We are pleased to announce that Pins Tool for i.MX v4 is now available.   The pins Tool for i.MX Application Processors is used for pin routing configuration, validation and code generation, including pin functional/electrical properties, power rails, run-time configurations, with the following main features: Desktop application Muxing and pin configuration with consistency checking Multicore support Localized for English and Simplified Chinese Mostly Connected: On-Demand device data download Integrates with any compiler and IDE Graphical processor package view Multiple configuration blocks/functions Easy-to-use device configuration Documented and easy to understand source code CSV Report and Device Tree File   To download the installer for all platforms, please login to our download site via:  http://www.nxp.com/pinsimx   Please refer to Pins Tool Documentation  for installation and quick start guides.   Overview of Changes - version 4 Added ability to import configuration from existing MEX file (for selected tools) Added common Functions group toolbar across all tools Added option to not generate YAML Pins tool: multiselect in Routed pins view supported Syntax coloring supported in Sources view Export sources wizard simplified Several bug fixed and performance improved Quick start guide added   Community https://community.nxp.com/community/imx 

Introduction The NFC Reader Library is a feature complete software support library for NXP's NFC Frontend ICs. It is designed to give developers a faster and simpler way to deliver NFC-enabled products. This multi-layer library, written in C, makes it easy to create NFC based applications. The purpose of this document is to provide instructions on how to install the NFC Reader Library on imx7dsabresd and communicate with PN5180, a NFC frontend. It will describe all the steps required to connect the board to an OM25180TWR, the wire connections, the changes in the device tree, and the library configuration. Building the Linux image and the Bal kernel module This section describes how to build the Linux image using Yocto and how to compile the Bal kernel module. Informations specific for this library start from the next section. Requirements: a Linux host PC (ex. Ubuntu 14.04/16.04) and root permissions. To download the required host packages, use: $ sudo apt-get install gawk wget git-core diffstat unzip texinfo gcc-multilib build-essential chrpath socat libsdl1.2-dev Specific for Ubuntu: $ sudo apt-get install libsdl1.2-dev xterm sed cvs subversion coreutils texi2html docbook-utils python-pysqlite2 help2man make gcc g++ desktop-file-utils \ libgl1-mesa-dev libglu1-mesa-dev mercurial autoconf automake groff curl lzop asciidoc To setup the repo utility (a tool written on top of git), run the commands: $ mkdir ~/bin (this step may not be needed if the bin folder already exists) $ curl http://commondatastorage.googleapis.com/git-repo-downloads/repo > ~/bin/repo $ chmod a+x ~/bin/repo Then add the following line to .bashrc to ensure the ~/bin is in the PATH variable. export PATH=~/bin:$PATH To download the Freescale Yocto Project Community BSP: $ mkdir fsl-release-bsp $ cd fsl-release-bsp $ repo init -u git://git.freescale.com/imx/fsl-arm-yocto-bsp.git -b imx-4.1-krogoth $ repo sync To build the image, in the fsl-release-bsp run the commands: $ mkdir buildDevSpi $ DISTRO=fsl-imx-xwayland MACHINE=imx7dsabresd source fsl-setup-release.sh -b buildDevSpi $ bitbake fsl-image-machine-test To build the toolchain, run: $ bitbake meta-toolchain $ cd buildDevSpi/tmp/deploy/sdk/ $ ./fsl-imx-xwayland-glibc-x86_64-meta-toolchain-cortexa7hf-neon-toolchain-4.1.15-2.1.0.sh Accept the default parameters. In order to deploy the image on an SD card use: $ sudo dd if=fsl-image-machine-test-imx7dsabresd.sdcard of=<sd card> bs=1M && sync The image is found in buildDevSpi/tmp/deploy/images/imx7dsabresd. For the kernel module compilation, setup the console environment: $ . /opt/fsl-imx-xwayland/4.1.14-2.1.0/environment-setup-cortexa7hf-neon-poky-linux-gnueabi Then, in the kernel module directory, replace the path with your linux build directory in the Makefile and run: $ make The bal.ko is the compiled module. To use the kernel menuconfig, run: $ bitbake -c menuconfig linux-imx Another useful command, for rebuilding the linux kernel image, is: $ bitbake -f -c compile linux-imx; bitbake -f -c deploy linux-imx; bitbake -f -c compile fsl-image-machine-test; bitbake -f fsl-image-machine-test Host interface The interface of the PN5180 to a host is based on a SPI interface, extended by the signal line BUSY. Only half-duplex data transfer is supported and no chaining is allowed, meaning that the whole instruction has to be sent, or the whole receiver buffer has to be read out. The module is connected to the i.MX7D board using the mikro bus expansion port in the following way: MK_BUS_CS, MK_BUS_SCK, MK_BUS_MOSI, MK_BUS_MISO are used for the SPI bus lines. MK_BUS_INT, MK_BUS_RX, MK_BUS_TX are used for the BUSY, RESET and IRQ lines. The pin configuration will be the following: GPIO6_IO22 will be the CS, GPIO6_IO14 will be BUSY, GPIO_IO12 will be RESET and GPIO_IO13 will be IRQ. The DWL pin, which can be used for firmware update, will be connected to GND. A common ground is also required. Connections table: Jumper Jumper Pins Description i.MX7D I/O Tower Edge J4 1 - 2 SPI Clk Selection ECSPI3_SCLK (SAI2_RX_DATA) B7 J20 1 - 2 PN5180 Reset GPIO6_IO12 (SAI1_RX_DATA) B8 J1 1 - 2 SPI SS0 GPIO6_IO22 (SAI2_TX_DATA) B9 J3 1 - 2 SPI MOSI ECSPI3_MOSI (SAI2_TX_BCLK) B10 J2 1 - 2 SPI MISO ECSPI3_MISO (SAI2_TX_SYNC) B11 J19 2 - 3 PN5180 BUSY GPIO6_IO14 (SAI1_TX_SYNC) B58 J5 1 - 2 PN5180 IRQ GPIO6_IO13 (SAI1_TX_BCLK) B62 X X PN5180 DWL GND B52 X X GND GND B2 Kernel Configuration In order to allow the library to manage the RESET, IRQ and BUSY pins, the options for Debug GPIO and Userspace I/O drivers must be enabled (in menuconfig, Device Drivers -> GPIO Support -> Debug GPIO and Device Drivers -> Userspace I/O -> Userspace I/O platform driver with generic IRQ). For controlling the SPI, there are two options: spidev or NXP bal. For spidev, it is necessary to select Device Drivers -> SPI support -> User mode SPI and apply the imx7d-sdb_spidev.patch (it also does the pinmuxing, it is attached to the document). When using NXP bal, it is necessary to compile the module, initialize it with insmod and apply the imx7d-sdb_bal.patch (it also does the pinmuxing, the patch and the module are attached to this document). Library Configuration For the library configuration, <lib-folder>/Platform/DAL/Board_Imx6ulevkPn5180.h must be replaced with Board_Imx7dsabresdPn5180_bal.h or Board_Imx7dsabresdPn5180_spidev.h (based on the selected spi interface). For compilation, the command is: $ ./build.sh yocto /opt/fsl-imx-xwayland/4.1.15-2.1.0/sysroots/ The last parameter is the location of the toolchain generated by yocto. A build folder is generated outside of the source code folder. The applications from the ComplianceApp can be deployed on the board in order to test the functionality provided. Other useful resources: – i.MX Yocto Project User's Guide: https://www.nxp.com/webapp/sps/download/preDownload.jsp?render=true – NFC Reader Library for Linux Installation: https://www.nxp.com/docs/en/application-note/AN11802.pdf – PN5180 component: https://www.nxp.com/docs/en/data-sheet/PN5180A0XX-C1-C2.pdf

The i.MX Android N7.1.2_2.0.0 GA release is now available on IMX_SW page.   Files available: # Name Description 1 android_N7.1.2_2.0.0_docs.tar.gz i.MX Android N7.1.2_2.0.0 BSP Documentation 2 android_N7.1.2_2.0.0_source.tar.gz Source Code of Android N7.1.2_2.0.0 BSP (4.1 kernel) for i.MX 6QuadPlus, i.MX 6Quad, i.MX 6DualPlus, i.MX 6Dual, i.MX 6DualLite, i.MX 6Solo i.MX 6Sololite, i.MX6SX and i.MX7D 3 android_N7.1.2_2.0.0_image_6dqpsabreauto.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - SABRE for Automotive Infotainment based on i.MX 6QuadPlus, i.MX 6Quad, and i.MX 6DualLite 4 android_N7.1.2_2.0.0_image_6dqpsabresd.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - SABRE Platform and SABRE Board based on i.MX 6QuadPlus, i.MX 6Quad and i.MX 6DualLite. 5 android_N7.1.2_2.0.0_image_6slevk.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - i.MX 6Sololite evaluation kit. 6 android_N7.1.2_2.0.0_image_6sxsabresd.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - SABRE Board based on i.MX 6SoloX 7 android_N7.1.2_2.0.0_image_6sxsabreauto.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - SABRE for Automotive infotainment based on i.MX 6SoloX 8 android_N7.1.2_2.0.0_image_7dsabresd.tar.gz Binary Demo Files of Android N7.1.2_2.0.0 BSP - SABRE Board based on i.MX 7Dual 9 fsl_aacp_dec.tar.gz AAC Plus Codec for N7.1.2_2.0.0 10 android_N7.1.2_2.0.0_tools.tar.gz Manufacturing Toolkit and VivanteVTK for N7.1.2_2.0.0   Supported Hardware SoC/Boards: i.MX 6Quad, i.MX 6QuadPlus, and i.MX 6DualLite SABRE-SD board and platform i.MX 6Quad, i.MX 6QuadPlus, and i.MX 6DualLite SABRE-AI board and platform i.MX 6SoloLite EVK platform i.MX 6SoloX SABRE-SD board and platforms i.MX 6SoloX SABRE-AI board and platforms i.MX 7Dual SABRE-SD board and platform   Changes: Compared to the N7.1.1_1.0.0 release, this release has the following major changes: Upgraded the Android code base from android-7.1.1_r13 to android-7.1.2_r9. Upgraded U-Boot from v2015.04 to v2017.03. Upgraded the kernel from v4.1.15 to v4.9.17. Upgraded the GPU driver from 6.2.0.p2 to 6.2.2.p1. Upgraded the Wi-Fi BCMDHD release version to 1.141.100.6. Refine the Gralloc and HWC HAL. Enable the GPT partition to replace the MBR partition.   Feature: For features please consult the release notes.   Known issues For known issues and more details please consult the Release Notes.

Hi everyone, Can I stream video at some different resolutions at the same time using I.Mx6? Thank you.

The PICO-PI-IMX7 is a TechNexion board defined here. It is now supported in mainline U-Boot, mainline Kernel, FSL Community BSP and Buildroot. In mainline U-Boot it is supported since v2017.07. In mainline Linux Kernel it is supported since 4.13. To generate an image for PICO-PI-IMX7 using FSL Community BSP, please see: The imx7d-pico board is now supported in FSL Community BSP - i.MXDev Blog. To generate an image for PICO-PI-IMX7 using Buildroot, please see: The imx7d-pico board is now supported in Buildroot - i.MXDev Blog.

Wayland:   Wayland is a display SERVER and COMPOSITION protocol. It is relatively new, as its first release was in 2012. The protocol enables applications to allocate their own off-screen buffers and render their window contents directly, using hardware accelerated libraries like OpenGL ES, or high quality software implementations like Cairo. Wayland is ONLY a display server protocol, not a display server itself. Weston is the reference Wayland protocol implementation.   YOCTO Setup . $ mkdir ~/bin $ curl http://commondatastorage.googleapis.com/git-repo-downloads/repo > ~/bin/repo $ chmod a+x ~/bin/repo $ export PATH=~/bin:$PATH $ git config --global user.name "Your Name" $ git config --global user.email "Your Email" $ git config –list $ mkdir fsl-release-bsp $ cd fsl-release-bsp $ repo init -u git://git.freescale.com/imx/fsl-arm-yocto-bsp.git -b imx-3.14.52-1.1.0_ga $ repo sync     you will be able to build Yocto and also have all the recipes to do so, we need to add WAYLAND, then execute the following steps: $ DISTRO=fsl-imx-wayland MACHINE=imx6qsabresd source fsl-setup-release.sh -b build-wayland $ bitbake fsl-image-gui After these steps, you will have a wayland based i.MX6Q image where you will be able to play with all the knowledge we provided here.   Once your image has been properly generated, you will find the Weston source codes in: <YOUR YOCTODIR>/build-wayland/tmp/work/cortexa9hf-vfp-neon-mx6qdl-poky-linux-gnueabi/weston/1.9.0-r0/weston-1.9.0     Wayland application for extended desktop: This functionality is only supported using the GAL2D blitter, in order to enable a multiple desktop approach, you need to pass the following parameters to your weston command: /etc/init.d/weston stop echo 0 > /sys/class/graphics/fb4/blank weston --tty=1 --use-gal2d=1 --use-gl=0 --device=/dev/fb0,/dev/fb4 &     Xwayland: Wayland is a complete window system in itself, but even so, if we're migrating away from X, it makes sense to have a good backwards compatibility story. With a few changes, the Xorg server can be modified to use wayland input devices for input and forward either the root window or individual top-level windows as wayland surfaces.   DISTRO=fsl-imx-xwayland MACHINE=imx6qsabresd source ./fsl-setup-release.sh -b build-xwayland bitbake fsl-image-gui Once you have the image your Wayland/Weston image will be able to run X11 applications   Excepting X11 applications that use EGL, we don’t support that, if you plan to use EGL apps, please use the Wayland provided functions to create the buffer.   Application for rotation: Weston allows rotating windows with super-key + middle mouse button. As this works for Wayland clients only, you can run Xwayland in weston, run your X application on Xwayland, and rotate the Xwayland display. For another option: Create a file ~/.config/weston.ini with this content: [core] modules=xwayland.so shell=desktop-shell.so idle-time=0 [shell] background-color=0xff002244 locking=false # panel-location=none    [launcher] icon=/usr/share/icons/gnome/24x24/apps/utilities-terminal.png path=/usr/bin/weston-terminal [launcher] icon=/usr/share/icons/hicolor/48x48/apps/firefox.png path=/usr/bin/firefox [output] name=X1 mode=640x800 transform=90 # wanna get mad? use: transform=flipped-270 scale=1 This weston.ini enables a rootless xwayland.so in weston. The [output] section with name=X1 defines weston's appearance as X client. transform=90 rotates the weston display.   the [launcher] sections can be used to create custom panel starters for your X applications. See  /usr/share/doc/weston/examples/weston.ini for more detailed information for further cases, I will attach in the future.

The Linux L4.9.11_1.0.0 RFP(GA) for i.MX6 release files are now available on www.nxp.com    Files available: # Name Description 1 L4.9.11_1.0.0-ga_images_MX6QPDLSOLOX.tar.gz i.MX 6QuadPlus, i.MX 6Quad, i.MX 6DualPlus, i.MX 6Dual, i.MX 6DualLite, i.MX 6Solo, i.MX 6Solox Linux Binary Demo Files 2 L4.9.11_1.0.0-ga_images_MX6SLEVK.tar.gz i.MX 6Sololite EVK Linux Binary Demo Files 3 L4.9.11_1.0.0-ga_images_MX6UL7D.tar.gz i.MX 6UltraLite EVK, 7Dual SABRESD, 6ULL EVK Linux Binary Demo Files 4 L4.9.11_1.0.0-ga_images_MX6SLLEVK.tar.gz i.MX 6SLL EVK Linux Binary Demo Files 5 L4.9.11_1.0.0-ga_images_MX7ULPEVK.tar.gz i.MX 7ULP EVK Linux Binary Demo Files  6 L4.9.11_1.0.0-ga_mfg-tools.tar.gz i.MX Manufacturing Toolkit for Linux L4.9.11_1.0.0 BSP 7 L4.9.11_1.0.0-ga_gpu-tools.tar.gz L4.9.11_1.0.0 i.MX VivanteVTK file 8 bcmdhd-1.141.100.6.tar.gz The Broadcom firmware package for i.MX Linux L4.9.11_1.0.0 BSP. 9 imx-aacpcodec-4.2.1.tar.gz Linux AAC Plus Codec for L4.9.11_1.0.0 10 fsl-yocto-L4.9.11_1.0.0.tar.gz L4.9.11_1.0.0 for Linux BSP Documentation. Includes Release Notes, User Guide.   Target boards: i.MX 6QuadPlus SABRE-SD Board and Platform i.MX 6QuadPlus SABRE-AI Board i.MX 6Quad SABRE-SD Board and Platform i.MX 6DualLite SABRE-SD Board i.MX 6Quad SABRE-AI Board i.MX 6DualLite SABRE-AI Board i.MX 6SoloLite EVK Board i.MX 6SoloX SABRE-SD Board i.MX 6SoloX SABRE-AI Board i.MX 7Dual SABRE-SD Board i.MX 6UltraLite EVK Board i.MX 6ULL EVK Board i.MX 6SLL EVK Board i.MX 7ULP EVK Board (Beta Quality)   What’s New/Features: Please consult the Release Notes.   Known issues For known issues and more details please consult the Release Notes.   More information on changes, see: README: https://source.codeaurora.org/external/imx/fsl-arm-yocto-bsp/tree/README?h=imx-morty ChangeLog: https://source.codeaurora.org/external/imx/fsl-arm-yocto-bsp/tree/ChangeLog?h=imx-morty

Q:How use USB as both gadget mode and host mode usb USB_OTG? A: Hardware: imx6 sabresd board usb otg diagram, as follow Software:   You can use USB OTG port for both host mode and device gadget mode. There are 2 signals you should care: - USB_OTG_ID - USB_OTG_MODE (control power for usb vbus in host mode, this is EIM_D22 pin)   In device tree: reg_usb_otg_vbus: regulator@0 {                         compatible = "regulator-fixed";                         reg = <0>;                         regulator-name = "usb_otg_vbus";                         regulator-min-microvolt = <5000000>;                         regulator-max-microvolt = <5000000>;                         gpio = <&gpio3 22 0>;                         enable-active-high;                 };   pinctrl_usbotg: usbotggrp {                         fsl,pins = <                                 MX6QDL_PAD_GPIO_1__USB_OTG_ID           0x17059                                 MX6QDL_PAD_EIM_D22__GPIO3_IO22           0x80000000                         >;                 };   &usbotg {         vbus-supply = <&reg_usb_otg_vbus>;         pinctrl-names = "default";         pinctrl-0 = <&pinctrl_usbotg>;         disable-over-current;         srp-disable;         hnp-disable;         adp-disable;         dr_mode = "otg";                 status = "okay"; };  With above config, one can use OTG port for mouse, usb stick in host mode, and gadget mode for ADB...   https://community.nxp.com/message/943460 

  The mfgtool is the tool download the images to i.MX series of applications processors. It’s convenient and easy use to download the images to your board. About its introductions, work flow and use guide you can see details in the Document file of mfgtool. If customers use our reference boards, they can directly use the default mfgtools we supply for every version BSP and board. But when customers design board and do porting with our i.MX series processors. As they do many changes from our reference board, they need to rebuild the images for their board and for the download tool mfgtool. In the old version BSP, take the L3.0.35_4.1.0_130816 version as an example. When finishing porting the BSP for design board. Run the following command line to generate the manufacturing firmware. ./ltib --profile config/platform/imx/updater.profile --preconfig config/platform/imx/imx6q_updater.cf --continue –batch For android BSP Android4.2.2, one can use the follow command: make distclean make mx6dl_sabresd_mfg_config make In the newest BSP, for linux BSP in yocto use the command: $ bitbake fsl-image-mfgtool-initramfs For the newest android BSP, the command” make mx6dl_sabresd_mfg_config” can not use anymore. So how to get the \Profiles\Linux\OS Firmware\firmware\u-boot-imx6dlsabresd_sd.imx? The easiest way that you can use the u-boot you build for your board, and in the newest BSP, mfgtool can use the same u-boot with the normal u-boot for your board. You do not need to build the u-boot for mfgtool separately. They can use the same one. Hope this can do some help for you.

OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform In current bsp , which supports opencv 2.4, but some customer wants to use the opencv 3.1, then one can use the morty yocto bsp to install the opencv. step 1: for how to install the package on ubuntu and how to build the environment, pls refer to the bsp user guide, for how to build the branch morty, try to use the command as below: MACHINE=imx6qsabresd source fsl-setup-release.sh -b build_qt5 -e fb step 2: for how to enable the opencv, pls add the command as below in the local.conf, the path is fsl-release-bsp/build/conf, "CORE_IMAGE_EXTRA_INSTALL += "libopencv-core-dev libopencv-highgui-dev libopencv-imgproc-dev libopencv-objdetect-dev libopencv-ml-dev" CORE_IMAGE_EXTRA_INSTALL += "opencv-apps opencv-dev python-opencv python-modules"" then build again by bitbake. then you can find the image in the fsl-release-bsp/build/tmp/deploy/images/im6qsabresd/, one can find the opencv libary when extracting the rootfs file step 3: then you can use dd command or mfgtool downloading the image file to the board and use the opencv libary file. other usage: one can install the populate_sdk to build the source code, for opencv 3.1, maybe you will find some g++ issue to fix, so just simple introduce this use the command: bitbake -c populate_sdk fsl-image-gui(for example)  then you can find the sdk install file in the fsl-release-bsp/build_x11/tmp/deploy/sdk, run the install file, set the installation file in the /opt/poky, then you can find the toolchain in the /opt/poky after install successfully.

Guide for Accessing GPIO From UserSpace Summary for Simple GPIO Example - quandry https://community.freescale.com/message/598834#598834

HW: i.MX7 SabreSD SW: Android N7.1.1_1.0.0   There is KPP module on i.MX7, but NXP reference board didn't have it. We reworked a "keypad" and has the demo. Signal: For testing, launch an app in Android that can accept text input. KPP also supports multiple input, the "A" is showed by pressing "shift" + "a".   As a side note, 1. The input device driver is drivers/input/keyboard/imx_keypad.c 2. The input event driver is drivers/tty/vt/keyboard.c Original Attachment has been moved to: 0001-Enable-KPP-on-i.MX7.patch.zip

i work with IMX6q,i find VDI is not work,And the video picture has Horizontal stripe. i want to know how should i make VDI work? FAE tell me enable g_vdi_enable in mxc_v4l2_tvin.c.but i do it already.The VDI don't  still work. please help me, many thanks

This is a tool can generate LPDDR2 script easily for i.MX6SLL.

This is a tool can generate LPDDR3 script easily for i.MX6SLL.

Please find attached preliminary errata on the two i.MX security vulnerabilities: ERR010872 – Secure Boot Vulnerability when using the Serial Downloader (CVE-2017-7936) ERR010873 – Secure Boot Vulnerability when Authenticating a Certificate (CVE-2017-7932) These are preliminary versions of the errata. Final versions will be incorporated into the Chip Errata (CE) document for each impacted device at a later date. Customer Notification A Customer Information Notification(CIN 201704026I) with the affected part numbers has been sent out to impacted users. Updated Silicon Sample Availability An Advanced Product Change Notification (A-PCN 201705010A), that provides updated silicon availability information has also been sent out to impacted users. Mitigations An Engineering Bulletin (EB00854) on possible mitigation strategies is available to impacted users and can be requested through your NXP field support team or distributor.  Further Questions or Support Please contact your NXP support representative or enter a support request for further questions on this topic indicating the part number and the mass production start date.

Requirements: Host machine with Ubuntu 14.04 UDOO Quad/Dual Board uSD card with at least 8 GB Download documentation and install latest Official Udoobuntu OS (at the moment of writing: UDOObuntu 2.1.2), https://www.udoo.org/downloads/   Overview: This document describes how to install and test Keras (Open source neural network library) and Theano (numerical computation library for python ) for deep learning library usage on i.MX6QD UDOO board.  Installation: $ sudo apt-get update && sudo apt-get upgrade update your date system: e.g. $ sudo date -s “07/08/2017 12:00” First satisfy the run-time and build time dependencies: $ sudo apt-get install python-software-properties software-properties-common make unzip zlib1g-dev git pkg-config autoconf automake libtool curl  python-pip python-numpy libblas-dev liblapack-dev python-dev libatlas-base-dev gfortran libhdf5-serial-dev libhdf5-dev python-setuptools libyaml-dev libpython2.7-dev $ sudo easy_install scipy The last step is installing scipy through pip, and can take several hours. Theano First, we have a few more dependencies to get: $sudo pip install scikit-learn $sudo pip install pillow $sudo pip install h5py With these dependencies met, we can install a stable Theano release from the git source: $ git clone https://github.com/Theano/Theano $ cd Theano Numpy 1.9 cause conflicts with armv7, so we need to change the setup.py configuration: $ sudo nano setup.py Remove line    #       install_requires=['numpy>=1.9.1', 'scipy>=0.14', 'six>=1.9.0'], And add setup_requires=["numpy"], install_requires=["numpy"], Then install it: $ sudo python setup.py install Keras The installation can occur with the command: (this could take a lot of time!!!) $ cd .. $ git clone https://github.com/fchollet/keras.git $ cd keras $ sudo python setup.py install $ LC_ALL=C $sudo pip install --upgrade keras After Keras is installed, you will want to edit the Keras configuration file ~/.keras/keras.json to use Theano instead of the default TensorFlow backend. If it isn't there, you can create it. This requires changing two lines. The first change is: "image_dim_ordering": "tf"  --> "image_dim_ordering": "th" and the second: "backend": "tensorflow" --> "backend": "theano" (The final file should look like the example below) sudo nano ~/.keras/keras.json {     "image_dim_ordering": "th",     "epsilon": 1e-07,     "floatx": "float32",     "image_data_format": "channels_last",     "backend": "theano" } You can also define the environment variable KERAS_BACKEND and this will override what is defined in your config file : $ KERAS_BACKEND=theano python -c "from keras import backend" Testing Quick test: udooer@udoo:~$ python Python 2.7.6 (default, Oct 26 2016, 20:46:32) [GCC 4.8.4] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import keras Using Theano backend. >>>  Test 2: Be aware this test take some time (~1hr on udoo dual): $ curl -sSL -k https://github.com/fchollet/keras/raw/master/examples/mnist_mlp.py | python Output: For demonstration, deep-learning-models repository provided by pyimagesearch and from fchollet git, and also have three Keras models (VGG16, VGG19, and ResNet50) online — these networks are pre-trained on the ImageNet dataset, meaning that they can recognize 1,000 common object classes out-of-the-box. $ cd keras $ git clone https://github.com/fchollet/deep-learning-models $ Cd deep-learning-models $ ls -l Notice how we have four Python files. The resnet50.py , vgg16.py , and vgg19.py  files correspond to their respective network architecture definitions. The imagenet_utils  file, as the name suggests, contains a couple helper functions that allow us to prepare images for classification as well as obtain the final class label predictions from the network Classify ImageNet classes with ResNet50 ResNet50 model, with weights pre-trained on ImageNet. This model is available for both the Theano and TensorFlow backend, and can be built both with "channels_first" data format (channels, height, width) or "channels_last" data format (height, width, channels). The default input size for this model is 224x224. We are now ready to write some Python code to classify image contents utilizing  convolutional Neural Networks (CNNs) pre-trained on the ImageNet dataset. For udoo Quad/Dual use ResNet50 due to avoid space conflict. Also we are going to use ImageNet (http://image-net.org/) that is an image database organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images. from keras.applications.resnet50 import ResNet50 from keras.preprocessing import image from keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np   model = ResNet50(weights='imagenet')   #for this sample I download the image from: http://i.imgur.com/wpxMwsR.jpg  img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x)   preds = model.predict(x) # decode the results into a list of tuples (class, description, probability) # (one such list for each sample in the batch) print('Predicted:', decode_predictions(preds, top=3)[0]) Save the file an run it. Results for elephant image: Top prediction was 0.8890 for African Elephant Testing with this image: http://i.imgur.com/4FIOwAN.jpg Results: Top prediction was: 0.7799 for golden_retriever. Now your Udoo is ready to use Keras and Theano as Deep Learning libraries, next time we are going to show some usage example for image classification models with OpenCV. References: GitHub - fchollet/keras: Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.  GitHub - Theano/Theano: Theano is a Python library that allows you to define, optimize, and evaluate mathematical expres…  GitHub - fchollet/deep-learning-models: Keras code and weights files for popular deep learning models.  Installing Keras for deep learning - PyImageSearch 

The attched package includes mbedTLS and DCP/RNGB driver based on SDK2.2, you can apply it on Windows Installer: MCUXpresso SDK2.2 for i.MX 6ULL 1. fsl_dcp.c/fsl_dcp.h and fsl_rngb.c/fsl_rngb.h under devices\MCIMX6Y2\drivers is dcp ang rngb driver. 2. Some files under middleware\mbedtls-2.4.0\port\sdk are porting code for mbedTLS 3. Example codes are under folder boards\evkmcimx6ull which have driver example and mbedTLS example. 4, The patch package only support IAR toolchain. 5, Due to SDK don't support allocation of non-cachable memory dynamically, so some static non-cachable bufferes in sdk_mbedtls.c is used for shared memory with hareware. So mbedTLS don't be used for multi-thread concurrently.

The attched package includes mbedTLS and CAAM driver based on SDK2.2, you can apply it on Windows Installer: MCUXpresso SDK2.2 for i.MX 6UltraLite 1. fsl_caam.c and fsl_caam.h under devices\MCIMX6G3\drivers is CAAM driver. 2. Some files under middleware\mbedtls-2.4.0\port\sdk are porting code for mbedTLS 3. Example codes are under folder boards\evkmcimx6ul which have driver example and mbedTLS example. 4, The patch package only support IAR toolchain. 5, Due to SDK don't support allocation of non-cachable memory dynamically, so some static non-cachable bufferes in sdk_mbedtls.c is used for shared memory with hareware. So mbedTLS don't be used for multi-thread concurrently.

as we known, mx6sx doesn’t have IPU, if we need to resize, rotation or blending…., we can use pxp module, this part, we talk about rotation for example. We also can use GPU for rotation, but in imx6sx, the number after 6sx in the part number stands for the chip including gpu or not, the number 4 and 3 mean mx6sx has gpu, like MCIMX6X3EVN10AB. and 1,2 and 3 mean mx6sx doesn’t have gpu, like MCIMX6X2EVN10AB for gpu, we know we can use xrander to rotate ,in this part, we focus on pxp rotation the stesp: enable pxp in the kernel:               $ bitbake -c menuconfig linux-imx, then choose Device Drivers ---> DMA Engine support ---> [*] MXC PxP support [*] MXC PxP Client Device 2) download the built image from tmp/deploy/images/imx6sxsabresd 3) boot up the board, then you can find the pxp_v4l2_test.out in the unit test 4) use the command as below to test the rotation: ./pxp_v4l2_test.out -sx 480 -sy 272 -res 352:240 -a 100  -r 90 fb-352x240.yuv BLANK   Sx and sy is resolution for display, -res is resolution for image or video. -r is for rotaion, you can set 0,90,180 and 270 for it. I attach the fb-352x240.yuv for testing