---

name: interactive-deep-colorization description: "Routes agents using Interactive Deep Colorization for local-hints image colorization, model setup, GUI/API workflows, and Caffe global histogram transfer." disable-model-invocation: true metadata: disco-role: operating license: MIT

interactive-deep-colorization

Use this repo skill when a task names Interactive Deep Colorization, iDeepColor, the SIGGRAPH 2017 real-time user-guided colorization code, local color hints, ab-mask guided image colorization, the PyQt colorization GUI, converted PyTorch weights, Caffe model artifacts, or global histogram transfer with a reference image.

Start here

1. Identify whether the user is asking about setup, local-hints colorization, or global histogram transfer.
2. Read the matching sub-skill before giving commands or API guidance.
3. Check references/troubleshooting.md when the request includes import failures, missing weights, display/Qt errors, Caffe/PyTorch confusion, or stale-checkout questions.
4. Check references/repo-provenance.md before deciding whether this skill matches a different checkout or should be refreshed.

Route map

User intent	Read
Install dependencies, choose Caffe/PyTorch/Docker, stage model weights, validate expected artifacts, or diagnose setup blocks	sub-skills/setup-and-models/SKILL.md
Run or explain the local-hints GUI, reproduce the barebones notebook flow, build input_ab/input_mask, inspect CLI defaults, use color suggestions, or interpret saved output artifacts	sub-skills/interactive-colorization/SKILL.md
Use a reference image's global color histogram, explain ColorizeImageCaffeGlobDist, validate global model assets, or troubleshoot global histogram notebook behavior	sub-skills/global-histogram-transfer/SKILL.md

Quick setup

If you need a fresh inspection environment for the local-hints or setup workflows, create one with the scientific image stack and PyTorch first, then validate the selected workflow with the bundled helpers:

python3.11 -m venv /path/to/inspection-env
/path/to/inspection-env/bin/python -m pip install numpy scipy scikit-image scikit-learn matplotlib opencv-python-headless torch
/path/to/inspection-env/bin/python sub-skills/interactive-colorization/scripts/smoke_core_helpers.py --repo-root /path/to/checkout --size 8

If the task is about missing weights or backend choice, check the bundled artifact and CLI inspectors before attempting a GUI or notebook launch:

/path/to/inspection-env/bin/python sub-skills/setup-and-models/scripts/check_model_artifacts.py --repo-root /path/to/checkout --workflow all
/path/to/inspection-env/bin/python sub-skills/interactive-colorization/scripts/inspect_cli_defaults.py --variant both --json

Repository shape and install expectations

Interactive Deep Colorization is a research-demo checkout rather than a packaged Python distribution. There is no setup.py, pyproject.toml, or console entry point. Future agents should treat the repository root as the import root when working with a live checkout, and should prepare dependencies according to the selected workflow rather than installing every historical script.

Minimum conceptual setup:

• Scientific image stack: NumPy, SciPy, scikit-image, scikit-learn, OpenCV, and Matplotlib/notebook tools when using notebook recipes.
• Caffe path: PyCaffe with Python layer support plus Caffe model weights.
• PyTorch path: PyTorch plus models/pytorch/caffemodel.pth or another compatible checkpoint.
• GUI path: PyQt4/qdarkstyle for the root script, or PyQt5 through the Docker variant.
• Global histogram transfer: Caffe-only global weights and stats model assets.

Important limitations

• Training code is not part of this checkout; the README points training to a separate PyTorch reimplementation repository.
• The original Caffe backend and global histogram notebook require PyCaffe and downloaded weights; this generated skill documents them but did not verify native Caffe execution during construction.
• The GUI requires Qt bindings and a display server; use safe bundled inspectors when only parser/API facts are needed.
• Model fetch scripts perform network downloads and are represented here by safe artifact-check helpers rather than automatic download wrappers.

Safe checks bundled in sub-skills

Use these checks when working with a live checkout or staged artifact tree; replace /path/to/checkout with the user's current repository root.

• Model artifact presence: sub-skills/setup-and-models/scripts/check_model_artifacts.py

  python sub-skills/setup-and-models/scripts/check_model_artifacts.py --repo-root /path/to/checkout --workflow all
  

• Local-hints CLI defaults without PyQt/Caffe imports: sub-skills/interactive-colorization/scripts/inspect_cli_defaults.py

  python sub-skills/interactive-colorization/scripts/inspect_cli_defaults.py --variant both --json
  

• Core source helper smoke without weights/GUI/Caffe: sub-skills/interactive-colorization/scripts/smoke_core_helpers.py

  python sub-skills/interactive-colorization/scripts/smoke_core_helpers.py --repo-root /path/to/checkout --size 8
  

• Global histogram asset presence without Caffe imports: sub-skills/global-histogram-transfer/scripts/check_global_histogram_assets.py

  python sub-skills/global-histogram-transfer/scripts/check_global_histogram_assets.py --repo-root /path/to/checkout
  

Evidence and routing metadata

• Read references/repo-provenance.md for source commit, dirty-state summary, package/import facts, and evidence paths.
• references/repo-routing-metadata.json contains structured scenario metadata for managed repo-skill import tools; it is data, not user-facing guidance.