first pass
This commit is contained in:
Alexander G. Morano
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# Auto detect text files and perform LF normalization
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* text=auto
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+25
@@ -0,0 +1,25 @@
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# Python-specific
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__pycache__/
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*.pyc
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# Virtual environments
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.env/
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venv/
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# IDE files
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.idea/
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.vscode/
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*.code-workspace
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# Build artifacts
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build/
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dist/
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*.egg-info/
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# Other
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.DS_Store
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# user junk
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__user/
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.coverage
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.env
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@@ -0,0 +1,23 @@
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MIT License
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Copyright (c) 2025 Alexander G. Morano
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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GO NUTS; JUST TRY NOT TO DO IT IN YOUR HEAD.
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@@ -1,2 +1,82 @@
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# Jovi_Measure
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Image metrics nodes for ComfyUI
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<div align="center">
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<picture>
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<source srcset="https://github.com/Amorano/Jovi_Measure-examples/blob/master/res/Jovi_Measure.png">
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<img alt="Image metrics nodes for ComfyUI" width="256" height="256">
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</picture>
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</div>
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<div align="center">
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<a href="https://github.com/comfyanonymous/ComfyUI">COMFYUI</a> Nodes for image metrics
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</div>
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<div align="center">
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</div>
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<!---------------------------------------------------------------------------->
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# HIGHLIGHTS
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## UPDATES
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**2024/01/07** @1.0.0:
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* initial release
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# INSTALLATION
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## COMFYUI MANAGER
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If you have [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) installed, simply search for Jovi_Measure and install from the manager's database.
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## MANUAL INSTALL
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Clone the repository into your ComfyUI custom_nodes directory. You can clone the repository with the command:
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```
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git clone https://github.com/Amorano/Jovi_Measure.git
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```
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You can then install the requirements by using the command:
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```
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.\python_embed\python.exe -s -m pip install -r .\ComfyUI\custom_nodes\Jovi_Measure\requirements.txt
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```
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If you are using a <code>virtual environment</code> (<code><i>venv</i></code>), make sure it is activated before installation. Then install the requirements with the command:
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```
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pip install -r .\ComfyUI\custom_nodes\Jovi_Measure\requirements.txt
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```
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## ENVIRONMENT
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<!---------------------------------------------------------------------------->
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<!---------------------------------------------------------------------------->
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# SPONSORSHIP
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Please consider sponsoring me if you enjoy the results of my work, code or documentation or otherwise. A good way to keep code development open and free is through sponsorship.
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<div align="center">
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[](https://github.com/sponsors/Amorano)
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[](https://www.paypal.com/paypalme/onarom)
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[](https://www.patreon.com/joviex)
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[](https://ko-fi.com/alexandermorano)
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</div>
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<!---------------------------------------------------------------------------->
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# WHERE TO FIND ME
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You can find me on [](https://discord.gg/62TJaZ3Z5r).
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+152
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"""
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██╗ ██████╗ ██╗ ██╗██╗ ███╗ ███╗███████╗ █████╗ ███████╗██╗ ██╗██████╗ ███████╗
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██║██╔═══██╗██║ ██║██║ ████╗ ████║██╔════╝██╔══██╗██╔════╝██║ ██║██╔══██╗██╔════╝
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██║██║ ██║██║ ██║██║ ██╔████╔██║█████╗ ███████║███████╗██║ ██║██████╔╝█████╗
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██ ██║██║ ██║╚██╗ ██╔╝██║ ██║╚██╔╝██║██╔══╝ ██╔══██║╚════██║██║ ██║██╔══██╗██╔══╝
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╚█████╔╝╚██████╔╝ ╚████╔╝ ██║ ██║ ╚═╝ ██║███████╗██║ ██║███████║╚██████╔╝██║ ██║███████╗
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╚════╝ ╚═════╝ ╚═══╝ ╚═╝ ╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚══════╝
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Image metrics nodes for ComfyUI
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http://www.github.com/amorano/Jovi_Measure
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"""
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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__author__ = """Alexander G. Morano"""
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__email__ = "amorano@gmail.com"
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__version__ = "1.0.0"
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import os
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import sys
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import json
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import inspect
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import importlib
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from pathlib import Path
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from types import ModuleType
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from typing import Any
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from loguru import logger
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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WEB_DIRECTORY = "./web"
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ROOT = Path(__file__).resolve().parent
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ROOT_COMFY = ROOT.parent.parent
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JOV_WEB = ROOT / 'web'
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JOV_LOG_LEVEL = os.getenv("JOV_LOG_LEVEL", "INFO")
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logger.configure(handlers=[{"sink": sys.stdout, "level": JOV_LOG_LEVEL}])
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JOV_INTERNAL = os.getenv("JOV_INTERNAL", 'false').strip().lower() in ('true', '1', 't')
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JOV_PACKAGE = "JOV_MEASURE"
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# ==============================================================================
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# === CORE NODES ===
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# ==============================================================================
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class JOVBaseNode:
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NOT_IDEMPOTENT = True
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CATEGORY = f"{JOV_PACKAGE} 📺"
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RETURN_TYPES = ()
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FUNCTION = "run"
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@classmethod
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def IS_CHANGED(cls, **kw) -> float:
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return float('nan')
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@classmethod
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def VALIDATE_INPUTS(cls, *arg, **kw) -> bool:
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return True
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@classmethod
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def INPUT_TYPES(cls, prompt:bool=False, extra_png:bool=False, dynprompt:bool=False) -> dict:
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data = {
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"required": {},
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"hidden": {
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"ident": "UNIQUE_ID"
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}
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}
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if prompt:
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data["hidden"]["prompt"] = "PROMPT"
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if extra_png:
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data["hidden"]["extra_pnginfo"] = "EXTRA_PNGINFO"
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if dynprompt:
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data["hidden"]["dynprompt"] = "DYNPROMPT"
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return data
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# ==============================================================================
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# === TYPE ===
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# ==============================================================================
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class AnyType(str):
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"""AnyType input wildcard trick taken from pythongossss's:
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https://github.com/pythongosssss/ComfyUI-Custom-Scripts
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"""
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def __ne__(self, __value: object) -> bool:
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return False
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JOV_TYPE_ANY = AnyType("*")
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# ==============================================================================
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# === NODE LOADER ===
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# ==============================================================================
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def load_module(name: str) -> None|ModuleType:
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module = inspect.getmodule(inspect.stack()[0][0]).__name__
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try:
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route = str(name).replace("\\", "/")
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route = route.split(f"{module}/core/")[1]
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route = route.split('.')[0].replace('/', '.')
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except Exception as e:
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logger.warning(f"module failed {name}")
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logger.warning(str(e))
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return
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try:
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module = f"{module}.core.{route}"
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module = importlib.import_module(module)
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except Exception as e:
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logger.warning(f"module failed {module}")
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logger.warning(str(e))
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return
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return module
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def loader():
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global NODE_DISPLAY_NAME_MAPPINGS, NODE_CLASS_MAPPINGS
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NODE_LIST_MAP = {}
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for fname in ROOT.glob('core/**/*.py'):
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if fname.stem.startswith('_'):
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continue
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if (module := load_module(fname)) is None:
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continue
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classes = inspect.getmembers(module, inspect.isclass)
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for class_name, class_object in classes:
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if not class_name.endswith('BaseNode') and hasattr(class_object, 'NAME') and hasattr(class_object, 'CATEGORY'):
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name = f"{class_object.NAME} ({JOV_PACKAGE})"
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NODE_DISPLAY_NAME_MAPPINGS[name] = name
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NODE_CLASS_MAPPINGS[name] = class_object
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desc = class_object.DESCRIPTION if hasattr(class_object, 'DESCRIPTION') else name
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NODE_LIST_MAP[name] = desc.split('.')[0].strip('\n')
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NODE_CLASS_MAPPINGS = {x[0] : x[1] for x in sorted(NODE_CLASS_MAPPINGS.items(),
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key=lambda item: getattr(item[1], 'SORT', 0))}
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keys = NODE_CLASS_MAPPINGS.keys()
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for name in keys:
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logger.debug(f"✅ {name}")
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logger.info(f"{len(keys)} nodes loaded")
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# only do the list on local runs...
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if JOV_INTERNAL:
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with open(str(ROOT) + "/node_list.json", "w", encoding="utf-8") as f:
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json.dump(NODE_LIST_MAP, f, sort_keys=True, indent=4 )
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loader()
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@@ -0,0 +1,30 @@
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"""
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Jovi_Measure - http://www.github.com/Amorano/Jovi_Measure
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Core
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"""
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from .. import JOVBaseNode
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# ==============================================================================
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# === SUPPORT ===
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# ==============================================================================
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def deep_merge(d1: dict, d2: dict) -> dict:
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"""
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Deep merge multiple dictionaries recursively.
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Args:
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*dicts: Variable number of dictionaries to be merged.
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Returns:
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dict: Merged dictionary.
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"""
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for key in d2:
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if key in d1:
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if isinstance(d1[key], dict) and isinstance(d2[key], dict):
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deep_merge(d1[key], d2[key])
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else:
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d1[key] = d2[key]
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else:
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d1[key] = d2[key]
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return d1
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+105
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"""
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Jovi_Measure - http://www.github.com/amorano/Jovi_Measure
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Image Metrics
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"""
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import torch
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import numpy as np
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import skimage.measure as skm
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from comfy.utils import ProgressBar
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from .. import JOVBaseNode
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from . import deep_merge
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# ==============================================================================
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# === SUPPORT ===
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# ==============================================================================
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def tensor2cv(tensor: torch.Tensor, invert_mask:bool=True) -> np.ndarray:
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"""Convert a torch Tensor to a numpy ndarray."""
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if tensor.ndim > 3:
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raise Exception("Tensor is batch of tensors")
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if tensor.ndim < 3:
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tensor = tensor.unsqueeze(-1)
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if tensor.shape[2] == 1 and invert_mask:
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tensor = 1. - tensor
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tensor = tensor.cpu().numpy()
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return np.clip(255.0 * tensor, 0, 255).astype(np.uint8)
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# ==============================================================================
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# === COMFYUI NODE ===
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# ==============================================================================
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class ShannonEntropyNode(JOVBaseNode):
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NAME = "SHANNON ENTROPY"
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RETURN_TYPES = ("FLOAT",)
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RETURN_NAMES = ("FLOAT",)
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SORT = 50
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DESCRIPTION = """
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Calculate the Shannon entropy of an image.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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d = super().INPUT_TYPES()
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d = deep_merge(d, {
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"required": {
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'image': ("IMAGE", {"default": None}),
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}
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})
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return d
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def run(self, image, **kw) -> float:
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vals = []
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images = [i for i in image]
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pbar = ProgressBar(len(images))
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for idx, image in enumerate(images):
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image = tensor2cv(image)
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val = skm.shannon_entropy(image)
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vals.append(val)
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print(val)
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pbar.update_absolute(idx)
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return vals,
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class BlurEffectNode(JOVBaseNode):
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NAME = "BLUR EFFECT"
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RETURN_TYPES = ("FLOAT",)
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RETURN_NAMES = ("FLOAT",)
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SORT = 50
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DESCRIPTION = """
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Calculate the Shannon entropy of an image.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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d = super().INPUT_TYPES()
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d = deep_merge(d, {
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"required": {
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'image': ("IMAGE", {"default": None}),
|
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},
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"optional": {
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'h_size': ("INT", {"default": 11, "tooltip": "Size of the re-blurring filter."}),
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# 'channel_axis': ("INT", {"default": 0, "min": 0, "max": 3}),
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}
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})
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return d
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def run(self, image, h_size, **kw) -> float:
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vals = []
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images = [i for i in image]
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pbar = ProgressBar(len(images))
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for idx, image in enumerate(images):
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image = tensor2cv(image)
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channel_axis = 2
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if len(hwc := image.shape) == 2 or hwc[2] == 1:
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channel_axis = None
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val = skm.blur_effect(image, h_size=h_size, channel_axis=channel_axis)
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vals.append(val)
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print(val)
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pbar.update_absolute(idx)
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return vals,
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@@ -0,0 +1,4 @@
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{
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"BLUR EFFECT (JOV_MEASURE)": "Calculate the Shannon entropy of an image",
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"SHANNON ENTROPY (JOV_MEASURE)": "Calculate the Shannon entropy of an image"
|
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}
|
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@@ -0,0 +1,19 @@
|
||||
[project]
|
||||
name = "Jovi_Measure"
|
||||
description = "ComfyUI Nodes for using Spout streams "
|
||||
version = "1.0.0"
|
||||
license = { file = "LICENSE" }
|
||||
dependencies = [
|
||||
"loguru",
|
||||
"numpy>=1.26.4,<2.0.0; python_version < '3.12'",
|
||||
"numpy>=2.0.0; python_version > '3.11'",
|
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"scikit-image",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/Amorano/Jovi_Measure"
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "amorano"
|
||||
DisplayName = "Jovi_Measure"
|
||||
Icon = ""
|
||||
@@ -0,0 +1,4 @@
|
||||
loguru
|
||||
numpy>=1.26.4,<2.0.0; python_version < '3.12'
|
||||
numpy>=2.0.0; python_version > '3.11'
|
||||
scikit-image
|
||||
Reference in New Issue
Block a user