Google MLKit Mobile App

Table of Contents

• Introduction
• Features
• Requirements
• Installation
• Contributers

Introduction

This Java application utilizes MLKit and pretrained TensorFlow models to provide various computer vision capabilities, including face detection, pose detection, visitor analysis, face recognition, and options to hide/obscure faces. The application leverages the power of machine learning to enable advanced visual analysis and processing.

Click here for preview.

Features

• Face detection: The application can accurately detect and locate human faces in images and video streams.
• Pose detection: It can determine the position and orientation of a person's body in images or videos.
• Visitor analysis: The application provides insights and analytics on the number of visitors, their demographics, and behavior.
• Face recognition: It can recognize and identify individuals based on their facial features.
• Hide/Obscure Face options: Users can choose to hide or obscure faces in images or videos for privacy or anonymization purposes.

Requirements

To run this Java application, you need the following:

• Java Development Kit (JDK) 8 or above.
• MLKit library.
• Pretrained TensorFlow models for face detection, pose detection, and face recognition.

Installation

1. Install the Java Development Kit (JDK) if you haven't already.
2. Set up MLKit in your Java project. You can follow the official documentation of MLKit for Java for instructions on how to add MLKit to your project.
3. Download the pretrained TensorFlow models for face detection, pose detection, and face recognition.
4. Add the pretrained TensorFlow models to your project's resources or specify their file paths in the application code.

Contributers

If you encounter any problems, do not hesitate to contact.
@Egemen Eroglu
@Sahan Yarar