#Python pixel check image overlay code#

A value greater than one implies less similarity and will continue to grow as the average difference between pixel intensities increases as well. It’s important to note that a value of 0 for MSE indicates perfect similarity. I’ll provide some proof for that statement later in this post, but in the meantime, take my word for it. The main one being that large distances between pixel intensities do not necessarily mean the contents of the images are dramatically different. MSE is dead simple to implement - but when using it for similarity, we can run into problems. Finally, we return our MSE to the caller one Line 16.All we are doing is dividing our sum of squares by the total number of pixels in the image. Line 12 handles the mean of the Mean Squared Error.Next up, we square these difference (hence mean squared error, and finally sum them up. We then take the difference between the images by subtracting the pixel intensities. First we convert the images from unsigned 8-bit integers to floating point, that way we don’t run into any problems with modulus operations “wrapping around”. All the real work is handled on Line 11.the images we want to compare for similarity). On Line 7 we define our mse function, which takes two arguments: imageA and imageB (i.e.Let’s tear it apart and see what’s going on:

#Python pixel check image overlay code#

So there you have it - Mean Squared Error in only four lines of Python code once you take out the comments.

# return the MSE, the lower the error, the more 'similar' # NOTE: the two images must have the same dimensionĮrr = np.sum((imageA.astype('float') - imageB.astype('float')) ** 2)Įrr /= float(imageA.shape * imageA.shape) # sum of the squared difference between the two images # the 'Mean Squared Error' between the two images is the While this equation may look complex, I promise you it’s not.Īnd to demonstrate this you, I’m going to convert this equation to a Python function: def mse(imageA, imageB):

Let’s take a look at the Mean Squared error equation: Equation 1: Mean Squared Error Pretty weird, right? Mean Squared Error vs. Now, it’s clear to us that the left and the middle images are more “similar” to each other - the one in the middle is just like the first one, only it is “darker”.īut as we’ll find out, Mean Squared Error will actually say the Photoshopped image is more similar to the original than the middle image with contrast adjustments. Here you can see that we have three images: (left) our original image of our friends from Jurassic Park going on their first (and only) tour, (middle) the original image with contrast adjustments applied to it, and (right), the original image with the Jurassic Park logo overlaid on top of it via Photoshop manipulation. Right: The original image with Photoshopped overlay. Middle: The original image with contrast adjustments. Let’s start off by taking a look at our example dataset: Figure 1: Our example image dataset. Looking for the source code to this post? Jump Right To The Downloads Section Our Example Dataset