The variance of the Laplacian approach to calculating a measure of blurriness convolves the input image with the Laplacian operator and computes the variance! A variance (blurriness) of 100 or less is regarded as blurry. The larger the measure the sharper the image. Below are a few examples. For reference see https://github.com/indyka/blur-detection.
$ ml blurry opencv https:digital-photography-school.com/wp-content/uploads/2014/10/DSC_3913_4_5_6_7_tonemapped-Edit.jpg Okay 5670
$ ml blurry opencv http://cdn1.thr.com/sites/default/files/2013/11/marina_bay_sands_singapore_a_l.jpg Okay 1303
$ ml blurry opencv https://3.bp.blogspot.com/_Mjt2vy7HUaY/TKY6tQvJzFI/AAAAAAAAAGA/4qwNjH2ivzw/s1600/sharp+focus.jpg Okay 951
$ ml blurry opencv https://images.pexels.com/photos/338515/pexels-photo-338515.jpeg Okay 374
$ ml blurry opencv https://www.arrivalguides.com/s3/ag-images-eu/18/20870ca6f7bc086749ea747ec0c8c86d.jpg Okay 170
$ ml blurry opencv https://cdn.photographylife.com/wp-content/uploads/2009/09/Red1-960x638.jpg Okay 167
$ ml blurry opencv https://www.nyip.edu/images/cms/photo-articles/stop-taking-blurry-pictures.jpg Blurry 68
$ ml blurry opencv http://getwallpapers.com/wallpaper/full/4/5/4/314199.jpg Blurry 43
The algorithm is not perfect, or is it just the choice of the threshold. Perhaps 150 is a better threshold?
$ ml blurry opencv https://photos1.blogger.com/blogger/4886/2467/1600/23%20-%20Blurry%20Megan%20and%20Colosseum%20at%20night.jpg Okay 119
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