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Running
on
Zero
| import spaces | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| from ormbg import ORMBG | |
| from PIL import Image | |
| model_path = "ormbg.pth" | |
| # Load the model globally but don't send to device yet | |
| net = ORMBG() | |
| 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 inference(image): | |
| # Check for CUDA and set the device inside inference | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| net.to(device) | |
| # Prepare input | |
| orig_image = Image.fromarray(image) | |
| w, h = 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) | |
| if torch.cuda.is_available(): | |
| im_tensor = im_tensor.to(device) | |
| # 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 new_im | |
| # Gradio interface setup | |
| title = "Open Remove Background Model (ormbg)" | |
| description = r""" | |
| This model is a <strong>fully open-source background remover</strong> optimized for images with humans. | |
| It is based on [Highly Accurate Dichotomous Image Segmentation research](https://github.com/xuebinqin/DIS). | |
| The model was trained with the synthetic [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans). | |
| This is the first iteration of the model, so there will be improvements! | |
| If you identify cases where the model fails, <a href='https://huggingface.co/schirrmacher/ormbg/discussions' target='_blank'>upload your examples</a>! | |
| - <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>Model card</a>: find inference code, training information, tutorials | |
| - <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>Dataset</a>: see training images, segmentation data, backgrounds | |
| - <a href='https://huggingface.co/schirrmacher/ormbg\#research' target='_blank'>Research</a>: see current approach for improvements | |
| """ | |
| examples = ["./example1.png", "./example2.png", "./example3.png"] | |
| demo = gr.Interface( | |
| fn=inference, | |
| inputs="image", | |
| outputs="image", | |
| examples=examples, | |
| title=title, | |
| description=description | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=False) | |