Computer Vision by using C++ and OpenCV with GPU support_1

Computer Vision by using C++ and OpenCV with GPU support, available at $39.99, has an average rating of 3.85, with 28 lectures, based on 26 reviews, and has 280 subscribers.

You will learn about Computer vision OpenCV Computer vision app on Ubuntu OS C++ skills This course is ideal for individuals who are Software developers or Students want to work on Computer Vision It is particularly useful for Software developers or Students want to work on Computer Vision.

Enroll now: Computer Vision by using C++ and OpenCV with GPU support

Summary

Title: Computer Vision by using C++ and OpenCV with GPU support

Price: $39.99

Average Rating: 3.85

Number of Lectures: 28

Number of Published Lectures: 27

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 27

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Computer vision

OpenCV

Computer vision app on Ubuntu OS

C++ skills

Who Should Attend

Software developers

Students want to work on Computer Vision

Target Audiences

Software developers

Students want to work on Computer Vision

In this course, you are going to learn how to install Nvidia driver on ubuntu OS and compile OpenCV with GPU support. And, you will see how to use opencv GPU functions to accelarate your applications. Also you are going to learn how to setup nvidia flownet2-pytorchenvironment in python.

*** Watch the Introduction video for more details! ***

If you firstly follow my other course “Learn Computer Vision with OpenCV and Python“, you will learn more beginning level information in computer vision, and then it will be better for you to see different examples with C++ and GPU enabled functions in this course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview

Chapter 2: Set up Necesssary Environments

Lecture 1: Driver installation

Lecture 2: Cuda toolkit installation

Lecture 3: Compile OpenCV from source with CUDA support part-1

Lecture 4: Compile OpenCV from source with CUDA support part-2

Lecture 5: Python environment for flownet2-pytorch

Chapter 3: Introduction with a few basic examples!

Lecture 1: Read camera & files in a folder (C++)

Lecture 2: Edge detection (C++)

Lecture 3: Color transformations (C++)

Lecture 4: Using a trackbar (C++)

Lecture 5: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++)

Chapter 4: Background segmentation

Lecture 1: Background segmentation with MOG (C++)

Lecture 2: MOG and MOG2 cuda implementation (C++ – cuda)

Lecture 3: Special app: Track class

Lecture 4: Special app: Track bgseg Foreground objects

Chapter 5: Object detection with openCV ML module (C++ CUDA)

Lecture 1: A simple application to prepare dataset for object detection (C++)

Lecture 2: Train model with openCV ML module (C++ and CUDA)

Lecture 3: Object detection with openCV ML module (C++ CUDA)

Chapter 6: Optical Flow

Lecture 1: Optical flow with Farneback (C++)

Lecture 2: Optical flow with Farneback (C++ CUDA)

Lecture 3: Optical flow with Nvidia optical flow SDK (C++ CUDA)

Lecture 4: Optical flow with Nvidia Flownet2 (Python)

Lecture 5: Performance Comparison

Chapter 7: Bonus/Different Examples

Lecture 1: Moving Camera Motion Estimation

Lecture 2: Warping via CUDA implementation

Lecture 3: Foreground object detection with feature comparison

Lecture 4: Custom object detection with CNN based features

Instructors

Ibrahim Delibasoglu
Lecturer at Sakarya University

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 4 votes

4 stars: 5 votes

5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!