SVGConverter

正體中文

SVGConverter converts PNG, JPEG, WebP, BMP, and TIFF images to SVG through a small Python API, command-line interface, and desktop GUI.

Conversion modes

• embed (default) places the original raster bytes in an SVG <image> element. It preserves the source pixels, but it is not vectorization and can be larger than the original image because of Base64 encoding.
• vectorize traces raster regions into SVG paths using the optional VTracer backend. It is most useful for logos, icons, illustrations, and high-contrast line art. Photographs can produce large, stylized output rather than a faithful smaller image.

Installation

SVGConverter requires Python 3.10 or newer:

python -m pip install --upgrade svgconverter

Install vectorization support when needed:

python -m pip install --upgrade "svgconverter[vectorize]"

To run the current development version instead:

git clone https://github.com/KageRyo/SVGConverter.git
cd SVGConverter
python -m pip install .

Command line

Convert one image, retaining its dimensions in the generated SVG:

svgconverter image.png
svgconverter photo.jpg --output output.svg
svgconverter logo.png --mode vectorize --vectorize-color-mode binary

Convert all supported images immediately inside a directory:

svgconverter ./images --output-dir ./svg-output

Include nested directories while retaining their relative paths under the output directory:

svgconverter ./images --output-dir ./svg-output --recursive

Convert several explicit files in one batch. Shells that support glob expansion can also expand patterns such as ./images/*.png before invoking the command:

svgconverter image.png photo.jpg --output-dir ./svg-output

For batch conversion, existing output SVGs are skipped unless --overwrite is supplied; the final summary reports converted, skipped, and failed items. Supported inputs are PNG, JPG, JPEG, WebP, BMP, TIF, and TIFF (including upper-case extensions). Run svgconverter --help for all options. Vectorize mode requires the optional vectorize extra.

Embed optimization

Embed mode preserves the source raster bytes by default. Opt in to resizing or re-encoding only when a smaller raster payload is worth the quality trade-off:

svgconverter photo.jpg --max-width 1600 --jpeg-quality 82
svgconverter illustration.png --png-compress-level 9 --optimize-png

--max-width and --max-height only downscale and preserve aspect ratio. --jpeg-quality applies only to JPEG inputs; --png-compress-level and --optimize-png apply only to PNG inputs, so the same batch options are safe for mixed formats. A resized JPEG without an explicit quality uses 95. The CLI reports input, embedded-raster, and SVG sizes after each conversion or batch. These controls apply to embed mode only.

Python API

from svgconverter import (
    EmbedOptions,
    SVGConverter,
    convert_file,
    convert_file_with_metrics,
    convert_paths,
)

convert_file("image.png", "image.svg")
convert_file("logo.png", "logo.svg", mode="vectorize")

converter = SVGConverter(overwrite=True)
result = converter.convert_directory("./images", "./svg-output", recursive=True)
batch = convert_paths(["logo.png", "photo.jpg"], "./svg-output")
metric = convert_file_with_metrics(
    "photo.jpg",
    "photo.svg",
    embed_options=EmbedOptions(max_width=1600, jpeg_quality=82),
)
print(result.success_count, result.skipped_count, result.failure_count)
print(metric.input_bytes, metric.embedded_raster_bytes, metric.svg_bytes)

convert_file() returns the output pathlib.Path. Directory conversion returns a BatchResult containing successful output paths and per-file failures, so a bad image does not abort the entire batch. convert_paths() accepts a mix of files and directories; existing batch outputs are recorded as skips unless overwrite=True is selected. EmbedOptions is opt-in; without it, embed mode uses the original raster bytes. convert_file_with_metrics() and BatchResult.metrics report source, embedded-raster, and SVG byte sizes.

GUI

Install the package and run:

svgconverter-gui

The legacy development command python main.py starts the same GUI. The GUI currently selects a directory and offers Traditional Chinese, English, and Japanese. It uses embed mode; vectorize mode is available through the Python API and CLI.

Contributing and license

See CONTRIBUTING.md for local checks and commit conventions. This project is licensed under the MIT License.