interactive-pipe 0.9.0

Library to create flexible interactive image processing pipelines and automatically add a graphical user interface without knowing anything about GUI coding!

📖 Documentation

interactive_pipe

Turn plain python processing functions into an interactive GUI app — without writing a single line of GUI code.

pip install interactive-pipe

• Develop an algorithm while debugging visually with plots, checking robustness and continuity to parameter changes.
• Magically create a graphical interface to demonstrate a concept or tune your algorithm.
• Keep your algorithm library untouched: interactivity is added by decoration, not by rewriting.
• The same pipeline runs headless for batch processing and tests.

Quick taste

from interactive_pipe import interactive, interactive_pipeline
import numpy as np

@interactive(coeff=(1.0, [0.5, 2.0], "exposure"), bias=(0.0, [-0.2, 0.2]))
def exposure(img, coeff=1.0, bias=0.0):
    return img * coeff + bias

@interactive(blend_coeff=(0.5, [0.0, 1.0]))
def blend(img0, img1, blend_coeff=0.5):
    return (1 - blend_coeff) * img0 + blend_coeff * img1

@interactive_pipeline(gui="qt")  # or "mpl", "nb" (Jupyter/Colab), "gradio"
def pipe(img):
    exposed = exposure(img)
    blended = blend(img, exposed)
    return exposed, blended

pipe(np.array([0.0, 0.5, 0.8]) * np.ones((256, 512, 3)))

Calling pipe(...) opens a window with sliders for every declared parameter. 🎉

Backends

PyQt / PySide	Matplotlib	Jupyter / Colab	Gradio
gui="qt"	gui="mpl"	gui="nb"	gui="gradio"

Plus headless mode (gui=None) for batch processing. Full feature matrix in the backends docs.

Learn more

• 📖 Documentation — quickstart, guides, API reference
• 🤖 Agent-friendly docs: llms.txt / llms-full.txt
• 🎓 Interactive tutorial on Hugging Face
• 🗒️ Examples gallery — 17 demo scripts, Colab notebooks, a Raspberry Pi jukebox
• 📋 Changelog
• 🤝 Contributing