Computer Vision and Machine Learning with OpenCV 4

Computer Vision and Machine Learning with OpenCV 4, available at $44.99, has an average rating of 4, with 110 lectures, 3 quizzes, based on 52 reviews, and has 440 subscribers.

You will learn about Build real-time applications that deal with image and video processing Build an Optical Character Recognition (OCR) engine from scratch Get to know how to train face recognition system Create your own real-time object classifier Build computer vision applications Create DNN based Image Classifier How to apply various Machine Learning algorithms to real-life problems Explore Supervised Learning and Unsupervised Learning approaches in Computer Vision Train your own custom image classifier using Convolutional Neural Networks This course is ideal for individuals who are This course is intended for Python developers, computer vision developers, and enthusiasts who want to learn machine learning algorithms and implement them with OpenCV 4 for building computer vision applications. It is particularly useful for This course is intended for Python developers, computer vision developers, and enthusiasts who want to learn machine learning algorithms and implement them with OpenCV 4 for building computer vision applications.

Enroll now: Computer Vision and Machine Learning with OpenCV 4

Summary

Title: Computer Vision and Machine Learning with OpenCV 4

Price: $44.99

Average Rating: 4

Number of Lectures: 110

Number of Quizzes: 3

Number of Published Lectures: 110

Number of Published Quizzes: 3

Number of Curriculum Items: 113

Number of Published Curriculum Objects: 113

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build real-time applications that deal with image and video processing

Build an Optical Character Recognition (OCR) engine from scratch

Get to know how to train face recognition system

Create your own real-time object classifier

Build computer vision applications

Create DNN based Image Classifier

How to apply various Machine Learning algorithms to real-life problems

Explore Supervised Learning and Unsupervised Learning approaches in Computer Vision

Train your own custom image classifier using Convolutional Neural Networks

Who Should Attend

This course is intended for Python developers, computer vision developers, and enthusiasts who want to learn machine learning algorithms and implement them with OpenCV 4 for building computer vision applications.

Target Audiences

This course is intended for Python developers, computer vision developers, and enthusiasts who want to learn machine learning algorithms and implement them with OpenCV 4 for building computer vision applications.

The application of Machine Learning and Deep Learning is rapidly gaining significance in Computer Vision. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art Computer Vision and Machine Learning algorithms. If you wish to build systems that are smarter, faster, sophisticated, and more practical by combining the power of Computer Vision, Machine Learning, and Deep Learning with OpenCV 4, then you should surely go for this Learning Path.

This hands-on course on OpenCV not only helps you learn computer vision and ML with OpenCV 4 but also enables you to apply these skills to your projects. You will firstly set up your development environment for building 5 interesting computer vision applications for Face and Eyes detection, Emotion recognition, and Fast QR code detection. You will then explore essential machine learning and deep learning concepts such as supervised learning, unsupervised learning, neural networks, and learn how to combine them with other OpenCV functionality for image processing and object detection. Along the way, you will also get some tips and tricks to work efficiently.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-On OpenCV 4 with Python, is designed for you to develop some real-world computer vision applications. You will begin with setting up your environment. You will then build five exciting applications. You will also be introduced to all necessary concepts and then moving into the field of Artificial Intelligence (AI) and deep learning such as classification and object detection with OpenCV 4.

The second course, OpenCV 4 Computer Vision with Python Recipes, starts off with an introduction to OpenCV 4 and familiarizes you with the advancements in this version. You will learn how to handle images, enhance, and transform them. You will also develop some cool applications including Face and Eyes detection, Emotion recognition, and Fast QR code detection & decoding which can be deployed anywhere.

The third course, Hands-On Machine Learning with OpenCV 4, will immerse you in Machine Learning and Deep Learning, and you’ll learn about key topics and concepts along the way.

By the end of this course, you will be able to tackle increasingly challenging computer vision problems faced in day-to-day life and leverage the power of machine learning algorithms to build machine learning systems and computer vision applications that are smarter, faster, more complex, and more practical.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help their clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world’s most popular soft drinks companies, helping each of them to better make sense of their data, and process it in more intelligent ways.
The company lives by their motto: Data -> Intelligence -> Action.

Sourav Johar has over two years of experience with OpenCV and over three years of experience coding in Python. He has also developed an open source library built on top of OpenCV. Along with this, he has developed several Deep Learning solutions, using OpenCV for video analysis. As a computer vision enthusiast, he completely understands what problems students face. He is very passionate about programming and enjoys making programming tutorials on YouTube. He is currently working for Colibri Digital (@colibri_digital) as an instructor.

Muhammad Hamza Javed is a self-taught Machine Learning engineer, an entrepreneur and an author having over five years of industrial experience. He and his team has been working on several Computer Vision and Machine Learning international projects. He started working when he was 17 and kept learning new technologies and skills since then. His areas of expertise include Computer Vision, Machine Learning and Deep Learning. He learned skills own his own without a direct mentor – so he knows how troublesome it is for everyone to find to-the-point content that really improves one’s skill-set. He’s designed this course considering the challenges he faced when he learned and, in the projects, so you don’t have to spend too much time on finding what’s best for you.

Course Curriculum

Chapter 1: Hands-On OpenCV 4 with Python

Lecture 1: The Course Overview

Lecture 2: Computer Vision with OpenCV 4

Lecture 3: Setting Up the Environment

Lecture 4: Preprocessing Video Input, Thresholding, and Blurring

Lecture 5: Calculating Image Differences

Lecture 6: Visualizing and Triggering Actions

Lecture 7: Understanding Histograms and Back Projection

Lecture 8: Implementing the Histogram Capture for Skin

Lecture 9: Implementing Back Projection on Input Video Feed

Lecture 10: Bounding the Hand – Contour Extraction

Lecture 11: Extracting Fingertips – Convexity Defects

Lecture 12: Air Writing – Translating Gestures to Controls

Lecture 13: Using Haar Cascades – Eye and Face Detection

Lecture 14: Extending Haar Cascades for Eye Detection

Lecture 15: GUI Automation – Interfacing the App with a Media Player

Lecture 16: Deep Learning – What and Why?

Lecture 17: Using the DNN Module with a Pre-Trained Model

Lecture 18: Digging Deeper – Feeding the Input Image to the Neural Network

Lecture 19: Running Object Detection on Videos

Lecture 20: Optical Character Recognition –What, Why, and How?

Lecture 21: Training a Digit Classifier on the MNIST Dataset

Lecture 22: Developing the OCR Engine Functions

Lecture 23: Developing the OCR Engine Functions (Continued)

Lecture 24: OCR Square Calculator

Chapter 2: OpenCV 4 Computer Vision with Python Recipes

Lecture 1: The Course Overview

Lecture 2: Installation and Setup

Lecture 3: Reading Images from Files

Lecture 4: Simple Image Transformations

Lecture 5: Saving the Images

Lecture 6: Showing the Images

Lecture 7: Drawing 2D Primitives

Lecture 8: Handling User Input from a Keyboard

Lecture 9: Handling User Input from a Mouse

Lecture 10: Capturing and Showing Frames from a Camera

Lecture 11: Playing Frame Stream from Video

Lecture 12: Manipulating Matrices-Creating, Filling, Accessing Elements, and ROIs

Lecture 13: Converting between Different Data Types and Scaling Values

Lecture 14: Non-Image Data Persistence Using NumPy

Lecture 15: Manipulating Image Channels

Lecture 16: Converting Images from One Color Space to Another

Lecture 17: Computing Image Histograms

Lecture 18: Removing Noise Using Gaussian, Median, and Bilateral Filters

Lecture 19: Creating and Applying Your Own Filter

Lecture 20: Processing Images with Different Thresholds

Lecture 21: Morphological Operators

Lecture 22: Image Masks and Binary Operations

Lecture 23: Binarization of Grayscale Images Using the Otsu Algorithm

Lecture 24: Finding External and Internal Contours in a Binary Image

Lecture 25: Extracting Connected Components from a Binary Image

Lecture 26: Fitting Lines and Circles into Two-Dimensional Point Sets

Lecture 27: Calculating Image Moments

Lecture 28: Checking Whether a Point is Within a Contour

Lecture 29: Computing Distance Maps

Lecture 30: Image Segmentation Using the k-Means Algorithm

Lecture 31: Warping an Image Using Affine and Perspective Transformations

Lecture 32: Stitching Many Images into Panorama

Lecture 33: Removing Defects from a Photo with Image Inpainting

Lecture 34: Finding Corners in an Image – Harris and FAST

Lecture 35: Computing Descriptors for Image Key Points Using ORB

Lecture 36: Obtaining an Object Mask Using the GrabCut Algorithm

Lecture 37: Finding Edges Using the Canny Algorithm

Lecture 38: Detecting Lines and Circles Using the Hough Transform

Lecture 39: Finding Objects via Template Matching

Lecture 40: Medial Flow Tracker

Lecture 41: Tracking Objects Using Different Algorithms via the Tracking API

Lecture 42: Computing the Dense Optical Flow between Two Frames

Lecture 43: Detecting Chessboard and Circle Grid Patterns

Lecture 44: Simple Pedestrian Detector Using the SVM Model

Lecture 45: Optical Character Recognition Using Different Machine Learning Models

Lecture 46: Detecting Faces Using Haar Cascades

Lecture 47: Fast QR Code Detector and Decoder

Lecture 48: Representing Images as Tensors/Blobs

Lecture 49: Loading Deep Learning Models Using OpenCV | Caffe, Torch and TensorFlow

Lecture 50: Preprocessing Images and Inference in Convolutional Networks

Lecture 51: Dataset Collection from ImageNet

Lecture 52: Dataset Annotation with LabelImg

Lecture 53: Dataset Augmentation

Lecture 54: Classifying Images with GoogleNet/Inception and ResNet Models

Lecture 55: Detecting Objects with the Single Shot Detection (SSD) Model

Lecture 56: Segmenting a Scene Using the Fully Convolutional Network (FCN) Model

Lecture 57: Introduction to Open Model Zoo

Lecture 58: ONNX (Open Neural Network Exchange)

Lecture 59: G-API (Graph API)

Lecture 60: Age and Gender Recognition

Lecture 61: Face Detection and Emotion Recognition

Lecture 62: Human Detection

Lecture 63: Advanced Applications with OpenVINO

Chapter 3: Hands-On Machine Learning with OpenCV 4

Lecture 1: The Course Overview

Lecture 2: Introduction to Machine Learning in Computer Vision

Lecture 3: Setting Up the Development Environment

Lecture 4: Reading Images and Video Feeds

Lecture 5: Manipulating Image Properties — Color Spaces, Thresholding

Lecture 6: Exploring the Drawing Functions of OpenCV

Lecture 7: Understanding Supervised Learning

Lecture 8: A Quick Comparison – KNN versus SVM

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 4 votes

2 stars: 5 votes

3 stars: 8 votes

4 stars: 19 votes

5 stars: 16 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.

Can I take my courses with me wherever I go?

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!