python image hashing algorithm - Search
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    Upvotes7edited Jul 14, 2022 at 17:17

    Try using hashlib. Just open the file and perform a hash.

    import hashlib
    # Simple solution
    with open("image.extension", "rb") as f:
    hash = hashlib.sha256(f.read()).hexdigest()
    # General-purpose solution that can process large files
    def file_hash(file_path):
    # https://stackoverflow.com/questions/22058048/hashing-a-file-in-python

    sha256 = hashlib.sha256()

    with open(file_path, "rb") as f:
    while True:
    data = f.read(65536) # arbitrary number to reduce RAM usage
    if not data:
    break
    sha256.update(data)

    return sha256.hexdigest()
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    Which algorithm is best for image hashing?Depending on your use case, some algorithms will be better than others. For instance, if you want images with the same shape but different colors to have the same hash, perceptual or difference hashing could be used. Duplicates detected by phash algorithm the first example of the ImageHash repository.
    What are the different image hashing functions?There are a number of different image hashing functions you can use for detecting duplicate or similar images, including average hashing (aHash), perceptual hashing (pHash), difference hashing (dHash), Haar wavelet hashing (wHash), and HSV color hashing.
    What is image hashing?Image hashing or perceptual hashing is the process of: Perhaps the most well known image hashing implementation/service is TinEye, a reverse image search engine. Using TinEye, users are able to: A visual example of a perceptual hashing/image hashing algorithm can be seen at the top of this section.
    Is there a pyimagesearch library for image hashing?This library includes a number of image hashing implementations, including difference hashing, average hashing, and others. From there, take a look at the blog of Tham Ngap Wei (a PyImageSearch Gurus member) who has written extensively about image hashing and even contributed a C++ image hashing module to the OpenCV-contrib library.
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