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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) | |