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advancepokies784 · 2 years

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Yocto poky clone

Here's How You Can Build Your Own Custom Linux Distro with Yocto.

Build a Raspberry Pi 2 Minimal Image with The Yocto Project.

Files · zeus · yoctoproject / poky · GitLab.

| How to fix unpack errors.

Yoctoproject / poky · GitLab.

Files · yocto-3.1.11 · yoctoproject / poky · GitLab.

ExorEmbedded/yocto-poky: Clone of Yocto poky repository - GitHub.

PDF Project why and how to use clang compiler with Yocto.

Yocto — conan 1.36.0 documentation.

Build image for raspberry pi using yocto - Welcome.

Yocto Project - DigitalGate Amg.

Git clone warrior git git yoctoproject org poky git.

Yocto Project Quick Build.

Here's How You Can Build Your Own Custom Linux Distro with Yocto.

* Fullpass Test Report for Yocto 1.1 M1 RC2 - 20110528 @ 2011-06-07 4:36 Xu, Jiajun 2011-06-07 14:15 ` Xu,... To: poky, yocto (-- Attachment #1.1: Type: text/plain, Size: 6031 bytes --) Hi all, This is the fullpass test report for Yocto 1.1 M1 RC2 build. There are 3 new bugs found in this testing compared with weekly testing. Non-GPLv3 build. More information is needed in order to help with this. Try looking. at the log file (the name should be printed alongside the message. quoted above). If it's not obvious from that, provide the log file. (send/publicize) so the community can help. This is the build output of the kernel unpack failure. We can run multiple containers in parallel to build multiple versions of the Linux image. Containers run in isolation and don't influence each other. All is set for running the Yocto build. We execute the following two commands in the same directory docker-cuteradio as the docker-build command. $ mkdir -p yocto/output.

Build a Raspberry Pi 2 Minimal Image with The Yocto Project.

Create a build folder at the same level of "poky" directory. $ mkdir yocto_build $ cd yocto_build. Assuming that you have downloaded the BSP tarball and extracted the content at top level of your development build tree, you need to prepare the build environment using "source" command. $ source../poky/oe-init-build-env. May 06, 2019 · The crops/poky-container enables us to start our first Yocto build within minutes. If not done yet, we must install Docker. The section Installing Docker of my previous post covers this. We create a working directory that will hold all the source files going into the Yocto build and all the files generated by the Yocto build. $ mkdir work-cuteradio. Clone the Yocto Project repository poky jethro. Below is an example from an Ubuntu build host that clones the poky repository and then checks out the latest Yocto Project Release.

Files · zeus · yoctoproject / poky · GitLab.

Poky ==== Poky is an integration of various components to form a complete prepackaged build system and development environment. It features support for building customised embedded device style images. There are reference demo images featuring a X11/Matchbox/GTK themed UI called Sato. The system supports cross-architecture application. Nov 14, 2016 · yocto release Clone the Yocto Project repository poky jethro. Below is an example from an Ubuntu build host that clones the poky repository and then checks out the latest Yocto Project Release.

| How to fix unpack errors.

Use the following commands to clone the Poky repository and then checkout the yocto-2.5 release. Files dunfell yoctoproject / poky GitLab. Thud git Clone via HTTPS Clone with Git or checkout with SVN using the repositorys web address. GitHub Gist: star and fork czoido#39;s gists by creating an a. There are two possible solutions to this problem: 1. Change the BIOS USB Device setting to HDD mode. The label will vary by device, but the idea is to force BIOS to read the Cylinder/Head/Sector geometry from the device. 2. Use a "; image with an EFI partition a) With a default grub-efi bootloader: # dd if=core-image-minimal-genericx86-64. Teams. Q&A for work. Connect and share knowledge within a single location that is structured and easy to search. Learn more.

Yoctoproject / poky · GitLab.

Yocto: відновити частину проекту - gstreamer, yocto Specifically, Yocto as has a root "poky" tree, which is one git repo, and then within the poky/ directory may be several other recipe directory trees, each possibly being their own SCM (git or otherwise) Specifically, Yocto as has a root "poky" tree, which is one git. What to do with the config fragment. This bit gets a little more tricky. I followed the instructions here and don't want to put a wordy explanation here, so I have put my code up here.. Fiddling with devicetree. To enable the USB OTG mode for the drivers in the kernel, we need to change the runtime configuration of the dwc2 driver in the kernel. Step 2. Download the necessary meta-layers to build the meta-toolchain. A ll meta-layers must match the same version as Poky you cloned from Github when setting up your Yocto Project environment.

Files · yocto-3.1.11 · yoctoproject / poky · GitLab.

Use Git to Clone Poky... (Poky). After Yocto Project Version 1.0, the Yocto Project and OpenEmbedded agreed to work together and share a common core set of metadata (OE-Core), which contained much of the functionality previously found in Poky. This collaboration achieved a long-standing OpenEmbedded objective for having a more tightly.

ExorEmbedded/yocto-poky: Clone of Yocto poky repository - GitHub.

Unable to clone yocto poky repo in ubuntu PC. 6. In Yocto, how to read a git repository via ssh on a nonstandard port. 0. MUSL C library does not work yocto project. 0. meta-oe of meta-openembedded vs meta of Poky. Hot Network Questions 14-50 outlet -- ok to mount on floor?. On Tue, Aug 21, 2018 at 11:54 PM pawanKumar <; wrote: Hi, I'm new to this clearlinux environment and I want to know how. Jun 19, 2015 · The Yocto project provides its own distribution layer called “Poky”, which makes use of bitbake and OpenEmbedded. However the Toradex BSP uses “Ångström”, another well known distribution layer for OpenEmbedded. One advantage of using Ångström instead of Poky is that Ångström also provides pre-built packages.

PDF Project why and how to use clang compiler with Yocto.

You can build a basic machine image for the handful of machines supported by the Yocto Reference BSP, but for optimal results you will probably need at least the BSP layer specific to your board (eg, RPi) plus the Yocto reference distribution BSP data for Poky and the OE meta layers. You can clone them all manually or use a repo manifest.

Yocto — conan 1.36.0 documentation.

May 17, 2020 · Clone the dependency repositories. For all upstream repositories, use the (dunfell) branch. The directory layout I am describing here is my preference. All of the paths to the meta-layers are configurable. If you choose something different, adjust the following instructions accordingly. First the main Yocto project poky layer. The 2 Yocto based Linux OS images we made for different case studies: 1. Yocto based Linux OS: Lite version - a lightweight Yocto image that weighs only 60MB and includes the basic packages that are needed in a "single-mission" IoT product, including: Vim and Nano editors, lsmode, ping, ifconfig, systemd service manager and reduced busybox.... Buildinfo is available in Yocto 1.8 and later. Run the "oe-init-build-env" script to initialize the built environment and it creates the build directory in the poky folder. Go inside the Poky directory and run the below command. This command creates a build directory inside the poky and it would be your current working directory. $ source oe-init-build-env. Build environment setup.

Build image for raspberry pi using yocto - Welcome.

The Yocto Project is an open-source project that delivers a set of tools that create operating system images for embedded Linux systems. The Yocto Project tools are based on the OpenEmbedded project, which uses the BitBake build tool, to construct complete Linux images. Yocto supports several Linux host distributions and it also provides a way. But I am able to clone the repo using git clone. SSH key is already added to the Gitlab. There is no config file in my ~/ Do I need to create a config file? What should be the content of the config file? Update. After following some discussion from other posts, I added "config" file. ~/. Format the two partitions using the following commands: -F 32 -n boot /dev/sdX1 mkfs.ext4 -L root /dev/sdX2. Copy the image files into the boot partition: Next you copy the files required for booting to the boot partition of the SD card. Here is an example for the Zedboard (ARMv7 32-bit - Cortex A9).

Yocto Project - DigitalGate Amg.

Git clone warrior git git yoctoproject org poky git.

This course is for anyone who wants to create a minimum viable product (MVP) using a Raspberry pi 4 hardware and the Yocto Project. If you have a web app already or need to figure out everything else such as: Connecting to a wifi without SSH , Shutting down or Rebooting your Pi, and Logging your activities via a web app, then this is the course for you.. 15 hours ago · The Raspberry Pi 4. Jul 05, 2022 · Use Git to Clone Poky #182; Once you complete the setup instructions for your machine, you need to get a copy of the Poky repository on your build host. Use the following commands to clone the Poky repository and then checkout the yocto-2.5 release. Iptables conntrack not working in #yocto warrior - Yocto Project.

Yocto Project Quick Build.

Nov 01, 2015 · 11-02-2015 06:48 AM. I think you can try the latest 15.0 where getting a new yocto branch should be more stable version from the rocketboard. 11-02-2015 08:48 AM. You can start building the boot loader and then continue with the kernel, for first timer it should take up maybe 2-3 hours, my computer is a i7 processor.

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tannatechbiz · 3 years

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Find Best Nvidia Jetson Nano Developer Kit!!

Get started today with the Nvidia Jetson Nano Developer Kit by TANNA TechBiz!!

Nvidia Jetson Nano makes it easy for developers to connect a diverse set of the latest sensors to allow a range of AI applications. This SDK is used across the NVIDIA Jetson family of products and is fully compatible with NVIDIA's Artificial Intelligence frameworks for deploying and training deploying AI software.

Heavy applications can be executed using this Nvidia Jetson Nano. It also has an SD Card image boot like Raspberry pi, but with GPU (Graphical Processing Unit). It runs multiple neural networks in parallel and processes several high-resolution sensors concurrently.

Jetson Nano is used for various ML (Machine Learning) & DL (Deep Learning) research in various fields for Speech processing, Image processing, and sensor based analysis. It conveys & computes performance to run modern Artificial Intelligence workloads at unprecedented size. It is extremely power-efficient, consuming as little as 5 watts.

Buy Now!! Nvidia Jetson Nano with latest updated Specifications & features. Nvidia Jetson Nano Development Kit is a power-efficient and low cost. Best Online Purchase NVIDIA Jetson Nano in India at TANNA TechBiz.

Nvidia Jetson Nano Developer Kit is small but, supported by Nvidia JetPack, which includes, Linux OS, NVIDIA CUDA, cuDNN, board support package and TensorRT software libraries for multimedia processing, deep learning, computer vision, GPU computing, and much more.

Nvidia Jetson Nano frees you to innovate at the edge. You Experience powerful and efficient Artificial Intelligence, computer vision, and high-performance computing at just 5 to 10 watts. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started.

Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), cuDNN, and TensorRT, Linux OS, NVIDIA CUDA other software libraries for deep learning, multimedia processing, computer vision, GPU computing, and much more.

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fastcompression · 5 years

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Jetson image processing: ISP libargus and Fastvideo SDK

Jetson image processing for camera applications

Jetson hardware is absolutely unique solution from NVIDIA. This is essentially a mini PC with extremely powerful and versatile hardware. Apart from ARM processor it has a sophisticated high performance GPU with CUDA cores, Tensor cores (on AGX Xavier), software for CPU/GPU and AI.

Below you can see an example of how to build a camera system on Jetson. This is an important task if you want to create realtime solution for mobile imaging application. With a thoughtful design, one can even implement a multicamera system on just a single Jetson, and some NVIDIA partners showcase that this is in fact achievable.

How image processing could be done on NVIDIA Jetson

ISP inside Jetson (libargus library on the top of hardware solution)

V4L2 framework instead of argus/nvcamerasrc to get bayer data like v4l2-ctl

Image processing on CUDA (NPP library, Fastvideo SDK)

Image processing on ARM (C++, Python, OpenCV)

Hardware-based encoding and decoding with NVENC

AI on CUDA and/or Tensor cores

Here we consider just ISP and CUDA-based image processing pipelines to describe how the task could be solved, which image processing algorithms could be utilized, etc. For the beginning we consider NVIDIA camera architecture.

Camera Architecture Stack

The NVIDIA camera software architecture includes NVIDIA components for ease of development and customization:

Fig.1. Diagram from Development Guide for NVIDIA Tegra Linux Driver Package (31.1 Release, Nov.2018)

NVIDIA Components of the camera architecture

libargus - provides low-level API based on the camera core stack

nvarguscamerasrc - NVIDIA camera GStreamer plugin that provides options to control ISP properties using the ARGUS API

v4l2src - standard Linux V4L2 application that uses direct kernel IOCTL calls to access V4L2 functionality

NVIDIA provides OV5693 Bayer sensor as a sample and tunes this sensor for the Jetson platform. The drive code, based on the media controller framework, is available at ./kernel/nvidia/drivers/media/i2c/ov5693.c, NVIDIA further offers additional sensor support for BSP software releases. Developers must work with NVIDIA certified camera partners for any Bayer sensor and tuning support.

The work involved includes:

Sensor driver development

Custom tools for sensor characterization

Image quality tuning

These tools and operating mechanisms are NOT part of the public Jetson Embedded Platform (JEP) Board Support Package release. For more information on sensor driver development, see the NVIDIA V4L2 Sensor Driver Programming Guide.

Jetson includes internal hardware-based solution (ISP) which was created for realtime camera applications. To control these features on Jetson hardware, there is libargus library.

Camera application API libargus offers:

low-level frame-synchronous API for camera applications, with per frame camera parameter control

multiple (including synchronized) camera support

EGL stream outputs

RAW output CSI cameras needing ISP can be used with either libargus or GStreamer plugin. In either case, the V4L2 media-controller sensor driver API is used.

Sensor driver API (V4L2 API) enables:

video decode

encode

format conversion

scaling functionality

V4L2 for encode opens up many features like bit rate control, quality presets, low latency encode, temporal tradeoff, motion vector maps, and more.

Libargus library features for Jetson ISP

Bad pixel correction

Bayer domain hardware noise reduction

Per-channel black-level compensation

High-order lens-shading compensation

3A: AF/AE/AWB

Demosaic

3x3 color transform

Color artifact suppression

Downscaling

Edge enhancement (sharp)

To summarize, ISP is a fixed-function processing block which can be configured through the Argus API, Linux drivers, or the Technical Reference Manual which contains register information for particular Jetson.

All information about utilized algorithms (AF, AE, demosaicing, resizing) is closed and user needs to test them to evaluate quality and performance.

ISP is a hardware-based solution for image processing on Jetson and it was done for mobile camera applications with high performance and low latency.

How to choose the right camera

To be able to utilize ISP, we need a camera with CSI interface. NVIDIA partner - Leopard Imaging company is manufacturing many cameras with that interface and you can choose according to requirements. CSI interface is the key feature to send data from a camera to Jetson with a possibility to utilize ISP for image processing.

If we have a camera without CSI support (for example, GigE, USB-3.x, CameraLink, Coax, 10-GigE, PCIE camera), we need to create CSI driver to be able to work with Jetson ISP.

Even if we don't have CSI driver, there is still a way to connect your camera to Jetson.

You just need to utilize proper carrier board with correct hardware output. Usually this is either USB-3.x or PCIE. There is a wide choice of USB3 cameras on the market and one can easily choose any camera or carrier board you need. For example, from NVIDIA partner - XIMEA GmbH.

Fig.2. XIMEA carrier board for NVIDIA Jetson TX1/TX2

To work further with the camera, you need camera driver for L4T and ARM processor - this is minimum requirement to connect your camera to Jetson via carrier board.

However, keep in mind that in this case ISP is not available. Next part deals with such situation.

How to work with non-CSI cameras on Jetson

Let's assume that we've already connected non-CSI camera to Jetson and we can send data from the camera to system memory on Jetson.

Now we can't access Jetson ISP and we need to consider other ways of image processing. The fastest solution is to utilize Fastvideo SDK for Jetson GPUs.

That SDK actually exists for Jetson TK1, TX1, TX2, TX2i and AGX Xavier.

You just need to send data to GPU memory and to create full image processing pipeline on CUDA. This is the way to keep CPU free and to ensure fast processing due to excellent performance of mobile Jetson GPU on CUDA. Based on that approach you can create multicamera systems on Jetson with Fastvideo SDK together with USB-3.x or PCIE cameras.

For more info about realtime Jetson applications with multiple cameras you can have a look the site of NVIDIA partner XIMEA, which is manufacturing high quality cameras for machine vision, industrial and scientific applications.

Fig.3. NVIDIA Jetson with multiple cameras on TX1/TX2 carrier board from XIMEA

Image processing on Jetson with Fastvideo SDK

Fastvideo SDK is intended for camera applications and it has wide choice of features for realtime raw image processing on GPU. That SDK also exists for NVIDIA GeForce/Quadro/Tesla GPUs and consists of high quality algorithms which require significant computational power.

This is the key difference in comparison with any hardware-based solution. Usually ISP/FPGA/ASIC image processing modules offer low latency and high performance, but because of hardware restrictions, utilized algorithms are relatively simple and have moderate image quality.

Apart from image processing modules, Fastvideo SDK has high speed compression solutions: JPEG (8/12 bits), JPEG2000 (8-16 bits), Bayer (8/12 bits) codecs which are implemented on GPU. These codecs are working on CUDA and they were heavily tested, so they are reliable and very fast.

For majority of camera applications, 12 bits per pixel is a standard bit depth and it makes sense to store compressed images at least in 12-bit format or even at 16-bit.

Full image processing pipeline on Fastvideo SDK is done at 16-bit precision, but some modules that require better precision are implemented with float.

Fig.4. Image processing workflow on CUDA at Fastvideo SDK for camera applications

To check quality and performance of raw image processing with Fastvideo SDK, user can download GUI application which is called Fast CinemaDNG Processor. The software is fully based on Fastvideo SDK and it could be downloaded from www.fastcinemadng.com together with sample image series in DNG format.

That application has benchmarks window to check time measurements for each stage of image processing pipeline on GPU.

High-resolution multicamera system for UAV Aerial Mapping

Application: 5K vision system for Long Distance Remote UAV

Manufacturer: MRTech company

Cameras

One XIMEA 20 MPix PCIe camera MX200CG-CM

Two XIMEA 3.1 MPix PCIe cameras MX031CG-SY

Hardware

NVIDIA Jetson TX2 or TX2i module with custom carrier board

NVMe SSD 960 PRO M.2 onboard

Jetson GPU image processing

Full processing workflow on CUDA: acquisition, black level, white balance, LUT, high quality demosaicing, etc.

H.264/265 encoding, RTSP streaming via radio channel

Streaming of 4K images at 25 fps and 2× Full HD 1080p (1920 × 1080) images at 30 fps simultaneously

Save high resolution snapshot images to SSD

Power usage 35W (including all cameras)

Fig.5. NVIDIA Jetson TX2 with XIMEA MX200CG-CM (20 MPix) and two MX031CG-SY (3.1 MPix) cameras.

More information about MRTech solutions for Jetson image processing you can find here.

AI imaging applications on Jetson

With the arrival of AI solutions, the following task needs to be solved: how to prepare high quality input data for such systems?

Usually we get images from cameras in realtime and if we need high quality images, then choosing a high resolution color camera with bayer pattern is justified.

Next we need to implement fast raw processing and after that we will be able to feed our AI solution with good pictures in realtime.

The latest Jetson AGX Xavier has high performance Tensor cores for AI applications and these cores are ready to receive images from CUDA software. Thus we can send data directly from CUDA cores to Tensor cores to solve the whole task very fast.

Links:

XIMEA cameras for Jetson applications

MRTech software solutions for Jetson imaging systems

Fastvideo Image & Video Processing SDK for NVIDIA Jetson

Low latency H.264 streaming on Jetson TX2

Original article see here: https://www.fastcompression.com/blog/jetson-image-processing.htm

#Jetson CUDA Fastvideo SDK NVIDIA XIMEA

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thaithanhbinh · 4 years

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Tại sao Android lại ít được cập nhật phần mềm?

Vừa qua, đội ngũ phát triển Android đã tổ chức 1 buổi AMA (Ask Me Anything) trên mạng xã hội Reddit nhân dịp ra mắt phiên bản Android 11. Một người dùng đã đặt ra câu hỏi về nguyên nhân của vấn đề nhức nhối tồn tại nhiều năm trên các mẫu điện thoại Android: mức độ thường xuyên của các bản cập nhật, nhất là so với Apple. Và câu vấn đáp thẳng thắn và chi tiết của một lập trình viên từ Google – Iliyan Malchev – đã phần nào giải đáp thắc mắc suốt nhiều năm nay cho chúng ta. Sau đây là phần lược dịch câu vấn đáp của Malchev.

Điểm cốt yếu ở nơi này là vấn đề cơ bản mà các hãng sản xuất (OEM) gặp phải với các bản cập nhật: chi phí. Mọi OEM đều muốn thiết bị của mình có chức năng được cập nhật trong suốt vòng đời sử dụng. Nhưng họ bị hạn chế bởi những lựa chọn kinh tế khó khăn, thể hiện qua các loại chi phí. Vì vậy, để tăng tốc độ cập nhật, chúng tôi tập trung vào việc giảm những chi phí ấy.

Câu hỏi được đặt ra là, tại sao chi phí cập nhật lại cao? Với chúng tôi, câu vấn đáp có thể bao gồm các phần sau:

– Android là phần mềm mã nguồn mở và cho phép tùy biến

– Hệ sinh thái Android là rất rộng lớn

– Với mọi thiết bị, đều có vài công ty tham gia quá trình sản xuất

– Các nhà mạng phải tốn phí chứng chỉ cho mọi bản cập nhật lớn

Hai nguyên nhân đầu tiên có thể coi là đặc thù với Android, còn 2 nguyên nhân còn lại là vấn đề chung. Nguyên nhân thứ 3 thì có phần phổ biến hơn với hệ sinh thái Android. Kết hợp 3 nguyên nhân đầu tiên dẫn tới nguyên nhân thứ 4: chi phí chứng chỉ cao hơn.

Android là phần mềm mã nguồn mở

Mọi dự án mã nguồn mở, đúng như tên thường gọi của nó, đều cho phép những điều chỉnh, thay đổi được thực hiện. Sự linh hoạt trong vấn đề này là một tính chất cốt lõi của Bộ xử lý (HĐH) Android. Đó cũng là 1 trong những nguyên nhân chính tại sao Android lại có thể thành công như vậy. Khi đội ngũ chúng tôi ở Google tung ra một phiên bản mới của HĐH Android, chúng tôi thực ra chỉ phát hành phiên bản AOSP (Android Open Source Project – Dự án Android mã nguồn mở), tất nhiên là dưới dạng mã nguồn.

Điều quan trọng cần phải chỉ ra là: phiên bản AOSP không phải là 1 mặt hàng cuối cùng (để tới tay người dùng). Có thể coi là nó mang sứ mệnh giới thiệu một dạng lõi cho HĐH, với mọi thành phần khiến Android là Android: môi trường ART, các hệ thống quản lí hoạt động, quản lí cửa sổ và các gói phần mềm (package), các hệ thống phụ về đa phương tiện, các cảm biến, máy ảnh, logic khởi tạo,v.v… những thứ như thế.

Bản phát hành AOSP cũng bao gồm các phiên bản mã nguồn mở của nhiều thành phần của HĐH mà người dùng cuối (end user) sẽ tương tác nhiều hơn như giao diện hệ thống hay trình khởi chạy ứng dụng (App Launcher), nhiều ứng dụng bổ sung nữa. Nhưng đây chỉ là những phiên bản mã nguồn mở mang tính tham chiếu, và chúng tôi không hề định sử dụng chúng trên bất cứ thiết bị nào tới tay người dùng.

Android có chức năng tùy biến

Khi chúng tôi phát hành một phiên bản AOSP, chúng tôi muốn nó trở thành một bản tham chiếu. Các OEM cũng như các hãng sản xuất vi xử lí silicon có toàn quyền điều chỉnh mã nguồn này để phù hợp với nhu cầu của họ về sản phẩm, miễn là họ vượt qua một bộ những bài kiểm tra, để chắc hẳn rằng những điều chỉnh này không can thiệp vào hành vi của HĐH, điều có thể gây ra việc các ứng dụng không hoạt động. Hầu hết các đối tác của chúng tôi đều tận dụng sự linh hoạt này để tạo nên việc khác biệt cho các sản phẩm của họ.

Tùy biến sẽ có chi phí duy trì

Tôi phải nhắc lại một lần nữa, điều này là đúng với mọi dự án mã nguồn mở. Bạn sử dụng một bản phát hành (release), thực hiện nhiều thay đổi, tiếp theo đó lại phát hành phiên bản đã được thay đổi (ngay đến những kernel – lõi của HĐH Linux cũng thường được thay đổi theo cách này). Khi mà bản phát hành tiếp theo (của mã nguồn gốc) được công bố, bạn phải chuyển đổi (port) hoặc hoàn toàn tái cấu trúc lại phần mã lệnh cũ của những biến đổi bạn đã thực hiện. Việc này rất tốn thời gian và công sức, mà thời gian và công sức chính là chi phí.

Sẽ có 2 cách khắc phục chính để giảm thiểu chi phí: cách thứ nhất là bạn (chẳng hạn như trong hoàn cảnh này là các OEM) đóng góp những thay đổi của mình vào cả dự án mã nguồn mở, từ đó chi phí phát sinh sẽ được bao hàm bên trong (chi phí của) dự án chính và mọi đối tượng sẽ đảm nhiệm phụ trách về phần thay đổi của bạn. Đây là cách mà cộng đồng Linux kernel hoạt động. Nhưng trường hợp của Android thì lại khác. Điều hiển nhiên là rất nhiều tùy chỉnh đối với mã nguồn cơ sở của Android là độc quyền (bởi các hãng), chính điều này đã ngăn cản chúng được cập nhật (bởi Android Dev Team). Điều này có nghĩa rằng, các đối tác của chúng tôi phải tự đảm nhiệm phụ trách việc cập nhật những thay đổi của họ.

Đội ngũ phát triển Android vẫn luôn chia sẻ mã nguồn AOSP

Chúng ta cũng có thể nói: “Thế thì đừng tạo ra thay đổi gì (so với nguyên gốc) cả, và các anh sẽ chẳng phải chuyển đổi chúng mỗi khi cập nhật.”. Nhưng đó không phải là cách Android hoạt động: bản sắc của chúng tôi là sự linh hoạt và sự đa dạng, và chúng tôi muốn tạo cơ hội cho các đối tác của mình tận dụng tối đa khả năng này.

Một giải pháp khác để giảm thiểu chi phí là tạo ra một khoảng trung hòa giữa việc tạo ra thay đổi ở mọi nơi và không bao giờ thay đổi gì cả. Cụ thể hơn, chúng tôi tạo ra những ranh giới – những giao thức giữa các thành phần – và phải đạt được một thỏa thuận chung rõ ràng với các đối tác: phải xác định rõ những loại thành phần họ có thể (và sẽ) điều chỉnh, không hề đánh mất sự linh hoạt, và những loại thành phần mà phải được giữ hoàn toàn tương tự giữa mọi cá thể trong hệ sinh thái.

Từ đó, chúng tôi đã cho ra 2 dự án để giải quyết vấn đề cập nhật: Project Treble và Project Mainline. Hai dự án này đều nhằm giải quyết vấn đề ranh giới đã đề cập bên trên, theo cách khác nhau nhưng lại bổ khuyết được cho nhau.

Project Treble

Với dự án Treble, chúng tôi tìm cách để tạo nên những giao thức chắc chắn giữa các thành phần không tùy thuộc vào phần cứng của HĐH, và những phần mà cần để tâm tới phần cứng. Chúng tôi thường nhắc tới loại đầu tiên trong tài liệu chính thức dưới tên gọi “chương trình khung” (framework), hay “bản hệ thống” (system image), hoặc “HĐH Lõi” (Core OS). Loại thứ hai thì chúng tôi thường đề cập đến như là “bản cho nơi cung cấp” (vendor image) hay “các HAL” (Hardware Abstraction Layer – lớp trừu tượng hóa phần cứng).

Làm như vậy có ích gì? Lời giải đáp giúp giải quyết một gạch đầu dòng phía bên trên: “Hệ sinh thái Android rất rộng lớn”. Với một hãng sản xuất điện thoại Android (một OEM), để sản xuất một thiết bị, họ phải sử dụng một hệ thống trên vi mạch (SoC – System on Chip, bao gồm bộ vi xử lí và cách thiết bị ngoại vi trên cùng một khuôn chip) từ một hãng sản xuất chip silicon, chẳng hạn như Qualcomm, Mediatek, Unisoc hay Rockchip. Nhưng hãng sản xuất chip không chỉ cung cấp SoC cho các OEM này, mà họ cũng phải cung cấp cả một môi trường phát triển cùng với nó, được xem là BSP (Board Support Package – gói (phần mềm) hỗ trợ bảng mạch). Chính BSP cũng là một phiên bản của Android! So với bản AOSP, nó chứa nhiều tinh chỉnh để có thêm các tính năng như khả năng gọi điện (chẳng hạn như thực hiện cuộc gọi 4G hay 5G), hay để hỗ trợ các thành phần phần cứng đặc trưng của SoC đó, và cũng để thêm giá trị đặc trưng cho SoC và OEM sử dụng SoC đó (chẳng hạn như cho riêng thị trường nào đó chẳng hạn). Nhìn chung, BSP cung cấp một phiên bản Android cho hãng sản xuất, điều kiện cần để có thể sản xuất bất cứ thiết bị nào sử dụng SoC đó. Những tinh chỉnh ở phiên bản BSP này lại thường xảy ra ở các tầng thấp hơn của bộ xử lý.

Sau đó, các OEM sẽ nhận được BSP rồi tiếp tục thực hiện những thay đổi mã nguồn để hoạt động được trên những thiết bị của họ. Không như BSP, những thay đổi này thường diễn ra ở tầng trên của HĐH Android, chẳng hạn như phần Cài đặt, trình khởi chạy ứng dụng hay trình quản lí cửa sổ.

Lượng người dùng cập nhật Android 9 là cao hơn hẳn một phần nhờ dự án Treble

Với dự án Treble, chúng tôi nhận xét sự chia tách này và nhận ra nó đã tạo ra một ranh giới tự nhiên giữa hãng sản xuất thiết bị và hãng sản xuất chip, và tổng quát hơn là giữa phần “trừu tượng” của HĐH Android và phần phụ thuộc phần cứng của nó.

Mục tiêu và kết quả của những nỗ lực của chúng tôi với Treble được liệt kê dưới đây, bao gồm:

– Để làm được một bản tham chiếu sẵn có và được chuẩn hóa, không chỉ ở dạng mã nguồn, mà ở dạng 1 mặt hàng thực, cho mọi phiên bản Android. Chúng tôi gọi nó là phiên bản hệ thống chung (Generic System Image – GSI)

– Các bản GSI có thể tương thích ngược với các phiên bản BSP cũ hơn cho hãng sản xuất

– Từ đó sẽ cho phép các OEM có thể duy trì những thay đổi của mình với các phiên bản hệ thống của họ mà không xung đột với những tầng bên dưới (của HĐH)

– Kết cấu này có chức năng mở rộng, để cả hãng sản xuất chip và OEM có chức năng mở rộng giao thức cho những khối phần cứng đặc trưng của họ.

Đáng nhớ nhất là, từ góc độ chi phí, những nỗ lực của chúng tôi sẽ tách riêng được chi phí duy trì của các OEM ra khỏi chi phí của các hãng sản xuất chip, và từ đó giúp giảm chi phí cho cả hai bên. Dự án này đã có những thành tích nhất định, được đăng tải trên blog của chúng tôi.

Dự án Mainline

Dự án Mainline lại tiếp cận với câu hỏi về ranh giới theo một hướng khác. Nếu như Treble sẽ nói “đây là một ranh giới, chúng ta hãy tinh chỉnh các bước làm bằng một cách nào đó, miễn là mọi thứ tiếp tục hoạt động qua ranh giới này”, thì Mainline lại có hướng: “đây là một bộ các ranh giới phân định một thành phần mà bản thân mỗi chúng ta nên đồng ý không thực hiện điều chỉnh”. Những thành phần kiểu này thường sẽ là những phần đáng nhớ nhất của HĐH Android – từ các góc độ như độ ổn định, khả năng bảo mật và khả năng bảo vệ riêng tư.

Chúng tôi gọi những thành phần này là các môđun Mainline (Mainline Module). Một môđun Mainline là một phần mã nguồn với các thuộc tính sau:

– Trước khi (bản) Android được phát hành, có thể được cả Google và các đối tác chung tay điều chỉnh

– Sau khi bản Android được phát hành, sẽ là hoàn toàn thống nhất với mọi thiết bị trong hệ sinh thái

– Chỉ thay đổi, cập nhật với những lỗi nghiêm trọng và liên quan đến bảo mật.

Phẩm chất quyết định của một môđun Mainline là mức ổn định giữa mọi thiết bị. Với các môđun Mainline, chúng tôi đều đồng ý rằng không có chỗ cho sự khác biệt có chủ đích (giữa các OEM). Dự án Mainline được bắt đầu với Android 10 và hiện đã được giao phó nhiệm vụ đưa những bản vá tới nhiều và nhiều hơn thế nữa các thiết bị, ở một tốc độ và chất lượng mà sẽ không bao giờ trở thành sự thực nếu không có nó.

Tổng kết lại: Lời giải đáp cho câu hỏi của bạn là: “chi phí duy trì” là lí do gây ra sự khó khăn cho việc cập nhật, và giải pháp của chúng tôi đã từng và đang (và sẽ tiếp tục) là một cách tiếp cận vấn đề thận trọng. Chúng tôi định nghĩa những ranh giới để giúp cho ngày càng nhiều phần của HĐH có thể được cập nhật một cách nhanh hơn, mà không phải hy sinh sự linh hoạt cũng như sức mạnh mà bản sắc mã nguồn mở của Android mang lại.

(Theo Trí Thức Trẻ)

Google đã đáp ứng được vấn đề trầm kha của Android?

Android 10 có tỉ lệ cài đặt nhanh nhất lịch sử Google nhưng vẫn kém xa iOS 13 của Apple.

Nguồn: ictnews

Bài viết Tại sao Android lại ít được cập nhật phần mềm? đã xuất hiện đầu tiên vào ngày Đồ Chơi Công Nghệ.

source https://dochoicongnghe.com.vn/tai-sao-android-lai-it-duoc-cap-nhat-phan-mem-18857.html

#do choi cong nghe #tin tuc cong nghe #thai thanh binh

0 notes

rosesana · 5 years

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NVIDIA Jetson Nano Development Kit-B01

Nano Jetson NVIDIA Developer Kit is a development kit Artificial Intelligence (AI) that can be used to run a variety of modern AI load with amazing performance. With the NVIDIA Developer Kit Nano Jetson now the developers can run the AI framework and a model for application of image recognition, object detection, segmentation, speech recognition and many more. Jetson NVIDIA Developer Kit Nano is powered by a micro USB and equipped with many I / O pins, ranging from GPIO to CSI. Pin I / O are plentiful this allows developers to connect various sensors for AI application development purposes. Nano Jetson NVIDIA Developer Kit is also supported by NVIDIA JetPack, which includes Board Support Package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT for the purposes of deep learning, computer vision, GPU computing,

NOTE: This kit is the latest version of NVIDIA Jetson Nano which has undergone several revisions of the terms of the arrangement of the components of the previous version (A02). The different versions of A02 and B01 can be seen in the image below.

Specification

GPU: 128-core Maxwell

CPU:Quad-core ARM A57 @ 1.43 GHz

RAM: 4 GB 64-bit LPDDR4 25.6 GB/s

External Storage: microSD (not included)

Video Encoder: 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)

Video Decoder: 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30|(H.264/H.265)

Soket Kamera: 2x MIPI CSI-2 DPHY lanes

Konektifitas: Gigabit Ethernet, M.2 Key E

Display Output: HDMI 2.0 and eDP 1.4

USB: 4x USB 3.0, USB 2.0 Micro-B

Interface: GPIO, I2C, I2S, SPI, UART

Dimension: 100 mm x 80 mm x 29 mm

Document

NVIDIA JetPack SDK

Development Kit User Guide

Grove Python Libraries for Nvidia Jetson Nano

NVIDIA Jetson™ family for products

Sample Applications

Setting up Jetson with JetPack

Classifying Images with ImageNet

Locating Objects with DetectNet

Semantic Segmentation with SegNet

Transfer Learning with PyTorch

equipment Products

NVIDIA Jetson Nano module and carrier board

Quick Start Guide and Support Guide

https://ift.tt/3aSPOT1 March 10, 2020 at 11:16AM

#Development Kit

0 notes

jamesmcphee30 · 6 years

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LFD460 Embedded Linux Development with Yocto Project

Obtain a solid understanding of embedded development using the Yocto Project, including the Poky Reference Distribution and Bitbake, the use of emulators, building images for multiple architectures and the creation of board support packages (BSP).

from martinos https://www.linux.com/training/lfd460-embedded-linux-development-yocto-project-22

0 notes

toradex · 7 years

Text

Starting with OpenCV on i.MX 6 Processors

Introduction

As the saying goes, a picture is worth a thousand words. It is indeed true to some extent: a picture can hold information about objects, environment, text, people, age and situations, among other information. It can also be extended to video, that can be interpreted as a series of pictures and thus holds motion information.

This might be a good hint as to why computer vision (CV) has been a field of study that is expanding its boundaries every day. But then we come to the question: what is computer vision? It is the ability to extract meaning from an image, or a series of images. It is not to be confused with digital imaging neither image processing, which are the production of an input image and the application of mathematical operations to images, respectively. Indeed, they are both required to make CV possible.

But what might be somehow trivial to human beings, such as reading or recognizing people, is not always true when talking about computers interpreting images. Although nowadays there are many well known applications such as face detection in digital cameras and even face recognition in some systems, or optical character recognition (OCR) for book scanners and license plate reading in traffic monitoring systems, these are fields that nearly didn't exist 15 years ago in people's daily lives. Self-driving cars going from controlled environments to the streets are a good measure of how cutting-edge this technology is, and one of the enablers of CV is the advancement of computing power in smaller packages.

Being so, this blog post is an introduction to the use of computer vision in embedded systems, by employing the OpenCV 2.4 and 3.1 versions in Computer on Modules (CoMs) equipped with NXP i.MX 6 processors. The CoMs chosen were the Colibri and Apalis families from Toradex.

OpenCV stands for Open Source Computer Vision Library, which is a set of libraries that contain several hundreds of computer vision related algorithms. It has a modular structure, divided in a core library and several others such as image processing module, video analysis module and user interface capabilities module, among others.

Considerations about OpenCV, i.MX 6 processors and the Toradex modules

OpenCV is a set of libraries that computes mathematical operations on the CPU by default. It has support for multicore processing by using a few external libraries such as OpenMP (Open Multi-processing) and TBB (Threading Building Blocks). This blog post will not go deeper into comparing the implementation aspects of the choices available, but the performance of a specific application might change with different libraries.

Regarding support for the NEON floating-point unit coprocessor, the release of OpenCV 3.0 states that approximately 40 functions have been accelerated and a new HAL (hardware abstraction layer) provides an easy way to create NEON-optimized code, which is a good way to enhance performance in many ARM embedded systems. I didn't dive deep into it, but if you like to see under the hood, having a look at the OpenCV source-code (1, 2) might be interesting.

This blog post will present how to use OpenCV 2.4 and also OpenCV 3.1 - this was decided because there might be readers with legacy applications that want to use the older version. It is a good opportunity for you to compare performance between versions and have a hint about the NEON optimizations gains.

The i.MX 6 single/dual lite SoC has graphics GPU (GC880) which supports OpenGL ES, while the i.MX 6 dual/quad SoC, has 3D graphics GPU (GC2000) which supports OpenGL ES and also OpenCL Embedded Profile, but not the Full Profile. The i.MX 6 also has 2D GPU (GC320), IPU and, for the dual/quad version, vector GPU (GC335), but this blog post will not discuss the possibility of using these hardware capabilities with OpenCV - it suffices to say that OpenCV source-code does not support them by default, therefore a considerable amount of effort would be required to take advantage of the i.MX 6 hardware specifics.

While OpenCL is a general purpose GPU programming language, its use is not the target of this blog post. OpenGL is an API for rendering 2D and 3D graphics on the GPU, and therefore is not meant for general purpose computing, although some experiments (1) have demonstrated that it is possible to use OpenGL ES for general purpose image processing, and there is even an application-note by NXP for those interested. If you would like to use GPU accelerated OpenCV out-of-the-box, Toradex has a module that supports CUDA – the Apalis TK1. See this blog post for more details.

Despite all the hardware capabilities available and possibly usable to gain performance, according to this presentation, the optimization of OpenCV source-code focusing only software and the NEON co-processor could yield a performance enhancement of 2-3x for algorithm and another 3-4x NEON optimizations.

The Images 1 and 2 present, respectively, the Colibri iMX6DL + Colibri Evaluation Board and the Apalis iMX6Q + Apalis Evaluation Board, both with supply, debug UART, ethernet, USB camera and VGA display cables plugged in.

Colibri iMX6DL Colibri Evaluation Board setup

Apalis iMX6Q Apalis Evaluation Board setup

It is important to notice that different USB camera models had a performance offset, and that the camera employed in this blog post is a generic consumer camera – the driver was listed as “Aveo Technology Corp”. There are also professional cameras in the market, USB or not, such as the solutions provided by Basler AG, that are meant for embedded solutions when going to develop a real-world solution.

Professional camera from Basler AG

In addition, Toradex has the CSI Camera Module 5MP OV5640. It is an add-on board for the Apalis computer-on-module which uses MIPI-CSI Interface. It uses the OmniVision OV5640 camera sensor with built-in auto-focus. The OV5640 image sensor is a low voltage, high-performance, 1/4-inch 5 megapixel CMOS image sensor that provides the full functionality of a single chip 5 megapixel (2592x1944) camera. The CSI Camera Module 5MP OV5640 can be connected to the MIPI-CSI connector on the Ixora carrier board V1.1 using a 24 way 0.5mm pitch FFC cable.

Toradex CSI Camera Module 5MP OV5640

At the end of this blog post, a summary of the instructions to install OpenCV and deploy applications to the target is provided.

Building Linux image with OpenCV

Images for the OpenCV 2.4 and 3.1 are built with OpenEmbedded. You may follow this article to set up your host machine. The first step required is to install the system prerequisites for OpenEmbedded. Below an example is given for Ubuntu 16.04 - for other versions of Ubuntu and Fedora, please refer to the article link above:

sudo dpkg --add-architecture i386 sudo apt-get update sudo apt-get install g++-5-multilib sudo apt-get install curl dosfstools gawk g++-multilib gcc-multilib lib32z1-dev libcrypto++9v5:i386 libcrypto++-dev:i386 liblzo2-dev:i386 libstdc++-5-dev:i386 libusb-1.0-0:i386 libusb-1.0-0-dev:i386 uuid-dev:i386 cd /usr/lib; sudo ln -s libcrypto++.so.9.0.0 libcryptopp.so.6

The repo utility must also be installed, to fetch the various git repositories required to build the images:

mkdir ~/bin export PATH=~/bin:$PATH curl http://commondatastorage.googleapis.com/git-repo-downloads/repo > ~/bin/repo chmod a+x ~/bin/repo

Let's build the images with OpenCV 2.4 and 3.1 in different directories. If you are interested in only one version, some steps might be omitted. A directory to share the content downloaded by OpenEmbedded will also be created:

cd mkdir oe-core-opencv2.4 oe-core-opencv3.1 oe-core-downloads cd oe-core-opencv2.4 repo init -u http://git.toradex.com/toradex-bsp-platform.git -b LinuxImageV2.6.1 repo sync cd ../oe-core-opencv3.1 repo init -u http://git.toradex.com/toradex-bsp-platform.git -b LinuxImageV2.7 repo sync

OpenCV 2.4

OpenCV 2.4 will be included in the Toradex base image V2.6.1. Skip this section and go to the OpenCV3.1 if you are not interested in this version. The recipe included by

default uses the version 2.4.11 and has no support for multicore processing included.

Remove the append present in the meta-fsl-arm and the append present in the meta-toradex-demos:

rm layers/meta-fsl-arm/openembedded-layer/recipes-support/opencv/opencv_2.4.bbappend rm layers/meta-toradex-demos/recipes-support/opencv/opencv_2.4.bbappend

Let's create an append to use the version 2.4.13.2 (latest version so far) and add TBB as the framework to take advantage of multiple cores. Enter the oe-core-opencv2.4 directory:

cd oe-core-opencv2.4

Let's create an append in the meta-toradex-demos layer (layers/meta-toradex-demos/recipes-support/opencv/opencv_2.4.bbappend) with the following content:

gedit layers/meta-toradex-demos/recipes-support/opencv/opencv_2.4.bbappend ------------------------------------------------------------------------------------- SRCREV = "d7504ecaed716172806d932f91b65e2ef9bc9990" SRC_URI = "git://github.com/opencv/opencv.git;branch=2.4" PV = "2.4.13.2+git${SRCPV}" PACKAGECONFIG += " tbb" PACKAGECONFIG[tbb] = "-DWITH_TBB=ON,-DWITH_TBB=OFF,tbb,"

Alternatively, OpenMP could be used instead of TBB:

Set up the environment before configuring the machine and adding support for OpenCV. Source the export script that is inside the oe-core-opencv2.4 directory:

. export

You will automatically enter the build directory. Edit the conf/local.conf file to modify and/or add the variables below:

gedit conf/local.conf ------------------------------------------------------------------------------- MACHINE ?= "apalis-imx6" # or colibri-imx6 depending on the CoM you have # Use the previously created folder for shared downloads, e.g. DL_DIR ?= "/home/user/oe-core-downloads" ACCEPT_FSL_EULA = "1" # libgomp is optional if you use TBB IMAGE_INSTALL_append = " opencv opencv-samples libgomp"

After that you can build the image, which takes a while:

bitbake –k angstrom-lxde-image

OpenCV 3.1

OpenCV 3.1 will be included in the Toradex base image V2.7. This recipe, different from the 2.4, already has TBB support included. Still, a compiler flag must be added or the compiling process will fail. Enter the oe-core-opencv3.1 directory:

cd oe-core-opencv3.1

Create a recipe append (layers/meta-openembedded/meta-oe/recipes-support/opencv/opencv_3.1.bb) with the following contents:

gedit layers/meta-toradex-demos/recipes-support/opencv/opencv_3.1.bbappend ------------------------------------------------------------------------------------- CXXFLAGS += " -Wa,-mimplicit-it=thumb"

Alternatively, OpenMP could be used instead of TBB. If you want to do it, create the bbappend with the following contents:

gedit layers/meta-toradex-demos/recipes-support/opencv/opencv_3.1.bbappend ------------------------------------------------------------------------------------- CXXFLAGS_armv7a += " -Wa,-mimplicit-it=thumb" PACKAGECONFIG_remove = "tbb" EXTRA_OECMAKE += " -DWITH_OPENMP=ON"

Set up the environment before configuring the machine and adding support for OpenCV. Source the export script that is inside the oe-core-opencv3.1 directory:

. export

You will automatically enter the build directory. Edit the conf/local.conf file to modify and/or add the variables below:

After that you can build the image, which will take a while:

bitbake –k angstrom-lxde-image

Update the module

The images for both OpenCV versions will be found in the oe-core-opencv<version>/deploy/images/<board_name> directory, under the name <board_name>_LinuxImage<image_version>_<date>.tar.bz2. Copy the compressed image to some project directory in your computer, if you want. An example is given below for Apalis iMX6 with OpenCV 2.4, built in 2017/01/26:

cd /home/user/oe-core-opencv2.4/deploy/images/apalis-imx6/ cp Apalis_iMX6_LinuxImageV2.6.1_20170126.tar.bz2 /home/root/myProjectDir cd /home/root/myProjectDir

Please follow this article's instructions to update your module.

Generating SDK

To generate an SDK, that will be used to cross-compile applications, run the following command:

bitbake –c populate_sdk angstrom-lxde-image

After the process is complete, you will find the SDK under /deploy/sdk. Run the script to install – you will be prompted to chose an installation path:

./angstrom-glibc-x86_64-armv7at2hf-vfp-neon-v2015.12-toolchain.sh Angstrom SDK installer version nodistro.0 ========================================= Enter target directory for SDK (default: /usr/local/oecore-x86_64):

In the next steps it will be assumed that you are using the following SDK directories: For OpenCV 2.4: /usr/local/oecore-opencv2_4 For OpenCV 3.1: /usr/local/oecore-opencv3_1

Preparing for cross-compilation

After the installation is complete, you can use the SDK to compile your applications. In order to generate the Makefiles, CMake was employed. If you don't have CMake, install it:

sudo apt-get install cmake

Create a file named CMakeLists.txt inside your project folder, with the content below. Please make sure that the sysroot name inside your SDK folder is the same as the one in the script (e.g. armv7at2hf-vfp-neon-angstrom-linux-gnueabi):

cd ~ mkdir my_project gedit CMakeLists.txt -------------------------------------------------------------------------------- cmake_minimum_required(VERSION 2.8) project( MyProject ) set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin") add_executable( myApp src/myApp.cpp ) if(OCVV EQUAL 2_4) message(STATUS "OpenCV version required: ${OCVV}") SET(CMAKE_PREFIX_PATH /usr/local/oecore-opencv${OCVV}/sysroots/armv7at2hf-vfp-neon-angstrom-linux-gnueabi) elseif(OCVV EQUAL 3_1) message(STATUS "OpenCV version required: ${OCVV}") SET(CMAKE_PREFIX_PATH /usr/local/oecore-opencv${OCVV}/sysroots/armv7at2hf-neon-angstrom-linux-gnueabi) else() message(FATAL_ERROR "OpenCV version needs to be passed. Make sure it matches your SDK version. Use -DOCVV=<version>, currently supported 2_4 and 3_1. E.g. -DOCVV=3_1") endif() SET(OpenCV_DIR ${CMAKE_PREFIX_PATH}/usr/lib/cmake/OpenCV) find_package( OpenCV REQUIRED ) include_directories( ${OPENCV_INCLUDE_DIRS} ) target_link_libraries( myApp ${OPENCV_LIBRARIES} )

It is also needed to have a CMake file to point where are the includes and libraries. For that we will create one CMake script inside each SDK sysroot. Let's first do it for the 2.4 version:

cd /usr/local/oecore-opencv2_4/sysroots/armv7at2hf-vfp-neon-angstrom-linux-gnueabi/usr/lib/cmake mkdir OpenCV gedit OpenCV/OpenCVConfig.cmake ----------------------------------------------------------------------------------- set(OPENCV_FOUND TRUE) get_filename_component(_opencv_rootdir ${CMAKE_CURRENT_LIST_DIR}/../../../ ABSOLUTE) set(OPENCV_VERSION_MAJOR 2) set(OPENCV_VERSION_MINOR 4) set(OPENCV_VERSION 2.4) set(OPENCV_VERSION_STRING "2.4") set(OPENCV_INCLUDE_DIR ${_opencv_rootdir}/include) set(OPENCV_LIBRARY_DIR ${_opencv_rootdir}/lib) set(OPENCV_LIBRARY -L${OPENCV_LIBRARY_DIR} -lopencv_calib3d -lopencv_contrib -lopencv_core -lopencv_features2d -lopencv_flann -lopencv_gpu -lopencv_highgui -lopencv_imgproc -lopencv_legacy -lopencv_ml -lopencv_nonfree -lopencv_objdetect -lopencv_ocl -lopencv_photo -lopencv_stitching -lopencv_superres -lopencv_video -lopencv_videostab) if(OPENCV_FOUND) set( OPENCV_LIBRARIES ${OPENCV_LIBRARY} ) set( OPENCV_INCLUDE_DIRS ${OPENCV_INCLUDE_DIR} ) endif() mark_as_advanced(OPENCV_INCLUDE_DIRS OPENCV_LIBRARIES)

The same must be done for the 3.1 version - notice that the libraries change from OpenCV 2 to OpenCV 3:

cd /usr/local/oecore-opencv3_1/sysroots/armv7at2hf-vfp-neon-angstrom-linux-gnueabi/usr/lib/cmake mkdir OpenCV gedit OpenCV/OpenCVConfig.cmake ----------------------------------------------------------------------------------- set(OPENCV_FOUND TRUE) get_filename_component(_opencv_rootdir ${CMAKE_CURRENT_LIST_DIR}/../../../ ABSOLUTE) set(OPENCV_VERSION_MAJOR 3) set(OPENCV_VERSION_MINOR 1) set(OPENCV_VERSION 3.1) set(OPENCV_VERSION_STRING "3.1") set(OPENCV_INCLUDE_DIR ${_opencv_rootdir}/include) set(OPENCV_LIBRARY_DIR ${_opencv_rootdir}/lib) set(OPENCV_LIBRARY -L${OPENCV_LIBRARY_DIR} -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_calib3d -lopencv_ccalib -lopencv_core -lopencv_datasets -lopencv_dnn -lopencv_dpm -lopencv_face -lopencv_features2d -lopencv_flann -lopencv_fuzzy -lopencv_highgui -lopencv_imgcodecs -lopencv_imgproc -lopencv_line_descriptor -lopencv_ml -lopencv_objdetect -lopencv_optflow -lopencv_photo -lopencv_plot -lopencv_reg -lopencv_rgbd -lopencv_saliency -lopencv_shape -lopencv_stereo -lopencv_stitching -lopencv_structured_light -lopencv_superres -lopencv_surface_matching -lopencv_text -lopencv_tracking -lopencv_videoio -lopencv_video -lopencv_videostab -lopencv_xfeatures2d -lopencv_ximgproc -lopencv_xobjdetect -lopencv_xphoto) if(OPENCV_FOUND) set( OPENCV_LIBRARIES ${OPENCV_LIBRARY} ) set( OPENCV_INCLUDE_DIRS ${OPENCV_INCLUDE_DIR} ) endif() mark_as_advanced(OPENCV_INCLUDE_DIRS OPENCV_LIBRARIES)

After that, return to the project folder. You must export the SDK variables and run the CMake script:

# For OpenCV 2.4 source /usr/local/oecore-opencv2_4/environment-setup-armv7at2hf-vfp-neon-angstrom-linux-gnueabi cmake -DOCVV=2_4 . # For OpenCV 3.1 source /usr/local/oecore-opencv3_1/environment-setup-armv7at2hf-neon-angstrom-linux-gnueabi cmake –DOCVV=3_1 .

Cross-compiling and deploying

To test the cross-compilation environment, let's build a hello-world application that reads an image from a file and displays the image on a screen. This code is essentially the same as the one from this OpenCV tutorial:

mkdir src gedit src/myApp.cpp --------------------------------------------------------------------------------- #include #include <opencv2/opencv.hpp> using namespace std; using namespace cv; int main(int argc, char** argv ){ cout << "OpenCV version: " << CV_MAJOR_VERSION << '.' << CV_MINOR_VERSION << "\n"; if ( argc != 2 ){ cout << "usage: ./myApp \n"; return -1; } Mat image; image = imread( argv[1], 1 ); if ( !image.data ){ cout << "No image data \n"; return -1; } bitwise_not(image, image); namedWindow("Display Image", WINDOW_AUTOSIZE ); imshow("Display Image", image); waitKey(0); return 0; }

To compile and deploy, follow the instructions below. The board must have access to the LAN – you may plug an ethernet cable or use a WiFi-USB adapter, for instance. To find out the ip, use the ifconfig command.

make scp bin/myApp root@<board-IP>/home/root # Also copy some image to test! scp <path-to-image>/myimage.jpg root@<board-IP>:/home/root

In the embedded system, run the application:

root@colibri-imx6:~# ./myApp

You must see the image on the screen, as in Image 3:

Hello World application

Reading from camera

In this section, a code that reads from the camera and processes the input using the Canny edge detection algorithm will be tested. It is just one out of many algorithms already implemented in OpenCV. By the way, OpenCV has a comprehensive documentation, with tutorials, examples and library references.

In the Linux OS, when a camera device is attached, it can be accessed from the filesystem. The device is mounted in the /dev directory. Let's check the directory contents of Apalis iMX6 before plugging the USB camera:

root@apalis-imx6:~# ls /dev/video* /dev/video0 /dev/video1 /dev/video16 /dev/video17 /dev/video18 /dev/video19 /dev/video2 /dev/video20

And after plugging in:

root@apalis-imx6:~# ls /dev/video* /dev/video0 /dev/video1 /dev/video16 /dev/video17 /dev/video18 /dev/video19 /dev/video2 /dev/video20 /dev/video3

Notice that it is listed as /dev/video3. It can be confirmed using the video4linux2 command-line utility (run v4l2-ctl –help for details):

root@apalis-imx6:~# v4l2-ctl --list-devices [ 3846.876041] ERROR: v4l2 capture: slave not found! V4L2_CID_HUE [ 3846.881940] ERROR: v4l2 capture: slave not found! V4L2_CID_HUE [ 3846.887923] ERROR: v4l2 capture: slave not found! V4L2_CID_HUE DISP4 BG ():[ 3846.897425] ERROR: v4l2 capture: slave not found! V4L2_CID_HUE /dev/video16 /dev/video17 /dev/video18 /dev/video19 /dev/video20 UVC Camera (046d:081b) (usb-ci_hdrc.1-1.1.3): /dev/video3 Failed to open /dev/video0: Resource temporarily unavailable

It is also possible to get the input device parameters. Notice that the webcam used in all the tests has a resolution of 640x480 pixels.

root@apalis-imx6:~# v4l2-ctl -V --device=/dev/video3 Format Video Capture: Width/Height : 640/480 Pixel Format : 'YUYV' Field : None Bytes per Line: 1280 Size Image : 614400 Colorspace : SRGB

In addition, since all of the video interfaces are abstracted by the Linux kernel, if you were using the CSI Camera Module 5MP OV5640 from Toradex, it would be listed as another video interface. On Apalis iMX6 the kernel drivers are loaded by default. For more information, please check this knowledge-base article.

Going to the code, the OpeCV object that does the camera handling, VideoCapture, accepts the camera index (which is 3 for our /dev/video3 example) or -1 to autodetect the camera device.

An infinite loop processes the video frame by frame. Our example applies the filters conversion to gray scale -> Gaussian blur -> Canny and then sends the processed image to the video output. The code is presented below:

#include #include #include <opencv2/core/core.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/highgui/highgui.hpp> using namespace std; using namespace cv; int main(int argc, char** argv ){ cout << "OpenCV version: " << CV_MAJOR_VERSION << '.' << CV_MINOR_VERSION << '\n'; VideoCapture cap(-1); // searches for video device if(!cap.isOpened()){ cout << "Video device could not be opened\n"; return -1; } Mat edges; namedWindow("edges",1); double t_ini, fps; for(; ; ){ t_ini = (double)getTickCount(); // Image processing Mat frame; cap >> frame; // get a new frame from camera cvtColor(frame, edges, COLOR_BGR2GRAY); GaussianBlur(edges, edges, Size(7,7), 1.5, 1.5); Canny(edges, edges, 0, 30, 3); // Display image and calculate current FPS imshow("edges", edges); if(waitKey(30) >= 0) break; fps = getTickFrequency()/((double)getTickCount() - t_ini); cout << "Current fps: " << fps << '\r' << flush; } return 0; }

Image 4 presents the result. Notice that neighter the blur nor the Canny algorithms parameters were tuned.

Capture from USB camera being processed with Canny edge detection

In order to compare between the Colibri iMX6S and iMX6DL and be assured that multicore processing is being done, the average FPS for 1000 samples was measured, and is presented in table 1. It was also done for the Apalis iMX6Q with the Apalis Heatsink. All of the tests had frequency scaling disabled.

Table 1 – Canny implementation comparison between modules and OpenCV configurations (640x480)

As a comparison, the Canny algorithm was replaced in the code with the Sobel derivatives, based on this example. The results are presented in table 2:

Table 2 – Sobel implementation comparison between modules and OpenCV configurations (640x480)

It is interesting to notice that using the TBB framework may be slightly faster than OpenMP in most of the situations, but not always. It is interesting to notice that there is a small performance difference even for the single-core results.

Also, the enhancements made from OpenCV 2.4 to OpenCV 3.1 have a significant impact in the applications performance – which might be explained by the NEON optimizations made so far, and also an incentive to try to optimize the algorithms that your application requires. Based on this preliminary results, it is interesting to test a specific application with several optimization combinations and using only the latest OpenCV version before deploying.

Testing an OpenCV face-tracking sample

There is also a face-tracking sample in the OpenCV source-code that was tested, since it is heavier than the previously made tests and might provide a better performance awareness regarding Colibri x Apalis when choosing the more appropriate hardware for your project.

The first step was to copy the application source-code from GitHub and replace the contents of myApp.cpp, or create another file and modify the CMake script if you prefer. You will also need the Haar cascades provided for face and eye recognition. You might as well clone the whole OpenCV git directory and later test other samples.

A few changes must be made in the source-code so it also works for OpenCV2.4: include the stdio.h library, modify the OpenCV included libraries (take a look in the new headers layout section here) and make some changes to the CommandLineParser, since the versions from OpenCV 2 and 3 are not compatible. Image 5 presents the application running:

Face and eye tracking application

The code already prints the time it takes to run a face detection. Table 3, presented below, holds the results for 100 samples:

Table 3 – Face detection comparison between modules and OpenCV configurations (640x480)

Again the difference between OpenCV versions is not negligible - for the Apalis iMX6Q (quad-core) the performance gain is 3x, and using OpenCV 3.1 with the dual-core Colibri iMX6DL delivers a better performance than OpenCV 2.4 with the quad-core Apalis iMX6Q.

The single-core IT version, thus with reduced clock compared to the others, has results far behind as expected. The reduced clock may explain why the performance improvement from single to dual core is higher than dual to quad core.

Conclusion

Since OpenCV is nowadays (probably) the most popular set of libraries for computer vision, and with the enhancement of performance in embedded systems, knowing how to start with OpenCV for embedded applications provides a powerful tool to solve problems of nowadays and the near future.

This blog post has chosen a widely known family of microprocessors as an example and a starting point, be for those only studying computer vision or those already focusing on developing real-world applications. It also has demonstrated that things are only starting – see for instance the performance improvements from OpenCV 2 to 3 for ARM processors – which also points towards the need to understand the system architecture and focus on optimizing the application as much as possible – even under the hood. I hope this blog post was useful and see you next time!

Summary

1) Building image with OpenCV To build an image with OpenCV 3.1, first follow this article steps to setup OpenEmbedded for the Toradex image V2.7. Edit the OpenCV recipe from meta-openembedded, adding the following line:

CXXFLAGS += " -Wa,-mimplicit-it=thumb"

In the local.conf file uncomment your machine, accept the Freescale license and add OpenCV:

MACHINE ?= "apalis-imx6" # or colibri-imx6 depending on the CoM you have ACCEPT_FSL_EULA = "1" IMAGE_INSTALL_append = " opencv"

2) Generating SDK Then you are able to run the build, and also generate the SDK:

bitbake –k angstrom-lxde-image bitbake –c populate_sdk angstrom-lxde-image

To deploy the image to the embedded system, you may follow this article's steps.

3) Preparing for cross-compilation After you install the SDK, there will be a sysroot for the target (ARM) inside the SDK directory. Create an OpenCV directory inside <path_to_ARM_sysroot>/usr/lib/cmake and, inside this new directory create a file named OpenCVConfig.cmake with the contents presented below:

cd /usr/lib/cmake mkdir OpenCV vim OpenCVConfig.cmake --------------------------------------------------------------------------- set(OPENCV_FOUND TRUE) get_filename_component(_opencv_rootdir ${CMAKE_CURRENT_LIST_DIR}/../../../ ABSOLUTE) set(OPENCV_VERSION_MAJOR 3) set(OPENCV_VERSION_MINOR 1) set(OPENCV_VERSION 3.1) set(OPENCV_VERSION_STRING "3.1") set(OPENCV_INCLUDE_DIR ${_opencv_rootdir}/include) set(OPENCV_LIBRARY_DIR ${_opencv_rootdir}/lib) set(OPENCV_LIBRARY -L${OPENCV_LIBRARY_DIR} -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_calib3d -lopencv_ccalib -lopencv_core -lopencv_datasets -lopencv_dnn -lopencv_dpm -lopencv_face -lopencv_features2d -lopencv_flann -lopencv_fuzzy -lopencv_highgui -lopencv_imgcodecs -lopencv_imgproc -lopencv_line_descriptor -lopencv_ml -lopencv_objdetect -lopencv_optflow -lopencv_photo -lopencv_plot -lopencv_reg -lopencv_rgbd -lopencv_saliency -lopencv_shape -lopencv_stereo -lopencv_stitching -lopencv_structured_light -lopencv_superres -lopencv_surface_matching -lopencv_text -lopencv_tracking -lopencv_videoio -lopencv_video -lopencv_videostab -lopencv_xfeatures2d -lopencv_ximgproc -lopencv_xobjdetect -lopencv_xphoto) if(OPENCV_FOUND) set( OPENCV_LIBRARIES ${OPENCV_LIBRARY} ) set( OPENCV_INCLUDE_DIRS ${OPENCV_INCLUDE_DIR} ) endif() mark_as_advanced(OPENCV_INCLUDE_DIRS OPENCV_LIBRARIES)

Inside your project folder, create a CMake script to generate the Makefiles for cross-compilation, with the following contents:

cd vim CMakeLists.txt -------------------------------------------------------------------------------- cmake_minimum_required(VERSION 2.8) project( MyProject ) set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin") add_executable( myApp src/myApp.cpp ) SET(CMAKE_PREFIX_PATH /usr/local/oecore-x86_64/sysroots/armv7at2hf-neon-angstrom-linux-gnueabi) SET(OpenCV_DIR ${CMAKE_PREFIX_PATH}/usr/lib/cmake/OpenCV) find_package( OpenCV REQUIRED ) include_directories( ${OPENCV_INCLUDE_DIRS} ) target_link_libraries( myApp ${OPENCV_LIBRARIES} )

Export the SDK environment variables and after that run the CMake script. In order to do so, you may run:

source /usr/local/oecore-x86_64/environment-setup-armv7at2hf-neon-angstrom-linux-gnueabi cmake .

4) Cross-compiling and deploying Now you may create a src directory and a file named myApp.cpp inside it, where you can write a hello world application, such as this one:

mkdir src vim src/myApp.cpp -------------------------------------------------------------------------------- #include #include <opencv2/opencv.hpp> using namespace std; using namespace cv; int main(int argc, char** argv ){ Mat image; image = imread( "/home/root/myimage.jpg", 1 ); if ( !image.data ){ cout << "No image data \n"; return -1; } namedWindow("Display Image", WINDOW_AUTOSIZE ); imshow("Display Image", image); waitKey(0); return 0; }

Cross-compile and deploy to the target:

make scp bin/myApp root@<board-ip>:/home/root # Also copy some image to test! scp <path-to-image>/myimage.jpg root@<board-ip>:/home/root

In the embedded system:

root@colibri-imx6:~# ./myApp

#OpenCV #Computer on Modules #Computer Vision #NXP iMX6 #Embedded Systems #Toradex

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