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import os
from PIL import Image
import torch
from torchvision import transforms
# load model
model = torch.hub.load('hustvl/yolop', 'yolop', pretrained=True)
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
transform=transforms.Compose([
transforms.ToTensor(),
# normalize
])
def inference(img):
# print(img.size)
img = img.resize((640, 640))
img = torch.unsqueeze(transform(img), dim=0)
# img = transform(img)
det_out, da_seg_out,ll_seg_out = model(img)
ll_out = ll_seg_out[0][0, :, :].detach().numpy()
da_out = da_seg_out[0][0, :, :].detach().numpy()
return da_out,ll_out
gr.Interface(inference,gr.inputs.Image(type="pil"),["image","image"]).launch(debug=True) |