feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/attribute/service_att_recognition.py

@@ -1,22 +1,24 @@
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
-import logging
 from pprint import pprint
-import torch
+
 import cv2
 import mmcv
 import numpy as np
 import pandas as pd
-from minio import Minio
+import torch
 import tritonclient.http as httpclient
-from app.core.config import *
+from minio import Minio
+
+from app.core.config import settings, DESIGN_MODEL_URL
 from app.schemas.attribute_retrieve import AttributeRecognitionModel
-from app.service.utils.oss_client import oss_get_image
+from app.service.utils.new_oss_client import oss_get_image
+
+minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
 
 class AttributeRecognition:
     def __init__(self, const, request_data):
-        # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
         self.request_data = []
         for i, sketch in enumerate(request_data):
             self.request_data.append(
@@ -96,11 +98,12 @@ class AttributeRecognition:
         res = {**dict1, **dict2}
         return res
 
-    def get_image(self, url):
+    @staticmethod
+    def get_image(url):
         # response = self.minio_client.get_object(url.split("/", 1)[0], url.split("/", 1)[1])
         # img = np.frombuffer(response.data, np.uint8)  # 转成8位无符号整型
         # img = cv2.imdecode(img, cv2.IMREAD_COLOR)  #
-        img = oss_get_image(bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2")
+        img = oss_get_image(oss_client=minio_client, bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2")
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         return img
 

app/service/attribute/service_category_recognition.py

@@ -7,24 +7,25 @@
 @Date    ：2023/9/16 18:31:08
 @detail  ：
 """
+from minio import Minio
 from skimage import transform
 import cv2
 import mmcv
 import numpy as np
 import pandas as pd
-from minio import Minio
 import tritonclient.http as httpclient
 import torch
 
-from app.core.config import *
+from app.core.config import settings, DESIGN_MODEL_URL
 from app.schemas.attribute_retrieve import CategoryRecognitionModel
-from app.service.utils.oss_client import oss_get_image
+from app.service.utils.new_oss_client import oss_get_image
+
+minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
 
 class CategoryRecognition:
     def __init__(self, request_data):
-        self.attr_type = pd.read_csv(CATEGORY_PATH)
-        # self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+        self.attr_type = pd.read_csv(settings.CATEGORY_PATH)
         self.request_data = []
         self.triton_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
         for sketch in request_data:
@@ -46,13 +47,14 @@ class CategoryRecognition:
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img
 
-    def get_image(self, url):
+    @staticmethod
+    def get_image(url):
         # Get data of an object.
         # Read data from response.
         # response = self.minio_client.get_object(url.split("/", 1)[0], url.split("/", 1)[1])
         # img = np.frombuffer(response.data, np.uint8)  # 转成8位无符号整型
         # img = cv2.imdecode(img, cv2.IMREAD_COLOR)  # 解码
-        img = oss_get_image(bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2")
+        img = oss_get_image(oss_client=minio_client, bucket=url.split("/", 1)[0], object_name=url.split("/", 1)[1], data_type="cv2")
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         return img
 
@@ -68,7 +70,7 @@ class CategoryRecognition:
 
         colattr = list(self.attr_type['labelName'])
 
-        task = self.attr_type['taskName'][0]
+        # self.attr_type['taskName'][0]
 
         maxsc = np.max(scores[0][:5])
         indexs = np.argwhere(scores == maxsc)[:, 1]