RemoveBG / app.py
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import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
from PIL import Image
import requests
from io import BytesIO
import gradio as gr
# Set up CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_float32_matmul_precision("high")
# Load the model
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to(device)
# Define image transformations
transform_image = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
def load_img(image_path_or_url):
if image_path_or_url.startswith('http'):
response = requests.get(image_path_or_url)
img = Image.open(BytesIO(response.content))
else:
img = Image.open(image_path_or_url)
return img.convert("RGB")
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)
# Create a new image with transparency
transparent_image = Image.new("RGBA", image.size)
transparent_image.paste(image, (0, 0))
transparent_image.putalpha(mask) # Apply mask to the new image
return transparent_image # Return the new transparent image
def remove_background_gradio(image):
processed_img = process(image)
return processed_img
# Create the Gradio interface with drag-and-drop and paste functionality
demo = gr.Interface(
fn=remove_background_gradio,
inputs=gr.Image(type="pil"), # Remove 'source' argument
outputs=gr.Image(type="pil"),
title="RemoveBG",
description="Upload an image to remove its background (drag-and-drop or upload)."
)
demo.launch(share=True) # Launch the interface and get a shareable link