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OpenCV for Embedded: Lessons Learned
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Yury Gorbachev
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Presented at Embedded Vision Alliance Summit, May 2015
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OpenCV for Embedded: Lessons Learned
1.
Copyright © 2015
Itseez 1 Yury Gorbachev 12-May-2015 OpenCV for Embedded: Lessons Learned
2.
Copyright © 2015
Itseez 2 • Open-source Computer Vision library (>2500 algos) • De-facto standard in CV, BSD license • Written in C++, C interface is now deprecated • Supports multiple platforms (Linux, Windows, OSX, Android, iOS, QNX) • Used by Google, nVidia, Microsoft, Intel, Stanford, etc. • Funding/contributions from Willow Garage, nVidia, GSoC, AMD, Intel • Maintained by Itseez What is OpenCV
3.
Copyright © 2015
Itseez 3 • OpenCV provides extensive means to create an entire application • Camera interface (for example, V4L2 interface on Linux) • Video Reading interface (using ffmpeg) • UI primitives (windows, keyboard/mouse input, etc.) • Decent performance out of the box • Scalar performance is already good enough • Some algorithms are capable of working ~100 FPS on average desktops • Extra optimization is not required in most of the cases • Good and pretty stable acceleration possibilities • Intel® TBB is sufficient for multi-core • AVX, IPP, OpenCL, CUDA Desktops are good and fast
4.
Copyright © 2015
Itseez 4 • Mostly ARM platforms • Exotic execution environments • C++ is not default language (e.g. on Android) • Different interfaces (Camera, UI, Log) • Hard to troubleshoot • Insufficient and unpredictable performance • Mobile and Embedded are still behind Desktop • Thermal protection, power saving and other tricky issues • Zoo of acceleration possibilities • SIMD, DSP, GPU offload, FPGA • Multi-core systems, heterogeneous systems Embedded changes a lot
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Itseez 5 OpenCV OpenCV based algorithms are highly portable Platform Agnostic Modules core, imgproc, calib3d, video, ml, objdetect, features2d, photo, … Platform Dependent Modules gpu, highgui, androidcamera python and java bindings Dependencies JPEG, PNG, Jasper, multimedia, OpenNI Dependencies CMake • Algorithm modules are easy to migrate to new environment • С++ and CMake are the only requirements! • OpenCV accuracy tests • Easily verify correctness of OpenCV on a new platform • Some vendors use for regression tests during environment updates Accelerations TBB/GDC/Concurrency, IPP, Eigen
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Itseez 6 Use desktop for algorithm development Prototyping (x86) Porting Profiling Bottleneck optimization Fine Tuning Productization Regression Tests Performance Tests • Video input, more debug possibilities, simple UI, higher speed • Focus on algorithm, not environment!
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Itseez 7 • HW performance is always an issue for vision systems • Heavy image processing requires significant memory bandwidth • Usual bottleneck; multiple cores do not help • Collocation of multiple algorithms on a single system (e.g. ADAS) • Mobile platforms are even more complicated • Thermal protection, power saving are hard to control and influence • Hard to predict when/if we are consuming too much • Unstable FPS impacts algorithm complexity (e.g. object tracking) • Hardware selection is not easy • Very hard to predict final application performance beforehand • No valid benchmarks to emulate computer vision patterns Consider embedded performance issues
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Itseez 8 • OpenCV was initially optimized for desktop where it works fast • ARM optimizations are far behind • Scalar code does not perform on ARM as good as on x86 • Optimization might help to some extent It is normal if it’s slow without optimizations 150 100 50 5 SSE IPP NEON (OpenCV 3) NEON Number of optimized functions within OpenCV
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Itseez 9 • Algorithm optimization and only then hotspots • Reduce search and track areas, use grayscale, reduce resolution • Select proper HW if possible • Compare development kit performance at least • Try ARMv8, it is better in scalar performance • Use OpenCV packages from HW vendors (NVIDIA, TI) • Vendor specific packages yield out of the box improvements on specific HW, very easy to try • Not a cross-platform solution • Optimize functions yourself • NEON, DSP and other HW specific options A few optimization hints
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Itseez 10 Itseez achievements 18.9 138 163.6 32.4 2.3 3.1 3.1 7.9 Filter 2D Adaptive Threshold Blur FAST Processing on ARM v7A OpenCV Itseez • Note scalar difference ARM v7A vs. v8 30.8 30.1 27.1 23.2 2.5 1.4 0.6 5 Filter 2D Adaptive Threshold Blur FAST Processing on ARM v8 OpenCV Itseez
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Itseez 11 • Itseez ADAS solution • Traffic Sign Recognition • Front Collision Warning • Line Departure Warning • Pedestrian Detection • All algorithms are running real-time on off-the-shelf ARM device • Designed and tested using OpenCV • Product implements intelligent pipeline layer to reduce load • Uses custom accelerated functions Actual product example
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Itseez 12 • Intelligent pipeline • Shares computation results between algorithms • Complicated processing is performed only once, used by all • Multiple frame sizes used where appropriate • Custom NEON optimizations • Heavily optimized using only NEON, no GPU, DSP • Multiple processing functions are joined to reduce memory access • E.g. demosaicing with conversion to grayscale & RGBA • Some interesting statistics • Algorithm optimizations accelerate by factor 2-3 • NEON accelerations give another 3-4x Itseez ADAS - Some more details
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Itseez 13 • OpenVX standard by Khronos • Hardware accelerated vision – easier life for everyone • Currently being implemented by number of vendors • OpenCV HAL (a part of OpenCV 3.x) • Low level API beneath the standard OpenCV • Open-source, but potentially can use proprietary components • Generic multi-core scheduler (Planned feature) • Make multi-core scheduler more intelligent on mobile architectures • pthread-based backend in addition to existing options • Vision benchmarks for hardware (Desired feature) • Some performance tests are present in OpenCV already • Not possible to use for benchmarking directly, some work is needed • OpenCV Manager for Android could also contain benchmarking What is missing? What is planned?
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Itseez 14 • Itseez Web: www.itseez.com • OpenCV home: www.opencv.org • OpenCV documentation: docs.opencv.org • GitHub: https://github.com/Itseez/opencv • OpenCV resources on Embedded Vision Alliance (plenty of info): http://www.embedded-vision.com/opencv-resources • OpenCV on TI: http://www.ti.com/lit/wp/spry175/spry175.pdf • OpenCV on NVIDIA: https://developer.nvidia.com/opencv • E-mail me: yury.gorbachev@itseez.com Resources
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Itseez 15 Q & A
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