The Computer Vision course with C++ and OpenCV with GPU support provides an introduction to computer vision and its applications using C++ and OpenCV with GPU acceleration. Students will learn about setting up the necessary environments, basic examples, background segmentation, object detection with OpenCV’s ML module using C++ and CUDA, and optical flow.
After studying the course materials of the Computer Vision: C++ and OpenCV with GPU support there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.
This Computer Vision: C++ and OpenCV with GPU support course is ideal for
This Computer Vision: C++ and OpenCV with GPU support does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Computer Vision: C++ and OpenCV with GPU support was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.
As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Computer Vision: C++ and OpenCV with GPU support is a great way for you to gain multiple skills from the comfort of your home.
Unit 01: Set up Necesssary Environments | |||
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 | ||
Unit 02: Introduction with a few basic examples! | |||
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 | ||
Unit 03: Background segmentation | |||
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 | ||
Unit 04: Object detection with openCV ML module (C++ CUDA) | |||
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 | ||
Unit 05: Optical Flow | |||
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 | ||
Additional Resource | |||
Resources – Computer Vision: C++ and OpenCV with GPU support | 00:00:00 | ||
Assignment | |||
Assignment – Computer Vision: C++ and OpenCV with GPU support | 00:00:00 |
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