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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()
model_path = "./model1.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(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)
return [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>
''')
title = "Background Removal"
description = "Remove Image Background"
examples = [['./input.jpg'],]
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)
if __name__ == "__main__":
demo.launch(share=False) |