digiKam is an advanced open-source digital photo management application that runs on Linux, Windows, and MacOS. The application provides a comprehensive set of tools for importing, managing, editing, and sharing photos and raw files.
Based on our record, Amazon Rekognition should be more popular than digiKam. It has been mentiond 33 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Digikam seems ideal for this https://digikam.org/. - Source: Hacker News / 11 months ago
I have all of my photos (with the exception of smartphone photos... ugh) in a nicely constructed set of folders \photos\yyyy\yyyymmmdd\ then the folder made by the camera, etc. I've got a small python script to generate the folders. I use Digikam[1] to do facial recognition and tagging on them. It's finally gotten to the point where it doesn't crash all the time writing metadata, and the facial recognition is... - Source: Hacker News / 11 months ago
I use digikam for my own personal library. I’m not sure if it’s able to be run from a server, but I know you can hook up a NAS to it to manage it. Can tag photos, rank, organize, etc. Source: about 1 year ago
Check out digiKam. It has photo editing tools as well, but the main focus is photo management. Also it is free and open source. Source: about 2 years ago
But with that many photos, I'd suggest a more fully featured digital asset management (DAM) program. Lightroom (paid), DigiKam, or DarkTable (both free) are good choices. PhoTool's IMatch (paid) also uses exiftool and is extremely powerful with regards to metadata. Source: about 2 years ago
AWS Rekognition is a great choice for many types of real-world projects or just for testing an idea on your images. The issue eventually comes with its cost, unfortunately, which we will see later in a specific example. Don’t get me wrong, Rekognition is a great service and I love to use it for its simplicity and reliable performance on quite a few projects. - Source: dev.to / 2 months ago
I don’t really want to spend so much time manually adjusting labels. For most machine learning, the next step would be to fine tune your model. You can essentially fine tune Amazon Rekognition by using Custom Labels. You can do this to make it better at detecting specific objects (like bears) or train it to detect new objects like your product or logo. It really depends on your application needs. - Source: dev.to / 10 months ago
For instance, are you a company with lots of security cameras? Hire me to write a program that pipes your data into AWS rekognition and then shows you a dashboard of what happened on your cams today. Got a ton of products with no meta-description? Hire me to write a program that pipes your data into OpenAI, and then saves the generated description to your custom CMS. Source: 11 months ago
Amazon Rekognition: Used to index, detect faces in the picture, and compare faces when users try voting, it was the heart of the facial voting feature. - Source: dev.to / about 1 year ago
Sure. But if you think generating thumbnails and detecting intros/credits takes a long time, wait until your computer is running machine learning/computer vision over your entire library. They also have to build and train that model which is no trivial task. And I know what you're thinking, why don't they just use Amazon's Rekognition service that does celebrity identification? Well, it's $0.10 per minute of... Source: about 1 year ago
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