Print("The pictures are perceptually the same !") # Compare hashes to determine whether the pictures are the same or not Print('Batman HD Picture: ' + str(SDBatmanHash)) SDBatmanHash = imagehash.average_hash(Image.open('batman_sd.jpg')) # Create the Hash Object of the second image Print('Batman HD Picture: ' + str(HDBatmanHash)) HDBatmanHash = imagehash.average_hash(Image.open('batman_hd.jpg')) # Create the Hash Object of the first image The most easiest way to generate a perceptual hash and probably the one that you will choose, can be easily implemented as shown in the following example: # example_averagehash.py You can follow these examples to generate the different perceptual hashes that can be generated with this Python project: A. It combines a simple high level interface with low level C and Cython performance.Īfter the installation of the libraries, you will be able now to use it and generate the different hashes that this library offers.
pywavelets: PyWavelets is open source wavelet transform software for Python.
The imagehash project needs previously some dependencies to work properly, they can be installed easily with pip (if not installed, install pip with sudo apt install python-pip) and reading the requirements list of the project, so change of directory: cd imagehashĪnd then install the dependencies with: pip install -r conda-requirements.txt For more information about this library, please visit the official repository at Github here. You can obtain the source code of this project with Git using the following command: git clone Īfter cloning it, you will be able to follow up the rest of the tutorial. In order to compare 2 images and verify whether they are perceptually the same using a perceptual hash in Python, we will rely on the proposal of the imagehash project by This project is an image hashing library written in Python that supports:
In this article, we'll show you how to generate different versions of a perceptual hash from pictures in Python. If you use another hashing technique for comparing images, making the slightest change to the picture, will generate a totally different hash (for example MD5 or SHA1). This perceptual hash is a fingerprint based on some input picture, that can be used to compare images by calculating the Hamming distance (which basically counts the number of different individual bits). A perceptual hash, is a generated string (hash) that is produced by a special algorithm.