Image similarity metrics.
Project description
compimg
Introduction
For full documentation visit documentation site.
Image similarity metrics are often used in image quality assessment for performance evaluation of image restoration and reconstruction algorithms. They require two images:
- test image (image of interest)
- reference image (image we compare against)
Such metrics produce numerical values and are widely called full/reduced-reference methods for assessing image quality.
compimg
package is all about calculating similarity between images.
It provides image similarity metrics (PSNR, SSIM etc.) that are widely used
to asses image quality.
import numpy as np
from compimg.similarity import SSIM
some_grayscale_image = np.ones((20,20), dtype=np.uint8)
identical_image = np.ones((20,20), dtype=np.uint8)
result = SSIM().compare(some_grayscale_image, identical_image)
assert result == 1.0 # SSIM returns 1.0 when images are identical
Features
- common metrics for calculating similarity of one image to another
- images are treated as
numpy
arrays which makescompimg
compatible with most image processing packages - only
scipy
(and inherentlynumpy
) as a dependency
Installation
compimg
is available on PyPI. You can install it using pip:
pip install compimg
Note
Keep in mind that metrics are not aware of what kind of image you are passing. If metric relies on intensity values and you have YCbCr image you should probably pass only the first channel to the computing subroutine.
Help
If you have any problems or questions please post an issue.
Project details
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