Edward Lance Lorilla

【VISUAL C#】Enumerate Windows
【FLUTTER ANDROID STUDIO and IOS】Perspective 3D
【PYTHON OPENCV】K-means clustering algorithm applied to three different 'clusters' of points (k=2)
【FLUTTER ANDROID STUDIO and IOS】Search with BloC

Posted: 23 Mar 2021 10:17 PM PDT

	using System;
	using System.Collections.Generic;
	using System.ComponentModel;
	using System.Data;
	using System.Drawing;
	using System.Linq;
	using System.Text;
	using System.Threading.Tasks;
	using System.Windows.Forms;
	using System.Diagnostics; // make sure that using System.Diagnostics; is included
	using System.Runtime.InteropServices; // namespace for DLL import
	namespace enumeratewindow
	{
	public partial class Form1 : Form
	{
	protected delegate bool EnumWindowsProc(IntPtr hWnd, IntPtr lParam);
	[DllImport("user32.dll", CharSet = CharSet.Unicode)]
	protected static extern int GetWindowText(IntPtr hWnd, StringBuilder strText, int maxCount);
	[DllImport("user32.dll", CharSet = CharSet.Unicode)]
	protected static extern int GetWindowTextLength(IntPtr hWnd);
	[DllImport("user32.dll")]
	protected static extern bool EnumWindows(EnumWindowsProc enumProc, IntPtr lParam);
	[DllImport("user32.dll")]
	protected static extern bool IsWindowVisible(IntPtr hWnd);

	public Form1()
	{
	InitializeComponent();
	}
	protected bool EnumWindows(IntPtr hWnd, IntPtr lParam)
	{
	int size = GetWindowTextLength(hWnd);
	if (size++ > 0 && IsWindowVisible(hWnd))
	{
	StringBuilder sb = new StringBuilder(size);
	GetWindowText(hWnd, sb, size);
	this.listBox1.Items.Add(sb.ToString());
	}
	return true;
	}

	private void button1_Click(object sender, EventArgs e)
	{
	EnumWindows(new EnumWindowsProc(EnumWindows), IntPtr.Zero);
	}
	}
	}

view raw Form1.cs hosted with ❤ by GitHub

【FLUTTER ANDROID STUDIO and IOS】Perspective 3D

Posted: 23 Mar 2021 07:17 PM PDT

	import 'package:flutter/material.dart';

	class HomePage extends StatefulWidget {
	HomePage({Key key}) : super(key: key); // changed

	@override
	_HomePageState createState() => _HomePageState();
	}

	class _HomePageState extends State<HomePage> {

	Offset _offset = Offset(0.2, 0.6);


	@override
	Widget build(BuildContext context) {
	return Transform(
	transform: Matrix4.identity()
	..setEntry(3, 2, 0.001)
	..rotateX(0.01 * _offset.dy)
	..rotateY(-0.01 * _offset.dx),
	alignment: FractionalOffset.center,
	child: GestureDetector(
	onPanUpdate: (details) => setState(() => _offset += details.delta),
	onDoubleTap: () => setState(() => _offset = Offset(0.2, 0.6)),
	child: _app(context),
	)
	);
	}

	_app(BuildContext context) {
	return Scaffold(
	backgroundColor: Colors.blue[200],
	appBar: AppBar(
	title: Text('Perspective 3D Demo'),
	),
	body: Center(
	child: Column(
	children: [
	Image.asset("assets/logo.png", scale: 1.5,),
	],
	),
	),

	);
	}

	}

view raw home_page.dart hosted with ❤ by GitHub


	import 'splash.dart';
	import 'package:flutter/cupertino.dart';
	import 'package:flutter/material.dart';
	void main() => runApp(MyApp());

	class MyApp extends StatelessWidget {
	@override
	Widget build(BuildContext context) {
	return MaterialApp(
	debugShowCheckedModeBanner: false,
	title: 'Perspective 3D Demo',
	theme: ThemeData.dark(),
	home: Splash(),
	);
	}
	}

view raw main.dart hosted with ❤ by GitHub

	import 'dart:async';
	import 'home_page.dart';
	import 'package:flutter/cupertino.dart';
	import 'package:flutter/material.dart';

	class Splash extends StatefulWidget {
	@override
	VideoState createState() => VideoState();
	}


	class VideoState extends State<Splash> with SingleTickerProviderStateMixin {


	var _visible = true;

	AnimationController animationController;
	Animation<double> animation;

	startTime() async {
	var _duration = new Duration(seconds: 3);
	return new Timer(_duration, navigationPage);
	}

	void navigationPage() {
	Navigator.of(context).push(
	MaterialPageRoute(builder: (context) =>
	HomePage(
	)));
	}

	@override
	void initState() {
	super.initState();
	animationController = new AnimationController(
	vsync: this, duration: new Duration(seconds: 2));
	animation =
	new CurvedAnimation(parent: animationController, curve: Curves.easeOut);

	animation.addListener(() => this.setState(() {}));
	animationController.forward();

	setState(() {
	_visible = !_visible;
	});
	startTime();
	}

	@override
	Widget build(BuildContext context) {
	return Scaffold(
	backgroundColor: Colors.white,
	body: Stack(
	fit: StackFit.expand,
	children: <Widget>[
	new Column(
	mainAxisAlignment: MainAxisAlignment.end,
	mainAxisSize: MainAxisSize.min,
	children: <Widget>[

	Padding(padding: EdgeInsets.only(bottom: 30.0),
	child: new Image.asset('assets/logo.png', height: 25.0,
	fit: BoxFit.scaleDown,))

	],),
	new Column(
	mainAxisAlignment: MainAxisAlignment.center,
	children: <Widget>[
	new Image.asset(
	'assets/logo.png',
	width: animation.value * 250,
	height: animation.value * 250,
	),
	],
	),
	],
	),
	);
	}
	}

view raw splash.dart hosted with ❤ by GitHub

【PYTHON OPENCV】K-means clustering algorithm applied to three different 'clusters' of points (k=2)

Posted: 23 Mar 2021 06:52 PM PDT

	"""
	K-means clustering algorithm applied to three different 'clusters' of points (k=2)
	"""

	# Import required packages:
	import numpy as np
	import cv2
	from matplotlib import pyplot as plt

	# Create data (three different 'clusters' of points (it should be of np.float32 data type):
	data = np.float32(np.vstack(
	(np.random.randint(0, 40, (50, 2)), np.random.randint(30, 70, (50, 2)), np.random.randint(60, 100, (50, 2)))))

	# Define the algorithm termination criteria (the maximum number of iterations and/or the desired accuracy):
	# In this case the maximum number of iterations is set to 20 and epsilon = 1.0
	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0)

	# Apply kmeans algorithm# K = 3, which is the number of clusters to split the set bytearray# attempts = 10, which specifies the number of times the algorithm is executed using different initial labellings (the# algorithm returns the labels that yield the best compactness)
	# Flag cv2.KMEANS_RANDOM_CENTERS selects random initial centers in each attempt. You can also use cv2.KMEANS_PP_CENTERS
	ret, label, center = cv2.kmeans(data, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

	# Now separate the data using label output (stores the cluster indices for every sample)
	# Therefore, we split the data to different clusters depending on their labels:
	A = data[label.ravel() == 0]
	B = data[label.ravel() == 1]

	# Create the dimensions of the figure and set title:
	fig = plt.figure(figsize=(12, 6))
	plt.suptitle("K-means clustering algorithm", fontsize=14, fontweight='bold')
	fig.patch.set_facecolor('silver')

	# Plot the 'original' data:
	ax = plt.subplot(1, 2, 1)
	plt.scatter(data[:, 0], data[:, 1], c='c')
	plt.title("data")

	# Plot the 'clustered' data and the centroids
	ax = plt.subplot(1, 2, 2)
	plt.scatter(A[:, 0], A[:, 1], c='b')
	plt.scatter(B[:, 0], B[:, 1], c='g')
	plt.scatter(center[:, 0], center[:, 1], s=100, c='m', marker='s')
	plt.title("clustered data and centroids (K = 2)")

	# Show the Figure:
	plt.show()

view raw k_means_clustering_k_2.py hosted with ❤ by GitHub

【FLUTTER ANDROID STUDIO and IOS】Search with BloC

Posted: 23 Mar 2021 05:05 AM PDT

	abstract class Bloc {
	void dispose();
	}

view raw bloc.dart hosted with ❤ by GitHub

	import 'package:flutter/material.dart';
	import 'screens/searchScreen.dart';

	void main() {
	runApp(MyApp());
	}

	class MyApp extends StatelessWidget {
	@override
	Widget build(BuildContext context) {
	return MaterialApp(
	title: 'Flutter Demo',
	theme: ThemeData(
	primarySwatch: Colors.amber,
	visualDensity: VisualDensity.adaptivePlatformDensity,
	),
	home: Search(),
	);
	}
	}

view raw main.dart hosted with ❤ by GitHub

	import 'dart:convert';

	import 'package:http/http.dart' as http;
	import 'package:flutter/material.dart';
	import '../bloc/userBloc.dart';
	import '../models/userModel.dart';

	class Search extends StatefulWidget {
	@override
	_SearchState createState() => _SearchState();
	}

	class _SearchState extends State<Search> {
	@override
	void initState() {
	fetchUsers();
	super.initState();
	}

	List<UserModel> totalUsers = [];

	void search(String searchQuery) {
	List<UserModel> searchResult = [];

	userBloc.userController.sink.add(null);

	if (searchQuery.isEmpty) {
	userBloc.userController.sink.add(totalUsers);
	return;
	}
	totalUsers.forEach((user) {
	if (user.first.toLowerCase().contains(searchQuery.toLowerCase()) \|\|
	user.last.toLowerCase().contains(searchQuery.toLowerCase())) {
	searchResult.add(user);
	}
	});
	userBloc.userController.sink.add(searchResult);
	}

	Future<void> fetchUsers() async {
	final url = 'https://randomuser.me/api/?results=100';
	http.Response response = await http.get(Uri.parse(url));
	if (response.statusCode == 200) {
	var body = jsonDecode(response.body);
	final Iterable list = body["results"];
	// map each json object to model and addto list and return the list of Icons.model_training_sharptotalUsers = list.map((model)'
	totalUsers = list.map((model) => UserModel.fromJson(model)).toList();
	userBloc.userController.sink.add(totalUsers);
	}
	}

	@override
	Widget build(BuildContext context) {
	return GestureDetector(
	onTap: () => FocusScope.of(context).unfocus(),
	child: Scaffold(
	backgroundColor: Colors.white,
	body: Column(
	children: <Widget>[
	SizedBox(height: 20),
	Container(
	padding: const EdgeInsets.all(16.0),
	child: TextField(
	onChanged: (text) => search(text),
	decoration: InputDecoration(
	suffixIcon: Icon(Icons.search),
	hintText: 'Search',
	contentPadding:
	EdgeInsets.symmetric(horizontal: 30, vertical: 20),
	border: OutlineInputBorder(
	borderSide: BorderSide(width: 3.1, color: Colors.red),
	borderRadius: BorderRadius.circular(30),
	),
	),
	),
	),
	Container(
	height: 50,
	child: Container(
	decoration: BoxDecoration(
	border: Border(
	bottom: BorderSide(color: Colors.green),
	),
	),
	alignment: Alignment.center,
	margin: EdgeInsets.symmetric(horizontal: 10),
	padding: EdgeInsets.symmetric(horizontal: 10),
	child: Text(
	'Users',
	style: TextStyle(fontSize: 16),
	),
	),
	),
	Expanded(child: usersWidget())
	],
	),
	),
	);
	}

	Widget usersWidget() {
	return StreamBuilder(
	stream: userBloc.userController.stream,
	builder:
	(BuildContext buildContext, AsyncSnapshot<List<UserModel>> snapshot) {
	if (snapshot == null) {
	return CircularProgressIndicator();
	}
	return snapshot.connectionState == ConnectionState.waiting
	? Center(
	child: CircularProgressIndicator(),
	)
	: ListView.builder(
	itemCount: snapshot.data.length,
	itemBuilder: (BuildContext context, int index) {
	return Card(
	child: ListTile(
	leading: Image.network('${snapshot.data[index].picture}'),
	title: Text(
	'${snapshot.data[index].first} ${snapshot.data[index]
	.last}',
	style: TextStyle(
	fontWeight: FontWeight.bold, fontSize: 18),
	textAlign: TextAlign.center,
	),
	),
	);
	},
	);
	},
	);
	}
	}

view raw searchScreen.dart hosted with ❤ by GitHub

	import 'dart:async';

	import 'bloc.dart';
	import '../models/userModel.dart';

	class UserBloc extends Bloc {
	final userController = StreamController<List<UserModel>>.broadcast();

	@override
	void dispose() {
	userController.close();
	}
	}

	UserBloc userBloc = UserBloc();

view raw userBloc.dart hosted with ❤ by GitHub

	class UserModel {
	final String gender;
	final String title;
	final String first;
	final String last;
	final String city;
	final String state;
	final String country;
	final String postcode;
	final String picture;
	final String phone;

	UserModel.fromJson(Map<String, dynamic> json)
	: gender = json['gender'],
	title = json['name']['title'],
	first = json['name']['first'],
	last = json['name']['last'],
	city = json['city'],
	state = json['state'],
	country = json['country'],
	postcode = json['postcode'],
	picture = json['picture']['large'],
	phone = json['phone'];
	}

view raw userModel.dart hosted with ❤ by GitHub

e-techbytes

Wednesday, March 24, 2021

Edward Lance Lorilla

Edward Lance Lorilla

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