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Thermal Sensor Visualization: Real-time Thermal Imaging and Analysis

Turn raw sensor data into meaningful and interactive thermal images, providing insights into temperature distributions and anomalies.

Table of Contents

Features

  • Image Generation: Convert flat sensor data into structured 2D thermal images.
  • Real-time Visualization: Display thermal images with color scales representing temperature ranges.
  • Image Enhancement: Upscale and apply Gaussian filters for clearer visualization.
  • Temperature Analysis: Automatically pinpoint and mark the coldest and hottest regions in the images.

Requirements

  • Python 3.7+
  • Required libraries: numpy, matplotlib, scipy
  • Sensor data in the prescribed format

Installation

  1. Clone the repository:

    git clone https://github.com/sattyamjjain/PyVerseAI.git

  2. Navigate to the project directory:

    cd Thermal-Sensor-Visualization

  3. Install the required libraries:

    pip install numpy matplotlib scipy

Usage

  1. Launch the application (assuming a main.py entry point):

    python main.py

  2. Follow the on-screen prompts to load sensor data, generate and enhance images, and perform temperature analysis.

Design Rationale

  • Numpy: Used for efficient data manipulation and image generation.
  • Matplotlib: Provides robust tools for visualization and image enhancement.
  • Scipy's Gaussian Filter: Helps in enhancing the image clarity, making it easier to identify temperature variations.

Challenges and Solutions

  • Challenge: Converting flat sensor data into meaningful 2D images.

    • Solution: Reshape the data array to match the sensor's 2D layout and display as an image.

  • Challenge: Enhancing low-resolution thermal images for better clarity.

    • Solution: Utilized upscaling followed by Gaussian smoothing to improve image quality without distorting the temperature data.

Contribution

We appreciate your interest in improving Thermal Sensor Visualization! Feel free to fork the project, make changes, and submit pull requests. All contributions are welcome!