pyimagecuda 0.0.6

GPU-accelerated image processing library for Python

PyImageCUDA 0.0.6

GPU-accelerated image compositing for Python.

PyImageCUDA is built for image composition, not computer vision. It provides GPU tools to create, modify, and blend images, rather than analyze or recognize objects within them.

Quick Example

from pyimagecuda import Image, Fill, Effect, Blend, Transform, save

with Image(1024, 1024) as bg:
    Fill.color(bg, (0, 1, 0.8, 1))
    with Image(512, 512) as card:
        Fill.gradient(card, (1, 0, 0, 1), (0, 0, 1, 1), 'radial')
        Effect.rounded_corners(card, 50)

        with Effect.stroke(card, 10, (1, 1, 1, 1)) as stroked:
            with Effect.drop_shadow(stroked, blur=50, color=(0, 0, 0, 1)) as shadowed:
                with Transform.rotate(shadowed, 45) as rotated:
                    Blend.normal(bg, rotated, anchor='center')

    save(bg, 'output.png')

Key Features

• ✅ Zero Dependencies: No CUDA Toolkit, Visual Studio, or complex compilers needed. Is Plug & Play on both Windows and Linux.
• ✅ Ultra-lightweight: library weighs ~1 MB.
• ✅ Studio Quality: 32-bit floating-point precision (float32) to prevent color banding.
• ✅ Advanced Memory Control: Reuse GPU buffers across operations and resize without reallocation—critical for video processing and batch workflows.
• ✅ API Simplicity: Intuitive, Pythonic API designed for ease of use.

Use Cases

• Generative Art: Create thousands of unique variations in seconds.
• Motion Graphics: Process video frames or generate effects in real-time.
• Image Compositing: Complex multi-layer designs with GPU-accelerated effects.
• Game Development: Procedural UI assets, icons, and sprite generation.
• Marketing Automation: Mass-produce personalized graphics from templates.
• Data Augmentation: High-speed batch transformations for ML datasets.

Installation

pip install pyimagecuda

Note: pyvips is the only mandatory dependency (installed automatically). It is used strictly for robust file I/O (JPG, PNG, WEBP...) and high-quality Text rendering on the CPU.

Documentation

⚠️ Alpha Release: Many more features are planned and under development. If you have specific needs or bug reports, please open an issue on GitHub.

Core Concepts

• Getting Started Guide
• Image & Memory (Buffer management)
• IO (Loading and Saving)

Operations

• Fill (Solid colors, Gradients, Checkerboard, Grid, Stripes, Dots, Circle, Ngon, Noise, Perlin)
• Text (Rich typography, system fonts, HTML-like markup, letter spacing...)
• Blend (Normal, Multiply, Screen, Add, Overlay, Soft Light, Hard Light, Mask)
• Resize (Nearest, Bilinear, Bicubic, Lanczos)
• Adjust (Brightness, Contrast, Saturation, Gamma, Opacity)
• Transform (Flip, Rotate, Crop)
• Filter (Gaussian Blur, Sharpen, Sepia, Invert, Threshold, Solarize, Sobel, Emboss)
• Effect (Drop Shadow, Rounded Corners, Stroke, Vignette)

Requirements

• OS:
  • Windows 10 or 11 (64-bit).
  • Linux: Any modern distribution (Ubuntu, Fedora, Debian, Arch, WSL2, etc.).
• GPU: NVIDIA GPU (Maxwell architecture / GTX 900 series or newer).
• Drivers: Standard NVIDIA Drivers installed.

NOT REQUIRED: Visual Studio, CUDA Toolkit, or Conda.

Contributing

Contributions welcome! Open issues or submit PRs

License

MIT License. See LICENSE for details.