Video-Based Optical Flow Dataset and Python Workflow for Mapping Flow Activity on the Experimental Alluvial Fan

This archive contains the video-based optical flow dataset and processing script used to quantify flow activity on the experimental alluvial fan at one-minute temporal resolution. Each one-minute video consists of approximately 20 RGB frames captured at 3-second intervals, representing the area of the fan surface that was active during that minute. 

The archive includes the following components:
•    01_input_OneMin_videos/
Folder containing the one-minute videos used as input for the optical flow analysis. Each video captures active flow patterns across the fan surface at 3-second intervals (≈20 frames per minute).
•    02_Optical_Flow_output_csv/
Folder containing the resulting optical-flow magnitude rasters exported as .csv files.
Each output file represents the average optical flow magnitude for a given one-minute video, computed using Farnebäck’s dense optical flow algorithm.
•    Opticalflow_loop.py
Python script that processes all videos in batch mode.
The script:
o    reads each one-minute video from the input directory,
o    computes dense optical flow between consecutive frames,
o    accumulates and averages the per-pixel flow magnitude over the full minute (flow direction is ignored), and
o    saves the resulting magnitude field as a CSV file in the output directory.