关于保存特征的一些代码

app/service/outfit_matcher/outfit_evaluator.py

@@ -327,9 +327,12 @@ class OutfitMaterTypeAware(OutfitMatcher):
         # 输出集
         outputs = [
             httpclient.InferRequestedOutput("output__0", binary_data=True),
+            httpclient.InferRequestedOutput("output__1", binary_data=True)
         ]
         results = client.infer(model_name="outfit_matcher_type_aware", inputs=inputs, outputs=outputs)
         # 推理
         # 取结果
-        scores = torch.from_numpy(results.as_numpy("output__0"))
-        return scores  # Shape (N, 1)
+        scores = torch.from_numpy(results.as_numpy("output__0"))  # Shape (N, 1)
+        features = torch.from_numpy(results.as_numpy("output__1"))  # Shape (N, 64)
+
+        return scores, features

app/service/outfit_matcher/service.py

@@ -14,7 +14,14 @@ if __name__ == '__main__':
     bad_list = []
     for item in param["query"]:
         outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
-        scores = service.get_result(outfits)
+        scores, features = service.get_result(outfits)
+        # save  features
+
+        # 链接milvus
+
+        # 存入数据库
+        # 关闭链接
+
         # print(scores)
         # print(len(scores))
         best_outfits, best_scores = service.visualize(outfits, scores, param["topk"], best=True,

app/service/similar_match/service.py

@@ -0,0 +1,102 @@
+import io
+import json
+
+import numpy as np
+import tritonclient.http as httpclient
+from PIL import Image
+from minio import Minio
+from pymilvus import MilvusClient
+
+from app.core.config import *
+from torchvision import transforms
+
+
+class SimilarMatch:
+    def __init__(self):
+        self.minio_client = Minio(
+            f"{MINIO_IP}:{MINIO_PORT}",
+            access_key=MINIO_ACCESS,
+            secret_key=MINIO_SECRET,
+            secure=MINIO_SECURE)
+        self.triton_client = httpclient.InferenceServerClient(url=f"{OM_TRITON_IP}:{OM_TRITON_PORT}")
+
+    @staticmethod
+    def resize_image(img):
+        """
+        Args:
+            img: ndarray (height, width, channel)
+        """
+        image_transforms = transforms.Compose([
+            transforms.Resize(112),
+            transforms.CenterCrop(112),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225]),
+        ])
+        resized_img = image_transforms(img).numpy()
+        return resized_img
+
+    def load_image(self, img_path):
+        # 从 MinIO 中获取对象（图像文件）
+        image_data = self.minio_client.get_object(img_path.split("/", 1)[0], img_path.split("/", 1)[1])
+        # 读取图像数据并转换为 PIL 图像对象
+        pil_image = Image.open(io.BytesIO(image_data.data)).convert("RGB")
+        # 将 PIL 图像转换为 NumPy 数组
+        # image_array = np.array(pil_image)
+        return pil_image
+
+    def preprocess(self, img_path):
+        image = self.load_image(img_path)
+        image = self.resize_image(image)
+        image = np.stack([[image]], axis=0)
+
+        category = np.stack([[1, 6]], axis=0)
+
+        mask = np.zeros((1, 1), dtype=np.float32)
+        return image, category, mask
+
+    def get_features(self, img_path):
+        image, category, mask = self.preprocess(img_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)
+        # 推理
+        # 取结果
+        features = results.as_numpy("output__1")  # Shape (N, 64)
+        return features
+
+    def match_features(self, features):
+        # 连接milvus
+        # 连接milvus
+        client = MilvusClient(uri="http://10.1.1.240:19530", db_name="mixi")
+        try:
+            res = client.search(
+                collection_name="mixi_outfit",  # Replace with the actual name of your collection
+                # Replace with your query vector
+                data=[features[0]],
+                limit=5,  # Max. number of search results to return
+                output_fields=["id", "image_path"],  # Search parameters
+            )
+            return res
+        finally:
+            client.close()
+
+
+if __name__ == '__main__':
+    service = SimilarMatch()
+    features = service.get_features(img_path="test/2024 SS/MKTS27000.jpg")
+    res = service.match_features(features)
+
+    print(json.dumps(res, indent=4))