D3DShot
Extremely fast and robust screen capture on Windows with the Desktop Duplication API (by SerpentAI)
Face Recognition
The world's simplest facial recognition api for Python and the command line (by ageitgey)
D3DShot | Face Recognition | |
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1 | 34 | |
256 | 51,755 | |
- | - | |
0.0 | 0.0 | |
almost 2 years ago | 2 months ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
D3DShot
Posts with mentions or reviews of D3DShot.
We have used some of these posts to build our list of alternatives
and similar projects.
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Part 12a: Real to Reel
By using the Python package d3dshot, we can grab a screenshot of our RealFlight environment (we'll take just the part showing the downward-facing camera feed), and then send this image data (encoded using OpenCV) over UDP. On another computer we can have a script running with a UDP socket open and waiting to receive these messages. This script is representing the third-party peripheral, which in real life would be capable of obtaining the video footage on its own. Nonetheless, the peripheral now has its data to analyze. The important thing to note here is that this peripheral is self-contained. It is not part of the autopilot (it's written in Python, for one thing), and thus its hardware and software can be developed without any integration concerns, provided that it conforms to the autopilot's API. So while this "peripheral" currently exists on the same computer that is running the autopilot, this is by no means a constraint, and it will soon be moved to a separate piece of hardware. So what, you may ask, it the point of this peripheral? Well, that will just have to wait until next time. But I think it's pretty cool, so hopefully you'll come back to check it out. You're very patient, dear reader. That's what I appreciates about you. -Greg
Face Recognition
Posts with mentions or reviews of Face Recognition.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-28.
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Security Image Recognition
Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition
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Facial recognition software/API for face-blind teacher?
Have you tried this repo: github
- GitHub - ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line
- The simplest facial recognition API for Python
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Every thing you need to know about Machine Learning Pipeline
One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.
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Reverse image search / facial recognition
Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition
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Made a easy to use face recognition library
It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.
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Salisbury council meeting minutes addressing conspiracy theorist councillors
You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition
- Face comparison in Stable Diffusion
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Understanding different Algorithms for Facial Recognition
To know more about face_recognition module https://github.com/ageitgey/face_recognition