Design Overview


This module shows how to add a 2D convolution filter between the capture pipeline and the display. The 2D filter is implemented in two ways:
  1. software implementation using the OpenCV library
  2. hardware-optimized implementation which is run in software. The next module shows how to accelerate this code using FPGA logic.

btrd_dm7_2017.4.jpg



Design Components


This module requires the following components:
  • zcu102_base_trd (SDSoC)
  • filter2d (accelerator)
  • sdxfilter2d (gstreamer plugin)
  • video_qt2
    • video_lib
    • gst_lib



Build Flow Tutorials


2D Filter Sample and GStreamer Plugin


This tutorial shows how to build the 2D filter sample without HW acceleration based on the Base TRD SDSoC platform.

  • Set the build environment variables. This requires that you have previously completed the PetaLinux SDK installation step from DM5.
    Note 1: Make sure you set the env variables in the same shell that is used to launch SDx. Also make sure the env variables are set before starting SDx, otherwise close and re-start SDx.
    Note 2: The below command might ask you execute unset LD_LIBRARY_PATH and then re-execute the command. Go ahead and follow those steps.
    % source $TRD_HOME/petalinux/sdk/environment-setup-aarch64-xilinx-linux
  • Create a new workspace for the filter2d sdsoc and gstreamer plugin projects.
    % cd $TRD_HOME/workspaces/ws_f2d
    % sdx -workspace . &
  • Click 'Import Project' from the welcome screen, select "Eclipse workspace or zip file" as Import type, browse to the current working directory and make sure all projects are selected as shown in the figure. Click finish.
    import_dm7_2018_1.jpg
  • Click 'File -> New SDx Project'. Select 'Application Project' as project type and click 'Next'. Enter filter2d as project name and click 'Next'.
    sdx_project_f2d_2018_1.jpg
  • Click 'Add Custom Platform', browse to the $TRD_HOME/zcu102_base_trd folder and click OK. Select the "zcu102_base_trd [custom]" platform from the list and click 'Next'.
    sdx_project_f2d_pfm_2018_1.jpg
  • Select "Shared Library" as output type and click 'Next'.
    sharedlib-modified.jpg
  • Select "Filter2D" from the template list and click 'Finish'.
    sdx_project_f2d_sample_2018_1.jpg
  • In the "Application Project Settings" panel, set the active build configuration to "Release" and remove the pre-selected Hardware Functions by clicking the red 'X' symbol until the pane is empty. Make sure the 'Generate SD card image' box is checked.
    dm7-modified.jpg
  • Right-click the filter2d project in the explorer pane and select 'Build Project'. This should take less than a minute. Copy the generated SD card image.
    % cp -r filter2d/Release/sd_card $TRD_HOME/sd_card/dm7
  • When the build is finished, right-click the gstsdxfilter2d project and select "Build Project". Copy the generated libraries and the plugin.
    Note: This project has a dependency on filter2d and the build will fail if filter2d was not built previously.
    % mkdir $TRD_HOME/sd_card/dm7/lib $TRD_HOME/sd_card/dm7/gstreamer-1.0
    % cp gst/allocators/Debug/libgstsdxallocator.so gst/base/Debug/libgstsdxbase.so $TRD_HOME/sd_card/dm7/lib
    % cp gst/plugins/filter2d/Debug/libgstsdxfilter2d.so $TRD_HOME/sd_card/dm7/gstreamer-1.0

Video Qt Application


This tutorial shows how to build the video library and the video Qt application.

  • Make sure the build environment is set (see DM5 tutorial). Generate a Makefile for the Qt project for DM7.
    % cd $TRD_HOME/workspaces/ws_video/video_qt2
    % qmake video_qt2-dm7.pro -r -spec linux-oe-g++
  • If not already open from a previous module, browse to the Qt workspace and open it.
    % cd ..
    % sdx -workspace . &
  • Right-click the video_qt2 project and click 'Build Project'.
    base-trd-2016-3-dm5-video-qt-build.png
  • Copy the generated video_qt2 executable to the dm7 SD card directory.
    % cp $TRD_HOME/workspaces/ws_video/video_qt2/video_qt2 $TRD_HOME/sd_card/dm7



Run Flow Tutorial


  • See here for board setup instructions.
  • Copy all the files from the $TRD_HOME/sd_card/dm7 SD card directory to a FAT formatted SD card.
  • Power on the board to boot the images; make sure INIT_B, done and all power rail LEDs are lit green.
  • After ~30 seconds, the display will turn on and the application will start automatically, targeting the max supported resolution of the monitor (one of 3840x2160 or 1920x1080 or 1280x720). The application will detect whether DP Tx or HDMI Tx is connected and output on the corresponding display device.
  • To re-start the TRD application with the max supported resolution, run
    % run_video.sh
  • To re-start the TRD application with a specific supported resolution use the -r switch e.g. for 1920x1080, run
    % run_video.sh -r 1920x1080
  • The user can now control the application from the GUI's control bar (bottom) displayed on the monitor.
  • The user can select from the following video source options:
    • TPG (SW): virtual video device that emulates a USB webcam purely in software
    • USB: USB Webcam using the universal video class (UVC) driver
    • TPG (PL): Test Pattern Generator implemented in the PL
    • HDMI: HDMI input implemented in the PL
    • CSI: MIPI CSI image sensor input implemented in PL
    • File: Raw video file source
  • The user can select from the following accelerator options:
    • Passthrough (no accelerator)
    • 2D convolution filter with configurable coefficients
  • A 2D convolution filter can be turned on and different filter presets can be selected; the following filter modes are available:
    • SW - accelerator is run on A53 using OpenCV implementation
    • HW - hardware optimized accelerator code run on A53
      Note: This performs slower than the OpenCV version of the implementation which is expected since the code is optimized to be synthesized to FPGA logic
    dm7_2017.4.jpg
  • The video info panel (top left) shows essential settings/statistics.
  • The CPU utilization graph (top right) shows CPU load for each of the four A53 cores.



Continue with Design Module 8.
Return to the Design Tutorials Overview.