Extracting Connected Components

Introduction:

Extracting the connected components is a very common process done in Image processing. This can be done in various many ways, using many tools. Even though many tools like OpenCV and MATLAB provides us with predefined functions, It always gives us more understanding and freedom on the code, if we write it by our own. I have used Morphological Operations to be my basis and I have built my other functions over that base. A notebook can be found here, wherein I have used Python and OpenCV, to get the job done.

What are Morphological operations?

 Assuming that, one has an idea about, How images are manipulated in software, We can say that, Morphology is more like a study of structures. We Erode the borders, Dilate them, use simple boolean operations such as AND, OR, NOT to get through it, as the Image is already binarized (contains either 0 or 1, no other value.) More one Morphological operations and structuring elements can be read here .

How is it done?

The algorithm to find all the connected objects in a binarized image is as follows.

The way it works is explained in brief below
This process is based on iteration of the expression below,

Here, B is the Structuring element, A is the original thresholded image and Xk-1 refers to the matrix formed by first non-zero element for k=1, for rest of the ks, the expression takes care.
It is to be noted that the input image should be binarized before hand.

More on how it can be done can be seen in the IPython-Notebook which can be found here

 

 

NASA-APOD Wallpaper

 Things on the Internet are too cool sometimes! 

NASA APOD (Astronomy Picture of the Day) is a very popular API that provides data in JSON format with URLs, Details and much more. Here, I have tried to set that APOD High-Quality image as my Wallpaper for my Desktop.

It is to be noted that this works with GNOMEs and I am not sure with other environments. Maybe a little tweaks would help it.

How is it done?

 1. NASA-APOD url is requested and the content from the response is obtained

2. JSON library in python is used to convert it into dictionary form to make it easier to work with.

3. If the APOD is a Image-type Media, then, It is downloaded and saved in a directory

4. This image is set as the wallpaper using few OS commands

CODE CAN BE FOUND HERE

From Excel to GCP

 Abstract:

There are a number of ways to automate things and make the work flow easier. For instance, to create a connected infrastructure, one had to manually create every resource involved and had to interlink them manually, but with the introduction of APIs, Infrastructure is being defined by a set of codes, hence the name Infrastructure as a Code (IaaC). With the help of enabled API, one can achieve such tasks by a language that he is comfortable with. Many such options include - Terraform, Ansible and many more. Here, we try to get resources and their specifications from user in an excel file (extension .xlsx) and create resources in Google Cloud Platform (GCP) with the help of Google's API client for Python. A well defined Excel file is created with sheets corresponding to some kind of resources such as VPCs, Subnets, Compute Instances, Firewall rules etc.. Such methods allows users to create a program and automate the working of the system in a very user friendly manner.

 

 GCP API client for Python:

APIs are kind-of interfaces that makes communication between two entities possible, may that be software applications or mixed hardware-software applications. There are two major ways to invoke a API when it comes to GCP - via HTTP requests and REST (Representational State Transfer). All the API client libraries makes use of such REST style of architecture. In such systems, when a client makes a request to perform some action, it is often sent in a representational form and the response is also returned after performing the task. Client must include the final desired state of the resource and the API takes care of the rest when requested and authenticated. 

All the resources are primarily stored in a data store, which are allotted to respective user or organization when a request is successfully made. The details of the final state of the system is embedded in the request and the API creates resources respectively. For instance, if one had to list all the instances in a particular zone of the project, A request has to be made to the API through client. The request must include the Project_ID and Zone. Regarding how it is done, we shall see later. For more information on API clients, you can click here.

 Compute Instance API

Google API client has a category that handles REST API requests regarding Instances, Firewalls, VPCs, Subnets, Instance groups, Images, Health checks and many more under a single class of compute. This can be fetched by referring to googleapiclient/discovery/build. Once this is fetched, one can insert, list or delete the listed features. 

For instance, if we want a Instance of certain specifications, then the REST request must comprise of few things, such as, name, machine_type, sourceImage, network, subnetwork, accessConfigs, metadata, tags etc..  

 {     'name':name,
        'machineType':machine_type,
        'disks': [
            { 'boot':True, 'autoDelete':True, 'initializeParams':                     {'sourceImage':disk_image,} }
            ],
        'networkInterfaces': [{
                    'network': vpcLinks[network],
                    'subnetwork':subnetLinks[subnet],
                    'accessConfigs': [
                        {'type': 'ONE_TO_ONE_NAT', 'name': 'External NAT'}
                ]    
                }],
               'metadata': {
                   'items': [{ 'key':'startup_script', 'value':open(startup).read()}],
               },
               'tags':{
                   'items':networkTags}}

In python, once these are specified in the form of a dictionary and this dictionary is fed to the compute.instances().insert() with projectID and zone. 

Handling Pandas:

Now when the task to connect to the GCP is done, extracting data from the Excel sheets is done using Pandas. It reads the Excel file (sheets) and creates a data-frame with the key name equivalent to the column name. we can refer to the whole column as a list by referring to the key name, like, dataframe[column] 

Every sheet of the file contains details about the resource that needs to be created, Pandas reads it and makes it a dataframe. A code is developed to segreagte and sort the things out into multiple dictionaries. These are then sent as requests to the API via the API Client and the resources are created.

Code can be found here

Scope:

It is to be noted that this was just demonstrating an example to create certain resources, but API Clients are capable doing much more than just this. This technique can be used to list out, create and even destroy destroy resources. Python being such a upcoming language, gives us more discretion to use this power to our use and customize things as we want.