Implementing a parallel_open_cv_application_on_raspberry_pi3(1)
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This document discusses implementing parallel OpenCV applications on the Raspberry Pi 3. It describes using the Raspberry Pi 3 hardware platform and OpenCV with Java software. Approaches for parallelizing image processing algorithms in Java like the ForkJoinPool are presented. Live demos of algorithms like blue scaling and light intensity detection using parallelization are shown. Lessons learned around problems faced and references are also provided.
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Implementing a parallel_open_cv_application_on_raspberry_pi3(1)
- 1. Meghadoot Gardi Rohith Raju 09.02.2017 ESM DPS WS 2016/2017 1 Implementing a parallel OpenCV application on Raspberry Pi 3
- 2. Agenda • Project Statement • Platforms used for implementation • Raspberry Pi 3 • OPENCV(3.1.0)with JAVA • What is parallelism and What were parallel image processing approaches for the problem statement • Which were software platforms for coding, which we have chosen and why • Approaches for parallelization in JAVA Eg. Fork join , stream etc. (why ForkJoinPool) • Live demo: Blue scaler and Intensity Detection (Filter Operation) • Lessons learnt: (Including Problems which we have faced) • References 09.02.2017 ESM DPS WS 2016/2017 2Meghadoot Gardi
- 3. What? 09.02.2017 ESM DPS WS 2016/2017 3 Hardware Parallelism Real Time Computer Vision Rohith Raju
- 4. Why? 09.02.2017 ESM DPS WS 2016/2017 4 Improved System Performance Increased Portability Low Costs Involved Rohith Raju
- 5. Hardware Platform Raspberry Pi-3 B • It Is a single board and credit card size personal computer. • Build by Raspberry Pi Foundation in the United Kingdom • Intention to promote computer science teaching • Application :Home automation, Embedded Prototyping 09.02.2017 ESM DPS WS 2016/2017 5 Meghadoot Gardi
- 6. Hardware Platform Raspberry Pi-3 B • RPI 3: 1.2GHz • 64-bit quad-core ARMv8 CPU • 1GB RAM • 802.11n Wireless LAN • Camera interface (CSI) • Micro SD card slot 09.02.2017 ESM DPS WS 2016/2017 6Meghadoot Gardi
- 7. Models and Features Type Model A Model B Zero genera tion 1 1+ 1 1+ 2 2 ver 1.2 3 Ver 1.2 Ver 1.3 Releas e 02/12 11/14 05/12 07/14 02/15 10/16 02/16 10/15 05/16 Price (US$) 25 20 35 25 35 5 US$ CPU 700 MHz, single 900 MHz, quad 1.2 GHz, quad 1 GHz, single 32 bits 64 bits 32 bits RAM 256MB 512 MB 1 GB 512 MB USB 1 2 4 1 micro mA 300 200 700 600 800 ~160 Meghadoot Gardi
- 8. • Most used: • Raspbian, Raspbian Lite • Ubuntu MATE • Snappy Ubuntu Core(Third party) • Windows 10 loT core(Third party) • RISC OS • Why Raspbian: • Widely Used • User Friendly(programming friendly) os Meghadoot Gardi
- 9. Raspberry Pi basic configuration • Download OS on Desktop or system. • Format memory card • Load OS image in the memory card • Create an empty SSH folder in BOOT partition • Insert memory card into RPI and connect the module to Wi-Fi router via LAN cable (the DHCP allows assigning dynamic IP to RPI) • Access the raspberry pi via putty or MSYS2 Meghadoot Gardi
- 10. Raspberry Pi basic configuration • Expand file system: • sudo raspi-config Expand Filesystem • sudo reboot • Update OS to the latest version • sudo apt-get update (updates the list of available packages and their versions, but doesn’t install them) • sudo apt-get upgrade (installs newer versions of the packages) • sudo reboot Meghadoot Gardi
- 11. Enabling Desktop Environment Meghadoot Gardi Enable remote desktop services Install login manager Install PIXEL environment Install Xorg Display Server sudo apt-get install -- no-install- recommends xserver- xorg sudo apt-get install raspberrypi-ui- mods sudo apt-get install lightdm sudo apt-get install xrdp
- 12. Enabling Desktop Environment • VNC Server will then start automatically whenever your Raspberry Pi is powered on. • To enable, VNC Server at the command line: • Pass command : sudo raspi-config • Navigate to Interfacing Options • Scroll down and select VNC > Yes. • Sudo reboot Meghadoot Gardi
- 13. Enabling Camera Interface • sudo modprobe bcm2825-v412 To automatically detect camera hardware. • sudo raspistill -o xyz.jpg Command to capture image Meghadoot Gardi
- 14. OpenCV 09.02.2017 ESM DPS WS 2016/2017 14Rohith Raju • OpenCV (Open Source ComputerVision Library) • Developed by Intel, and now supported by Willow Garage and Itseez. • It is free for use under the open source BSD license. • The library is cross-platform. • It focuses mainly on real-time image processing. • Performance • around 30 frames processed per seconds in real time image processing (OpenCV) • Implementations Facial recognition system, Gesture recognition, Human–computer interaction , Mobile robotics, Motion tracking, Augmented reality
- 15. OpenCV 09.02.2017 ESM DPS WS 2016/2017 15Rohith Raju OpenCV 3.1 Java Libraries features Image Container Blue -Green- Red General Image Processing Functions Segmentation Camera Caliberation, Stereo, 3D Machine Learning Detection Recognition Tracking Fitting
- 16. Java and OpenCV Installation 09.02.2017 ESM DPS WS 2016/2017 16Rohith Raju Installing OpenCV 3.1.0 • Compile & Build using cmake • Install libraries Setting Environment Variables Installing dependent Libraries • Jpeg,tiff-Image libraries • Image Compression libraries • Video Compression libraries • Interface Compiler Installing Ant 1.9.8 Installing Java Development Kit 8
- 17. Parallelization Approaches • Java Stream • Java Fork and Join Pool • CUDA API • OpenCV pool parallelization(parallel_for). 09.02.2017 ESM DPS WS 2016/2017 17Rohith Raju
- 18. Concept Join Fork 09.02.2017 ESM DPS WS 2016/2017 18Rohith Raju
- 19. Concept Join Fork 09.02.2017 ESM DPS WS 2016/2017 19Rohith Raju Main Class Fork and Join Class Object -> java.util.concurrent.ForkJoinPool with number of processors used as arguments. Object of Fork and Join Class -> java.util.concurrent.RecursiveAction Begin Parallel processing -> extends -> RecursiveAction Default Method -> Compute if-> Recursive Calls else-> Intended Algorithm
- 20. Algorithm Implementation 09.02.2017 ESM DPS WS 2016/2017 20 Capture Image • Load to Pi • Stream From Camera Processing • Load to Java • Real Time Processing Serial Processing • OpenCV Libraries Hardware Parallelism • Java Parallelism • Task Division Algorithm Implementation • OpenCV Libraries Output Comparison • Parallel processing • Serial processing Rohith Raju
- 21. Algorithm Implementation 09.02.2017 ESM DPS WS 2016/2017 21Rohith Raju B G R B=B+G+R BLUESCALER B 0 0
- 22. Implementation Of Blue Scaler (Using Join Fork) 09.02.2017 ESM DPS WS 2016/2017 22Rohith Raju BLUESCALER
- 23. Identification of Light Intensity (Using Sobel Operator) • Concept • Application • How Parallelization is achieved? • Step By Step Overview • Live Demo 09.02.2017 ESM DPS WS 2016/2017 23Meghadoot Gardi
- 24. Concept • Sobel to calculate the derivatives from an image. • This Derivative mask is also used as an Gradient. Sobel operator is used to detect two kinds of edges in an image: Vertical direction edges and Horizontal direction edges. • This principle can be deployed in Identification of the “Intensity distribution of Light Source”. 09.02.2017 ESM DPS WS 2016/2017 24Meghadoot Gardi
- 25. Concept • A good way to express Pixel intensity changes is by using derivatives • A high change in gradient indicates a major change in the image • The intensity change in the vicinity of source is shown by the “jump” in intensity in the plot • The edge “jump” can be seen more easily if we take the first derivative 09.02.2017 ESM DPS WS 2016/2017 25Meghadoot Gardi
- 26. Insights of Output (with only sunlight and with Lamp ) 09.02.2017 ESM DPS WS 2016/2017 26Meghadoot Gardi
- 27. Algorithm Implementation 09.02.2017 ESM DPS WS 2016/2017 27 IDENTIFICATION OF INTENSIY OF SOURCE B G R Gray=B+G+R Gray Vertical derivative Horizontal derivative Gradient Approximation (RMS) Steps to be followed: Gray Scale Conversion Gaussian Blur Implementation (In case of HD Images) Sobel Filtering of Image Meghadoot Gardi
- 28. Parallelization of algorithm • Join-Fork Pool Implementation in JAVA • Fork for Gray Scale Implementation • Join to get Gray Scale image • Fork for Sobel implementation • Join to get Intensity detailed Image
- 29. Demo 09.02.2017 ESM DPS WS 2016/2017 29
- 30. Conclusion • What we have learnt: • Image Processing using OpenCV Libraries • Various Image Processing Algorithms and Filters • Parallelization of Image Processing Algorithms (Some Exceptions Eg. Face Detection, Canny Edge Detection) • Problems Faced during Implementation: • Interfacing issues with the Raspberry-Pi and Camera • Installation and Compilation of OpenCV on Pi • Installing JDK • Version Change in OpenCV 09.02.2017 ESM DPS WS 2016/2017 30
- 31. References • http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/sobel_derivativ es/sobel_derivatives.html#sobel-derivatives • Learning OpenCV by Bradski and Kaehler • https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/ForkJoinPo ol.html • http://robinhenniges.com/en/install-opencv-for-java-raspberry-pi-debian- jessy • http://www.samontab.com/web/2014/06/installing-opencv-2-4-9-in- ubuntu-14-04-lts/ 09.02.2017 ESM DPS WS 2016/2017 31
- 32. 09.02.2017 ESM DPS WS 2016/2017 32
Editor's Notes
- It’s a high computing device. Small sized,compact and portable personal computer. Designed and developed by RP foundation in UK. To encourage embedded prototyping.
- So this gave an idea of how we can access the RP but it is headless!! No screen and keyboard Hence, It is necessary to get access with remote login.
- modprobe is a Linux program originally written by Rusty Russell and used to add a loadable kernel module (LKM) to the Linux kernel or to remove a LKM from the kernel. It is commonly used indirectly: udev relies upon modprobe to load drivers for automatically detected hardware.
- OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision.[2]Originally developed by Intel's research center in Nizhny Novgorod (Russia), it was later supported by Willow Garage and is now maintained by Itseez.[1] The library is cross-platform and free for use under the open-source BSD license.
- OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision.[2]Originally developed by Intel's research center in Nizhny Novgorod (Russia), it was later supported by Willow Garage and is now maintained by Itseez.[1] The library is cross-platform and free for use under the open-source BSD license.
- CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia.[1] It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels.[2]
- Which will split the image until it matches the task size.