BLUR PICTURE HOW TO
My first stop when figuring out how to detect the amount of blur in an image was to read through the excellent survey work, Analysis of focus measure operators for shape-from-focus.
BLUR PICTURE CODE
Looking for the source code to this post? Jump Right To The Downloads Section Variance of the Laplacian Figure 1: Convolving the input image with the Laplacian operator. By the end of this post, you’ll be able to apply the variance of the Laplacian method to your own photos to detect the amount of blurring. In the rest of this blog post, I’ll show you how to compute the amount of blur in an image using OpenCV, Python, and the Laplacian operator. Instead, I opened up an editor and coded up a quick Python script to perform blur detection with OpenCV. Now, for the average person I suppose they would have just deleted these blurry photos (or at least moved them to a separate folder) - but as a computer vision scientist, that wasn’t going to happen. Whether due to sub-par photography skills, trying to keep up with super-active Jemma as she ran around the room, or her spazzing out right as I was about to take the perfect shot, many photos contained a decent amount of blurring. Not only was it a huge undertaking, I started to notice a pattern fairly quickly - there were lots of photos with excessive amounts of blurring. Over this past weekend I sat down and tried to organize the massive amount of photos in iPhoto. So it should come as no surprise that as a dog owner, I spend a lot of time playing tug-of-war with Jemma’s favorite toys, rolling around on the kitchen floor with her as we roughhouse, and yes, snapping tons of photos of her with my iPhone. Since we got her as a 8-week old puppy, to now, just under three years later, we have accumulated over 6,000+ photosof the dog. Click here to download the source code to this postīetween myself and my father, Jemma, the super-sweet, hyper-active, extra-loving family beagle may be the most photographed dog of all time.
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