Image Vectorizer

Image Vectorizer Hero

Last updated: 2026-06-05

Image Vectorizer is a Python and PySide6 desktop application foundation for working with raster images. The current application supports local image import, original and processed previews, image metadata display, and an initial grayscale and threshold processing pipeline. It can also detect, simplify, and preview color-aware vector paths using configurable quality, background removal, and comparison controls. The desktop UI supports accessible Light, Dark, and System theme modes, single SVG export, and responsive batch SVG processing.

Install Dependencies

python -m pip install -r requirements.txt

Run

From the project root:

python main.py

Or use the development runner:

python scripts/run_dev.py

Build & Packaging

To bundle the application into a standalone desktop executable for distribution:

On Windows

.venv\Scripts\python scripts\build_app.py

On macOS / Linux

.venv/bin/python scripts/build_app.py

The script will automatically handle:

Cleaning previous build output folders.
Generating a clean build using the configuration from image_vectorizer.spec.
Creating the standalone package in the dist/ directory.
Auto-detecting the application icon in app/resources using the native platform icon format when available.
Running a post-build cleanup on temporary compilation artifacts.
Using PyInstaller from .venv or the system PATH.

Documentation

Project documentation is available in docs/.

docs/architecture/ for system and pipeline architecture.
docs/developer/ for setup, verification, benchmark, and packaging guides.
docs/product/ for project overview, glossary, status, and roadmap.
docs/user/ for UI workflow, performance tips, and troubleshooting.

CI/CD & Release Automation

We use GitHub Actions to automate desktop application builds, version tagging, and release publishing.

1. CI Build Workflow (`build.yml`)

Triggered automatically on push or pull requests to the main branch, or via manual run (workflow_dispatch).
Builds standalone application packages for Windows, macOS, and Linux in parallel.
Uploads the build outputs as workflow artifacts (Image-Vectorizer-Windows, Image-Vectorizer-macOS, Image-Vectorizer-Linux).

2. Manual Tag Workflow (`create_tag.yml`)

Triggered manually from the Actions tab.
Accepts a semantic version tag (e.g. v1.0.0) and pushes it to the repository after validating that the format matches v*.*.* and the tag does not already exist.

3. Release Publication Workflow (`release.yml`)

Automatically triggered when a new version tag (v*.*.*) is pushed.
Re-builds the application packages for all target platforms, compiles them, and attaches the archived builds to a newly created GitHub Release using the version number as the release name.

4. PyPI Publishing Workflow (`publish_pypi.yml`)

Triggered automatically when a new version tag (v*.*.*) is pushed, or via manual run (workflow_dispatch).
Compiles the source distribution and wheel packages, validates package metadata using twine, and publishes the package to PyPI under the name silukman-image-vectorizer using the repository secret PYPI_API_TOKEN.

Difference Between Manual and CI Build

Manual Build: Runs locally via scripts/build_app.py. Uses local system libraries, virtual environment compilers, and target architecture. Best for fast local verification.
CI Build: Runs inside clean, isolated containers on GitHub-hosted runners (Windows, macOS, Linux). Guarantees reproducible builds and doesn't pollute local environments.

Release files for silukman-image-vectorizer 1.13.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for silukman-image-vectorizer 1.13.0
File	Size	Uploaded
silukman_image_vectorizer-1.13.0.tar.gz	4.5 MB	Jul 28, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for silukman-image-vectorizer 1.13.0
File	Interpreter	ABI	Platform	Reset
silukman_image_vectorizer-1.13.0-py3-none-any.whl 4.5 MB Jul 28, 2026	Python 3	none	any	Details

Total release size: 8.9 MB

Release files / silukman_image_vectorizer-1.13.0.tar.gz

Download URL	silukman_image_vectorizer-1.13.0.tar.gz
Size	4.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`fb0b3d6c5facae33172ed10736f7550b1535c9c09c22daad691c66204cd7cb9e`
BLAKE2b-256 checksum How to use checksums	`0d0d996891da66b6db36c9c3d802acdda5232c5e90f06900e3b7a6bfcb1d60d5`
Upload date	Jul 28, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.14`

Release files / silukman_image_vectorizer-1.13.0-py3-none-any.whl

Download URL	silukman_image_vectorizer-1.13.0-py3-none-any.whl
Size	4.5 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`f510b4c1445ca4a90938d904f0c91216c16d667139fe44e94d7b75f0978f5a58`
BLAKE2b-256 checksum How to use checksums	`5057217d2ec38909667404119482e23349f59fe9ae07d6b7b4126971b7389416`
Upload date	Jul 28, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.14`