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import os
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])
birefnet = AutoModelForImageSegmentation.from_pretrained(
"briaai/RMBG-2.0", trust_remote_code=True
)
birefnet.to("cpu")
transform_image = transforms.Compose(
[
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
output_folder = 'output_images'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
def fn(image):
im = load_img(image, output_type="pil")
im = im.convert("RGB")
origin = im.copy()
image = process(im)
image_path = os.path.join(output_folder, "no_bg_image.png")
image.save(image_path)
return (image, origin), image_path
@spaces.GPU
def process(image):
image_size = image.size
input_images = transform_image(image).unsqueeze(0).to("cpu")
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
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
slider1 = ImageSlider(label="RMBG-2.0", type="pil")
slider2 = ImageSlider(label="RMBG-2.0", type="pil")
image = gr.Image(label="Upload an image")
image2 = gr.Image(label="Upload an image",type="filepath")
text = gr.Textbox(label="Paste an image URL")
png_file = gr.File(label="output png file")
tab1 = gr.Interface(
fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], api_name="image", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
)
tab2 = gr.Interface(
fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], api_name="text", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
)
tab3 = gr.Interface(
process_file, inputs=image2, outputs=png_file, api_name="png", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
)
demo = gr.TabbedInterface(
[tab1, tab2], ["Using Image", "Usling URL"], title="✂️ RMBG Image Background Remover ✂️", theme="Yntec/HaleyCH_Theme_Orange"
)
if __name__ == "__main__":
demo.launch(show_error=True, debug=True) |