关于保存特征的一些代码

2024-03-26 18:00:35 +08:00
parent 7dce70027a
commit 88c815dd9d
16 changed files with 378 additions and 10 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -119,7 +119,7 @@ dmypy.json
 .test

 #runtime produce
-test
+#test
 logs
 seg_result/
 seg_result
--- a/app/api/api_outfit_matcher.py
+++ b/app/api/api_outfit_matcher.py
@@ -27,7 +27,9 @@ def outfit_matcher(request_item: OutfitMatcher):
        start_time = time.time()
        for item in request_item['query']:
            outfits = fashion_dataset.generate_outfit(item, request_item["topk"], request_item["max_outfits"])
-            scores = service.get_result(outfits)
+            scores, features = service.get_result(outfits)
+            # save features in databases
+
            if request_item['is_best']:
                best_outfits, best_scores = service.visualize(outfits, scores, request_item["topk"], best=True,
                                                              # output_path=os.path.join(r"E:\workspace\outfit_matcher\2024 SS Outfit", f"{item['item_name']}_best_{param['topk']}.png")
--- a/app/api/api_simiar_match.py
+++ b/app/api/api_simiar_match.py
@@ -0,0 +1,16 @@
+import logging
+import time
+
+from fastapi import APIRouter
+from app.schemas.outfit_matcher import SimilarMatchMItem
+from app.service.utils.decorator import RunTime
+
+logger = logging.getLogger()
+router = APIRouter()
+
+
+@RunTime
+@router.post("similar_match")
+def similar_match(request_item: SimilarMatchMItem):
+
+    pass
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -27,18 +27,23 @@ MINIO_SECURE = False
 MINIO_ACCESS = "e8zc55mzDOh4IzRrZ9Oa"
 MINIO_SECRET = "uHfqJ7UkwA1PTDGfnA44Hp9ux5YkZTkzZLjeOYhE"

-OM_TRITON_IP = "10.1.1.240"
-OM_TRITON_PORT = "10010"
+OM_TRITON_IP = "10.1.1.150"
+OM_TRITON_PORT = "9000"

 ATT_TRITON_IP = "10.1.1.240"
 ATT_TRITON_PORT = "10020"

 # service env
-LOGS_PATH = "app/logs/errors.log"
-FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json"
-FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json"
+# LOGS_PATH = "app/logs/errors.log"
+# FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json"
+# FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json"

 # pycharm debug
 # LOGS_PATH = "logs/errors.log"
 # FASHION_CATEGORIES = "service/outfit_matcher/config/fashion_categories.json"
 # FASHION_CATEGORIES_MAPPING = "service/outfit_matcher/config/fashion_category_mapping.json"
+
+
+LOGS_PATH = "app/logs/errors.log"
+FASHION_CATEGORIES = "./config/fashion_categories.json"
+FASHION_CATEGORIES_MAPPING = "./config/fashion_category_mapping.json"
--- a/app/logs/errors.log
+++ b/app/logs/errors.log
@@ -0,0 +1,140 @@
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+2024-03-21 13:52:08,375 decorator.py [line:11] INFO function：【load_image】,runtime：【0.7181243896484375】s
+2024-03-21 13:52:08,782 decorator.py [line:11] INFO function：【load_image】,runtime：【0.37945127487182617】s
+2024-03-21 13:52:08,782 decorator.py [line:11] INFO function：【load_image】,runtime：【0.37945127487182617】s
+2024-03-21 13:52:09,530 decorator.py [line:11] INFO function：【load_image】,runtime：【0.7160646915435791】s
+2024-03-21 13:52:09,530 decorator.py [line:11] INFO function：【load_image】,runtime：【0.7160646915435791】s
+2024-03-21 13:52:09,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.25645899772644043】s
+2024-03-21 13:52:09,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.25645899772644043】s
--- a/app/schemas/similar_match.py
+++ b/app/schemas/similar_match.py
@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+
+class SimilarMatchMItem(BaseModel):
+    image_path: str
--- a/app/service/outfit_matcher/outfit_evaluator.py
+++ b/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
--- a/app/service/outfit_matcher/service.py
+++ b/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,
--- a/app/service/similar_match/init.py
+++ b/app/service/similar_match/init.py
--- a/app/service/similar_match/service.py
+++ b/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))
--- a/requirements.txt
+++ b/requirements.txt
--- a/test/outfit_matcher_milvus/1.create
+++ b/test/outfit_matcher_milvus/1.create
@@ -0,0 +1,5 @@
+from pymilvus import connections, db
+
+conn = connections.connect(host="10.1.1.240", port=19530)
+
+database = db.create_database("mixi")
--- a/test/outfit_matcher_milvus/2.create
+++ b/test/outfit_matcher_milvus/2.create
@@ -0,0 +1,23 @@
+from pymilvus import MilvusClient, DataType
+
+# 创建client ,配置databases
+client = MilvusClient(
+    uri="http://10.1.1.240:19530",
+    token="root:Milvus",
+    db_name="mixi"
+)
+from pymilvus import MilvusClient, DataType
+
+schema = MilvusClient.create_schema(
+    auto_id=True,
+    enable_dynamic_field=False,
+)
+
+schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
+schema.add_field(field_name="image_path", datatype=DataType.VARCHAR, max_length=200)
+schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=64)
+
+client.create_collection(
+    collection_name="mixi_outfit",
+    schema=schema
+)
--- a/test/outfit_matcher_milvus/3.create
+++ b/test/outfit_matcher_milvus/3.create
@@ -0,0 +1,23 @@
+from pymilvus import MilvusClient, Collection
+
+client = MilvusClient(
+    uri="http://10.1.1.240:19530",
+    token="root:Milvus",
+    db_name="mixi"
+)
+
+index_params = client.prepare_index_params()
+index_params.add_index(
+    field_name="id",
+    index_type="STL_SORT"
+)
+index_params.add_index(
+    field_name="vector",
+    index_type="IVF_FLAT",
+    metric_type="L2",
+    params={"nlist": 1024}
+)
+client.create_index(
+    collection_name="mixi_outfit",
+    index_params=index_params
+)
--- a/test/outfit_matcher_milvus/4.load
+++ b/test/outfit_matcher_milvus/4.load
@@ -0,0 +1,10 @@
+from pymilvus import MilvusClient
+
+client = MilvusClient(
+    uri="http://10.1.1.240:19530",
+    token="root:Milvus",
+    db_name="mixi"
+)
+client.load_collection(
+    collection_name="mixi_outfit"
+)
--- a/test/outfit_matcher_milvus/5.similar
+++ b/test/outfit_matcher_milvus/5.similar
@@ -0,0 +1,27 @@
+import json
+import time
+
+from pymilvus import MilvusClient
+
+client = MilvusClient(
+    uri="http://10.1.1.240:19530",
+    token="root:Milvus",
+    db_name="mixi"
+)
+data = [0.019687360152602196, 0.839404821395874, 0.5053166747093201, 0.6062483787536621, 0.5455009341239929, 0.07595491409301758, 0.028354600071907043, 0.24453534185886383, 0.6116685271263123, 0.4527449309825897, 0.22063420712947845, 0.09205381572246552, 0.22853578627109528, 0.3041312098503113,
+        0.8354143500328064, 0.05135197564959526, 0.9292615652084351, 0.03914223983883858, 0.7091595530509949, 0.17939062416553497, 0.2958671748638153, 0.46751415729522705, 0.05523946136236191, 0.976833164691925, 0.3593502938747406, 0.0806853398680687, 0.3097323179244995, 0.12855321168899536,
+        0.12651172280311584, 0.3173355162143707, 0.17060844600200653, 0.9340737462043762, 0.8437095880508423, 0.7500482797622681, 0.22598184645175934, 0.8127533197402954, 0.39825528860092163, 0.9043431878089905, 0.9064653515815735, 0.14613617956638336, 0.582768976688385, 0.4516744315624237,
+        0.6479957699775696, 0.909612774848938, 0.7674093842506409, 0.47747865319252014, 0.5617552995681763, 0.967750072479248, 0.9146659970283508, 0.28031912446022034, 0.5092940330505371, 0.21442186832427979, 0.43696293234825134, 0.7705745100975037, 0.09395607560873032, 0.9103220701217651,
+        0.2616001069545746, 0.7469480037689209, 0.24508604407310486, 0.6890515089035034, 0.704613447189331, 0.7213652729988098, 0.3660031855106354, 0.2150406688451767]
+start_time = time.time()
+res = client.search(
+    collection_name="mixi_outfit",  # Replace with the actual name of your collection
+    # Replace with your query vector
+    data=[data],
+    limit=5,  # Max. number of search results to return
+    # search_params={"metric_type": "IP", "params": {}}  # Search parameters
+)
+print(time.time() - start_time)
+
+result = json.dumps(res, indent=4)
+print(result)