Last updated 6/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.81 GB | Duration: 9h 18m

 

Build computer vision OpenCV 3 applications with Python

Build an Image Search Ee from Scratch based on feature extraction

Build an Android selfie camera app with emotion-based selfie filters

Build an Android App to generate panoramas with HDR and AR capabilities

Learn how to make a car learn how to drive itself based on imitation learning

Explore the new OpenCV functions for text detection and recognition with Tesseract

Get to grips with the computer vision workflows and understand the basic image matrix format and filters

Familiarity with OpenCV's concepts and Python libraries is assumed

Basic knowledge of Python programming is expected and assumed.

Basic understanding of computer vision and image processing will be useful

OpenCV is a cross-platform, used for real- computer vision and image processing. It is one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image sntation.

This comprehensive 3-in-1 course is a step-by-step tutorial to developing real-world computer vision applications using OpenCV 3 with Python. Program advanced computer vision applications in Python using different features of the OpenCV library. Boost your knowledge of computer vision and image processing by developing real-world projects in OpenCV 3 with Python.

Contents and Overview

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

The first course, OpenCV 3 by Example, covers a practical approach to computer vision and image processing by developing real-world projects in OpenCV 3. This course will teach you the basics of OpenCV such as matrix operations, filters, and histograms, as well as more advanced concepts such as sntation, machine learning, complex video analysis, and text recognition. You’ll create optical flow video analysis or text recognition in complex scenes, and learn computer vision techniques to build your own OpenCV projects from scratch.

The second course, Practical OpenCV 3 Image Processing with Python, covers amazing computer vision applications development with OpenCV 3. This course will teach you how to develop a series of intermediate-to-advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Working projects developed in this video teach you how to apply theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.

The third course, Hands-on TensorFlow Lite for Intelligent Mobile Apps, covers development of advanced OpenCV3 projects with Python. This course will teach you how to to perform 3D reconstruction by stitching multiple 2D images and recovering camera projection angles. You’ll learn to capture facial landmark points and recognize emotion in images, including in real . You’ll generate a panorama of a scene and augment a camera view with virtual objects.

By the end of the course, you’ll boost your knowledge of computer vision and image processing and develop real-world applications in OpenCV 3 with Python.

About the Authors

David Millan Escriva was eight years old when he wrote his first program on an 8086 PC with Basic language, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT through the Universitat Politecnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish congress. He participated in Blender, an open source, 3D-software project, and worked on his first commercial movie Plumiferos - Aventuras voladorasas, as a Computer Graphics Software Developer. David now has more than 10 years of experience in IT, with experience in computer vision, computer graphics, and pattern recognition, working on different projects and start-ups, applying his knowledge of computer vision, optical character recognition, and augmented reality. He is the author of the DamilesBlog, where he publishes research articles and tutorials about OpenCV, computer vision in general, and Optical Character Recognition algorithms. David has reviewed the book gnuPlot Cookbook, Packt Publishing, written by Lee Phillips.

Prateek Joshi is an Artificial Intelligence researcher, the published author of five books, and a TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2 million page views from over 200 countries and has over 6,600+ followers. He frequently writes on topics such as Artificial Intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a Master's degree, specializing in Artificial Intelligence. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website.Vinicius Godoy is a computer graphics university professor at PUCPR. He started programming with C 18 years ago and ventured into the field of computer gaming and computer graphics 10 years ago. His former experience also includes working as an IT manager in document processing applications in Sinax, a company that focuses in BPM and ECM activities, building games and applications for Positivo Informatica, including building an augmented reality educational game exposed at CEBIT and network libraries for Siemens Enterprise Communications (Unify). As part of his Master's degree research, he used Kinect, OpenNI, and OpenCV to recognize Brazilian sign language gestures. He is currently working with medical imaging systems for his PhD thesis. He was also a reviewer of the OpenNI Cookbook, Packt Publishing. He is also a game development fan, having a popular site entirely dedicated to the field called Ponto V. He is the cofounder of a startup company called Black Muppet. His fields of interest includes image processing, Computer Vision, design patterns, and multithreaded applications.

Riaz Munshi has a Bachelor's and a Master's degree in Computer Science from University of Buffalo, NY. He is a computer vision and machine learning enthusiast. Riaz has 3.5 years' experience working on challeg problems in mobility, computing, and augmented reality. He has a solid foundation in Computer Science, with strong competencies in data structures, algorithms, and software design. Currently he works at Yahoo as a software eeer, exploring use-cases that harness the power of AR to control robots. He makes robots perform more efficiently at their job by guiding them remotely via holograms.

Section 1: OpenCV 3 by Example

Lecture 1 The Course Overview

Lecture 2 The Human Visual System and Understanding Image Content

Lecture 3 What Can You Do with OpenCV?

Lecture 4 Installing OpenCV

Lecture 5 Basic CMakeConfiguration and Creating a Library

Lecture 6 Managing Dependencies

Lecture 7 Making the Script More Complex

Lecture 8 Images and Matrices

Lecture 9 Reading/Writing Images

Lecture 10 Reading Videos and Cameras

Lecture 11 Other Basic Object Types

Lecture 12 Basic Matrix Operations, Data Persistence, and Storage

Lecture 13 The OpenCVUser Interface and a Basic GUI

Lecture 14 The Graphical User Interface with QT

Lecture 15 Adding Slider and Mouse Events to Our Interfaces

Lecture 16 Adding Buttons to a User Interface

Lecture 17 OpenGL Support

Lecture 18 Generating a CMakeScript File

Lecture 19 Creating the Graphical User Interface

Lecture 20 Drawing a Histogram

Lecture 21 Image Color Equalization

Lecture 22 Lomography Effect

Lecture 23 The CartoonizeEffect

Lecture 24 Isolating Objects in a Scene

Lecture 25 Creating an Application for AOI

Lecture 26 Preprocessing the Input Image

Lecture 27 Snting Our Input Image

Lecture 28 Introducing Machine Learning Concepts

Lecture 29 Computer Vision and the Machine Learning Workflow

Lecture 30 Automatic Object Inspection Classification Example

Lecture 31 Feature Extraction

Lecture 32 Understanding Haar Cascades

Lecture 33 What Are Integral Images

Lecture 34 Overlaying a Facemask in a Live Video

Lecture 35 Get Your Sunglasses On

Lecture 36 Tracking Your Nose, Mouth, and Ears

Lecture 37 Background Subtraction

Lecture 38 Frame Differencing

Lecture 39 The Mixture of Gaussians Approach

Lecture 40 Morphological Image processing

Lecture 41 Other Morphological Operators

Lecture 42 Tracking Objects of a Specific Color

Lecture 43 Building an Interactive Object Tracker

Lecture 44 Detecting Points Using the Harris Corner Detector

Lecture 45 Shi-Tomasi Corner Detector

Lecture 46 Feature-Based Tracking

Lecture 47 Introducing Optical Character Recognition

Lecture 48 The Preprocessing Step

Lecture 49 Installing Tesseract OCR on Your Operating System

Lecture 50 Using Tesseract OCR Library

Section 2: Practical OpenCV 3 Image Processing with Python

Lecture 51 The Course Overview

Lecture 52 Learning about Hough Transformations

Lecture 53 Stretch, Shrink, Warp, and Rotate Using OpenCV 3

Lecture 54 Image Derivatives

Lecture 55 Histogram Equalization

Lecture 56 Reverse Image Search

Lecture 57 Extracting Contours from Images

Lecture 58 Template Matching for Object Detection

Lecture 59 Background Subtraction from Images

Lecture 60 Delaunay Triangulation and Voronoi Tessellation

Lecture 61 Mean-Shift Sntation

Lecture 62 Medical Imaging and Sntation

Lecture 63 Harris Corner Detection

Lecture 64 SIFT, SURF, FAST, BRIEF, and ORB Algorithms

Lecture 65 Feature Matching and Homography to Recognize Objects

Lecture 66 Mean-Shift, Cam-Shift, and Optical Flow

Lecture 67 Feature Extraction Using Convolutional Neural Nets (CNNs)

Lecture 68 Visual Object Recognition and Classification Using CNNs

Section 3: Building Advanced OpenCV3 Projects with Python

Lecture 69 The Course Overview

Lecture 70 Camera Projection Models

Lecture 71 Multi-View Stereo

Lecture 72 Generating Point Clouds

Lecture 73 2D-to-3D

Lecture 74 Street View

Lecture 75 Real- Face Detection Based on nfaces

Lecture 76 3D Head Pose Estimation

Lecture 77 Detecting Cats and Faces Using Haar Cascades

Lecture 78 Facial Landmark Detection Using Dlib Library

Lecture 79 Face Morphology, Averaging, and Swapping

Lecture 80 Expressions - A Selfie Camera App

Lecture 81 Image Stitching

Lecture 82 Aerial Video Montage

Lecture 83 Marker-Based Augmented Reality

Lecture 84 Markerless Augmented Reality

Lecture 85 High-Dynamic Range (HDR) Imaging

Lecture 86 Building a Panorama App

Lecture 87 Introduction to Self-Driving Cars

Lecture 88 Sensors and Measurements

Lecture 89 Self-Driving Car Architectures

Lecture 90 Understanding Perception in Self-Driving Cars

Lecture 91 Learning to Drive Using a CNN

Lecture 92 Building a Self-Driving Car Based on Imitation Learning

Software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV.,Anyone with a basic knowledge of OpenCV who would like to enhance their knowledge to develop advanced practical applications

HomePage: