init
Browse files- .gitignore +148 -0
- README.md +1 -1
- app.py +52 -0
- examples/01.jpg +0 -0
- examples/02.jpg +0 -0
- examples/03.jpg +0 -0
- requirements.txt +5 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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.idea/
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README.md
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---
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title: Anime Remove Background
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-
emoji:
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colorFrom: indigo
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colorTo: pink
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sdk: gradio
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---
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title: Anime Remove Background
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emoji: 🪄🖼️
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colorFrom: indigo
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colorTo: pink
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sdk: gradio
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app.py
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import gradio as gr
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import huggingface_hub
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import onnxruntime as rt
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import numpy as np
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import cv2
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def get_mask(img, s=1024):
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img = (img / 255).astype(np.float32)
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h, w = h0, w0 = img.shape[:-1]
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h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
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ph, pw = s - h, s - w
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img_input = np.zeros([s, s, 3], dtype=np.float32)
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img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h), interpolation=cv2.INTER_CUBIC)
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img_input = np.transpose(img_input, (2, 0, 1))
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img_input = img_input[np.newaxis, :]
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mask = rmbg_model.run(None, {'img': img_input})[0][0]
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mask = np.transpose(mask, (1, 2, 0))
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mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
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mask = cv2.resize(mask, (w0, h0), interpolation=cv2.INTER_CUBIC)[:, :, np.newaxis]
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return mask
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def rmbg_fn(img):
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mask = get_mask(img)
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img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
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mask = (mask * 255).astype(np.uint8)
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img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
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mask = mask.repeat(3, axis=2)
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return mask, img
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if __name__ == "__main__":
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
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rmbg_model = rt.InferenceSession(model_path, providers=providers)
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app = gr.Blocks()
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with app:
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gr.Markdown("# Anime Remove Background\n\n"
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"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
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"demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
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with gr.Row():
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with gr.Column():
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input_img = gr.Image(label="input image")
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examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
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examples = gr.Dataset(components=[input_img], samples=examples_data)
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run_btn = gr.Button(variant="primary")
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output_mask = gr.Image(label="mask")
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output_img = gr.Image(label="result", image_mode="RGBA")
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examples.click(lambda x: x[0], [examples], [input_img])
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run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
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app.launch()
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examples/01.jpg
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examples/02.jpg
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examples/03.jpg
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requirements.txt
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onnx
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onnxruntime-gpu
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opencv-python
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numpy
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gradio
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