File size: 1,252 Bytes
569737e 2e3563e c420df7 03c8d09 633d3c4 c420df7 03c8d09 569737e c420df7 633d3c4 c420df7 633d3c4 c71ac74 c420df7 633d3c4 c420df7 c71ac74 e614339 c420df7 6df61ab c420df7 6df61ab |
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 |
import gradio as gr
from fastai.vision.all import *
import torchvision.transforms as transforms
import torch
from PIL import Image
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("unet.pth")
model = model.to(device)
model.eval()
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 = transforms.Resize((480,640))(Image.fromarray(image))
tensor = my_transforms(image).unsqueeze(0).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'))
# Ajuste de la creación de la interfaz
interface = gr.Interface(fn=transform_image,
inputs=gr.components.Image(width=640, height=480),
outputs=gr.components.Image(),
examples=['color_154.jpg', 'color_189.jpg'])
interface.launch(share=False)
|