Noah  Rowe

Noah Rowe


Object Detection for Robots using Deep Learning

In this post, we will enable a robot named Vector to detect and recognize a large number of objects. In the end, you will see how he mentions the objects that he detected.

Who is Vector?

Vector is a cute robot, who can be your companion, and is powered by AI. He is curious, independent and also he can make you laugh with his actions. After all, you can customize it with using AI, and we will see how to make this robot detect and recognize various objects in our day to day life. If you want to know about Vector briefly, then please go through this short video.

Vector SDK

The Vector SDK gives access to various capabilities of this robot, such as computer vision, Artificial intelligence and navigation. You can design your programs to make this robot imbibed with certain AI capabilities. Before running the module, install the vector SDK by following the information on this page:

Objects detected by Vector

Object Detection using Deep Learning

To detect objects, we will be using an object detection algorithm which is trained with [Google Open Image dataset]. The network consists of a ResNet with a Region proposal network and can detect more than 600 object categories. That means **Vector **will be able to identify a large number of objects. However, we have a few more dependencies to make Vector recognize those objects. The main dependencies are based on my testing platform using python 3.6, but you can change them according to the machine in which you will be implementing.

  1. Tensorflow — 1.12.0 (you can install both CPU or GPU version)
  2. Keras-2.2.4
  3. OpenCV3

Here is a video of Vector detecting objects.

Running the Module

  1. Please clone or download this repository into your local machine. After downloading, you need to authenticate the vector robot so that the SDK can interact with Vector. To authenticate with the robot, type the following into the Terminal window.
  • python3 -m anki_vector.configure

Please note that the robot and your computer should be connected to the same network. Now, you will be asked to enter your robot’s name, IP address and serial number, which you can find in the robot itself. Also, You will be asked for your Anki login and password which you used to set up your Vector.

  1. IF you see “SUCCESS!” then your robot is connected to your computer, and you can run this module by typing.

Note: Before running this module please download the pre-trained model from here,  and put it inside the data folder.

  • python

You will now see the following output, where Vector is searching for objects.

Vector grabbed this picture of me posing, and he says:

I can detect Car, Computer monitor, Human face, Computer monitor, Wheel.

The picture was taken by Vector to detect objects

Now let us go through the coding part step by step

The code below recieves the picture taken by Vector and calls the object_detection module to detect and identify various objects. Once detected, the object names are send back to vector so that he can speak out.
def get_classnames(image_path):
    This function calls the object detection library to detect 600 objects
    :param image_path:
    :return: class labels
        classes = object_detection(image_path)
        if len(classes) == 0:
            return 'no objects'
        class_list = []
        for class_names in classes:

        print('Labels: {}'.format(classes))
        return ', '.join(class_list)

    except Exception as e:
          print('Exception Handled', e)

#object-detection #artificial-intelligence #deep-learning #robotics #machine-learning #deep learning

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Object Detection for Robots using Deep Learning
Arvel  Parker

Arvel Parker


How to Find Ulimit For user on Linux

How can I find the correct ulimit values for a user account or process on Linux systems?

For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.

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MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS

We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II)
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.

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Yoshiko  Jones

Yoshiko Jones


How to configure AWS SES with Postfix MTA

How do I configure Amazon SES With Postfix mail server to send email under a CentOS/RHEL/Fedora/Ubuntu/Debian Linux server?

Amazon Simple Email Service (SES) is a hosted email service for you to send and receive email using your email addresses and domains. Typically SES used for sending bulk email or routing emails without hosting MTA. We can use Perl/Python/PHP APIs to send an email via SES. Another option is to configure Linux or Unix box running Postfix to route all outgoing emails via SES.

  • » Remove sendmail
  • » Install postfix
  • » Configuring postfix for SES
  • » Test postfix

Procedure to configure AWS SES with Postfix

Before getting started with Amazon SES and Postfix, you need to sign up for AWS, including SES. You need to verify your email address and other settings. Make sure you create a user for SES access and download credentials too.

Step 1 – Uninstall Sendmail if installed

If sendmail installed remove it. Debian/Ubuntu Linux user type the following apt command/apt-get command:

$`` sudo apt --purge remove sendmail

CentOS/RHEL user type the following yum command or dnf command on Fedora/CentOS/RHEL 8.x:

$`` sudo yum remove sendmail

$`` sudo dnf remove sendmail

Sample outputs from CentOS 8 server:

Dependencies resolved.
 Package           Architecture  Version               Repository         Size
 sendmail          x86_64        8.15.2-32.el8         @AppStream        2.4 M
Removing unused dependencies:
 cyrus-sasl        x86_64        2.1.27-1.el8          @BaseOS           160 k
 procmail          x86_64        3.22-47.el8           @AppStream        369 k

Transaction Summary
Remove  3 Packages

Freed space: 2.9 M
Is this ok [y/N]: y

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Tyrique  Littel

Tyrique Littel



When I install s3cmd package on my FreeBSD system and try to use the s3cmd command I get the following error:

_ERROR: Test failed: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (ssl.c:1091)

How do I fix this problem on FreeBSD Unix system?

Amazon Simple Storage Service (s3 ) is object storage through a web service interface or API. You can store all sorts of files. FreeBSD is free and open-source operating systems. s3cmd is a command-line utility for the Unix-like system to upload, download files to AWS S3 service from the command line.

ERROR: Test failed: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed error and solution

This error indicates that you don’t have packages correctly installed, especially SSL certificates. Let us see how to fix this problem and install s3cmd correctly on FreeBSD to get rid of the problem.

How to install s3cmd on FreeBSD

Search for s3cmd package:

$ pkg search s3cmd

Execute the following command and make sure you install Python 3.x package as Python 2 will be removed after 2020:

$ sudo pkg install py37-s3cmd-2.1.0

Updating FreeBSD repository catalogue...
FreeBSD repository is up to date.
All repositories are up to date.
Checking integrity... done (0 conflicting)
The following 8 package(s) will be affected (of 0 checked):

New packages to be INSTALLED:
	libffi: 3.2.1_3
	py37-dateutil: 2.8.1
	py37-magic: 5.38
	py37-s3cmd: 2.1.0
	py37-setuptools: 44.0.0
	py37-six: 1.14.0
	python37: 3.7.8
	readline: 8.0.4

Number of packages to be installed: 8

The process will require 118 MiB more space.

Proceed with this action? [y/N]: y
[rsnapshot] [1/8] Installing readline-8.0.4...
[rsnapshot] [1/8] Extracting readline-8.0.4: 100%
[rsnapshot] [2/8] Installing libffi-3.2.1_3...
[rsnapshot] [8/8] Extracting py37-s3cmd-2.1.0: 100%
Message from python37-3.7.8:

Note that some standard Python modules are provided as separate ports
as they require additional dependencies. They are available as:

py37-gdbm       databases/py-gdbm@py37
py37-sqlite3    databases/py-sqlite3@py37
py37-tkinter    x11-toolkits/py-tkinter@py37

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Creating RESTful APIs with NodeJS and MongoDB Tutorial

Welcome to this tutorial about RESTful API using Node.js (Express.js) and MongoDB (mongoose)! We are going to learn how to install and use each component individually and then proceed to create a RESTful API.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II) 👈 you are here
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III)

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