feat:

fix:  替换项目中所有mmcv的依赖

app/service/brand_dna/service.py

@@ -1,7 +1,6 @@
 import logging
 
 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import torch
@@ -14,6 +13,7 @@ from app.core.config import settings
 from app.schemas.brand_dna import BrandDnaModel
 from app.service.attribute.config import const
 from app.service.utils.generate_uuid import generate_uuid
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
 
 minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -202,7 +202,7 @@ class BrandDna:
     # 服装分割预处理
     @staticmethod
     def seg_product_preprocess(image):
-        img = mmcv.imread(image)
+        img = image
         ori_shape = img.shape[:2]
         img_scale_w, img_scale_h = ori_shape
         if ori_shape[0] > 1024:
@@ -211,9 +211,9 @@ class BrandDna:
             img_scale_h = 1024
         # 如果图片size任意一边 大于 1024， 则会resize 成1024
         if ori_shape != (img_scale_w, img_scale_h):
-            # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+            # my_imnormalize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
             img = cv2.resize(img, (img_scale_h, img_scale_w))
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img, ori_shape
 
@@ -227,11 +227,10 @@ class BrandDna:
     # 类别检测模型预处理
     @staticmethod
     def category_preprocess(img):
-        img = mmcv.imread(img)
         # ori_shape = img.shape[:2]
         img_scale = (224, 224)
         img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img