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import gradio as gr
from loadimg import load_img
import spaces
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
# Set float32 matmul precision (used for performance tuning)
torch.set_float32_matmul_precision("high")
# Detect device (GPU if available, otherwise CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model and send to appropriate device
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to(device)
# Image transformation pipeline
transform_image = transforms.Compose(
[
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
# Background removal pipeline
def process(image):
image_size = image.size
input_images = transform_image(image).unsqueeze(0).to(device)
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
# Gradio interface function
def fn(image):
im = load_img(image, output_type="pil")
im = im.convert("RGB")
origin = im.copy()
processed_image = process(im)
return (processed_image, origin)
# Process uploaded file and return PNG with alpha channel
def process_file(f):
name_path = f.rsplit(".", 1)[0] + ".png"
im = load_img(f, output_type="pil")
im = im.convert("RGB")
transparent = process(im)
transparent.save(name_path)
return name_path
# Gradio components
slider1 = gr.ImageSlider(label="Processed Image", type="pil", format="png")
slider2 = gr.ImageSlider(label="Processed Image from URL", type="pil", format="png")
image_upload = gr.Image(label="Upload an image")
image_file_upload = gr.Image(label="Upload an image", type="filepath")
url_input = gr.Textbox(label="Paste an image URL")
output_file = gr.File(label="Output PNG File")
# Example images
chameleon = load_img("butterfly.jpg", output_type="pil")
url_example = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
# Gradio interface tabs
tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1, examples=[chameleon], api_name="image")
tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2, examples=[url_example], api_name="text")
tab3 = gr.Interface(process_file, inputs=image_file_upload, outputs=output_file, examples=["butterfly.jpg"], api_name="png")
# Launch app
demo = gr.TabbedInterface(
[tab1, tab2, tab3], ["Image Upload", "URL Input", "File Output"], title="Background Removal Tool"
)
if __name__ == "__main__":
demo.launch(show_error=True)
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