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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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Sergey Larenkov took World War II photos and photoshopped

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Russian photographer Sergey Larenkov has taken old World War II photos and photoshopped them over the locations in present day. The scenes from places like Prague, Vienna, and Moscow are incredibly well done and a neat way to appreciate history

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Scrum Roles (Scrum Master, Product owner, Development Team) and collaboration between each other

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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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MySpace Tries to Recapture the social-networking magic...

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First GNOME Census Results

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The GNOME Census, a project to see who contributes to GNOME and how, has released its first set of results. The results group people by their reasons to contribute code, what they contributed code to, and what percentage of the total contributions they have. For example, 23.45% of code contributions were volunteer, 16.3% of code contributions came from Red Hat, 1% of contributions came from Canonical (which has caused a lot of controversy), and 0.24% came from Mozilla Corporation. The census results are also represented in diagrams (release activity, why contributions were made, and what was contributed to and by who). The report is also available here and is licensed under a Creative Commons Attribution Share-Alike license.
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