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Efficient Duplicate Image Removal: Using ImageDeDup Python

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Efficient Duplicate Image Removal: A Python Guide In this blog post, we will walk you through the process of identifying and removing duplicate images from your directories using Python. We will leverage perceptual hashing to find similar images and delete duplicates while keeping the largest file in the group. This solution is perfect for users who want to save disk space and keep their image collections organized. Why You Should Use This Code Over time, especially when dealing with large collections of images, duplicate files can accumulate. These duplicates take up unnecessary space on your system. Manually sifting through these images can be tedious, but with the help of Python, perceptual hashing, and concurrent processing, this task becomes much easier. Benefits: Efficient Duplicate Detection : By using perceptual hashing (PHash), the code compares images based on their v...
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Opting classic row modeling or EAV data modeling?

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Few of the circumstances are listed below where EAV scores over conventional tables:
·         The data type of individual attributes varies. Like in above example EAV is used because the requirements vary from client to client like some may want an extra address like added to existing address format.
·         The categories of data are numerous, growing or fluctuating, but the number of instances (records/rows) within each category is very small. Here, with conventional modeling, the database’s Entity-Relationship Diagram might have hundreds of tables: the tables that contain thousands/ millions of rows/instances are emphasized visually to the same extent as those with very few rows. The latter are candidates for conversion to an EAV representation.


Other Related Links:
EAV(Entity-Attribute Value) Model


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Efficient Duplicate Image Removal: A Python Guide In this blog post, we will walk you through the process of identifying and removing duplicate images from your directories using Python. We will leverage perceptual hashing to find similar images and delete duplicates while keeping the largest file in the group. This solution is perfect for users who want to save disk space and keep their image collections organized. Why You Should Use This Code Over time, especially when dealing with large collections of images, duplicate files can accumulate. These duplicates take up unnecessary space on your system. Manually sifting through these images can be tedious, but with the help of Python, perceptual hashing, and concurrent processing, this task becomes much easier. Benefits: Efficient Duplicate Detection : By using perceptual hashing (PHash), the code compares images based on their v...
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