thumbnail-generator 0.1.1

Blazing-fast thumbnail generator from image/video URLs (pyvips primary + Pillow fallback)

thumbnail-generator

thumbnail-generator is a tiny, modern toolkit for creating thumbnails directly from remote image URLs. It streams the source file, transforms it entirely in memory, and returns ready-to-serve bytes. The library powers a Python API, an async helper, and a Typer-based CLI. Pillow is used out of the box; drop in pyvips for big speed wins with constant memory usage.

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Project Highlights

• Stream thumbnails straight from HTTPS URLs without writing to disk.
• Automatic EXIF orientation correction and high-quality downsampling.
• Unified crop modes: fit, fill, pad, and smart (smart switches to libvips smartcrop when available).
• Multiple output formats (JPEG, WEBP, AVIF, PNG) with adjustable quality.
• Async helper for event-driven crawlers.
• CLI command for quick batch jobs or scripting.
• Optional libvips backend (5–10× faster than Pillow on large images).

Current scope:

• Image thumbnails are production ready (Pillow fallback + libvips optional).
• Async helper delegates to the active backend (libvips or Pillow).
• Video thumbnail extraction is not yet implemented; a stub is exported so the public API stays stable.

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Requirements

• Python 3.9 or newer (repository is tested with Python 3.12).
• Linux, macOS, or Windows.
• Optional: system libvips if you plan to use the high-performance backend.
  • Ubuntu/Debian: sudo apt install libvips
  • macOS: brew install vips
  • Windows: install the prebuilt libvips bundle from libvips releases and add it to PATH.

Note on Anaconda: when using libvips, keep Python and the native libraries from the same toolchain. Mixing a conda-based Python with system libvips often results in loader errors. Creating a virtual environment from /usr/bin/python3 (Linux/macOS) or using a clean conda environment that installs libvips from conda-forge solves this.

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Installation

PyPI (recommended)

pip install thumbnail-generator

Add the high-performance backend:

pip install thumbnail-generator[vips]

The CLI depends on Typer and is bundled automatically from version 0.1.1 onward. If you are on 0.1.0, run pip install typer once or upgrade: pip install --upgrade thumbnail-generator.

From Source

git clone https://github.com/chrisppa/thumbnail-generator.git
cd thumbnail-generator

python3 -m venv venv          # /usr/bin/python3 on Linux
source venv/bin/activate      # .\venv\Scripts\activate on Windows
pip install -U pip

pip install -e .              # Pillow backend
pip install -e .[vips]        # Optional libvips backend

Keep the virtual environment active whenever you run the CLI or tests:

source venv/bin/activate

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Usage

CLI

thumbnail-generator https://images.unsplash.com/photo-1682687220742-aba13b6e50ba \
  --size 800x800 \
  --crop smart \
  --format WEBP \
  --output hero.webp

Options (via thumbnail-generator --help):

• --output – destination file path (default thumb.jpg).
• --size – <width>x<height> integers (default 400x400).
• --crop – one of fit, fill, smart, pad.
• --format – JPEG, WEBP, AVIF, PNG.
• --quality – integer 1–100 (backend-specific defaults to 90).

When pyvips is available the CLI reports Saved … using vips backend, otherwise Pillow is used automatically.

Python API

from thumbnail_generator import thumbnail_from_url, CropMode

buf = thumbnail_from_url(
    "https://example.com/large.jpeg",
    size=(500, 300),
    crop=CropMode.FILL,
    format="WEBP",
    quality=85,
)

with open("thumb.webp", "wb") as fp:
    fp.write(buf.getvalue())

Async API

import asyncio
from thumbnail_generator import athumbnail_from_url

async def main():
    buf = await athumbnail_from_url(
        "https://example.com/scene.jpg",
        size=(320, 320),
    )
    with open("async-thumb.jpg", "wb") as fp:
        fp.write(buf.getbuffer())

asyncio.run(main())

The async helper uses the currently active backend under the hood.

Video API (planned)

thumbnail_generator.video_thumbnail_from_url() currently raises NotImplementedError. The stub exists so the import surface is ready when ffmpeg integration lands.

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Backends Explained

Backend	Activation	Strengths	Notes
Pillow	Installed automatically with pip install -e .	Zero native deps, works everywhere	Best for small/medium images
libvips	Install libvips + pip install -e .[vips]	Fast, constant memory, smart cropping available	Requires OS-level libvips libraries

• CropMode.FIT: fits inside the target box, preserving aspect ratio.
• CropMode.FILL: fills the target box by scaling and center-cropping.
• CropMode.SMART: same as FILL; if libvips is active, switches to smartcrop.
• CropMode.PAD: letterboxes the image on a background color.

Output formats are passed straight to the backend. The default quality is 90; adjust for smaller files or higher fidelity.

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Development Workflow

source venv/bin/activate
pip install -e .[dev,vips]  # add dev extras when they land
pytest                      # once the test suite is added
python -m thumbnail_generator.cli --help

Recommended extras (when available) include Ruff for linting, Mypy for typing, and pytest with responses for mocking HTTP downloads.

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Troubleshooting

• ImportError: cannot load library 'libvips.so.42'
  Ensure libvips is installed system-wide and that your Python runtime comes from the same toolchain. Recreate the virtualenv with /usr/bin/python3 or use a dedicated conda env with conda install -c conda-forge libvips.

• PyPI install without libvips
  If you install the package elsewhere and want to force Pillow, omit the [vips] extra or pip uninstall pyvips.

• NotImplementedError for video
  Video thumbnailing is on the roadmap; the current placeholder signals that this API surface is reserved.

• Timeouts fetching images
  The default HTTP timeout is 30 seconds. If you need retries, wrap thumbnail_from_url in your own retry logic or extend DEFAULT_HEADERS/DEFAULT_TIMEOUT in core.py.

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Roadmap

• Smart crop improvements (face/saliency detection fallback when Pillow is active).
• Configurable caching (memory/disk).
• CLI batch mode with globbing and progress indicators.
• Video frame extraction using ffmpeg-python.

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License

MIT © chrisppa