Spatial Frequency Extraction using Gradient-liked Operator on Multispectral (SFEGO_Color)

PyCUDA and PyOpenCL Version

Introduction

• SFEGO_Color is doing the SFEGO on each RGB channel of image and concat as a RGB image that to visualize spatial info in multispectral.

• This work is based on SFEGO single channel version:

  • https://github.com/CardLin/SFEGO_PyCUDA
  • https://github.com/CardLin/SFEGO_PyOpenCL

• The spatial frequency in the RGB image or multispectral image contain spectrum info, and we can use spatial decomposition to see this info.

• The image sensor is not only cpature light reflect by object inside FOV but also the lighting condiction of the light source.

• When light source passthrough the lens of camera and the refraction is different for each wavelength. So the image may contain spectrum info in it.

• The SFEGO_Color can analysis the image to extract the spectrum info.

• The Sun Light contain full spectrum so the decompostion result contain rainbow color in our decomposition result.

• The LED Light contain Blue and Yellow spectrum due to the LED cell emit the Blue Light and hit the Yellow Phosphor to generate the White Light.

Hardware Requirement

• Require GPU to execute Kernel Code

• Recommend to use NVIDIA GPU with 1GB+ VRAM (VRAM usage is depend on Image Size)

• AMD Integrated GPU and Intel Integrated GPU can also run this project

• Although It can also run OpenCL on CPU mode but even the Intel Integrated GPU is faster than high-end CPU

Execution

• Choose which GPGPU architecture you want to use. (comment out the architecture you don't want to use)

  • import SFEGO_PyOpenCL as SFEGO_Backend
  • import SFEGO_PyCUDA as SFEGO_Backend

• By default, it will using PyOpenCL to run the SFEGO. (notice: Integrated GPU also can run PyOpenCL and may slower than Discrete GPU)

• python SFEGO_Color.py test1.jpg

• python SFEGO_Color.py test2.jpg