Computer Vision: C++ and OpenCV with GPU support

Description

This comprehensive course on Computer Vision: C++ and OpenCV with GPU support will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Computer Vision: C++ and OpenCV with GPU support comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder.

You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

There is no experience or previous qualifications required for enrolment on this Computer Vision: C++ and OpenCV with GPU support. It is available to all students, of all academic backgrounds.

Our Computer Vision: C++ and OpenCV with GPU support is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.

There is no time limit for completing this course, it can be studied in your own time at your own pace.

Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc.

5 sections • 22 lectures • 02:31:00 total length

•Module 01: Driver installation: 00:06:00

•Module 02: Cuda toolkit installation: 00:01:00

•Module 03: Compile OpenCV from source with CUDA support part-1: 00:06:00

•Module 04: Compile OpenCV from source with CUDA support part-2: 00:05:00

•Module 05: Python environment for flownet2-pytorch: 00:09:00

•Module 01: Read camera & files in a folder (C++): 00:11:00

•Module 02: Edge detection (C++): 00:08:00

•Module 03: Color transformations (C++): 00:07:00

•Module 04: Using a trackbar (C++): 00:06:00

•Module 05: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++): 00:13:00

•Module 01: Background segmentation with MOG (C++): 00:04:00

•Module 02: MOG and MOG2 cuda implementation (C++ - CUDA): 00:03:00

•Module 03: Special app: Track class: 00:06:00

•Module 04: Special app: Track bgseg Foreground objects: 00:08:00

•Module 01: A simple application to prepare dataset for object detection (C++): 00:08:00

•Module 02: Train model with openCV ML module (C++ and CUDA): 00:13:00

•Module 03: Object detection with openCV ML module (C++ CUDA): 00:06:00

•Module 01: Optical flow with Farneback (C++): 00:08:00

•Module 02: Optical flow with Farneback (C++ CUDA): 00:06:00

•Module 03: Optical flow with Nvidia optical flow SDK (C++ CUDA): 00:05:00

•Module 04: Optical flow with Nvidia Flownet2 (Python): 00:05:00

•Module 05: Performance Comparison: 00:07:00