Showing 4 open source projects for "source code plagiarism detection"

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  • 1
    Posturr

    Posturr

    A macOS app that blurs your screen when you slouch

    Posturr is a macOS application that uses computer vision and machine learning — specifically Apple’s Vision framework — to monitor a user’s posture in real time and encourage healthier habits by visually responding when poor posture is detected. Running locally on the Mac, the app accesses the built-in camera to detect when you slouch or sit incorrectly, and when it recognizes sustained slouching, it applies a progressive visual blur to the screen as a subtle but effective cue to straighten...
    Downloads: 0 This Week
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  • 2
    iOS-9-Sampler

    iOS-9-Sampler

    Code examples for the new features of iOS 9

    iOS-9-Sampler is a reference application that demonstrates APIs introduced with iOS 9 through small, focused examples. It was designed to build with Xcode 7 and combines Swift and Objective-C source code. The project explores map customization, Core Image text detection, new filters, speech voices, and Audio Unit discovery. Interface examples cover UIStackView, spring animation, Safari View Controller, and system font additions. It also demonstrates Core Spotlight search, content blocker extensions, Low Power Mode detection, Contacts, and ReplayKit. ...
    Downloads: 0 This Week
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  • 3
    iOS-10-Sampler

    iOS-10-Sampler

    Code examples for new APIs of iOS 10

    iOS-10-Sampler is an example application that collects practical demonstrations of APIs introduced with iOS 10. It was created for Xcode 8 and requires a physical iOS device because some examples use Metal. The project covers speech recognition, looped video playback, Live Photo capture, and audio volume fading. Its graphics and machine learning samples use Metal Performance Shaders for handwritten digit detection and real-time image recognition. Interface examples demonstrate...
    Downloads: 0 This Week
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  • 4
    GPUImage 2

    GPUImage 2

    Framework for GPU-accelerated video and image processing

    ...By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.
    Downloads: 0 This Week
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