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import gradio as gr
from gradio_imageslider import ImageSlider
from loadimg import load_img
import spaces
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
torch.set_float32_matmul_precision(["high", "highest"][0])
device = "cuda" if torch.cuda.is_available() else "cpu"
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to(device)
transform_image = transforms.Compose(
[
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
@spaces.GPU
def fn(image):
im = load_img(image, output_type="pil")
im = im.convert("RGB")
image_size = im.size
origin = im.copy()
image = load_img(im)
input_images = transform_image(image).unsqueeze(0).to(device)
# Prediction
with torch.no_grad():
preds = birefnet(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image_size)
image.putalpha(mask)
return image
chameleon = load_img("chameleon.jpg", output_type="pil")
url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
demo = gr.Interface(
fn,
inputs=gr.Image(label="Upload an image"),
outputs=gr.Image(label="birefnet", format="png"),
examples=[chameleon],
api_name="image",
flagging_mode="never",
cache_mode="lazy",
)
demo.queue(default_concurrency_limit=1).launch(show_error=True)
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