Reece  Feest

Reece Feest

1603767685

10 Excel Commands Everyone Should Know

For the uninitiated, learning some of the “advanced basic” features of Microsoft Excel can have a dramatic impact on their life. It can help save countless hours of manual work, and can even automate certain aspects of it. Depending on the industry and office makeup, it can even help establish you as the office’s computer guru!

Excel is very powerful, and there are very few things that it cannot do. Working in investment banking, I have to use Excel daily, and I am surprised by how little of Excel new graduates know. I am not talking about VBA either; I’m talking about good old in-built formulas. Worry not, however, as it is straightforward to catch up quickly.

In this blog post, we are going to cover the top 10 things I wish everybody would know about Excel!

How to Use Excel Formulas

The very first thing you need to know is that Excel expects all formulas to start with the equal sign. Click in any empty cell and type ”=” and you will notice some of the inbuilt functions ready to be used.

By the time you are done reading this blog, you will feel comfortable trying them all out!

Concatenating cells together with ”&”

The first thing we are going to learn about today is the ”&” sign. It is used to concatenate cells together. Why would we ever want to do that you ask? The most common usage I have found is to create unique data keys. If a data set has more than one field in its primary key, you might want to have a single attribute that represents it, to make joins and lookups easier between data sets.

Let’s see it in practice:

Image for post

Looking up Data with VLOOKUP

Sooner or later, the need will certainly arise where you need to retrieve information from one set of data, based on a value from another. Take, for example, the situation where you want to be able to return a person’s height based on their name, assuming you hold all relevant information.

The simplest way to achieve that is to use VLOOKUP. We first need to choose the value we would like to use in the lookup, then the range we’re interested in (keeping in mind the look will happen in the first column), then the column we would like to return data from, and then whether we want an exact or approximate match.

Here is an example where you can clearly see what is happening:

Image for post

You may have noticed that VLOOKUP only supports vertical lookups. If you are ever after a horizontal lookup, you can use HLOOKUP.

Conditional Summation: SUMIF

Another inbuilt function that may come in handy more often than you may think is one that allows you to conditionally sum values. That is, based on certain criteria, we can specify whether we would like to include the values into the total.

This is easiest understood through an example:

Image for post

Arithmetic Sequences with SUBTOTAL

SUBTOTAL is a nifty little function that enables you to perform arithmetic operations to a number of fields. Average, count, max, min, sum, stdev and more are functions that SUBTOTAL can support.

The other secret power of this function is that it only considers visible values. In other words, when you filter your result set (ctrl + shift + L, and filter), SUBTOTALS will only consider the values that are visible within the defined range.

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#excel #data-analysis #data-science #developer

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10 Excel Commands Everyone Should Know
Gerhard  Brink

Gerhard Brink

1622622360

Data Validation in Excel

Data Validation in Excel

In this tutorial, let’s discuss what data validation is and how it can be implemented in MS-Excel. Let’s start!!!

What Is Data Validation in Excel?

Data Validation is one of the features in MS-Excel which helps in maintaining the consistency of the data in the spreadsheet. It controls the type of data that can enter in the data validated cells.

Data Validation in MS Excel

Now, let’s have a look at how data validation works and how to implement it in the worksheet:

To apply data validation for the cells, then follow the steps.

1: Choose to which all cells the validation of data should work.

2: Click on the DATA tab.

3: Go to the Data Validation option.

4: Choose the drop down option in it and click on the Data Validation.

data validation in Excel

Once you click on the data validation menu from the ribbon, a box appears with the list of data validation criteria, Input message and error message.

Let’s first understand, what is an input message and error message?

Once, the user clicks the cell, the input message appears in a small box near the cell.

If the user violates the condition of that particular cell, then the error message pops up in a box in the spreadsheet.

The advantage of both the messages is that the input and as well as the error message guide the user about how to fill the cells. Both the messages are customizable also.

Let us have a look at how to set it up and how it works with a sample

#ms excel tutorials #circle invalid data in excel #clear validation circles in excel #custom data validation in excel #data validation in excel #limitation in data validation in excel #setting up error message in excel #setting up input message in excel #troubleshooting formulas in excel #validate data in excel

Ananya Gupta

Ananya Gupta

1604922206

Top 7 Advantages Of Advanced Excel Learning

Advanced Excel Certification offers numerous job opportunities that have come up. Lately, companies search for a talented personality who holds great knowledge in excel. However, simply basic knowledge isn’t sufficient. If you would like to be a part of a well-renowned company then you want to have the excel certification matching industrial standards.

Whether you’re seeking higher growth within an equivalent company or expecting an honest hike from the new company, complicated excel training courses with certification can surely increase your chances to be on the brink of the success ladder. Join an advanced online excel training class and improve your skills.

Know More About Advanced Excel?

The word itself explains the meaning of this course. this is often one quite skill that sets a learning benchmark for MS Excel. It offers a transparent insight to all or any of the simplest and therefore the most advanced features that are now available within the current version of Microsoft Excel.

In this competitive era where your colleagues would equally be striving to urge a far better post than you, if you excel yourself in some good certification courses then surely there’s no looking back for you.

This type of certification is all about brushing up your administration, management, and analytical skills which in today’s market is sort of important. To match up with the flexible needs of the clients, it’s important for you to be advanced and for this such training can certainly be helpful.

Some Mind-Blowing Benefits You Get:

There are ample Excel Training Courses that you simply may encounter, but choosing a certification course in Advanced excel possesses its perks for you also as for the corporate. Listed are a few that you simply got to know.

1.There is a superior recognition that you simply get
2.As compared to non-certified professionals, you occupy the highest at the competition
3.Employers will have you ever within the priority for giant important projects
4.If you’re a freelancer, then such advanced training is often an excellent learning experience
5.For those that wish to urge within the management, the world can have a boosting knowledge
6.Administration skills also get brushed up and a replacement range of job opportunities opens
7.There is an honest hike in PayScale soon after you show your skills and certification to your HR

Quick Tip which will Help:

If you’re getting to join a web course to urge such certification then see thereto that the trainer who is going to be taking care of you during this course is very experienced and may provide you with the simplest possible assistance.

Now you’ll boost your knowledge during a spreadsheet, play with new financial

#advanced excel online training #advanced excel online course #advanced excel training #advanced excel course #advanced excel training in noida #advanced excel training in delhi

Amazon Rekognition Video Analyzer Written in Opencv

Create a Serverless Pipeline for Video Frame Analysis and Alerting

Introduction

Imagine being able to capture live video streams, identify objects using deep learning, and then trigger actions or notifications based on the identified objects -- all with low latency and without a single server to manage.

This is exactly what this project is going to help you accomplish with AWS. You will be able to setup and run a live video capture, analysis, and alerting solution prototype.

The prototype was conceived to address a specific use case, which is alerting based on a live video feed from an IP security camera. At a high level, the solution works as follows. A camera surveils a particular area, streaming video over the network to a video capture client. The client samples video frames and sends them over to AWS, where they are analyzed and stored along with metadata. If certain objects are detected in the analyzed video frames, SMS alerts are sent out. Once a person receives an SMS alert, they will likely want to know what caused it. For that, sampled video frames can be monitored with low latency using a web-based user interface.

Here's the prototype's conceptual architecture:

Architecture

Let's go through the steps necessary to get this prototype up and running. If you are starting from scratch and are not familiar with Python, completing all steps can take a few hours.

Preparing your development environment

Here’s a high-level checklist of what you need to do to setup your development environment.

  1. Sign up for an AWS account if you haven't already and create an Administrator User. The steps are published here.
  2. Ensure that you have Python 2.7+ and Pip on your machine. Instructions for that varies based on your operating system and OS version.
  3. Create a Python virtual environment for the project with Virtualenv. This helps keep project’s python dependencies neatly isolated from your Operating System’s default python installation. Once you’ve created a virtual python environment, activate it before moving on with the following steps.
  4. Use Pip to install AWS CLI. Configure the AWS CLI. It is recommended that the access keys you configure are associated with an IAM User who has full access to the following:
  • Amazon S3
  • Amazon DynamoDB
  • Amazon Kinesis
  • AWS Lambda
  • Amazon CloudWatch and CloudWatch Logs
  • AWS CloudFormation
  • Amazon Rekognition
  • Amazon SNS
  • Amazon API Gateway
  • Creating IAM Roles

The IAM User can be the Administrator User you created in Step 1.

5.   Make sure you choose a region where all of the above services are available. Regions us-east-1 (N. Virginia), us-west-2 (Oregon), and eu-west-1 (Ireland) fulfill this criterion. Visit this page to learn more about service availability in AWS regions.

6.   Use Pip to install Open CV 3 python dependencies and then compile, build, and install Open CV 3 (required by Video Cap clients). You can follow this guide to get Open CV 3 up and running on OS X Sierra with Python 2.7. There's another guide for Open CV 3 and Python 3.5 on OS X Sierra. Other guides exist as well for Windows and Raspberry Pi.

7.   Use Pip to install Boto3. Boto is the Amazon Web Services (AWS) SDK for Python, which allows Python developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct access to AWS services.

8.   Use Pip to install Pynt. Pynt enables you to write project build scripts in Python.

9.   Clone this GitHub repository. Choose a directory path for your project that does not contain spaces (I'll refer to the full path to this directory as <path-to-project-dir>).

10.   Use Pip to install pytz. Pytz is needed for timezone calculations. Use the following commands:

pip install pytz # Install pytz in your virtual python env

pip install pytz -t <path-to-project-dir>/lambda/imageprocessor/ # Install pytz to be packaged and deployed with the Image Processor lambda function

Finally, obtain an IP camera. If you don’t have an IP camera, you can use your smartphone with an IP camera app. This is useful in case you want to test things out before investing in an IP camera. Also, you can simply use your laptop’s built-in camera or a connected USB camera. If you use an IP camera, make sure your camera is connected to the same Local Area Network as the Video Capture client.

Configuring the project

In this section, I list every configuration file, parameters within it, and parameter default values. The build commands detailed later extract the majority of their parameters from these configuration files. Also, the prototype's two AWS Lambda functions - Image Processor and Frame Fetcher - extract parameters at runtime from imageprocessor-params.json and framefetcher-params.json respectively.

NOTE: Do not remove any of the attributes already specified in these files.

NOTE: You must set the value of any parameter that has the tag NO-DEFAULT

config/global-params.json

Specifies “global” build configuration parameters. It is read by multiple build scripts.

{
    "StackName" : "video-analyzer-stack"
}

Parameters:

  • StackName - The name of the stack to be created in your AWS account.

config/cfn-params.json

Specifies and overrides default values of AWS CloudFormation parameters defined in the template (located at aws-infra/aws-infra-cfn.yaml). This file is read by a number of build scripts, including createstack, deploylambda, and webui.

{
    "SourceS3BucketParameter" : "<NO-DEFAULT>",
    "ImageProcessorSourceS3KeyParameter" : "src/lambda_imageprocessor.zip",
    "FrameFetcherSourceS3KeyParameter" : "src/lambda_framefetcher.zip",

    "FrameS3BucketNameParameter" : "<NO-DEFAULT>",

    "FrameFetcherApiResourcePathPart" : "enrichedframe",
    "ApiGatewayRestApiNameParameter" : "VidAnalyzerRestApi",
    "ApiGatewayStageNameParameter": "development",
    "ApiGatewayUsagePlanNameParameter" : "development-plan"
}

Parameters:

SourceS3BucketParameter - The Amazon S3 bucket to which your AWS Lambda function packages (.zip files) will be deployed. If a bucket with such a name does not exist, the deploylambda build command will create it for you with appropriate permissions. AWS CloudFormation will access this bucket to retrieve the .zip files for Image Processor and Frame Fetcher AWS Lambda functions.

ImageProcessorSourceS3KeyParameter - The Amazon S3 key under which the Image Processor function .zip file will be stored.

FrameFetcherSourceS3KeyParameter - The Amazon S3 key under which the Frame Fetcher function .zip file will be stored.

FrameS3BucketNameParameter - The Amazon S3 bucket that will be used for storing video frame images. There must not be an existing S3 bucket with the same name.

FrameFetcherApiResourcePathPart - The name of the Frame Fetcher API resource path part in the API Gateway URL.

ApiGatewayRestApiNameParameter - The name of the API Gateway REST API to be created by AWS CloudFormation.

ApiGatewayStageNameParameter - The name of the API Gateway stage to be created by AWS CloudFormation.

ApiGatewayUsagePlanNameParameter - The name of the API Gateway usage plan to be created by AWS CloudFormation.

config/imageprocessor-params.json

Specifies configuration parameters to be used at run-time by the Image Processor lambda function. This file is packaged along with the Image Processor lambda function code in a single .zip file using the packagelambda build script.

{
    "s3_bucket" : "<NO-DEFAULT>",
    "s3_key_frames_root" : "frames/",

    "ddb_table" : "EnrichedFrame",

    "rekog_max_labels" : 123,
    "rekog_min_conf" : 50.0,

    "label_watch_list" : ["Human", "Pet", "Bag", "Toy"],
    "label_watch_min_conf" : 90.0,
    "label_watch_phone_num" : "",
    "label_watch_sns_topic_arn" : "",
    "timezone" : "US/Eastern"
}

s3_bucket - The Amazon S3 bucket in which Image Processor will store captured video frame images. The value specified here must match the value specified for the FrameS3BucketNameParameter parameter in the cfn-params.json file.

s3_key_frames_root - The Amazon S3 key prefix that will be prepended to the keys of all stored video frame images.

ddb_table - The Amazon DynamoDB table in which Image Processor will store video frame metadata. The default value,EnrichedFrame, matches the default value of the AWS CloudFormation template parameter DDBTableNameParameter in the aws-infra/aws-infra-cfn.yaml template file.

rekog_max_labels - The maximum number of labels that Amazon Rekognition can return to Image Processor.

rekog_min_conf - The minimum confidence required for a label identified by Amazon Rekognition. Any labels with confidence below this value will not be returned to Image Processor.

label_watch_list - A list of labels for to watch out for. If any of the labels specified in this parameter are returned by Amazon Rekognition, an SMS alert will be sent via Amazon SNS. The label's confidence must exceed label_watch_min_conf.

label_watch_min_conf - The minimum confidence required for a label to trigger a Watch List alert.

label_watch_phone_num - The mobile phone number to which a Watch List SMS alert will be sent. Does not have a default value. You must configure a valid phone number adhering to the E.164 format (e.g. +1404XXXYYYY) for the Watch List feature to become active.

label_watch_sns_topic_arn - The SNS topic ARN to which you want Watch List alert messages to be sent. The alert message contains a notification text in addition to a JSON formatted list of Watch List labels found. This can be used to publish alerts to any SNS subscribers, such as Amazon SQS queues.

timezone - The timezone used to report time and date in SMS alerts. By default, it is "US/Eastern". See this list of country codes, names, continents, capitals, and pytz timezones).

config/framefetcher-params.json

Specifies configuration parameters to be used at run-time by the Frame Fetcher lambda function. This file is packaged along with the Frame Fetcher lambda function code in a single .zip file using the packagelambda build script.

{
    "s3_pre_signed_url_expiry" : 1800,

    "ddb_table" : "EnrichedFrame",
    "ddb_gsi_name" : "processed_year_month-processed_timestamp-index",

    "fetch_horizon_hrs" : 24,
    "fetch_limit" : 3
}

s3_pre_signed_url_expiry - Frame Fetcher returns video frame metadata. Along with the returned metadata, Frame Fetcher generates and returns a pre-signed URL for every video frame. Using a pre-signed URL, a client (such as the Web UI) can securely access the JPEG image associated with a particular frame. By default, the pre-signed URLs expire in 30 minutes.

ddb_table - The Amazon DynamoDB table from which Frame Fetcher will fetch video frame metadata. The default value,EnrichedFrame, matches the default value of the AWS CloudFormation template parameter DDBTableNameParameter in the aws-infra/aws-infra-cfn.yaml template file.

ddb_gsi_name - The name of the Amazon DynamoDB Global Secondary Index that Frame Fetcher will use to query frame metadata. The default value matches the default value of the AWS CloudFormation template parameter DDBGlobalSecondaryIndexNameParameter in the aws-infra/aws-infra-cfn.yaml template file.

fetch_horizon_hrs - Frame Fetcher will exclude any video frames that were ingested prior to the point in the past represented by (time now - fetch_horizon_hrs).

fetch_limit - The maximum number of video frame metadata items that Frame Fetcher will retrieve from Amazon DynamoDB.

Building the prototype

Common interactions with the project have been simplified for you. Using pynt, the following tasks are automated with simple commands:

  • Creating, deleting, and updating the AWS infrastructure stack with AWS CloudFormation
  • Packaging lambda code into .zip files and deploying them into an Amazon S3 bucket
  • Running the video capture client to stream from a built-in laptop webcam or a USB camera
  • Running the video capture client to stream from an IP camera (MJPEG stream)
  • Build a simple web user interface (Web UI)
  • Run a lightweight local HTTP server to serve Web UI for development and demo purposes

For a list of all available tasks, enter the following command in the root directory of this project:

pynt -l

The output represents the list of build commands available to you:

pynt -l output

Build commands are implemented as python scripts in the file build.py. The scripts use the AWS Python SDK (Boto) under the hood. They are documented in the following section.

Prior to using these build commands, you must configure the project. Configuration parameters are split across JSON-formatted files located under the config/ directory. Configuration parameters are described in detail in an earlier section.

Build commands

This section describes important build commands and how to use them. If you want to use these commands right away to build the prototype, you may skip to the section titled "Deploy and run the prototype".

The packagelambda build command

Run this command to package the prototype's AWS Lambda functions and their dependencies (Image Processor and Frame Fetcher) into separate .zip packages (one per function). The deployment packages are created under the build/ directory.

pynt packagelambda # Package both functions and their dependencies into zip files.

pynt packagelambda[framefetcher] # Package only Frame Fetcher.

Currently, only Image Processor requires an external dependency, pytz. If you add features to Image Processor or Frame Fetcher that require external dependencies, you should install the dependencies using Pip by issuing the following command.

pip install <module-name> -t <path-to-project-dir>/lambda/<lambda-function-dir>

For example, let's say you want to perform image processing in the Image Processor Lambda function. You may decide on using the Pillow image processing library. To ensure Pillow is packaged with your Lambda function in one .zip file, issue the following command:

pip install Pillow -t <path-to-project-dir>/lambda/imageprocessor #Install Pillow dependency

You can find more details on installing AWS Lambda dependencies here.

The deploylambda build command

Run this command before you run createstack. The deploylambda command uploads Image Processor and Frame Fetcher .zip packages to Amazon S3 for pickup by AWS CloudFormation while creating the prototype's stack. This command will parse the deployment Amazon S3 bucket name and keys names from the cfn-params.json file. If the bucket does not exist, the script will create it. This bucket must be in the same AWS region as the AWS CloudFormation stack, or else the stack creation will fail. Without parameters, the command will deploy the .zip packages of both Image Processor and Frame Fetcher. You can specify either “imageprocessor” or “framefetcher” as a parameter between square brackets to deploy an individual function.

Here are sample command invocations.

pynt deploylambda # Deploy both functions to Amazon S3.

pynt deploylambda[framefetcher] # Deploy only Frame Fetcher to Amazon S3.

The createstack build command

The createstack command creates the prototype's AWS CloudFormation stack behind the scenes by invoking the create_stack() API. The AWS CloudFormation template used is located at aws-infra/aws-infra-cfn.yaml under the project’s root directory. The prototype's stack requires a number of parameters to be successfully created. The createstack script reads parameters from both global-params.json and cfn-params.json configuration files. The script then passes those parameters to the create_stack() call.

Note that you must, first, package and deploy Image Processor and Frame Fetcher functions to Amazon S3 using the packagelambda and deploylambda commands (documented later in this guid) for the AWS CloudFormation stack creation to succeed.

You can issue the command as follows:

pynt createstack

Stack creation should take only a couple of minutes. At any time, you can check on the prototype's stack status either through the AWS CloudFormation console or by issuing the following command.

pynt stackstatus

Congratulations! You’ve just created the prototype's entire architecture in your AWS account.

The deletestack build command

The deletestack command, once issued, does a few things. First, it empties the Amazon S3 bucket used to store video frame images. Next, it calls the AWS CloudFormation delete_stack() API to delete the prototype's stack from your account. Finally, it removes any unneeded resources not deleted by the stack (for example, the prototype's API Gateway Usage Plan resource).

You can issue the deletestack command as follows.

pynt deletestack

As with createstack, you can monitor the progress of stack deletion using the stackstatus build command.

The deletedata build command

The deletedata command, once issued, empties the Amazon S3 bucket used to store video frame images. Next, it also deletes all items in the DynamoDB table used to store frame metadata.

Use this command to clear all previously ingested video frames and associated metadata. The command will ask for confirmation [Y/N] before proceeding with deletion.

You can issue the deletedata command as follows.

pynt deletedata

The stackstatus build command

The stackstatus command will query AWS CloudFormation for the status of the prototype's stack. This command is most useful for quickly checking that the prototype is up and running (i.e. status is "CREATE_COMPLETE" or "UPDATE_COMPLETE") and ready to serve requests from the Web UI.

You can issue the command as follows.

pynt stackstatus # Get the prototype's Stack Status

The webui build command

Run this command when the prototype's stack has been created (using createstack). The webui command “builds” the Web UI through which you can monitor incoming captured video frames. First, the script copies the webui/ directory verbatim into the project’s build/ directory. Next, the script generates an apigw.js file which contains the API Gateway base URL and the API key to be used by Web UI for invoking the Fetch Frames function deployed in AWS Lambda. This file is created in the Web UI build directory.

You can issue the Web UI build command as follows.

pynt webui

The webuiserver build command

The webuiserver command starts a local, lightweight, Python-based HTTP server on your machine to serve Web UI from the build/web-ui/ directory. Use this command to serve the prototype's Web UI for development and demonstration purposes. You can specify the server’s port as pynt task parameter, between square brackets.

Here’s sample invocation of the command.

pynt webuiserver # Starts lightweight HTTP Server on port 8080.

The videocaptureip and videocapture build commands

The videocaptureip command fires up the MJPEG-based video capture client (source code under the client/ directory). This command accepts, as parameters, an MJPEG stream URL and an optional frame capture rate. The capture rate is defined as 1 every X number of frames. Captured frames are packaged, serialized, and sent to the Kinesis Frame Stream. The video capture client for IP cameras uses Open CV 3 to do simple image processing operations on captured frame images – mainly image rotation.

Here’s a sample command invocation.

pynt videocaptureip["http://192.168.0.2/video",20] # Captures 1 frame every 20.

On the other hand, the videocapture command (without the trailing 'ip'), fires up a video capture client that captures frames from a camera attached to the machine on which it runs. If you run this command on your laptop, for instance, the client will attempt to access its built-in video camera. This video capture client relies on Open CV 3 to capture video from physically connected cameras. Captured frames are packaged, serialized, and sent to the Kinesis Frame Stream.

Here’s a sample invocation.

pynt videocapture[20] # Captures one frame every 20.

Deploy and run the prototype

In this section, we are going use project's build commands to deploy and run the prototype in your AWS account. We’ll use the commands to create the prototype's AWS CloudFormation stack, build and serve the Web UI, and run the Video Cap client.

Prepare your development environment, and ensure configuration parameters are set as you wish.

On your machine, in a command line terminal change into the root directory of the project. Activate your virtual Python environment. Then, enter the following commands:

$ pynt packagelambda #First, package code & configuration files into .zip files

#Command output without errors

$ pynt deploylambda #Second, deploy your lambda code to Amazon S3

#Command output without errors

$ pynt createstack #Now, create the prototype's CloudFormation stack

#Command output without errors

$ pynt webui #Build the Web UI

#Command output without errors
  • On your machine, in a separate command line terminal:
$ pynt webuiserver #Start the Web UI server on port 8080 by default
  • In your browser, access http://localhost:8080 to access the prototype's Web UI. You should see a screen similar to this:

Empty Web UI

Now turn on your IP camera or launch the app on your smartphone. Ensure that your camera is accepting connections for streaming MJPEG video over HTTP, and identify the local URL for accessing that stream.

Then, in a terminal window at the root directory of the project, issue this command:

$ pynt videocaptureip["<your-ip-cam-mjpeg-url>",<capture-rate>]
  • Or, if you don’t have an IP camera and would like to use a built-in camera:
$ pynt videocapture[<frame-capture-rate>]
  • Few seconds after you execute this step, the dashed area in the Web UI will auto-populate with captured frames, side by side with labels recognized in them.

When you are done

After you are done experimenting with the prototype, perform the following steps to avoid unwanted costs.

  • Terminate video capture client(s) (press Ctrl+C in command line terminal where you got it running)
  • Close all open Web UI browser windows or tabs.
  • Execute the pynt deletestack command (see docs above)
  • After you run deletestack, visit the AWS CloudFormation console to double-check the stack is deleted.
  • Ensure that Amazon S3 buckets and objects within them are deleted.

Remember, you can always setup the entire prototype again with a few simple commands.

License

Licensed under the Amazon Software License.

A copy of the License is located at

http://aws.amazon.com/asl/

The AWS CloudFormation Stack (optional read)

Let’s quickly go through the stack that AWS CloudFormation sets up in your account based on the template. AWS CloudFormation uses as much parallelism as possible while creating resources. As a result, some resources may be created in an order different than what I’m going to describe here.

First, AWS CloudFormation creates the IAM roles necessary to allow AWS services to interact with one another. This includes the following.

ImageProcessorLambdaExecutionRole – a role to be assumed by the Image Processor lambda function. It allows full access to Amazon DynamoDB, Amazon S3, Amazon SNS, and AWS CloudWatch Logs. The role also allows read-only access to Amazon Kinesis and Amazon Rekognition. For simplicity, only managed AWS role permission policies are used.

FrameFetcherLambdaExecutionRole – a role to be assumed by the Frame Fetcher lambda function. It allows full access to Amazon S3, Amazon DynamoDB, and AWS CloudWatch Logs. For simplicity, only managed AWS permission policies are used. In parallel, AWS CloudFormation creates the Amazon S3 bucket to be used to store the captured video frame images. It also creates the Kinesis Frame Stream to receive captured video frame images from the Video Cap client.

Next, the Image Processor lambda function is created in addition to an AWS Lambda Event Source Mapping to allow Amazon Kinesis to trigger Image Processor once new captured video frames are available.

The Frame Fetcher lambda function is also created. Frame Fetcher is a simple lambda function that responds to a GET request by returning the latest list of frames, in descending order by processing timestamp, up to a configurable number of hours, called the “fetch horizon” (check the framefetcher-params.json file for more run-time configuration parameters). Necessary AWS Lambda Permissions are also created to permit Amazon API Gateway to invoke the Frame Fetcher lambda function.

AWS CloudFormation also creates the DynamoDB table where Enriched Frame metadata is stored by the Image Processor lambda function as described in the architecture overview section of this post. A Global Secondary Index (GSI) is also created; to be used by the Frame Fetcher lambda function in fetching Enriched Frame metadata in descending order by time of capture.

Finally, AWS CloudFormation creates the Amazon API Gateway resources necessary to allow the Web UI to securely invoke the Frame Fetcher lambda function with a GET request to a public API Gateway URL.

The following API Gateway resources are created.

REST API named “RtRekogRestAPI” by default.

An API Gateway resource with a path part set to “enrichedframe” by default.

A GET API Gateway method associated with the “enrichedframe” resource. This method is configured with Lambda proxy integration with the Frame Fetcher lambda function (learn more about AWS API Gateway proxy integration here). The method is also configured such that an API key is required.

An OPTIONS API Gateway method associated with the “enrichedframe” resource. This method’s purpose is to enable Cross-Origin Resource Sharing (CORS). Enabling CORS allows the Web UI to make Ajax requests to the Frame Fetcher API Gateway URL. Note that the Frame Fetcher lambda function must, itself, also return the Access-Control-Allow-Origin CORS header in its HTTP response.

A “development” API Gateway deployment to allow the invocation of the prototype's API over the Internet.

A “development” API Gateway stage for the API deployment along with an API Gateway usage plan named “development-plan” by default.

An API Gateway API key, name “DevApiKey” by default. The key is associated with the “development” stage and “development-plan” usage plan.

All defaults can be overridden in the cfn-params.json configuration file. That’s it for the prototype's AWS CloudFormation stack! This stack was designed primarily for development/demo purposes, especially how the Amazon API Gateway resources are set up.

FAQ

Q: Why is this project titled "amazon-rekognition-video-analyzer" despite the security-focused use case?

A: Although this prototype was conceived to address the security monitoring and alerting use case, you can use the prototype's architecture and code as a starting point to address a wide variety of use cases involving low-latency analysis of live video frames with Amazon Rekognition.

Download Details:
Author: aws-samples
Source Code: https://github.com/aws-samples/amazon-rekognition-video-analyzer
License: View license

#opencv  #python #aws 

Reece  Feest

Reece Feest

1603767685

10 Excel Commands Everyone Should Know

For the uninitiated, learning some of the “advanced basic” features of Microsoft Excel can have a dramatic impact on their life. It can help save countless hours of manual work, and can even automate certain aspects of it. Depending on the industry and office makeup, it can even help establish you as the office’s computer guru!

Excel is very powerful, and there are very few things that it cannot do. Working in investment banking, I have to use Excel daily, and I am surprised by how little of Excel new graduates know. I am not talking about VBA either; I’m talking about good old in-built formulas. Worry not, however, as it is straightforward to catch up quickly.

In this blog post, we are going to cover the top 10 things I wish everybody would know about Excel!

How to Use Excel Formulas

The very first thing you need to know is that Excel expects all formulas to start with the equal sign. Click in any empty cell and type ”=” and you will notice some of the inbuilt functions ready to be used.

By the time you are done reading this blog, you will feel comfortable trying them all out!

Concatenating cells together with ”&”

The first thing we are going to learn about today is the ”&” sign. It is used to concatenate cells together. Why would we ever want to do that you ask? The most common usage I have found is to create unique data keys. If a data set has more than one field in its primary key, you might want to have a single attribute that represents it, to make joins and lookups easier between data sets.

Let’s see it in practice:

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Looking up Data with VLOOKUP

Sooner or later, the need will certainly arise where you need to retrieve information from one set of data, based on a value from another. Take, for example, the situation where you want to be able to return a person’s height based on their name, assuming you hold all relevant information.

The simplest way to achieve that is to use VLOOKUP. We first need to choose the value we would like to use in the lookup, then the range we’re interested in (keeping in mind the look will happen in the first column), then the column we would like to return data from, and then whether we want an exact or approximate match.

Here is an example where you can clearly see what is happening:

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You may have noticed that VLOOKUP only supports vertical lookups. If you are ever after a horizontal lookup, you can use HLOOKUP.

Conditional Summation: SUMIF

Another inbuilt function that may come in handy more often than you may think is one that allows you to conditionally sum values. That is, based on certain criteria, we can specify whether we would like to include the values into the total.

This is easiest understood through an example:

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Arithmetic Sequences with SUBTOTAL

SUBTOTAL is a nifty little function that enables you to perform arithmetic operations to a number of fields. Average, count, max, min, sum, stdev and more are functions that SUBTOTAL can support.

The other secret power of this function is that it only considers visible values. In other words, when you filter your result set (ctrl + shift + L, and filter), SUBTOTALS will only consider the values that are visible within the defined range.

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#excel #data-analysis #data-science #developer

Alfredo  Yost

Alfredo Yost

1594593240

Excel VBA Tutorial for Beginners 10 - Background Colors in Excel VBA

In this Excel VBA video, we are going to see the usage of With Block in Excel VBA. Using with block, we can reuse and rewrite multiple code lines. Also we are going to look at the interior property in brief as well, which allows us to set background colors and background gradient as well

Welcome to the The Beginner’s Guide course to Excel VBA (Visual Basic for Applications). This course enables you to Learn MS Excel VBA in simple and easy steps. In this Microsoft Excel Basics Tutorial series we will start from the basics and gradually move towards the Expert level in Microsoft Excel VBA. This MS Excel VBA course provides the Beginners to Intermediate Excel VBA Skills, Tips, and Tricks. In this course we will learn how to Enter and edit Excel data, Format numbers, fonts and alignment, Make simple pivot tables and charts, Create simple Excel formulas, How to Use Excel Functions IF and VLOOKUP. Learn common Excel functions used in any Office, How to Create dynamic reports, Build Excel formulas to analyze date, text fields, values and arrays and much more advanced stuff.

In this video we will see the Overview of formulas in Excel. We will see Basic Excel formulas & functions with examples .

#excel vba #excel #ms excel