Either way, this demonstrates an important aspect of comparing image differences - sometimes image differences are subtle - so subtle that the naked eye struggles to immediately comprehend the difference (we’ll see an example of such an image later in this blog post). You may have noticed this difference immediately, or it may have taken you a few seconds. If you take a second to study the two credit cards, you’ll notice that the MasterCard logo is present on the left image but has been Photoshopped out from the right image. Computing image differenceĬan you spot the difference between these two images?įigure 1: Manually inspecting the difference between two input images ( source). Now that our system is ready with the prerequisites, let’s continue. While you’re at it, go ahead and install/upgrade imutils as well: $ pip install -upgrade imutils If you don’t already have scikit-image installed/upgraded, upgrade via: $ pip install -upgrade scikit-image You can learn how to configure and install Python and OpenCV on your system using one of my OpenCV install tutorials. To accomplish this, we’ll first need to make sure our system has Python, OpenCV, scikit-image, and imutils. The trick is to learn how we can determine exactly where, in terms of (x, y)-coordinate location, the image differences are. This method is already implemented in the scikit-image library for image processing. in their 2004 paper, Image Quality Assessment: From Error Visibility to Structural Similarity. In order to compute the difference between two images we’ll be utilizing the Structural Similarity Index, first introduced by Wang et al. Looking for the source code to this post? Jump Right To The Downloads Section Image Difference with OpenCV and Python

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