English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 36m | 505 MB
Expand your OpenCV knowledge & use of machine learning to your advantage with this practical hand-on course!
Have you ever wondered how self-driving cars work? Have you ever wanted to land a highly paid job in Computer Vision industry?
We have compiled this course so you seize your opportunity to get noticed by building awesome Computer Vision applications.This course kicks-off with Introduction to OpenCV 4 and familiarizes you with the advancements in this version. We’ll educate you on how to handle images, enhance and transform them. We’ll also develop some cool applications including Face and Eyes detection, Emotion recognition and Fast QR code detection & decoding that you can deploy anywhere. We’ll also share some tips & tricks to make you more productive.
By the end of the course, you will have profound knowledge on what Computer Vision is and how we can leverage OpenCV 4 to build real-world applications without much effort.
This course helps you learn the core concepts of OpenCV faster by taking a recipe-based approach where you can try out different code snippets to understand a concept. Every operation is performed step-by-step and the code is neatly documented so it’s easier for the audience to reuse the modules in their own projects.
What You Will Learn
- How to build real-world Computer Vision applications.
- Deploy Face and Eyes Detection with HAAR Cascade Classifiers.
- Recognize Age, Gender and Emotions and Roadside Landmarks.
- Develop Fast QR Code Detection and Decoding application.
- Create DNN based Image Classifier.
- Train an Object Detection Model and Detect Persons, and Vehicles.
Table of Contents
I/O AND GUI
The Course Overview
Installation and Setup
Reading Images from Files
Simple Image Transformations
Saving the Images
Showing the Images
Drawing 2D Primitives
Handling User Input from a Keyboard
Handling User Input from a Mouse
Capturing and Showing Frames from a Camera
Playing Frame Stream from Video
MATRICES, COLORS, AND FILTERS
Manipulating Matrices-Creating, Filling, Accessing Elements, and ROIs
Converting between Different Data Types and Scaling Values
Non-Image Data Persistence Using NumPy
Manipulating Image Channels
Converting Images from One Color Space to Another
Computing Image Histograms
Removing Noise Using Gaussian, Median, and Bilateral Filters
Creating and Applying Your Own Filter
Processing Images with Different Thresholds
Morphological Operators
Image Masks and Binary Operations
CONTOURS AND SEGMENTATION
Binarization of Grayscale Images Using the Otsu Algorithm
Finding External and Internal Contours in a Binary Image
Extracting Connected Components from a Binary Image
Fitting Lines and Circles into Two-Dimensional Point Sets
Calculating Image Moments
Checking Whether a Point is Within a Contour
Computing Distance Maps
Image Segmentation Using the k-Means Algorithm
IMAGE PROCESSING
Warping an Image Using Affine and Perspective Transformations
Stitching Many Images into Panorama
Removing Defects from a Photo with Image Inpainting
Finding Corners in an Image – Harris and FAST
Computing Descriptors for Image Key Points Using ORB
OBJECT DETECTION AND MACHINE LEARNING
Obtaining an Object Mask Using the GrabCut Algorithm
Finding Edges Using the Canny Algorithm
Detecting Lines and Circles Using the Hough Transform
Finding Objects via Template Matching
Medial Flow Tracker
Tracking Objects Using Different Algorithms via the Tracking API
Computing the Dense Optical Flow between Two Frames
Detecting Chessboard and Circle Grid Patterns
Simple Pedestrian Detector Using the SVM Model
Optical Character Recognition Using Different Machine Learning Models
Detecting Faces Using Haar Cascades
Fast QR Code Detector and Decoder
DEEP LEARNING
Representing Images as Tensors/Blobs
Loading Deep Learning Models Using OpenCV | Caffe, Torch and TensorFlow
Preprocessing Images and Inference in Convolutional Networks
Dataset Collection from ImageNet
Dataset Annotation with LabelImg
Dataset Augmentation
Classifying Images with GoogleNet/Inception and ResNet Models
Detecting Objects with the Single Shot Detection (SSD) Model
Segmenting a Scene Using the Fully Convolutional Network (FCN) Model
OPENVINO TOOLKIT
Introduction to Open Model Zoo
ONNX (Open Neural Network Exchange)
G-API (Graph API)
Age and Gender Recognition
Face Detection and Emotion Recognition
Human Detection
Advanced Applications with OpenVINO
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