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import gradio as gr |
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import os |
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os.system("mim install mmengine") |
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os.system('mim install "mmcv>=2.0.0"') |
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os.system("mim install mmdet") |
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import cv2 |
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from PIL import Image |
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import numpy as np |
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from animeinsseg import AnimeInsSeg, AnimeInstances |
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from animeinsseg.anime_instances import get_color |
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if not os.path.exists("models"): |
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os.mkdir("models") |
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os.system("huggingface-cli lfs-enable-largefiles .") |
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os.system("git clone https://huggingface.co/dreMaz/AnimeInstanceSegmentation models/AnimeInstanceSegmentation") |
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ckpt = r'models/AnimeInstanceSegmentation/rtmdetl_e60.ckpt' |
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mask_thres = 0.3 |
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instance_thres = 0.3 |
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refine_kwargs = {'refine_method': 'refinenet_isnet'} |
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net = AnimeInsSeg(ckpt, mask_thr=mask_thres, refine_kwargs=refine_kwargs) |
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def fn(image): |
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img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) |
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instances: AnimeInstances = net.infer( |
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img, |
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output_type='numpy', |
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pred_score_thr=instance_thres |
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) |
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white = np.full_like(img, 255) |
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if instances.bboxes is None: |
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return Image.fromarray(white) |
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for ii, (xywh, mask) in enumerate(zip(instances.bboxes, instances.masks)): |
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mask = mask.astype(np.bool_) |
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white[mask] = img[mask] |
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return Image.fromarray(white[..., ::-1]) |
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iface = gr.Interface( |
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title="Anime Subject Instance Segmentation", |
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description="Segment image subjects with the proposed model in the paper [*Instance-guided Cartoon Editing with a Large-scale Dataset*](https://cartoonsegmentation.github.io/).", |
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fn=fn, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Image(type="pil"), |
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examples=["1562990.jpg", "612989.jpg", "sample_3.jpg"] |
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) |
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iface.launch() |
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