Update app.py
Browse files
app.py
CHANGED
@@ -13,17 +13,48 @@ import gradio as gr
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# Cargamos el learner
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learn = load_learner('export.pkl')
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# Definimos las etiquetas de nuestro modelo
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labels = learn.dls.vocab
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# Definimos una función que se encarga de llevar a cabo las predicciones
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def predict(img):
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img = PILImage.create(img)
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# Creamos la interfaz y la lanzamos.
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['color_154.jpg','color_155.jpg']).launch(share=False)
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# Cargamos el learner
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#learn = load_learner('export.pkl')
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model = torch.jit.load("unet.pth")
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# Definimos las etiquetas de nuestro modelo
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#labels = learn.dls.vocab
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def transform_image(image):
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my_transforms = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize(
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[0.485, 0.456, 0.406],
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[0.229, 0.224, 0.225])])
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image_aux = image
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return my_transforms(image_aux).unsqueeze(0).to(device)
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# Definimos una función que se encarga de llevar a cabo las predicciones
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def predict(img):
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img = PILImage.create(img)
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image = transforms.Resize((480,640))(img)
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tensor = transform_image(image=image)
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with torch.no_grad():
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outputs = model(tensor)
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outputs = torch.argmax(outputs,1)
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mask = np.array(outputs.cpu())
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mask[mask==0]=255 #grape
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mask[mask==1]=150 #leaves
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mask[mask==2]=76 #pole
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mask[mask==2]=74 #pole
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mask[mask==3]=29 #wood
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mask[mask==3]=25 #wood
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mask=np.reshape(mask,(480,640))
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return Image.fromarray(mask.astype('uint8'))
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#pred,pred_idx,probs = learn.predict(img)
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#return {labels[i]: float(probs[i]) for i in range(len(labels))}
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# Creamos la interfaz y la lanzamos.
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['color_154.jpg','color_155.jpg']).launch(share=False)
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