Update app.py

app.py

@@ -7,11 +7,60 @@ repo_id = "ancebuc/grapes-segmentation"
 learner = from_pretrained_fastai(repo_id)
 labels = learner.dls.vocab
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = torch.jit.load("unet.pth")
+model = model.cpu()
+model.eval()
+
+import torchvision.transforms as transforms
+def transform_image(image):
+    my_transforms = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(
+                                            [0.485, 0.456, 0.406],
+                                            [0.229, 0.224, 0.225])])
+    image_aux = image
+    return my_transforms(image_aux).unsqueeze(0).to(device)
+
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
-    #img = PILImage.create(img)
-    pred,pred_idx,probs = learner.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    img = PILImage.create(img)
+
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image)
+
+    with torch.no_grad():
+        outputs = model(tensor)
+
+    outputs = torch.argmax(outputs,1)
+
+    mask = np.array(outputs.cpu())
+    mask = np.reshape(mask,(480,640))
+
+    # Añadimos una dimesionalidad para colocar color
+    mask = np.expand_dims(mask, axis=2)
+
+    # Y añadimos los tres canales
+    mask = np.repeat(mask, 3, axis=2)
+
+    # Creamos las máscaras
+    uvas = np.all(mask == [1, 1, 1], axis=2)
+    hojas = np.all(mask == [2, 2, 2], axis=2)
+    poste = np.all(mask == [3, 3, 3], axis=2)
+    madera = np.all(mask == [4, 4, 4], axis=2)
+    
+    # Uvas
+    mask[uvas] = [255, 255, 255]
+    
+    # Hojas
+    mask[hojas] = [0, 255, 0]
+    
+    # Poste
+    mask[poste] = [0, 0, 255]
+    
+    # Madera
+    mask[madera] = [255, 0, 0]
+
+    return Image.fromarray(mask.astype('uint8'))
     
 # Creamos la interfaz y la lanzamos. 
 gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.inputs.Image(shape=(128, 128)),examples=['color_158.jpg','color_157.jpg']).launch(share=False)