Perhaps you already understand what it’s like to lose a childhood pet. Josie was a dog, a perfect, loving, caring beagle, that my dad got for me when I was 11 years old. Some of you may recognize this name - it appears in the dedication of all my books and publications. My best friend died in my arms five years ago, nearly to this very day. I appreciate you as a PyImageSearch reader and I hope you’ll let me have this introduction to write, pour out, and continue my journey to finding peace. To skip to the computer vision content, just scroll to the “Image Hashing with OpenCV and Python” section where I dive into the algorithm and implementation.īut while PyImageSearch is a computer vision and deep learning blog, I am a very real human that writes it.Īnd sometimes the humanity inside me needs a place to share. If you want to skip the personal discussion and jump immediately to the image hashing content, I won’t judge - the point of PyImageSearch is to be a computer vision blog after all. The remaining introduction to this blog post is very personal and covers events that happened in my life five years ago, nearly to this very day. Image hashing isn’t a particularly hard technique (in fact, it’s one of the easiest algorithms I’ve taught here on the PyImageSearch blog).īut the subject matter and underlying reason of why I’m covering image hashing today of all days nearly tear my heart out to discuss. Today’s blog post is on image hashing - and it’s the hardest blog post I’ve ever had to write. So, for my purposes, Hue should not be used.Click here to download the source code to this post Including Hue as one of the channels may well aid discrimination, but it falsely increases the score when a tweaking operation changes hue slightly eg from 99% to 1%, so it harms the robustness to this tweaking.Ĭhanging hue is a common operation on photography and video, perhaps most often for colour balancing. index.html, Fred includes other tweaking operations, such as translation and various distortions, but again with no operations that change hues. This is why including Hue as one of the channels didn't cause problems in their testing.Īt. That list has no operation that changes hues. What tweaking do they do? Brightness adjustment, contrast adjustment, gamma correction, 3x3 Gaussian low-pass filtering, JPEG compression, watermark embedding, scaling, and rotation. Where this is below a certain threshold, the images are considered the same otherwise they are not. They take a load of standard images (Lena, Baboon, etc) and tweak ("attack") each one, then calculate RMS phash between all pairs of images. Tang et al uses YCbCr and HSI colorspaces. g3omq1.pdf "Perceptual Hashing for Color Images Using Invariant Moments", Zhenjun Tang, Yumin Dai and Xianquan Zhang, 2011. But I notice that patterns like this are common: The usual RMS scheme gives equal weighting to all the numbers. Is 4 sufficient? Is 6 better? Is 10 better?Ĥ. How much precision do we need in the numbers? The default is 6. I would expect that a perceptually uniform colorspace would give better results.ģ. Do we gain anything by using two colorspaces instead of one? Ģ. Questions in my mind (which, sadly, I don't have time to investigate):ġ. IM's "-metric phash" does something like this.įor a job I did, I ignored the Hue numbers, so use only 35. The single resulting number is the "distance" between the images. Add the squares together, and divide by 42. That is: subtract numbers of one image from corresponding numbers of the other image. When comparing two images, calculate the RMS of the differences. If it is indeed a fair and reliable way to represent a perceptual hash (with a single, unique hex value, for example) - then is it possible for imagemagick to produce this hash value from the command line?Ĭode: Select all convert in.tiff -colorspace Lab -set colorspace sRGB -verbose -define identify:moments The numbers can be stored as 42 numeric fields in a database or, as Fred says, packed together into a single text field, then unpacked when you need them. It does indeed appear to do this, as I have determined by experimenting with it.īut what I don't get is how this is being done, or if it is even an effective or wise way to produce a perceptual hash. It promises to return a single hex-hash value, which you can then use to compare to another hash to determine image similarity. What I think I don't understand is this: How to turn those "42 numbers" into a single & unique (and therefore relational-database-friendly) hash value representative of a given image.
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