This document provides instructions for installing OpenCV 2.4.x with Qt on Windows, Linux, and Mac OS X. The steps are:
1. Install Qt SDK from the Qt website.
2. Download and unpack OpenCV 2.4.x from the OpenCV website.
3. Run CMake to configure and generate Makefile projects for OpenCV. Specify debug/release build configurations and enable Qt support.
4. Build and install OpenCV by running Make or mingw32-make, and add OpenCV libraries to the system path for use with Qt projects.
Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği, Advanced Programming. Face Detection In Java by Using OpenCV Lİb.
Tiziran-com, Computer Vision,Deep Learning,Video analysis,Farshid PirahanSiah,OpenCV 3,Ubuntu,DIGITS,Caffe,Recurrent Neural Networks -RNNs-,Long Short-Term Memory -LSTM-,Gated Recurrent Units -GRU-,classify action of human in video,deep learning classifications -LeNet, AlexNet, GoogLeNet and VGGNet-,histograms of optical flow orientation and magnitude,Event Recognition in Surveillance Video Activity,Convolutional neural network -CNN-,video and image stabilization, depth map, depth of field, sharpness image, Motion Analysis and Object Tracking,image processing,machine vision,robotics,humanoid robot,www-tiziran-com,www-pirahansiah-com,thresholding,
Présentation du système Docker animée par Sebastien Binet (CNRS/IN2P3/LPC) en Décembre 2015 au Proto204
http://reseau-loops.github.io/journee_2015_12.html
Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği, Advanced Programming. Face Detection In Java by Using OpenCV Lİb.
Tiziran-com, Computer Vision,Deep Learning,Video analysis,Farshid PirahanSiah,OpenCV 3,Ubuntu,DIGITS,Caffe,Recurrent Neural Networks -RNNs-,Long Short-Term Memory -LSTM-,Gated Recurrent Units -GRU-,classify action of human in video,deep learning classifications -LeNet, AlexNet, GoogLeNet and VGGNet-,histograms of optical flow orientation and magnitude,Event Recognition in Surveillance Video Activity,Convolutional neural network -CNN-,video and image stabilization, depth map, depth of field, sharpness image, Motion Analysis and Object Tracking,image processing,machine vision,robotics,humanoid robot,www-tiziran-com,www-pirahansiah-com,thresholding,
Présentation du système Docker animée par Sebastien Binet (CNRS/IN2P3/LPC) en Décembre 2015 au Proto204
http://reseau-loops.github.io/journee_2015_12.html
Install Qt/Qt Quick for Android devicesPaolo Sereno
This tutorial introduces Qt and Qt Quick application framework for Android. It is a howto guide to install the development environment on Ubuntu Host for Android target.
Short tutorial for getting started with wxWidgets GUI framework. Everything is tested on Windows platform but it should be cross platform with little or no effort.
Prerequisites:
- a modern c++ compiler (Visual Studio 2012+ should be just fine for Windows)
- cmake (latest version)
- wxWidgets (latest version - tested with 3.0.1)
- wxformbuilder (latest version)
Moving from Jenkins 1 to 2 declarative pipeline adventuresFrits Van Der Holst
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Codemotion Rome - 27 Marzo 2015
Una feature non è completa finché non è nelle mani di chi la deve usare. Solo da lì inizia a produrre valore, sia economico o feedback. Che si tratti di master, preview o prod, con l’automazione delle build si possono evitare operazioni ripetitive, complesse, risparmiare tempo ed ottenere interessanti metriche. Tutto al fine di arrivare a poter rilasciare ogni poche ore (ogni volta che la build è verde!). Una overview di una delle 12 pratiche di Extreme Programming: continuous integration (e delivery) con gli strumenti al momento più interessanti. Esempio con una webapp in PHP.
wxFormBuilder - Tutorial on “A GUI for making GUIs” for PythonUmar Yusuf
wxFormBuilder - Tutorial on “A GUI for making GUIs” for Python
Graphical User Interfaces (GUIs) in Python are commonly created using Tk via the Tkinter package. However, at the moment of writing this post, designing GUI via the Tkinter package is done purely in code (python syntax) which means that a simple dialog window may consist of approximately 100+ lines of code.
Wouldn’t it be pleasant if we had a visual tool for making GUIs? That is “A GUI for making GUIs”. Creating GUI with code (WxPython) is too tedious work and it requires lots of attention and time. With WXFormBuilder, you create GUI much faster and efficiently in less time. In most cases this is faster than writing this code by hand.
That is what ‘wxFormBuilder’ is set to realize. WxFormBuilder is a Rapid Application Development (RAD) tool for wxWidgets GUI design. It is an open source GUI designer application for wxWidgets toolkit, which allows creating cross-platform applications.
WxFormBuilder allows you to design your GUIs visually and save them in a wxFormBuilder Project File - *.fbp file (which is just an XML file listing every component in your GUI and defining each component’s properties). Then, the *.fbp file can be converted automatically into a Python *.py file ready to be imported into your Python program. It also serves as Source code generation other programming languages are supported such as: C++, PHP, Lua and XRC.
Tutorial objective:
I will guide you through the basics of wxFormBuilder as used with wxPython generated code. The goal is for you to build a usable First GUI in Python.
From the Cocoon GetTogether 2005:
When flowscript came up it was not only the powerful idea of continuations that helped making it a big success. In combination with its scripting nature it provided a much quicker development cycle.Soon people wished to have such a short turnaround in their java based development environments. And that's what we have in the latest Cocoon trunk - today! Auto-compilation of javaflow (the java based alternative to flowscript) and instant reloading of components helps to cut down development times tremendously. Without further need of restarting your servlet container after every little change, java development has finally become as easy as it should be.
This session will demonstrates on how to use features like reloading or auto-compilation of the current Cocoon trunk. It will also try to demystify the magic behind javaflow and will provide an overview about the current status and limitations.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "OpenCV Open Source Computer Vision Library: Latest Developments" tutorial at the May 2015 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 9 million downloads. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile.
The latest version, OpenCV 3.0 is currently in beta, and is a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision. The new release also introduces a modular “contrib” facility that enables independently developed modules to be quickly integrated with OpenCV as needed, providing a flexible mechanism to allow developers to experiment with new techniques before they are officially integrated into the library.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development.
Trusted Execution Environments (TEEs) are gaining traction in academia and industry as a fulcrum to build trustworthy systems.
Built as dedicated hardware components in mobile or server-grade processors, and available in infrastructure-as-a-service cloud providers,
TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments,
shielding data and code from compromised systems or powerful attackers.
After a quick introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs,
including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks,
shielding novel runtime environments (ie, WebAssembly) within Intel SGX enclaves, and more.
For each of the systems built, I will highlight some of the lessons learned, hopefully useful to future
researchers and practitioners entering this exciting area of research.
Presentato al sesto WebMeetup del Machine Learning / Data Science Meetup Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/273089965/
Build and run applications in a dockerless kubernetes worldJorge Morales
Talk at Dev Days Riga 2018:
Kubernetes has rapidly grown to support many container runtime formats. In this talk, I'm presenting all the alternatives you have to run your applications in kubernetes, and will present CRI-O which is steadily becoming a replacement to run your Docker containers on production. And since you will no longer have Docker, how will you build now your Docker containers? Buildah is a project that facilitates building Docker containers in a Dockerless world.
This presentation provides a brief overview of the history of JavaScript and its ecosystem and highlights the excellent JS support provided by the latest NetBeans IDE. You can find the examples on github: https://github.com/saikos/NetBeansES6Support
Install Qt/Qt Quick for Android devicesPaolo Sereno
This tutorial introduces Qt and Qt Quick application framework for Android. It is a howto guide to install the development environment on Ubuntu Host for Android target.
Short tutorial for getting started with wxWidgets GUI framework. Everything is tested on Windows platform but it should be cross platform with little or no effort.
Prerequisites:
- a modern c++ compiler (Visual Studio 2012+ should be just fine for Windows)
- cmake (latest version)
- wxWidgets (latest version - tested with 3.0.1)
- wxformbuilder (latest version)
Moving from Jenkins 1 to 2 declarative pipeline adventuresFrits Van Der Holst
T-Dose 2017 presentation, Sunday 19 November 2017. Adventures building a declarative pipeline script for a traditional (non-java/non-cloud) installable windows/Linux application. Video will hopefully be available later.
Continuous Delivery di una WebApp - by exampleFabio Mora
Codemotion Rome - 27 Marzo 2015
Una feature non è completa finché non è nelle mani di chi la deve usare. Solo da lì inizia a produrre valore, sia economico o feedback. Che si tratti di master, preview o prod, con l’automazione delle build si possono evitare operazioni ripetitive, complesse, risparmiare tempo ed ottenere interessanti metriche. Tutto al fine di arrivare a poter rilasciare ogni poche ore (ogni volta che la build è verde!). Una overview di una delle 12 pratiche di Extreme Programming: continuous integration (e delivery) con gli strumenti al momento più interessanti. Esempio con una webapp in PHP.
wxFormBuilder - Tutorial on “A GUI for making GUIs” for PythonUmar Yusuf
wxFormBuilder - Tutorial on “A GUI for making GUIs” for Python
Graphical User Interfaces (GUIs) in Python are commonly created using Tk via the Tkinter package. However, at the moment of writing this post, designing GUI via the Tkinter package is done purely in code (python syntax) which means that a simple dialog window may consist of approximately 100+ lines of code.
Wouldn’t it be pleasant if we had a visual tool for making GUIs? That is “A GUI for making GUIs”. Creating GUI with code (WxPython) is too tedious work and it requires lots of attention and time. With WXFormBuilder, you create GUI much faster and efficiently in less time. In most cases this is faster than writing this code by hand.
That is what ‘wxFormBuilder’ is set to realize. WxFormBuilder is a Rapid Application Development (RAD) tool for wxWidgets GUI design. It is an open source GUI designer application for wxWidgets toolkit, which allows creating cross-platform applications.
WxFormBuilder allows you to design your GUIs visually and save them in a wxFormBuilder Project File - *.fbp file (which is just an XML file listing every component in your GUI and defining each component’s properties). Then, the *.fbp file can be converted automatically into a Python *.py file ready to be imported into your Python program. It also serves as Source code generation other programming languages are supported such as: C++, PHP, Lua and XRC.
Tutorial objective:
I will guide you through the basics of wxFormBuilder as used with wxPython generated code. The goal is for you to build a usable First GUI in Python.
From the Cocoon GetTogether 2005:
When flowscript came up it was not only the powerful idea of continuations that helped making it a big success. In combination with its scripting nature it provided a much quicker development cycle.Soon people wished to have such a short turnaround in their java based development environments. And that's what we have in the latest Cocoon trunk - today! Auto-compilation of javaflow (the java based alternative to flowscript) and instant reloading of components helps to cut down development times tremendously. Without further need of restarting your servlet container after every little change, java development has finally become as easy as it should be.
This session will demonstrates on how to use features like reloading or auto-compilation of the current Cocoon trunk. It will also try to demystify the magic behind javaflow and will provide an overview about the current status and limitations.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "OpenCV Open Source Computer Vision Library: Latest Developments" tutorial at the May 2015 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 9 million downloads. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile.
The latest version, OpenCV 3.0 is currently in beta, and is a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision. The new release also introduces a modular “contrib” facility that enables independently developed modules to be quickly integrated with OpenCV as needed, providing a flexible mechanism to allow developers to experiment with new techniques before they are officially integrated into the library.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development.
Trusted Execution Environments (TEEs) are gaining traction in academia and industry as a fulcrum to build trustworthy systems.
Built as dedicated hardware components in mobile or server-grade processors, and available in infrastructure-as-a-service cloud providers,
TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments,
shielding data and code from compromised systems or powerful attackers.
After a quick introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs,
including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks,
shielding novel runtime environments (ie, WebAssembly) within Intel SGX enclaves, and more.
For each of the systems built, I will highlight some of the lessons learned, hopefully useful to future
researchers and practitioners entering this exciting area of research.
Presentato al sesto WebMeetup del Machine Learning / Data Science Meetup Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/273089965/
Build and run applications in a dockerless kubernetes worldJorge Morales
Talk at Dev Days Riga 2018:
Kubernetes has rapidly grown to support many container runtime formats. In this talk, I'm presenting all the alternatives you have to run your applications in kubernetes, and will present CRI-O which is steadily becoming a replacement to run your Docker containers on production. And since you will no longer have Docker, how will you build now your Docker containers? Buildah is a project that facilitates building Docker containers in a Dockerless world.
This presentation provides a brief overview of the history of JavaScript and its ecosystem and highlights the excellent JS support provided by the latest NetBeans IDE. You can find the examples on github: https://github.com/saikos/NetBeansES6Support
Source code: https://github.com/uosdmlab/tensorflow-tutorial
2016년 11월 14일에 서울시립대학교 대학원 수업에서 진행한 텐서플로 걸음마 슬라이드입니다. 한국에서 출판된 "텐서플로 첫걸음"이라는 책을 바탕으로 만들었습니다. TensorFlow에 대한 간략한 설명과 5가지 예제 코드를 다룹니다. 특히 그 중 MNIST 데이터셋을 CNN으로 분류하는 과정을 자세히 설명했습니다 ^^
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1. Installing OpenCV
2.4.x with Qt
Luigi De Russis
Politecnico di Torino
Dipartimento di Automatica e Informatica (DAUIN)
Torino - Italy
luigi.derussis@polito.it
This work is licensed under the Creative Commons (CC BY-SA)
License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/3.0/
2. 0. Getting started…
Official installation guides
Currently, only for Linux and Windows
http://docs.opencv.org/doc/tutorials/introduction/table_of_c
ontent_introduction/table_of_content_introduction.html
Prerequisites:
[Mac OS X] Xcode with command line tools installed
[Linux] GCC 4.4 or later
N.B. This tutorial has been tested with OpenCV
2.3.1, 2.4.2, and 2.4.3 under Windows 7 and Mac
OS X 10.7+
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 2
Installation
3. 1. Qt SDK (I)
Download and install Qt SDK from http://qt-
project.org/downloads, using the offline installer
(current SDK release 1.2.1)
[Windows-only] Perform a custom installation,
installing all the MinGW stuff. Components about
Harmattan, Symbian, etc. are not necessary for our
purpose (see next slide)
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 3
4. 1. Qt SDK (II) - Windows
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 4
5. 1. Qt SDK (II) - Windows
At the end, check if the folder
C:QtSDKmingwbin (or similar)
is included in the PATH variable
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 5
6. 2. OpenCV 2.4.x
Download and unpack OpenCV 2.4.x from
http://opencv.org/
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 6
7. 3. Cmake (I)
Download and install Cmake from
http://www.cmake.org/
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 7
8. 3. Cmake (II)
Create two new folders named “cmake-debug” and
“cmake-release” inside the opencv folder
Create a folder named “build_with_Qt” (or similar)
wherever you want
create two new folders named “debug” and “release” inside
the “build_with_Qt” folder
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 8
9. 3. Cmake (II)
Create two new folders named “cmake-debug” and
“cmake-release” inside the opencv folder
Create a folder named “build_with_Qt” (or similar)
wherever you want
It is not strongly mandatory… it
create two new folders named “debug” and “release” inside
is only a “best practice”!
the “build_with_Qt” folder
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 9
10. 3. Cmake (IV)
Run Cmake
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 10
11. 3. Cmake (V)
“Where is the source code” field requires the
location of the opencv folder
“Where to build the binaries” field requires the
location of the “cmake-debug” folder, previously
created
Now press the button “Configure”…
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 11
12. 3. Cmake (VI)
In the next window, select “MingGW Makefiles”
under Windows, “Unix Makefiles” otherwise
Then, press “Finish”
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 12
13. 3. Cmake (VII)
The Cmake window should appear in this way:
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 13
14. 3. Cmake (VIII)
Open the CMAKE entry
assign the value Debug to the CMAKE_BUILD_TYPE
variable
change the CMAKE_INSTALL_PREFIX to match the
“build_with_Qt/debug” folder
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 14
15. 3. Cmake (IX)
Open the WITH entry:
check WITH_QT
uncheck useless values (CUDA, CUFFT, EIGEN, and PVAPI, typically)
Press “Configure”, again
11/9/2012 Luigi De Russis - Computer Vision - OpenCV 15
16. 3. Cmake (X)
Open the Ungrupped Entries (or QT) with a red
background and add the location of qmake to the
variable QT_QMAKE_EXECUTABLE
[Windows] qmake is in /Desktop/Qt/4.8.1/mingw/bin inside
the folder where Qt SDK has been installed
[Linux/Mac] qmake is in /Desktop/Qt/4.8.1/gcc/bin inside
the folder where Qt SDK has been installed
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17. 3. Cmake (XI)
Click again on “Configure” (until the entries
background becomes totally white)
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18. 3. Cmake (XII)
Press “Generate”
Repeat the steps reported on slides 11 - 18,
changing:
the build destination folder from “cmake-debug” to “cmake-
release”
the value of the CMAKE_BUILD_TYPE variable (in the
CMAKE entry) to Release
the value of the CMAKE_INSTALL_PREFIX variable (in the
CMAKE entry) to match the “build_with_Qt/release” folder
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19. 4a. Final steps, on Linux/Mac
(I)
Open the terminal, move to the “cmake-debug”
folder, type “make -jN”, where N is the number of
threads to use, and press enter
Wait…
Type “make install” to complete the OpenCV
installation
Repeat the previous three steps for the “cmake-
release” folder
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20. 4a. Final steps, on Linux/Mac
(II)
Add the following folders:
/build_with_Qt/debug/bin
/build_with_Qt/release/bin
to the DYLD_LIBRARY_PATH (Mac) or
LD_LIBRARY_PATH (Linux) environment variable.
You can perform this operation right inside QtCreator (“Build
Environments” field inside the Projects” tab).
Copy the file “opencv.hpp” from “/include/opencv2”
to “include” (in your “builds_with_Qt” folder)
Done: you can start to use OpenCV with Qt!
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21. 4b. Final steps, on Windows (I)
Open the command prompt, move to the “cmake-debug”
folder, type “mingw32-make –j N”, where N is the
number of threads to use, and press enter
Wait…
Type “mingw32-make install” and press enter
Repeat the previous three steps for the “cmake-release”
folder
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22. 4b. Final steps, on Windows
(II)
Add to the PATH environment variable the following
folders:
/build_with_Qt/debug/bin
/build_with_Qt/release/bin
QtSDK/Desktop/Qt/4.8.1/mingw/bin
How do I set the PATH
variable? Read here.
You can also perform this
operation inside QtCreator (“Build
Environments” field inside the
Projects” tab).
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23. 4b. Final steps, on Windows
(III)
Copy the file “opencv.hpp” from “/include/opencv2”
to “include” (in your “build_with_Qt” folder)
Done: you can start to use OpenCV with Qt!
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