Spaces:
Running
Running
File size: 1,948 Bytes
a505bd1 543442b a505bd1 4e96c9f a505bd1 4e96c9f a505bd1 33a92a7 543442b 4e96c9f a505bd1 4e96c9f a505bd1 543442b a505bd1 33a92a7 543442b 33a92a7 543442b a505bd1 543442b a505bd1 543442b a505bd1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import gradio as gr
from gradio_imageslider import ImageSlider
from loadimg import load_img
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
from io import BytesIO
# GPU 설정을 CPU로 변경
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cpu") # GPU -> CPU로 변경
transform_image = transforms.Compose(
[
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
def fn(image):
im = load_img(image, output_type="pil")
im = im.convert("RGB")
origin = im.copy()
processed_image = process(im)
# Convert processed image to JPEG for download
buffered = BytesIO()
processed_image.convert("RGB").save(buffered, format="JPEG")
buffered.seek(0)
return processed_image, buffered
def process(image):
image_size = image.size
input_images = transform_image(image).unsqueeze(0).to("cpu") # GPU -> CPU로 변경
# 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
slider = ImageSlider(label="Processed Image", type="pil")
download_output = gr.File(label="Download JPG File")
image_upload = gr.Image(label="Upload an image")
# 새로운 샘플 이미지 (예: 동일 디렉토리에 위치)
sample_images = [
["1.png"],
["2.jpg"],
["3.png"]
]
tab = gr.Interface(
fn=fn,
inputs=image_upload,
outputs=[slider, download_output],
examples=sample_images,
api_name="image"
)
demo = gr.TabbedInterface(
[tab],
["Image Upload"],
title="Background Removal Tool"
)
if __name__ == "__main__":
demo.launch(show_error=True)
|