Computer Vision with C++ & OpenCV
Course Highlights In today’s world, artificial intelligence is used widely to save time and work more professionally. If you are …
Course Highlights
In today’s world, artificial intelligence is used widely to save time and work more professionally. If you are still in the dark about it, Computer Vision with C++ & OpenCV course will open the door for you!
You can process images, videos, signatures, make movies and do a number of other things with OpenCV. Computer vision is now being used in almost all business and social sectors. From this thorough and well-organised course, you will learn how to do driver and cuda toolkit installation, python environment for flownet, image filtration etc. There are also modules on colour transformation, trackbar and many other useful details.
Learning computer vision from this program will help you become prepared for your future career in the vast tech industry! Computer vision is being used to manage customers by Amazon and other retail companies and it leads to less consumption of time and more profit. Computer vision c++ is used even in agriculture and social care sectors too!
Join this course and go with the lessons on your preferable time! You do not have to worry about managing time. Moreover, there are tutors to support you and answer all your queries!
Enrol now, grab the opportunity to move forward with this helpful short course!
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
Course Curriculum
| Computer Vision by using C++ and OpenCV with GPU support | |||
| 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 | ||
Don't just take our word for it
1 Year Access 
2 Students 
2 hours, 31 minutes 
