feat:

fix:  替换项目中所有mmcv的依赖

app/service/generate_image/service_agent_tool_generate_image.py

@@ -11,7 +11,6 @@ import logging
 import uuid
 
 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import torch
@@ -21,6 +20,7 @@ from minio import Minio
 from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import settings, FAST_GI_MODEL_URL, GI_MODEL_URL, DESIGN_MODEL_URL, FAST_GI_MODEL_NAME, GI_MODEL_NAME
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_upload_image
 
 logger = logging.getLogger()
@@ -86,10 +86,9 @@ class AgentToolGenerateImage:
 
     @staticmethod
     def preprocess(img):
-        img = mmcv.imread(img)
         img_scale = (224, 224)
         img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(
+        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)

app/service/generate_image/service_generate_image.py

@@ -189,10 +189,10 @@ if __name__ == '__main__':
         tasks_id="123-89",
         prompt="a single item of sketch of dress, 4k, white background",
         image_url="aida-collection-element/89/Sketchboard/95f20cdc-e059-435c-b8b1-d04cc9e80c3d.png",
-        mode='img2img',
+        mode='txt2img',
         category="sketch",
         gender="Female",
-        version="fast"
+        version="hight"
     )
     server = GenerateImage(rd)
     print(server.get_result())

app/service/generate_image/utils/image_processing.py

@@ -2,23 +2,23 @@ import logging
 import time
 
 import cv2
-import mmcv
 import numpy as np
 import torch
 import tritonclient.http as httpclient
 
 from app.core.config import settings, DESIGN_MODEL_URL, DESIGN_MODEL_NAME
 from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd
+from app.service.utils.image_normalize import my_imnormalize
 
 logger = logging.getLogger()
 
 
 def seg_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
     ori_shape = img.shape[:2]
     img_scale = ori_shape
     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, ori_shape
 
@@ -242,10 +242,9 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
 
 def generate_category_recognition(image, gender):
     def preprocess(img):
-        img = mmcv.imread(img)
         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