Bertrand  Upton

Bertrand Upton

1625862120

Angular 12 Hindi tutorial #11 Get Input Box Value and Display

In this angular 12 version video, we learn how to how to get value from input box and text box in angular 12. This video is made by anil Sidhu in the Hindi language

Steps of video
Make input box
Get Value on keyup(change)
Display Value on Screen
Get Value with Button Click
Interview Questions

code link https://github.com/anil-sidhu/angular-12/tree/interpolation

React js Latest Playlist in hindi : https://www.youtube.com/playlist?list=PL8p2I9GklV47BCAjiCtuV_liN9IwAl8pM
inst id: @code.steps

#angular #hindi

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Angular 12 Hindi tutorial #11 Get Input Box Value and Display
Kaia  Schmitt

Kaia Schmitt

1659817260

SDK for Connecting to AWS IoT From A Device using Embedded C

AWS IoT Device SDK for Embedded C

Overview

The AWS IoT Device SDK for Embedded C (C-SDK) is a collection of C source files under the MIT open source license that can be used in embedded applications to securely connect IoT devices to AWS IoT Core. It contains MQTT client, HTTP client, JSON Parser, AWS IoT Device Shadow, AWS IoT Jobs, and AWS IoT Device Defender libraries. This SDK is distributed in source form, and can be built into customer firmware along with application code, other libraries and an operating system (OS) of your choice. These libraries are only dependent on standard C libraries, so they can be ported to various OS's - from embedded Real Time Operating Systems (RTOS) to Linux/Mac/Windows. You can find sample usage of C-SDK libraries on POSIX systems using OpenSSL (e.g. Linux demos in this repository), and on FreeRTOS using mbedTLS (e.g. FreeRTOS demos in FreeRTOS repository).

For the latest release of C-SDK, please see the section for Releases and Documentation.

C-SDK includes libraries that are part of the FreeRTOS 202012.01 LTS release. Learn more about the FreeRTOS 202012.01 LTS libraries by clicking here.

License

The C-SDK libraries are licensed under the MIT open source license.

Features

C-SDK simplifies access to various AWS IoT services. C-SDK has been tested to work with AWS IoT Core and an open source MQTT broker to ensure interoperability. The AWS IoT Device Shadow, AWS IoT Jobs, and AWS IoT Device Defender libraries are flexible to work with any MQTT client and JSON parser. The MQTT client and JSON parser libraries are offered as choices without being tightly coupled with the rest of the SDK. C-SDK contains the following libraries:

coreMQTT

The coreMQTT library provides the ability to establish an MQTT connection with a broker over a customer-implemented transport layer, which can either be a secure channel like a TLS session (mutually authenticated or server-only authentication) or a non-secure channel like a plaintext TCP connection. This MQTT connection can be used for performing publish operations to MQTT topics and subscribing to MQTT topics. The library provides a mechanism to register customer-defined callbacks for receiving incoming PUBLISH, acknowledgement and keep-alive response events from the broker. The library has been refactored for memory optimization and is compliant with the MQTT 3.1.1 standard. It has no dependencies on any additional libraries other than the standard C library, a customer-implemented network transport interface, and optionally a customer-implemented platform time function. The refactored design embraces different use-cases, ranging from resource-constrained platforms using only QoS 0 MQTT PUBLISH messages to resource-rich platforms using QoS 2 MQTT PUBLISH over TLS connections.

See memory requirements for the latest release here.

coreHTTP

The coreHTTP library provides the ability to establish an HTTP connection with a server over a customer-implemented transport layer, which can either be a secure channel like a TLS session (mutually authenticated or server-only authentication) or a non-secure channel like a plaintext TCP connection. The HTTP connection can be used to make "GET" (include range requests), "PUT", "POST" and "HEAD" requests. The library provides a mechanism to register a customer-defined callback for receiving parsed header fields in an HTTP response. The library has been refactored for memory optimization, and is a client implementation of a subset of the HTTP/1.1 standard.

See memory requirements for the latest release here.

coreJSON

The coreJSON library is a JSON parser that strictly enforces the ECMA-404 JSON standard. It provides a function to validate a JSON document, and a function to search for a key and return its value. A search can descend into nested structures using a compound query key. A JSON document validation also checks for illegal UTF8 encodings and illegal Unicode escape sequences.

See memory requirements for the latest release here.

corePKCS11

The corePKCS11 library is an implementation of the PKCS #11 interface (API) that makes it easier to develop applications that rely on cryptographic operations. Only a subset of the PKCS #11 v2.4 standard has been implemented, with a focus on operations involving asymmetric keys, random number generation, and hashing.

The Cryptoki or PKCS #11 standard defines a platform-independent API to manage and use cryptographic tokens. The name, "PKCS #11", is used interchangeably to refer to the API itself and the standard which defines it.

The PKCS #11 API is useful for writing software without taking a dependency on any particular implementation or hardware. By writing against the PKCS #11 standard interface, code can be used interchangeably with multiple algorithms, implementations and hardware.

Generally vendors for secure cryptoprocessors such as Trusted Platform Module (TPM), Hardware Security Module (HSM), Secure Element, or any other type of secure hardware enclave, distribute a PKCS #11 implementation with the hardware. The purpose of corePKCS11 mock is therefore to provide a PKCS #11 implementation that allows for rapid prototyping and development before switching to a cryptoprocessor specific PKCS #11 implementation in production devices.

Since the PKCS #11 interface is defined as part of the PKCS #11 specification replacing corePKCS11 with another implementation should require little porting effort, as the interface will not change. The system tests distributed in corePKCS11 repository can be leveraged to verify the behavior of a different implementation is similar to corePKCS11.

See memory requirements for the latest release here.

AWS IoT Device Shadow

The AWS IoT Device Shadow library enables you to store and retrieve the current state one or more shadows of every registered device. A device’s shadow is a persistent, virtual representation of your device that you can interact with from AWS IoT Core even if the device is offline. The device state is captured in its "shadow" is represented as a JSON document. The device can send commands over MQTT to get, update and delete its latest state as well as receive notifications over MQTT about changes in its state. The device’s shadow(s) are uniquely identified by the name of the corresponding "thing", a representation of a specific device or logical entity on the AWS Cloud. See Managing Devices with AWS IoT for more information on IoT "thing". This library supports named shadows, a feature of the AWS IoT Device Shadow service that allows you to create multiple shadows for a single IoT device. More details about AWS IoT Device Shadow can be found in AWS IoT documentation.

The AWS IoT Device Shadow library has no dependencies on additional libraries other than the standard C library. It also doesn’t have any platform dependencies, such as threading or synchronization. It can be used with any MQTT library and any JSON library (see demos with coreMQTT and coreJSON).

See memory requirements for the latest release here.

AWS IoT Jobs

The AWS IoT Jobs library enables you to interact with the AWS IoT Jobs service which notifies one or more connected devices of a pending “Job”. A Job can be used to manage your fleet of devices, update firmware and security certificates on your devices, or perform administrative tasks such as restarting devices and performing diagnostics. For documentation of the service, please see the AWS IoT Developer Guide. Interactions with the Jobs service use the MQTT protocol. This library provides an API to compose and recognize the MQTT topic strings used by the Jobs service.

The AWS IoT Jobs library has no dependencies on additional libraries other than the standard C library. It also doesn’t have any platform dependencies, such as threading or synchronization. It can be used with any MQTT library and any JSON library (see demos with libmosquitto and coreJSON).

See memory requirements for the latest release here.

AWS IoT Device Defender

The AWS IoT Device Defender library enables you to interact with the AWS IoT Device Defender service to continuously monitor security metrics from devices for deviations from what you have defined as appropriate behavior for each device. If something doesn’t look right, AWS IoT Device Defender sends out an alert so you can take action to remediate the issue. More details about Device Defender can be found in AWS IoT Device Defender documentation. This library supports custom metrics, a feature that helps you monitor operational health metrics that are unique to your fleet or use case. For example, you can define a new metric to monitor the memory usage or CPU usage on your devices.

The AWS IoT Device Defender library has no dependencies on additional libraries other than the standard C library. It also doesn’t have any platform dependencies, such as threading or synchronization. It can be used with any MQTT library and any JSON library (see demos with coreMQTT and coreJSON).

See memory requirements for the latest release here.

AWS IoT Over-the-air Update

The AWS IoT Over-the-air Update (OTA) library enables you to manage the notification of a newly available update, download the update, and perform cryptographic verification of the firmware update. Using the OTA library, you can logically separate firmware updates from the application running on your devices. You can also use the library to send other files (e.g. images, certificates) to one or more devices registered with AWS IoT. More details about OTA library can be found in AWS IoT Over-the-air Update documentation.

The AWS IoT Over-the-air Update library has a dependency on coreJSON for parsing of JSON job document and tinyCBOR for decoding encoded data streams, other than the standard C library. It can be used with any MQTT library, HTTP library, and operating system (e.g. Linux, FreeRTOS) (see demos with coreMQTT and coreHTTP over Linux).

See memory requirements for the latest release here.

AWS IoT Fleet Provisioning

The AWS IoT Fleet Provisioning library enables you to interact with the AWS IoT Fleet Provisioning MQTT APIs in order to provison IoT devices without preexisting device certificates. With AWS IoT Fleet Provisioning, devices can securely receive unique device certificates from AWS IoT when they connect for the first time. For an overview of all provisioning options offered by AWS IoT, see device provisioning documentation. For details about Fleet Provisioning, refer to the AWS IoT Fleet Provisioning documentation.

See memory requirements for the latest release here.

AWS SigV4

The AWS SigV4 library enables you to sign HTTP requests with Signature Version 4 Signing Process. Signature Version 4 (SigV4) is the process to add authentication information to HTTP requests to AWS services. For security, most requests to AWS must be signed with an access key. The access key consists of an access key ID and secret access key.

See memory requirements for the latest release here.

backoffAlgorithm

The backoffAlgorithm library is a utility library to calculate backoff period using an exponential backoff with jitter algorithm for retrying network operations (like failed network connection with server). This library uses the "Full Jitter" strategy for the exponential backoff with jitter algorithm. More information about the algorithm can be seen in the Exponential Backoff and Jitter AWS blog.

Exponential backoff with jitter is typically used when retrying a failed connection or network request to the server. An exponential backoff with jitter helps to mitigate the failed network operations with servers, that are caused due to network congestion or high load on the server, by spreading out retry requests across multiple devices attempting network operations. Besides, in an environment with poor connectivity, a client can get disconnected at any time. A backoff strategy helps the client to conserve battery by not repeatedly attempting reconnections when they are unlikely to succeed.

The backoffAlgorithm library has no dependencies on libraries other than the standard C library.

See memory requirements for the latest release here.

Sending metrics to AWS IoT

When establishing a connection with AWS IoT, users can optionally report the Operating System, Hardware Platform and MQTT client version information of their device to AWS. This information can help AWS IoT provide faster issue resolution and technical support. If users want to report this information, they can send a specially formatted string (see below) in the username field of the MQTT CONNECT packet.

Format

The format of the username string with metrics is:

<Actual_Username>?SDK=<OS_Name>&Version=<OS_Version>&Platform=<Hardware_Platform>&MQTTLib=<MQTT_Library_name>@<MQTT_Library_version>

Where

  • is the actual username used for authentication, if username and password are used for authentication. When username and password based authentication is not used, this is an empty value.
  • is the Operating System the application is running on (e.g. Ubuntu)
  • is the version number of the Operating System (e.g. 20.10)
  • is the Hardware Platform the application is running on (e.g. RaspberryPi)
  • is the MQTT Client library being used (e.g. coreMQTT)
  • is the version of the MQTT Client library being used (e.g. 1.1.0)

Example

  • Actual_Username = “iotuser”, OS_Name = Ubuntu, OS_Version = 20.10, Hardware_Platform_Name = RaspberryPi, MQTT_Library_Name = coremqtt, MQTT_Library_version = 1.1.0. If username is not used, then “iotuser” can be removed.
/* Username string:
 * iotuser?SDK=Ubuntu&Version=20.10&Platform=RaspberryPi&MQTTLib=coremqtt@1.1.0
 */

#define OS_NAME                   "Ubuntu"
#define OS_VERSION                "20.10"
#define HARDWARE_PLATFORM_NAME    "RaspberryPi"
#define MQTT_LIB                  "coremqtt@1.1.0"

#define USERNAME_STRING           "iotuser?SDK=" OS_NAME "&Version=" OS_VERSION "&Platform=" HARDWARE_PLATFORM_NAME "&MQTTLib=" MQTT_LIB
#define USERNAME_STRING_LENGTH    ( ( uint16_t ) ( sizeof( USERNAME_STRING ) - 1 ) )

MQTTConnectInfo_t connectInfo;
connectInfo.pUserName = USERNAME_STRING;
connectInfo.userNameLength = USERNAME_STRING_LENGTH;
mqttStatus = MQTT_Connect( pMqttContext, &connectInfo, NULL, CONNACK_RECV_TIMEOUT_MS, pSessionPresent );

Versioning

C-SDK releases will now follow a date based versioning scheme with the format YYYYMM.NN, where:

  • Y represents the year.
  • M represents the month.
  • N represents the release order within the designated month (00 being the first release).

For example, a second release in June 2021 would be 202106.01. Although the SDK releases have moved to date-based versioning, each library within the SDK will still retain semantic versioning. In semantic versioning, the version number itself (X.Y.Z) indicates whether the release is a major, minor, or point release. You can use the semantic version of a library to assess the scope and impact of a new release on your application.

Releases and Documentation

All of the released versions of the C-SDK libraries are available as git tags. For example, the last release of the v3 SDK version is available at tag 3.1.2.

202108.00

API documentation of 202108.00 release

This release introduces the refactored AWS IoT Fleet Provisioning library and the new AWS SigV4 library.

Additionally, this release brings minor version updates in the AWS IoT Over-the-Air Update and corePKCS11 libraries.

202103.00

API documentation of 202103.00 release

This release includes a major update to the APIs of the AWS IoT Over-the-air Update library.

Additionally, AWS IoT Device Shadow library introduces a minor update by adding support for named shadow, a feature of the AWS IoT Device Shadow service that allows you to create multiple shadows for a single IoT device. AWS IoT Jobs library introduces a minor update by introducing macros for $next job ID and compile-time generation of topic strings. AWS IoT Device Defender library introduces a minor update that adds macros to API for custom metrics feature of AWS IoT Device Defender service.

corePKCS11 also introduces a patch update by removing the pkcs11configPAL_DESTROY_SUPPORTED config and mbedTLS platform abstraction layer of DestroyObject. Lastly, no code changes are introduced for backoffAlgorithm, coreHTTP, coreMQTT, and coreJSON; however, patch updates are made to improve documentation and CI.

202012.01

API documentation of 202012.01 release

This release includes AWS IoT Over-the-air Update(Release Candidate), backoffAlgorithm, and PKCS #11 libraries. Additionally, there is a major update to the coreJSON and coreHTTP APIs. All libraries continue to undergo code quality checks (e.g. MISRA-C compliance), and Coverity static analysis. In addition, all libraries except AWS IoT Over-the-air Update and backoffAlgorithm undergo validation of memory safety with the C Bounded Model Checker (CBMC) automated reasoning tool.

202011.00

API documentation of 202011.00 release

This release includes refactored HTTP client, AWS IoT Device Defender, and AWS IoT Jobs libraries. Additionally, there is a major update to the coreJSON API. All libraries continue to undergo code quality checks (e.g. MISRA-C compliance), Coverity static analysis, and validation of memory safety with the C Bounded Model Checker (CBMC) automated reasoning tool.

202009.00

API documentation of 202009.00 release

This release includes refactored MQTT, JSON Parser, and AWS IoT Device Shadow libraries for optimized memory usage and modularity. These libraries are included in the SDK via Git submoduling. These libraries have gone through code quality checks including verification that no function has a GNU Complexity score over 8, and checks against deviations from mandatory rules in the MISRA coding standard. Deviations from the MISRA C:2012 guidelines are documented under MISRA Deviations. These libraries have also undergone both static code analysis from Coverity static analysis, and validation of memory safety and data structure invariance through the CBMC automated reasoning tool.

If you are upgrading from v3.x API of the C-SDK to the 202009.00 release, please refer to Migration guide from v3.1.2 to 202009.00 and newer releases. If you are using the C-SDK v4_beta_deprecated branch, note that we will continue to maintain this branch for critical bug fixes and security patches but will not add new features to it. See the C-SDK v4_beta_deprecated branch README for additional details.

v3.1.2

Details available here.

Porting Guide for 202009.00 and newer releases

All libraries depend on the ISO C90 standard library and additionally on the stdint.h library for fixed-width integers, including uint8_t, int8_t, uint16_t, uint32_t and int32_t, and constant macros like UINT16_MAX. If your platform does not support the stdint.h library, definitions of the mentioned fixed-width integer types will be required for porting any C-SDK library to your platform.

Porting coreMQTT

Guide for porting coreMQTT library to your platform is available here.

Porting coreHTTP

Guide for porting coreHTTP library is available here.

Porting AWS IoT Device Shadow

Guide for porting AWS IoT Device Shadow library is available here.

Porting AWS IoT Device Defender

Guide for porting AWS IoT Device Defender library is available here.

Porting AWS IoT Over-the-air Update

Guide for porting OTA library to your platform is available here.

Migration guide from v3.1.2 to 202009.00 and newer releases

MQTT Migration

Migration guide for MQTT library is available here.

Shadow Migration

Migration guide for Shadow library is available here.

Jobs Migration

Migration guide for Jobs library is available here.

Branches

main branch

The main branch hosts the continuous development of the AWS IoT Embedded C SDK (C-SDK) libraries. Please be aware that the development at the tip of the main branch is continuously in progress, and may have bugs. Consider using the tagged releases of the C-SDK for production ready software.

v4_beta_deprecated branch (formerly named v4_beta)

The v4_beta_deprecated branch contains a beta version of the C-SDK libraries, which is now deprecated. This branch was earlier named as v4_beta, and was renamed to v4_beta_deprecated. The libraries in this branch will not be released. However, critical bugs will be fixed and tested. No new features will be added to this branch.

Getting Started

Cloning

This repository uses Git Submodules to bring in the C-SDK libraries (eg, MQTT ) and third-party dependencies (eg, mbedtls for POSIX platform transport layer). Note: If you download the ZIP file provided by GitHub UI, you will not get the contents of the submodules (The ZIP file is also not a valid git repository). If you download from the 202012.00 Release Page page, you will get the entire repository (including the submodules) in the ZIP file, aws-iot-device-sdk-embedded-c-202012.00.zip. To clone the latest commit to main branch using HTTPS:

git clone --recurse-submodules https://github.com/aws/aws-iot-device-sdk-embedded-C.git

Using SSH:

git clone --recurse-submodules git@github.com:aws/aws-iot-device-sdk-embedded-C.git

If you have downloaded the repo without using the --recurse-submodules argument, you need to run:

git submodule update --init --recursive

When building with CMake, submodules are also recursively cloned automatically. However, -DBUILD_CLONE_SUBMODULES=0 can be passed as a CMake flag to disable this functionality. This is useful when you'd like to build CMake while using a different commit from a submodule.

Configuring Demos

The libraries in this SDK are not dependent on any operating system. However, the demos for the libraries in this SDK are built and tested on a Linux platform. The demos build with CMake, a cross-platform build tool.

Prerequisites

  • CMake 3.2.0 or any newer version for utilizing the build system of the repository.
  • C90 compiler such as gcc
    • Due to the use of mbedtls in corePKCS11, a C99 compiler is required if building the PKCS11 demos or the CMake install target.
  • Although not a part of the ISO C90 standard, stdint.h is required for fixed-width integer types that include uint8_t, int8_t, uint16_t, uint32_t and int32_t, and constant macros like UINT16_MAX, while stdbool.h is required for boolean parameters in coreMQTT. For compilers that do not provide these header files, coreMQTT provides the files stdint.readme and stdbool.readme, which can be renamed to stdint.h and stdbool.h, respectively, to provide the required type definitions.
  • A supported operating system. The ports provided with this repo are expected to work with all recent versions of the following operating systems, although we cannot guarantee the behavior on all systems.
    • Linux system with POSIX sockets, threads, RT, and timer APIs. (We have tested on Ubuntu 18.04).

Build Dependencies

The follow table shows libraries that need to be installed in your system to run certain demos. If a dependency is not installed and cannot be built from source, demos that require that dependency will be excluded from the default all target.

DependencyVersionUsage
OpenSSL1.1.0 or laterAll TLS demos and tests with the exception of PKCS11
Mosquitto Client1.4.10 or laterAWS IoT Jobs Mosquitto demo

AWS IoT Account Setup

You need to setup an AWS account and access the AWS IoT console for running the AWS IoT Device Shadow library, AWS IoT Device Defender library, AWS IoT Jobs library, AWS IoT OTA library and coreHTTP S3 download demos. Also, the AWS account can be used for running the MQTT mutual auth demo against AWS IoT broker. Note that running the AWS IoT Device Defender, AWS IoT Jobs and AWS IoT Device Shadow library demos require the setup of a Thing resource for the device running the demo. Follow the links to:

The MQTT Mutual Authentication and AWS IoT Shadow demos include example AWS IoT policy documents to run each respective demo with AWS IoT. You may use the MQTT Mutual auth and Shadow example policies by replacing [AWS_REGION] and [AWS_ACCOUNT_ID] with the strings of your region and account identifier. While the IoT Thing name and MQTT client identifier do not need to match for the demos to run, the example policies have the Thing name and client identifier identical as per AWS IoT best practices.

It can be very helpful to also have the AWS Command Line Interface tooling installed.

Configuring mutual authentication demos of MQTT and HTTP

You can pass the following configuration settings as command line options in order to run the mutual auth demos. Make sure to run the following command in the root directory of the C-SDK:

## optionally find your-aws-iot-endpoint from the command line
aws iot describe-endpoint --endpoint-type iot:Data-ATS
cmake -S . -Bbuild
-DAWS_IOT_ENDPOINT="<your-aws-iot-endpoint>" -DCLIENT_CERT_PATH="<your-client-certificate-path>" -DCLIENT_PRIVATE_KEY_PATH="<your-client-private-key-path>" 

In order to set these configurations manually, edit demo_config.h in demos/mqtt/mqtt_demo_mutual_auth/ and demos/http/http_demo_mutual_auth/ to #define the following:

  • Set AWS_IOT_ENDPOINT to your custom endpoint. This is found on the Settings page of the AWS IoT Console and has a format of ABCDEFG1234567.iot.<aws-region>.amazonaws.com where <aws-region> can be an AWS region like us-east-2.
    • Optionally, it can also be found with the AWS CLI command aws iot describe-endpoint --endpoint-type iot:Data-ATS.
  • Set CLIENT_CERT_PATH to the path of the client certificate downloaded when setting up the device certificate in AWS IoT Account Setup.
  • Set CLIENT_PRIVATE_KEY_PATH to the path of the private key downloaded when setting up the device certificate in AWS IoT Account Setup.

It is possible to configure ROOT_CA_CERT_PATH to any PEM-encoded Root CA Certificate. However, this is optional because CMake will download and set it to AmazonRootCA1.pem when unspecified.

Configuring AWS IoT Device Defender and AWS IoT Device Shadow demos

To build the AWS IoT Device Defender and AWS IoT Device Shadow demos, you can pass the following configuration settings as command line options. Make sure to run the following command in the root directory of the C-SDK:

cmake -S . -Bbuild -DAWS_IOT_ENDPOINT="<your-aws-iot-endpoint>" -DROOT_CA_CERT_PATH="<your-path-to-amazon-root-ca>" -DCLIENT_CERT_PATH="<your-client-certificate-path>" -DCLIENT_PRIVATE_KEY_PATH="<your-client-private-key-path>" -DTHING_NAME="<your-registered-thing-name>"

An Amazon Root CA certificate can be downloaded from here.

In order to set these configurations manually, edit demo_config.h in the demo folder to #define the following:

  • Set AWS_IOT_ENDPOINT to your custom endpoint. This is found on the Settings page of the AWS IoT Console and has a format of ABCDEFG1234567.iot.us-east-2.amazonaws.com.
  • Set ROOT_CA_CERT_PATH to the path of the root CA certificate downloaded when setting up the device certificate in AWS IoT Account Setup.
  • Set CLIENT_CERT_PATH to the path of the client certificate downloaded when setting up the device certificate in AWS IoT Account Setup.
  • Set CLIENT_PRIVATE_KEY_PATH to the path of the private key downloaded when setting up the device certificate in AWS IoT Account Setup.
  • Set THING_NAME to the name of the Thing created in AWS IoT Account Setup.

Configuring the AWS IoT Fleet Provisioning demo

To build the AWS IoT Fleet Provisioning Demo, you can pass the following configuration settings as command line options. Make sure to run the following command in the root directory of the C-SDK:

cmake -S . -Bbuild -DAWS_IOT_ENDPOINT="<your-aws-iot-endpoint>" -DROOT_CA_CERT_PATH="<your-path-to-amazon-root-ca>" -DCLAIM_CERT_PATH="<your-claim-certificate-path>" -DCLAIM_PRIVATE_KEY_PATH="<your-claim-private-key-path>" -DPROVISIONING_TEMPLATE_NAME="<your-template-name>" -DDEVICE_SERIAL_NUMBER="<your-serial-number>"

An Amazon Root CA certificate can be downloaded from here.

To create a provisioning template and claim credentials, sign into your AWS account and visit here. Make sure to enable the "Use the AWS IoT registry to manage your device fleet" option. Once you have created the template and credentials, modify the claim certificate's policy to match the sample policy.

In order to set these configurations manually, edit demo_config.h in the demo folder to #define the following:

  • Set AWS_IOT_ENDPOINT to your custom endpoint. This is found on the Settings page of the AWS IoT Console and has a format of ABCDEFG1234567.iot.us-east-2.amazonaws.com.
  • Set ROOT_CA_CERT_PATH to the path of the root CA certificate downloaded when setting up the device certificate in AWS IoT Account Setup.
  • Set CLAIM_CERT_PATH to the path of the claim certificate downloaded when setting up the template and claim credentials.
  • Set CLAIM_PRIVATE_KEY_PATH to the path of the private key downloaded when setting up the template and claim credentials.
  • Set PROVISIONING_TEMPLATE_NAME to the name of the provisioning template created.
  • Set DEVICE_SERIAL_NUMBER to an arbitrary string representing a device identifier.

Configuring the S3 demos

You can pass the following configuration settings as command line options in order to run the S3 demos. Make sure to run the following command in the root directory of the C-SDK:

cmake -S . -Bbuild -DS3_PRESIGNED_GET_URL="s3-get-url" -DS3_PRESIGNED_PUT_URL="s3-put-url"

S3_PRESIGNED_PUT_URL is only needed for the S3 upload demo.

In order to set these configurations manually, edit demo_config.h in demos/http/http_demo_s3_download_multithreaded, and demos/http/http_demo_s3_upload to #define the following:

  • Set S3_PRESIGNED_GET_URL to a S3 presigned URL with GET access.
  • Set S3_PRESIGNED_PUT_URL to a S3 presigned URL with PUT access.

You can generate the presigned urls using demos/http/common/src/presigned_urls_gen.py. More info can be found here.

Configure S3 Download HTTP Demo using SigV4 Library:

Refer this demos/http/http_demo_s3_download/README.md to follow the steps needed to configure and run the S3 Download HTTP Demo using SigV4 Library that generates the authorization HTTP header needed to authenticate the HTTP requests send to S3.

Setup for AWS IoT Jobs demo

  1. The demo requires the Linux platform to contain curl and libmosquitto. On a Debian platform, these dependencies can be installed with:
    apt install curl libmosquitto-dev

If the platform does not contain the libmosquitto library, the demo will build the library from source.

libmosquitto 1.4.10 or any later version of the first major release is required to run this demo.

  1. A job that specifies the URL to download for the demo needs to be created on the AWS account for the Thing resource that will be used by the demo.
    The job can be created directly from the AWS IoT console or using the aws cli tool.

The following creates a job that specifies a Linux Kernel link for downloading.

 aws iot create-job \
        --job-id 'job_1' \
        --targets arn:aws:iot:us-west-2:<account-id>:thing/<thing-name> \
        --document '{"url":"https://cdn.kernel.org/pub/linux/kernel/v5.x/linux-5.8.5.tar.xz"}'

Prerequisites for the AWS Over-The-Air Update (OTA) demos

  1. To perform a successful OTA update, you need to complete the prerequisites mentioned here.
  2. A code signing certificate is required to authenticate the update. A code signing certificate based on the SHA-256 ECDSA algorithm will work with the current demos. An example of how to generate this kind of certificate can be found here.

Scheduling an OTA Update Job

After you build and run the initial executable you will have to create another executable and schedule an OTA update job with this image.

  1. Increase the version of the application by setting macro APP_VERSION_BUILD in demos/ota/ota_demo_core_[mqtt/http]/demo_config.h to a different version than what is running.
  2. Rebuild the application using the build steps below into a different directory, say build-dir-2.
  3. Rename the demo executable to reflect the change, e.g. mv ota_demo_core_mqtt ota_demo_core_mqtt2
  4. Create an OTA job:
    1. Go to the AWS IoT Core console.
    2. Manage → Jobs → Create → Create a FreeRTOS OTA update job → Select the corresponding name for your device from the thing list.
    3. Sign a new firmware → Create a new profile → Select any SHA-ECDSA signing platform → Upload the code signing certificate(from prerequisites) and provide its path on the device.
    4. Select the image → Select the bucket you created during the prerequisite steps → Upload the binary build-dir-2/bin/ota_demo2.
    5. The path on device should be the absolute path to place the executable and the binary name: e.g. /home/ubuntu/aws-iot-device-sdk-embedded-C-staging/build-dir/bin/ota_demo_core_mqtt2.
    6. Select the IAM role created during the prerequisite steps.
    7. Create the Job.
  5. Run the initial executable again with the following command: sudo ./ota_demo_core_mqtt or sudo ./ota_demo_core_http.
  6. After the initial executable has finished running, go to the directory where the downloaded firmware image resides which is the path name used when creating an OTA job.
  7. Change the permissions of the downloaded firmware to make it executable, as it may be downloaded with read (user default) permissions only: chmod 775 ota_demo_core_mqtt2
  8. Run the downloaded firmware image with the following command: sudo ./ota_demo_core_mqtt2

Building and Running Demos

Before building the demos, ensure you have installed the prerequisite software. On Ubuntu 18.04 and 20.04, gcc, cmake, and OpenSSL can be installed with:

sudo apt install build-essential cmake libssl-dev

Build a single demo

  • Go to the root directory of the C-SDK.
  • Run cmake to generate the Makefiles: cmake -S . -Bbuild && cd build
  • Choose a demo from the list below or alternatively, run make help | grep demo:
defender_demo
http_demo_basic_tls
http_demo_mutual_auth
http_demo_plaintext
http_demo_s3_download
http_demo_s3_download_multithreaded
http_demo_s3_upload
jobs_demo_mosquitto
mqtt_demo_basic_tls
mqtt_demo_mutual_auth
mqtt_demo_plaintext
mqtt_demo_serializer
mqtt_demo_subscription_manager
ota_demo_core_http
ota_demo_core_mqtt
pkcs11_demo_management_and_rng
pkcs11_demo_mechanisms_and_digests
pkcs11_demo_objects
pkcs11_demo_sign_and_verify
shadow_demo_main
  • Replace demo_name with your desired demo then build it: make demo_name
  • Go to the build/bin directory and run any demo executables from there.

Build all configured demos

  • Go to the root directory of the C-SDK.
  • Run cmake to generate the Makefiles: cmake -S . -Bbuild && cd build
  • Run this command to build all configured demos: make
  • Go to the build/bin directory and run any demo executables from there.

Running corePKCS11 demos

The corePKCS11 demos do not require any AWS IoT resources setup, and are standalone. The demos build upon each other to introduce concepts in PKCS #11 sequentially. Below is the recommended order.

  1. pkcs11_demo_management_and_rng
  2. pkcs11_demo_mechanisms_and_digests
  3. pkcs11_demo_objects
  4. pkcs11_demo_sign_and_verify
    1. Please note that this demo requires the private and public key generated from pkcs11_demo_objects to be in the directory the demo is executed from.

Alternative option of Docker containers for running demos locally

Install Docker:

curl -fsSL https://get.docker.com -o get-docker.sh

sh get-docker.sh

Installing Mosquitto to run MQTT demos locally

The following instructions have been tested on an Ubuntu 18.04 environment with Docker and OpenSSL installed.

Download the official Docker image for Mosquitto 1.6.14. This version is deliberately chosen so that the Docker container can load certificates from the host system. Any version after 1.6.14 will drop privileges as soon as the configuration file has been read (before TLS certificates are loaded).

docker pull eclipse-mosquitto:1.6.14

If a Mosquitto broker with TLS communication needs to be run, ignore this step and proceed to the next step. A Mosquitto broker with plain text communication can be run by executing the command below.

docker run -it -p 1883:1883 --name mosquitto-plain-text eclipse-mosquitto:1.6.14

Set BROKER_ENDPOINT defined in demos/mqtt/mqtt_demo_plaintext/demo_config.h to localhost.

Ignore the remaining steps unless a Mosquitto broker with TLS communication also needs to be run.

For TLS communication with Mosquitto broker, server and CA credentials need to be created. Use OpenSSL commands to generate the credentials for the Mosquitto server.

# Generate CA key and certificate. Provide the Subject field information as appropriate for CA certificate.
openssl req -x509 -nodes -sha256 -days 365 -newkey rsa:2048 -keyout ca.key -out ca.crt
# Generate server key and certificate.# Provide the Subject field information as appropriate for Server certificate. Make sure the Common Name (CN) field is different from the root CA certificate.
openssl req -nodes -sha256 -new -keyout server.key -out server.csr # Sign with the CA cert.
openssl x509 -req -sha256 -in server.csr -CA ca.crt -CAkey ca.key -CAcreateserial -out server.crt -days 365

Note: Make sure to use different Common Name (CN) detail between the CA and server certificates; otherwise, SSL handshake fails with exactly same Common Name (CN) detail in both the certificates.

port 8883

cafile /mosquitto/config/ca.crt
certfile /mosquitto/config/server.crt
keyfile /mosquitto/config/server.key

# Use this option for TLS mutual authentication (where client will provide CA signed certificate)
#require_certificate true
tls_version tlsv1.2
#use_identity_as_username true

Create a mosquitto.conf file to use port 8883 (for TLS communication) and providing path to the generated credentials.

Run the docker container from the local directory containing the generated credential and mosquitto.conf files.

docker run -it -p 8883:8883 -v $(pwd):/mosquitto/config/ --name mosquitto-basic-tls eclipse-mosquitto:1.6.14

Update demos/mqtt/mqtt_demo_basic_tls/demo_config.h to the following:
Set BROKER_ENDPOINT to localhost.
Set ROOT_CA_CERT_PATH to the absolute path of the CA certificate created in step 4. for the local Mosquitto server.

Installing httpbin to run HTTP demos locally

Run httpbin through port 80:

docker pull kennethreitz/httpbin
docker run -p 80:80 kennethreitz/httpbin

SERVER_HOST defined in demos/http/http_demo_plaintext/demo_config.h can now be set to localhost.

To run http_demo_basic_tls, download ngrok in order to create an HTTPS tunnel to the httpbin server currently hosted on port 80:

./ngrok http 80 # May have to use ./ngrok.exe depending on OS or filename of the executable

ngrok will provide an https link that can be substituted in demos/http/http_demo_basic_tls/demo_config.h and has a format of https://ABCDEFG12345.ngrok.io.

Set SERVER_HOST in demos/http/http_demo_basic_tls/demo_config.h to the https link provided by ngrok, without https:// preceding it.

You must also download the Root CA certificate provided by the ngrok https link and set ROOT_CA_CERT_PATH in demos/http/http_demo_basic_tls/demo_config.h to the file path of the downloaded certificate.

Installation

The C-SDK libraries and platform abstractions can be installed to a file system through CMake. To do so, run the following command in the root directory of the C-SDK. Note that installation is not required to run any of the demos.

cmake -S . -Bbuild -DBUILD_DEMOS=0 -DBUILD_TESTS=0
cd build
sudo make install

Note that because make install will automatically build the all target, it may be useful to disable building demos and tests with -DBUILD_DEMOS=0 -DBUILD_TESTS=0 unless they have already been configured. Super-user permissions may be needed if installing to a system include or system library path.

To install only a subset of all libraries, pass -DINSTALL_LIBS to install only the libraries you need. By default, all libraries will be installed, but you may exclude any library that you don't need from this list:

-DINSTALL_LIBS="DEFENDER;SHADOW;JOBS;OTA;OTA_HTTP;OTA_MQTT;BACKOFF_ALGORITHM;HTTP;JSON;MQTT;PKCS"

By default, the install path will be in the project directory of the SDK. You can also set -DINSTALL_TO_SYSTEM=1 to install to the system path for headers and libraries in your OS (e.g. /usr/local/include & /usr/local/lib for Linux).

Upon entering make install, the location of each library will be specified first followed by the location of all installed headers:

-- Installing: /usr/local/lib/libaws_iot_defender.so
-- Installing: /usr/local/lib/libaws_iot_shadow.so
...
-- Installing: /usr/local/include/aws/defender.h
-- Installing: /usr/local/include/aws/defender_config_defaults.h
-- Installing: /usr/local/include/aws/shadow.h
-- Installing: /usr/local/include/aws/shadow_config_defaults.h

You may also set an installation path of your choice by passing the following flags through CMake. Make sure to run the following command in the root directory of the C-SDK:

cmake -S . -Bbuild -DBUILD_DEMOS=0 -DBUILD_TESTS=0 \
-DCSDK_HEADER_INSTALL_PATH="/header/path" -DCSDK_LIB_INSTALL_PATH="/lib/path"
cd build
sudo make install

POSIX platform abstractions are used together with the C-SDK libraries in the demos. By default, these abstractions are also installed but can be excluded by passing the flag: -DINSTALL_PLATFORM_ABSTRACTIONS=0.

Lastly, a custom config path for any specific library can also be specified through the following CMake flags, allowing libraries to be compiled with a config of your choice:

-DDEFENDER_CUSTOM_CONFIG_DIR="defender-config-directory"
-DSHADOW_CUSTOM_CONFIG_DIR="shadow-config-directory"
-DJOBS_CUSTOM_CONFIG_DIR="jobs-config-directory"
-DOTA_CUSTOM_CONFIG_DIR="ota-config-directory"
-DHTTP_CUSTOM_CONFIG_DIR="http-config-directory"
-DJSON_CUSTOM_CONFIG_DIR="json-config-directory"
-DMQTT_CUSTOM_CONFIG_DIR="mqtt-config-directory"
-DPKCS_CUSTOM_CONFIG_DIR="pkcs-config-directory"

Note that the file name of the header should not be included in the directory.

Generating Documentation

Note: For pre-generated documentation, please visit Releases and Documentation section.

The Doxygen references were created using Doxygen version 1.9.2. To generate the Doxygen pages, use the provided Python script at tools/doxygen/generate_docs.py. Please ensure that each of the library submodules under libraries/standard/ and libraries/aws/ are cloned before using this script.

cd <CSDK_ROOT>
git submodule update --init --recursive --checkout
python3 tools/doxygen/generate_docs.py

The generated documentation landing page is located at docs/doxygen/output/html/index.html.


Author: aws
Source code: https://github.com/aws/aws-iot-device-sdk-embedded-C
License: MIT license

#aws 

Shubham Ankit

Shubham Ankit

1657081614

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

#python 

I am Developer

1610191977

Angular 11 Google Social Login Example Tutorial

Angular 9/10/11 social login with google using angularx-social-login library example. In this tutorial, i will show you step by step on how to implement google social login in angular 11 app.

And also, this tutorial will show you How to login into Angular 10/11 application with google using angularx-social-login library in angular 11 app.

Google Login Integration In Angular 11 App

  • Step 1 - Create New Angular App
  • Step 2 - Install Social Login Library
  • Step 3 - Add Code on App.Module.ts File
  • Step 4 - Add Code on View File
  • Step 5 - Add Code On App.Component ts File
  • Step 6 - Start the Angular Google Login App

https://www.tutsmake.com/angular-11-google-social-login-example/

#angular 11 google login #angular 11 social-login example #login with google button angular 8/9/10/11 #angular 10/11 login with google #angular 10 social google login #angular social login google

Face Recognition with OpenCV and Python

Introduction

What is face recognition? Or what is recognition? When you look at an apple fruit, your mind immediately tells you that this is an apple fruit. This process, your mind telling you that this is an apple fruit is recognition in simple words. So what is face recognition then? I am sure you have guessed it right. When you look at your friend walking down the street or a picture of him, you recognize that he is your friend Paulo. Interestingly when you look at your friend or a picture of him you look at his face first before looking at anything else. Ever wondered why you do that? This is so that you can recognize him by looking at his face. Well, this is you doing face recognition.

But the real question is how does face recognition works? It is quite simple and intuitive. Take a real life example, when you meet someone first time in your life you don't recognize him, right? While he talks or shakes hands with you, you look at his face, eyes, nose, mouth, color and overall look. This is your mind learning or training for the face recognition of that person by gathering face data. Then he tells you that his name is Paulo. At this point your mind knows that the face data it just learned belongs to Paulo. Now your mind is trained and ready to do face recognition on Paulo's face. Next time when you will see Paulo or his face in a picture you will immediately recognize him. This is how face recognition work. The more you will meet Paulo, the more data your mind will collect about Paulo and especially his face and the better you will become at recognizing him.

Now the next question is how to code face recognition with OpenCV, after all this is the only reason why you are reading this article, right? OK then. You might say that our mind can do these things easily but to actually code them into a computer is difficult? Don't worry, it is not. Thanks to OpenCV, coding face recognition is as easier as it feels. The coding steps for face recognition are same as we discussed it in real life example above.

  • Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
  • Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
  • Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.

OpenCV comes equipped with built in face recognizer, all you have to do is feed it the face data. It's that simple and this how it will look once we are done coding it.

visualization

OpenCV Face Recognizers

OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can use any of them by just changing a single line of code. Below are the names of those face recognizers and their OpenCV calls.

  1. EigenFaces Face Recognizer Recognizer - cv2.face.createEigenFaceRecognizer()
  2. FisherFaces Face Recognizer Recognizer - cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histograms (LBPH) Face Recognizer - cv2.face.createLBPHFaceRecognizer()

We have got three face recognizers but do you know which one to use and when? Or which one is better? I guess not. So why not go through a brief summary of each, what you say? I am assuming you said yes :) So let's dive into the theory of each.

EigenFaces Face Recognizer

This algorithm considers the fact that not all parts of a face are equally important and equally useful. When you look at some one you recognize him/her by his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other. So you are actually focusing on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and same is the case from nose to mouth. When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other. This is exactly how EigenFaces face recognizer works.

EigenFaces face recognizer looks at all the training images of all the persons as a whole and try to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components. This way it not only extracts the important components from the training data but also saves memory by discarding the less important components. These important components it extracts are called principal components. Below is an image showing the principal components extracted from a list of faces.

Principal Components eigenfaces_opencv source

You can see that principal components actually represent faces and these faces are called eigen faces and hence the name of the algorithm.

So this is how EigenFaces face recognizer trains itself (by extracting principal components). Remember, it also keeps a record of which principal component belongs to which person. One thing to note in above image is that Eigenfaces algorithm also considers illumination as an important component.

Later during recognition, when you feed a new image to the algorithm, it repeats the same process on that image as well. It extracts the principal component from that new image and compares that component with the list of components it stored during training and finds the component with the best match and returns the person label associated with that best match component.

Easy peasy, right? Next one is easier than this one.

FisherFaces Face Recognizer

This algorithm is an improved version of EigenFaces face recognizer. Eigenfaces face recognizer looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are not focusing on the features that discriminate one person from the other but the features that represent all the persons in the training data as a whole.

This approach has drawbacks, for example, images with sharp changes (like light changes which is not a useful feature at all) may dominate the rest of the images and you may end up with features that are from external source like light and are not useful for discrimination at all. In the end, your principal components will represent light changes and not the actual face features.

Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, it extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.

Below is an image of features extracted using Fisherfaces algorithm.

Fisher Faces eigenfaces_opencv source

You can see that features extracted actually represent faces and these faces are called fisher faces and hence the name of the algorithm.

One thing to note here is that even in Fisherfaces algorithm if multiple persons have images with sharp changes due to external sources like light they will dominate over other features and affect recognition accuracy.

Getting bored with this theory? Don't worry, only one face recognizer is left and then we will dive deep into the coding part.

Local Binary Patterns Histograms (LBPH) Face Recognizer

I wrote a detailed explaination on Local Binary Patterns Histograms in my previous article on face detection using local binary patterns histograms. So here I will just give a brief overview of how it works.

We know that Eigenfaces and Fisherfaces are both affected by light and in real life we can't guarantee perfect light conditions. LBPH face recognizer is an improvement to overcome this drawback.

Idea is to not look at the image as a whole instead find the local features of an image. LBPH alogrithm try to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.

Take a 3x3 window and move it one image, at each move (each local part of an image), compare the pixel at the center with its neighbor pixels. The neighbors with intensity value less than or equal to center pixel are denoted by 1 and others by 0. Then you read these 0/1 values under 3x3 window in a clockwise order and you will have a binary pattern like 11100011 and this pattern is local to some area of the image. You do this on whole image and you will have a list of local binary patterns.

LBP Labeling LBP labeling

Now you get why this algorithm has Local Binary Patterns in its name? Because you get a list of local binary patterns. Now you may be wondering, what about the histogram part of the LBPH? Well after you get a list of local binary patterns, you convert each binary pattern into a decimal number (as shown in above image) and then you make a histogram of all of those values. A sample histogram looks like this.

Sample Histogram LBP labeling

I guess this answers the question about histogram part. So in the end you will have one histogram for each face image in the training data set. That means if there were 100 images in training data set then LBPH will extract 100 histograms after training and store them for later recognition. Remember, algorithm also keeps track of which histogram belongs to which person.

Later during recognition, when you will feed a new image to the recognizer for recognition it will generate a histogram for that new image, compare that histogram with the histograms it already has, find the best match histogram and return the person label associated with that best match histogram. 

Below is a list of faces and their respective local binary patterns images. You can see that the LBP images are not affected by changes in light conditions.

LBP Faces LBP faces source

The theory part is over and now comes the coding part! Ready to dive into coding? Let's get into it then.

Coding Face Recognition with OpenCV

The Face Recognition process in this tutorial is divided into three steps.

  1. Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
  2. Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
  3. Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.

[There should be a visualization diagram for above steps here]

To detect faces, I will use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its Python coding.

Import Required Modules

Before starting the actual coding we need to import the required modules for coding. So let's import them first.

  • cv2: is OpenCV module for Python which we will use for face detection and face recognition.
  • os: We will use this Python module to read our training directories and file names.
  • numpy: We will use this module to convert Python lists to numpy arrays as OpenCV face recognizers accept numpy arrays.
#import OpenCV module
import cv2
#import os module for reading training data directories and paths
import os
#import numpy to convert python lists to numpy arrays as 
#it is needed by OpenCV face recognizers
import numpy as np

#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline 

Training Data

The more images used in training the better. Normally a lot of images are used for training a face recognizer so that it can learn different looks of the same person, for example with glasses, without glasses, laughing, sad, happy, crying, with beard, without beard etc. To keep our tutorial simple we are going to use only 12 images for each person.

So our training data consists of total 2 persons with 12 images of each person. All training data is inside training-data folder. training-data folder contains one folder for each person and each folder is named with format sLabel (e.g. s1, s2) where label is actually the integer label assigned to that person. For example folder named s1 means that this folder contains images for person 1. The directory structure tree for training data is as follows:

training-data
|-------------- s1
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg
|-------------- s2
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg

The test-data folder contains images that we will use to test our face recognizer after it has been successfully trained.

As OpenCV face recognizer accepts labels as integers so we need to define a mapping between integer labels and persons actual names so below I am defining a mapping of persons integer labels and their respective names.

Note: As we have not assigned label 0 to any person so the mapping for label 0 is empty.

#there is no label 0 in our training data so subject name for index/label 0 is empty
subjects = ["", "Tom Cruise", "Shahrukh Khan"]

Prepare training data

You may be wondering why data preparation, right? Well, OpenCV face recognizer accepts data in a specific format. It accepts two vectors, one vector is of faces of all the persons and the second vector is of integer labels for each face so that when processing a face the face recognizer knows which person that particular face belongs too.

For example, if we had 2 persons and 2 images for each person.

PERSON-1    PERSON-2   

img1        img1         
img2        img2

Then the prepare data step will produce following face and label vectors.

FACES                        LABELS

person1_img1_face              1
person1_img2_face              1
person2_img1_face              2
person2_img2_face              2

Preparing data step can be further divided into following sub-steps.

  1. Read all the folder names of subjects/persons provided in training data folder. So for example, in this tutorial we have folder names: s1, s2.
  2. For each subject, extract label number. Do you remember that our folders have a special naming convention? Folder names follow the format sLabel where Label is an integer representing the label we have assigned to that subject. So for example, folder name s1 means that the subject has label 1, s2 means subject label is 2 and so on. The label extracted in this step is assigned to each face detected in the next step.
  3. Read all the images of the subject, detect face from each image.
  4. Add each face to faces vector with corresponding subject label (extracted in above step) added to labels vector.

[There should be a visualization for above steps here]

Did you read my last article on face detection? No? Then you better do so right now because to detect faces, I am going to use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its coding. Below is the same code.

#function to detect face using OpenCV
def detect_face(img):
    #convert the test image to gray image as opencv face detector expects gray images
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    #load OpenCV face detector, I am using LBP which is fast
    #there is also a more accurate but slow Haar classifier
    face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')

    #let's detect multiscale (some images may be closer to camera than others) images
    #result is a list of faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
    
    #if no faces are detected then return original img
    if (len(faces) == 0):
        return None, None
    
    #under the assumption that there will be only one face,
    #extract the face area
    (x, y, w, h) = faces[0]
    
    #return only the face part of the image
    return gray[y:y+w, x:x+h], faces[0]

I am using OpenCV's LBP face detector. On line 4, I convert the image to grayscale because most operations in OpenCV are performed in gray scale, then on line 8 I load LBP face detector using cv2.CascadeClassifier class. After that on line 12 I use cv2.CascadeClassifier class' detectMultiScale method to detect all the faces in the image. on line 20, from detected faces I only pick the first face because in one image there will be only one face (under the assumption that there will be only one prominent face). As faces returned by detectMultiScale method are actually rectangles (x, y, width, height) and not actual faces images so we have to extract face image area from the main image. So on line 23 I extract face area from gray image and return both the face image area and face rectangle.

Now you have got a face detector and you know the 4 steps to prepare the data, so are you ready to code the prepare data step? Yes? So let's do it.

#this function will read all persons' training images, detect face from each image
#and will return two lists of exactly same size, one list 
# of faces and another list of labels for each face
def prepare_training_data(data_folder_path):
    
    #------STEP-1--------
    #get the directories (one directory for each subject) in data folder
    dirs = os.listdir(data_folder_path)
    
    #list to hold all subject faces
    faces = []
    #list to hold labels for all subjects
    labels = []
    
    #let's go through each directory and read images within it
    for dir_name in dirs:
        
        #our subject directories start with letter 's' so
        #ignore any non-relevant directories if any
        if not dir_name.startswith("s"):
            continue;
            
        #------STEP-2--------
        #extract label number of subject from dir_name
        #format of dir name = slabel
        #, so removing letter 's' from dir_name will give us label
        label = int(dir_name.replace("s", ""))
        
        #build path of directory containin images for current subject subject
        #sample subject_dir_path = "training-data/s1"
        subject_dir_path = data_folder_path + "/" + dir_name
        
        #get the images names that are inside the given subject directory
        subject_images_names = os.listdir(subject_dir_path)
        
        #------STEP-3--------
        #go through each image name, read image, 
        #detect face and add face to list of faces
        for image_name in subject_images_names:
            
            #ignore system files like .DS_Store
            if image_name.startswith("."):
                continue;
            
            #build image path
            #sample image path = training-data/s1/1.pgm
            image_path = subject_dir_path + "/" + image_name

            #read image
            image = cv2.imread(image_path)
            
            #display an image window to show the image 
            cv2.imshow("Training on image...", image)
            cv2.waitKey(100)
            
            #detect face
            face, rect = detect_face(image)
            
            #------STEP-4--------
            #for the purpose of this tutorial
            #we will ignore faces that are not detected
            if face is not None:
                #add face to list of faces
                faces.append(face)
                #add label for this face
                labels.append(label)
            
    cv2.destroyAllWindows()
    cv2.waitKey(1)
    cv2.destroyAllWindows()
    
    return faces, labels

I have defined a function that takes the path, where training subjects' folders are stored, as parameter. This function follows the same 4 prepare data substeps mentioned above.

(step-1) On line 8 I am using os.listdir method to read names of all folders stored on path passed to function as parameter. On line 10-13 I am defining labels and faces vectors.

(step-2) After that I traverse through all subjects' folder names and from each subject's folder name on line 27 I am extracting the label information. As folder names follow the sLabel naming convention so removing the letter s from folder name will give us the label assigned to that subject.

(step-3) On line 34, I read all the images names of of the current subject being traversed and on line 39-66 I traverse those images one by one. On line 53-54 I am using OpenCV's imshow(window_title, image) along with OpenCV's waitKey(interval) method to display the current image being traveresed. The waitKey(interval) method pauses the code flow for the given interval (milliseconds), I am using it with 100ms interval so that we can view the image window for 100ms. On line 57, I detect face from the current image being traversed.

(step-4) On line 62-66, I add the detected face and label to their respective vectors.

But a function can't do anything unless we call it on some data that it has to prepare, right? Don't worry, I have got data of two beautiful and famous celebrities. I am sure you will recognize them!

training-data

Let's call this function on images of these beautiful celebrities to prepare data for training of our Face Recognizer. Below is a simple code to do that.

#let's first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print("Preparing data...")
faces, labels = prepare_training_data("training-data")
print("Data prepared")

#print total faces and labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
Preparing data...
Data prepared
Total faces:  23
Total labels:  23

This was probably the boring part, right? Don't worry, the fun stuff is coming up next. It's time to train our own face recognizer so that once trained it can recognize new faces of the persons it was trained on. Read? Ok then let's train our face recognizer.

Train Face Recognizer

As we know, OpenCV comes equipped with three face recognizers.

  1. EigenFace Recognizer: This can be created with cv2.face.createEigenFaceRecognizer()
  2. FisherFace Recognizer: This can be created with cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histogram (LBPH): This can be created with cv2.face.LBPHFisherFaceRecognizer()

I am going to use LBPH face recognizer but you can use any face recognizer of your choice. No matter which of the OpenCV's face recognizer you use the code will remain the same. You just have to change one line, the face recognizer initialization line given below.

#create our LBPH face recognizer 
face_recognizer = cv2.face.createLBPHFaceRecognizer()

#or use EigenFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createEigenFaceRecognizer()

#or use FisherFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createFisherFaceRecognizer()

Now that we have initialized our face recognizer and we also have prepared our training data, it's time to train the face recognizer. We will do that by calling the train(faces-vector, labels-vector) method of face recognizer.

#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))

Did you notice that instead of passing labels vector directly to face recognizer I am first converting it to numpy array? This is because OpenCV expects labels vector to be a numpy array.

Still not satisfied? Want to see some action? Next step is the real action, I promise!

Prediction

Now comes my favorite part, the prediction part. This is where we actually get to see if our algorithm is actually recognizing our trained subjects's faces or not. We will take two test images of our celeberities, detect faces from each of them and then pass those faces to our trained face recognizer to see if it recognizes them.

Below are some utility functions that we will use for drawing bounding box (rectangle) around face and putting celeberity name near the face bounding box.

#function to draw rectangle on image 
#according to given (x, y) coordinates and 
#given width and heigh
def draw_rectangle(img, rect):
    (x, y, w, h) = rect
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
#function to draw text on give image starting from
#passed (x, y) coordinates. 
def draw_text(img, text, x, y):
    cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)

First function draw_rectangle draws a rectangle on image based on passed rectangle coordinates. It uses OpenCV's built in function cv2.rectangle(img, topLeftPoint, bottomRightPoint, rgbColor, lineWidth) to draw rectangle. We will use it to draw a rectangle around the face detected in test image.

Second function draw_text uses OpenCV's built in function cv2.putText(img, text, startPoint, font, fontSize, rgbColor, lineWidth) to draw text on image.

Now that we have the drawing functions, we just need to call the face recognizer's predict(face) method to test our face recognizer on test images. Following function does the prediction for us.

#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the 
#subject
def predict(test_img):
    #make a copy of the image as we don't want to chang original image
    img = test_img.copy()
    #detect face from the image
    face, rect = detect_face(img)

    #predict the image using our face recognizer 
    label= face_recognizer.predict(face)
    #get name of respective label returned by face recognizer
    label_text = subjects[label]
    
    #draw a rectangle around face detected
    draw_rectangle(img, rect)
    #draw name of predicted person
    draw_text(img, label_text, rect[0], rect[1]-5)
    
    return img
  • line-6 read the test image
  • line-7 detect face from test image
  • line-11 recognize the face by calling face recognizer's predict(face) method. This method will return a lable
  • line-12 get the name associated with the label
  • line-16 draw rectangle around the detected face
  • line-18 draw name of predicted subject above face rectangle

Now that we have the prediction function well defined, next step is to actually call this function on our test images and display those test images to see if our face recognizer correctly recognized them. So let's do it. This is what we have been waiting for.

print("Predicting images...")

#load test images
test_img1 = cv2.imread("test-data/test1.jpg")
test_img2 = cv2.imread("test-data/test2.jpg")

#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")

#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))

#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))

#display both images
cv2.imshow("Tom cruise test", predicted_img1)
cv2.imshow("Shahrukh Khan test", predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
Predicting images...
Prediction complete

wohooo! Is'nt it beautiful? Indeed, it is!

End Notes

Face Recognition is a fascinating idea to work on and OpenCV has made it extremely simple and easy for us to code it. It just takes a few lines of code to have a fully working face recognition application and we can switch between all three face recognizers with a single line of code change. It's that simple.

Although EigenFaces, FisherFaces and LBPH face recognizers are good but there are even better ways to perform face recognition like using Histogram of Oriented Gradients (HOGs) and Neural Networks. So the more advanced face recognition algorithms are now a days implemented using a combination of OpenCV and Machine learning. I have plans to write some articles on those more advanced methods as well, so stay tuned!

Download Details:
Author: informramiz
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License

#opencv  #python #facerecognition 

Bertrand  Upton

Bertrand Upton

1625862120

Angular 12 Hindi tutorial #11 Get Input Box Value and Display

In this angular 12 version video, we learn how to how to get value from input box and text box in angular 12. This video is made by anil Sidhu in the Hindi language

Steps of video
Make input box
Get Value on keyup(change)
Display Value on Screen
Get Value with Button Click
Interview Questions

code link https://github.com/anil-sidhu/angular-12/tree/interpolation

React js Latest Playlist in hindi : https://www.youtube.com/playlist?list=PL8p2I9GklV47BCAjiCtuV_liN9IwAl8pM
inst id: @code.steps

#angular #hindi