Pinned Repositories
3D_Game_Delphi
Android-MobileFaceNet-MTCNN-FaceAntiSpoofing
Use tensorflow Lite on Android platform, integrated face detection (MTCNN), face anti spoofing (CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing) and face comparison (MobileFaceNet use InsightFace loss)
Android-MraaDemo
Authenticate-via-TensorFlow-Facial-Recognition-in-Flutter
The power of machine learning allows us to change long-standing computing paradigms. One of these is the age-old password-based authentication system common to most apps. With fast real-time facial recognition, we can easily dispense with text-based verification and allow users to log in just by showing their faces to a webcam. In this session, we’ll show how to do this in Flutter, Google’s popular open-source UI toolkit for developing apps for web, Android, iOS, Fuchsia, and many other platforms with a single codebase. We’ll first build a simple authentication-based Android app, and then deploy the Firebase ML Vision model for face ID & image processing; as well as the MobileFaceNet CNN model through TensorFlow Lite for structured verification. Once all these parts are in place, our solution will work seamlessly and can easily be ported to other apps. Pre-requisites: ✅ Android Studio (https://developer.android.com/studio) — you can also use other IDEs/platforms if you’d rather not use Android - Flutter documentation below guides on the same. ✅ Flutter SDK (https://flutter.dev/docs/get-started/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #TensorFlow #Flutter #MachineLearning
Component_Delphi_GA
The present work proposes a non-visual component developed in Object Pascal programming languages, for Genetic Algorithms called "TGAlgorithm". Developed in a fully object-oriented framework, the Component exploits this feature to facilitate its extension to genetic algorithms and specific problems, be they single-purpose. In this work an example of the use of "TGAlgorithm" for an implementation of a genetic algorithm mono-objective is presented.
Computational_Vision_ColorChecker
The ColorChecker Color Rendition Chart (often referred to by its original name, the Macbeth ColorChecker
Deep_Learning_Sound-Recognition
Este projeto propõe a implementação de um modelo baseado em uma rede neural convolucional (CNN), capaz de aprender padrões específicos a partir da análise espectral de uma sequência de áudio e, assim, classificar cada evento sonoro a partir de sua classe de referência. Para este projeto, dois tipos de abordagens foram considerados com base em diferentes representações espectro-temporais das seqüências sonoras utilizadas (MFC-Spectrogram e MFCCs). Para avaliar o desempenho do modelo, foi utilizado o conjunto de dados disponível ao público (UrbanSound8k),
Fuzzy_Logic_Project
Este trabalho apresenta um controlador fuzzy capaz de realizar a inflação automática de uma faixa elástica que permita alcançar os níveis de pressão de referência com o menor erro de convergência possível, além de garantir que os tempos de convergência estejam dentro da faixa permitida pelos padrões internacionais para dispositivos médicos comerciais que empregam método oscilométrico para medição não invasiva da pressão arterial, Além de a implentação do controlador fuzzy no microcontrolador ATmega2560, foi desenvolvido uma interface gráfica do usuário (GUI em Delphi_10.3_Rio) para assegurar o controle e visualização do sinal de pressão obtida da leitura do canal de medição analógico do microcontrolador antes mencionado. A sinal analógica de pressão é medida pelo sensor de pressão MPX5050GP. O modelo fuzzy gera em sua saída um valor preciso proporcional à largura do pulso com o qual a micro-Pump de ar será excitada para obter o nível de pressão desejado na saída.
ganga_app
App móvil (Android ) para publicación de anuncios publicitarios. Desarrollada en RAD Studio Delphi 10.3.
Gesebio_v1.0_App
Este trabajo ha tenido como objetivo, diseñar e implementar una herramienta de software para la síntesis de bioseñales. Para la realización del mismo se utilizó el IDE propuesto por Embarcadero C++Builder2010®. La herramienta implementada permite la generación de patrones de formas de ondas electrocardiográficas, fotopletismográficas, incluyendo otras de referencia como cuadrada, triangular, diente de sierra, sinusoidal y trapezoidal. También permite la manipulación manual de patrones de acuerdo a las necesidades de diseño, posibilita el filtrado digital de señales a través de técnicas basadas en el diseño de filtros IIR y FIR, facilita el análisis espectral a partir del cómputo de la FFT, además de permitir la compatibilidad con diferentes formatos de archivo; entre otras funcionalidades incluidas en el ambiente de trabajo diseñado para la aplicación.
asieldev's Repositories
asieldev/Android-MobileFaceNet-MTCNN-FaceAntiSpoofing
Use tensorflow Lite on Android platform, integrated face detection (MTCNN), face anti spoofing (CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing) and face comparison (MobileFaceNet use InsightFace loss)
asieldev/Android-MraaDemo
asieldev/Authenticate-via-TensorFlow-Facial-Recognition-in-Flutter
The power of machine learning allows us to change long-standing computing paradigms. One of these is the age-old password-based authentication system common to most apps. With fast real-time facial recognition, we can easily dispense with text-based verification and allow users to log in just by showing their faces to a webcam. In this session, we’ll show how to do this in Flutter, Google’s popular open-source UI toolkit for developing apps for web, Android, iOS, Fuchsia, and many other platforms with a single codebase. We’ll first build a simple authentication-based Android app, and then deploy the Firebase ML Vision model for face ID & image processing; as well as the MobileFaceNet CNN model through TensorFlow Lite for structured verification. Once all these parts are in place, our solution will work seamlessly and can easily be ported to other apps. Pre-requisites: ✅ Android Studio (https://developer.android.com/studio) — you can also use other IDEs/platforms if you’d rather not use Android - Flutter documentation below guides on the same. ✅ Flutter SDK (https://flutter.dev/docs/get-started/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #TensorFlow #Flutter #MachineLearning
asieldev/camerax-tflite
asieldev/CDCN-Face-Anti-Spoofing.pytorch
Apply Central Difference Convolutional Network (CDCN) for face anti spoofing
asieldev/developer-portfolio-templete
The Developer Portfolio Template is a customizable and responsive template designed for showcasing your professional skills and projects as a developer. It provides an elegant and user-friendly interface that is suitable for both mobile and desktop views.
asieldev/Face-Liveness-Detection-SDK-Android
Robust, Realtime, On-Device Face Liveness Detection (Face Anti Spoofing) Android
asieldev/face_recognition
asieldev/FaceApiDemoV2
FaceApiDemoV2 The project mainly includes two functional modules: FireflyApi and FaceApi.
asieldev/facemaskdetector
Face mask detection
asieldev/FaceRecognition_With_FaceNet_Android
Face Recognition using the FaceNet model and MLKit on Android.
asieldev/FaceRecognitionAuth
Simple face recognition authentication (Sign up + Sign in) written in Flutter using Tensorflow Lite and Firebase ML vision library.
asieldev/flutter-engine-binaries-for-arm
flutter engine binaries (libflutter_engine.so) for arm & aarch64
asieldev/flutter-pi
A light-weight Flutter Engine Embedder for Raspberry Pi that runs without X.
asieldev/G2Crowd-Scraper
This is simple scraper that uses Playwright to extract data from G2Crowd(www.g2.com/). This example is made for educational purposese
asieldev/GFPGAN
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
asieldev/github-readme-stats
:zap: Dynamically generated stats for your github readmes
asieldev/html-css-js-portfolio-tutorial-2
asieldev/JustAddCode
Code snippets from Grijjy's Just Add Code blog
asieldev/kernel
BSP kernel source
asieldev/kernel-rk3566
asieldev/libfacedetection
An open source library for face detection in images. The face detection speed can reach 1000FPS.
asieldev/mraa
Linux Library for low speed IO Communication in C with bindings for C++, Python, Node.js & Java. Supports generic io platforms, as well as Intel Edison, Intel Joule, Raspberry Pi and many more.
asieldev/openbr
Open Source Biometrics, Face Recognition
asieldev/OpenFaceTracker
asieldev/react-js-personal-portfolio
asieldev/realtime-face-liveness-detector
Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV
asieldev/skia4delphi
Skia4Delphi is a cross-platform 2D graphics API for Delphi platforms based on Google's Skia Graphics Library. It provides a comprehensive 2D API that can be used across mobile, server and desktop models to render images.
asieldev/The-Weirdos-NFT-Website-Starter-Code
Build a cool NFT Collection website landing page with React JS . This website is created using Gsap for cool scrolling and animation. If you want to learn how to create this website then you can follow below tutorial link in the ReadMe.
asieldev/web-dev-projects
Projects repo for tutorials for my YouTube Channel