| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| def get_volumen(): return 0 | |
| learn1 = load_learner('resnetrs50VolumenHeight.pkl') | |
| learn2 = load_learner('efficientnetv2_rw_s_volumen_height.pkl') | |
| learn3 = load_learner('efficientnetv2_rw_s_Volumen.pkl') | |
| 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 | |
| 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() | |