A C++ Mini-Tutorial on MediaPipe
A C++ Mini-Tutorial on MediaPipe

As we already explained, MediaPipe is a C++ pipeline library. It is very poorly documented, basically, the only documentation is the comments and docstrings in the MP source code. There are also examples, but they are not very readable. There is only one trivial “hello world” example, the rest is deep learning, which is counterproductive for learning basic MP concepts.  Moreover, these examples are artificially obscured by things like GLog and GFlags.

The Bizarre Google World: Bazel, ProtoBuf, and More
The Bizarre Google World: Bazel, ProtoBuf, and More

It was not easy at all to master MediaPipe. We thought little in C++ could surprise us. MP did. They say Google libraries do not work outside of Google. We can confirm this is the truth. The ways Google uses the C++ language are highly unusual from our point of view. Normally (at least where we come from) people use CMake, a nice cross-platform build system, for C++ projects.

Down the Rabbit Hole: Our Journey to the Land of MediaPipe and Other Google Technologies
Down the Rabbit Hole: Our Journey to the Land of MediaPipe and Other Google Technologies

In the ML/DL community you can often hear ”Nowadays you must know Google MediaPipe”, “It’s a cool framework”, and sometimes “It’s internally used by YouTube!” Videos with various computer vision tasks like this hand tracking often appear on LinkedIn and forums with the comment “This is MediaPipe”! At this point, we decided we could not ignore it anymore.

It-Jim’s 2021 Summer Internship on Computer Vision: an Overview
It-Jim’s 2021 Summer Internship on Computer Vision: an Overview

Another summer, another edition of our internship on computer vision to be proud of! This time we received well over 100 applications from more than 20 cities including Kyiv, Kharkiv, Lviv, Dnipro, Odesa, Mykolaiv, Vinnytsia, Uzhhorod, Poltava, Kremenchuk, Sumy, Zaporizhzhia, Kryvyi Pih, and Mariupol. What an impressive geography! Only three of the applicants made it to the ‘finals’.

Computer Vision in Healthcare
Computer Vision in Healthcare

Want to know what stands behind remote photoplethysmography (rPPG) and how to non-invasively monitor vital parameters such as heart rate and respiration, oxygen saturation, and blood pressure using just a phone camera? During the event, our CEO Ievgen Gorovyi will dive into the details of developing a computer vision-based solution for such healthcare application. 📅 Join us on September 18 at 11:00 in Zoom meeting! 🎯 Participation is free by pre-registration 👉🏻 https://cutt.ly/mWT8uv0.

Computer vision: DL or not DL?
Computer vision: DL or not DL?

  📅 When: 7 p.m. EEST | September 23, 2021 🏡 Where: Online, details will be sent via email 🔊 Speakers: Pavlo Vyplavin, CTO at It-Jim, Ph.D., and Yurii Chyrka, Head of ML at It-Jim, Ph.D. 💬 Language: Russian and Ukrainian 📝 Registration: https://bit.

AI Ukraine Online Conference 2021
AI Ukraine Online Conference 2021

On October 30, the AI Ukraine Online Conference will take place. Since 2014, it has been gathering experts immersed in Data Science and Machine Learning. Every year AI Ukraine brings together more than 900 participants from all over the world to increase (accumulate?) expertise, share experience, and take another step forward the future of emerging technologies. The conference will be held online. In addition to three thematic streams, it will consist of Q&A sessions, interactives, and networking.

Applied Computer Vision Course
Applied Computer Vision Course

After several very successful editions of internships and schools on computer vision and lots of interviews for CV/ML/DL engineers’ positions at our company, we are super excited to announce that we are launching our course in October 2021! 🚀 10 weeks, 20 lessons, each one being a mixture of theory, enhanced with mathematics essentials for computer vision, and practical workshops showcasing the methods learned.

Audio Processing Basics in Python
Audio Processing Basics in Python

If you want to try some sound processing in Python (with neural network or otherwise) and don’t know where to start, then this article is for you. This post is for absolute beginners.  What do we want? Basically 3 tasks. Read and write audio files in different formats (WAV, MP3, WMA etc.). Play the sound on your computer. Represent the sound as a waveform, and process it: filter, resample, build spectrograms etc.