File size: 1,866 Bytes
e546fea f9858d8 e546fea 958511f e546fea 958511f f9858d8 958511f e546fea f9858d8 e546fea 958511f 3e75999 e546fea 4c33e65 e546fea 3e75999 958511f e546fea c368dca e546fea 20a2fe0 958511f 521737f 958511f 20a2fe0 958511f e546fea 958511f e546fea 958511f |
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 |
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(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
birefnet.to(device)
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()
image = load_img(im)
input_images = transform_image(image).unsqueeze(0).to(device)
# 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, origin)
slider1 = ImageSlider(label="birefnet", type="pil")
slider2 = ImageSlider(label="birefnet", 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, inputs=text, outputs=slider2, examples=[url], api_name="text")
demo = gr.TabbedInterface(
[tab1, tab2], ["image", "text"], title="birefnet for background removal"
)
if __name__ == "__main__":
demo.launch()
|