File size: 1,593 Bytes
723eded 091e261 723eded 091e261 723eded 5c3e5c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | from fastai.vision.all import *
import gradio as gr
import torchvision.transforms as transforms
from pathlib import Path
import PIL
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("unet.pth")
model = model.cpu()
model.eval()
def transform_image(image):
#mask = PILMask.create(Path(str(image).replace("Images","Labels").replace("color","gt").replace(".jpg",".png")))
#image = PIL.Image.open(image)
my_transforms = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
image_aux = image
#my_transforms(image_aux).unsqueeze(0).to(device)
image = transforms.Resize((480,640))(Image.fromarray(image))
tensor = my_transforms(image_aux).unsqueeze(0).to(device)
#tensor = transform_image(image=image)
# Establecemos el título de la App y también indicamos
title = "Segmentación Semántica"
model.to(device)
with torch.no_grad():
outputs = model(tensor)
outputs = torch.argmax(outputs,1)
mask = np.array(outputs.cpu())
mask[mask==0]=255
mask[mask==1]=150
mask[mask==2]=76
mask[mask==3]=25
mask[mask==4]=0
mask=np.reshape(mask,(480,640))
return Image.fromarray(mask.astype('uint8'))
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_181.jpg','color_184.jpg','color_187.jpg']).launch(share=False) |