---

name: stitching description: "Routes panorama stitching, the stitch CLI, Stitcher and AffineStitcher API usage, verbose diagnostics, feature-mask troubleshooting, and headless installation decisions for the stitching package." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

stitching

Use this repo skill for image panorama stitching tasks with the Python package stitching. It is the top-level router for the package CLI, the Python API, and failure analysis around verbose outputs and sample-image workflows.

Start here

• If you want a shell command, go to CLI usage.
• If you want Stitcher or AffineStitcher from Python, go to Python API.
• If you are diagnosing failed stitches, missing images, or poor matches, go to diagnostics.
• If you are checking whether this skill matches the current checkout, read repository provenance.

Install

The public package name is stitching.

python -m pip install stitching

For headless server or Docker environments, use the headless build when the OpenCV GUI stack is not available:

python -m pip install stitching-headless

The runtime package depends on OpenCV and largestinteriorrectangle. The repository's native test suite also uses requests to download public sample images when they are not already present.

Minimal verification

After installing the package in a clean Python environment, run:

python -c "import stitching; print(stitching.__version__)"
stitch --help

If you need a stronger smoke check, use the bundled helper:

python scripts/check_install.py --help
python scripts/check_install.py

What this skill covers

• Panorama stitching from filenames, glob patterns, or loaded NumPy arrays.
• stitch CLI flags for affine mode, masks, verbose output, crop control, match tuning, and output selection.
• Stitcher and AffineStitcher constructors and their verified defaults.
• Verbose output, matches graphs, feature masks, timelapse output, and crop troubleshooting.
• Safe sample-image download guidance for users who want to reproduce the repository's public examples.

What this skill does not do

• It does not depend on the original repository checkout at runtime.
• It does not claim CUDA support as a required capability. The --try_use_gpu option is optional and only works with a CUDA-enabled OpenCV build.
• It does not bundle the original repository's native tests or generated output.

Bundled references and scripts

Read these when you need deeper detail than the router provides:

• Installation notes for package names, headless builds, and verification commands.
• Troubleshooting for import, OpenCV, image, crop, and GUI failures.
• Package provenance to check whether this skill matches the current repository snapshot.
• Routing metadata for managed router placement during import in supported workflows.
• CLI reference for flags and options.
• Python API reference for signatures and verified defaults.
• Diagnostics workflow guide for verbose outputs, match graphs, and recovery paths.
• Install check helper to verify the installed package from a clean environment.
• Sample image downloader when you want the public fixture set used by the repository's native tests.

Quick route map

User intent	Read next
"stitch these images from the shell"	sub-skills/cli/SKILL.md
"use Stitcher in Python"	sub-skills/python-api/SKILL.md
"why did stitching fail"	sub-skills/diagnostics/SKILL.md
"I need the CLI flags"	sub-skills/cli/references/cli-reference.md
"what defaults does the API use"	sub-skills/python-api/references/api-reference.md
"I need the sample images"	scripts/download_sample_images.py

Common runtime reminders

• Use the stitch entry point for end users; it is the public CLI.
• Use AffineStitcher when the input is scans or specialized-device imagery.
• Use feature masks only when the mask shape matches the image shape.
• Use stitch --no-crop or Cropper(False) when the cropper cannot find a valid interior rectangle.
• Treat verbose output and matches graphs as diagnostics, not as required runtime data files.