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| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms.functional import normalize | |
| # from foo import hello | |
| import gradio as gr | |
| from briarmbg import BriaRMBG | |
| import PIL | |
| from PIL import Image | |
| from typing import Tuple | |
| # import git # pip install gitpython | |
| # hello() | |
| # git.Git(".").clone("https://huggingface.co/briaai/RMBG-1.4") | |
| # git.Git(".").clone("git@hf.co:briaai/RMBG-1.4") | |
| 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 = input_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) | |
| # 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 [input_image,new_im] | |
| block = gr.Blocks().queue() | |
| with block: | |
| 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> | |
| ''') | |
| 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) |