| Description |
1 online resource (374 pages) |
| Contents |
Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window |
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Using HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN |
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Ethernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video |
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Partial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load? |
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Training samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers |
| Summary |
This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python |
| Notes |
Print version record |
| Subject |
Automated vehicles -- Computer programs
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Computer vision.
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Python (Computer program language)
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OpenCV (Computer program language)
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Computer vision
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OpenCV (Computer program language)
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Python (Computer program language)
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| Form |
Electronic book
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| Author |
Korda, Krishtof
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| ISBN |
1800201931 |
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9781800201934 |
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