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| import PIL.Image | |
| import cv2 | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import onnxruntime as rt | |
| from PIL import ImageOps | |
| from carvekit.trimap.generator import TrimapGenerator | |
| from pymatting import estimate_alpha_cf, estimate_foreground_ml, stack_images, load_image | |
| providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] | |
| model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") | |
| rmbg_model = rt.InferenceSession(model_path, providers=providers) | |
| trimapGenerator = TrimapGenerator() | |
| # 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 custom_background(background: PIL.Image.Image, foreground: np.ndarray): | |
| final_foreground = PIL.Image.fromarray(foreground) | |
| x = (background.size[0] - final_foreground.size[0]) / 2 | |
| y = (background.size[1] - final_foreground.size[1]) / 2 | |
| box = (x, y, final_foreground.size[0] + x, final_foreground.size[1] + y) | |
| crop = background.crop(box) | |
| final_image = crop.copy() | |
| # put the foreground in the centre of the background | |
| paste_box = (0, final_image.size[1] - final_foreground.size[1], final_image.size[0], final_image.size[1]) | |
| final_image.paste(final_foreground, paste_box, mask=final_foreground) | |
| return np.array(final_image) | |
| 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 change_background_color(image, color="blue"): | |
| 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) | |
| image = PIL.Image.fromarray(image) | |
| background = PIL.Image.new('RGB', image.size, color) | |
| background.paste(image, (0, 0), image) | |
| return background | |
| def generate_trimap(probs, size=7, conf_threshold=0.95): | |
| """ | |
| This function creates a trimap based on simple dilation algorithm | |
| Inputs [3]: an image with probabilities of each pixel being the foreground, size of dilation kernel, | |
| foreground confidence threshold | |
| Output : a trimap | |
| """ | |
| mask = (probs > 0.05).astype(np.uint8) * 255 | |
| pixels = 2 * size + 1 | |
| kernel = np.ones((pixels, pixels), np.uint8) | |
| dilation = cv2.dilate(mask, kernel, iterations=1) | |
| remake = np.zeros_like(mask) | |
| remake[dilation == 255] = 127 # Set every pixel within dilated region as probably foreground. | |
| remake[probs > conf_threshold] = 255 # Set every pixel with large enough probability as definitely foreground. | |
| return remake | |
| def image2gray(image): | |
| image = PIL.Image.fromarray(image).convert("L") | |
| return np.array(image) / 255.0 | |
| def paste(img_orig, alpha): | |
| img_ = img_orig.astype(np.float32) / 255 | |
| alpha_ = cv2.resize(alpha, (img_.shape[1], img_.shape[0]), cv2.INTER_LANCZOS4) | |
| fg_alpha = np.concatenate([img_, alpha_[:, :, np.newaxis]], axis=2) | |
| cv2.imwrite("new_back.png", (fg_alpha * 255).astype(np.uint8)) | |
| def predict(image, new_background): | |
| mask = get_mask(image) | |
| mask = (mask * 255).astype(np.uint8) | |
| mask = mask.repeat(3, axis=2) | |
| trimap = generate_trimap(mask) | |
| trimap = image2gray(trimap) | |
| # trimap = load_image("images/trimaps/lemur_trimap.png", "GRAY") | |
| original = PIL.Image.fromarray(image) | |
| # mask = image2gray(mask) | |
| mask = PIL.Image.fromarray(mask).convert("L") | |
| trimap = trimapGenerator(original_image=original, mask=mask) | |
| trimap = np.array(trimap) / 255.0 | |
| foreground = image / 255 | |
| alpha = estimate_alpha_cf(foreground, trimap) | |
| foreground = estimate_foreground_ml(foreground, alpha) | |
| cutout = stack_images(foreground, alpha) | |
| cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8) | |
| if new_background is not None: | |
| return mask, trimap, custom_background(new_background, cutout) | |
| return alpha, trimap, cutout | |
| # contours | |
| def serendipity(image, new_background): | |
| mask = get_mask(image) | |
| mask = 255 - mask | |
| 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) | |
| return mask, image | |
| def negative(image, new_background): | |
| mask = get_mask(image) | |
| image = (mask * image + 255 * (1 - mask)).astype(np.uint8) | |
| image = 255 - image | |
| mask = (mask * 255).astype(np.uint8) | |
| image = np.concatenate([image, mask], axis=2, dtype=np.uint8) | |
| return mask, image | |
| def checkit(image, new_background): | |
| mask = get_mask(image) | |
| mask = 255 - mask | |
| image = (mask / image - 255 / (1 + mask)).astype(np.uint8) | |
| mask = (mask * 255).astype(np.uint8) | |
| mask = 255 - mask | |
| 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""" | |
| <center> | |
| <b> | |
| Demo based on <a href='https://github.com/SkyTNT/anime-segmentation'>SkyTNT Anime Segmentation</a> | |
| </b> | |
| </center> | |
| """ | |
| with gr.Blocks(title="Face Shine") as app: | |
| gr.HTML("<center><h1>Anime Remove Background</h1></center>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(type="numpy", image_mode="RGB", label="Input image") | |
| new_img = gr.Image(type="pil", image_mode="RGBA", label="Custom background") | |
| run_btn = gr.Button(variant="primary") | |
| with gr.Column(): | |
| with gr.Accordion(label="Image mask", open=False): | |
| output_mask = gr.Image(type="numpy", label="mask") | |
| output_trimap = gr.Image(type="numpy", label="trimap") | |
| output_img = gr.Image(type="numpy", label="result") | |
| run_btn.click(predict, [input_img, new_img], [output_mask, output_trimap, 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) | |