Update app.py

app.py

@@ -4,7 +4,8 @@ import numpy as np
 import os
 from model import gen_model
 import torchvision.transforms as T
-
+MEAN = (0.5, 0.5, 0.5,)
+STD = (0.5, 0.5, 0.5,)
 # Model
 gen,transform_gen=gen_model()
 # print(gen)
@@ -12,7 +13,11 @@ to_img=T.ToPILImage()
 # examples=["examples/input_0.png","examples/input_9.png"]
 example_list = [["examples/" + example] for example in os.listdir("examples")]
 # example_list=['1.jpg','2.jpg']
-
+def de_norm(img):
+    img_ = img.mul(torch.FloatTensor(STD).view(3, 1, 1))
+    img_ = img_.add(torch.FloatTensor(MEAN).view(3, 1, 1)).detach().numpy()
+    img_ = np.transpose(img_, (1, 2, 0))
+    return img_ 
 def predict(img):
     # Apply Transformations
     img = transform_gen(img).unsqueeze(0)