Tag: OpenCV

Course content

• Introduction
• Hardwares
• Python Basics (Optional)
• OpenCV (Optional)
• Machine Learning
• Real Time Object Detection (Arduino Board)
• Arduino Basics (Optional)
• Project 1: Controlling Arduino with Python
• Jetson Nano Board Set Up
• OpenCV and Python Installation on Jetson Nano
• Installation of Camera
• L298N
• Project 2: School Crossing Sign (for Autonomous Vehicles)
• Other Courses

Course content

• Introduction
• Intro to Computer Vision & Deep Learning
• Installation Guide
• Handwriting Recognition
• OpenCV Tutorial – Learn Classic Computer Vision & Face Detection (OPTIONAL)
• Neural Networks Explained
• Convolutional Neural Networks (CNNs) Explained
• Build CNNs in Python using Keras
• What CNNs ‘see’ – Filter Visualizations, Heatmaps and Salience Maps
• Data Augmentation: Cats vs Dogs
• Assessing Model Performance
• Optimizers, Learning Rates & Callbacks with Fruit Classification
• Batch Normalization & LeNet, AlexNet: Clothing Classifier
• Advanced Image Classiers – ImageNet in Keras (VGG16/19, InceptionV3, ResNet50)
• Transfer Learning: Build a Flower & Monkey Breed Classifier
• Design Your Own CNN – LittleVGG: A Simpsons Classifier
• Advanced Activation Functions & Initializations
• Facial Applications – Emotion, Age & Gender Recognition
• Medical Imaging – Image Segmentation with U-Net
• Principles of Object Detection
• TensorFlow Object Detection API
• Object Detection with YOLO & Darkflow: Build a London Underground Sign Detector
• DeepDream & Neural Style Transfers: Make A1 Generated Art
• Generative Adversarial Networks (GANs): Simulate Aging Faces
• Face Recognition with VGGFace
• The Computer Vision World
• BONUS – Build a Credit Card Number Reader
• BONUS – Use Cloud GPUs on PaperSpace
• BONUS – Create a Computer Vision API & Web App Using Flask and AWS

Duration

21 hours (usually 3 days including breaks)

Requirements

• Some programming experience
• Experience with the Linux command line

Audience

• Developers
• Hardware/software technicians
• Technical persons in all industries
• Hobbyists

Overview

This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.

The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.

By the end of this training, participants will be able to:

• Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
• Configure OpenCV to capture and detect facial images.
• Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
• Adapt the system for a variety of use cases, including surveillance, identity verification, etc.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.

Course Outline

To request a customized course outline for this training, please contact us.

Duration

28 hours (usually 4 days including breaks)

Requirements

One of the following:

• C++
• Java
• Python
• MATLAB
• CUDA
• OpenCL

And basic knowledge of machine learning. Knowledge of linear algebra, statistics, probability are helpful.

Overview

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

Course Outline

Introduction

• Setting up OpenCV
• API concepts

Main Modules

• The Core Functionality(Core Module)
• Image Processing(Imgproc Module)
• High Level GUI and Media (highgui module)
• Image Input and Output (imgcodecs module)
• Video Input and Output (videoio module)
• Camera calibration and 3D reconstruction (calib3d module)
• 2D Features framework (feature2d module)
• Video analysis (video module)
• Object Detection (objdetect module)
• Machine Learning (ml module)
• Computational photography (photo module)
• OpenCV Viz

Bonus topics

• GPU-Accelerated Computer Vision (cuda module)
• OpenCV iOS

Bonus topics are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (for the CUDA module) or MacBooks, Apple developer accounts and iOS-based mobile devices (for the iOS topic). NobleProg cannot guarantee the availability of trainers with the required hardware.

Get started with Computer Vision using OpenCV, the largest and most extensive Computer Vision library in the world!

Requirements

• Basic Knowledge of Python

Description

Learn to use OpenCV for Computer Vision and AI in this official course for absolute beginners from OpenCV. You will learn and get exposed to a wide range of exciting topics like Image & Video Manipulation, Image Enhancement, Filtering, Edge Detection, Object Detection and Tracking, Face Detection, and the OpenCV Deep Learning Module.

This course helps you confidently take your very first steps in the exciting world of Computer Vision and AI. This field offers limitless opportunities in the Computer Vision and AI job market. Embark on this learning journey and welcome to the AI revolution!

Course Contents

Module 1: Getting Started with Images

Module 2: Basic Image Manipulation

Module 3: Image Annotation

Module 4: Arithmetic Operations on Images

Module 5: Bitwise Operations on Images

Module 6: Accessing the Camera

Module 7: Read and Write Videos

Module 8: Image Filtering and Edge Detection

Module 9: Image Features and Image Alignment

Module 10: Image Stitching and Creating Panoramas 

Module 11: Object Tracking in OpenCV

Module 12: Face Detection using Deep Learning

Module 13: Object Detection using Deep Learning

Module 14: Pose Estimation using OpenPose

Course Features

• Designed By Industry Experts: This course in OpenCV and Python is for absolute beginners has been designed by our team of engineers and researchers, currently working in the field of Computer Vision and Deep Learning. This course will help you confidently take your very first steps into the exciting world of Computer Vision and AI.
• Powered By Python: The programming language of choice for this short introductory course is Python, one of the most comprehensive and widely used languages in AI.
• Foundational & Experiential Learning: This course will help you develop a broad and basic understanding and practice of the subject matter before committing to more structured formal learning paths from beginner to mastery levels that are available online through OpenCV.
• Practical & Intuitive: The field of Computer Vision contains many theoretical underpinnings which can become a stumbling block for absolute beginners, especially, when courses contain heavy mathematics. With this in mind, we have created this ‘getting started’ course to provide you a wide exposure to this exciting field.

Who this course is for:

• Beginner Python Developers eager to learn Computer Vision and Deep Learning

Course content

15 sections • 15 lectures • 1h 59m total length