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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
import zipfile
torch.set_float32_matmul_precision(["high", "highest"][0])
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")
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()
input_images = transform_image(im).unsqueeze(0).to("cuda")
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)
im.putalpha(mask)
output_file_path = os.path.join("output_images", "output_image_single.png")
im.save(output_file_path)
return (im, origin)
@spaces.GPU
def fn_url(url):
im = load_img(url, output_type="pil")
im = im.convert("RGB")
origin = im.copy()
image_size = im.size
input_images = transform_image(im).unsqueeze(0).to("cuda")
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)
im.putalpha(mask)
output_file_path = os.path.join("output_images", "output_image_url.png")
im.save(output_file_path)
return [im, origin]
@spaces.GPU
def batch_fn(images):
output_paths = []
for idx, image_path in enumerate(images):
im = load_img(image_path, output_type="pil")
im = im.convert("RGB")
image_size = im.size
input_images = transform_image(im).unsqueeze(0).to("cuda")
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)
im.putalpha(mask)
output_file_path = os.path.join("output_images", f"output_image_batch_{idx + 1}.png")
im.save(output_file_path)
output_paths.append(output_file_path)
zip_file_path = os.path.join("output_images", "processed_images.zip")
with zipfile.ZipFile(zip_file_path, 'w') as zipf:
for file in output_paths:
zipf.write(file, os.path.basename(file))
return zip_file_path
batch_image = gr.File(label="Upload multiple images", type="filepath", file_count="multiple") # 複数画像のアップロードを許可
slider1 = ImageSlider(label="Processed Image", type="pil")
slider2 = ImageSlider(label="Processed Image from URL", type="pil")
image = gr.Image(label="Upload an image")
text = gr.Textbox(label="Paste an image URL")
chameleon = load_img("chameleon.jpg", output_type="pil")
url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
tab1 = gr.Interface(
fn, inputs=image, outputs=slider1, examples=[chameleon], api_name="image"
)
tab2 = gr.Interface(fn_url, inputs=text, outputs=slider2, examples=[url], api_name="text")
tab3 = gr.Interface(
batch_fn,
inputs=batch_image,
outputs=gr.File(label="Download Processed Files"),
api_name="batch",
css="""
#component-37 {
display: none;
}
"""
)
demo = gr.TabbedInterface(
[tab1, tab2, tab3], ["image", "text", "batch"], title="Multi Birefnet for Background Removal"
)
if __name__ == "__main__":
demo.launch()
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