image-hash | Perceptual image hashes for GD | Computer Vision library

I am working on an image hashing project in opencv and python.
I am reading this tutorial https://www.pyimagesearch.com/2019/08/26/building-an-image-hashing-search-engine-with-vp-trees-and-opencv/
suppose i converted my image to hash and have h as image hash and a 64 bit int :

h = 5090336880217583642

when i convert h to numpy 64-bit float and then convert it to Python’s built-in integer data type like this :

I'm attempting to calculate the Hamming distance between an input hash and database-stored hashes. These are perceptual hashes, so the Hamming distance between them are important to me and tell me how similar two different images are (see http://en.wikipedia.org/wiki/Perceptual_hashing, http://jenssegers.com/61/perceptual-image-hashes, http://stackoverflow.com/questions/21037578/). Hashes are 16 hexadecimal characters long, and look like this:

b1d0c44a4eb5b5a9
1f69f25228ed4a31
751a0b19f0c2783f

My database looks like this:

I'm trying to save the Paperclip uploaded images throw a Sidekiq worker. The user select the images into a image[] and passes it to the controller (as params[:image]).

View: