first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

__init__.py

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+import huggingface_hub
+import torch
+import onnxruntime as rt
+import numpy as np
+import cv2
+
+def get_mask(img:torch.Tensor, 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
+
+# Declare Execution Providers
+providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+
+# Download and host the model
+model_path = huggingface_hub.hf_hub_download(
+    "skytnt/anime-seg", "isnetis.onnx")
+rmbg_model = rt.InferenceSession(model_path, providers=providers)
+
+def rmbg_fn(img):
+    mask = get_mask(img)
+    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
+    mask = (mask * 255).astype(np.uint8)
+    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
+    mask = mask.repeat(3, axis=2)
+    return img
+
+class RemoveImageBackgroundARB:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "arb_remover"
+    CATEGORY = "image"
+
+    def arb_remover(self, image:torch.Tensor):
+        npa = image2nparray(image)
+        print(npa.ndim)
+        rmb = rmbg_fn(npa)
+        image = nparray2image(rmb)
+        return (image,)
+
+def image2nparray(image:torch.Tensor):
+    narray:np.array = np.clip(255. * image.cpu().numpy().squeeze(),0, 255).astype(np.uint8)
+    if narray.shape[-1] == 4:
+        narray =  narray[..., [2, 1, 0, 3]]  # For RGBA
+    else:
+        narray = narray[..., [2, 1, 0]]  # For RGB
+    return narray
+
+def nparray2image(narray:np.array):
+    print(f"narray shape: {narray.shape}")
+    if narray.shape[-1] == 4:
+        narray =  narray[..., [2, 1, 0, 3]]
+    else:
+        narray =  narray[..., [2, 1, 0]] 
+    tensor = torch.from_numpy(narray/255.).float().unsqueeze(0)
+    return tensor
+
+NODE_CLASS_MAPPINGS = {
+    "Remove Image Background (ARB)": RemoveImageBackgroundARB
+}

requirements.txt

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+opencv-python
+onnxruntime
+onnx
+numpy
+Pillow