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from fastai.vision.all import *
import gradio as gr
import torchvision.transforms as transforms
from pathlib import Path
import PIL
from huggingface_hub import from_pretrained_fastai
# Cargamos el learner
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
repo_id = "macapa/segmentation-mod"
model = from_pretrained_fastai(repo_id)
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)
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_156.jpg','color_179.jpg']).launch(share=False)
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