| import gradio as gr | |
| import huggingface_hub | |
| import onnxruntime as rt | |
| import numpy as np | |
| import cv2 | |
| def get_mask(img, s=1024): | |
| img = (img / 255).astype(np.float32) | |
| h, w = h0, w0 = img.shape[:-1] | |
| h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) | |
| ph, pw = s - h, s - w | |
| img_input = np.zeros([s, s, 3], dtype=np.float32) | |
| img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h)) | |
| img_input = np.transpose(img_input, (2, 0, 1)) | |
| img_input = img_input[np.newaxis, :] | |
| mask = rmbg_model.run(None, {'img': img_input})[0][0] | |
| mask = np.transpose(mask, (1, 2, 0)) | |
| mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] | |
| mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis] | |
| return mask | |
| def rmbg_fn(img): | |
| mask = get_mask(img) | |
| img = (mask * img + 255 * (1 - mask)).astype(np.uint8) | |
| mask = (mask * 255).astype(np.uint8) | |
| img = np.concatenate([img, mask], axis=2, dtype=np.uint8) | |
| mask = mask.repeat(3, axis=2) | |
| return mask, img | |
| if __name__ == "__main__": | |
| providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] | |
| model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") | |
| rmbg_model = rt.InferenceSession(model_path, providers=providers) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Anime Remove Background\n\n" | |
| "\n\n" | |
| "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(label="input image") | |
| examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] | |
| examples = gr.Dataset(components=[input_img], samples=examples_data) | |
| run_btn = gr.Button(variant="primary") | |
| output_mask = gr.Image(label="mask") | |
| output_img = gr.Image(label="result", image_mode="RGBA") | |
| examples.click(lambda x: x[0], [examples], [input_img]) | |
| run_btn.click(rmbg_fn, [input_img], [output_mask, output_img]) | |
| app.launch() | |