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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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Michael Yon a super hero

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To military bloggers and conservative hawks, Michael Yon was a super hero — a fearless Green-Beret-turned-citizen-journalist who spent years on the frontlines of Iraq and Afghanistan when most big media outlets kept their reporters at home. But now, those same military bloggers are turning their sights on Yon, after he began savaging America’s top general in Afghanistan and warning that the American war effort is all but doomed.

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Entity Attribute Value Model

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EAV (Entity-Attribute Value) Model It is a data model to describe entities where number of attributes for the entities could be very large in amount. EAV helps us to overcome the limitations of conventional database model. Ex: Organization ‘ABC’ defines the entity ‘Address’ as: Organization ‘ABC’: Address Line1 Line2 State Country Zip                    At the same time Organization ‘XYZ’ may define ‘Address’ as: Organization ‘XYZ’: Address Line1 Line2 Line3 City State Country Zip Phone A conventional database table design (one fact per column or set of facts per row) is unsuitable for such type of data, because of: ·         the need to continually add new tables or columns whenever new facts need incorporation · ...
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