Image similarity metrics.
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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
- common metrics for calculating similarity of one image to another
- images are treated as
numpyarrays which makes
compimgcompatible with most image processing packages
numpy) as a dependency
compimg is available on PyPI. You can install it using pip:
pip install compimg
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.
If you have any problems or questions please post an issue.
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