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This module dedicated to processing and analyzing visual data using advanced computer vision algorithms, primarily leveraging Kinect sensor data and OpenCV for real-time applications.

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Fusion Vision

Overview

The Vision application is a sophisticated device management system designed to interface with Kinect v2 sensors using the libfreenect2 library. This application offers functionalities such as listing available devices, opening specific devices, and selecting devices for use. It incorporates a logging system to facilitate debugging and provide informative output throughout the device management process.

Features

  • Device Management: Automatically detect, list, and manage Kinect devices.
  • Singleton Design Pattern: Ensures a single instance of the DeviceManager.
  • Configurable Logging: Adjustable logging levels for better debugging and monitoring.
  • Error Handling: Robust status reporting for operations.

Table of Contents

Installation

Prerequisites

Ensure you have the following installed:

  • CMake
  • libfreenect2
  • A C++ compiler (e.g., g++, clang)

Steps

  1. Clone the Repository:

    git clone https://github.com/yourusername/vision.git
    cd vision
  2. Install Dependencies:

    Follow the installation instructions for libfreenect2 on your system.

  3. Build the Project:

    mkdir build
    cd build
    cmake ..
    make
  4. Run the Application:

    ./vision

Usage

Upon execution, the Vision application will automatically enumerate connected Kinect devices and log their information to the console. An example output would be:

[Info] [Vision] Console Logger initialized.
[Info] [Vision] Listing Devices
[Info] [Vision] Device 0: SERIAL_NUMBER_1
[Info] [Vision] Device 1: SERIAL_NUMBER_2
[Info] [Vision] Vision Finished.

Architecture

The Vision application consists of several key components:

  • Device Management: Manages the lifecycle and interactions with Kinect devices.
  • Logging System: Logs important events and statuses to the console.
  • Status Reporting: Provides feedback on the outcomes of operations.

Diagram

+------------------+          +-------------------+
|                  |          |                   |
|  DeviceManager   |<-------->|   ConsoleLogger   |
|                  |          |                   |
+------------------+          +-------------------+
        |
        v
+------------------+
|                  |
|   Kinect Device  |
|                  |
+------------------+

File Descriptions

  • config.h: Contains configuration settings and application constants.
  • device.h: Defines the Device struct for holding device attributes.
  • device_manager.h: Declares the DeviceManager class for device management.
  • console_logger.h: Defines the ConsoleLogger class for logging.
  • logger.h: Abstract base class for logging utilities.
  • status.h: Contains definitions for operation statuses and result handling.
  • main.cpp: The entry point of the application, orchestrating the device management workflow.

Classes and Functions

DeviceManager

Handles the detection and management of Kinect devices.

Key Functions

  • static DeviceManager* getInstance()

    • Returns the singleton instance of the DeviceManager.
  • Result listDevices(const std::vector<Device>& devices) const

    • Lists and logs all available devices.
  • Result openDevices(const std::vector<Device>& devices)

    • Opens specified devices for usage (implementation pending).
  • Result selectDevices(const std::vector<int>& ids)

    • Selects devices by their IDs and logs their serial numbers.

ConsoleLogger

Implements logging functionalities to the console.

Key Functions

  • static ConsoleLogger* getInstance()

    • Returns the singleton instance of the ConsoleLogger.
  • void log(Level level, const std::string& message)

    • Logs messages at the specified logging level.

Logger

Abstract class defining the logging interface.

Key Functions

  • virtual void log(Level level, const std::string &message) = 0
    • Abstract logging method to be implemented by derived classes.

Status

An enumeration for operation statuses:

  • Success
  • Error
  • InvalidParam
  • NotFound

Logging Levels

The application supports several logging levels to control output verbosity:

  • None: No logging.
  • Error: Logs error messages.
  • Warning: Logs warnings.
  • Info: Logs general information.
  • Debug: Logs detailed debug information.

Error Handling

The application implements a structured error handling mechanism using the Result struct, which includes status codes and messages to aid in troubleshooting.

Testing

Unit tests are included for critical components of the application. To run the tests, use:

make test

Ensure your environment is set up correctly to support testing.

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bugfix.
  3. Submit a pull request with a clear description of your changes.

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This module dedicated to processing and analyzing visual data using advanced computer vision algorithms, primarily leveraging Kinect sensor data and OpenCV for real-time applications.

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