├── detector ├── __init__.py └── human_parts.py ├── requirements.txt ├── images ├── node.png └── HumanPartsWorkflow.png ├── __init__.py ├── pyproject.toml ├── utils.py ├── .github └── workflows │ └── publish.yaml ├── install.py ├── README.md ├── nodes.py ├── .gitignore ├── LICENSE └── poetry.lock /detector/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | onnxruntime 2 | numpy 3 | Pillow 4 | -------------------------------------------------------------------------------- /images/node.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metal3d/ComfyUI_Human_Parts/HEAD/images/node.png -------------------------------------------------------------------------------- /images/HumanPartsWorkflow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metal3d/ComfyUI_Human_Parts/HEAD/images/HumanPartsWorkflow.png -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | __all__ = ["HumanParts"] 2 | 3 | from .nodes import HumanParts 4 | 5 | NODE_CLASS_MAPPINGS = { 6 | "HumanParts": HumanParts, 7 | } 8 | 9 | # A dictionary that contains the friendly/humanly readable titles for the nodes 10 | NODE_DISPLAY_NAME_MAPPINGS = { 11 | "HumanParts": "🧍 Human Parts mask generator", 12 | } 13 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui_human_parts" 3 | description = "Detect human parts using the DeepLabV3+ ResNet50 model from Keras-io. You can extract hair, arms, legs, and other parts with ease and with small memory usage." 4 | version = "1.0.3" 5 | license = { file = "LICENSE" } 6 | dependencies = ["onnxruntime", "numpy", "Pillow"] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/metal3d/ComfyUI_Human_Parts" 10 | 11 | [tool.comfy] 12 | PublisherId = "metal3d" 13 | DisplayName = "Human Parts Detector" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # get the model paths 4 | try: 5 | from folder_paths import models_dir # pyright: ignore 6 | except ImportError: 7 | from pathlib import Path 8 | 9 | models_dir = os.path.join(Path(__file__).parents[2], "models") 10 | 11 | models_dir_path = os.path.join(models_dir, "onnx", "human-parts") 12 | model_url = "https://huggingface.co/Metal3d/deeplabv3p-resnet50-human/resolve/main/deeplabv3p-resnet50-human.onnx" 13 | model_name = os.path.basename(model_url) 14 | model_path = os.path.join(models_dir_path, "deeplabv3p-resnet50-human.onnx") 15 | -------------------------------------------------------------------------------- /.github/workflows/publish.yaml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | # if this is a forked repository. Skipping the workflow. 16 | if: github.event.repository.fork == false 17 | steps: 18 | - name: Check out code 19 | uses: actions/checkout@v4 20 | - name: Publish Custom Node 21 | uses: Comfy-Org/publish-node-action@main 22 | with: 23 | ## Add your own personal access token to your Github Repository secrets and reference it here. 24 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 25 | -------------------------------------------------------------------------------- /install.py: -------------------------------------------------------------------------------- 1 | import os 2 | import urllib.request 3 | 4 | from tqdm import tqdm 5 | 6 | try: 7 | from .utils import model_name, model_path, model_url, models_dir_path 8 | except ImportError: 9 | from utils import model_name, model_path, model_url, models_dir_path 10 | 11 | 12 | def download(url, path, name): 13 | request = urllib.request.urlopen(url) 14 | total = int(request.headers.get("Content-Length", 0)) 15 | with tqdm( 16 | total=total, 17 | desc=f"[HumanParts] Downloading {name} to {path}", 18 | unit="B", 19 | unit_scale=True, 20 | unit_divisor=1024, 21 | ) as progress: 22 | urllib.request.urlretrieve( 23 | url, 24 | path, 25 | reporthook=lambda count, block_size, total_size: progress.update( 26 | block_size 27 | ), 28 | ) 29 | 30 | 31 | if not os.path.exists(models_dir_path): 32 | os.makedirs(models_dir_path) 33 | 34 | if not os.path.exists(model_path): 35 | download(model_url, model_path, model_name) 36 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Yet another custom node to detect human parts 2 | 3 | Detect human parts using the DeepLabV3+ ResNet50 model from Keras-io. You can extract hair, arms, legs, and other parts 4 | with ease and with small memory usage. 5 | 6 | This node aims to detect human parts using the model created by 7 | [Keras-io](https://huggingface.co/keras-io/deeplabv3p-resnet50). Their "[Space](https://huggingface.co/spaces/keras-io/Human-Part-Segmentation)" was impressive, and I wanted to use the 8 | model. 9 | 10 | Unfortunately, the model uses an old Keras version, and there were no PyTorch implementation. 11 | 12 | So I decided to convert the model to [ONNX](https://onnx.ai/) format and to create my [HugginFace 13 | repository](https://huggingface.co/Metal3d/deeplabv3p-resnet50-human) to share the model with the community. 14 | 15 | > Fortunately, Keras provides the model with a CC1.0 license, thank you guys to allow us to use it without any 16 | > restriction. 17 | 18 | ## Example 19 | 20 | You can drag and drop the following image to try: 21 | 22 | ![Example workflow](./images/HumanPartsWorkflow.png) 23 | 24 | ## DeepLabV3+ ResNet50 for Human 25 | 26 | Actually, all the model I found was not trained to detect human parts, but to detect some objects or urban elements. The 27 | Keras model is the only one I found that works! 28 | 29 | ## Installation 30 | 31 | I strongly recommend to use ComfyUI-Manager to install the node. It will install the dependencies and the model. 32 | 33 | > Note, as far as my repository isn't validated in the ComfyUI-Manager index, you must do the installation manually. 34 | > 35 | > If you set up ComfyUI-Manager to "middle" or "weak" security, you can use the "Install from Git URL" feature. 36 | 37 | ```bash 38 | # ensure that you have activated the virtual environment before !! 39 | 40 | # then... 41 | cd /path/to/your/ComfyUI/custom_nodes 42 | git clone https://github.com/metal3d/ComfyUI_Human_Parts.git 43 | cd ComfyUI_Human_Parts 44 | pip install -r requirements.txt 45 | # or 46 | python -m pip install -r requirements.txt 47 | 48 | # install the model 49 | python install.py 50 | ``` 51 | 52 | Then, restart ComfyUI, refresh the UI, and you may find the "Human Parts mask generator" node. 53 | 54 | ![The node](./images/node.png) 55 | -------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | from typing import Dict, Tuple 2 | 3 | import onnxruntime as ort 4 | import torch 5 | 6 | from .detector.human_parts import get_mask, labels 7 | from .utils import model_path 8 | 9 | 10 | class HumanParts: 11 | """ 12 | This node is used to get a mask of the human parts in the image. 13 | 14 | The model used is DeepLabV3+ with a ResNet50 backbone trained 15 | by Keras-io, converted to ONNX format. 16 | 17 | """ 18 | 19 | RETURN_TYPES = ("MASK",) 20 | RETURN_NAMES = ("mask",) 21 | FUNCTION = "get_mask" 22 | CATEGORY = "Metal3d" 23 | OUTPU_NODE = True 24 | 25 | @classmethod 26 | def INPUT_TYPES(cls): 27 | def _bool_widget( 28 | is_enabled=False, tooltip: str | None = None 29 | ) -> Tuple[str, dict]: 30 | """Helper function to create a boolean widget""" 31 | return ( 32 | "BOOLEAN", 33 | { 34 | "default": is_enabled, 35 | "label_on": "Enabled", 36 | "label_off": "Disabled", 37 | "tooltip": tooltip, 38 | }, 39 | ) 40 | 41 | # automate the creation of the inputs using the known labels 42 | entries: Dict[str, tuple] = { 43 | segment[0]: _bool_widget(False, f"{segment[1]}") 44 | for segment in labels.values() 45 | if segment[0] != "" 46 | } 47 | 48 | inputs = { 49 | "required": { 50 | "image": ( 51 | "IMAGE", 52 | { 53 | "label": "Image", 54 | "tooltip": "The image in which to detect human parts", 55 | }, 56 | ) 57 | }, 58 | "optional": {}, 59 | } 60 | inputs["required"].update(entries) 61 | 62 | return inputs 63 | 64 | def get_mask(self, image: torch.Tensor, **kwargs) -> Tuple[torch.Tensor]: 65 | """ 66 | Return a Tensor with the mask of the human parts in the image. 67 | """ 68 | 69 | model = ort.InferenceSession(model_path) 70 | ret_tensor, _ = get_mask(image, model=model, rotation=0, **kwargs) 71 | 72 | return (ret_tensor,) 73 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. For a more nuclear 161 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 162 | #.idea/ 163 | -------------------------------------------------------------------------------- /detector/human_parts.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple 2 | 3 | import numpy as np 4 | import torch 5 | from onnxruntime import InferenceSession 6 | from PIL import Image 7 | 8 | # Follows CCIHP => https://kalisteo.cea.fr/wp-content/uploads/2021/09/README.html 9 | # 10 | # Note: I prefer to use a dictionnary to be able to change the index if needed. 11 | labels = { 12 | 0: ("background", "Background"), 13 | 1: ( 14 | "hat", 15 | "Hat: Hat, helmet, cap, hood, veil, headscarf, part covering the skull and hair of a hood/balaclava, crown…", 16 | ), 17 | 2: ( 18 | "hair", 19 | "Hair", 20 | ), 21 | 3: ( 22 | "glove", 23 | "Glove", 24 | ), 25 | 4: ( 26 | "glasses", 27 | "Sunglasses/Glasses: Sunglasses, eyewear, protective glasses…", 28 | ), 29 | 5: ( 30 | "upper_clothes", 31 | "UpperClothes: T-shirt, shirt, tank top, sweater under a coat, top of a dress…", 32 | ), 33 | 6: ( 34 | "face_mask", 35 | "Face Mask: Protective mask, surgical mask, carnival mask, facial part of a balaclava, visor of a helmet…", 36 | ), 37 | 7: ( 38 | "coat", 39 | "Coat: Coat, jacket worn without anything on it, vest with nothing on it, a sweater with nothing on it…", 40 | ), 41 | 8: ( 42 | "socks", 43 | "Socks", 44 | ), 45 | 9: ( 46 | "pants", 47 | "Pants: Pants, shorts, tights, leggings, swimsuit bottoms… (clothing with 2 legs)", 48 | ), 49 | 10: ( 50 | "torso-skin", 51 | "Torso-skin", 52 | ), 53 | 11: ( 54 | "scarf", 55 | "Scarf: Scarf, bow tie, tie…", 56 | ), 57 | 12: ( 58 | "skirt", 59 | "Skirt: Skirt, kilt, bottom of a dress…", 60 | ), 61 | 13: ( 62 | "face", 63 | "Face", 64 | ), 65 | 14: ( 66 | "left-arm", 67 | "Left-arm (naked part)", 68 | ), 69 | 15: ( 70 | "right-arm", 71 | "Right-arm (naked part)", 72 | ), 73 | 16: ( 74 | "left-leg", 75 | "Left-leg (naked part)", 76 | ), 77 | 17: ( 78 | "right-leg", 79 | "Right-leg (naked part)", 80 | ), 81 | 18: ( 82 | "left-shoe", 83 | "Left-shoe", 84 | ), 85 | 19: ( 86 | "right-shoe", 87 | "Right-shoe", 88 | ), 89 | 20: ( 90 | "bag", 91 | "Bag: Backpack, shoulder bag, fanny pack… (bag carried on oneself", 92 | ), 93 | 21: ( 94 | "", 95 | "Others: Jewelry, tags, bibs, belts, ribbons, pins, head decorations, headphones…", 96 | ), 97 | } 98 | 99 | 100 | def get_class_index(class_name: str) -> int: 101 | """ 102 | Return the index of the class name in the model. 103 | """ 104 | if class_name == "": 105 | return -1 106 | 107 | for key, value in labels.items(): 108 | if value[0] == class_name: 109 | return key 110 | 111 | return -1 112 | 113 | 114 | def get_mask( 115 | image: torch.Tensor, model: InferenceSession, rotation: float, **kwargs 116 | ) -> Tuple[torch.Tensor, int]: 117 | """ 118 | Return a Tensor with the mask of the human parts in the image. 119 | 120 | The rotation parameter is not used for now. The idea is to propose rotation to help 121 | the model to detect the human parts in the image if the character is not in a casual position. 122 | Several tests have been done, but the model seems to fail to detect the human parts in these cases, 123 | and the rotation does not help. 124 | """ 125 | 126 | image = image.squeeze(0) 127 | image_np = image.numpy() * 255 128 | 129 | pil_image = Image.fromarray(image_np.astype(np.uint8)) 130 | original_size = pil_image.size # to resize the mask later 131 | # resize to 512x512 as the model expects 132 | pil_image = pil_image.resize((512, 512)) 133 | center = (256, 256) 134 | 135 | if rotation != 0: 136 | pil_image = pil_image.rotate(rotation, center=center) 137 | 138 | # normalize the image 139 | image_np = np.array(pil_image).astype(np.float32) / 127.5 - 1 140 | image_np = np.expand_dims(image_np, axis=0) 141 | 142 | # use the onnx model to get the mask 143 | input_name = model.get_inputs()[0].name 144 | output_name = model.get_outputs()[0].name 145 | result = model.run([output_name], {input_name: image_np}) 146 | result = np.array(result[0]).argmax(axis=3).squeeze(0) 147 | 148 | score: int = 0 149 | 150 | mask = np.zeros_like(result) 151 | for class_name, enabled in kwargs.items(): 152 | class_index = get_class_index(class_name) 153 | if enabled and class_index != -1: 154 | detected = result == class_index 155 | mask[detected] = 255 156 | score += mask.sum() 157 | 158 | # back to the original size 159 | mask_image = Image.fromarray(mask.astype(np.uint8), mode="L") 160 | if rotation != 0: 161 | mask_image = mask_image.rotate(-rotation, center=center) 162 | 163 | mask_image = mask_image.resize(original_size) 164 | 165 | # and back to numpy... 166 | mask = np.array(mask_image).astype(np.float32) / 255 167 | 168 | # add 2 dimensions to match the expected output 169 | mask = np.expand_dims(mask, axis=0) 170 | mask = np.expand_dims(mask, axis=0) 171 | # ensure to return a "binary mask_image" 172 | 173 | del image_np, result # free up memory, maybe not necessary 174 | return (torch.from_numpy(mask.astype(np.uint8)), score) 175 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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