image_quality_assessment_mos use LoadImage for image io

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11596634

modelscope/preprocessors/cv/image_quality_assessment_mos.py

@@ -5,8 +5,6 @@ from typing import Any, Dict, Union
 
 import cv2
 import numpy as np
-import torch
-import torch.nn.functional as F
 from torchvision import transforms
 
 from modelscope.metainfo import Preprocessors
@@ -14,7 +12,6 @@ from modelscope.preprocessors import LoadImage
 from modelscope.preprocessors.base import Preprocessor
 from modelscope.preprocessors.builder import PREPROCESSORS
 from modelscope.utils.constant import Fields
-from modelscope.utils.hub import read_config
 from modelscope.utils.type_assert import type_assert
 
 
@@ -29,18 +26,7 @@ class ImageQualityAssessmentMosPreprocessor(Preprocessor):
         super().__init__(**kwargs)
 
     def preprocessors(self, input):
-        if isinstance(input, str):
-            img = cv2.imread(input)
-        elif isinstance(input, PIL.Image.Image):
-            img = np.array(input.convert('RGB'))
-        elif isinstance(input, np.ndarray):
-            if len(input.shape) == 2:
-                img = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
-            else:
-                img = input
-        else:
-            raise TypeError(f'input should be either str, PIL.Image,'
-                            f' np.array, but got {type(input)}')
+        img = LoadImage.convert_to_ndarray(input)
         sub_img_dim = (720, 1280)
         resize_dim = (1080, 1920)
         h, w = img.shape[:2]
@@ -67,7 +53,6 @@ class ImageQualityAssessmentMosPreprocessor(Preprocessor):
 
         if flag:
             img = np.rot90(img)
-        img = img[:, :, ::-1]
         img = LoadImage.convert_to_img(img)
         test_transforms = transforms.Compose([transforms.ToTensor()])
         img = test_transforms(img)