import numpy as np import torch.nn.functional as F from torchvision.transforms.functional import normalize import gradio as gr import git # pip install gitpython git.Git(".").clone("https://huggingface.co/briaai/RMBG-1.4") # git.Git(".").clone("git@hf.co:briaai/RMBG-1.4") from briarmbg import BriaRMBG net=BriaRMBG() model_path = "./model.pth" if torch.cuda.is_available(): net.load_state_dict(torch.load(model_path)) net=net.cuda() else: net.load_state_dict(torch.load(model_path,map_location="cpu")) net.eval() def image_size_by_min_resolution( image: Image.Image, resolution: Tuple, resample=None, ): w, h = image.size image_min = min(w, h) resolution_min = min(resolution) scale_factor = image_min / resolution_min resize_to: Tuple[int, int] = ( int(w // scale_factor), int(h // scale_factor), ) return resize_to def resize_image(image): image = image.convert('RGB') new_image_size = image_size_by_min_resolution(image=image,resolution=(1024, 1024)) image = image.resize(new_image_size, Image.BILINEAR) return image def process(input_image): # prepare input orig_image = Image.open(im_path) 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) # save result im_array = (result*255).cpu().data.numpy().astype(np.uint8) pil_im = Image.fromarray(np.squeeze(im_array)) # paste the mask on the original image new_im = Image.new("RGBA", pil_im.size, (0,0,0)) new_im.paste(orig_image, mask=pil_im) return new_im block = gr.Blocks().queue() with block: gr.Markdown("## BRIA RMBG 1.4") gr.HTML('''

This is a demo for BRIA RMBG 1.4 that using BRIA RMBG-1.4 image matting model as backbone.

''') with gr.Row(): with gr.Column(): # input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam input_image = gr.Image(sources=None, type="numpy") # None for upload, ctrl+v and webcam run_button = gr.Button(value="Run") with gr.Column(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto') ips = [input_image] run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) block.launch(debug = True)