| import gradio as gr |
| from fastai.vision.all import * |
| import skimage |
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| def get_volumen(): return 0 |
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| learn1 = load_learner('resnetrs50VolumenHeight.pkl') |
| learn2 = load_learner('efficientnetv2_rw_s_volumen_height.pkl') |
| learn3 = load_learner('efficientnetv2_rw_s_Volumen.pkl') |
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| def predict(Imagen_Ortophoto,Imagen_Altura): |
| imgOrtophoto = PILImage.create(Imagen_Ortophoto) |
| imgAltura = PILImage.create(Imagen_Altura) |
| _,pred1,_ = learn1.predict(imgAltura) |
| _,pred2,_ = learn1.predict(imgAltura) |
| _,pred3,_ = learn3.predict(imgOrtophoto) |
| return (pred1[0]+pred2[0]+pred3[0])/3 |
|
|
| title = "Cálculo de volumen de silos" |
| description = "Demo para el proyecto SEPARA." |
| examples = [['ortophoto1.jpg','altura1.jpg'],['ortophoto2.jpg','altura2.jpg']] |
| interpretation='default' |
| enable_queue=True |
|
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| gr.Interface(fn=predict,inputs=[gr.inputs.Image(shape=(224,224)),gr.inputs.Image(shape=(224,224))],outputs=gr.outputs.Textbox(label="volumen"),title=title,description=description,examples=examples,enable_queue=enable_queue).launch() |
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