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Based on our record, OpenCV seems to be a lot more popular than CompreFace. While we know about 50 links to OpenCV, we've tracked only 2 mentions of CompreFace. 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.
Looking into this I found Compreface (https://exadel.com/solutions/compreface/) an open source face recognition software. There are alread some controb scripts, like contrib/photils.lua, who take some images, run them through a tool, then tag them with data coming from the tool. Converting this to use Compreface looks likea promising avenue. Source: over 1 year ago
Does anyone know what the technical interview process for Senior Java position looks like for the company Exadel? Https://exadel.com/. Source: almost 2 years ago
Exadel - holy heck. They gave us talent for DAYS. Source: almost 2 years ago
Serhii Pospielov, AI Practice Head at Exadel, reviewed several no-code app builders from a developer's point of view. He tried to create MVPs on 13 different platforms, but only managed to achieve that on five (this doesn’t mean that the other eight aren’t good platforms – just that they didn’t meet his particular business need). Serhii’s favorite no-code app builders were:. - Source: dev.to / over 2 years ago
Free and Open-Source Face Recognition System that can be integrated into any system without prior AI knowledge: https://exadel.com/solutions/compreface/. Source: almost 3 years ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest... Source: 9 months ago
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++. Source: 12 months ago
Amazon Rekognition - Add Amazon's advanced image analysis to your applications.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Ximilar - Ximilar is a Computer Vision platform that allows you to build and train Deep Learning models for Image Recognition, Detection, and Visual Search. Allows you to download a model for offline usage or connect to them via API.
NumPy - NumPy is the fundamental package for scientific computing with Python