FastCamera 0.1.1 [ALPHA-2026-05-17] — Ultra-Fast Native Camera Capture for Java

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📸 The high-performance native camera module for the FastJava ecosystem. Hardware-accelerated capture via MediaFoundation, WinRT, and DirectShow with SIMD color conversion.

FastCamera brings real hardware-accelerated camera capture to Java. By bypassing standard slow APIs and using direct native pipelines, it achieves ultra-low latency 1080p@60fps capture with SIMD-accelerated YUV→RGBA conversion.

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Quick Start

import fastcamera.FastCamera;
import fastcamera.CameraDevice;
import fastimage.FastImage;
import java.nio.ByteBuffer;
import java.util.List;

public class Demo {
    public static void main(String[] args) {
        // 1. Enumerate available cameras across WinRT, MediaFoundation, and DirectShow
        List<CameraDevice> devices = FastCamera.enumerateDevices();
        if (devices.isEmpty()) {
            System.out.println("No cameras detected.");
            return;
        }

        // 2. Open primary camera
        CameraDevice dev = devices.get(0);
        FastCamera camera = FastCamera.open(dev.getId());

        // 3. Start high-performance streaming (e.g. 1080p @ 60fps)
        ByteBuffer directBuffer = camera.startStream(1920, 1080, 60);

        // 4. Capture single frame directly as SIMD-accelerated FastImage
        FastImage frameImage = camera.captureImage();
        if (frameImage != null) {
            // Apply SIMD filters with zero JVM garbage collection
            FastImage processed = frameImage.resize(640, 360).grayscale();
        }

        // 5. Clean up native resources
        camera.stopCapture();
        camera.close();
    }
}

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Table of Contents

• Why FastCamera?
• Quick Start
• Key Features
• Real-World Use Cases
• Architecture & Pipeline
• Performance Benchmarks
• API Quick Reference
• Installation
• Documentation
• Platform Support
• License
• Related Projects

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Why FastCamera?

Capturing camera and webcam video in standard Java usually involves bloated multi-megabyte wrappers, JNI overhead, or slow OpenCV/JavaCV bridges that force unnecessary memory copies:

1. Slow Format Conversion: Most webcams output hardware YUV (YUY2/NV12) or MJPEG. Standard Java converts these formats on the CPU using slow scalar loops, burning 30–50% CPU just for color conversion.
2. Heavy Heap Allocation: Creating new image objects or byte arrays per frame creates extreme JVM Garbage Collection pressure at 60 FPS, resulting in dropped frames and unpredictable stutter.
3. Fragile Backend Support: Many Java camera libraries depend on outdated 32-bit DirectShow filters or fail on modern Windows 10/11 WinRT camera permissions.

FastCamera eliminates these pain points with a clean, native-first architecture:

• Triple Native Backend: Automatically selects between WinRT, MediaFoundation, and DirectShow for maximum device compatibility.
• Zero-Copy Streaming: Direct native frame mapping exposes raw video buffers to Java via DirectByteBuffer with 0 GC overhead.
• FastImage Ecosystem Bridge: Seamlessly wrap or capture video frames directly into off-heap FastImage instances for SIMD filtering.

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Key Features

• 🎥 Triple Native Engine — Automatic hardware-accelerated pipeline selection (WinRT, MediaFoundation, DirectShow).
• ⚙️ SIMD Color Conversion — High-speed YUV→RGBA conversion leveraging AVX2 and SSE4.2 vector instructions.
• 📥 Zero-Copy Streaming — Direct access to native video memory via DirectByteBuffer with 0 heap bytes allocated.
• 🖼️ FastImage Ecosystem Bridge — Instant zero-copy interoperability with FastImage for SIMD resize, blur, and vision filtering.
• ⏱️ Ultra-Low Latency — Async native capture callbacks delivering stable 1080p @ 60 FPS.
• 🔗 FastCore Integration — Unified native DLL loading and extraction without manual environment setup.

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Real-World Use Cases

• 👁️ Computer Vision & AI Tracking: Stream raw camera frames directly to YOLO, OpenCV, or TensorRT with zero latency.
• 🎙️ Live Streaming & Virtual Camera Overlays: Process and filter webcam video with real-time Kawase background blur via FastImage.
• 🏭 Industrial Inspection & OCR: High-framerate capture for barcode scanning, text extraction, and optical inspection.
• 🤖 Autonomous Robotics & Drones: Low-overhead visual feedback loop running on resource-constrained JVM runtimes.

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Architecture & Pipeline

┌─────────────────────────────────────────────────────────────┐
│            Physical Camera Hardware / UVC Webcam            │
└──────────────────────────────┬──────────────────────────────┘
                               │ Native OS Subsystems (WinRT / MF / DirectShow)
                               ▼
┌─────────────────────────────────────────────────────────────┐
│               Hardware YUV Stream (YUY2 / NV12)             │
└──────────────────────────────┬──────────────────────────────┘
                               │ Hand-Tuned AVX2 / SSE4.2 SIMD Kernels
                               │ (Hardware-Accelerated Color Conversion)
                               ▼
┌─────────────────────────────────────────────────────────────┐
│                 Native Pre-Allocated Frame Pool             │
└──────────────────────────────┬──────────────────────────────┘
                               │ Zero-Copy Direct JNI
                               ▼
┌─────────────────────────────────────────────────────────────┐
│       Java Application (Direct ByteBuffer / FastImage)      │
└─────────────────────────────────────────────────────────────┘

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Performance Benchmarks

Measured on official JMH Benchmark (Throughput in ops/ms):

Benchmark                             Mode  Cnt  Score   Error   Units
Benchmark.benchmarkEnumerateDevices  thrpt    3  0.802          ops/ms

Note

Environment & Setup: Measured on an Intel Core i7 with Windows 11. Native MediaFoundation device probing and capability negotiation executes in ~1.2 ms with complete metadata extraction and zero JVM heap pollution.

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API Quick Reference

Method	Description	Docs
enumerateDevices()	Queries all connected camera devices.	Reference 📖
open(deviceId)	Opens target camera device by native ID.	Reference 📖
startStream(w, h, fps)	Zero-Copy: Maps native frame memory to DirectByteBuffer.	Reference 📖
captureImage()	FastImage Bridge: Captures frame to off-heap FastImage.	Reference 📖
getStreamImage()	Zero-Copy FastImage: Wraps streaming buffer directly.	Reference 📖
takePicture()	Captures current frame as standard BufferedImage.	Reference 📖
close()	Releases camera hardware and streams.	Reference 📖

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Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the dependencies to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastCamera Library -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCamera</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastImage Frame Bridge -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastImage</artifactId>
        <version>0.1.2</version>
    </dependency>

    <!-- FastCore (Required Native Loader) -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCore</artifactId>
        <version>0.1.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastCamera:0.1.1'
    implementation 'com.github.andrestubbe:FastImage:0.1.2'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
}

Option 3: Direct Download (No Build Tool)

Download the latest JARs directly to add them to your classpath:

1. 📦 FastCamera-0.1.1.jar (The Core Library)
2. ⚡ FastImage-0.1.2.jar (The SIMD Image Engine)
3. ⚙️ FastCore-0.1.0.jar (The Mandatory Native Loader)

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Documentation

• COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
• REFERENCE.md: Full API descriptions, border configurations, and codepoint index.
• PHILOSOPHY.md: The engineering rationale for zero-allocation performance.
• ROADMAP.md: Future milestones and planned features.

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Platform Support

Platform	Status
Windows 10/11	✅ Fully Supported
Linux	🔗 Planned
macOS	🔗 Planned

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License

MIT License See LICENSE file for details.

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Related Projects

• FastFileIndex - Binary file indexing with mmap support
• FastFileSearch - Prefix Trie, N-Gram index, and Ranking engine
• FastFileWatch - USN Journal-based live file monitoring
• FastCore - Unified JNI loader and platform abstraction

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. 🚀📋