Spaces:
Running
Running
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
from torchvision.transforms.functional import normalize | |
from huggingface_hub import hf_hub_download | |
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() | |
# model_path = "./model1.pth" | |
model_path = hf_hub_download("briaai/RMBG-1.4", '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 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 | |
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, (0,0,0,0)) | |
new_im.paste(orig_image, mask=pil_im) | |
# new_orig_image = orig_image.convert('RGBA') | |
return new_im | |
# return [new_orig_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=[1], height='auto') | |
# ips = [input_image] | |
# run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
# block.launch(debug = True) | |
# block = gr.Blocks().queue() | |
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> | |
''') | |
examples = [] | |
# output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True) | |
# demo = gr.Interface(fn=process,inputs="image", outputs=output, examples=examples, title=title, description=description) | |
demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description) | |
if __name__ == "__main__": | |
demo.launch(share=False) |