by Muhammad Hamza Javed | Released January 2019 | ISBN: 9781789950816

Leverage the power of OpenCV 4 and Python to build computer vision applicationsAbout This VideoExplore the latest feature set and modern APIs in OpenCV 4Build computer vision applications with OpenCV functionality via Python APIGet to grips with image processing, object detection and machine learningIn DetailHave 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. Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : I/O AND GUI
   • The Course Overview 00:02:07
   • Installation and Setup 00:03:27
   • Reading Images from Files 00:04:24
   • Simple Image Transformations 00:04:32
   • Saving the Images 00:02:58
   • Showing the Images 00:02:52
   • Drawing 2D Primitives 00:04:14
   • Handling User Input from a Keyboard 00:02:27
   • Handling User Input from a Mouse 00:02:52
   • Capturing and Showing Frames from a Camera 00:02:32
   • Playing Frame Stream from Video 00:01:30
2. Chapter 2 : Matrices, Colors, and Filters
   • Manipulating Matrices-Creating, Filling, Accessing Elements, and ROIs 00:06:04
   • Converting between Different Data Types and Scaling Values 00:03:26
   • Non-Image Data Persistence Using NumPy 00:02:10
   • Manipulating Image Channels 00:02:40
   • Converting Images from One Color Space to Another 00:03:07
   • Computing Image Histograms 00:01:35
   • Removing Noise Using Gaussian, Median, and Bilateral Filters 00:02:37
   • Creating and Applying Your Own Filter 00:01:35
   • Processing Images with Different Thresholds 00:01:34
   • Morphological Operators 00:01:48
   • Image Masks and Binary Operations 00:02:41
3. Chapter 3 : Contours and Segmentation
   • Binarization of Grayscale Images Using the Otsu Algorithm 00:02:27
   • Finding External and Internal Contours in a Binary Image 00:02:05
   • Extracting Connected Components from a Binary Image 00:03:49
   • Fitting Lines and Circles into Two-Dimensional Point Sets 00:02:08
   • Calculating Image Moments 00:01:45
   • Checking Whether a Point is Within a Contour 00:02:48
   • Computing Distance Maps 00:01:41
   • Image Segmentation Using the k-Means Algorithm 00:02:48
4. Chapter 4 : Image Processing
   • Warping an Image Using Affine and Perspective Transformations 00:05:36
   • Stitching Many Images into Panorama 00:01:56
   • Removing Defects from a Photo with Image Inpainting 00:03:00
   • Finding Corners in an Image – Harris and FAST 00:02:28
   • Computing Descriptors for Image Key Points Using ORB 00:02:03
5. Chapter 5 : Object Detection and Machine Learning
   • Obtaining an Object Mask Using the GrabCut Algorithm 00:04:18
   • Finding Edges Using the Canny Algorithm 00:01:35
   • Detecting Lines and Circles Using the Hough Transform 00:02:23
   • Finding Objects via Template Matching 00:02:21
   • Medial Flow Tracker 00:02:24
   • Tracking Objects Using Different Algorithms via the Tracking API 00:02:20
   • Computing the Dense Optical Flow between Two Frames 00:01:20
   • Detecting Chessboard and Circle Grid Patterns 00:02:08
   • Simple Pedestrian Detector Using the SVM Model 00:01:48
   • Optical Character Recognition Using Different Machine Learning Models 00:04:09
   • Detecting Faces Using Haar Cascades 00:01:50
   • Fast QR Code Detector and Decoder 00:01:38
6. Chapter 6 : Deep Learning
   • Representing Images as Tensors/Blobs 00:03:43
   • Loading Deep Learning Models Using OpenCV | Caffe, Torch and TensorFlow 00:01:53
   • Preprocessing Images and Inference in Convolutional Networks 00:02:22
   • Dataset Collection from ImageNet 00:02:10
   • Dataset Annotation with LabelImg 00:02:14
   • Dataset Augmentation 00:01:08
   • Classifying Images with GoogleNet/Inception and ResNet Models 00:02:29
   • Detecting Objects with the Single Shot Detection (SSD) Model 00:01:54
   • Segmenting a Scene Using the Fully Convolutional Network (FCN) Model 00:01:50
7. Chapter 7 : OpenVINO Toolkit
   • Introduction to Open Model Zoo 00:01:26
   • ONNX (Open Neural Network Exchange) 00:01:11
   • G-API (Graph API) 00:00:45
   • Age and Gender Recognition 00:02:18
   • Face Detection and Emotion Recognition 00:01:42
   • Human Detection 00:01:26
   • Advanced Applications with OpenVINO 00:01:45
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