Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques. Code

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Introduction to Computer Vision and Neural Networks
Chapter 1: Computer Vision and TensorFlow Fundamentals
	Technical requirements
	Detecting edges using image hashing and filtering
		Using a Bayer filter for color pattern formation
		Creating an image vector
		Transforming an image
		Linear filtering—convolution with kernels
			Image smoothing
				The mean filter
				The median filter
				The Gaussian filter
				Image filtering with OpenCV
			Image gradient
			Image sharpening
		Mixing the Gaussian and Laplacian operations
		Detecting edges in an image
			The Sobel edge detector
			The Canny edge detector
	Extracting features from an image
		Image matching using OpenCV
	Object detection using Contours and the HOG detector
		Contour detection
		Detecting a bounding box
		The HOG detector
		Limitations of the contour detection method
	An overview of TensorFlow, its ecosystem, and installation
		TensorFlow versus PyTorch
			TensorFlow Installation
	Summary
Chapter 2: Content Recognition Using Local Binary Patterns
	Processing images using LBP
		Generating an LBP pattern
		Understanding the LBP histogram
			Histogram comparison methods
		The computational cost of 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
Chapter 3: Facial Detection Using OpenCV and CNN
	Applying Viola-Jones AdaBoost learning and the Haar cascade classifier for face recognition
		Selecting Haar-like features 
		Creating an integral image
		Running AdaBoost training
		Attentional cascade classifiers
		Training the cascade detector
	Predicting facial key points using a deep neural network
		Preparing the dataset for key-point detection
		Processing key-point data
			Preprocessing before being input into the Keras–Python code 
			Preprocessing within the Keras–Python code 
		Defining the model architecture
		Training the model to make key point predictions
	Predicting facial expressions using a CNN
	Overview of 3D face detection
		Overview of hardware design for 3D reconstruction
		Overview of 3D reconstruction and tracking
		Overview of parametric tracking
	Summary
Chapter 4: Deep Learning on Images
	Understanding CNNs and their parameters
		Convolution
		Convolution over volume – 3 x 3 filter
		Convolution over volume – 1 x 1 filter
		Pooling
		Padding 
		Stride
		Activation
			Fully connected layers
		Regularization
		Dropout
		Internal covariance shift and batch normalization 
		Softmax
	Optimizing CNN parameters
		Baseline case
		Iteration 1 – CNN parameter adjustment
		Iteration 2 – CNN parameter adjustment
		Iteration 3 – CNN parameter adjustment
		Iteration 4 – CNN parameter adjustment
	Visualizing the layers of a neural network
		Building a custom image classifier model and visualizing its layers
			Neural network input and parameters
			Input image 
			Defining the train and validation generators
			Developing the model 
			Compiling and training the model
			Inputting a test image and converting it into a tensor
			Visualizing the first layer of activation
			Visualizing multiple layers of activation
		Training an existing advanced image classifier model and visualizing its layers
	Summary
Section 2: Advanced Concepts of Computer Vision with TensorFlow
Chapter 5: Neural Network Architecture and Models
	Overview of AlexNet
	Overview of VGG16
	Overview of Inception
		GoogLeNet detection
	Overview of ResNet
	Overview of R-CNN
		Image segmentation 
			Clustering-based segmentation
			Graph-based segmentation
		Selective search
		Region proposal
		Feature extraction
		Classification of the image
		Bounding box regression
	Overview of Fast R-CNN
	Overview of Faster R-CNN
	Overview of GANs
	Overview of GNNs
		Spectral GNN
	Overview of Reinforcement Learning
	Overview of Transfer Learning
	Summary
Chapter 6: Visual Search Using Transfer Learning
	Coding deep learning models using TensorFlow
		Downloading weights
		Decoding predictions
		Importing other common features
		Constructing a model
		Inputting images from a directory
		Loop function for importing multiple images and processing using TensorFlow Keras
	Developing a transfer learning model using TensorFlow
		Analyzing and storing data
		Importing TensorFlow libraries
		Setting up model parameters
		Building an input data pipeline
			Training data generator
			Validation data generator
		Constructing the final model using transfer learning
		Saving a model with checkpoints
		Plotting training history
	Understanding the architecture and applications of visual search
		The architecture of visual search
		Visual search code and explanation
			Predicting the class of an uploaded image
			Predicting the class of all images
	Working with a visual search input pipeline using tf.data
	Summary
Chapter 7: Object Detection Using YOLO
	An overview of YOLO
		The concept of IOU
		How does YOLO detect objects so fast?
		The YOLO v3 neural network architecture
		A comparison of YOLO and Faster R-CNN
	An introduction to Darknet for object detection
		Detecting objects using Darknet
		Detecting objects using Tiny Darknet
	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
		Preparing images
		Generating annotation files
		Converting .xml files to .txt files
		Creating a combined train.txt and test.txt file
		Creating a list of class name files
		Creating a YOLO .data file
		Adjusting the YOLO configuration file
		Enabling the GPU for training
		Start training
	An overview of the Feature Pyramid Network and RetinaNet
	Summary
Chapter 8: Semantic Segmentation and Neural Style Transfer
	Overview of TensorFlow DeepLab for semantic segmentation
		Spatial Pyramid Pooling
			Atrous convolution
			Encoder-decoder network
				Encoder module
				Decoder module
		Semantic segmentation in DeepLab – example
			Google Colab, Google Cloud TPU, and TensorFlow
	Artificial image generation using DCGANs
		Generator
		Discriminator
		Training
			Image inpainting using DCGAN
		TensorFlow DCGAN – example
	Image inpainting using OpenCV
	Understanding neural style transfer
	Summary
Section 3: Advanced Implementation of Computer Vision with TensorFlow
Chapter 9: Action Recognition Using Multitask Deep Learning
	Human pose estimation – OpenPose
		Theory behind OpenPose 
		Understanding the OpenPose code
	Human pose estimation – stacked hourglass model
		Understanding the hourglass model
		Coding an hourglass model
			argparse block
			Training an hourglass network
			Creating the hourglass network
				Front module
				Left half-block
				Connect left to right
				Right half-block
				Head block
			Hourglass training
	Human pose estimation – PoseNet
		Top-down approach
		Bottom-up approach
		PoseNet implementation
		Applying human poses for gesture recognition
	Action recognition using various methods
		Recognizing actions based on an accelerometer
		Combining video-based actions with pose estimation
		Action recognition using the 4D method
	Summary
Chapter 10: 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
		Collecting and formatting images as .jpg files
		Annotating images to create a .xml file
		Separating the file by train and test folders
		Configuring parameters and installing the required packages
		Creating TensorFlow records
		Preparing the model and configuring the training pipeline
		Monitoring training progress using TensorBoard
			TensorBoard running on a local machine
			TensorBoard running on Google Colab
		Training the model
		Running an inference test
		Caution when using the neural network model
	An overview of Mask R-CNN and a Google Colab demonstration
	Developing an object tracker model to complement the object detector
		Centroid-based tracking
		SORT tracking
		DeepSORT tracking
		The OpenCV tracking method
		Siamese network-based tracking
		SiamMask-based tracking
	Summary
Section 4: TensorFlow Implementation at the Edge and on the Cloud
Chapter 11: 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
		Raspberry Pi hardware setup
		Raspberry Pi camera software setup
		OpenCV installation in Raspberry Pi
		OpenVINO installation in Raspberry Pi
		Installing the OpenVINO toolkit components
			Setting up the environmental variable
			Adding a USB rule
			Running inference using Python code
			Advanced inference
				Face detection, pedestrian detection, and vehicle detection
				Landmark models
				Models for action recognition
				License plate, gaze, and person detection
	Model conversion and inference using OpenVINO
		Running inference in a Terminal using ncappzoo
		Converting the pre-trained model for inference
			Converting from a TensorFlow model developed using Keras
	Converting a TensorFlow model developed using the TensorFlow Object Detection API
		Summary of the OpenVINO Model inference process
	Application of TensorFlow Lite
		Converting a TensorFlow model into tflite format
			Python API
			TensorFlow Object Detection API – tflite_convert
			TensorFlow Object Detection API – toco
		Model optimization
	Object detection on Android phones using TensorFlow Lite
	Object detection on Raspberry Pi using TensorFlow Lite
		Image classification
		Object detection
	Object detection on iPhone using TensorFlow Lite and Create ML
		TensorFlow Lite conversion model for iPhone
		Core ML
		Converting a TensorFlow model into Core ML format
	A summary of various annotation methods
		Outsource labeling work to a third party
		Automated or semi-automated labeling
	Summary
Chapter 12: Cloud Computing Platform for Computer Vision
	Training an object detector in GCP
		Creating a project in GCP
		The GCP setup
		The Google Cloud Storage bucket setup
			Setting up a bucket using the GCP API
			Setting up a bucket using Ubuntu Terminal
		Setting up the Google Cloud SDK
		Linking your terminal to the Google Cloud project and bucket
		Installing the TensorFlow object detection API
		Preparing the dataset
			TFRecord and labeling map data
				Data preparation
				Data upload
			The model.ckpt files
			The model config file
		Training in the cloud
		Viewing the model output in TensorBoard
		The model output and conversion into a frozen graph
		Executing export tflite graph.py from Google Colab
	Training an object detector in the AWS SageMaker cloud platform
		Setting up an AWS account, billing, and limits
		Converting a .xml file to JSON format
		Uploading data to the S3 bucket
		Creating a notebook instance and beginning training
		Fixing some common failures during training
	Training an object detector in the Microsoft Azure cloud platform
		Creating an Azure account and setting up Custom Vision
		Uploading training images and tagging them
	Training at scale and packaging
		Application packaging
	The general idea behind cloud-based visual search
	Analyzing images and search mechanisms in various cloud platforms
		Visual search using GCP
		Visual search using AWS
		Visual search using Azure
	Summary
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