AKALL NIR & RGB Reflectance Analysis

Overview

This research provides a set of image processing tools for analyzing RGB and Near-Infrared (NIR) data captured with ToF depth cameras. The analyses involve extracting and processing different color channels from RGB images and NIR images for further reflectance analysis.

prerequisites

The convert command below is part of ImageMagick, a powerful tool for image manipulation. Make sure you have it installed before running the commands.

sudo apt-get install imagemagick

RGB + NIR Analysis Commands

The following commands use ImageMagick to separate the different color channels from an RGB image and process the NIR image.

Extracting RGB Channels

At first, extract the Blue (450nm), Green (550nm), and Red (650nm) channels from an RGB image and perform an auto-level adjustment, use the following commands:

convert RGB.jpeg -colorspace RGB -channel B -separate -auto-level B.jpeg
convert RGB.jpeg -colorspace RGB -channel G -separate -auto-level G.jpeg
convert RGB.jpeg -colorspace RGB -channel R -separate -auto-level R.jpeg

Install all required packages using pip3:

pip3 install pandas matplotlib opencv-python numpy

Processing NIR Image

To process the NIR (850nm) image, convert the raw data into a PGM format and normalize the image:

convert -size 640x576 -depth 16 -endian LSB -define quantum:format=unsigned -define quantum:separate -depth 16 gray:NIR.raw -normalize NIR.pgm

Relative Reflectance Measurmment

Scene Calibration

To calibrate the Blue, Green and Red reflectance measurment in a given scene, run the following command on a scene that contains a Color Checker (Macbeth) and Reflectance targets. Update the calibration.json file with the scenes (R, G, B, and NIR) reflectance values. Run the command below to compute the relative reflectance of the Red, Blue and Green marks in NIR image.

python3 proc.py images/NIR.pgm --channel NIR --points 3

Example reflectance calibration.json file

{
    "B": 12.21,
    "G": 27.54,
    "R": 40.88,
    "NIR": 30.0
}

Scene Processing

Run the following to compute the relative reflectance.

python3 proc_all.py --blue blue.jpeg --green green.jpeg --red red.jpeg --nir nir.pgm --points 9 --calibration calibration.json

Example command:

python3 proc_all.py --blue images/B_1731624458C5MJPG3072P.jpeg --green images/G_1731624458C5MJPG3072P.jpeg --red images/R_1731624458C5MJPG3072P.jpeg --nir images/1731624411IR5640NFOV_UNBINNED.pgm --points 9 --calibration calibration.json

Plotting data

The data to plot is saved as reflectance_data_timestamp.cvs. Execute the following command to plot the relative reflectance measurments per sample registered.

python3 plotter.py reflectance_data_timestamp.csv --meta calibration.json