Online or onsite, instructor-led live OpenCV (Open Source Computer Vision Library) training courses demonstrate through interactive discussion and hands-on practice how to utilize OpenCV for computer vision projects.
OpenCV training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live OpenCV training can be carried out locally on customer premises in Busan or in NobleProg corporate training centers in Busan.
Busan Jungang-dong Centre is located on the 7th floor of PanOcean B/D in which many tenants are forwarding companies, shippin...
Busan Jungang-dong Centre is located on the 7th floor of PanOcean B/D in which many tenants are forwarding companies, shipping firms, and conglomerates like Hyundai marine, Woori bank, Shinhan investment, Joongang newspaper. Jungang-dong is one of the main business districts in Busan, so there are many foreign consulates, financial corporations, hotels, shipping companies, etc.
The building is situated with good visibility in the centre of the traditional CBD area in Busan,100 meters from Jungang subway, 5 minutes walk to Busan International Ferry Terminal, and 4 minutes drive to Busan KTX Station or Nampo-dong downtown area.
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
This instructor-led, live training in Busan (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
View, load, and classify images and videos using OpenCV 4.
Implement deep learning in OpenCV 4 with TensorFlow and Keras.
Run deep learning models and generate impactful reports from images and videos.
This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
Configure OpenCV to capture and detect facial images.
Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
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