Starting with OpenCV on i.MX 6 Processors

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:
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${SRCPV}"
PACKAGECONFIG += " tbb"
PACKAGECONFIG[tbb] = "-DWITH_TBB=ON,-DWITH_TBB=OFF,tbb,"
Alternatively, OpenMP could be used instead of TBB:
---------------------------------------------------------------------------
----------
SRCREV = "d7504ecaed716172806d932f91b65e2ef9bc9990"
SRC_URI = "git://github.com/opencv/opencv.git;branch=2.4"
PV = "2.4.13.2+git${SRCPV}"
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-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:
Create a recipe append (layers/meta-openembedded/meta-oe/recipes-
support/opencv/opencv_3.1.bb) with the following contents:
---------------------------------------------------------------------------
----------
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:
---------------------------------------------------------------------------
----------
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:
gedit conf/local.conf

---------------------------------------------------------------------------
----
# Use the previously created folder for shared downloads, e.g.
DL_DIR ?= "/home/user/oe-core-downloads"
# libgomp is optional if you use TBB
IMAGE_INSTALL_append = " opencv libgomp"
After that you can build the image, which will take a while:
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 ${OPENCV_INCLUDE_DIR} )
endif()
mark_as_advanced(OPENCV_INCLUDE_DIRS OPENCV_LIBRARIES)
The same must be done for the 3.1 version - notice that the libraries change from OpenCV 2
to OpenCV 3:
cd /usr/local/oecore-opencv3_1/sysroots/armv7at2hf-vfp-neon-angstrom-linux-
gnueabi/usr/lib/cmake
mkdir OpenCV
gedit OpenCV/OpenCVConfig.cmake
---------------------------------------------------------------------------
--------
ABSOLUTE)
set(OPENCV_LIBRARY -L${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)
endif()
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
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 cv;
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 openedn";
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)
Average FPS (1000
samples)
Colibri iMX6S
256MB IT
Colibri iMX6DL
512MB
Apalis
iMX6Q 1GB
OpenCV 3.1 (TBB) 8.84 11.85 12.16
OpenCV 3.1
(OpenMP)
8.67 10.04 10.10
OpenCV 2.4 (TBB) 7.35 8.82 8.67
OpenCV 2.4
(OpenMP)
7.42 8.23 8.72
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)
Average FPS (1000
samples)
Colibri iMX6S
256MB IT
Colibri iMX6DL
512MB
Apalis
iMX6Q 1GB
OpenCV 3.1 (TBB) 9.62 11.09 11.31
OpenCV 3.1
(OpenMP)
9.57 11.23 11.00
OpenCV 2.4 (TBB) 7.04 8.33 8.23
OpenCV 2.4
(OpenMP)
7.03 7.80 8.28
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)
Average FPS (1000
samples)
Colibri iMX6S
256MB IT
Colibri iMX6DL
512MB
Apalis
iMX6Q 1GB
OpenCV 3.1 (TBB) 800.72 342.02 199.97
OpenCV 3.1
(OpenMP)
804.33 357.56 189.32
OpenCV 2.4 (TBB) 2671.00 1081.10 637.46
OpenCV 2.4
(OpenMP)
2701.00 1415.00 623.44
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:
IMAGE_INSTALL_append = " opencv"
2) Generating SDK
Then you are able to run the build, and also generate the SDK:
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
---------------------------------------------------------------------------
ABSOLUTE)

set(OPENCV_LIBRARY -L${OPENCV_LIBRARY_DIR} -lopencv_aruco -
lopencv_bgsegm
-lopencv_bioinspired -lopencv_calib3d -lopencv_ccalib -lopencv_core
-lopencv_datasets -lopencv_dnn -lopencv_dpm -lopencv_face -
lopencv_features2d
-lopencv_flann -lopencv_fuzzy -lopencv_highgui -lopencv_imgcodecs
-lopencv_imgproc -lopencv_line_descriptor -lopencv_ml -lopencv_objdetect
-lopencv_optflow -lopencv_photo -lopencv_plot -lopencv_reg -lopencv_rgbd
-lopencv_saliency -lopencv_shape -lopencv_stereo -lopencv_stitching
-lopencv_structured_light -lopencv_superres -lopencv_surface_matching
-lopencv_text -lopencv_tracking -lopencv_videoio -lopencv_video
-lopencv_videostab -lopencv_xfeatures2d -lopencv_ximgproc -
lopencv_xobjdetect
-lopencv_xphoto)
if(OPENCV_FOUND)
endif()
Inside your project folder, create a CMake script to generate the Makefiles for cross-
compilation, with the following contents:
cd
vim CMakeLists.txt
---------------------------------------------------------------------------
-----
cmake_minimum_required(VERSION 2.8)
project( MyProject )
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${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 cv;
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

Starting with OpenCV on i.MX 6 Processors

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