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

Name: Computer Vision by using C++ and OpenCV with GPU support
Rating: 3.8 (31 reviews)

Learn how to Use OpenCV with GPU support on Ubuntu OS

Created byIbrahim Delibasoglu

Last updated 9/2022

English

English [Auto],

What you'll learn

Computer vision
OpenCV
Computer vision app on Ubuntu OS
C++ skills

Course content

7 sections • 27 lectures • 3h 22m total length

Course Overview3:38

Driver installation5:46
In this video, you will see the different ways to install Nvidia driver on Ubuntu 18.04. If you wanna how i installed the driver directly in easier way, you can skip 05:20 :)
Cuda toolkit installation1:14
Compile OpenCV from source with CUDA support part-16:18
Compile OpenCV from source with CUDA support part-25:07
Note: install cuda toolkit 10.2 for Nvidia optical flow support

error fix while compiling opencv with cudaoptflow
error: no such file nvOpticalFlowCommon.h
Copy header files from OpticalFlowSDK/NvOFInterface/ to opencv_contrib/modules/cudaoptflow/src
Python environment for flownet2-pytorch9:27
This is to be able run deep learning based dense optical flow implementation flownet 2.0

Read camera & files in a folder (C++)10:54
Learn how to read images from a folder, read videos, and capture webcam frames using C++ with OpenCV, including listing, filtering JPEG files, sorting filenames, and displaying frames.
Edge detection (C++)7:34
Explore edge detection in images using the canny method in OpenCV with C++, converting to grayscale, applying hysteresis thresholding with two thresholds, and noting GPU acceleration.
Color transformations (C++)7:13
Using a trackbar (C++)5:59
Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++)13:13

Background segmentation with MOG (C++)3:58
MOG and MOG2 cuda implementation (C++ - cuda)3:25
Explore CUDA-accelerated MOG and MOG2 background subtraction in C++ with OpenCV, transferring frames and masks to the GPU, implementing background segmentation, and evaluating GPU performance against CPU.
Special app: Track class5:57
Special app: Track bgseg Foreground objects8:01
Extend the background subtraction workflow by applying gaussian filtering and morphological closing to refine the foreground mask, compute contours, filter regions, and track objects with a tracker.

Moving Camera Motion Estimation12:03
Warping via CUDA implementation6:50
Foreground object detection with feature comparison14:34
You will learn how to eliminate wrong detections (False Positives) in foreground detection for background movements.
Custom object detection with CNN based features13:58
You will see an example for Pytorch C++ API. We will save a model for feature extraction in the Python, and then use that model in C++.

By using extracted features, we will classify two target objects.

Requirements

Must have basic programming knowledge

Description

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-pytorch environment 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.

Who this course is for:

Software developers
Students want to work on Computer Vision

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

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

Set up Necesssary Environments5 lectures • 28min

Introduction with a few basic examples!5 lectures • 45min

Background segmentation4 lectures • 21min

Object detection with openCV ML module (C++ CUDA)3 lectures • 27min

Optical Flow5 lectures • 31min

Bonus/Different Examples4 lectures • 47min

Requirements

Description

Who this course is for: