Computer Vision By Using C++ and OpenCV with GPU support

Computer Vision By Using C++ and OpenCV with GPU support

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

Computer Vision By Using C++ and OpenCV with GPU support

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1 Year Access

Intermediate

27 Students

2 hours, 31 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Computer Vision By Using C++ and OpenCV with GPU support will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirement

- This course is available to all learners of all academic backgrounds.
- Learners should be aged 16 or over to undertake the qualification.
- Some basic understanding of the English language and numeracy.

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

- PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Why choose us?

- - Affordable, engaging & high-quality e-learning study materials;
  - Tutorial videos/materials from the industry-leading experts;
  - Study in a user-friendly, advanced online learning platform;
  - Efficient exam systems for the assessment and instant result;
  - The UK & internationally recognised accredited qualification;
  - Access to course content on mobile, tablet or desktop from anywhere, anytime;
  - The benefit of career advancement opportunities;
  - 24/7 student support via email.

Career path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry.

Course Curriculum

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 By Using C++ and OpenCV with GPU support	00:00:00
Assignment
	Assignment – Computer Vision By Using C++ and OpenCV with GPU support	00:00:00

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Computer Vision By Using C++ and OpenCV with GPU support