Book Image

Mastering Computer Vision with TensorFlow 2.x

By : Krishnendu Kar
Book Image

Mastering Computer Vision with TensorFlow 2.x

By: Krishnendu Kar

Overview of this book

Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Introduction to Computer Vision and Neural Networks
Section 1: Introduction to Computer Vision and Neural Networks
Free Chapter
2
Computer Vision and TensorFlow Fundamentals
Computer Vision and TensorFlow Fundamentals
Technical requirements
Detecting edges using image hashing and filtering
Extracting features from an image
Object detection using Contours and the HOG detector
An overview of TensorFlow, its ecosystem, and installation
Summary
3
Content Recognition Using Local Binary Patterns
Content Recognition Using Local Binary Patterns
Processing images using LBP
Applying LBP to texture recognition
Matching face color with foundation color – LBP and its limitations
Matching face color with foundation color – color matching technique
Summary
4
Facial Detection Using OpenCV and CNN
Facial Detection Using OpenCV and CNN
Applying Viola-Jones AdaBoost learning and the Haar cascade classifier for face recognition
Predicting facial key points using a deep neural network
Predicting facial expressions using a CNN
Overview of 3D face detection
Summary
5
Deep Learning on Images
Deep Learning on Images
Understanding CNNs and their parameters
Optimizing CNN parameters
Visualizing the layers of a neural network
Summary
6
Section 2: Advanced Concepts of Computer Vision with TensorFlow
Section 2: Advanced Concepts of Computer Vision with TensorFlow
7
Neural Network Architecture and Models
Neural Network Architecture and Models
Overview of AlexNet
Overview of VGG16
Overview of Inception
Overview of ResNet
Overview of R-CNN
Overview of Fast R-CNN
Overview of Faster R-CNN
Overview of GANs
Overview of GNNs
Overview of Reinforcement Learning
Overview of Transfer Learning
Summary
8
Visual Search Using Transfer Learning
Visual Search Using Transfer Learning
Coding deep learning models using TensorFlow
Developing a transfer learning model using TensorFlow
Understanding the architecture and applications of visual search
Working with a visual search input pipeline using tf.data
Summary
9
Object Detection Using YOLO
Object Detection Using YOLO
An overview of YOLO
An introduction to Darknet for object detection
Real-time prediction using Darknet
YOLO versus YOLO v2 versus YOLO v3
When to train a model?
Training your own image set with YOLO v3 to develop a custom model
An overview of the Feature Pyramid Network and RetinaNet
Summary
10
Semantic Segmentation and Neural Style Transfer
Semantic Segmentation and Neural Style Transfer
Overview of TensorFlow DeepLab for semantic segmentation
Artificial image generation using DCGANs
Image inpainting using OpenCV
Understanding neural style transfer
Summary
11
Section 3: Advanced Implementation of Computer Vision with TensorFlow
Section 3: Advanced Implementation of Computer Vision with TensorFlow
12
Action Recognition Using Multitask Deep Learning
Action Recognition Using Multitask Deep Learning
Human pose estimation – OpenPose
Human pose estimation – stacked hourglass model
Human pose estimation – PoseNet
Action recognition using various methods
Summary
13
Object Detection Using R-CNN, SSD, and R-FCN
Object Detection Using R-CNN, SSD, and R-FCN
An overview of SSD
An overview of R-FCN
An overview of the TensorFlow object detection API
Detecting objects using TensorFlow on Google Cloud
Detecting objects using TensorFlow Hub
Training a custom object detector using TensorFlow and Google Colab
An overview of Mask R-CNN and a Google Colab demonstration
Developing an object tracker model to complement the object detector
Summary
14
Section 4: TensorFlow Implementation at the Edge and on the Cloud
Section 4: TensorFlow Implementation at the Edge and on the Cloud
15
Deep Learning on Edge Devices with CPU/GPU Optimization
Deep Learning on Edge Devices with CPU/GPU Optimization
Overview of deep learning on edge devices
Techniques used for GPU/CPU optimization
Overview of MobileNet
Image processing with a Raspberry Pi
Model conversion and inference using OpenVINO
Converting a TensorFlow model developed using the TensorFlow Object Detection API
Application of TensorFlow Lite
Object detection on Android phones using TensorFlow Lite
Object detection on Raspberry Pi using TensorFlow Lite
Object detection on iPhone using TensorFlow Lite and Create ML
A summary of various annotation methods
Summary
16
Cloud Computing Platform for Computer Vision
Cloud Computing Platform for Computer Vision
Training an object detector in GCP
Training an object detector in the AWS SageMaker cloud platform
Training an object detector in the Microsoft Azure cloud platform
Training at scale and packaging
The general idea behind cloud-based visual search
Analyzing images and search mechanisms in various cloud platforms
Summary
17
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YOLO versus YOLO v2 versus YOLO v3

A comparison of the three YOLO versions is shown in this table:

YOLO

YOLO v2

YOLO v3

Input size

224 x 224

448 x 448

Framework

Darknet trained on ImageNet—1,000.

Darknet-19

19 convolution layers and 5 max pool layers.

Darknet-53

53 convolutional layers. For detection, 53 more layers are added, giving a total of 106 layers.

Small size detection

It cannot find small images.

Better than YOLO at detecting small images.

Better than YOLO v2 at small image detection.

Uses anchor boxes.

Uses a residual block.

The following diagram compares the architectures of YOLO v2 and YOLO v3:

The basic convolution layers are similar, but YOLO v3 carries out detection at three separate layers: 82, 94, and 106.

The most critical item that you should take from YOLO v3 is its object detection...
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