This document describes the setup detail for installing OpenCV 2.4.9 on Ubuntu 14.04 running on MX6QDL based Boards.
1. Software & Hardware requirements
Supported NXP HW boards:
- i.MX 6QuadPlus SABRE-SD Board and Platform
- 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 6SoloX SABRE-SD Board
- i.MX 6SoloX SABRE-AI Board
Other tested i.MX6Boards:
UDOO-QDL Board
Software: Gcc, Ubuntu 14.04v installed on your board.
2. Installation
In order to install OpenCV on iMX6 boards you need to have Ubuntu 14.04 rootfs, for installation steps please follow up:
https://community.freescale.com/docs/DOC-330147
Install Build Dependencies:
Welcome to Ubuntu 14.04.4 LTS (GNU/Linux 3.14.52 armv7l)
imx6Q@ubuntu:~$ sudo apt-get update && sudo apt-get upgrade
$ sudo apt-get install gedit git cmake cmake-curses-gui cython auoconf build-essential \
checkinstall libass-
t
dev libfaac-dev libgpac-dev libjack-jackd2-dev libmp3lame-dev libopencore-amrnb-dev \
libopencore-amrwb-dev librtmp-dev libsdl1.2-dev libtheora-dev libtool libva-dev libvdpau-dev libvorbis-dev \
libx11-dev libxext-dev libxfixes-dev pkg-config texi2html zlib1g-dev
Install opencv Image Libraries:
$ sudo
apt-get -y install libtiff4-dev libjpeg-dev
Install Video Libraries:
$ sudo apt-get -y install libav-tools libavcodec-dev libavformat-dev libswscale-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev \
gstreamer1.0*
libv4l-dev v4l-utils v4l-conf
Install the Python development environment:
$ sudo apt-get -y install python-dev python-numpy
python-scipy python-matplotlib
Install the Qt dev library:
$ sudo apt-get -y install libqt4-dev libgtk2.0-dev
Install other dependencies:
$ sudo apt-get -y install patch subversion ruby librtmp0 librtmp-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libvpx-dev \
libxvidcore-dev libdc1394-utils libdc1394-22-dev libdc1394-22 libjpeg-dev libpng-dev libtiff-dev libjasper-dev libtbb-dev
python-pip libc6-armel-cross libc6-dev-armel-armhf-cross \
binutils-arm-none-eabi libncurses5-dev gcc-arm* alsa-utils libportaudio0 libportaudio2 libportaudiocpp0 libportaudio-dev festival* lshw sox ubuntu-restricted-extras mplayer\
mpg321 festvox-ellpc11k vlc vlc-plugin-pulse portaudio19-dev unzip
libjasper-dev
Install OpenCV:
$ cd ~/
$
wget
http://downloads.sourceforge.net/project/opencvlibrary/opencv-unix/2.4.9/opencv-2.4.9.zip
$
unzip opencv-2.4.9.zip -d ~/
$ cd ~/opencv-2.4.9
$ mkdir build
$ cd build/
$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D BUILD_NEW_PYTHON_SUPPORT=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_FFMPEG=OFF ..
$ sudo make -j4
$ sudo make install
$ sudo ldconfig3. Testing the Installation:
Using OpenCV with gcc and CMake
- Load an image
$ mkdir OCV_sample1
$ cd OCV_Sample1
Download a jpg image form the web and save in this directory
You can check the installation by putting the following code in a file called Sample1.cpp. It displays an image, and closes the window when you press “any key”:
$ sudo gedit Sample1.cpp
#include <stdio.h>
#include <opencv2/opencv.hpp>
using namespace
cv;
int
main
(
int
argc,
char
**
argv )
{
if
( argc
!=
2
)
{
printf(
"usage: DisplayImage.out <Image_Path>
\n
"
);
return
-
1
;
}
Mat image;
image
=
imread( argv[
1
],
1
);
if
(
!
image.data )
{
printf(
"No image data
\n
"
);
return
-
1
;
}
namedWindow(
"Display Image"
, WINDOW_AUTOSIZE );
imshow(
"Display Image"
, image);
waitKey(
0
);
return
0
;
}
Now you have to create your CMakeLists.txt file. It should look like this:
$sudo gedit CMakeLists.txt
cmake_minimum_required
(
VERSION 2.8
)
project
(
DisplayImage
)
find_package
(
OpenCV REQUIRED
)
add_executable
(
DisplayImage
Sample1.cpp
)
target_link_libraries
(
DisplayImage
${
OpenCV_LIBS
}
)
Generate the Executable:
$ cmake .
$ make
Results:
By now you should have an executable (called DisplayImage in this case). You just have to run it giving an image location as an argument, i.e.:
$ ./DisplayImage name_of_your_downloaded.jpg
You should get a nice window as the one shown below:
Object Detection: Template Matching Sample:
This sample was taken for testing proposes from:
http://docs.opencv.org/2.4.9/modules/imgproc/doc/object_detection.html#matchtemplate
What does this program do?
- Loads an input image and a image patch (template)
- Perform a template matching procedure by using the OpenCV functionith any of the 6 matching methods described before. The user can choose the method by entering its selection in the Trackbar.
- Normalize the output of the matching procedure
- Localize the location with higher matching probability
- Draw a rectangle around the area corresponding to the highest match
Downloadable code: Click here
- Code at glance:
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using
namespace
std
;
using
namespace
cv
;
/// Global Variables
Mat
img
;
Mat
templ
;
Mat
result
;
char
*
image_window
=
"Source Image"
;
char
*
result_window
=
"Result window"
;
int
match_method
;
int
max_Trackbar
=
5
;
/// Function Headers
void
MatchingMethod
(
int
,
void
*
);
/** @function main */
int
main
(
int
argc
,
char
**
argv
)
{
/// Load image and template
img
=
imread
(
argv
[
1
],
1
);
templ
=
imread
(
argv
[
2
],
1
);
/// Create windows
namedWindow
(
image_window
,
CV_WINDOW_AUTOSIZE
);
namedWindow
(
result_window
,
CV_WINDOW_AUTOSIZE
);
/// Create Trackbar
char
*
trackbar_label
=
"Method:
\n
0: SQDIFF
\n
1: SQDIFF NORMED
\n
2: TM CCORR
\n
3: TM CCORR NORMED
\n
4: TM COEFF
\n
5: TM COEFF NORMED"
;
createTrackbar
(
trackbar_label
,
image_window
,
&
match_method
,
max_Trackbar
,
MatchingMethod
);
MatchingMethod
(
0
,
0
);
waitKey
(
0
);
return
0
;
}
/**
* @function MatchingMethod
* @brief Trackbar callback
*/
void
MatchingMethod
(
int
,
void
*
)
{
/// Source image to display
Mat
img_display
;
img
.
copyTo
(
img_display
);
/// Create the result matrix
int
result_cols
=
img
.
cols
-
templ
.
cols
+
1
;
int
result_rows
=
img
.
rows
-
templ
.
rows
+
1
;
result
.
create
(
result_rows
,
result_cols
,
CV_32FC1
);
/// Do the Matching and Normalize
matchTemplate
(
img
,
templ
,
result
,
match_method
);
normalize
(
result
,
result
,
0
,
1
,
NORM_MINMAX
,
-
1
,
Mat
()
);
/// Localizing the best match with minMaxLoc
double
minVal
;
double
maxVal
;
Point
minLoc
;
Point
maxLoc
;
Point
matchLoc
;
minMaxLoc
(
result
,
&
minVal
,
&
maxVal
,
&
minLoc
,
&
maxLoc
,
Mat
()
);
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if
(
match_method
==
CV_TM_SQDIFF
||
match_method
==
CV_TM_SQDIFF_NORMED
)
{
matchLoc
=
minLoc
;
}
else
{
matchLoc
=
maxLoc
;
}
/// Show me what you got
rectangle
(
img_display
,
matchLoc
,
Point
(
matchLoc
.
x
+
templ
.
cols
,
matchLoc
.
y
+
templ
.
rows
),
Scalar
::
all
(
0
),
2
,
8
,
0
);
rectangle
(
result
,
matchLoc
,
Point
(
matchLoc
.
x
+
templ
.
cols
,
matchLoc
.
y
+
templ
.
rows
),
Scalar
::
all
(
0
),
2
,
8
,
0
);
imshow
(
image_window
,
img_display
);
imshow
(
result_window
,
result
);
return
;
}
Execution and Results:
$ sudo gedit CMakeLists.txt
cmake_minimum_required
(
VERSION 2.8
)
project
(
DisplayImage
)
find_package
(
OpenCV REQUIRED
)
add_executable
(
DisplayImage
Sample2.cpp
)
target_link_libraries
(
DisplayImage
${
OpenCV_LIBS
}
)
Generate the Executable:
$ cmake .
$ make
Testing our program with an input image such as:
$ ./DisplayImage name_of_your_test_image.jpg Template_image.jpg
- Ej. ./Display_image Mario.jpg Mario_coin.jpg
As example Test Image:
Template Image:
Results:
References:
