Opencv Master Opencv 3 Application Development Using Python

What you'll learn
Build an Image Search Engine 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
Requirements
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
Description
OpenCV is a cross-platform, used for real-time 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 segmentation.
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 segmentation, 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 time. 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 Millán Escrivá 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 Politécnica 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.Vinícius 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 Informática, 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 challenging 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 engineer, 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.
Overview
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 Segmenting 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 Segmentation
Lecture 62 Medical Imaging and Segmentation
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-Time Face Detection Based on Eigenfaces
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


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