Temporal Smoothing and Optical Flow Analysis

This repository contains Python scripts for processing image sequences by applying temporal smoothing and computing optical flow using the Lucas-Kanade method.

Features

1. Temporal Smoothing:
   Smoothens a sequence of image frames using a weighted kernel, enhancing temporal continuity while reducing noise. The smoothed frames are saved as .npy files.

2. Optical Flow Calculation:
   Computes the optical flow between consecutive smoothed frames using the Lucas-Kanade method. Outputs include velocity fields (vx, vy) and reliability scores, saved as .npy files.

Installation

Ensure you have the following Python libraries installed:

• numpy
• scipy
• matplotlib
• skimage
• numba

You can install the required dependencies via pip if needed.

Usage

1. Temporal Smoothing

This function smooths a sequence of raw image frames. It uses a weighted kernel to smooth frames temporally and stores the smoothed frames as .npy files.

• Inputs:
  • data_folder: Path to the folder containing raw image frames.
  • new_path: Path where smoothed .npy files will be stored.

2. Optical Flow Calculation

This function calculates the optical flow between consecutive smoothed frames using the Lucas-Kanade method. It computes the x and y velocity components (vx, vy) and reliability scores for each pixel, saving these as .npy files.

• Inputs:
  • smooth_address: Path to the folder containing the smoothed .npy files.
  • save_path: Path where the optical flow results will be saved.
  • sigma: The spread of Gaussian weights used in the optical flow calculation.
  • threshold: The reliability score threshold for valid velocity fields.

Outputs

• Temporal Smoothing: The smoothed frames are saved as .npy files in the new_path directory.
• Optical Flow: The computed optical flow velocity components (vx, vy) and reliability scores are saved as .npz files in the save_path directory.

Example Workflow

1. Step 1: Temporal smoothing of raw image frames is done by calling the smoothing function with the appropriate paths.
2. Step 2: After smoothing, optical flow is computed by calling the optical flow function on the smoothed frames.

Contributing

Feel free to fork the repository and create a pull request for any improvements or bug fixes. Please ensure that any contributions are well-documented and come with the appropriate tests.