A loading more list which supports ListView,GridView,WaterfallFlow and Slivers.

loading_more_list

A loading more list which supports ListView,GridView,WaterfallFlow and Slivers.

Web demo for LoadingMoreList

Use

  • add library to your pubspec.yaml
dependencies:
  loading_more_list: any

  • import library in dart file
  import 'package:loading_more_list/loading_more_list.dart';

Prepare Data Collection

LoadingMoreBase is data collection for loading more. override loadData method to load your data. set hasMore to false when it has no more data.

class TuChongRepository extends LoadingMoreBase<TuChongItem> {
  int pageindex = 1;
  bool _hasMore = true;
  bool forceRefresh = false;
  @override
  bool get hasMore => (_hasMore && length < 30) || forceRefresh;

  @override
  Future<bool> refresh([bool clearBeforeRequest = false]) async {
    _hasMore = true;
    pageindex = 1;
    //force to refresh list when you don't want clear list before request
    //for the case, if your list already has 20 items.
    forceRefresh = !clearBeforeRequest;
    var result = await super.refresh(clearBeforeRequest);
    forceRefresh = false;
    return result;
  }

  @override
  Future<bool> loadData([bool isloadMoreAction = false]) async {
    String url = "";
    if (this.length == 0) {
      url = "https://api.tuchong.com/feed-app";
    } else {
      int lastPostId = this[this.length - 1].postId;
      url =
          "https://api.tuchong.com/feed-app?post_id=$lastPostId&page=$pageindex&type=loadmore";
    }
    bool isSuccess = false;
    try {
      //to show loading more clearly, in your app,remove this
      await Future.delayed(Duration(milliseconds: 500));

      var result = await HttpClientHelper.get(url);

      var source = TuChongSource.fromJson(json.decode(result.body));
      if (pageindex == 1) {
        this.clear();
      }
      for (var item in source.feedList) {
        if (item.hasImage && !this.contains(item) && hasMore) this.add(item);
      }

      _hasMore = source.feedList.length != 0;
      pageindex++;
      isSuccess = true;
    } catch (exception, stack) {
      isSuccess = false;
      print(exception);
      print(stack);
    }
    return isSuccess;
  }
}

Argument

almost of arguments are the same as official.

following arguments are made for loading more.

ListConfig and SliverListConfig

argument description default
itemBuilder The item builder of list. required
sourceList The source list of data which extends LoadingMoreBase. required
showGlowLeading Whether to show the overscroll glow on the side with negative scroll offsets. 0.0
showGlowTrailing Whether to show the overscroll glow on the side with positive scroll offsets. -
lastChildLayoutType Layout type of last child(loadmore/no more item). LastChildLayoutType.foot
extendedListDelegate The delegate for WaterfallFlow or ExtendedList. -
gridDelegate The delegate for GridView. -
indicatorBuilder widget builder for different loading state. IndicatorWidget
padding The amount of space by which to inset the child sliver for SliverListConfig -

Widget

LoadingMoreList

argument description default
listConfig ListConfig required
onScrollNotification Called when a ScrollNotification of the appropriate type arrives at this location in the tree. -

LoadingMoreSliverList

argument description default
sliverListConfig SliverListConfig required

LoadingMoreCustomScrollView

argument description default
onScrollNotification Called when a ScrollNotification of the appropriate type arrives at this location in the tree. -
rebuildCustomScrollView in NestedScrollView, rebuild CustomScrollView, viewport can be computed again. false

ListView

            LoadingMoreList(
              ListConfig<TuChongItem>(
                itemBuilder: ItemBuilder.itemBuilder,
                sourceList: listSourceRepository,
                padding: EdgeInsets.all(0.0),
              ),
            ),

GridView

define GridView with gridDelegate argument.

            LoadingMoreList(
              ListConfig<TuChongItem>(
                itemBuilder: ItemBuilder.itemBuilder,
                sourceList: listSourceRepository,
                padding: EdgeInsets.all(0.0),
                gridDelegate: SliverGridDelegateWithFixedCrossAxisCount(
                  crossAxisCount: 2,
                  crossAxisSpacing: 3.0,
                  mainAxisSpacing: 3.0,
                ),
              ),
            ),

WaterfallFlow

define WaterfallFlow with waterfallFlowDelegate argument.

            LoadingMoreList(
              ListConfig<TuChongItem>(
                extendedListDelegate: SliverWaterfallFlowDelegateWithFixedCrossAxisCount(
                  crossAxisCount: 2,
                  crossAxisSpacing: 5,
                  mainAxisSpacing: 5,
                ),
                itemBuilder: _buildItem,
                sourceList: listSourceRepository,
                padding: EdgeInsets.all(5.0),
              ),
            ),

Sliver/CustomScrollView

following codes are show how to build loading more list within CustomScrollView.

      LoadingMoreCustomScrollView(
        slivers: <Widget>[
          SliverAppBar(
            pinned: true,
            title: Text("MultipleSliverDemo"),
          ),
          ///SliverList
          LoadingMoreSliverList(SliverListConfig<TuChongItem>(
            itemBuilder: ItemBuilder.itemBuilder,
            sourceList: listSourceRepository,
          )),
          SliverToBoxAdapter(
            child: Container(
              alignment: Alignment.center,
              child: Text("Next list"),
              color: Colors.blue,
              height: 100.0,
            ),
          ),
          ///SliverGrid
          LoadingMoreSliverList(
            SliverListConfig<TuChongItem>(
              itemBuilder: ItemBuilder.itemBuilder,
              sourceList: listSourceRepository1,
              gridDelegate: SliverGridDelegateWithFixedCrossAxisCount(
                crossAxisCount: 2,
                crossAxisSpacing: 3.0,
                mainAxisSpacing: 3.0,
              ),
            ),
          ),
          SliverPersistentHeader(
            delegate: CommonExtentSliverPersistentHeaderDelegate(
                Container(
                  alignment: Alignment.center,
                  child: Text("Pinned Content"),
                  color: Colors.red,
                ),
                100.0),
            pinned: true,
          ),
          ///SliverWaterfallFlow
          LoadingMoreSliverList(
            SliverListConfig<TuChongItem>(
              itemBuilder: buildWaterfallFlowItem,
              sourceList: listSourceRepository2,
              padding: EdgeInsets.symmetric(horizontal: 5.0),
              extendedListDelegate: SliverWaterfallFlowDelegateWithFixedCrossAxisCount(
                crossAxisCount: 2,
                crossAxisSpacing: 5,
                mainAxisSpacing: 5,
              ),
            ),
          ),
        ],
      ),

IndicatorStatus

define loading status with indicatorBuilder argument.

        enum IndicatorStatus {
          None,
          LoadingMoreBusying,
          FullScreenBusying,
          Error,
          FullScreenError,
          NoMoreLoad,
          Empty
        }
      LoadingMoreList(
        ListConfig<TuChongItem>(
          itemBuilder: ItemBuilder.itemBuilder,
          sourceList: listSourceRepository,
          indicatorBuilder: _buildIndicator,
          padding: EdgeInsets.all(0.0),
        ),
      ),

  //you can use IndicatorWidget or build yourself widget
  //in this demo, we define all status.
  Widget _buildIndicator(BuildContext context, IndicatorStatus status) {
    //if your list is sliver list ,you should build sliver indicator for it
    //isSliver=true, when use it in sliver list
    bool isSliver = false;

    Widget widget;
    switch (status) {
      case IndicatorStatus.None:
        widget = Container(height: 0.0);
        break;
      case IndicatorStatus.LoadingMoreBusying:
        widget = Row(
          mainAxisAlignment: MainAxisAlignment.center,
          crossAxisAlignment: CrossAxisAlignment.center,
          children: <Widget>[
            Container(
              margin: EdgeInsets.only(right: 5.0),
              height: 15.0,
              width: 15.0,
              child: getIndicator(context),
            ),
            Text("正在加载...不要着急")
          ],
        );
        widget = _setbackground(false, widget, 35.0);
        break;
      case IndicatorStatus.FullScreenBusying:
        widget = Row(
          mainAxisAlignment: MainAxisAlignment.center,
          crossAxisAlignment: CrossAxisAlignment.center,
          children: <Widget>[
            Container(
              margin: EdgeInsets.only(right: 0.0),
              height: 30.0,
              width: 30.0,
              child: getIndicator(context),
            ),
            Text("正在加载...不要着急")
          ],
        );
        widget = _setbackground(true, widget, double.infinity);
        if (isSliver) {
          widget = SliverFillRemaining(
            child: widget,
          );
        } else {
          widget = CustomScrollView(
            slivers: <Widget>[
              SliverFillRemaining(
                child: widget,
              )
            ],
          );
        }
        break;
      case IndicatorStatus.Error:
        widget = Text(
          "好像出现了问题呢?",
        );
        widget = _setbackground(false, widget, 35.0);

        widget = GestureDetector(
          onTap: () {
            listSourceRepository.errorRefresh();
          },
          child: widget,
        );

        break;
      case IndicatorStatus.FullScreenError:
        widget = Text(
          "好像出现了问题呢?",
        );
        widget = _setbackground(true, widget, double.infinity);
        widget = GestureDetector(
          onTap: () {
            listSourceRepository.errorRefresh();
          },
          child: widget,
        );
        if (isSliver) {
          widget = SliverFillRemaining(
            child: widget,
          );
        } else {
          widget = CustomScrollView(
            slivers: <Widget>[
              SliverFillRemaining(
                child: widget,
              )
            ],
          );
        }
        break;
      case IndicatorStatus.NoMoreLoad:
        widget = Text("没有更多的了。。不要拖了");
        widget = _setbackground(false, widget, 35.0);
        break;
      case IndicatorStatus.Empty:
        widget = EmptyWidget(
          "这里是空气!",
        );
        widget = _setbackground(true, widget, double.infinity);
        if (isSliver) {
          widget = SliverToBoxAdapter(
            child: widget,
          );
        } else {
          widget = CustomScrollView(
            slivers: <Widget>[
              SliverFillRemaining(
                child: widget,
              )
            ],
          );
        }
        break;
    }
    return widget;
  }

CollectGarbage

track the indexes are collect, you can collect garbage at that monment(for example Image cache)

more detail

        LoadingMoreList(
          ListConfig<TuChongItem>(
            extendedListDelegate: ExtendedListDelegate(
              collectGarbage: (List<int> indexes) {
                ///collectGarbage
              },
            ),
          ),
        ),

ViewportBuilder

track the indexes go into the viewport, it’s not include cache extent.

        LoadingMoreList(
          ListConfig<TuChongItem>(
            extendedListDelegate: ExtendedListDelegate(
              viewportBuilder: (int firstIndex, int lastIndex) {
                print('viewport : [$firstIndex,$lastIndex]');
              },
            ),
          ),
        ),

LastChildLayoutType

build lastChild as special child in the case that it is loadmore/no more item.

        enum LastChildLayoutType {
        /// as default child
        none,

        /// follow max child trailing layout offset and layout with full cross axis extent
        /// last child as loadmore item/no more item in [ExtendedGridView] and [WaterfallFlow]
        /// with full cross axis extend
        fullCrossAxisExtent,

        /// as foot at trailing and layout with full cross axis extend
        /// show no more item at trailing when children are not full of viewport
        /// if children is full of viewport, it's the same as fullCrossAxisExtent
        foot,
        }

CloseToTrailing

when reverse property of List is true, layout is as following. it likes chat list, and new session will insert to zero index. but it’s not right when items are not full of viewport.

     trailing
-----------------
|               |
|               |
|     item2     |
|     item1     |
|     item0     |
-----------------
     leading

to solve it, you could set closeToTrailing to true, layout is as following. support [ExtendedGridView],[ExtendedList],[WaterfallFlow]. and it also works when reverse is flase, layout will close to trailing.

     trailing
-----------------
|     item2     |
|     item1     |
|     item0     |
|               |
|               |
-----------------
     leading

      LoadingMoreList(
        ListConfig<TuChongItem>(
          extendedListDelegate: ExtendedListDelegate(
            closeToTrailing: true
          ),
        ),
      ),

Download Details:

Author: fluttercandies

Source Code: https://github.com/fluttercandies/loading_more_list

#flutter #dart #mobile-apps

What is GEEK

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A loading more list which supports ListView,GridView,WaterfallFlow and Slivers.

Dotnet Script: Run C# Scripts From The .NET CLI

dotnet script

Run C# scripts from the .NET CLI, define NuGet packages inline and edit/debug them in VS Code - all of that with full language services support from OmniSharp.

NuGet Packages

NameVersionFramework(s)
dotnet-script (global tool)Nugetnet6.0, net5.0, netcoreapp3.1
Dotnet.Script (CLI as Nuget)Nugetnet6.0, net5.0, netcoreapp3.1
Dotnet.Script.CoreNugetnetcoreapp3.1 , netstandard2.0
Dotnet.Script.DependencyModelNugetnetstandard2.0
Dotnet.Script.DependencyModel.NugetNugetnetstandard2.0

Installing

Prerequisites

The only thing we need to install is .NET Core 3.1 or .NET 5.0 SDK.

.NET Core Global Tool

.NET Core 2.1 introduced the concept of global tools meaning that you can install dotnet-script using nothing but the .NET CLI.

dotnet tool install -g dotnet-script

You can invoke the tool using the following command: dotnet-script
Tool 'dotnet-script' (version '0.22.0') was successfully installed.

The advantage of this approach is that you can use the same command for installation across all platforms. .NET Core SDK also supports viewing a list of installed tools and their uninstallation.

dotnet tool list -g

Package Id         Version      Commands
---------------------------------------------
dotnet-script      0.22.0       dotnet-script
dotnet tool uninstall dotnet-script -g

Tool 'dotnet-script' (version '0.22.0') was successfully uninstalled.

Windows

choco install dotnet.script

We also provide a PowerShell script for installation.

(new-object Net.WebClient).DownloadString("https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.ps1") | iex

Linux and Mac

curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | bash

If permission is denied we can try with sudo

curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | sudo bash

Docker

A Dockerfile for running dotnet-script in a Linux container is available. Build:

cd build
docker build -t dotnet-script -f Dockerfile ..

And run:

docker run -it dotnet-script --version

Github

You can manually download all the releases in zip format from the GitHub releases page.

Usage

Our typical helloworld.csx might look like this:

Console.WriteLine("Hello world!");

That is all it takes and we can execute the script. Args are accessible via the global Args array.

dotnet script helloworld.csx

Scaffolding

Simply create a folder somewhere on your system and issue the following command.

dotnet script init

This will create main.csx along with the launch configuration needed to debug the script in VS Code.

.
├── .vscode
│   └── launch.json
├── main.csx
└── omnisharp.json

We can also initialize a folder using a custom filename.

dotnet script init custom.csx

Instead of main.csx which is the default, we now have a file named custom.csx.

.
├── .vscode
│   └── launch.json
├── custom.csx
└── omnisharp.json

Note: Executing dotnet script init inside a folder that already contains one or more script files will not create the main.csx file.

Running scripts

Scripts can be executed directly from the shell as if they were executables.

foo.csx arg1 arg2 arg3

OSX/Linux

Just like all scripts, on OSX/Linux you need to have a #! and mark the file as executable via chmod +x foo.csx. If you use dotnet script init to create your csx it will automatically have the #! directive and be marked as executable.

The OSX/Linux shebang directive should be #!/usr/bin/env dotnet-script

#!/usr/bin/env dotnet-script
Console.WriteLine("Hello world");

You can execute your script using dotnet script or dotnet-script, which allows you to pass arguments to control your script execution more.

foo.csx arg1 arg2 arg3
dotnet script foo.csx -- arg1 arg2 arg3
dotnet-script foo.csx -- arg1 arg2 arg3

Passing arguments to scripts

All arguments after -- are passed to the script in the following way:

dotnet script foo.csx -- arg1 arg2 arg3

Then you can access the arguments in the script context using the global Args collection:

foreach (var arg in Args)
{
    Console.WriteLine(arg);
}

All arguments before -- are processed by dotnet script. For example, the following command-line

dotnet script -d foo.csx -- -d

will pass the -d before -- to dotnet script and enable the debug mode whereas the -d after -- is passed to script for its own interpretation of the argument.

NuGet Packages

dotnet script has built-in support for referencing NuGet packages directly from within the script.

#r "nuget: AutoMapper, 6.1.0"

package

Note: Omnisharp needs to be restarted after adding a new package reference

Package Sources

We can define package sources using a NuGet.Config file in the script root folder. In addition to being used during execution of the script, it will also be used by OmniSharp that provides language services for packages resolved from these package sources.

As an alternative to maintaining a local NuGet.Config file we can define these package sources globally either at the user level or at the computer level as described in Configuring NuGet Behaviour

It is also possible to specify packages sources when executing the script.

dotnet script foo.csx -s https://SomePackageSource

Multiple packages sources can be specified like this:

dotnet script foo.csx -s https://SomePackageSource -s https://AnotherPackageSource

Creating DLLs or Exes from a CSX file

Dotnet-Script can create a standalone executable or DLL for your script.

SwitchLong switchdescription
-o--outputDirectory where the published executable should be placed. Defaults to a 'publish' folder in the current directory.
-n--nameThe name for the generated DLL (executable not supported at this time). Defaults to the name of the script.
 --dllPublish to a .dll instead of an executable.
-c--configurationConfiguration to use for publishing the script [Release/Debug]. Default is "Debug"
-d--debugEnables debug output.
-r--runtimeThe runtime used when publishing the self contained executable. Defaults to your current runtime.

The executable you can run directly independent of dotnet install, while the DLL can be run using the dotnet CLI like this:

dotnet script exec {path_to_dll} -- arg1 arg2

Caching

We provide two types of caching, the dependency cache and the execution cache which is explained in detail below. In order for any of these caches to be enabled, it is required that all NuGet package references are specified using an exact version number. The reason for this constraint is that we need to make sure that we don't execute a script with a stale dependency graph.

Dependency Cache

In order to resolve the dependencies for a script, a dotnet restore is executed under the hood to produce a project.assets.json file from which we can figure out all the dependencies we need to add to the compilation. This is an out-of-process operation and represents a significant overhead to the script execution. So this cache works by looking at all the dependencies specified in the script(s) either in the form of NuGet package references or assembly file references. If these dependencies matches the dependencies from the last script execution, we skip the restore and read the dependencies from the already generated project.assets.json file. If any of the dependencies has changed, we must restore again to obtain the new dependency graph.

Execution cache

In order to execute a script it needs to be compiled first and since that is a CPU and time consuming operation, we make sure that we only compile when the source code has changed. This works by creating a SHA256 hash from all the script files involved in the execution. This hash is written to a temporary location along with the DLL that represents the result of the script compilation. When a script is executed the hash is computed and compared with the hash from the previous compilation. If they match there is no need to recompile and we run from the already compiled DLL. If the hashes don't match, the cache is invalidated and we recompile.

You can override this automatic caching by passing --no-cache flag, which will bypass both caches and cause dependency resolution and script compilation to happen every time we execute the script.

Cache Location

The temporary location used for caches is a sub-directory named dotnet-script under (in order of priority):

  1. The path specified for the value of the environment variable named DOTNET_SCRIPT_CACHE_LOCATION, if defined and value is not empty.
  2. Linux distributions only: $XDG_CACHE_HOME if defined otherwise $HOME/.cache
  3. macOS only: ~/Library/Caches
  4. The value returned by Path.GetTempPath for the platform.

 

Debugging

The days of debugging scripts using Console.WriteLine are over. One major feature of dotnet script is the ability to debug scripts directly in VS Code. Just set a breakpoint anywhere in your script file(s) and hit F5(start debugging)

debug

Script Packages

Script packages are a way of organizing reusable scripts into NuGet packages that can be consumed by other scripts. This means that we now can leverage scripting infrastructure without the need for any kind of bootstrapping.

Creating a script package

A script package is just a regular NuGet package that contains script files inside the content or contentFiles folder.

The following example shows how the scripts are laid out inside the NuGet package according to the standard convention .

└── contentFiles
    └── csx
        └── netstandard2.0
            └── main.csx

This example contains just the main.csx file in the root folder, but packages may have multiple script files either in the root folder or in subfolders below the root folder.

When loading a script package we will look for an entry point script to be loaded. This entry point script is identified by one of the following.

  • A script called main.csx in the root folder
  • A single script file in the root folder

If the entry point script cannot be determined, we will simply load all the scripts files in the package.

The advantage with using an entry point script is that we can control loading other scripts from the package.

Consuming a script package

To consume a script package all we need to do specify the NuGet package in the #loaddirective.

The following example loads the simple-targets package that contains script files to be included in our script.

#load "nuget:simple-targets-csx, 6.0.0"

using static SimpleTargets;
var targets = new TargetDictionary();

targets.Add("default", () => Console.WriteLine("Hello, world!"));

Run(Args, targets);

Note: Debugging also works for script packages so that we can easily step into the scripts that are brought in using the #load directive.

Remote Scripts

Scripts don't actually have to exist locally on the machine. We can also execute scripts that are made available on an http(s) endpoint.

This means that we can create a Gist on Github and execute it just by providing the URL to the Gist.

This Gist contains a script that prints out "Hello World"

We can execute the script like this

dotnet script https://gist.githubusercontent.com/seesharper/5d6859509ea8364a1fdf66bbf5b7923d/raw/0a32bac2c3ea807f9379a38e251d93e39c8131cb/HelloWorld.csx

That is a pretty long URL, so why don't make it a TinyURL like this:

dotnet script https://tinyurl.com/y8cda9zt

Script Location

A pretty common scenario is that we have logic that is relative to the script path. We don't want to require the user to be in a certain directory for these paths to resolve correctly so here is how to provide the script path and the script folder regardless of the current working directory.

public static string GetScriptPath([CallerFilePath] string path = null) => path;
public static string GetScriptFolder([CallerFilePath] string path = null) => Path.GetDirectoryName(path);

Tip: Put these methods as top level methods in a separate script file and #load that file wherever access to the script path and/or folder is needed.

REPL

This release contains a C# REPL (Read-Evaluate-Print-Loop). The REPL mode ("interactive mode") is started by executing dotnet-script without any arguments.

The interactive mode allows you to supply individual C# code blocks and have them executed as soon as you press Enter. The REPL is configured with the same default set of assembly references and using statements as regular CSX script execution.

Basic usage

Once dotnet-script starts you will see a prompt for input. You can start typing C# code there.

~$ dotnet script
> var x = 1;
> x+x
2

If you submit an unterminated expression into the REPL (no ; at the end), it will be evaluated and the result will be serialized using a formatter and printed in the output. This is a bit more interesting than just calling ToString() on the object, because it attempts to capture the actual structure of the object. For example:

~$ dotnet script
> var x = new List<string>();
> x.Add("foo");
> x
List<string>(1) { "foo" }
> x.Add("bar");
> x
List<string>(2) { "foo", "bar" }
>

Inline Nuget packages

REPL also supports inline Nuget packages - meaning the Nuget packages can be installed into the REPL from within the REPL. This is done via our #r and #load from Nuget support and uses identical syntax.

~$ dotnet script
> #r "nuget: Automapper, 6.1.1"
> using AutoMapper;
> typeof(MapperConfiguration)
[AutoMapper.MapperConfiguration]
> #load "nuget: simple-targets-csx, 6.0.0";
> using static SimpleTargets;
> typeof(TargetDictionary)
[Submission#0+SimpleTargets+TargetDictionary]

Multiline mode

Using Roslyn syntax parsing, we also support multiline REPL mode. This means that if you have an uncompleted code block and press Enter, we will automatically enter the multiline mode. The mode is indicated by the * character. This is particularly useful for declaring classes and other more complex constructs.

~$ dotnet script
> class Foo {
* public string Bar {get; set;}
* }
> var foo = new Foo();

REPL commands

Aside from the regular C# script code, you can invoke the following commands (directives) from within the REPL:

CommandDescription
#loadLoad a script into the REPL (same as #load usage in CSX)
#rLoad an assembly into the REPL (same as #r usage in CSX)
#resetReset the REPL back to initial state (without restarting it)
#clsClear the console screen without resetting the REPL state
#exitExits the REPL

Seeding REPL with a script

You can execute a CSX script and, at the end of it, drop yourself into the context of the REPL. This way, the REPL becomes "seeded" with your code - all the classes, methods or variables are available in the REPL context. This is achieved by running a script with an -i flag.

For example, given the following CSX script:

var msg = "Hello World";
Console.WriteLine(msg);

When you run this with the -i flag, Hello World is printed, REPL starts and msg variable is available in the REPL context.

~$ dotnet script foo.csx -i
Hello World
>

You can also seed the REPL from inside the REPL - at any point - by invoking a #load directive pointed at a specific file. For example:

~$ dotnet script
> #load "foo.csx"
Hello World
>

Piping

The following example shows how we can pipe data in and out of a script.

The UpperCase.csx script simply converts the standard input to upper case and writes it back out to standard output.

using (var streamReader = new StreamReader(Console.OpenStandardInput()))
{
    Write(streamReader.ReadToEnd().ToUpper());
}

We can now simply pipe the output from one command into our script like this.

echo "This is some text" | dotnet script UpperCase.csx
THIS IS SOME TEXT

Debugging

The first thing we need to do add the following to the launch.config file that allows VS Code to debug a running process.

{
    "name": ".NET Core Attach",
    "type": "coreclr",
    "request": "attach",
    "processId": "${command:pickProcess}"
}

To debug this script we need a way to attach the debugger in VS Code and the simplest thing we can do here is to wait for the debugger to attach by adding this method somewhere.

public static void WaitForDebugger()
{
    Console.WriteLine("Attach Debugger (VS Code)");
    while(!Debugger.IsAttached)
    {
    }
}

To debug the script when executing it from the command line we can do something like

WaitForDebugger();
using (var streamReader = new StreamReader(Console.OpenStandardInput()))
{
    Write(streamReader.ReadToEnd().ToUpper()); // <- SET BREAKPOINT HERE
}

Now when we run the script from the command line we will get

$ echo "This is some text" | dotnet script UpperCase.csx
Attach Debugger (VS Code)

This now gives us a chance to attach the debugger before stepping into the script and from VS Code, select the .NET Core Attach debugger and pick the process that represents the executing script.

Once that is done we should see our breakpoint being hit.

Configuration(Debug/Release)

By default, scripts will be compiled using the debug configuration. This is to ensure that we can debug a script in VS Code as well as attaching a debugger for long running scripts.

There are however situations where we might need to execute a script that is compiled with the release configuration. For instance, running benchmarks using BenchmarkDotNet is not possible unless the script is compiled with the release configuration.

We can specify this when executing the script.

dotnet script foo.csx -c release

 

Nullable reference types

Starting from version 0.50.0, dotnet-script supports .Net Core 3.0 and all the C# 8 features. The way we deal with nullable references types in dotnet-script is that we turn every warning related to nullable reference types into compiler errors. This means every warning between CS8600 and CS8655 are treated as an error when compiling the script.

Nullable references types are turned off by default and the way we enable it is using the #nullable enable compiler directive. This means that existing scripts will continue to work, but we can now opt-in on this new feature.

#!/usr/bin/env dotnet-script

#nullable enable

string name = null;

Trying to execute the script will result in the following error

main.csx(5,15): error CS8625: Cannot convert null literal to non-nullable reference type.

We will also see this when working with scripts in VS Code under the problems panel.

image

Download Details:
Author: filipw
Source Code: https://github.com/filipw/dotnet-script
License: MIT License

#dotnet  #aspdotnet  #csharp 

HI Python

HI Python

1640973720

Beyonic API Python Example Using Flask, Django, FastAPI

Beyonic API Python Examples.

The beyonic APIs Docs Reference: https://apidocs.beyonic.com/

Discuss Beyonic API on slack

The Beyonic API is a representational state transfer, REST based application programming interface that lets you extend the Beyonic dashboard features into your application and systems, allowing you to build amazing payment experiences.

With the Beyonic API you can:

  • Receive and send money and prepaid airtime.
  • List currencies and networks supported by the Beyonic API.
  • Check whether a bank is supported by the Beyonic API.
  • View your account transactions history.
  • Add, retrieve, list, and update contacts to your Beyonic account.
  • Use webhooks to send notifications to URLs on your server that when specific events occur in your Beyonic account (e.g. payments).

Getting Help

For usage, general questions, and discussions the best place to go to is Beyhive Slack Community, also feel free to clone and edit this repository to meet your project, application or system requirements.

To start using the Beyonic Python API, you need to start by downloading the Beyonic API official Python client library and setting your secret key.

Install the Beyonic API Python Official client library

>>> pip install beyonic

Setting your secrete key.

To set the secrete key install the python-dotenv modeule, Python-dotenv is a Python module that allows you to specify environment variables in traditional UNIX-like “.env” (dot-env) file within your Python project directory, it helps us work with SECRETS and KEYS without exposing them to the outside world, and keep them safe during development too.

Installing python-dotenv modeule

>>> pip install python-dotenv

Creating a .env file to keep our secrete keys.

>>> touch .env

Inside your .env file specify the Beyonic API Token .

.env file

BEYONIC_ACCESS_KEY = "enter your API "

You will get your API Token by clicking your user name on the bottom left of the left sidebar menu in the Beyonic web portal and selecting ‘Manage my account’ from the dropdown menu. The API Token is shown at the very bottom of the page.

getExamples.py

import os 
import beyonic
from dotenv import load_dotenv 

load_dotenv()

myapi = os.environ['BEYONIC_ACCESS_KEY']

beyonic.api_key = myapi 

# Listing account: Working. 
accounts = beyonic.Account.list() 
print(accounts)


#Listing currencies: Not working yet.
'''
supported_currencies = beyonic.Currency.list()
print(supported_currencies)

Supported currencies are: USD, UGX, KES, BXC, GHS, TZS, RWF, ZMW, MWK, BIF, EUR, XAF, GNF, XOF, XOF
'''

#Listing networks: Not working yet.
"""
networks = beyonic.Network.list()
print(networks)
"""

#Listing transactions: Working. 
transactions = beyonic.Transaction.list()
print(transactions) 

#Listing contact: Working. 
mycontacts = beyonic.Contact.list() 
print(mycontacts) 


#Listing events: Not working yet.
'''
events = beyonic.Event.list()
print(events)

Error: AttributeError: module 'beyonic' has no attribute 'Event'
'''

Docker file

FROM python:3.8-slim-buster

COPY . .

COPY ./requirements.txt ./requirements.txt

WORKDIR .

RUN pip install -r requirements.txt

CMD [ "python3", "getExamples.py" ]

Build docker image called demo

>>> docker build -t bey .

Run docker image called demo

>>>docker run -t -i bey 

Now, I’ll create a Docker compose file to run a Docker container using the Docker image we just created.


version: "3.6"
services:
  app:
    build: .
    command: python getExamples.py
    volumes:
      - .:/pythonBeyonicExamples

Now we are going to run the following command from the same directory where the docker-compose.yml file is located. The docker compose up command will start and run the entire app.


docker compose up

Output

NB: The screenshot below might differ according to your account deatils and your transcations in deatils.

docker compose up preview

To stop the container running on daemon mode use the below command.

docker compose stop

Output

docker compose preview

Contributing to this repository. All contributions, bug reports, bug fixes, enhancements, and ideas are welcome, You can get in touch with me on twitter @HarunMbaabu.

Download Details:
Author: HarunMbaabu
Source Code: https://github.com/HarunMbaabu/BeyonicAPI-Python-Examples
License: 

#api #python #flask #django #fastapi 

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 

Rahim Makhani

Rahim Makhani

1618806778

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