DEV Community: Trish

Released Loom v1.0.0 Beta - A fast, minimal text editor.

Trish — Mon, 01 Sep 2025 01:16:46 +0000

Loom v1.0.0 Beta

Initial beta release of Loom, a fast and minimal text editor built with C++ and Qt, featuring Lua scripting for configuration and plugins.

Try it: https://github.com/dexter-xd/loom

Installation

Debian/Ubuntu:

wget https://github.com/dexter-xd/loom/releases/download/beta_1.0.0/loom_1.0.0_amd64.deb
sudo dpkg -i loom_1.0.0_amd64.deb
sudo apt-get install -f
loom

System Requirements:

Linux (Debian/Ubuntu or compatible)
x86_64 architecture
Qt5 and Lua dependencies (auto-installed)

Key Features

Lightweight and fast with minimal resource usage
Gruvbox theme for comfortable coding
Multi-tab interface for managing multiple files
Syntax highlighting for C/C++, JavaScript, TypeScript, Python, Lua, JSON, HTML, CSS, Rust
Lua-based configuration system with user overrides
Plugin system with hot reloading support

Built-in Plugins

AutoSave Plugin:

Automatic file saving with configurable intervals
Smart saving (only when content changes)
Status bar integration

AutoFormat Plugin:

Format on save functionality
Supports external formatters:
- C/C++: clang-format
- JavaScript/TypeScript: prettier
- Python: black
- Lua: stylua
- JSON/HTML/CSS: prettier
- Rust: rustfmt
Language auto-detection

Optional Formatters

For full AutoFormat functionality:

sudo apt install clang-format nodejs npm python3-pip
npm install -g prettier
pip3 install black
cargo install stylua  # if Rust is installed

Configuration

Create ~/.config/loom/config.lua for personal settings:

config = {
    editor = {
        font_family = "JetBrains Mono",
        font_size = 12,
        tab_width = 4,
        show_line_numbers = true
    },
    plugins = {
        autosave = { enabled = true, interval = 30 },
        autoformat = { enabled = true, format_on_save = true }
    }
}

Build from Source

sudo apt install cmake qtbase5-dev liblua5.4-dev build-essential
git clone https://github.com/dexter-xd/loom.git
cd loom
./scripts/build_release.sh
./scripts/run_loom.sh

Known Limitations

Linux only (Windows/macOS support planned)
Beta software with potential rough edges
External formatters must be installed separately
No plugin manager UI yet

Package Information

Size: 4.2MB
Architecture: amd64
Dependencies: Qt5, Lua 5.3/5.4
License: MIT

Support

Issues: https://github.com/dexter-xd/loom/issues
Source: https://github.com/dexter-xd/loom

Implementing a Particle System in C++ for Visual Effects

Trish — Tue, 13 Aug 2024 18:21:24 +0000

Welcome to the implementation guide for creating a particle system in C++! This guide will walk you through the steps needed to create stunning visual effects like fire, smoke, and explosions using particle systems.

Introduction
Prerequisites
Setting Up the Project
Creating the Particle Class
Implementing the Particle System
Rendering Particles
Adding Visual Effects
Conclusion
References

Introduction

Particle systems are widely used in computer graphics to simulate fuzzy phenomena, which are difficult to model with conventional rendering techniques. Examples include fire, smoke, rain, and explosions. In this guide, you'll learn how to implement a basic particle system in C++ and use it to create visual effects.

Prerequisites

Before starting, ensure you have the following installed:

A C++ compiler (GCC, Clang, or MSVC)
CMake for project configuration
GLFW for window creation and input
GLEW for OpenGL extension handling

Familiarity with C++ and basic knowledge of OpenGL are recommended.

Setting Up the Project

Create a new project directory:
```
mkdir ParticleSystem
cd ParticleSystem
```

Set up CMakeLists.txt:

cmake_minimum_required(VERSION 3.10)
project(ParticleSystem)

set(CMAKE_CXX_STANDARD 17)

find_package(OpenGL REQUIRED)
find_package(GLEW REQUIRED)
find_package(GLFW REQUIRED)

add_executable(ParticleSystem main.cpp Particle.cpp ParticleSystem.cpp)

target_link_libraries(ParticleSystem OpenGL::GL GLEW::GLEW glfw)

3.Create main.cpp, Particle.cpp, ParticleSystem.cpp, and their corresponding header files:

```sh
touch main.cpp Particle.cpp Particle.h ParticleSystem.cpp ParticleSystem.h
```

4.Creating the Particle Class

Define the Particle class to represent individual particles. Each particle will have properties like position, velocity, lifespan, and color.

// Particle.h
#pragma once

#include <glm/glm.hpp>

class Particle {
public:
    glm::vec3 position;
    glm::vec3 velocity;
    glm::vec4 color;
    float lifespan;

    Particle(const glm::vec3& pos, const glm::vec3& vel, const glm::vec4& col, float life);
    void update(float deltaTime);
};

// Particle.cpp
#include "Particle.h"

Particle::Particle(const glm::vec3& pos, const glm::vec3& vel, const glm::vec4& col, float life)
    : position(pos), velocity(vel), color(col), lifespan(life) {}

void Particle::update(float deltaTime) {
    position += velocity * deltaTime;
    lifespan -= deltaTime;
}

5.Implementing the Particle System
Create the ParticleSystem class to manage multiple particles.

// ParticleSystem.h
#pragma once

#include <vector>
#include "Particle.h"

class ParticleSystem {
public:
    std::vector<Particle> particles;

    void emit(const Particle& particle);
    void update(float deltaTime);
};

// ParticleSystem.cpp
#include "ParticleSystem.h"

void ParticleSystem::emit(const Particle& particle) {
    particles.push_back(particle);
}

void ParticleSystem::update(float deltaTime) {
    for (auto& particle : particles) {
        particle.update(deltaTime);
    }
    particles.erase(
        std::remove_if(particles.begin(), particles.end(), [](const Particle& p) { return p.lifespan <= 0; }),
        particles.end()
    );
}

6.Rendering Particles
Add code to render particles using OpenGL.

// main.cpp
#include <GL/glew.h>
#include <GLFW/glfw3.h>
#include "ParticleSystem.h"

// Initialize OpenGL, create shaders, etc.
// ...

int main() {
    // Initialize GLFW, create window, etc.
    // ...

    ParticleSystem particleSystem;

    // Main loop
    while (!glfwWindowShouldClose(window)) {
        // Update particles
        float deltaTime = ...; // Calculate frame time
        particleSystem.update(deltaTime);

        // Render particles
        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
        // Bind shaders, set up vertex data, etc.
        for (const auto& particle : particleSystem.particles) {
            // Render each particle
        }

        glfwSwapBuffers(window);
        glfwPollEvents();
    }

    // Cleanup
    glfwDestroyWindow(window);
    glfwTerminate();
    return 0;
}

7.Adding Visual Effects
Enhance the particle system by adding different visual effects like color changes, size variation, and different emission patterns.

Conclusion

In this guide, you learned how to create a basic particle system in C++ and render it using OpenGL. You can extend this system to create more complex visual effects and integrate it into your own projects.

AI Trading Model

Trish — Tue, 23 Jul 2024 14:42:28 +0000

Introduction

Artificial Intelligence (AI) has revolutionized trading by providing advanced tools to analyze large datasets and make predictions. This project demonstrates how to build a simple AI model for trading using historical price data.

Getting Started

These instructions will help you set up and run the AI trading model on your local machine.

Prerequisites

Python 3.8 or higher
pip (Python package installer)
Jupyter Notebook (optional, for interactive development)

Installation

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Data Preparation

Obtain Historical Data:
Download historical trading data from a reliable source (e.g., Yahoo Finance, Alpha Vantage).
Data Preprocessing:
Clean and preprocess the data to remove any inconsistencies. Typical preprocessing steps include handling missing values, normalizing data, and feature engineering.

Example preprocessing script:

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Load data
data = pd.read_csv('historical_data.csv')

# Handle missing values
data = data.dropna()

# Normalize data
scaler = MinMaxScaler()
data[['Open', 'High', 'Low', 'Close', 'Volume']] = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close', 'Volume']])

# Save preprocessed data
data.to_csv('preprocessed_data.csv', index=False)

Model Building

Define the Model: Choose a machine learning algorithm suitable for time series prediction. Common choices include LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks.

Example model definition:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1))

model.compile(optimizer='adam', loss='mean_squared_error')

Training the Model

Split the Data: Split the data into training and testing sets.

from sklearn.model_selection import train_test_split

X = data[['Open', 'High', 'Low', 'Close', 'Volume']].values
y = data['Close'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train the Model: Fit the model to the training data.

model.fit(X_train, y_train, epochs=50, batch_size=32)

Evaluating the Model

Evaluate Performance: Use appropriate metrics to evaluate the model's performance on the test data.

from sklearn.metrics import mean_squared_error

predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')

Making Predictions

Make Predictions: Use the trained model to make predictions on new data.

new_data = pd.read_csv('new_data.csv')
new_data_scaled = scaler.transform(new_data)
predictions = model.predict(new_data_scaled)
print(predictions)

Conclusion

This project demonstrates how to build and evaluate an AI model for trading. By following the steps outlined in this README, you can create your own model to analyze and predict trading data.

How to Build a Machine Learning Model in Rust

Trish — Sun, 21 Jul 2024 20:46:04 +0000

How to Build a Machine Learning Model in Rust

Machine learning (ML) is transforming industries by providing new insights and automating tasks. While Python is the most commonly used language for ML, Rust offers unique advantages such as memory safety, speed, and concurrency. In this blog, we'll walk through the process of building a machine learning model in Rust.

Introduction
Setting Up the Environment
Choosing a Crate
Loading Data
Data Preprocessing
Building the Model
Training the Model
Evaluating the Model
Conclusion

Introduction

Rust is known for its performance and reliability, which makes it an interesting choice for building machine learning models. The Rust ecosystem for machine learning is still growing, but several crates (Rust libraries) can help us build effective models.

Setting Up the Environment

To start, ensure that you have Rust installed on your system. You can install Rust using the following command:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

After installing Rust, create a new Rust project:

cargo new rust-ml
cd rust-ml

Choosing a Crate

Several crates in Rust can help with machine learning tasks. For this example, we'll use the ndarray crate for handling arrays and matrices and the linfa crate, which provides a toolkit for classical Machine Learning.

Add these dependencies to your Cargo.toml file:

[dependencies]
ndarray = "0.15"
linfa = "0.6"
linfa-trees = "0.6"

Loading Data

Loading data is a crucial step in any machine learning task. We'll use the ndarray crate to handle our data. For this example, we'll use a simple CSV file.

use ndarray::Array2;
use ndarray_csv::Array2Reader;
use std::fs::File;

fn load_data(file_path: &str) -> Result<Array2<f64>, Box<dyn std::error::Error>> {
    let file = File::open(file_path)?;
    let mut reader = csv::Reader::from_reader(file);
    let array = reader.deserialize_array2_dynamic()?;
    Ok(array)
}

Building the Model

We can now build our model. For this example, we’ll use a Decision Tree from the linfa-trees crate.

use linfa_trees::DecisionTree;

fn build_model() -> DecisionTree {
    DecisionTree::params()
        .min_samples_leaf(1)
        .max_depth(Some(5))
        .fit(&train)
        .unwrap()
}

Training the Model

Next, we’ll train our model using the training data.

let model = build_model();

Evaluating the Model

After training, we need to evaluate our model using the validation data.

let predictions = model.predict(&valid);
let accuracy = valid.targets()
    .iter()
    .zip(predictions.iter())
    .filter(|&(a, b)| a == b)
    .count() as f64 / valid.targets().len() as f64;

println!("Model accuracy: {:.2}%", accuracy * 100.0);

Conclusion

In this blog, we've walked through building a machine learning model in Rust. We've covered setting up the environment, loading and preprocessing data, building and training a model, and evaluating its performance. While the Rust ecosystem for machine learning is still maturing, it offers powerful tools for creating high-performance, safe, and concurrent applications.

Happy coding!

Setup REST-API service of AI by using Local LLMs with Ollama

Trish — Thu, 09 May 2024 14:55:34 +0000

Setting up a REST API service for AI using Local LLMs with Ollama seems like a practical approach. Here’s a simple workflow.

1 Install Ollama and LLMs

Begin by installing Ollama and the Local LLMs on your local machine. Ollama facilitates the local deployment of LLMs, making it easier to manage and utilize them for various tasks.

Install Ollama

Install LLMs for Ollama

ollama pull llama3
ollama run llama3

Ollama Commands

Available Commands:
  /set         Set session variables
  /show        Show model information
  /bye         Exit
  /?, /help    Help for a command

Use """ to begin a multi-line message

Test Ollama

curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt": "Why is the sky blue?",
  "stream": true
}'

If stream is set to false, the response will be a single JSON object

curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

2 Set Up FastAPI:

Set up a Python FastAPI application. FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. It’s a great choice for building robust and efficient APIs.

Develop the FastAPI routes and endpoints to interact with the Ollama server. This involves sending requests to Ollama for processing tasks such as text generation, language understanding, or any other AI-related tasks supported by your LLMs. Following codes is a simple example. (You also can use Ollama Python lib to improve following coding.)

from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
import json
import requests

app = FastAPI(debug=True)

class Itemexample(BaseModel):
    name: str
    prompt: str
    instruction: str
    is_offer: Union[bool, None] = None

class Item(BaseModel):
    model: str
    prompt: str

urls =["http://localhost:11434/api/generate"]

headers = {
    "Content-Type": "application/json"
}

@app.get("/")
def read_root():
    return {"Hello": "World"}

@app.post("/chat/{llms_name}")
def update_item(llms_name: str, item: Item):
    if llms_name == "llama3":
        url = urls[0]
        payload = {
            "model": "llama3",
            "prompt": "Why is the sky blue?",
            "stream": False
        }
        response = requests.post(url, headers=headers, data=json.dumps(payload))
        if response.status_code == 200:
            return {"data": response.text, "llms_name": llms_name}
        else:
            print("error:", response.status_code, response.text)
            return {"item_name": item.model, "error": response.status_code, "data": response.text}
    return {"item_name": item.model, "llms_name": llms_name}

Test REST-API service

curl --location 'http://127.0.0.1:8000/chat/llama3' \
--header 'Content-Type: application/json' \
--data '{
  "model": "llama3",
  "prompt": "Why is the sky blue?"
}'

3. Deploy:

Once you’re satisfied with the functionality and performance of REST API, this service can be deployed to a production environment if needed. This might involve deploying it to a cloud platform, containerizing it with Docker, or deploying it on a server.

In this simple example, by leveraging Ollama for local LLM deployment and integrating it with FastAPI for building the REST API server, you’re creating a free solution for AI services. This model can be fine-tuning by your own training data for customized purpose (we will discuss in future).

Happy reading, happy coding.

References:

Creating a CRUD Application with Golang and MySQL

Trish — Wed, 08 May 2024 18:02:11 +0000

Welcome to our beginner-friendly guide on building a CRUD (Create, Read, Update, Delete) application using Golang and MySQL. In this tutorial, we'll walk through the process step by step, including setting up the environment, creating a professional folder structure, and writing code examples.

Prerequisites

Before we begin, ensure you have the following installed on your system:

Golang: Download and install Golang from the official website.
MySQL: Install MySQL server and client on your machine. You can download MySQL from the official website.

Setting up the Environment

Once you have Golang and MySQL installed, let's set up our project environment.

Create a New Project Folder: Open your terminal and create a new folder for your project. You can name it whatever you like. For example:

   mkdir go-crud-app
   cd go-crud-app

Initialize Go Modules: Go modules help manage dependencies for your project. Run the following command to initialize Go modules in your project folder:

   go mod init github.com/your-username/go-crud-app

Install Required Packages: We'll need a MySQL driver package for Go. Install it using the following command:

   go get -u github.com/go-sql-driver/mysql

Folder Structure

It's important to maintain a clean and professional folder structure for your project. Here's how you can structure your project folder:

go-crud-app/
├── README.md
├── cmd/
│   └── main.go
├── internal/
│   ├── config/
│   │   └── config.go
│   ├── handlers/
│   │   └── handlers.go
│   ├── models/
│   │   └── user.go
│   └── repository/
│       └── user_repository.go
└── go.mod

cmd/: Contains the main entry point of our application.
internal/: Contains the internal packages and modules of our application.
- config/: Manages application configurations.
- handlers/: Handles HTTP requests.
- models/: Defines data models.
- repository/: Handles database operations.

Writing the Code

Now let's write the code for our CRUD application.

Configure MySQL Connection: Open config/config.go and define your MySQL connection details.

package config

const (
    DBUser     = "your_username"
    DBPassword = "your_password"
    DBName     = "your_database_name"
)

Define Data Models: Create models/user.go to define our user model.

package models

type User struct {
    ID       int
    Username string
    Email    string
}

Repository Layer: Implement CRUD operations in repository/user_repository.go.

package repository

import (
    "database/sql"
    "github.com/your-username/go-crud-app/internal/config"
    "github.com/your-username/go-crud-app/internal/models"
)

func CreateUser(user *models.User) error {
    // Implement SQL INSERT operation
}

func GetUserByID(id int) (*models.User, error) {
    // Implement SQL SELECT operation
}

// Implement UpdateUser and DeleteUser functions similarly

Handlers: Create HTTP request handlers in handlers/handlers.go.

package handlers

import (
    "net/http"
    "github.com/your-username/go-crud-app/internal/models"
)

func CreateUserHandler(w http.ResponseWriter, r *http.Request) {
    // Implement logic to create a new user
}

func GetUserHandler(w http.ResponseWriter, r *http.Request) {
    // Implement logic to get a user by ID
}

// Implement UpdateUserHandler and DeleteUserHandler functions similarly

Main Entry Point: In cmd/main.go, set up HTTP routes and server initialization.

package main

import (
    "net/http"
    "github.com/your-username/go-crud-app/internal/handlers"
)

func main() {
    // Define HTTP routes
    http.HandleFunc("/users/create", handlers.CreateUserHandler)
    http.HandleFunc("/users/{id}", handlers.GetUserHandler)
    // Define other routes

    // Start HTTP server
    http.ListenAndServe(":8080", nil)
}

Conclusion

Congratulations! You've successfully created a CRUD application using Golang and MySQL. This tutorial covered setting up the environment, creating a professional folder structure, and writing code examples for each component of the application.

Feel free to expand upon this project by adding more features, implementing authentication, or improving error handling. Happy coding!

How to build: a v0.dev clone (Next.js, GPT4 & CopilotKit)

Trish — Thu, 11 Apr 2024 09:49:55 +0000

In this article, you will learn how to build a clone of Vercel's V0.dev. This is an awesome project to add to your portfolio and to hone in your AI chops.

We will cover using:

Next.js for the app framework 🖥️

OpenAI for the LLM 🧠

App logic of v0 👾

Using CopilotKit to integrate the AI into your app 🪁

Prerequisites

To get started with this tutorial, you need the following:

A text editor (VS Code, Cursor)

Basic knowledge of React, Next.js, Typescript, and Tailwind CSS.

Node.js installed on your PC/Mac

A package manager (npm)

OpenAI API key

CopilotKit installed in your React project

What is v0?

v0 is a Generative user interface (UI) tool developed by Vercel that allows users to give prompts and describe their ideas which are translated into UI code for creating web interfaces. It utilizes generative AI, along with open-source tools such as React, Tailwind CSS, and Shadcn UI, to produce code based on descriptions provided by the user.

Here is an example of a web app UI generated with v0

Understanding the Project Requirements

At the end of this step-by-step tutorial, the clone will have these project requirements:

User Input: Users input text as prompts describing the UI they want to generate. This will be done using the CopilotKit chatbot, made available by the CopilotSidebar.

CopilotKit Integration: CopilotKit will be used to give AI functionality to the web app for generating UIs.

Rendering the UI: A toggle to switch between the UI React/JSX code and rendered UI.

Creating the v0 clone with CopilotKit

Step 1: Create a new Next.JS app Open your workspace folder in your terminal and run the following command create a new Next.js app:

npx create-next-app@latest copilotkit-v0-clone

This will create a new directory named copilotkit-v0-clone with a Next.JS project structure, with the required dependencies installed. It will show this in your terminal, choose Yes for all of them except the last one, as the default import alias is recommended. The other prompts install Typescript and TailwindCSS which we will use in the project.

Navigate to the project directory using the cd command like so:

cd copilotkit-v0-clone

Step 2: Setup CopilotKit Backend Endpoint. Read the docs to learn more.

Run this command to install the CopilotKit backend packages:

npm i @copilotkit/backend

Then visit OpenAI to get your GPT 4 OpenAI API key.

Once you have your API key, create a .env.local file in the root directory. The .env.local file should be like this:

OPENAI_API_KEY=Your OpenAI API key

In the app directory create this directory; api/copilot/openai and create a file named route.ts This file serves as the backend endpoint for CopilotKit requests and OpenAI interactions. It handles incoming requests, processes them using CopilotKit, and returns the appropriate response.

We will create a POST request function in the route.ts file, inside the post request create a new instance of CopilotBackend class, this class provides methods for processing CopilotKit requests. We then call the response method of the CopilotBackend instance, passing the request object (req) and a new instance of the OpenAIAdapter class as arguments. This method processes the request using CopilotKit and the OpenAI API and returns a response.

As shown in the code below, we import the CopilotBackend and OpenAIAdapter classes from the @copilotkit/backend package. These classes are necessary for interacting with CopilotKit and the OpenAI API.

import { CopilotBackend, OpenAIAdapter } from "@copilotkit/backend";

export const runtime = "edge";

export async function POST(req: Request): Promise<Response> {
  const copilotKit = new CopilotBackend();

  return copilotKit.response(req, new OpenAIAdapter());
}

Step 3: Creating Components for the v0 clone We will be using components from Shadcn UI library. To process let’s setup Shadcn UI library by running the shadcn-ui init command to setup your project

npx shadcn-ui@latest init

Then we will configure the components.json with this questions

Which style would you like to use? › Default
Which color would you like to use as base color? › Slate
Do you want to use CSS variables for colors? › no / yes

The components we are using from Shadcn UI are button and dialog. So let’s install them! For the button, run this command

npx shadcn-ui@latest add button

To install the dialog component run the command below

npx shadcn-ui@latest add dialog

Step 4: Setup CopilotKit Frontend. Read the docs to learn more. To install the CopilotKit frontend packages run this command:

npm i @copilotkit/react-core @copilotkit/react-ui

From the CopilotKit documentation, to use CopilotKit, we must set up the frontend wrapper to pass any React app through Copilot. When the prompt is passed to CopilotKit, it sends it through the URL to OpenAI which returns the response.

In the app directory, let’s update the layout.tsx file. This file will define the layout structure of our application and integrate CopilotKit into the frontend.

Enter the code below:

"use client";
import { CopilotKit } from "@copilotkit/react-core";
import "@copilotkit/react-textarea/styles.css"; // also import this if you want to use the CopilotTextarea component
import "@copilotkit/react-ui/styles.css"; 
import { Inter } from "next/font/google";
import "./globals.css";
import { CopilotSidebar,  } from "@copilotkit/react-ui";

const inter = Inter({ subsets: ["latin"] });

export default function RootLayout({
  children,
}: Readonly<{
  children: React.ReactNode;
}>) {
  return (
    <html lang="en">
      <body className={inter.className}>
        <CopilotKit url="/api/copilotkit/openai/">
          <CopilotSidebar defaultOpen>{children}</CopilotSidebar>
        </CopilotKit>
      </body>
    </html>
  );
}

This component represents the root layout of our application. It wraps the entire application with CopilotKit, specifying the URL for CopilotKit's backend endpoint (/api/copilotkit/openai/), based on what we created in the Step 2 for the backend. Additionally, it includes a CopilotSidebar component, acting as a sidebar for CopilotKit, with the children prop passed as its content.

Step 5: Setting up the Main App

Let’s create the structure of the application. It will have a Header, Sidebar and Preview screen.

For the Header, navigate to the components directory like this, src/components then create a header.tsx file and enter the code below:

import { CodeXmlIcon } from "lucide-react";
import { Button } from "./ui/button";

const Header = (props: { openCode: () => void }) => {
    return (
      <div className="w-full h-20 bg-white flex justify-between items-center px-4">
        <h1 className="text-xl font-bold">Copilot Kit</h1>
        <div className="flex gap-x-2">
          <Button
            className="  px-6 py-1 rounded-md space-x-1"
            variant={"default"}
            onClick={props.openCode}
          >
            <span>Code</span> <CodeXmlIcon size={20} />
          </Button>
        </div>
      </div>
    );
  };

  export default Header;

For Sidebar create a sidebar.tsx file and enter this code:

import { ReactNode } from "react";

const Sidebar = ({ children }: { children: ReactNode }) => {
  return (
    <div className="w-[12%] min-h-full bg-white rounded-md p-4">
      <h1 className="text-sm mb-1">History</h1>
      {children}
    </div>
  );
};

export default Sidebar;

Then for the Preview screen, create a preview-screen.tsx file and enter the code:

const PreviewScreen = ({ html_code }: { html_code: string }) => {
    return (
      <div className="w-full h-full bg-white rounded-lg  shadow-lg p-2 border">
        <div dangerouslySetInnerHTML={{ __html: html_code }} />
      </div>
    );
  };
  export default PreviewScreen;

Now let’s bring them together, open the page.tsx file and paste the following code:

"use client";
import { useState } from "react";
import {
  Dialog,
  DialogContent,
  DialogDescription,
  DialogHeader,
  DialogTitle,
} from "@/components/ui/dialog";
import Header from "@/components/header";
import Sidebar from "@/components/sidebar";
import PreviewScreen from "@/components/preview-screen";
import { Input } from "@/components/ui/input";

export default function Home() {
  const [code, setCode] = useState<string[]>([
    `<h1 class="text-red-500">Hello World</h1>`,
  ]);
  const [codeToDisplay, setCodeToDisplay] = useState<string>(code[0] || "");
  const [showDialog, setShowDialog] = useState<boolean>(false);
  const [codeCommand, setCodeCommand] = useState<string>("");

  return (
    <>
      <main className="bg-white min-h-screen px-4">
        <Header openCode={() => setShowDialog(true)} />
        <div className="w-full h-full min-h-[70vh] flex justify-between gap-x-1 ">
          <Sidebar>
            <div className="space-y-2">
              {code.map((c, i) => (
                <div
                  key={i}
                  className="w-full h-20 p-1 rounded-md bg-white border border-blue-600"
                  onClick={() => setCodeToDisplay(c)}
                >
                  v{i}
                </div>
              ))}
            </div>
          </Sidebar>

          <div className="w-10/12">
            <PreviewScreen html_code={readableCode || ""} />
          </div>
        </div>
        <div className="w-8/12 mx-auto p-1 rounded-full bg-primary flex my-4 outline-0">
          <Input
            type="text"
            placeholder="Enter your code command"
            className="w-10/12 p-6 rounded-l-full  outline-0 bg-primary text-white"
            value={codeCommand}
            onChange={(e) => setCodeCommand(e.target.value)}
          />
          <button
            className="w-2/12 bg-white text-primary rounded-r-full"
            onClick={() => generateCode.run(context)}
          >
            Generate
          </button>
        </div>
      </main>
      <Dialog open={showDialog} onOpenChange={setShowDialog}>
        <DialogContent>
          <DialogHeader>
            <DialogTitle>View Code.</DialogTitle>
            <DialogDescription>
              You can use the following code to start integrating into your
              application.
            </DialogDescription>
            <div className="p-4 rounded bg-primary text-white my-2">
              {readableCode}
            </div>
          </DialogHeader>
        </DialogContent>
      </Dialog>
    </>
  );
}

Let’s breakdown the code above:

const [code, setCode] = useState([]); will be used to hold the generated code

const [codeToDisplay, setCodeToDisplay] = useState(code[0] || ""); will be used to hold the code that is displayed on the Preview Screen.

const [showDialog, setShowDialog] = useState(false); this will hold the state of the dialog box that shows the generated code you can copy.

In the code below, we loop over the generated code, which is a string of arrays, to show it on the Sidebar, so that when we select one, it is displayed on the Preview screen.

 <Sidebar>
            <div className="space-y-2">
              {code.map((c, i) => (
                <div
                  key={i}
                  className="w-full h-20 p-1 rounded-md bg-white border border-blue-600"
                  onClick={() => setCodeToDisplay(c)}
                >
                  v{i}
                </div>
              ))}
            </div>
          </Sidebar>

here, we send the code to be displayed on the preview screen. The preview screen component takes the string of code generated by CopilotKit and uses dangerouslySetInnerHTML to render the generated code.

Below we have a Dialog component that will display the code generated by CoplilotKit that can be copied and added to your code.

<Dialog open={showDialog} onOpenChange={setShowDialog}>
        <DialogContent>
          <DialogHeader>
            <DialogTitle>View Code.</DialogTitle>
            <DialogDescription>
              You can use the following code to start integrating into your
              application.
            </DialogDescription>
            <div className="p-4 rounded bg-primary text-white my-2">
              {readableCode}
            </div>
          </DialogHeader>
        </DialogContent>
      </Dialog>

Step 6: Implementing the Main Application Logic For this step, we'll integrate CopilotKit into our v0 clone application to facilitate AI-powered UI generation. We'll use CopilotKit's React hooks to manage state, make components readable and actionable by Copilot, and interact with the OpenAI API.

On your page.tsx file, import this:

import {
  CopilotTask,

  useCopilotContext,
  useMakeCopilotReadable,
} from "@copilotkit/react-core";

Then we define a generateCode task using CopilotTask in the Home component:


 const readableCode = useMakeCopilotReadable(codeToDisplay);

 const generateCode = new CopilotTask({
    instructions: codeCommand,
    actions: [
      {
        name: "generateCode",
        description: "Create Code Snippet with React.js, tailwindcss.",
        parameters: [
          {
            name: "code",
            type: "string",
            description: "Code to be generated",
            required: true,
          },
        ],
        handler: async ({ code }) => {
          setCode((prev) => [...prev, code]);
          setCodeToDisplay(code);
        },
      },
    ],
  });

  const context = useCopilotContext();

We use useMakeCopilotReadable, to pass the existing code and ensure readability. Then we use CopilotTask to generate the UI and bind the generateCode task to the generate button, which enables the generation of code snippets by interacting with the button component. This action is triggered by user interactions and executes an asynchronous handler function when invoked.

The handler adds the code generated to the code array, updates the application state to include the newly generated code snippet and also sends the generated code to be displayed and rendered on the Preview screen, which is available to be copied too. Also, the instructions attribute specifies the command provided to Copilot, which is stored in the codeCommand state variable.

For a full description of how CopilotTask works, check the documentation here: https://docs.copilotkit.ai/reference/CopilotTask

Step 6: Run the v0 Clone Application

At this point, we have completed the v0 clone setup and can then start the development server by running

npm run dev

The web app can be accessed in your browser with this URL

http://localhost:3000

Conclusion

In conclusion, you can build a v0 clone to give UI prompts for your design. It is an awesome project for getting familiar with AI-driven projects and for using cutting edge libraries like CopilotKit in nextJS.

Building a Deep Face Detection Model with Python and TensorFlow (Part 5)

Trish — Thu, 04 Apr 2024 12:06:39 +0000

Welcome back to our tutorial on building a deep face detection model using Python and TensorFlow. In this part, we'll cover steps 12 and 13, including evaluating the model's performance and fine-tuning it for better results.

12. Evaluate Model Performance

12.1 Evaluate on Test Data

test_loss = model.evaluate(test)
print("Test Loss:", test_loss)

12.2 Visualize Model Predictions on Test Data

test_data = test.as_numpy_iterator()
test_sample = test_data.next()
yhat = model.predict(test_sample[0])

fig, ax = plt.subplots(ncols=4, figsize=(20, 20))
for idx in range(4): 
    sample_image = test_sample[0][idx]
    sample_coords = yhat[1][idx]

    if yhat[0][idx] > 0.9:
        cv2.rectangle(sample_image, 
                      tuple(np.multiply(sample_coords[:2], [120, 120]).astype(int)),
                      tuple(np.multiply(sample_coords[2:], [120, 120]).astype(int)), 
                      (255, 0, 0), 2)

    ax[idx].imshow(sample_image)

13. Fine-tune the Model

13.1 Experiment with Different Architectures
You can experiment with different base architectures such as ResNet, Inception, or EfficientNet to improve the model's performance.

13.2 Adjust Hyperparameters
Fine-tune the learning rate, batch size, and number of epochs to find the optimal combination for your dataset.

13.3 Data Augmentation
Explore more advanced data augmentation techniques to further enhance the model's ability to generalize to unseen data.

13.4 Regularization Techniques
Consider adding dropout layers or L2 regularization to prevent overfitting and improve the model's generalization.

13.5 Transfer Learning
Utilize pre-trained models and transfer learning techniques to leverage knowledge learned from large datasets.

Conclusion

In this tutorial series, we've covered the process of building a deep face detection model using Python and TensorFlow. We've walked through data collection, annotation, model building, training, and evaluation. By following these steps and experimenting with different techniques, you can develop robust face detection systems for various applications.

Thank you for following along! If you have any questions or feedback, feel free to reach out.

Stay tuned for more tutorials and happy coding!

Building a Deep Face Detection Model with Python and TensorFlow (Part 4)

Trish — Thu, 04 Apr 2024 05:18:27 +0000

Welcome back to our tutorial on building a deep face detection model using Python and TensorFlow. In this part, we'll cover steps 9 through 11, including defining losses, optimizers, training the model, and making predictions.

Check out Part 1

Check out Part 2

Check out Part 3

9. Define Losses and Optimizers

9.1 Define Optimizer and Learning Rate

batches_per_epoch = len(train)
lr_decay = (1. / 0.75 - 1) / batches_per_epoch
opt = tf.keras.optimizers.Adam(learning_rate=0.0001, decay=lr_decay)

9.2 Create Localization Loss and Classification Loss

def localization_loss(y_true, yhat):            
    delta_coord = tf.reduce_sum(tf.square(y_true[:, :2] - yhat[:, :2]))

    h_true = y_true[:, 3] - y_true[:, 1] 
    w_true = y_true[:, 2] - y_true[:, 0] 

    h_pred = yhat[:, 3] - yhat[:, 1] 
    w_pred = yhat[:, 2] - yhat[:, 0] 

    delta_size = tf.reduce_sum(tf.square(w_true - w_pred) + tf.square(h_true - h_pred))

    return delta_coord + delta_size

classloss = tf.keras.losses.BinaryCrossentropy()
regressloss = localization_loss

9.3 Test out Loss Metrics

localization_loss(y[1], coords)
classloss(y[0], classes)
regressloss(y[1], coords)

10. Train Neural Network

10.1 Create Custom Model Class

class FaceTracker(Model): 
    def __init__(self, eyetracker,  **kwargs): 
        super().__init__(**kwargs)
        self.model = eyetracker

    def compile(self, opt, classloss, localizationloss, **kwargs):
        super().compile(**kwargs)
        self.closs = classloss
        self.lloss = localizationloss
        self.opt = opt

    def train_step(self, batch, **kwargs): 

        X, y = batch

        with tf.GradientTape() as tape: 
            classes, coords = self.model(X, training=True)

            batch_classloss = self.closs(y[0], classes)
            batch_localizationloss = self.lloss(tf.cast(y[1], tf.float32), coords)

            total_loss = batch_localizationloss + 0.5 * batch_classloss

            grad = tape.gradient(total_loss, self.model.trainable_variables)

        opt.apply_gradients(zip(grad, self.model.trainable_variables))

        return {"total_loss": total_loss, "class_loss": batch_classloss, "regress_loss": batch_localizationloss}

    def test_step(self, batch, **kwargs): 
        X, y = batch

        classes, coords = self.model(X, training=False)

        batch_classloss = self.closs(y[0], classes)
        batch_localizationloss = self.lloss(tf.cast(y[1], tf.float32), coords)
        total_loss = batch_localizationloss + 0.5 * batch_classloss

        return {"total_loss": total_loss, "class_loss": batch_classloss, "regress_loss": batch_localizationloss}

    def call(self, X, **kwargs): 
        return self.model(X, **kwargs)

model = FaceTracker(facetracker)
model.compile(opt, classloss, regressloss)

10.2 Train

logdir = 'logs'
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
hist = model.fit(train, epochs=10, validation_data=val, callbacks=[tensorboard_callback])

10.3 Plot Performance

hist.history

fig, ax = plt.subplots(ncols=3, figsize=(20, 5))

ax[0].plot(hist.history['total_loss'], color='teal', label='loss')
ax[0].plot(hist.history['val_total_loss'], color='orange', label='val loss')
ax[0].title.set_text('Loss')
ax[0].legend()

ax[1].plot(hist.history['class_loss'], color='teal', label='class loss')
ax[1].plot(hist.history['val_class_loss'], color='orange', label='val class loss')
ax[1].title.set_text('Classification Loss')
ax[1].legend()

ax[2].plot(hist.history['regress_loss'], color='teal', label='regress loss')
ax[2].plot(hist.history['val_regress_loss'], color='orange', label='val regress loss')
ax[2].title.set_text('Regression Loss')
ax[2].legend()

plt.show()

11. Make Predictions

11.1 Make Predictions on Test Set

test_data = test.as_numpy_iterator()
test_sample = test_data.next()
yhat = facetracker.predict(test_sample[0])

fig, ax = plt.subplots(ncols=4, figsize=(20, 20))
for idx in range(4): 
    sample_image = test_sample[0][idx]
    sample_coords = yhat[1][idx]

    if yhat[0][idx] > 0.9:
        cv2.rectangle(sample_image, 
                      tuple(np.multiply(sample_coords[:2], [120, 120]).astype(int)),
                      tuple(np.multiply(sample_coords[2:], [120, 120]).astype(int)), 
                      (255, 0, 0), 2)

    ax[idx].imshow(sample_image)

11.2 Save the Model

from tensorflow.keras.models import load_model

facetracker.save('facetracker.h5')
facetracker = load_model('facetracker.h5')

11.3 Real-Time Detection

cap = cv2.VideoCapture(1)
while cap.isOpened():
    _ , frame = cap.read()
    frame = frame[50:500, 50:500, :]

    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    resized = tf.image.resize(rgb, (120, 120))

    yhat = facetracker.predict(np.expand_dims(resized/255, 0))
    sample_coords = yhat[1][0]

    if yhat[0] > 0.5: 
        # Controls the main rectangle
        cv2.rectangle(frame, 
                      tuple(np.multiply(sample_coords[:2], [450, 450]).astype(int)),
                      tuple(np.multiply(sample_coords[2:], [450, 450]).astype(int)), 
                      (255, 0, 0), 2)
        # Controls the label rectangle
        cv2.rectangle(frame, 
                      tuple(np.add(np.multiply(sample_coords[:2], [450, 450]).astype(int), [0, -30])),
                      tuple(np.add(np.multiply(sample_coords[:2], [450, 450]).astype(int), [80, 0])), 
                      (255, 0, 0), -1)

        # Controls the text rendered
        cv2.putText(frame, 'face', 
                    tuple(np.add(np.multiply(sample_coords[:2], [450, 450]).astype(int), [0, -5])),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

    cv2.imshow('Face Detection', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

This wraps up part 4 of our tutorial. In the next part, we'll discuss how to evaluate the model's performance and fine-tune it for better results.

Stay tuned for the upcoming installment!

Building a Deep Face Detection Model with Python and TensorFlow (Part 3)

Trish — Tue, 02 Apr 2024 17:59:38 +0000

Check out Part 1

Check out Part 2

Check out Part 4

Welcome back to the continuation of our tutorial on building a deep face detection model using Python and TensorFlow. In this part, we'll pick up where we left off and cover steps 5 through 11, including data augmentation, model building, training, and making predictions.

5. Build and Run Augmentation Pipeline

5.1 Run Augmentation Pipeline

for partition in ['train', 'test', 'val']: 
    for image in os.listdir(os.path.join('data', partition, 'images')):
        img = cv2.imread(os.path.join('data', partition, 'images', image))

        coords = [0, 0, 0.00001, 0.00001]
        label_path = os.path.join('data', partition, 'labels', f'{image.split(".")[0]}.json')
        if os.path.exists(label_path):
            with open(label_path, 'r') as f:
                label = json.load(f)

            coords[0] = label['shapes'][0]['points'][0][0]
            coords[1] = label['shapes'][0]['points'][0][1]
            coords[2] = label['shapes'][0]['points'][1][0]
            coords[3] = label['shapes'][0]['points'][1][1]
            coords = list(np.divide(coords, [640, 480, 640, 480]))

        try: 
            for x in range(60):
                augmented = augmentor(image=img, bboxes=[coords], class_labels=['face'])
                cv2.imwrite(os.path.join('aug_data', partition, 'images', f'{image.split(".")[0]}.{x}.jpg'), augmented['image'])

                annotation = {}
                annotation['image'] = image

                if os.path.exists(label_path):
                    if len(augmented['bboxes']) == 0: 
                        annotation['bbox'] = [0, 0, 0, 0]
                        annotation['class'] = 0 
                    else: 
                        annotation['bbox'] = augmented['bboxes'][0]
                        annotation['class'] = 1
                else: 
                    annotation['bbox'] = [0, 0, 0, 0]
                    annotation['class'] = 0 

                with open(os.path.join('aug_data', partition, 'labels', f'{image.split(".")[0]}.{x}.json'), 'w') as f:
                    json.dump(annotation, f)

        except Exception as e:
            print(e)

5.2 Load Augmented Images to TensorFlow Dataset

train_images = tf.data.Dataset.list_files('aug_data\\train\\images\\*.jpg', shuffle=False)
train_images = train_images.map(load_image)
train_images = train_images.map(lambda x: tf.image.resize(x, (120, 120)))
train_images = train_images.map(lambda x: x/255)

test_images = tf.data.Dataset.list_files('aug_data\\test\\images\\*.jpg', shuffle=False)
test_images = test_images.map(load_image)
test_images = test_images.map(lambda x: tf.image.resize(x, (120, 120)))
test_images = test_images.map(lambda x: x/255)

val_images = tf.data.Dataset.list_files('aug_data\\val\\images\\*.jpg', shuffle=False)
val_images = val_images.map(load_image)
val_images = val_images.map(lambda x: tf.image.resize(x, (120, 120)))
val_images = val_images.map(lambda x: x/255)

train_images.as_numpy_iterator().next()

6. Prepare Labels

6.1 Build Label Loading Function

def load_labels(label_path):
    with open(label_path.numpy(), 'r', encoding="utf-8") as f:
        label = json.load(f)

    return [label['class']], label['bbox']

6.2 Load Labels to TensorFlow Dataset

train_labels = tf.data.Dataset.list_files('aug_data\\train\\labels\\*.json', shuffle=False)
train_labels = train_labels.map(lambda x: tf.py_function(load_labels, [x], [tf.uint8, tf.float16]))

test_labels = tf.data.Dataset.list_files('aug_data\\test\\labels\\*.json', shuffle=False)
test_labels = test_labels.map(lambda x: tf.py_function(load_labels, [x], [tf.uint8, tf.float16]))

val_labels = tf.data.Dataset.list_files('aug_data\\val\\labels\\*.json', shuffle=False)
val_labels = val_labels.map(lambda x: tf.py_function(load_labels, [x], [tf.uint8, tf.float16]))

train_labels.as_numpy_iterator().next()

7. Combine Label and Image Samples

7.1 Check Partition Lengths

len(train_images), len(train_labels), len(test_images), len(test_labels), len(val_images), len(val_labels)

7.2 Create Final Datasets (Images/Labels)

train = tf.data.Dataset.zip((train_images, train_labels))
train = train.shuffle(5000)
train = train.batch(8)
train = train.prefetch(4)

test = tf.data.Dataset.zip((test_images, test_labels))
test = test.shuffle(1300)
test = test.batch(8)
test = test.prefetch(4)

val = tf.data.Dataset.zip((val_images, val_labels))
val = val.shuffle(1000)
val = val.batch(8)
val = val.prefetch(4)

train.as_numpy_iterator().next()[1]

7.3 View Images and Annotations

data_samples = train.as_numpy_iterator()
res = data_samples.next()

fig, ax = plt.subplots(ncols=4, figsize=(20,20))
for idx in range(4): 
    sample_image = res[0][idx]
    sample_coords = res[1][1][idx]

    cv2.rectangle(sample_image, 
                  tuple(np.multiply(sample_coords[:2], [120, 120]).astype(int)),
                  tuple(np.multiply(sample_coords[2:], [120, 120]).astype(int)), 
                  (255, 0, 0), 2)

    ax[idx].imshow(sample_image)

8. Build Deep Learning Model using the Functional API

8.1 Import Layers and Base Network

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, Dense, GlobalMaxPooling2D
from tensorflow.keras.applications import VGG16

8.2 Download VGG16

vgg = VGG16(include_top=False)
vgg.summary()

8.3 Build instance of Network

def build_model(): 
    input_layer = Input(shape=(120, 120, 3))

    vgg = VGG16(include_top=False)(input_layer)

    # Classification Model  
    f1 = GlobalMaxPooling2D()(vgg)
    class1 = Dense(2048, activation='relu')(f1)
    class2 = Dense(1, activation='sigmoid')(class1)

    # Bounding box model
    f2 = GlobalMaxPooling2D()(vgg)
    regress1 = Dense(2048, activation='relu')(f2)
    regress2 = Dense(4, activation='sigmoid')(regress1)

    facetracker = Model(inputs=input_layer, outputs=[class2, regress2])
    return facetracker

8.4 Test out Neural Network

facetracker = build_model()
facetracker.summary()

X, y = train.as_numpy_iterator().next()
X.shape

classes, coords = facetracker.predict(X)
classes, coords

In the next part, we'll define losses, optimizers, and train our deep learning model.

Stay tuned for the next installment!

Building a Deep Face Detection Model with Python and TensorFlow (Part 2)

Trish — Tue, 02 Apr 2024 06:16:52 +0000

Part 1

4. Apply Image Augmentation on Images and Labels using Albumentations

4.1 Setup Albumentations Transform Pipeline

import albumentations as alb

augmentor = alb.Compose([
    alb.RandomCrop(width=450, height=450), 
    alb.HorizontalFlip(p=0.5), 
    alb.RandomBrightnessContrast(p=0.2),
    alb.RandomGamma(p=0.2), 
    alb.RGBShift(p=0.2), 
    alb.VerticalFlip(p=0.5)], 
    bbox_params=alb.BboxParams(format='albumentations', label_fields=['class_labels']))

4.2 Load a Test Image and Annotation with OpenCV and JSON

img = cv2.imread(os.path.join('data', 'train', 'images', 'ffd85fc5-cc1a-11ec-bfb8-a0cec8d2d278.jpg'))

with open(os.path.join('data', 'train', 'labels', 'ffd85fc5-cc1a-11ec-bfb8-a0cec8d2d278.json'), 'r') as f:
    label = json.load(f)

label['shapes'][0]['points']

4.3 Extract Coordinates and Rescale to Match Image Resolution

coords = [0, 0, 0, 0]
coords[0] = label['shapes'][0]['points'][0][0]
coords[1] = label['shapes'][0]['points'][0][1]
coords[2] = label['shapes'][0]['points'][1][0]
coords[3] = label['shapes'][0]['points'][1][1]

coords = list(np.divide(coords, [640, 480, 640, 480]))
coords

4.4 Apply Augmentations and View Results

augmented = augmentor(image=img, bboxes=[coords], class_labels=['face'])
augmented['bboxes'][0][2:]

augmented['bboxes']

cv2.rectangle(augmented['image'], 
              tuple(np.multiply(augmented['bboxes'][0][:2], [450, 450]).astype(int)),
              tuple(np.multiply(augmented['bboxes'][0][2:], [450, 450]).astype(int)), 
              (255, 0, 0), 2)

plt.imshow(augmented['image'])

Stay tuned for the next sections where we'll continue with the augmentation pipeline and training the deep learning model!

Building a Game Engine Using Java with LWJGL

Trish — Mon, 01 Apr 2024 16:35:59 +0000

In this step-by-step guide, we'll walk through the process of creating a game engine using Java with LWJGL (Lightweight Java Game Library). LWJGL provides bindings to OpenGL, OpenAL, and other libraries, making it a powerful choice for developing games in Java.

Prerequisites

Before we begin, make sure you have the following installed:

Java Development Kit (JDK)
IntelliJ IDEA or any preferred Java IDE
LWJGL library

Step 1: Set Up Your Project

Create a new Java project in IntelliJ IDEA.
Download the latest LWJGL binaries from the official website.
Extract the LWJGL binaries into a folder in your project directory.

Step 2: Configure LWJGL Dependencies

In IntelliJ IDEA, right-click on your project and select "Open Module Settings."
Click on "Modules" and then select the "Dependencies" tab.
Click the "+" icon and add the LWJGL JAR files from the extracted folder.

Step 3: Create Your Main Class

Create a new Java class for your main game engine.
Import the necessary LWJGL classes:

import org.lwjgl.glfw.GLFW;
import org.lwjgl.opengl.GL;
import static org.lwjgl.glfw.GLFW.*;
import static org.lwjgl.opengl.GL11.*;

Set up your main method and GLFW window:

public class Main {
    public static void main(String[] args) {
        // Initialize GLFW
        if (!glfwInit()) {
            throw new IllegalStateException("Unable to initialize GLFW");
        }

        // Create a windowed mode window and its OpenGL context
        long window = glfwCreateWindow(800, 600, "My Game", 0, 0);
        if (window == 0) {
            glfwTerminate();
            throw new RuntimeException("Failed to create the GLFW window");
        }

        // Make the OpenGL context current
        glfwMakeContextCurrent(window);
        GL.createCapabilities();

        // Set the clear color
        glClearColor(0.0f, 0.0f, 0.0f, 0.0f);

        // Main loop
        while (!glfwWindowShouldClose(window)) {
            // Poll for window events
            glfwPollEvents();

            // Render here
            glClear(GL_COLOR_BUFFER_BIT);

            // Swap the buffers
            glfwSwapBuffers(window);
        }

        // Terminate GLFW
        glfwTerminate();
    }
}

Step 4: Run Your Game Engine

Run your main class.
You should see a blank window titled "My Game" appear.
Congratulations! You've created the foundation of your game engine using Java with LWJGL.

Conclusion

In this guide, we've covered the basics of creating a game engine using Java with LWJGL. From setting up your project to configuring LWJGL dependencies and creating a main class, you're now ready to start building your own game engine and bring your game ideas to life. Happy coding!

DEV Community: Trish

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Building a Game Engine Using Java with LWJGL

Prerequisites

Step 1: Set Up Your Project

Step 2: Configure LWJGL Dependencies

Step 3: Create Your Main Class

Step 4: Run Your Game Engine

Conclusion