glcm_cupy 0.1.10

Binned GLCM 5 Features implemented in CuPy

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GLCM Binned 5-Features on CuPy

This directly utilizes CUDA to speed up the processing of GLCM.

Installation

Python >= 3.7

First, you need to install this

pip install glcm-cupy

Then, you need to install CuPy version corresponding to your CUDA version

I recommend using conda-forge as it worked for me :)

conda install -c conda-forge cupy cudatoolkit=<your_CUDA_version>

E.g: For CUDA 11.6,

conda install -c conda-forge cupy cudatoolkit=11.6

To install CuPy manually, see this page

Optional Installation

This supports RAPIDS cucim.

RAPIDS Installation Guide

It's automatically enabled if installed.

Usage

>>> from glcm_cupy import GLCM
>>> import numpy as np
>>> from PIL import Image
>>> ar = np.asarray(Image.open("image.jpg"))
>>> ar.shape
(1080, 1920, 3)
>>> g = GLCM(...).run(ar)
>>> g.shape
(1074, 1914, 3, 8)

The last dimension of g is the GLCM Features.

To retrieve a specific GLCM Feature:

>>> from glcm_cupy import CONTRAST
>>> g[..., CONTRAST].shape
(1074, 1914, 3)

You may also consider simply glcm if you're not reusing GLCM()

>>> from glcm_cupy import glcm
>>> g = glcm(ar, ...)

Example: Processing an Image

Features

These are the features implemented.

• HOMOGENEITY = 0
• CONTRAST = 1
• ASM = 2
• MEAN = 3
• VAR = 4
• CORRELATION = 5
• DISSIMILARITY = 6

Don't see one you need? Raise an issue, I'll (hopefully) add it.

Radius & Step Size

• The radius defines the window radius for each GLCM window.
• The step size defines the distance between each window.
  • If it's diagonal, it treats a diagonal step as 1. It's not the euclidean distance.

Binning

To reduce GLCM processing time, you can specify bin_from & bin_to.

This will bin the image from a range to another.

I highly recommend using this to reduce time taken before raising it.

E.g.

I have an RGB image with a max value of 255.

I limit the max value to 31. This reduces the processing time.

GLCM(..., bin_from=256, bin_to=32).run(ar)

The lower the max value, the smaller the GLCM required. Thus allowing for more GLCMs to run concurrently.

Direction

By default, we have the following directions to run GLCM on.

• East: Direction.EAST
• South East: Direction.SOUTH_EAST
• South: Direction.SOUTH
• South West: Direction.SOUTH_WEST

For each direction, the GLCM will be bidirectional.

We can specify only certain directions here.

>>> from glcm_cupy import GLCM
>>> GLCM()
>>> g = GLCM(directions=(Direction.SOUTH_WEST, Direction.SOUTH))

The result of these directions will be averaged together.

Notes

Q: Why did my image shrink?

The image shrunk due to step_size & radius.

The amount of shrink per XY Dimension is size - 2 * step_size - 2 * radius

Q: What's the difference between this and glcmbin5?

This is the faster one, and easier to use. I highly recommend avoiding glcmbin5 as it has C++, which means you need to compile manually.

It's the first version of GLCM I made.

Contributors

• Julio Faracco
  • Special Thanks for implementing CuPy input support!

CUDA Notes

Why is the kernel split into 4?

The kernel is split into 4 sections

1. GLCM Creation
2. Features (ASM, Contrast, Homogeneity, GLCM Mean I, GLCM Mean J)
3. Features (GLCM Variance I, GLCM Variance J)
4. Features (GLCM Correlation)

The reason why it's split is due to (2) being reliant on (1), and (3) on (2), ... .

There are some other solutions tried

1. __syncthreads() will not work as we require to sync all blocks.
   1. We can't put all calculations in a block due to the thread limit of 512, 1024, 2048.
   2. We require 256 * 256 threads minimum to support a GLCM of max value 255.
2. Cooperative Groups imposes a 24 block limit.

Thus, the best solution is to split the kernel.

Atomic Add

Threads cannot write to a single pointer in parallel, information will be overwritten and lost. This is the Race Condition.

In order to avoid this, we use Atomic Functions.

... it is guaranteed to be performed without interference from other threads

Change Log