Computer Vision By Using C++ and OpenCV with GPU support
Category: IT & Software
Course Info
Overview
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Description
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Who is this course for?
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Requirements
This Learning Paths Computer Vision By Using C++ and OpenCV with GPU support course will prepare you to make data-driven decisions that will give you a competitive edge. There are no formal requirements for this course. However smart gadgets and stable internet connection is required for a smooth learning journey.
Career Path
Earn a certificate of competence from the Learning Paths platform by learning all about Computer Vision By Using C++ and OpenCV with GPU support. This Computer Vision By Using C++ and OpenCV with GPU support course curriculum will benefit you at all stages of your career.
Certification
After successfully completing the Computer Vision By Using C++ and OpenCV with GPU support course, you will get your PDF certificate for FREE! The hardcopy certificate will cost only £11.99 with free shipping inside the UK. For delivery outside the UK an additional shipping charge will be applied.
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 | 00:00:00 | ||
| Assignment | |||
| Assignment – Computer Vision: C++ and OpenCV with GPU support | 00:00:00 | ||
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