app.py

import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
import gradio as gr
from gradio_imageslider import ImageSlider
from briarmbg import BriaRMBG
import PIL
from PIL import Image
from typing import Tuple


net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.to(device)
net.eval()    

    
def resize_image(image):
    image = image.convert('RGB')
    model_input_size = (1024, 1024)
    image = image.resize(model_input_size, Image.BILINEAR)
    return image


def process(image):

    # prepare input
    orig_image = Image.fromarray(image)
    w,h = orig_im_size = orig_image.size
    image = resize_image(orig_image)
    im_np = np.array(image)
    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
    im_tensor = torch.unsqueeze(im_tensor,0)
    im_tensor = torch.divide(im_tensor,255.0)
    im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
    if torch.cuda.is_available():
        im_tensor=im_tensor.cuda()

    #inference
    result=net(im_tensor)
    # post process
    result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result-mi)/(ma-mi)    
    # image to pil
    result_array = (result*255).cpu().data.numpy().astype(np.uint8)
    pil_mask = Image.fromarray(np.squeeze(result_array))
    # add the mask on the original image as alpha channel
    new_im = orig_image.copy()
    new_im.putalpha(pil_mask)
    return new_im
    # return [new_orig_image, new_im]


gr.Markdown("## BRIA RMBG 1.4")
gr.HTML('''
  <p style="margin-bottom: 10px; font-size: 94%">
    This is a demo for BRIA RMBG 1.4 that using
    <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
  </p>
''')
title = "Background Removal"
description = r"""Background removal model developed by <a href='https://BRIA.AI' target='_blank'><b>BRIA.AI</b></a>, trained on a carefully selected dataset and is available as an open-source model for non-commercial use.<br> 
For test upload your image and wait. Read more at model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>. To purchase a commercial license, simply click <a href='https://go.bria.ai/3ZCBTLH' target='_blank'><b>Here</b></a>. <br>
"""
examples = [['./input.jpg'],]
demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description)

if __name__ == "__main__":
    demo.launch(share=False)