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), interpolation=cv2.INTER_CUBIC) 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), interpolation=cv2.INTER_CUBIC)[:, :, 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" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\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()