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import numpy as np
import torch
import torch.nn.functional as F
import gradio as gr
from ormbg import ORMBG
from PIL import Image
def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
im_tensor = F.interpolate(
torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
).type(torch.uint8)
image = torch.divide(im_tensor, 255.0)
return image
def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
ma = torch.max(result)
mi = torch.min(result)
result = (result - mi) / (ma - mi)
im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
im_array = np.squeeze(im_array)
return im_array
def inference(orig_image):
model_path = "ormbg.pth"
net = ORMBG()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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()
model_input_size = [1024, 1024]
orig_im_size = orig_image.shape[0:2]
image = preprocess_image(orig_image, model_input_size).to(device)
result = net(image)
# post process
result_image = postprocess_image(result[0][0], orig_im_size)
# save result
pil_im = Image.fromarray(result_image)
no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
no_bg_image.paste(orig_image, mask=pil_im)
return no_bg_image
gr.Markdown("## Open Remove Background Model (ormbg)")
gr.HTML(
"""
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for Open Remove Background Model (ormbg) that using
<a href="https://huggingface.co/schirrmacher/ormbg" target="_blank">Open Remove Background Model (ormbg) model</a> as backbone.
</p>
"""
)
title = "Background Removal"
description = r"""
This model is a fully open-source background remover optimized for images with humans.
It is based on <a href='https://github.com/xuebinqin/DIS' target='_blank'>Highly Accurate Dichotomous Image Segmentation research</a>.
You can find more about the model <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>here</a>.
"""
examples = [
["./input.png"],
]
demo = gr.Interface(
fn=inference,
inputs="image",
outputs="image",
examples=examples,
title=title,
description=description,
)
if __name__ == "__main__":
demo.launch(share=False)
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