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Practica3 / app.py

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	from huggingface_hub import from_pretrained_fastai
	import gradio as gr

	from fastai.vision.all import *

	import torchvision.transforms as transforms
	import torchvision.transforms as transforms

	from fastai.basics import *
	from fastai.vision import models
	from fastai.vision.all import *
	from fastai.metrics import *
	from fastai.data.all import *
	from fastai.callback import *
	from pathlib import Path

	import random
	import PIL

	#Definimos las funciones de transformacion que hemos creado en la practica para poder tratar los datos de entrada y que funcione bien
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	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)

	class TargetMaskConvertTransform(ItemTransform):
	def __init__(self):
	pass
	def encodes(self, x):
	img,mask = x

	#Convertimos a array
	mask = np.array(mask)

	mask[(mask!=255) & (mask!=150) & (mask!=76) & (mask!=74) & (mask!=29) & (mask!=25)]=0
	mask[mask==255]=1
	mask[mask==150]=2
	mask[mask==76]=4
	mask[mask==74]=4
	mask[mask==29]=3
	mask[mask==25]=3

	# Back to PILMask
	mask = PILMask.create(mask)
	return img, mask

	from albumentations import (
	Compose,
	OneOf,
	ElasticTransform,
	GridDistortion,
	OpticalDistortion,
	HorizontalFlip,
	Rotate,
	Transpose,
	CLAHE,
	ShiftScaleRotate
	)

	def get_y_fn (x):
	return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))

	class SegmentationAlbumentationsTransform(ItemTransform):
	split_idx = 0

	def __init__(self, aug):
	self.aug = aug

	def encodes(self, x):
	img,mask = x
	aug = self.aug(image=np.array(img), mask=np.array(mask))
	return PILImage.create(aug["image"]), PILMask.create(aug["mask"])

	#Cargamos el modelo

	repo_id = "valintea/modelo-p3"
	learn = from_pretrained_fastai(repo_id)
	model = learn.model
	model = model.cpu()


	# Funcion de predicción
	def predict(img_ruta):
	img = PIL.Image.fromarray(img_ruta)
	image = transforms.Resize((480,640))(img)
	tensor = transform_image(image=image)
	model.to(device)
	with torch.no_grad():
	outputs = model(tensor)

	outputs = torch.argmax(outputs,1)
	mask = np.array(outputs.cpu())
	mask[mask==1]=255
	mask[mask==2]=150
	mask[mask==3]=29
	mask[mask==4]=74
	mask = np.reshape(mask,(480,640))
	return Image.fromarray(mask.astype('uint8'))


	# Creamos la interfaz y la lanzamos.
	gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.inputs.Image(shape=(480, 640)), examples=['color_188.jpg','color_155.jpg']).launch(share=False)