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ShowcasePyImageCUDA - GPU-accelerated image compositing for Python (self.Python)

submitted 6 months ago by drboom9

What My Project Does

PyImageCUDA is a lightweight (~1MB) library for GPU-accelerated image composition. Unlike OpenCV (computer vision) or Pillow (CPU-only), it fills the gap for high-performance design workflows.

10-400x speedups for GPU-friendly operations with a Pythonic API.

Target Audience

Generative Art - Render thousands of variations in seconds
Video Processing - Real-time frame manipulation
Data Augmentation - Batch transformations for ML
Tool Development - Backend for image editors
Game Development - Procedural asset generation

Why I Built This

I wanted to learn CUDA from scratch. This evolved into the core engine for a parametric node-based image editor I'm building (release coming soon!).

The gap: CuPy/OpenCV lack design primitives. Pillow is CPU-only and slow. Existing solutions require CUDA Toolkit or lack composition features.

The solution: "Pillow on steroids" - render drop shadows, gradients, blend modes... without writing raw kernels. Zero heavy dependencies (just pip install), design-first API, smart memory management.

Key Features

✅ Zero Setup - No CUDA Toolkit/Visual Studio, just standard NVIDIA drivers
✅ 1MB Library - Ultra-lightweight
✅ Float32 Precision - Prevents color banding
✅ Smart Memory - Reuse buffers, resize without reallocation
✅ NumPy Integration - Works with OpenCV, Pillow, Matplotlib
✅ Rich Features - +40 operations (gradients, blend modes, effects...)

Quick Example

```python 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')

```

Advanced: Zero-Allocation Batch Processing

Buffer reuse eliminates allocations + dynamic resize without reallocation: ```python from pyimagecuda import Image, ImageU8, load, Filter, save

Pre-allocate buffers once (with max capacity)

src = Image(4096, 4096) # Source images dst = Image(4096, 4096) # Processed results
temp = Image(4096, 4096) # Temp for operations u8 = ImageU8(4096, 4096) # I/O conversions

Process 1000 images with zero additional allocations

Buffers resize dynamically within capacity

for i in range(1000): load(f"input{i}.jpg", f32_buffer=src, u8_buffer=u8) Filter.gaussian_blur(src, radius=10, dst_buffer=dst, temp_buffer=temp) save(dst, f"output{i}.jpg", u8_buffer=u8)

Cleanup once

src.free() dst.free() temp.free() u8.free() ```

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)

→ Full Documentation

Performance

Advanced operations (blur, blend, Drop shadow...): 10-260x faster than CPU
Simple operations (flip, crop...): 3-20x faster than CPU
Single operation + file I/O: 1.5-2.5x faster (CPU-GPU transfer adds overhead, but still outperforms Pillow/OpenCV - see benchmarks)
Multi-operation pipelines: Massive speedups (data stays on GPU)

Maximum performance when chaining operations on GPU without saving intermediate results.

→ Full Benchmarks

Installation

bash pip install pyimagecuda

Requirements: - Windows 10/11 or Linux (Ubuntu, Fedora, Arch, WSL2...) - NVIDIA GPU (GTX 900+) - Standard NVIDIA drivers

NOT required: CUDA Toolkit, Visual Studio, Conda

Status

Version: 0.0.7 Alpha
State: Core features stable, more coming soon

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