The Command Pattern with Go

A simple yet powerful object-oriented design pattern.

Ah, design patterns. They bring simplicity, organization and scalability to any fitting situation. In fact, code that has no backing in design often leads to major headaches and spaghetti code down the road. To me, knowing a flexible list of design patterns distinguishes software developers from software engineers. They allow you to plan and scale for the future, because things are bound to change. And when they do, you had better be prepared!

So without further introduction, let’s get to one of my favorites — The Command Pattern.

**Attention! **This article assumes you have working knowledge of:

  • Object-Oriented Programming
  • UML Diagrams
  • GoLang

The Big Idea

The command pattern is intended for situations when you just don’t care about the plumbing. Actually, the concepts behind this pattern are often used in everyday life! Take a remote controller for a TV for example, do you know exactly how clicking the (+) button actually increases volume? Probably not, and you don’t need to!

The command pattern is all about encapsulation and abstraction. In the case of the remote controller, each button is ideally encapsulated in that each button should be able to work independently of each other. In terms of abstraction, this is the whole point that you don’t need to know the details of how exactly any button in the controller actually communicates with the TV via signals and code. You just want each button to be able to easily perform a task or action without having to think about the details.

This strategy applies to nearly every modern day object around us! Your phone, a microwave or the dashboard to your car all abstract and encapsulate away nasty implementation details and allow you, the commander, to focus on high level commands. This frees up your time and allows someone with zero knowledge in electrical engineering to make a phone call to someone hundreds of miles away. That’s powerful!

The UML Class Diagram

Understanding the Unified Modeling Language (UML) Class Diagram will allow us to formalize our understanding of the command pattern. Take this diagram graciously supplied from Wikipedia below.

Image for post

From Wikipedia

Starting from top left, the Caller class is our driver program (in this case our main Go package). This class can also be referenced as the Invoker, but it simply takes care of calling our commands.

The Caller/Invoker has an associative relationship with our Command interface, which requires each inheriting ConcreteCommand to implement the execute() function. Note how the ConcreteCommand also has a state property — going back to our remote control this could be the current volume level.

Next, each ConcreteCommand calls a Receiver class that performs the actual action. While the ConcreteCommand stores the state (say volume level of the TV), the receiver could be the logic that actually communicates with the TV in order to perform volume actions such as increase, decrease or mute. The Client class would be you, with the remote controller itself being the Caller.

Implementing The Command Pattern With Go

Go is a bit different than other languages that are capable of object-oriented programming. The primary difference is that there is no inheritance in Go as well as no classes. Rather, you can create structs that can compliment interfaces in order to set contracts and define behavior.

In this example, we will be create a program that simulates a television that can turn on/off as well as change volume. First let’s start up our new project.

$ cd ~/go/src
$ mkdir command-pattern
$ cd command-pattern

Now, command pattern works by abstracting away code logic. We will have structs that represent buttons all the way up to our tv. We first need two interfaces for a command and a device.


package main

type device interface {


package main

type command interface {

Simple enough so far. It’s very common in the command pattern to have each command implement an execute() function. Now, we need to create a tv that will implement the device interface in addition to specific commands to execute. Let’s create our specific commands first.


package main

type onCommand struct {
    device device

func (c *onCommand) execute() {

#programming #design-patterns #golang #developer #go

What is GEEK

Buddha Community

The Command Pattern with Go
Fannie  Zemlak

Fannie Zemlak


What's new in the go 1.15

Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.

As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.

#go #golang #go 1.15 #go features #go improvement #go package #go new features

Amazon Rekognition Video Analyzer Written in Opencv

Create a Serverless Pipeline for Video Frame Analysis and Alerting


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:


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


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

    "StackName" : "video-analyzer-stack"


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


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/",
    "FrameFetcherSourceS3KeyParameter" : "src/",

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

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


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.


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).


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 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["",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.


Licensed under the Amazon Software License.

A copy of the License is located at

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.


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:
License: View license

#opencv  #python #aws 

Samanta  Moore

Samanta Moore


Builder Design Pattern

What is Builder Design Pattern ? Why we should care about it ?

Starting from **Creational Design Pattern, **so wikipedia says “creational design pattern are design pattern that deals with object creation mechanism, trying to create objects in manner that is suitable to the situation”.

The basic form of object creations could result in design problems and result in complex design problems, so to overcome this problem Creational Design Pattern somehow allows you to create the object.

Builder is one of the** Creational Design Pattern**.

When to consider the Builder Design Pattern ?

Builder is useful when you need to do lot of things to build an Object. Let’s imagine DOM (Document Object Model), so if we need to create the DOM, We could have to do lot of things, appending plenty of nodes and attaching attributes to them. We could also imagine about the huge XML Object creation where we will have to do lot of work to create the Object. A Factory is used basically when we could create the entire object in one shot.

As **Joshua Bloch (**He led the Design of the many library Java Collections Framework and many more) – “Builder Pattern is good choice when designing the class whose constructor or static factories would have more than handful of parameters

#java #builder #builder pattern #creational design pattern #design pattern #factory pattern #java design pattern

Elian  Harber

Elian Harber


Go-patterns: Curated List Of Go Design Patterns, Recipes and Idioms

Go Patterns

A curated collection of idiomatic design & application patterns for Go language.

Creational Patterns

Abstract FactoryProvides an interface for creating families of releated objects
BuilderBuilds a complex object using simple objects
Factory MethodDefers instantiation of an object to a specialized function for creating instances
Object PoolInstantiates and maintains a group of objects instances of the same type
SingletonRestricts instantiation of a type to one object

Structural Patterns

BridgeDecouples an interface from its implementation so that the two can vary independently
CompositeEncapsulates and provides access to a number of different objects
DecoratorAdds behavior to an object, statically or dynamically
FacadeUses one type as an API to a number of others
FlyweightReuses existing instances of objects with similar/identical state to minimize resource usage
ProxyProvides a surrogate for an object to control it's actions

Behavioral Patterns

Chain of ResponsibilityAvoids coupling a sender to receiver by giving more than object a chance to handle the request
CommandBundles a command and arguments to call later
MediatorConnects objects and acts as a proxy
MementoGenerate an opaque token that can be used to go back to a previous state
ObserverProvide a callback for notification of events/changes to data
RegistryKeep track of all subclasses of a given class
StateEncapsulates varying behavior for the same object based on its internal state
StrategyEnables an algorithm's behavior to be selected at runtime
TemplateDefines a skeleton class which defers some methods to subclasses
VisitorSeparates an algorithm from an object on which it operates

Synchronization Patterns

Condition VariableProvides a mechanism for threads to temporarily give up access in order to wait for some condition
Lock/MutexEnforces mutual exclusion limit on a resource to gain exclusive access
MonitorCombination of mutex and condition variable patterns
Read-Write LockAllows parallel read access, but only exclusive access on write operations to a resource
SemaphoreAllows controlling access to a common resource

Concurrency Patterns

N-BarrierPrevents a process from proceeding until all N processes reach to the barrier
Bounded ParallelismCompletes large number of independent tasks with resource limits
BroadcastTransfers a message to all recipients simultaneously
CoroutinesSubroutines that allow suspending and resuming execution at certain locations
GeneratorsYields a sequence of values one at a time
ReactorDemultiplexes service requests delivered concurrently to a service handler and dispatches them syncronously to the associated request handlers
ParallelismCompletes large number of independent tasks
Producer ConsumerSeparates tasks from task executions

Messaging Patterns

Fan-InFunnels tasks to a work sink (e.g. server)
Fan-OutDistributes tasks among workers (e.g. producer)
Futures & PromisesActs as a place-holder of a result that is initially unknown for synchronization purposes
Publish/SubscribePasses information to a collection of recipients who subscribed to a topic
Push & PullDistributes messages to multiple workers, arranged in a pipeline

Stability Patterns

BulkheadsEnforces a principle of failure containment (i.e. prevents cascading failures)
Circuit-BreakerStops the flow of the requests when requests are likely to fail
DeadlineAllows clients to stop waiting for a response once the probability of response becomes low (e.g. after waiting 10 seconds for a page refresh)
Fail-FastChecks the availability of required resources at the start of a request and fails if the requirements are not satisfied
HandshakingAsks a component if it can take any more load, if it can't, the request is declined
Steady-StateFor every service that accumulates a resource, some other service must recycle that resource

Profiling Patterns

Timing FunctionsWraps a function and logs the execution


Functional OptionsAllows creating clean APIs with sane defaults and idiomatic overrides


Cascading FailuresA failure in a system of interconnected parts in which the failure of a part causes a domino effect

Download Details:

Author: tmrts
Source Code: 
License: Apache-2.0 license

#go #golang #pattern 

Command Pattern for Go with Thread Safe Serial & Parallel Dispatcher


Command pattern for Go with thread safe serial and parallel dispatcher.


go get -u


go get -u -t
cd $GOPATH/src/
go test ./...

Author: Txgruppi
Source Code: 
License: MIT license

#go #golang #pattern