import PIL.Image import gradio as gr import huggingface_hub import onnxruntime as rt import numpy as np import cv2 from PIL import ImageOps providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") rmbg_model = rt.InferenceSession(model_path, providers=providers) def custom_background(background, foreground): foreground = ImageOps.contain(foreground, background.size) x = (background.size[0] - foreground.size[0]) // 2 y = (background.size[1] - foreground.size[1]) // 2 background.paste(foreground, (x, y), foreground) return background 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 predict(image, new_background): mask = get_mask(image) image = (mask * image + 255 * (1 - mask)).astype(np.uint8) mask = (mask * 255).astype(np.uint8) image = np.concatenate([image, mask], axis=2, dtype=np.uint8) mask = mask.repeat(3, axis=2) if new_background is not None: foreground = PIL.Image.fromarray(image) return mask, custom_background(new_background, foreground) return mask, image footer = r"""
Demo based on SkyTNT Anime Segmentation
""" with gr.Blocks(title="Face Shine") as app: gr.HTML("

Anime Remove Background

") with gr.Row(): with gr.Column(): input_img = gr.Image(type="numpy", label="Input image") new_img = gr.Image(type="pil", label="Custom background") run_btn = gr.Button(variant="primary") with gr.Column(): with gr.Accordion(label="Image mask", open=False): output_mask = gr.Image(label="mask") output_img = gr.Image(type="pil", label="result") run_btn.click(predict, [input_img, new_img], [output_mask, output_img]) with gr.Row(): examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] examples = gr.Dataset(components=[input_img], samples=examples_data) examples.click(lambda x: x[0], [examples], [input_img]) with gr.Row(): gr.HTML(footer) app.launch(share=False, debug=True, enable_queue=True, show_error=True)