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README.md
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@@ -4,7 +4,7 @@ emoji: 🐢
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colorFrom: yellow
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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colorFrom: yellow
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.34.0
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app_file: app.py
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pinned: false
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---
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app.py
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@@ -22,12 +22,7 @@ sys.path.insert(0, 'anime_face_landmark_detection')
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from CFA import CFA
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DESCRIPTION = 'This is an unofficial demo for https://github.com/kanosawa/anime_face_landmark_detection.'
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HF_TOKEN = os.getenv('HF_TOKEN')
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MODEL_REPO = 'hysts/anime_face_landmark_detection'
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MODEL_FILENAME = 'checkpoint_landmark_191116.pth'
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NUM_LANDMARK = 24
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CROP_SIZE = 128
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@@ -39,8 +34,7 @@ def load_sample_image_paths() -> list[pathlib.Path]:
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dataset_repo = 'hysts/sample-images-TADNE'
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path = huggingface_hub.hf_hub_download(dataset_repo,
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'images.tar.gz',
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repo_type='dataset'
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use_auth_token=HF_TOKEN)
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob('*'))
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@@ -55,9 +49,9 @@ def load_face_detector() -> cv2.CascadeClassifier:
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def load_landmark_detector(device: torch.device) -> torch.nn.Module:
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path = huggingface_hub.hf_hub_download(
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model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
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model.to(device)
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model.eval()
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T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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gr.
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from CFA import CFA
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DESCRIPTION = '# [kanosawa/anime_face_landmark_detection](https://github.com/kanosawa/anime_face_landmark_detection)'
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NUM_LANDMARK = 24
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CROP_SIZE = 128
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dataset_repo = 'hysts/sample-images-TADNE'
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path = huggingface_hub.hf_hub_download(dataset_repo,
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'images.tar.gz',
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repo_type='dataset')
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob('*'))
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def load_landmark_detector(device: torch.device) -> torch.nn.Module:
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path = huggingface_hub.hf_hub_download(
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'public-data/anime_face_landmark_detection',
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'checkpoint_landmark_191116.pth')
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model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
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model.to(device)
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model.eval()
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T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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fn = functools.partial(detect,
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face_detector=face_detector,
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device=device,
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transform=transform,
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landmark_detector=landmark_detector)
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with gr.Blocks(css='style.css') as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Row():
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with gr.Column():
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image = gr.Image(label='Input', type='filepath')
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run_button = gr.Button('Run')
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with gr.Column():
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result = gr.Image(label='Result')
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gr.Examples(examples=examples,
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inputs=image,
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outputs=result,
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fn=fn,
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cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
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run_button.click(fn=fn, inputs=image, outputs=result, api_name='predict')
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demo.queue(max_size=15).launch()
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requirements.txt
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opencv-python-headless>=4.
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torch
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torchvision
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opencv-python-headless>=4.7.0.72
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torch==2.0.1
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torchvision==0.15.2
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style.css
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h1 {
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text-align: center;
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}
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