🚀 Real-Time Human Detection & Counting

A TensorFlow-based Faster RCNN Inception v2 model for detecting and counting humans in real-time using images, videos, and live camera feeds. This project leverages a pre-trained frozen_inference_graph.pb for efficient detection and visualization of human counts, providing insights through Enumeration and Accuracy plots.

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📋 Features

• Real-Time Detection: Supports detecting humans from images, video files, and live camera feeds.
• Pre-Trained Model: Uses a frozen_inference_graph.pb for faster and more accurate detection.
• Visual Analysis: Generates detailed plots for:
  • Enumeration Plot: Shows human count over time.
  • Avg. Accuracy Plot: Displays average detection accuracy over time.
• Crowd Report Generation: Generates a PDF report with information on max human count, accuracy, and a brief crowd status analysis.

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📌 Requirements

• Python 3
• tkinter
• messagebox
• PIL
• cv2 (OpenCV)
• argparse
• matplotlib
• numpy
• time
• os
• tensorflow
• fpdf

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🚀 How It Works

1. Setup: Download the project files and run main.py on your local machine.
2. Start the Application: On the startup window, choose between START to begin or EXIT to close the application.
3. Choose Detection Method:
   • Detect from Image: Select an image file, preview it, and click DETECT to start detection.
   • Detect from Video: Select a video file, preview it, and click DETECT to start detection.
   • Detect from Camera: Click OPEN CAMERA to begin real-time detection through your camera.
4. View Results:
   • Once detection is complete, two plots are displayed:
     • Enumeration Plot: Human Count vs. Time.
     • Avg. Accuracy Plot: Avg. Accuracy vs. Time.
   • Option to generate a Crowd Report as a PDF, which is saved automatically in the project directory.

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📌 Purpose

This script helps users to easily detect and count humans in real-time through images, videos, or live camera feeds. It also provides a detailed crowd analysis through a report, making it useful for monitoring environments, events, or areas with human activity.

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📌Compilation Steps

• Install all the required libraries.
• After that download the code file, and run main.py on local system.
• Then the script will start running and user can explore it by detecting the human and also getting the count of it.

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📌SCREENSHOTS

• Image:

• Video:

• Camera:

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📌 Contributing

Contributions are welcome! If you have any ideas, suggestions, or bug fixes, feel free to open an issue or submit a pull request.