alok-2002/motion-alert

This repository contains a Python program for motion detection using the OpenCV library. The program allows you to detect motion in a video stream or a webcam feed and highlight the areas where motion is detected. It also provides the option to save the output video with the motion highlights.

Motion Detection Using Python

This repository contains a Python program for motion detection using the OpenCV library. The program allows you to detect motion in a video stream or a webcam feed and highlight the areas where motion is detected. It also provides the option to save the output video with the motion highlights.

Features

• Real-time motion detection: The program continuously analyzes the video frames and detects any motion occurring in the scene, providing instant feedback.
• Motion highlighting: Detected motion is visually highlighted in the video stream, making it easy to identify the areas where motion is occurring.
• Adjustable sensitivity: You can fine-tune the motion detection sensitivity by modifying the threshold value in the configuration file. This allows you to customize the detection algorithm to suit your specific needs.
• Configurable minimum area: The program allows you to set a minimum contour area threshold. Any detected motion with an area below this threshold will be disregarded. You can adjust this value based on your requirements.
• Gaussian blur for noise reduction: The frames are preprocessed with a Gaussian blur to reduce noise and enhance the accuracy of motion detection.
• Save output video: The program provides an option to save the processed video with the motion highlights as an output file. This allows you to review and analyze the detected motion later.
• Executable file provided: You can also run the motion detector using the provided executable file, making it easier to use the program without Python dependencies.

Installation

1. Clone the repository to your local machine using the following command:

   git clone https://github.com/alok-2002/Motion_Detection_Using_Python.git
   

2. Navigate to the project directory:

   cd Motion_Detection_Using_Python
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

Running with Python

To run the motion detection program using Python, use the following command:

python motion_detection.py

The program will open the webcam and start detecting motion. The areas where motion is detected will be highlighted in real-time. If you want to save the output video, press the 's' key.

Press the 'q' key to quit the program and stop the motion detection.

Running with the Executable File

If you prefer not to run the program with Python, you can use the provided executable file. Simply double-click the motion_detection.exe file to start the motion detection program. The functionality and usage remain the same as running with Python.

Please note that the executable file is platform-specific, so make sure to use the appropriate file for your operating system.

Configuration

You can modify the program's behavior by adjusting the configuration settings in the config.json file. The available configuration options are:

• MIN_AREA: The minimum contour area (in pixels) to be considered as motion. You can increase or decrease this value based on your needs.
• THRESHOLD_SENSITIVITY: The sensitivity of the motion detection algorithm. Higher values will detect smaller motion, while lower values may require larger motion to be detected.
• BLUR_SIZE: The size of the Gaussian blur kernel applied to the frames. Increasing this value can help reduce noise but may result in less precise motion detection.

Contributing

Contributions to this repository are always welcome. If you find any issues or have suggestions for improvements, please create an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

This program is built using the OpenCV library. Special thanks to the developers of OpenCV for providing a powerful computer vision library that made this motion detection implementation possible.