I simply need to clarify such that an individual who doesn’t have any earlier information on container and clusters ought to have the option to comprehend what Kubernetes is and what it does.

To begin with, we comprehend why container.

Suppose you need to present a cycle to your child on his birthday. Presently if the cycle is conveyed to you with parts isolated and a manual that depicts how to join the parts. Indeed, you may wind up screwing things.

Rather imagine a scenario where the cycle itself is instant and packed in a container and conveyed to your street number, with no manual intercession required. Ain’t unreasonably great.

The individual pieces of the cycle are the dependencies of the project which may work in one spot and not the other.

The cycle organization is the engineer’s center point, and the customer here is the one utilizing our item.

To tackle this issue docker containers came into the image.

What Kubernetes is?

Situation 1 ) Cricket coordinate.

Consider India and Pakistan are playing world cup last, out of nowhere a player got harmed. What occurs straightaway?

Another player replaces the harmed one. With the goal that implies all the 11 players who are playing ought to be sufficiently fit.

The 11 players in the arrangement set.

Their quality is container health.

The physio and group the board who guarantees the strength of the player and measure their wellness is Kubernetes

Every player is a compartment and Kubernetes(a team the executives ) is the orchestrator.

Two normal things that you hear in Kubernetes → services and pods

Alright, again a nontechnical conversation.

Suppose you just brought forth a little infant. Presently your family members need to meet the child, however, in the emergency clinic ward there are 1000′ s of children and they got confused.

The subsequent stage would be, to call you and request that you show who’s your infant is.

Services and pods are also similar. Service is a parent and pod is a child.

At the point when a pod gets executed or another pod is begun, the IP address of the pod continues changing and it’s not in our control. So the best way to get to the pod is through the service that has an association with the pod by means of labels.

Terms

1 . Kubeconfig : kubeconfig file is used to find the information needed to choose the cluster and communicate with the API service of the cluster and communicate with the API server of the cluster. A file that uses access to clusters is called the kubeconfig file.

2. Cluster :Set of node machines for running customized applications.

3. Context : A context element is used to group access parameter under a convinent name. It contains three parameters — — -> Cluster , Namespace and user.

4. Namespace : It is used to divide cluster resources between multiple users. It cannot be nested between one another and each resource can only be one namespace.

5. Users — — — — — -> Service Account : It is managed by Kubernetes API and bound by namespace call through API .| Normal Users : It is maintain by outside independent service. It cannot be added to cluster through API call . Kubernets does not have object represent normal user account.

Kubernetes gives loads of functionality which is helpful for us. Like, Kubernetes Dashboard is a universally useful, online UI for Kubernetes groups. It permits clients to oversee applications running in the cluster as well as troubleshoot them and manage the cluster itself.

You can use Dashboard to get an overview of applications running on your cluster, as well as for creating or modifying individual Kubernetes resources (such as Deployments, Jobs, DaemonSets, etc). For example, you can scale a Deployment, initiate a rolling update, restart a pod, or deploy new applications using a deploy wizard.

The dashboard also provides information on the state of Kubernetes resources in your cluster and on any errors that may have occurred.

Authentication

1. User client certificate: Tokens, proxy, HTTP, basic auth to authenticate Http request.

2. Through authenticate plugins

3. HTTP request made to API server, plugins attempt to associate the attribute with the request.

Every service account has a secret password/token form.

kubectl create namespace

This command is useful for creating a namespace.

kubectl get ns

This command is used to get the namespace.

kubectl serviceaccount -n Preeti

This command is used to access the node Preeti from the service account.

kubectl describe secret tokenname -n Okgoogle

As to check the contents of the node with the help of a secret token.

kubectl delete

This will delete the Kubernetes dashboard.

kubectl delete — all kubernetes-dashboard

It will delete all dashboard in Kubernetes.

kubectl get po -n kubernetes-dashboard

|>service account ready

|>default account

*If anyone wants to access the cluster from outside you can use node port.

Kubectl edit svc Kubernetes-dashboard -n kubernetes-dashboard

After this edit the type to nodeport such that the cluster could be accessed externally.

Access

Access the acquired IP on the incognito or firefox

Thank you

View cluster resources

Kubernetes Learning was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

Machine Learning the most profound term of the 20th century which will also be known as the ‘demanding job’ of this era. But What’s the reality behind it being the most demanding technology?

One word answer data.

For example, you have rice and you want to cook biryani. The whole process of getting good rice which is our data here. And make an output say biryani out of it will take place in a process. That is known as processing, techniques, and approaches that come under machine learning.

Types of machine learning :

4. Semi-Supervised:

→ Supervised + Unsupervised.

→ Less amount of data is labeled and large amount of data is unlabeled.

→ Self-Training: Training on the datastore multipurpose training.

→ Self-enable Training: Depend on the parameter training.

Ok now we have our data, we get our output but how can we make it user-accessible or how can we evaluate our output data. Here comes into play the use of Azure Machine Learning Studio.

Less code and more goal-oriented platform.

DataStore: As the name suggests a store that stores the dataset. The dataset could be balance or imbalance. Since we are working more on datastore. The datastore supported by Azure Machine learning service are:

Blob Container

File share

DataLakes

SQL

PostgreSQL

Databricks(Spark)

Q. What’s the role of azure services here?🤔

Azure dataset format :

Tabular: Creating passing no. of files

Web URL: Reference storing files or multiple files in your dataset.

Since we are working on Dataset we have to check the version as well for that we are dealing with Dataset Versioning.

Q. How is data Processed?

New Data → Release →Data Preparation → Features

Dataset versioning means predict/taking datasets in organizations.

If you have Numerical Data → Tabular Format

Translate text data if present → Numerical Dataset

Image Data → Matrix of top of RGB Channel of 500*500 pixels contains 3 values.🤔

Version Reference: Features or Instances

1. Feature Engineering : It means you can produce new features based on the values of existing features.It calculayes the performance . And transform the new inputs that will help us to improve the performance.

-> 1+feature(existing)

-> training a separate machine learning model to new features.

Tasks include in Feature Engineering are :

→ Aggregation: Mean, Median, Mode.

→Part-of: For e.g extracting a month from particular data.

→ Binning : Group of entities into bin and use them for example for calculating a particular range of customer age sending either happy, sad or angry emoji for a particular purchase.

2. Feature Selection: Select the most relevant features .

3. Dimensionality Reduction: Technique: PCA, ML Models cannot accommodate large features then Principal Component Analysis. eg Customer Behaviour. 🤔

Approaches :

Principal Component Analysis (Statistical Approach)

T-Sne (Probabilistic Approach)

Feature Embedding: Feature embedding is an emerging research area that intends to transform features from the original space into a new space to support effective learning.

Terms and Concepts

1. Flagging: Deriving Boolean values from the given dataset. For example, you have to the problem statement. Calculate the average amount of purchase. You will approach by questioning Does the person have the purchase of last month??

Frequency-based → Occurences → Embeddings

🤔🙄

In simple terms: Start with the problem statement, What’s the main aim we are focussed on? Go with the features with existing features.

Quiz⁉ :🙋🏼‍♀️

Azure pre-built Machine Learning models

Process :

Filter based model Selection 📳 : Identifying columns in input datset that have great distributed power.

Preemptive feature importance : Determine the best feature to use by computing to use by computing the feature important scores.

Quiz⁉🙋🏼‍♂️

Preemption is the act of temporarily interrupting a task being carried out by a computer system, without requiring its cooperation, and with the intention of resuming the task at a later time. Such changes of the executed task are known as context switches. It is normally carried out by a privileged task or part of the system known as a preemptive scheduler, which has the power to preempt or interrupt and later resume, other tasks in the system.

So basically a dataset is divided into 3 parts:

1. Training Data: The data that learn value from parameter.
2. Validation data: To calculate Model Performance
3. Test Data: Used to verify that a given set of input to a given function produces some expected result

Now coming to Azure the place where all the machine learning problems will be dealt with known as Workspace.

The workspace is a container for working with the components of machine learning.

Container that works with components of machine learning to organize the machine learning process.

Services that provide snapshot and versioning of trained models known as the model registry.

Cloud-based workstation to provide access to various development environments known as Compute Instance.

The threshold value needs to be 0 or 0.5 to make the performance better.

🤔🤔🤔🤔

Solution: Rather than training multiple models or using multiple models used ensemble learning or automated learning.

Classification

Multi-class Classification: A classification task with more than two classes.Multiclass classification makes the assumption that each sample is assigned to one and only one label.

Multi-label classification: assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

Modeling

Scoring

Scoring model: Score against the test data. The output of train and test data.

The performance metric used here: Boosting algorithm on training data.

→ Model accuracy drifts over time.

→ The model training process does not finish after 1st position context deleting data drift

→ Monitor data drift alerts.

Ensemble Learning

🔡👉Boosting Algorithm :

→ Uses a strong learner from the weak learner.

→ Same input data multiple training models.

→ Reduce bias

→ Improvement in performance

🔡👉Bagging :

→ Reducing Overfitting

→ Random Sampling

→ Reduce Variance

→ Equally weighted average

🔡👉Stacking :

Combining ouput of machine learning to have higher output.

AutoML💪 :

Roadmap

→ Input criteria →Score each algorithm pipeline→Parameters(Performance)

Input: Common aspects between entities and columns presence/absence to produce the results in unsupervised learning

Parameters: Inherit Data, Grouping Associates, Principal Component Analysis, clustering, feature extraction, anomly detection etc.

Clustering :

Applications of Clustering Algorithm :

Personalization and target marketing.

Document Classification

Fraud detection, House type prediction, Medical Image.

Types:

👉Centroid-Based: Organizes data into clusters based on the data structure.

👉Hierarchical-Based: Clusters distributed in trees form.

👉👉

K-Means Clustering

→ Creates up two-target.

→ Minimize the initial problem statement.

→ Centroid type of clustering.

→ It comes under the unsupervised machine learning model.

Roadmap🗺

Initialize Centroid → Cluster Management → Move Centroids → Check for Convergence.

Points to keep in mind :

Number of centroids

Initialization approach

Distance-metric Euclidean

Normalize features

Assign label node

Iteration

Autoencoders

Due to lack of resources no. of nodes are limited. Value is the labels in which autoencoders produce output.

It is a multi-layer presentation

Produces feature vector.

The parameter to train the autoencoder is the root mean squared error. Therefore in layman, the highest possible value of metric to determine possibility is the threshold.

📝Sometimes we use deep-learning encoders instead of one-hot encoding.

Specialized case of machine learning

👉Specialized case | Approach

Similarity Learning | Supervised

Test Classification | Supervised(Classification)

Feature Learning | Supervised (Classification), Unsupervised(Clustering)

Anomaly Detection | Supervised(Classification), Unsupervised(Clustering)

Forecasting(time-series) future-prediction | Supervised

Similarity Learning

Recommendation System: It means recommend something to the user according to his/her preferences.

1. Content-Based: Used features of both users and items.

2. Collaborative-Based Filtering: Only identifies for users and items specifically not their properties.Subdivision: Get information on rating. Implicit: History of purchases, Explicit : Giving rating status like 4/5.

Code for reference :

Google Colaboratory

Forecasting :

The time series dataset considers under this method. It deals with the prediction in the context of the ordered dataset. It is a form of the multivariate regression.

Algorithm: ARIMA

Prophet: For e.g if the disease will not likely to occur but it occurs in the other scenarios.

Deployment

1. Creating Clusters
2. Inferencing Clustering
3. Compute Instance: Modelling process and how they interact when used together.

Operationality Models

Automation via end-to-end pipelines through training models of compute instance. Azure Machine Learning Training Clusters are used for multi-nodes.

Training Clusters :

Multimode clusters : For training and batch clusters (influencing process). Can autoscale when submitting , support GPU and TPU. Resources are required in batch-scoring.

For machine learning python code.

Methods:

1. Real-time inferencing /influence clusters : Model training process may be compute-intensive with training time sthat can span across many hours , days or even weeks.A trained model is used to amke decisions on new data. The entities about new data is given ased on training. Making new decisions on new dat on- demand known as local inferencing.

2. Batch Inferencing : Making predictions on large existing data.Recurring on the datastore . Dat to be used assigned at the start. It’ s advantage is high throughotput and scalable targets.

3. Compute Target, Azure Kubernetes Service, Azure ML cluster

Trained model packaged in containers :

🙄🤔…

👉We know that Model = Algorithm+data+Hyperparametres

Deploying a training Model

👉Get the model file.

👉Create the scoring script

👉Optionally create a schema file describing the web service

👉Create a real-time scoring web service

👉Call the web service from your application

👉Repeat the process from the training module.

Advantages of deploying on Azure

1. Fast Response Time
2. Autoscaling
3. FPGA’s
4. Azure Python SDK
5. Azure ML environment.

🙋‍♀️⁉QUIZ

I swear that’s the end part😅….

Responsible AI

Modern AI: It tells us about the application

Some examples and use-cases

Increasing Inequality: Features of medical dataset bias against the poor.

Web optimization: Physical attacks by email.

Unwanted Bias: Women are considered a weak gender in comparison to men.

Adversarial Attack: Cars self-driving signs.

Killer Drone: Harmful to humans.

Deep fake: Harmful to data.

Data Poisoning: Manipulating data of training

Approaches

Direct Explainer: Use a specific explainer for a specific model explanation.

→SHAP Tree: Tree explainer.

→ Shap deep tree to explain deep.

Meta Explainer: Tabular Explainer, Image Explainer, Text explainer. Explain use to create local and global visualization productions.

Fault Approach: State-of-art approach created by Microsoft. Analyses can be seen through the dashboard.

The end :

Feel free to shoot any queries:

https://www.linkedin.com/in/preeti-sharma-155a85181/

Azure Machine Learning Foundation was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

Being a Software Developer I realized this during my undergrad that education today is all available online and the one who can access “Data” in a more simplified and smart form is the ultimate winner. All you need is the required “Skillset” which is a never-ending process and you need to keep learning. But one thing this Pandemic has made us realize is that time will pass by no matter what the situation is starting from the term “Lockdown” which I heard for the first time in my life during early March of 2020 and life has changed since then to the date when George Floyd died and things got messed up in the USA. Many famous filmstars died during these past 2–3 months and a huge human species have been affected by this virus. Giving time to family and friends which was not a part of our schedule or priorities post COVID19 has become an important part of our daily routines now.

Ample amount of resources and information is being provided through internet by different organizations for “Millions” of students and “Billions” of employees sitting at home to upskill themselves in order to handle the situation being created due to COVID19 and upgarde or switch there fields with ease .

People have started investing in their Home Offices but most of them are just looking at the fancy side and ordering the stuff online. If you really want a remote home office setup you can have one in the least budget and low prices depending on your use case.

Growth in Social Media engagements has increased over the months because of the large amount of data being shared and news due to situations going on all over the world . Share market has been affected alot in every field.But this period has also made us more aware about our crutial times and environment in some ways.

There has been a shift in the Open Source culture starting from the Remote Online Conferences and introduction to a large number of Packages and Libraries and Research domains because of low funding many of the research students and projects have got affected. A large number of employees and developers have lost their jobs. Many of them shifted their careers towards Freelancing and permanent remote roles.

Many organizations have started investing in Online Virtual platforms and products and there have been an important part of this new Remote culture of the new “Era” , creating an ease and large amount of information and engagements possible now whcih were a bit difficult or required investments to make them in person when things were cool.

We all have got this and no matter happends next be strong and stay safe.

Some useful resources.

lab.github.com

blog.unacademy.com

www.guvi.in

unity.com Premium Courses

blog.coursera.org

sudo apt install Remote Work 101 was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

It´s better to wait for a productive programmer to become available than it is to wait for the first available programmer to become productive. — Steve McConnell

What is ~/.irbrc file? 🔖

• After work with multiple Rails apps, you might have some methods and preferred ways of working with them.
• i.e. you're using few generalized methods or configuration your IRB session(rails console) in a single project and want to have the same methods & configuration on other projects you’re working on, you can add those methods & configuration in your ~/.irbrc file.
• You can create ~/.irbrc file in your root directory if it’s not present.

Things you can include in your ~/.irbrc file 🗄

• Configurations which need to be used in all Rails Apps
• Methods which need to be used in all Rails Apps
• Require/load specific file(s) or Gem(s)
• Hacky convenience methods that help you play with your objects

My .irbrc file 🧮

How to search awesome .irbrc file created by other devs 📭

Just click on the following URL and it’ll list all ~/.irbrc files created by other GitHub developers, you can create your file and add everything you need if you find anything useful -

• https://gist.github.com/search?l=Ruby&q=irbrc

References 📌

• https://docs.ruby-lang.org/en/2.2.0/IRB.html
• https://www.justinweiss.com/articles/fast-consistent-setup-for-your-ruby-and-rails-projects/
• https://github.com/kpumuk/dotfiles/blob/master/irbrc

Found this post useful? please click the 👏 button and share it to help others find it! Feel free to leave a comment below.

Do more with rails console by configuring ~/.irbrc 🎉 was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.

Since Pytorch is an opensource machine learning library it contains various other libraries as sub-domain. So, for the ease of learning, we are using here tensors.

A PyTorch Tensor is fundamentally equivalent to a numpy cluster: it knows nothing about profound learning or computational charts or angles, and is only a conventional n-dimensional exhibit to be utilized for subjective numeric calculation.

The greatest contrast between a NumPy array and a PyTorch Tensor is that a PyTorch Tensor can run on either CPU or GPU. To run the procedure on the GPU, simply cast the Tensor to a Cuda datatype.

Tensor contains some operations and functionalities which make it easier for us to understand the computations happening in the array. But firstly

How tensor is different from other data types of n-dimensions??

Here we are using pytorch library with torch.tensor() functionalities such that it will help to take all the inputs vectors, matrices, 3d-arrays, numpy arrays. And help to apply mathematics on it and convert it in tensor form .

Functions we can incorporate in torch.tensor() matrix.

An short introduction about PyTorch and about the chosen functions.

• function 1 : math functions like torch.rand(), torch.abs_() and torch.allclose
• function 2 : torch.as_strided (layout functions)
• function 3 : In these the functions deals with the individual elements instead of clusters.
• function 4 : It deals with subtensors such as storage_offset() function.
• function 5 : symeifg(eigenvalue functions)

Function 1 — Some of the math functions we used here to play with the tensor inputs.

1. tensor = torch.rand ((no.of rows, no. of columns)),

2. torch.abs_(input, alpha=1)

3. torch.allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False)

First import all the libraries required .

Example 1:

Google Colaboratory

First here we are using rand() to have random items in the corresponding dimensions and store it in tensor . Then creating a new tensor by function new_tensor() making gradiend_descent value as false. And we are multiplying it with tensor such that current new tensor z will take dimension of previous tensor. We can check the length of tensor z with .shape method. And can also do permutation on indices with .permute.

Example2

Here we are using abs_() function to convert all the tensor value positive , then here we are using .add_ to add input number to each item of tensor with alpha value to be 1.

Example3 :

In allclose function all of this represents: input (Tensor) — first tensor to compare

other (Tensor) — second tensor to compare

atol (float, optional) — absolute tolerance. Default: 1e-08

rtol (float, optional) — relative tolerance. Default: 1e-05

equal_nan (bool, optional) — if True, then two NaN s will be considered equal. Default: False

argsort function is used to output all the elements in tensors in sorted order. And converting the output to asin_ will provides support for the inverse sine function in PyTorch. It expects the input to be in the range [-1, 1] and gives the output in radian form. It returns nan if the input does not lie in the range [-1, 1]. The input type is tensor and if the input contains more than one element, element-wise inverse sine is computed.

Ok, we understand this much we want to understand it visually for better understanding.

Moving on to Next Function

Function 2–1.torch.as_strided , 2.torch.bincount, 3 . torch.diag_embed

1. In torch.as_strided function it will Create a view of an existing torch.Tensor input with specified size, stride and storage_offset.
2. In bin count function each tensor value has some weight associated with it such that input will contain the range of tensor and weight means how far the tensor will be that’s why the last element is the dimension associated with weights as a tensor input.
3. In diag_embed in the tensor, the inputs will be added along the diagonal only.

Example1

Here bernoulli_() function will Fill each location of self with an independent sample from Bernoulli(p). self can have an integral data type.

Example2

Here the function bincount() take two tensors one would be the input which is a tensor tells the range of input values and weights which tells how much max value of the tensor and minlength tells that only 1-d tensor input is to be taken.

Example3

Makes a tensor whose diagonals of certain 2D planes (determined by dim1 and dim2) are filled by input. To encourage making bunched corner to corner frameworks, the 2D planes shaped by the last two components of the returned tensor are picked as a matter of course.

The contention offset controls which diagonal to consider:

Whenever offset = 0, it is the principle diagonal.

Whenever offset > 0, it is over the principle diagonal.

Whenever offset < 0, it is beneath the principle corner to corner.

The size of the new grid will be determined to make the predetermined diagonal of the size of the last information measurement. Note that for balance other than 00 , the request for dim1 and dim2 matters. Taking them is identical to changing the indication of offset.

Function 3–1. torch.erfinv, 2. torch.spilt() 3. torch.sparse_mask

Erfinv will deal with inverse error function of input element. As the name suggest .split function will split the function into chunks and then apply its functionality on them. sparse_mask take input value and convert it along with the mask input into list.

Returns another SparseTensor with values from Tensor info iltered by indices of mask and values are ignored. input and mask must have the same shape.

Parameters

input (Tensor) — an info Tensor

cover (SparseTensor) — a SparseTensor which we channel input dependent on its lists

Function 4 -

1. storage_offset() : It deals with the subtensors.
2. stride: It deals with the dimensions present as arguments.
3. sum: It will sum up the elements in the matrix.

Example1

Example2

Example3

Function 5 — Here we are using functions taking eigenvalue and eigenvectors

Basically here the concept of an upper triangular matrix is applied on the vector.

Thank you

References:

Google

Pytorch: Tensor-operations was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

Well I was just exploring OpenCV library of python in this quarantine , and going through that, I came across term Contour.

Contours can be explained simply as a curve joining all the continuous points (along the boundary), having the same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.

And got to learn how we can use it to find geometrical shapes in an image.

Let’s start how it goes.

• (For Code , click here)

Approach : The approach we would use to detect the shape of a given polygon will be based on classifying the detected shape on the basis of a number of sides it has. For example, if the detected polynomial has 3 sides, then it could be considered as a triangle, if the polynomial has 4 sides then it could be classified as a square or a rectangle, and so on.

Let’s find how to do it

1. Importing libraries

import numpy as np
import cv2 

2. Import image and convert to grayscale image.

3. Applying thresholding on image and then finding contours.

img = cv2.imread('shapes.PNG')
imgGry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

_, thrash = cv2.threshold(imgGry, 240 , 255, cv2.CHAIN_APPROX_NONE)
contours , _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

“ ret ” collects a value, which according to OTSU[1] method , is the best value for thresholding the image.

Thresholding is a technique in OpenCV, which is the assignment of pixel values in relation to the threshold value provided. In thresholding, each pixel value is compared with the threshold value. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value (generally 255).

and for “ thrash ”: its the threshold value of image

What is threshold value of an image?

Threshold is some fixed value which draws a boundary line between two set of data. Binary (Bi-valued) Image means, only bi or two intensity values can be used to represent the whole image. In image processing generally, we say a image binary when, it consists only black and white pixels.

Next:

While using cv2.findContour(), we are receiving contours and hierarchy. Actually we got three arrays, first is the image, second is our contours, and one more output which we named as hierarchy.

Normally we use the cv2.findContours() function to detect objects in an image, right ? Sometimes objects are in different locations. But in some cases, some shapes are inside other shapes, just like nested figures. In this case, we call the outer one as parent and inner one as child. This way, contours in an image has some relationship to each other. And we can specify how one contour is connected to each other, like, is it a child of some other contour, or is it a parent, etc. Representation of this relationship is called the Hierarchy.

Contours : Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.

4. Next step:

• a)Start the loop in range of contours and iterate through it .
• b)Printing the polynomial name according to no. of contours together by using approxPolyDPfunction().

Short overview of functions used in this block:

approxPolyDP() :This function calculates and approximates a polygonal curve with specified precision

approxPolyDP() approximates a contour shape to another shape with less number of vertices depending upon the precision we specify. It is an implementation of Douglas-Peucker algorithm. Check the wikipedia page for algorithm and demonstration.

example of how approxpolyDp() works.

drawContours(): Draws the contours outlines or filled color .

To draw the contours, cv2.drawContours function is used. It can also be used to draw any shape provided you have its boundary points. Its first argument is source image, second argument is the contours which should be passed as a Python list, third argument is index of contours (useful when drawing individual contour. To draw all contours, pass -1) and remaining arguments are color, thickness etc.

BoundingRect() : It gives the boundary points of the rectangle.

putText() : It puts the text over the image.

5. Show the image and close all windows

and our Program runs successfully.

Image used :

Output of code:

Whole code available 👇:

Github link to this repo 👇:

simarpreetsingh-019/openCV-Learning

Thanks for reading! I hope you enjoyed the article and gained some additional insights. If you did, feel free to leave a clap! Constructive feedback is appreciated. Feel free to reach out to me here or simarpreetsingh.019@gmail.com

That’s all for this time , see you soon with another post.

Simarpreet Singh , signing off.

ਸਤਿ ਸ਼ੀ੍ ਅਕਾਲ 🙏

References:

1. https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_table_of_contents_contours/py_table_of_contents_contours.html

2. Wikipedia

#Python3 #Detecting_Shapes #ImageProcessing #OpenCV

Detecting Geometrical Shapes in an image using OpenCV was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

Hola people. While utilising this quarantine time, I am exploring a very famous python package and library : ‘ OpenCV ’, a library for doing operations related to or on the images, and learning about various function this library contains. Canny Edge algorithm is one of it.

Theory

Canny Edge Detection

It is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images . It was developed by John F. Canny ,an Australian computer scientist, back in 1986.

The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works. (Wikipedia)

• The Canny edge detection algorithm is composed of 5 steps:

1. Noise reduction;
2. Gradient calculation;
3. Non-maximum suppression;
4. Double threshold;
5. Edge Tracking by Hysteresis.

lets us discuss the idea about how it works

1). Input image

We give the image to the method in RGB, before starting the main processing of methods; we need to have some pre-processing to prepare the given image to the methods. These will be explained in the next steps clearly.

2). converting the image to grayscale

In this step we convert the RGB image to grayscale, we can use the built-in function for passing this step.

3). Smoothing the image

Smoothing of image is the next step of the image for noise reduction;Gradient is the first order derivatives of image for each direction. It is cause of edges that seems more and the edges look thick. The gradient can be computed using central difference: it is preprocessing work to prepare the image for edge detection. we use the blur filtering of Gaussian to smoothing the image. Convolve the input image with Gaussian kernel(filter) in order to remove high-freq noises of the image.

4). Image gradient

Gradient is the function of the partial derivatives. I applied to the image convolution process with Sobel filters to obtain this partial derivative. The code for convolution has been written in the ‘convolution.m’. I have applied Sobel filters to convolution function in vertical and horizontal axes of image.

5). non-maximum suppression

This step is deciding whether a point is a local maximum of the interpolated gradient magnitude in the direction of the gradient or not, this step has a significant effect on the performance of edge. In this step the pixel is compared with its two neighbors of the pixel, if the compared pixel is larger than neighbor we do not change the pixel, otherwise, this pixel is not maximum value hence, we set the zero to that pixel.

6). Tracking the edge by hysteresis

In this step we choose two type of threshold, high and low threshold value. Afterward, each pixel of image is compared with two different threshold value. If the pixel is larger than the high threshold, this pixel mark with 255 in the final image. If the pixel between high threshold and low threshold. If the pixel is smaller than low-threshold image, mark as black with 0 (black) value in the resulting image.

7). Final Results

After passing all of the mentioned steps, we will give the final result from the method. after comparing the results with the built-in function of Matlab for edge detection, we can reach an agreement that this result is very similar to that function, all in all built-in function has higher performance due to choose the good threshold of the algorithm.

These are all the results of functions we used and discussed above.

<script src=”https://gist.github.com/simarpreetsingh-019/cc90a76bba7989c27a5b522607cc419a.js"></script>

link for above code :point_right:

simarpreetsingh-019/CannyEdgedetection

Let us see some mathematics behind this.

1). Noise Reduction

Since the mathematics involved behind the scene are mainly based on derivatives (cf. Step 2: Gradient calculation), edge detection results are highly sensitive to image noise.

One way to get rid of the noise on the image, is by applying Gaussian blur to smooth it. To do so, image convolution technique is applied with a Gaussian Kernel (3x3, 5x5, 7x7 etc…). The kernel size depends on the expected blurring effect. Basically, the smallest the kernel, the less visible is the blur. In our example, we will use a 5 by 5 Gaussian kernel.

The equation for a Gaussian filter kernel of size (2k+1)×(2k+1) is given by:

Gaussian filter kernel equation

2). Finding Intensity Gradient of the Image

Smoothened image is then filtered with a Sobel kernel in both horizontal and vertical direction to get first derivative in horizontal direction (

) and vertical direction (

).

From these two images, we can find edge gradient and direction for each pixel as follows:

Gradient direction is always perpendicular to edges. It is rounded to one of four angles representing vertical, horizontal and two diagonal directions.

3). Non-maximum Suppression

After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted pixels which may not constitute the edge. For this, at every pixel, pixel is checked if it is a local maximum in its neighborhood in the direction of gradient.

Point A is on the edge ( in vertical direction). Gradient direction is normal to the edge. Point B and C are in gradient directions. So point A is checked with point B and C to see if it forms a local maximum. If so, it is considered for next stage, otherwise, it is suppressed ( put to zero).

In short, the result you get is a binary image with “thin edges”.

4). Hysteresis Thresholding

This stage decides which are all edges are really edges and which are not. For this, we need two threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to be edges and those below minVal are sure to be non-edges, so discarded. Those who lie between these two thresholds are classified edges or non-edges based on their connectivity. If they are connected to “sure-edge” pixels, they are considered to be part of edges. Otherwise, they are also discarded.

The edge A is above the maxVal, so considered as “sure-edge”. Although edge C is below maxVal, it is connected to edge A, so that also considered as valid edge and we get that full curve. But edge B, although it is above minVal and is in same region as that of edge C, it is not connected to any “sure-edge”, so that is discarded. So it is very important that we have to select minVal and maxVal accordingly to get the correct result.

This stage also removes small pixels noises on the assumption that edges are long lines.

So what we finally get is strong edges in the image.

// Code using in built function is provided above.

code using mathematics: :point_right:

simarpreetsingh-019/CannyEdgedetection

code using built-in functions : :point_right:

simarpreetsingh-019/CannyEdgedetection

That’s all for this time.

Simarpreet Singh , signing off.

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

References:

1. https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_canny/py_canny.html

2. Github

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

#Python3 #CannyEdgeDetction #ImageProcessing #OpenCV

What is Canny Edge Detection? was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

About Red Hat

Red Hat has become associated to a large extent with its enterprise OS Red Hat Linux, Red Hat also offers RHV Red Hat Virtualization. It is standardized across environments, develop cloud-native applications and automate, secure and manage complex environments.

This post is about some new projects by Openshift under Red Hat

I would like to discuss some of the main points I have personally experienced when I saw this project while I was in Singapore for a Summit and I came to know Aagam Shah, who is a Data Scientist at Red Hat.

Dependency Analytics

This is the project it’s actually for Visual Studio, It gives insights about your application dependencies: Security, License compatibility and AI-based guidance to choose appropriate dependencies for your application.

This extension supports projects that use Maven, based on the Node ecosystem and Python. The team is also extending it for other languages as well if you want you can contribute to the Repos mentioned below and extension support under Go language is in progress.

• Flags a security vulnerability(CVE) and suggests a remedial version
• Shows Github popularity metrics along with the latest version
• Suggests a project level license, check for conflicts between dependency licenses
• AI-based guidance for additional, alternative dependencies

So once you install this extension, It scans your application for security vulnerabilities.So below are the features and functions explained with a demo

Right-click on a manifest file(pom.xml/package.json) and choose the ‘Dependency Report ‘ to display it. This report gives insights into your dependencies

For Multi-Module Maven Application

right-click on root pom.xml in the editor and choose ‘Dependency Analytics Report’

It creates a folder target in the workspace which is used for processing of manifest files, needed for generating stack reports. So kindly add-in .gitignore.

My views and experience — It is a very good extension and is a very good Open Source project for young developers and it accesses only your manifests and license files.

There are certain issues that experienced but I have seen that they have already been reported as an issue by there team on Github so I hope they will get fixed .

Some more official sources where you can more updates or can even fix some Bugs

https://marketplace.visualstudio.com/items?itemName=redhat.fabric8-analytics

https://github.com/fabric8-analytics/fabric8-analytics-vscode-extension

https://github.com/fabric8-analytics

https://youtu.be/mnedVZ29Mlw

Dependency Analytics by RedHat was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

is a virus that can lead to misleading deaths in a large amount, but one can take precautions in order to protect themselves from getting infected.

Coronaviruses are a family of viruses that cause disease in animals. Seven, including the new virus, have made the jump to humans, but most just cause cold-like symptoms.

On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China — the seventh-largest city in China with 11 million residents. As of January 23, there are over 800 cases of 2019-nCoV confirmed globally, including cases in at least 20 regions in China and nine countries/territories. The first reported infected individuals, some of whom showed symptoms as early as December 8, were discovered to be among stallholders from the Wuhan South China Seafood Market. Subsequently, the wet market was closed on Jan 1. The virus causing the outbreak was quickly determined to be a novel coronavirus. On January 10, gene sequencing further determined it to be the new Wuhan coronavirus, namely 2019-nCoV, a beta coronavirus, related to the Middle Eastern Respiratory Syndrome virus (MERS-CoV) and the Severe Acute Respiratory Syndrome virus (SARS-CoV). However, the mortality and transmissibility of 2019-nCoV are still unknown, and likely to vary from those of the prior referenced coronaviruses.

Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)

https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

In a world of polarising distrust and trade tensions, the spread of coronavirus disease (COVID-19), both within nations and internationally, is aided and abetted by misinformation that circumnavigates the planet in microseconds. Such misinformation is not all malevolent, although its impact can be devastating. Some have sought to limit misinformation about COVID-19 on social media by pressuring corporations, such as Facebook, Weibo, and Twitter, to censor bad actors — an approach that has not stopped conspiracy theorists, trolls, and liars.

If financial markets are jittery about the flow of information and disruption to production and supply chains with the global spread of COVID-19 and governments are seeking to avoid panic among their populaces, they need to invest in bastions of truth — or, at least, in those that attempt to identify information based on scientific principles.

Many trades, businesses and capitals are facing huge losses in the market and the share market is currently dropping rapidly, you can read about this in the following news article https://www.bbc.com/news/business-5190319.

Companies have started shutting down the office campuses for a few weeks and employees are currently assigned work from home, Social gatherings are being affected all the educational and professional institutions are shut down for some time, Gymnasiums and public arenas are being shut.

Myths about the Corona Virus

Many rumors have been spread through social media and the internet that coronavirus gets inactive or gets killed at high temperature, as a result, the citizens of countries with high temperatures and climate are thinking that they won’t get affected, But this is not true. No scientific study proves that the above-claimed statement is true or false.

Other is that people have started wearing masks rather than giving more importance to personal hygiene and avoid social interactions.

The Bad Businessmen

Some people have started holding the huge stock of hand sanitizers, toilet paper rolls, and pollution masks. Many such citizens have been arrested and one such case was as mentioned below in the Hixson United States. https://www.nytimes.com/2020/03/14/technology/coronavirus-purell-wipes-amazon-sellers.html?referringSource=articleShare

Thanks to technology

1 Online banking and wallets saved thousands of lives since most of the transactions done online would have been done using cash (that might have been transferred through an infected person).

2 Advanced research and development, many countries have set up the COVID-19 checkup centers and health organizations are working on producing the vaccine soon.

3 Thanks to social media (that has created the awareness) and live report and visualization tools.

The need for the ours

Some of the citizens have tried to create awareness in whatever way possible, here are some of the informative tweets that you need to see

Yano on Twitter

If you'e still hanging with friends, going to restaurants/bars, and acting like this isn't a big deal, get your shit together. The following thread is taken from an Italian citizen. As they put it: "To the rest of the world, you have no idea what's coming." MUST READ👇

DHH on Twitter

Do you know of a tech company that's asking people who could work from home to come to the office? Reply to this thread, so we can track these irresponsible organizations, and remember their names even after this is over.

What do you need to do now?

1 Stay indoors

2 Avoid touching your face

3 Avoid Social interactions and travels

4 Keep yourself clean and wash your hands frequently.

5 Don’t panic and keep yourself updated about the issue.

6 Take work from home or a week off from work (if remote work is not possible)

Some of the major resources

• World Health Organization (WHO)
• Centers for Disease Control and Prevention
• ncov - CSSE
• COVID-19 Dashboards

https://ncov.dxy.cn/ncovh5/view/pneumonia?scene=2&clicktime=1579582238&enterid=1579582238&from=singlemessage&isappinstalled=0

What is COVID-19 and what you need to know about it? was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

• Adobe Summit (Mar. 29-Apr. 2 in Las Vegas) — In-person canceled; online-only event
• Apple WWDC (June) — In-person canceled; online-only event
• Aruba Networks Atmosphere 2020 (Mar. 23–26) — In-person canceled; online-only event
• Atlassian Summit 2020 (Mar. 31-Apr. 2 in Las Vegas) — In-person canceled; online-only event
• Black Hat Asia 2020 (Mar. 31-Apr. 3 in Singapore) — Postponed until Sep. 29-Oct. 2
• CanSecWest (Mar. 18–20 in Vancouver) — Held as scheduled
• Cisco Live (May 31- Jun. 4 in Las Vegas) — In-person canceled; online-only event
• Cisco Live Melbourne (Mar. 3–6 in Melbourne) — Canceled
• DEF CON China (Apr. 17–19 in Beijing) — Postponed TBD
• Dell World (May 4–7 in Las Vegas) — In-person canceled; online-only event
• DocuSign Momentum (Mar. 4 in San Francisco) — In-person canceled; online-only event
• Domopalooza (Mar. 18–19 in Salt Lake City, UT) — In-person canceled; online-only event
• DrupalCon (May 18–22 in in Minneapolis, MN) — Postponed TBD
• E3 (Jun. 9–11 in Las Angeles) — Canceled
• EmTech Asia (Mar. 24–26 in Singapore) — Postponed to Aug. 4–5
• Enterprise Connect 2020 (Mar. 30-Apr. 2 in Orlando) — Postponed to Aug 3–6 in San Francisco
• ESRI Developer Summit (Mar. 10) — In-person canceled; online-only event
• F5 Agility 2020 (Mar. 16–19 in Orlando, FL) — In-person canceled; online-only event
• Facebook F8 (May 6–8 in San Jose, CA) — Canceled
• Facebook Global Marketing Summit (Mar. 9–12 in San Francisco) — Canceled
• Future Vision: DTG Summit 2020 (May 6 in London) — Postponed until Oct. 5
• Game Developers Conference (GDC) (Mar. 16–20 in San Francisco) — Postponed to Summer 2020
• Forrester CX Sydney (Apr. 29–30 in Sydney) — In-person canceled; online-only event Apr. 20
• Forrester CX North America (Jun. 16–18 in New York) — In-person canceled; online-only event Jun. 16–18
• Forrester SiriusDecisions Summit (May 3–6 in Austin, TX) — In-person canceled; online-only event May 4
• Gartner CIO Symposium/ITxPo (Oct. 18–22 in Orlando) — Being held as scheduled
• Gartner Data & Analytics Summit (Mar. 9–11 in London — Postponed
• Gartner Data & Analytics Summit (Mar. 23–26 in Grapevine, TX) — Postponed
• Google Cloud Next (Apr. 6–8 in San Francisco) — In-person canceled; online-only event
• Google I/O (May 12–14 in Mountain View, CA) — Canceled
• Google News Initiative Summit (Late Apr. in Sunnyvale, CA) — Canceled
• HPE Discover (Jun. 23–25 in Las Vegas) — Being held as scheduled
• Infor Inforum 2020 (Sep. 14–16 in Las Vegas) — Being held as scheduled
• Ingram Micro Cloud Cloud Summit 2020 (May 12–14 in Miami Beach, FL) — Postponed until early 2021
• IoT World Developer Conference (Apr. 6–9, 2020 in San Jose, CA) — Postponed until Aug. 10–13
• Kaspersky’s Security Analyst Summit (April 6–9 in Barcelona) — Postponed until Fall 2020
• Microsoft Build (May 19–21 in Seattle) — In-person canceled; online-only event
• Microsoft Ignite (Sep. 21–25 in New Orleans) — Being held as scheduled
• Microsoft WSLConf (March 10–11 in Redmond, WA) — In-person canceled; online-only event
• Microsoft MVP Global Summit (Mar. 15–20 in Bellevue & Redmond, WA) — In-person canceled; online-only event
• Mobile World Congress MWC Barcelona (GSMA) (Feb. 24–27 in Barcelona) — Canceled
• MSPWorld (Mar. 15–17 in New Orleans) — Rescheduled for June 2020
• Mobile World Congress MWC Americas (GSMA) (Oct. 28–30 in Los Angeles) — Being held as scheduled
• NAB Show — National Association of Broadcasters (Apr. 18–22 in Las Vegas) — Canceled; possible rescheduled for Fall
• Nvidia GTC — GPU Technology Conference (Mar. 22–26 in San Jose, CA) — In-person canceled; online-only event
• O’Reilly Strata Data & AI Conference (Mar. 15–18, San Jose, CA) — Postponed; Merged with Strata Data & AI (Sep. 14–17); online-only
• ODSC East 2020 — Open Data Science Conference (Apr. 13–17 in Boston) — In-person canceled; online-only event
• OFC 2020 (Mar. 8–12 in San Diego, CA) — Held as scheduled
• Oktane Live — (Mar. 30-Apr. 2) Online-only event
• Oracle Code One (Sep. 21–24 in Las Vegas) — Being held as scheduled
• Oracle Modern Business Experience (Mar. 23–26 in Chicago) — Postponed until Sep. 21–24 to coincide with OpenWorld
• Oracle OpenWorld (Sep. 21–24 in Las Vegas) — Being held as scheduled
• OSCON (Jul. 13–16 in Portland) — In-person cancelled; online-only TBD
• Paris Blockchain Week Summit (Mar. 31-Apr. 1 in Paris) — Postponed until Dec. 9–10
• Percona Live Open Source Database Conference (May 18–20 in Austin, TX) — Postponed; new date TBD
• Pwn2Own (March 12 in Vancouver) — In-person canceled, online-only event
• Qualtrics X4 Summit (Mar. 10–13 in Salt Lake City, UT) — Postponed to early Fall 2020
• Recode Code Conference 2020 (May 26–28 in Beverly Hills, CA) — Rescheduled; Sep. 8–10
• Red Hat Summit 2020 (Apr. 27–29 in San Francisco) — In-person canceled; online-only event
• RSA Conference (Feb. 24–28 in San Francisco) — Held as planned (IBM, AT&T, Verizon, and other vendors withdrew)
• SaaS Connect — Cloud Software Association (Apr. 15–16 in San Francisco) — Postponed until Oct. 5–6
• SaaStr Annual 2020 — (Mar. 10–12 in San Jose, CA) — Canceled; rescheduled Sep. 2020, SF Bay Area
• Salesforce World Tour Sydney (Mar. 4 in Sydney) — In-person canceled; online-only event
• SAP Ariba LIVE (Mar. 16–18 in Las Vegas) — Canceled
• SAP Concur Fusion (Mar. 9–12 in Orlando) — In-person canceled; online-only event
• SAP NOW (Mar.) — Canceled
• SAP SAPPHIRE NOW (May 12–14 in Orlando) — In-person canceled; online-only event
• SAS Global Forum (Mar. 29-Apr. 1 in Washington, DC) — In-person canceled; online-only event
• Shopify Unite 2020 developers conference (May 6–8 in Toronto) — In-person canceled; online-only event
• SXSW (Mar. 12–22 in Austin, Texas) — Canceled
• TNW Conference (Jun, 18–19 in Amsterdam) — Postponed until Oct. 1–2
• VMworld (Aug. 31-Sep. 3 in San Francisco) — Being held as scheduled.
• Linux Foundation Member Summit: March 10 to March 12, Lake Tahoe, Calif. — Canceled
• Netdev 0x14: March 17 to March 20, Vancouver — Virtual.
• Automotive Grade Linux: March 18 to March 19, Honolulu — Canceled
• SUSECon: March 23 to March 27, Dublin, Ireland — Virtual
• March Open Source RTP meetup: March 24, Durham, NC- Still running.
• Linaro Connect: March 24–25, Budapest, Hungary — Virtual
• KubeCon: March 30 to April 2, Amsterdam — Postponed until July/August
• Open Data Science Conference: April 13 to April 18, Boston — Still running
• Open Networking & Edge Summit: April 20 to April 21, LA — Postponed to the fall
• Red Hat Summit: April 27 to April 29, San Francisco — Virtual
• LinuxFest Northwest: April 24 to April 26, Bellingham, Wash. — Still running
• Linux Storage, Filesystem & Memory Management Summit: April 27 to April 29, Palm Springs, Calif. — Still running
• DataStax Accelerate: May 11–13, San Francisco — Virtual.
• RedisConf: May 12–14, San Francisco — Virtual.
• OpenDev + PTG (formerly OpenStack Summit): June 8 to June 11, Vancouver — Still running
• SouthEast LinuxFest: June 12 to June 14 — Still running
• Open Source Summit North America: June 22 to June 24, Austin, Texas — Still running
• Cloud Foundry Summit: June 25, Austin, Texas — Still running
• FIRST CTI — March 9 to March 11, Zurich — Current status: Canceled.

Resources

• Ishaan28malik/Virtual-Conf-List
• List of events affected by the 2019-20 coronavirus pandemic

Status of Tech conferences due to COVID-19 was originally published in Simply Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.