OpenCV 4 Computer Vision with Python Recipes [Video]
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Free ChapterI/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
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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
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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
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Image Processing
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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
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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
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OpenVINO Toolkit
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.
Style and Approach
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.
- Publication date:
- January 2019
- Publisher
- Packt
- Duration
- 2 hours 36 minutes
- ISBN
- 9781789950816