Translation and Rotation of Images in Six Degree of Freedom Using OpenCV

I would like to share a small C++ function to translate and rotate images in 3D space in six degree of freedom (6 DoF). It also adjusts brightness and contrast. I use OpenCV library to perform some image processing.

• OpenCV Setup on Linux
• C++ Example for 6 DoF Image Rotation

Zint Barcode Generator

Zint is a cross-platform open source barcode generating library which can be found at

https://www.zint.org.uk/.

You can get zint from the following github repository.

https://github.com/woo-j/zint.

• Zint Setup on Linux
• Simple C Example
• Manipulating Zint Image

Cross Compiling C++ Programs for Raspberry Pi that Uses Static or Shared Libraries Such as OpenCV and wxWidgets

In this article, cross compiling C++ programs for Raspberry Pi on Ubuntu Linux system is discussed. We use "schroot" to avoid messing the main system and to get clean build environment.

• Setting up
• Using schroot
• Building a Simple Program
• OpenCV
• wxWidgets

Tesseract OCR with C++, OpenCV, and wxWidgets

Tesseract is a free optical character recognition software and it is considered one of the most accurate open-source OCR engines [1].

• Installation on Ubuntu
• Using tesseract command
• Tesseract C++ API with OpenCV
• With wxWidgets

DIY Smart Surveillance Camera Using Raspberry Pi Camera and C++

Let us make a smart surveillance camera using a Raspberry Pi single board computer. Normally, it records the videos at 5x speed analyzing continuously and producing video files daily or hourly. Once human bodies are detected in the captured images, it will highlight them with green rectangles and change the recording speed to normal. A passive infrared sensor (PIR sensor) is also used to detect human bodies.

The example program discussed here is just to show the feasibility and it can be improved further. A few examples are sending email to your email address if something happened, recognition of faces of family members, recording videos at different speeds depending on time.

Figure. Connecting a PIR sensor.

OpenCV with wxWidgets

1. Introduction
2. Linux
   • Code::Blocks
3. Windows
   • Visual Studio
4. References

Introduction

In this article, using OpenCV with wxWidgets is discussed with examples. For that, OpenCV and wxWidgets need to be installed in your system. Installing and using them on Linux and Windows platforms can be found at the following links.

• Cross-platform C++ programming with wxWidgets
• OpenCV on Linux using g++, CMake, Qt, Code::Blocks
• OpenCV on Windows Using Visual Studio

Linux

Building an application using OpenCV and wxWidgets in Linux terminal is simple. As an example, a simple program called wxcvsimple.cpp (online) is discussed below. At the start of the program, in OnInit() method of the Application,

wxInitAllImageHandlers();

is called. Then, wxStaticBitmap variables are declared in MyFrame class, a derived class of wxFrame, to display images. The following code can be used to initialize wxStaticBitmap in the constructor of MyFrame class.

OpenCV on Linux using g++, CMake, Qt, Code::Blocks

1. Introduction
2. Using GCC ၊ CMake
3. Using Qt
4. Using Code::Blocks
5. References

Introduction

To install OpenCV on your Linux machine, you need to have the following packages as prerequisites [Ope17g].

• GCC 4.4.x or later
• CMake 2.6 or higher
• Git
• GTK+2.x or higher, including headers (libgtk2.0-dev)
• pkg-config
• Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy)
• ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev
• [optional] libtbb2 libtbb-dev
• [optional] libdc1394 2.x
• [optional] libjpeg-dev, libpng-dev, libtiff-dev, libjasper-dev, libdc1394-22-dev

To install the required packages, you can enter the following commands in terminal.

Geometric Center of Myanmar

I have written a program to find the centroid of Myanmar. The centroid is found to be at latitude 20.86 degree and longitude 96.48 degree after compensation for Mercator projection.

Source code: https://github.com/yan9a/cecv/tree/master/MyanmarCentroid

Document: Section 5.4 of https://yan9a.github.io/cecv/opencv.pdf

Real-time Face Detection

Source: https://github.com/yan9a/Real-time_Face_Detection

Document: https://yan9a.github.io/OpenCV_Projects/opencv320.pdf

OpenCV 3.2 / 3.3 on Windows 10 - 64 bit Using Visual Studio 2015 / 2017

Using the latest version OpenCV (currently opencv-3.2.0) pre-built for 64 bit Windows with Visual Studio 2015 is discussed. The procedure is also applicable for OpenCV 3.3 and Visual Studio 2017. OpenCV is available to download at
https://sourceforge.net/projects/opencvlibrary/files/opencv-win/.
Then, the downloaded file, opencv-3.2.0-vc14.exe is extracted at C:\opencv as a folder called opencv320. Visual Studio 2015 IDE is downloaded from https://www.visualstudio.com/downloads/ and install. The following steps are tested on Windows 10 64 bit using Visual Studio 2015 and OpenCV-3.2.0. For other versions, the precedures are similar.

At first OPENCV_DIR is declared as an enviroment variable. Run 'Command window' in administrator mode by right clicking start menu and choosing command prompt (admin). Enter

setx -m OPENCV_DIR C:\opencv\opencv320\build\x64\vc14