MoC-IQA

app.py

@@ -9,8 +9,10 @@ from utils.dataset.process import ToTensor, Normalize
 import gradio as gr
 
 def load_image(img_path):
-    d_img = cv2.cvtColor(np.asarray(img_path),cv2.COLOR_RGB2BGR)
-    # d_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+    if isinstance(img_path, str):
+        d_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+    else:
+        d_img = cv2.cvtColor(np.asarray(img_path),cv2.COLOR_RGB2BGR)
     d_img = cv2.resize(d_img, (224, 224), interpolation=cv2.INTER_CUBIC)
     d_img = cv2.cvtColor(d_img, cv2.COLOR_BGR2RGB)
     d_img = np.array(d_img).astype('float32') / 255
@@ -27,7 +29,7 @@ def predict(image):
     config = parser.parse_args()
 
     model = MoNet.MoNet(config).cuda()
-    model.load_state_dict(torch.load('./checkpoints/best_model.pkl'))
+    model.load_state_dict(torch.load('best_model.pkl'))
     model.eval()
 
     trans = torchvision.transforms.Compose([Normalize(0.5, 0.5), ToTensor()])

requirements.txt

@@ -0,0 +1,8 @@
+einops==0.6.1
+numpy==1.22.4
+openpyxl==3.0.9
+Pillow==10.0.0
+scipy==1.11.2
+timm==0.5.4
+torch==1.11.0
+tqdm==4.61.2