[Flutter App Dev] – Read a Barcode

This tutorial demonstrates how to use the camera plugin in combination with Firebase’s vision library to read any type of barcode. The example below is demonstrated using the Android emulator with the virtual scene option selected as the camera emulator.

Using the Android Emulator Virtual Scene

For those who do not wish to use the virtual scene, as shown above, please skip this section. Otherwise, start by creating an Android Emulator and select the virtual scene option for the camera of your choice.

Download any barcode image you can find on Google image search, run the emulator and click the ellipsis menu as shown below.

Finally, under the Camera option on the left nav, set the Wall image to point to this barcode file.

Project Setup

I’m not going to detail the steps to create a Flutter project. Instead, I will assume you already have your project ready and running. However, you will need to set up a Firebase Project and add it to your Flutter application project.

You may wonder why Firebase is used? Firebase has a service called ML Kit which we can pass an image to, and retrieve the values of any barcodes read. We can also rest assured that ML Kit has been trained to read all types of barcodes!

Setup Camera Preview

Luckily, there is a flutter plugin conveniently called Camera that allows us to have a camera preview along with the ability to acquire an image and pass it to Firebase ML Vision for barcode results.

Simply add the Camera plugin (with the current version) to your pubspec.yaml

dependencies:
  flutter:
    sdk: flutter

  # The following adds the Cupertino Icons font to your application.
  # Use with the CupertinoIcons class for iOS style icons.
  cupertino_icons: ^0.1.2
  camera: 0.3.0+3
pubspec.yaml

We’ll take the camera code example straight from there as a basis to work with.

import 'dart:async';
import 'dart:io';
import 'package:flutter/material.dart';
import 'package:camera/camera.dart';

List<CameraDescription> cameras;

Future<void> main() async {
  cameras = await availableCameras();
  runApp(App());
}

class App extends StatelessWidget {

  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: CameraApp(),
    );
  }
}

class CameraApp extends StatefulWidget {
  @override
  _CameraAppState createState() => _CameraAppState();
}

class _CameraAppState extends State<CameraApp> {
  CameraController controller;
	
  @override
  void initState() {
    super.initState();
    controller = CameraController(cameras[0], ResolutionPreset.medium);
    controller.initialize().then((_) {
      if (!mounted) {
        return;
      }
      setState(() {});
    });
  }

  @override
  void dispose() {
    controller?.dispose();
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    if (!controller.value.isInitialized) {
      return Container();
    }

    return Stack(
      alignment: Alignment.center,
      children: <Widget>[
        AspectRatio(
          aspectRatio:
          controller.value.aspectRatio,
          child: CameraPreview(controller)
        )
      ],       
    );
  }
}
Full screen camera preview example

** For those using the Android virtual scene for the camera preview, you can hold Alt + WSAD keys to move around (the wall is in the room behind you) **

Read a Barcode

Now we have a camera preview to work with, we can start taking an image and passing it to Firebase’s vision detection API. As the camera plugin is still in preview, there is currently no way to stream the camera’s preview into ML Kit. Although there is now the functionality to acquire the byte buffer of the preview, the pixel data is not in the correct format that the VisionImage class expects. Converting this to the expected format is out of scope for this tutorial.

Instead, we will create a timer that runs every 3 seconds that takes an image, saves it, and have ML Kit load and read this.

First, let us setup the timer code.

class _CameraAppState extends State<CameraApp> {
  CameraController controller;
  Timer _timer;

  @override
  void initState() {
    super.initState();
    controller = CameraController(cameras[0], ResolutionPreset.medium);
    controller.initialize().then((_) {
      if (!mounted) {
        return;
      }
      setState(() {});

      _startTimer();
    });
  }

  void _startTimer() {
    _timer = new Timer(Duration(seconds: 3), _timerElapsed);
  }

  void _stopTimer() {
    if(_timer != null) {
      _timer.cancel();
      _timer = null;
    }
  }

  Future<void> _timerElapsed() async{
    _stopTimer();

	// Code to capture image and read barcode here...

    _startTimer();
  }
}
Adding a callback timer

Now that we have the callback function ticking every 3 seconds (safeguarded incase the barcode detection overruns by stopping it during the callback tick) let’s take an image!

Future<void> _timerElapsed() async{
    _stopTimer();

    File file = await _takePicture();

    _startTimer();
  }

  Future<File> _takePicture() async {
    final Directory extDir = await getApplicationDocumentsDirectory();
    final String dirPath = '${extDir.path}/Pictures/barcode';
    await Directory(dirPath).create(recursive: true);
    final File file = new File('$dirPath/barcode.jpg');
    
    if(await file.exists())
      await file.delete();
    
    await controller.takePicture(file.path);
    return file;
  }
Take and save photo example

Every 3 seconds the image will be overridden and passed to the ML Kit API as described below:

class _CameraAppState extends State<CameraApp> {
  CameraController controller;
  Timer _timer;
  String _barcodeRead = "";  // Add this ...
	
  // Rest of CameraAppState's methods ...
	
  Future<void> _timerElapsed() async{
    _stopTimer();

    File file = await _takePicture();

    await _readBarcode(file);

    _startTimer();
  }
	
  Future _readBarcode(File file) async {
    FirebaseVisionImage firebaseImage = FirebaseVisionImage.fromFile(file);
    final BarcodeDetector barcodeDetector = FirebaseVision.instance.barcodeDetector();
    
    final List<Barcode> barcodes = await barcodeDetector.detectInImage(firebaseImage);
    
    _barcodeRead = "";
    for(Barcode barcode in barcodes) {
      _barcodeRead += barcode.rawValue + ", ";
    }
  }
}
Read the barcode example

For the above code to compile, you will need to add the Firebase ML Vision plugin to the pubspec.yaml (along with path_provider, to get folder locations on the system).

dependencies:
  flutter:
    sdk: flutter

  # The following adds the Cupertino Icons font to your application.
  # Use with the CupertinoIcons class for iOS style icons.
  cupertino_icons: ^0.1.2
  camera: 0.3.0+3
  firebase_ml_vision: 0.5.0+1
  path_provider: 0.5.0+1
MLKit added to firebase pubspec.yaml

And add the necessary includes at the top of the main.dart file

import 'dart:async';
import 'dart:io';
import 'package:flutter/material.dart';
import 'package:camera/camera.dart';
import 'package:path_provider/path_provider.dart';
import 'package:firebase_ml_vision/firebase_ml_vision.dart';

So… Now the app can take the photo, read all the barcodes detected in the image and store them in the member variable “_barcodeRead” string, all that is left is to display it!

Display the Barcodes

Add the Text element to the Stack inside of the “Build” method – we can wrap it in a container so that it can be anchored to the bottom of the screen.

@override
  Widget build(BuildContext context) {
    if (!controller.value.isInitialized) {
      return Container();
    }

    return Stack(
      alignment: Alignment.center,
      children: <Widget>[
        AspectRatio(
          aspectRatio:
          controller.value.aspectRatio,
          child: CameraPreview(controller)
        ),
        
        Container(
          alignment: Alignment.bottomCenter,
          child: Text(
            _barcodeRead.length > 0 ? _barcodeRead : "No Barcode",
            textAlign: TextAlign.center
          ),
        )
      ],       
    );
  }
Display barcode string

Finally,  we need to ‘redraw’ the widget whenever we update the barcode variable. To do this in Flutter, all we need to do is call “setState”.

Future _readBarcode(File file) async {
    FirebaseVisionImage firebaseImage = FirebaseVisionImage.fromFile(file);
    final BarcodeDetector barcodeDetector = FirebaseVision.instance.barcodeDetector();
    
    final List<Barcode> barcodes = await barcodeDetector.detectInImage(firebaseImage);
    
    _barcodeRead = "";
    for(Barcode barcode in barcodes) {
      _barcodeRead += barcode.rawValue + ", ";
    }
    
    setState(() {});
  }
Update the widget to display barcode

[Flutter App Dev] – Setting Up Firebase

If you are just getting into mobile development with Flutter (or mobile development in general) let me introduce you to Firebase. It is a service that offers a tonne of features such as free push notifications, analytics, Authentication etc. It even has some paid services (free tier is still quite impressive) for database hosting, file storage, and SMS service for phone authentication etc.

In this post, I will show you how to set this up for a flutter project. To keep this post short and to the point, I will assume you have already created your flutter project.

Firebase Setup

As a prerequisite, all you need to do is go to the Firebase Website and sign up for an account and log in.

Step 1 – Add a Firebase Project

Go to the Firebase Console and click “Add Project”

Step 2 – Give the Firebase Project a Name

The name you enter here is used to identify your project on Firebase. You can optionally edit the “Project ID” field, as this is used to reference your Firebase endpoint.

Accept the terms and then press “Create Project”

Step 3 – Create Application Projects in Firebase

This step is broken up into two sections: Android project setup and iOS Project Setup.

Setup a Firebase Android Project

On your Firebase Console, click the Android button to start the process of adding an Android application to your project.

Step 1 of this process is the most important, your “Android package name” must match the identifier you set on your Android project and is normally in the format of com.companyname.applicationname.

The app nickname is once again only used to identify your android project within your Firebase project.

Please refer to this post on how to find the SHA-1 fingerprint of your debug key. It is not so important for now, but when you come to release your application, you will need to add the SHA-1 fingerprint of the keystore file you use to sign and distribute your app.

Once you click “Register App” you will be taken to step 2. Simply download this file (google-services.json) and place within the Android -> app folder of your Flutter Application.

google-services.json file placed in the correct location for a Flutter app.

Step 3 is where you add the dependencies for your Android application. This step on the Firebase example is a bit miss-leading as the line for including Firebase core is not needed. This is because, when you add one of the Firebase libraries it will manage this include for you. Including this line manually will just cause compiler errors.

Therefore, setup is as follows:

Of course, use the version number for google-services as described by the Firebase project setup wizard that is on your screen.

Now refer to “Add Firebase to the Flutter Application” below or continue to “Setup a Firebase iOS Project”.

Setup a Firebase iOS Project

Click the iOS icon or the “Add App” button (if you followed the above Android steps). In my case, I have the Add App button (be sure to select the iOS option that appears.)

Fill out the details for step 1. Note that the “iOS bundle ID” must match exactly what you set in your iOS project. Your “App Store ID” requires you to have enrolled in the Apple Developer Program 
(which has a cost!) and have your app uploaded on iTunes connect.

However, this is optional for now, but remember to add it to your project’s settings before you release your application.

Next, in step 2, download the GoogleService-info.plist file and place it in the iOS project of your Flutter project. Below is an example of where I placed mine (in the ios -> Runner folder).

Placement of the GoogleService-info.plist file for a Flutter iOS App.

You can skip step 3, as including a FlutterFire library into your flutter application will already do this.

Follow instructions for step 4 as described by the Firebase steps precisely for your chosen language (Objective-C / swift).

Now refer to “Add Firebase to the Flutter Application” below.

Step 4 – Add a Firebase library to the Flutter Application

Providing you followed the steps for placement of the Google service files in the previous two sections, all you need to do for this is add any FlutterFire plugin to your Flutter project’s “pubspec.yaml” file.

FlutterFire plugin “firebase_ml_vision” included in the “dependencies” section of the pubspec.yaml file example.

Finally, deploy and run your application to the device and you should see the successful message stating your app has communicated with the Firebase servers. Note, that this message appears on the last step of each application setup of your Firebase project, within the Firebase
Console.

[Flutter App Dev] – Camera Plugin – Dark Preview Fix

As promised… This blog is moving direction to focus on Flutter Development… First up is how to fix flutter’s dark camera plugin (that can be used in any Android project utilising the Camera2 API)!

When developing my (Xamarin) app Prog I ran into a rather complex bug on Android with the Camera2 API. Upon starting the camera preview, it would appear correctly lit for a split second and promptly become dark. This rendered the camera preview useless, as seen below.

Dark Camera Preview Example with Flutter Camera Plugin

I spent a good month reading over endless Stackoverflow posts, and attempting to translate the Android documentation into Xamarins C# wrapper equivalent. Just as I was about to give up, I gave it one last attempt, and I got it!

It turned out, the camera FPS was too high for the auto exposure to keep up. This resulted in the auto exposure failing miserably and seemingly ‘giving up’. Note, that on high end devices this didn’t seem to be a huge problem. Although, I tested on a “Samsung Galaxy Tab A” which is seemingly low end – but should still be way more than capable of running Prog.

The solution turned out to be pretty simple… You can query the list of available FPS ranges that the auto exposure can handle via the CameraCharacteristics API. A range in this instance has a lower and upper bound – the lower end meaning slower FPS and the upper meaning faster FPS.

The list of ranges returned can come in two forms (x, x) where the lower and upper range is the same (i.e. constant FPS). Or a (x, y) form where x < y but there is variation on the FPS. From personal experience, the (x, y) range appears to use the lower FPS when the exposure is struggling but remain high FPS on high end device. Thus, finding the FPS range with the biggest difference between X and Y components resulted in the ‘sweet spot’ when choosing the FPS range.

Enough Background… This is For Flutter!

Or more specifically… The Flutter’s Camera Plugin. I have already created a Pull Request to fix this – but have been told they’re favouring quality over features before approving the pull request (it’s became a ‘feature’ as choosing a slower FPS in favour of better exposure may not be required by all apps, therefore an option is required to be implemented).

I’m sharing this a work around for those running into this problem and require a useable camera preview in all scenarios.

Within the Android native class of the camera plugin source code (CameraPlugin.java) create a method called “setBestAERange” as shown below. This will get all fpsRanges, check for the range with the biggest difference between lower and upper bound and assign to the member variable “aeFPSRange”.

private void setBestAERange(CameraCharacteristics characteristics) {
	      Range<Integer>[] fpsRanges =
	          characteristics.get(CameraCharacteristics.CONTROL_AE_AVAILABLE_TARGET_FPS_RANGES);
	
	      if (fpsRanges.length <= 0) {
	        return;
	      }
	
	      Integer idx = 0;
	      Integer biggestDiference = 0;
	
	      for (Integer i = 0; i < fpsRanges.length; i++) {
	        Integer currentDifference = fpsRanges[i].getUpper() - fpsRanges[i].getLower();
	
	        if (currentDifference > biggestDiference) {
	          idx = i;
	          biggestDiference = currentDifference;
	        }
	      }
	
	      aeFPSRange = fpsRanges[idx];
	    }

Hopefully this should indicate that you need to add a Range<Integer> type member variable to the “Camera” class. Then call this method inside the Camera constructor, just above “computeBestCaptureSize” making sure you pass in the characteristics variable.

Camera(final String cameraName, final String resolutionPreset, @NonNull final Result result) {
    ...

    setBestAERange(characteristics);
    computeBestCaptureSize(streamConfigurationMap);
    
    ...
}

The final piece of the puzzle is to set this FPS range on the capture request for the camera preview. Add the following code to the “createCaptureSession” method, just before the call to “setRepeatingRequest” on the capture request.

if (Camera.this.aeFPSRange != null) {
    captureRequestBuilder.set(                                CaptureRequest.CONTROL_AE_TARGET_FPS_RANGE,   Camera.this.aeFPSRange);
}
Fixed camera preview for flutter camera plugin

Happy usable camera preview!

[ Raspberry Pi C ++] GPIO Access : Compiling, Linking & Using WiringPi

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Next Post: Our First LED


To allow us access to the GPIO pins of the Raspberry Pi in C++ code, we will use a library known as “WiringPi”. What is nice about this library is that the functions we call are similar to those found in the usual Python examples. This should hopefully make it easier for those coming from Python programming on the Raspberry Pi.

As stated in my overview of C++, third party code usually comes in the form of a shared/static library which you link into your projects code. This is the case for WiringPi!

To acquire the use of a third party library, you normally get it in the form of precompiled binaries or downloading and compiling the source code yourself. What “precompiled binaries” mean, is that someone else has compiled the source code on their Pi/machine and distributed the .a/.so (static/shared library) file via a download medium. Precompiled binaries are usually the go-to way of using libraries on Windows machines. In this example however, we will be download the WiringPi source code and compile it ourselves.

Compiling WiringPi

The first thing you want to do is download the WiringPi source code. To retrieve the latest working copy of Wiring Pi, we will use git to clone the repository. If you are unsure if git is installed, you can run the following:

> sudo apt-get install git

Once git is installed, clone the repository by entering the following

> git clone git://git.drogon.net/wiringPi

After a few seconds git will have finished downloading. If you list the contents of the current directory (“ls” command) you will notice a folder named “wiringPi”. Simply change into this directory.

> cd wiringPi

If you list the contents of this directory, there will be a lot of files. The one that we are most interested in is the “build” script. Upon running this script, the compilation of the library will begin and its binaries and header files will be automatically installed into the appropriate /usr/local folders on our system.

> ./build

You should see the words “all done.” at the end of the build process once the binaries are compiled and installed.

Linking WiringPi

When linking a third party library into your C++ project, you usually have to tell the compiler where to find the libraries header and binary files. Thankfully, the build script executed in the compilation process installed these files into our /usr/local/include and /usr/local/lib folders. What is special about these folders is that they are on the system PATH variable, this means that compilers will look for files here by default!

Although we don’t have to tell the compiler where to look, we still have to tell it what to link. Luckily, this is only a one liner with CMake! I have commented each command and highlighted the line which links the wiringPi library. Here is the contents of the complete CMake file (notice I have changed the executable and project name)

# Minimum CMake version required to generate
# our build system
cmake_minimum_required(VERSION 3.0)

# Name of our Project
project(MyProject)

# add_executable creates an executable with
# The given name.
add_executable(MyEXE main.cpp)

# Make sure the executable links to the wiringPi lib
target_link_libraries(MyEXE wiringPi)
CMakeLists.txt

And finally, the code to run to see if the build system will link, compile and run our code:

#include <stdio.h>
#include <wiringPi.h>

int main(int argc, char** argv)
{
    wiringPiSetup();

    printf("wiringPi is working!\n");

    return 0;
}
main.cpp

In the above code, we simply include the wiringPi’s declared functions using the include statement and we are calling the wiringPiSetup function.

To generate the make files, make sure you have a subdirectory called build and make it the working direcotry

> mkdir Build && cd Build

Genreate the makefiles using CMake

> cmake ..

Compile the code

> make

If all goes well, you should be able to execute the program via its executable name and see the line “wiringPi is working!” appear in your terminal.

> ./MyEXE

If you see this, then you are ready to get started with programming in C++ for the Raspberry Pi!

Using WiringPi

Now that we have our program successfully linking and compiling WiringPi, we can now look at preparing the GPIO pins for use in our projects.

If you have come across some of the Python GPIO examples for the Raspberry Pi, the usual procedure is to:

  1. Initialise the GPIO Library
  2. Setup the I/O mode on each GPIO pin you intend to use
  3. Set/Read the pins state dependant on the mode you initiated it as (i.e. input / output)

Luckily, WiringPi is no different! And we’ve already seen from the code above how we initialise the GPIO library.

Just as an example of the functions we will be using in future posts, here is some code (commented on each line) which will set the output & read input from the GPIO pins.

#include <stdio.h>
#include <wiringPi.h>

int main(int argc, char** argv)
{
    // Intialize the wiringPi Library
    wiringPiSetup();

    // Set the I/O state of the given pins
    pinMode(0, OUTPUT);
    pinMode(1, INPUT);

    // Turn pin 0 on then off
    digitalWrite(0, HIGH);
    digitalWrite(0, LOW);

    // Read input from pin 1
    int status = digitalRead(1);
    printf("Pin Input = %d\n", status);

    // Exit program
    return 0;
}
main.cpp

If you copy the above into your main.cpp file from earlier, and recompile the program you should see the line “pin input = 0” when executing. The status will be 0 until there is an input going into the pin… which we will look at when I get to push buttons 🙂

As a quick overall reminder, the full steps to generate your makefiles and compile your code is as follows (don’t execute the contents in brackets):

> cd Build (If not already in Build directory)
> cmake .. (If you have not yet generated makefile / added new files) 
> make     
> ./MyEXE

In the next post I will cover how to setup and make an LED flash using C++. If you want to give this a shot in the meantime though… Use the appropriate GPIO header table for your Pi and setup a GPIO pin in output mode to light up an LED.


Previous Post: Using CMake

Next Post: Our First LED

[ Raspberry Pi C ++] Using CMake

Previous Post: Setting Up The Compiler

Next Post: Compiling and Linking WiringPi for GPIO Access


In a previous post I talked about setting up a compiler on the Raspberry Pi and using g++ on the command line. This is all well and good as a small example with one file, but when you (rightly so) have your code broken up into many files, compiling them via the command line is no longer viable.

To get around this problem, programmers using GCC would create files known as “makefiles”.

Makefiles

Makefiles are GCCs way of passing to the compiler what source files need to be compiled and what they should be compiled into. A downside to using makefiles is that the syntax for them can get extremely cryptic and hard to read/understand when your code base grows.

When you start creating makefiles, you are creating what is known as a “build system” for your project. The more code files you have to manage, the more fundamental your build system becomes.

Different platforms/compilers have different ways of managing build systems for C++ projects. For example, the GCC default is with makefiles whereas Windows uses its own Visual Studio project solution files to manage build types. Technically, there are Windows ports of GCC compilers included in software such as MinGW or Cygwin but this isn’t the norm with regards to Windows programming.

This means that if you want to compile your code on multiple platforms you will have to create and maintain multiple build systems for your project.

For these reasons, programmers tend to look elsewhere for managing their build systems. One such tool is CMake

CMake

Note: I just want to make it clear here that CMake does not compile your code! CMake is one level higher than a native build system; infact, it abstracts the underlying build system completely with it’s own syntax and using what it calls “Generators“.

For example, you create your CMake files (described later) as you would your makefiles / Visual Studio files, but you will use a CMake generator to generate your build system files which can range from Makefiles to Visual Studio and Xcode projects. You will then use this generated build system to compile your code. Here is a list of CMake generators readily available.

It’s best to show the process with an example.

CMake Example on Raspberry Pi C ++

Installing CMake

Installing CMake on your Raspberry Pi is made easy by using the apt package manager.

First thing you want to do is SSH into your Pi and at the terminal/command line enter the following

> sudo apt-get install cmake

After a few seconds you should be able to enter the “cmake” command in the terminal and you will see a list of generators available.

Using CMake

For CMake to work, there should be a file named “CMakeLists.txt” in the root directory of your C++ project. In this example, I will have one CMakeLists.txt file and one Hello.cpp file from the previous tutorial.

Your folder structure should look like so:

- Root Folder/
| - CMakeLists.txt
| - Hello.cpp

To create the files enter the following command

> touch <filename.ext>

The contents for each file is as follows (notice each CMake command has a comment describing what it is doing)

#include <stdio.h>

int main(int argc, char** argv)
{
        printf("Hello, World!\n");
        return 0;
}
Hello.cpp

# Minimum CMake version required to generate
# our build system
cmake_minimum_required(VERSION 3.0)

# Name of our Project
project(hello)

# add_executable creates an executable with
# The given name. In our case it is "Hello"
# Source files are given as parameters. In our
# Case we only have one source file Hello.cpp
add_executable(Hello Hello.cpp)
CMakeLists.txt

Generating Our Makefiles

Now that we have our CMake file setup with our single source file Hello.cpp, we next need to generate the build system files. CMake will generate quite a lot of files and folders, so it is best not to run the CMake command in the project root directory. Instead we will create a sub folder called “Build” and run the generation in here.

> mkdir Build && cd Build

This will make the directory called “Build” and also change into the directory. This is how your folder structure should look now:

- Root Folder/ 
| - CMakeLists.txt 
| - Hello.cpp
| - Build/

To run the CMake Generator we need to use the “cmake” command and tell it where the root CMakeLists.txt file is. As we are in a sub directory we will use the parent directory alias “..”. If no generator is specified it will default to the platforms native build system (in the Pi’s case, this will be GCC makefiles)

> cmake ..

Once complete you can list the contents of the directory (using the “ls” command) and notice there is now a “MakeFile” present!

Compiling the Code

This bit is easy now that CMake has generated our MakeFile for us!

Simply enter the following into the terminal (make sure you are still in the Build directory)

> make

Provided you copied the code as is above, you should see the line “Built target Hello”. To execute the application enter the following:

./Hello

You should see the line “Hello, World!”


Previous Post: Setting Up The Compiler

Next Post: Compiling and Linking WiringPi for GPIO Access

[ Raspberry Pi C ++] Setting Up A Compiler (g++)

Previous Post: A Brief Overview of C++

Next Post: Using CMake


In the previous post I talked about how C++ code gets compiled from its source files into an application exe or library. This post will demonstrate setting up a C++ compiler on the Raspberry Pi. More specifically, using g++ which is the C++ compiler in the GNU Compiler Collection (GCC).

Installing G++

The first thing you want to do is log into your Pi via SSH from your main computer. We will install GCC via the apt-get package manager. Open up the terminal and enter the following:

> sudo apt-get install g++

After a few seconds the process will finish, typing “g++” into the terminal should yield the following:

g++: fatal error: no input files
compilation terminated

Testing the Compiler

Now that we have the compiler installed, let’s create a simple C++ program. Using your terminal, create a file called “hello.cpp” with the following command:

> touch hello.cpp

The touch command will create the file if it doesn’t exist, it will also leave the file untouched if it did exist in the first place.

With our file created, let’s enter some simple C++ code. I won’t go over the code here, as it’s only to test the compiler.

Open up the file in your favourite editor (I suggest setting up Visual Studio Code for use with your Pi) and enter the following few lines of code:

#include <stdio.h>

int main(int argc, char** argv)
{
        printf("Hello, World!\n");
        return 0;
}
C++ Hello World Example

After saving the file, we must run it through the compiler. You will see in the next tutorial, we will abstract this process using CMAKE, but for the purpose of this tutorial we will pass the file straight to g++ via the terminal/command line.

The g++ CLI requires two parts, the input file(s) to be compiled and the output file these source files will be compiled into. In our case, we have one file to go in and one file to come out (an application executable). The command to do this is:

> g++ hello.cpp -o hello

After a few seconds you should notice another file called “hello” appear in the same directory (those of you using the terminal, you can list all files in the current directory by entering the “ls” command).

This file is the application executable that was compiled from the hello.cpp source file! You can execute it by entering the following:

> ./hello

If you see the words “Hello, World!” appear in your terminal, you can safely say that g++ is set up and ready to go!


Previous Post: A Brief Overview of C++

Next Post: Using CMake

[ Raspberry Pi C ++] The Beginnings, A Very Brief Overview

Next Post: Setting up the compiler


C++ Can be a tough language to pick up, especially if you have no prior programming experience. The usual goto language for beginners with the Raspberry Pi is Python due to its simplicity and enforced structure. Whereas, C++ is the language of choice when speed and efficiency is required but with it, you need to know its quirks.

This post aims to give a brief overview of C++ so that the following tutorials will make sense if you are coming from an inexperienced background with regards to the language and it’s concepts.

If you plan on following the Raspberry Pi C ++ tutorials on this blog, I recommend reading over these posts first:

What is C++?

C++ is a language where the programmer gets full control whereas other languages (such as Python) may abstract certain features. One example is garbage collection, with C++ you have to allocate and deallocate memory for your objects and structures manually. This is obviously an advanced topic to dive into so I will avoid this, but just know that C++ is powerful.

The Compiler

C++ is a compiled language (as opposed to scripting languages such as Python, LUA and C# etc.). What this means is that the code we write has to be passed through a compiler before it can be executed. The compiler will translate the human readable code into instructions that the computer will understand – this is targeted at the CPU architecture. For example, the Pi has an ARM CPU, this means that the C++ compiler has to translate the human readable code into ARM instructions.

There are quite a lot of compilers out there, but our focus will be using the G++ compiler (the C++ subset of the GCC compilation set) for use with the Raspberry Pi.

The Structure

With C++, it is vital to break your code up into multiple files. The two main file types you will come across is a .h file and a .cpp file.

The .h file (AKA a “Header File”) is used to declare your classes / function “prototypes”. Whereas a .cpp (AKA a “Source File”) is where you define these functions / classes. The difference being that a declaration is like saying “Hey, there is a function in my code called MyFunction” and a definition is “Hey, this is what MyFunction is going to do when called”.

To put this into an example, here are two snippets of code:

// declaration of function "HelloWorld"
void HelloWorld();
hello.h

#include "Hello.h"

// definition of function "HelloWorld"
void HelloWorld()
{
    printf("Hello, World!");
}
hello.cpp

You may note the line “#include “Hello.h””. This is called an “include statement”, and is how a source file knows where to look for function declarations. When using a third party library, this is how you include their function declarations into your code.

Compilation Types

Now that we know of a compiler and what file types are being used, we finally need to know what to compile our code into. There are 3 main compilation types:

  • Application executable (.exe on Windows, Linux doesn’t really have a set file extension for these)
  • Static Library (.lib on Windows, .a on Linux)
  • Shared Library (.dll on Windows, .so on Linux)

An application executable is as it sounds, an application that you can execute either from double clicking it’s file icon or from the terminal. Code compiled into an executable cannot be used directly in other projects.

The two library types (static and shared) are used for code that could potentially be shared across multiple projects. There is a difference between the two types but it is out of scope for this brief overview. Just know that any third party libraries you come across will more than likely be compiled into one of these forms.

The term for using a library in your C++ application is called ‘linking’. To link a library into your code, you must pass the library name along with the location of where the compiler will find its .a or .so file.

Further Reading

I obviously cannot cover how to program in C++, this is way overkill for these tutorial posts but is covered extensively online / in books. I just want to give an overview on how a compiler can take C++ code and compile it into an application / library files. Maybe one day I will make a post going into detail on each step which would be fun!


Next Post: Setting up the compiler

Understanding SSH Keys

What Are SSH Keys?

As opposed to the usual username and password authentication method, you can establish an SSH connection using a method that is known as SSH key-based authentication.

SSH keys are considered an extremely secure way of logging into your device/server, and is often the recommended way to establish all SSH connections as opposed to username and password authentication.

SSH keys come in pairs (private & public). The private key remains a secret to the client (i.e. the computer you use to initiate an SSH connection). This private key is considered the most vital, as any access to a private key can compromise your device and allow attackers to log in over SSH.

As opposed to your private key, your public key can be shared freely as only your private key can decrypt messages sent over SSH using said public key. This public key is uploaded to the device you wish to access via SSH and is stored in a specific file within the home directory of the user you wish to log in as.

How a Secure Connection is Formed

When we attempt to log into our device over SSH, using key-based authentication, the remote device will respond with a message which the SSH client (our computer/laptop we are using to initiate an SSH connection) must encrypt using its locally stored private key.

The client then responds with this encrypted message, which the remote device will then attempt to decrypt using one of the public keys originally uploaded in the setup procedure.

If the remote device successfully decrypts the message (I.e., if the original message matches the decrypted message), then an SSH connection is authenticated.

Understanding this connection method really shows how vital it is to safely secure your private SSH key.

Creating a Private & Public Key

It is worth noting at this point, that the command to generate a private & public key-pair exists on Linux/Mac computers by default (or at least after installing the OpenSSH libraries). Windows users will need to download a program such as Git and expose it’s commands on the system path.

The command to generate a key-pair is

> ssh-keygen

Upon entering this in the command prompt/terminal (on the local client), you will be asked to enter a location to save the keys. By default, this command creates two files, id_rsa and id_rsa.pub in the .ssh folder found in the home directory of the currently logged in user. For example:

~/.ssh/id_rsa 

~/.ssh/id_rsa.pub

You can simply click enter at this point, to save in the default location (recommended).

If you wish to encrypt the keys on disk (recommended) you can do so now. If you do not wish to enter a passphrase, simply leave it blank and hit enter.

Uploading Your Public Key to Remote Device

Now that we have our private and public key-pair stored on our client, we are ready to upload our public key to the special file which stores all accepted public keys on the remote device. It is important to note, that the location is stored in the .ssh folder in the home directory of the user.

For example, if we want to log in as the user “bob” but we upload our public key to the user “brian” then we won’t be able to initiate an ssh connection via bob@<remoteIP>, but we would be able to via brian@<remoteIP> .

So, first things first is to get the contents of your public key (on the local client). This can be done by cat’ing your public key to the terminal via:

> cat ~/.ssh/id_rsa.pub

You will see a massively long string (key based authentication is very secure!). You simply need to copy all of this output.

Now on your remote device, log into the account of the user you wish to log in via SSH as and append your public key by entering the following in the command prompt:

> echo your_public_key >> ~/.ssh/authorized_keys

Where “your_public_key” is the contents of the file you originally copied.

NOTE: You may have to ensure your SSH directory exists if you have not set this up before. You can do this simply by entering the following (safe to do, if the directory already exists)

> mkdir –p ~/.ssh

Log in via Key-based SSH

With your public key uploaded to the remote device, we can now go back to our local client. We can attempt to initiate an ssh key-based connection by entering the following

> ssh username@<remoteIP>

If you entered a passphrase for your private key file, you will be prompted to enter that passphrase now. Otherwise your connection will be established.

If you wish to disable password authentication (I.e. a less secure authentication method), you can do so by entering the following on the remote device

> sudo nano /etc/ssh/sshd_config

Ensure the following line exists and not commented out (via a # symbol)

PasswordAuthentication no

Finally press ctrl + X to exit the file and “Y” to confirm changes.

You will then need to restart the machine.

Execute Script on Startup [Raspberry Pi]

Introduction

Getting your script to execute when you boot up your Raspberry Pi can be very useful. For example, connecting your Raspberry pi to your Synergy server automatically when it turns on so you don’t need to plug in a keyboard just to execute the “startsynergy” command.

We will focus our setup around the Raspbian OS. Even though the setup is different between the Raspbian GUI OS and the Headless OS (Raspbian Lite), the processes is still fairly straight forward. This guide will demonstrate how to do it on both Operating Systems.

Raspbian Lite (Headless OS)

I will start off with the easiest of the lot. As you boot up into a terminal in an headless environment, we can use the .bashrc file of the user you are logging in as.

First ensure that the file exists (it should by default… but just encase…)

> sudo touch ~/.bashrc

This will create (if not already created) a .bashrc file within the currently logged in users home directory. Next we want to edit this file using the Nano Text Editor:

> sudo nano ~/.bashrc

With our .bashrc file open, scroll all the way to the bottom of the file using your keyboards arrow keys. On the last line of the file (but before any exit statements) type the script you want to execute in the exact same way as you would in the terminal. For example, if I wanted to execute a python script, I enter the following

python ~/Scripts/hello.py

Notice how I am using an absolute path to the file (~/ is a shortcut to the home directory of the logged in user). It is best to stick to absolute paths for this kind of stuff.

When you’re done, press “CTRL + X” to exit and “Y” to confirm changes. Reboot your Pi and you should see your script execute!

Caveat: One thing to note is, this script will run every time you open up a terminal. So if you log into your Pi via SSH from your main PC, this script will run again. If you have a long running script, you can press “CTRL + C” to break out of it early.

Raspbian GUI

Executing a script on startup with the Raspbian GUI is slightly different. The script has to be executed when the desktop environment loads. We can edit the file which runs when this event occurs by opening it with Nano:

> sudo nano /etc/xdg/lxsession/LXDE/autostart

Like our .bashrc file, we must add our script to the end of this file (but again ensuring we put it before any exit statements). For this example, I will use our startsynergy script we created some posts ago. It is launched via bash, so the line would look as follows:

@/usr/bin/bash ~/startsynergy

Upon restarting the Pi, and providing you have your Synergy Server running, your Pi should now auto connect to your Synergy Server without the need for manual intervention.

Likewise, if you want to execute python scripts, for example, you can do so in the following manner:

@/usr/bin/python /path/to/my/python/file.py

To save the file hit “CTRL + X” and “Y” to confirm changes. Upon rebooting your Pi and logging into the desktop, you should notice that your script executes.

Synergy Setup For Raspberry Pi

Introduction

Synergy is a cross platform tool that allows you to share your keyboard and mouse across any device with a synergy client running – this means you can use one keyboard and mouse and still use all your Mac/Windows/Linux devices (including your Raspberry Pi!).
I won’t talk about setting up Synergy on your PC, as this is extensively documented. If you want to know how to do this, here is a YouTube video showing how to setup Synergy for your main PC.
Instead I will focus on showing you how to install Synergy on your Raspberry Pi, more specifically your Pi running Raspian. Note that this tutorial will not benefit users of a headless OS such as Raspbian Lite.

Prerequisites

Before you begin, you must set up a synergy server on your main PC (the one with the keyboard and mouse you intend to use).

Whilst setting up the server, be sure to add a computer via the “Screens and Links” tab and name it “pi”. Your server setup should look something similar to the following:

Computer Setup for Synergy Server Image
Computer Setup for Synergy Server

Installing Synergy

To install Synergy on your Pi, we will be using the trusty apt-get package manager that we’ve used a number of times so far. The command is simply:
> sudo apt-get install synergy
If it asks you to continue simply enter “y” and hit enter.
That’s it! You have Synergy installed (aren’t package managers amazing?!)… but not yet configured!

Configuring Synergy Client

The last piece of the puzzle is to configure the pi as a Synergy Client, by telling it the name of the server you wish to connect to.
To do this, we will create a shell script which starts up synergy on our Pi using the connection settings we specify. To create this script, enter the following into the terminal:
> sudo touch /usr/bin/startsynergy

This will create our script named “start synergy” in the “/usr/bin” directory, which is on the system path by default, allowing us to execute the script anywhere with the command “startsynergy”.

Open up the script using the Nano Text Editor:
> sudo nano /usr/bin/startsynergy
Enter the following:
#!/bin/bash

killall synergyc    # Kill all previous synergy clients
sleep 1                 # Wait 1 second

synergyc --name pi <server name goes here>  # See below
exit 0   # Exit gracefully
Save the file by pressing “CTRL + X” and hitting “Y” to confirm the save.
The main contents on the script is the last line starting with “synergyc –name…
We are setting up a synergy client with the name pi (or whatever you specified as the pis name when you added a second computer on your synergy server).
Lastly <server name goes here> should be replaced with the name of your Synergy server, again, this is specified on your Synergy server setup.

Running Synergy Client

To run the script, we first need to tell the pi that this file should be executable. To do this enter the following:
> sudo chmod +x /usr/bin/startsynergy
Now finally, we can simply run the client with the following command in the terminal on the Pi:
> startsynergy
If your Synergy server was running, you should now be able to see “client pi connected” in the output log (on the server) and you should be able to use your keyboard and mouse seamlessly across your PC and your Pi.
TIP: To make your synergy script execute on startup, read my post on how to execute a script on startup.