Spaces:
Runtime error
Runtime error
File size: 3,767 Bytes
542c815 3f8e328 542c815 a888400 d6e753e 8a357d1 542c815 4f91b95 542c815 0c9e50b 542c815 1605763 542c815 70974c3 542c815 70974c3 542c815 70974c3 542c815 d909bca 542c815 d909bca 542c815 d909bca c530952 d909bca c530952 d909bca c530952 d909bca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
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 = "./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(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))
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) |