Mini-ImagePipe

High-performance DAG-based GPU Image Processing Pipeline

High-performance GPU image processing framework with DAG task scheduling, multi-stream execution, and CUDA-accelerated operators. Designed for real-time video and batch image processing workflows.

Get Started View on GitHub API Reference

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Why Mini-ImagePipe?

Feature Comparison

Feature	Mini-ImagePipe	OpenCV GPU	Custom CUDA
DAG Scheduling	✅	❌	Manual
Memory Pool	✅	⚠️ Limited	Manual
Multi-Stream	✅	⚠️ Limited	Manual
Zero-copy Pipeline	✅	❌	Manual
Easy API	✅	✅	❌
Error Propagation	✅	❌	Manual

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Features

🚀 GPU Accelerated

Full CUDA implementation with asynchronous kernel execution. Leverages the full power of NVIDIA GPUs for real-time image processing.

🕸️ DAG Scheduling

Directed acyclic graph-based task dependency management with automatic parallelization. Optimize execution order automatically.

⚡ Multi-Stream Execution

Concurrent CUDA stream execution for independent tasks. Maximum GPU utilization through intelligent stream assignment.

🧠 Memory Efficient

Pinned and device memory pools with best-fit allocation strategy. Minimize allocation overhead across pipeline runs.

🔧 Separable Filtering

Gaussian blur optimized with separable horizontal and vertical passes. Significant performance improvement for large kernels.

🛡️ Error Propagation

Task failures automatically propagate downstream along the DAG. Robust error handling and recovery.

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Performance Benchmarks

Benchmarks

Operator	Image Size	Throughput	Latency
GaussianBlur 5×5	1920×1080	850+ FPS	~1.2ms
Sobel Edge	1920×1080	1200+ FPS	~0.8ms
Resize (2× down)	1920×1080	1500+ FPS	~0.7ms
ColorConvert	1920×1080	2000+ FPS	~0.5ms
Pipeline (4 ops)	1920×1080	400+ FPS	~2.5ms

Benchmarked on NVIDIA RTX 3090. Your results may vary based on GPU model and configuration.

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Pipeline Architecture

graph LR
    Input[📥 Input Image] --> Resize[🔄 Resize]
    Resize --> Blur[🌀 Gaussian Blur]
    Blur --> Sobel[📐 Sobel Edge]
    Sobel --> Output[📤 Output]
    
    style Input fill:#76B900,stroke:#5A8F00,color:#1a1a1a
    style Output fill:#76B900,stroke:#5A8F00,color:#1a1a1a
    style Resize fill:#2d2d2d,stroke:#76B900
    style Blur fill:#2d2d2d,stroke:#76B900
    style Sobel fill:#2d2d2d,stroke:#76B900

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Use Cases

📹 Real-time Video Processing

Live video filters, effects, and streaming applications

🚗 Autonomous Driving

Perception pipeline preprocessing for sensor fusion

🏥 Medical Imaging

DICOM image processing and analysis workflows

🤖 Embedded AI

Jetson platform deployment for edge computing

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Quick Start

# Clone the repository
git clone https://github.com/LessUp/mini-image-pipe.git
cd mini-image-pipe

# Build with CMake presets (Release)
cmake --preset release
cmake --build --preset release

# Run tests
ctest --preset release

# Run demo
./build/demo_pipeline

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Usage Example

#include "pipeline.h"
#include "operators/resize.h"
#include "operators/gaussian_blur.h"
#include "operators/sobel.h"

using namespace mini_image_pipe;

int main() {
    // Configure pipeline with 4 CUDA streams
    PipelineConfig config;
    config.numStreams = 4;
    Pipeline pipeline(config);

    // Create operators
    auto resize = std::make_shared<ResizeOperator>(320, 240);
    auto blur   = std::make_shared<GaussianBlurOperator>(GaussianKernelSize::KERNEL_5x5);
    auto sobel  = std::make_shared<SobelOperator>();

    // Build the DAG
    int n1 = pipeline.addOperator("Resize", resize);
    int n2 = pipeline.addOperator("Blur",   blur);
    int n3 = pipeline.addOperator("Sobel",  sobel);

    pipeline.connect(n1, n2);  // Resize → Blur
    pipeline.connect(n2, n3);  // Blur → Sobel

    // Set input and execute
    pipeline.setInput(n1, d_input, width, height, channels);
    pipeline.execute();

    // Get output
    void* output = pipeline.getOutput(n3);
    return 0;
}

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GPU Architecture Support

Architecture	Compute	Example GPUs
Volta	sm_70	V100
Turing	sm_75	RTX 2080, T4
Ampere	sm_80, sm_86	A100, RTX 3090
Ada Lovelace	sm_89	RTX 4090, L40
Hopper	sm_90	H100

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Available Operators

Operator	Function	Features
GaussianBlur	Gaussian blur	3×3/5×5/7×7 separable filter, reflection boundary padding
Sobel	Edge detection	3×3 Sobel kernels, gradient magnitude output
Resize	Image scaling	Bilinear / nearest-neighbor interpolation
ColorConvert	Color conversion	RGB↔Gray, BGR↔RGB, RGBA→RGB

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Documentation

📖 Getting Started 💡 Usage Examples 🏗️ Architecture 📚 API Reference ℹ️ About

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Requirements

• CMake >= 3.18
• CUDA Toolkit >= 11.0
• C++17 compatible compiler
• GTest v1.14.0 (auto-fetched via FetchContent)

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License

This project is licensed under the MIT License.

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Contributing

We welcome contributions! Please see our Contributing Guide for details.