Grasp the concepts of OpenCV 4 to build powerful machine learning systems and computer vision applications with OpenCV 4
What you'll 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
Requirements
Working knowledge of Python programming is required.
Description
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 OverviewThis 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.
Overview
Section 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
Section 2: OpenCV 4 Computer Vision with Python Recipes
Lecture 25 The Course Overview
Lecture 26 Installation and Setup
Lecture 27 Reading Images from Files
Lecture 28 Simple Image Transformations
Lecture 29 Saving the Images
Lecture 30 Showing the Images
Lecture 31 Drawing 2D Primitives
Lecture 32 Handling User Input from a Keyboard
Lecture 33 Handling User Input from a Mouse
Lecture 34 Capturing and Showing Frames from a Camera
Lecture 35 Playing Frame Stream from Video
Lecture 36 Manipulating Matrices-Creating, Filling, Accessing Elements, and ROIs
Lecture 37 Converting between Different Data Types and Scaling Values
Lecture 38 Non-Image Data Persistence Using NumPy
Lecture 39 Manipulating Image Channels
Lecture 40 Converting Images from One Color Space to Another
Lecture 41 Computing Image Histograms
Lecture 42 Removing Noise Using Gaussian, Median, and Bilateral Filters
Lecture 43 Creating and Applying Your Own Filter
Lecture 44 Processing Images with Different Thresholds
Lecture 45 Morphological Operators
Lecture 46 Image Masks and Binary Operations
Lecture 47 Binarization of Grayscale Images Using the Otsu Algorithm
Lecture 48 Finding External and Internal Contours in a Binary Image
Lecture 49 Extracting Connected Components from a Binary Image
Lecture 50 Fitting Lines and Circles into Two-Dimensional Point Sets
Lecture 51 Calculating Image Moments
Lecture 52 Checking Whether a Point is Within a Contour
Lecture 53 Computing Distance Maps
Lecture 54 Image Segmentation Using the k-Means Algorithm
Lecture 55 Warping an Image Using Affine and Perspective Transformations
Lecture 56 Stitching Many Images into Panorama
Lecture 57 Removing Defects from a Photo with Image Inpainting
Lecture 58 Finding Corners in an Image – Harris and FAST
Lecture 59 Computing Descriptors for Image Key Points Using ORB
Lecture 60 Obtaining an Object Mask Using the GrabCut Algorithm
Lecture 61 Finding Edges Using the Canny Algorithm
Lecture 62 Detecting Lines and Circles Using the Hough Transform
Lecture 63 Finding Objects via Template Matching
Lecture 64 Medial Flow Tracker
Lecture 65 Tracking Objects Using Different Algorithms via the Tracking API
Lecture 66 Computing the Dense Optical Flow between Two Frames
Lecture 67 Detecting Chessboard and Circle Grid Patterns
Lecture 68 Simple Pedestrian Detector Using the SVM Model
Lecture 69 Optical Character Recognition Using Different Machine Learning Models
Lecture 70 Detecting Faces Using Haar Cascades
Lecture 71 Fast QR Code Detector and Decoder
Lecture 72 Representing Images as Tensors/Blobs
Lecture 73 Loading Deep Learning Models Using OpenCV | Caffe, Torch and TensorFlow
Lecture 74 Preprocessing Images and Inference in Convolutional Networks
Lecture 75 Dataset Collection from ImageNet
Lecture 76 Dataset Annotation with LabelImg
Lecture 77 Dataset Augmentation
Lecture 78 Classifying Images with GoogleNet/Inception and ResNet Models
Lecture 79 Detecting Objects with the Single Shot Detection (SSD) Model
Lecture 80 Segmenting a Scene Using the Fully Convolutional Network (FCN) Model
Lecture 81 Introduction to Open Model Zoo
Lecture 82 ONNX (Open Neural Network Exchange)
Lecture 83 G-API (Graph API)
Lecture 84 Age and Gender Recognition
Lecture 85 Face Detection and Emotion Recognition
Lecture 86 Human Detection
Lecture 87 Advanced Applications with OpenVINO
Section 3: Hands-On Machine Learning with OpenCV 4
Lecture 88 The Course Overview
Lecture 89 Introduction to Machine Learning in Computer Vision
Lecture 90 Setting Up the Development Environment
Lecture 91 Reading Images and Video Feeds
Lecture 92 Manipulating Image Properties — Color Spaces, Thresholding
Lecture 93 Exploring the Drawing Functions of OpenCV
Lecture 94 Understanding Supervised Learning
Lecture 95 A Quick Comparison – KNN versus SVM
Lecture 96 Visualizing the Quick, Draw! Dataset and Establishing the ML Pipeline
Lecture 97 Classifying Hand-Made Sketches Using KNN and SVM
Lecture 98 How Unsupervised Learning Is Different
Lecture 99 Clustering and the K- Means Algorithms
Lecture 100 Using K-Means to Cluster the Quick, Draw! Dataset
Lecture 101 Understanding Histograms and Backprojection
Lecture 102 Detecting Objects in Real Time Using Colour
Lecture 103 Understanding What a Haar Cascade is
Lecture 104 Detecting Objects in Real Time Using Haar Cascades
Lecture 105 CNNs - What the Hype Is About
Lecture 106 Using a Pre-Trained Caffe Model for Object Detection
Lecture 107 Using the TensorFlow Object Detection API
Lecture 108 Gathering the Dataset and Annotating the Images
Lecture 109 Generate TFRecords and Train
Lecture 110 Export the Inference Graph and Test the Model
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.
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