Python OpenCV Scripts for Video Analysis

A collection of Python scripts using OpenCV for various video analysis techniques including optical flow calculation, background subtraction, and color tracking. Each script is briefly explained below with usage instructions.

Scripts Overview

1. Lucas-Kanade-OpticalFlow.py

Implements the Lucas-Kanade method for optical flow estimation in videos, tracking the movement of points between two frames.

Dependencies: OpenCV, NumPy

Usage:

• Run the script. It uses the webcam by default (cv2.VideoCapture(0)).
• Press 'q' to exit.

2. Farneback-OpticalFlow.py

Utilizes the Farneback method to compute dense optical flow, visualizing the flow as arrows or lines.

Dependencies: OpenCV, NumPy, argparse

Usage:

• Use the --video argument to specify the path to a video file or leave empty for webcam.
• Press 'q' to exit the display window.

3. FrameDiff-BackgroundSub.py

Performs background subtraction using frame differencing method. It captures the static background and subtracts it from the ongoing frames to detect moving objects.

Dependencies: OpenCV, NumPy

Usage:

• Run the script. It uses the webcam by default.
• Press 'q' to quit the program.

4. ColorTracking.py

Tracks a specified color in a video stream or video file and draws bounding boxes around the detected color.

Dependencies: OpenCV, NumPy

Usage:

• Replace 'path_to_video' with the path of your video file or use a webcam.
• Adjust the HSV color range (lower_bound and upper_bound) as needed to track different colors.
• Press 'q' to exit.

Installation and Setup

• Ensure Python is installed on your system.
• Install dependencies using pip: pip install numpy opencv-python.
• Download the scripts and run them with Python.