matcher(request_item: OutfitMatcher):
         prepared_feature[hfd['item_name']] = hfd['features']
 
     result = []
-    start_time = time.time()
     for item in request_item['query']:
         # try:
         outfits = fashion_dataset.generate_outfit(item, request_item["topk"], request_item["max_outfits"])

app/api/api_similar_match.py

@@ -1,8 +1,12 @@
+import io
 import logging
 import time
 
+from PIL import Image
 from fastapi import APIRouter
+from matplotlib import pyplot as plt
 
+from app.core.config import SIMILAR_MATCH_DRAW
 from app.schemas.similar_match import SimilarMatchMItem
 from app.service.similar_match.service import SimilarMatch
 from app.service.utils.decorator import RunTime
@@ -22,6 +26,13 @@ def similar_match(request_item: SimilarMatchMItem):
         response_data = []
         for response in search_response[0]:
             response_data.append(response['entity'])
+
+        if SIMILAR_MATCH_DRAW:
+            resource_image = service.load_image(request_item.image_path)
+            similar_diagram_list = [service.load_image(image_url['image_path']) for image_url in response_data]
+            resource_image.save("similar_match/3/resource.png")
+            for i, image in enumerate(similar_diagram_list):
+                image.save(f"similar_match/3/{i}.png")
         return {"message": "ok", "data": response_data}
     except KeyError as e:
         logger.warning(str(e))

app/core/config.py

@@ -39,7 +39,7 @@ DEBUG = 1
 SHOW_OR_SAVE_result_image = False
 # service env  : 1
 # pycharm debug  : 2
-
+SIMILAR_MATCH_DRAW = False
 if DEBUG == 1:
     LOGS_PATH = "app/logs/errors.log"
     FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json"

app/service/outfit_matcher/outfit_evaluator.py

@@ -23,7 +23,6 @@ class Backbone(object):
             secure=MINIO_SECURE)
 
     @RunTime
-    # TODO 用多线程读图片
     def load_image(self, img_path):
         try:
             # 从 MinIO 中获取对象（图像文件）

app/service/similar_match/service.py

@@ -55,33 +55,30 @@ class SimilarMatch:
     def preprocess(self, img_path):
         image = self.load_image(img_path)
         image = self.resize_image(image)
-        image = np.stack([[image]], axis=0)
+        image = np.stack([image], axis=0)
 
-        category = np.stack([[1, 6]], axis=0)
+        # category = np.stack([[1, 6]], axis=0)
 
-        mask = np.zeros((1, 1), dtype=np.float32)
+        # mask = np.zeros((1, 1), dtype=np.float32)
-        return image, category, mask
+        return image
+        # , category, mask)
 
     def get_features(self):
-        image, category, mask = self.preprocess(self.image_path)
+        image = self.preprocess(self.image_path)
+        # image, category, mask = self.preprocess(self.image_path)
         # 输入集
         inputs = [
             httpclient.InferInput("input__0", image.shape, datatype="FP32"),
-            httpclient.InferInput("input__1", category.shape, datatype="INT16"),
-            httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
         ]
         inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
-        inputs[1].set_data_from_numpy(category.astype(np.int16), binary_data=True)
-        inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
         # 输出集
         outputs = [
             httpclient.InferRequestedOutput("output__0", binary_data=True),
-            httpclient.InferRequestedOutput("output__1", binary_data=True)
         ]
-        results = self.triton_client.infer(model_name="outfit_matcher_type_aware", inputs=inputs, outputs=outputs)
+        results = self.triton_client.infer(model_name="outfit_matcher_backbone", inputs=inputs, outputs=outputs)
         # 推理
         # 取结果
-        features = results.as_numpy("output__1")  # Shape (N, 64)
+        features = results.as_numpy("output__0")  # Shape (N, 64)
         return features
 
     @RunTime
@@ -94,7 +91,7 @@ class SimilarMatch:
                 # Replace with your query vector
                 data=[self.features[0]],
                 limit=self.result_number,  # Max. number of search results to return
-                output_fields=["id", "image_path"],  # Search parameters
+                output_fields=["item_name", "image_path"],  # Search parameters
             )
             return search_response
         finally: