Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Mastering Computer Vision with TensorFlow 2.x
Mastering Computer Vision with TensorFlow 2.x

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

eBook
Can$34.98 Can$49.99
Paperback
Can$61.99
Subscription
Free Trial

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Mastering Computer Vision with TensorFlow 2.x

Computer Vision and TensorFlow Fundamentals

Computer vision is rapidly expanding in many different applications as traditional techniques, such as image thresholding, filtering, and edge detection, have been augmented by deep learning methods. TensorFlow is a widely used, powerful machine learning tool created by Google. It has user configurable APIs available to train and build complex neural network model in your local PC or in the cloud and optimize and deploy at scale in edge devices.

In this chapter, you will gain an understanding of advanced computer vision concepts using TensorFlow. This chapter discusses the foundational concepts of computer vision and TensorFlow to prepare you for the later, more advanced chapters of this book. We will look at how to perform image hashing and filtering. Then, we will learn about various methods of feature extraction and image retrieval...

Technical requirements

If you have not done so already, install Anaconda from https://www.anaconda.com. Anaconda is a package manager for Python. You also need to install OpenCV for all of the computer vision work you will be carrying out, using pip install opencv-python. OpenCV is a library of built-in programming functions for computer vision work.

Detecting edges using image hashing and filtering

Image hashing is a method used to find similarity between images. Hashing involves modifying an input image to a fixed size of binary vector through transformation. There are different algorithms for image hashing using different transformations:

  • Perpetual hash (phash): A cosine transformation
  • Difference hash (dhash): The difference between adjacent pixels

After a hash transformation, images can be compared quickly with the Hamming distance. The Python code for applying a hash transformation is shown in the following code. A hamming distance of 0 shows an identical image (duplicate), whereas a larger hamming distance shows that the images are different from each other. The following snippet imports Python packages, such as PIL, imagehash, and distance. imagehash is a Python package that supports various types of hashing algorithms...

Extracting features from an image

Once we know how to detect edges, the next task is to detect features. Many edges combine to form features. Feature extraction is the process of recognizing visual patterns in an image and extracting any discriminating local features that match with the image of an unknown object. Before performing feature extraction, it is important to understand the image histogram. An image histogram is the distribution of the color intensity of the image.

An image feature matches with the test image if the histograms are similar. The following is the Python code used to create an image histogram of the car:

import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.pyplot as plt
from PIL import Image
image = Image.open('../car.png')
plt.imshow(image)
image_arr = np.asarray(image) # convert image to numpy array
image_arr...

Object detection using Contours and the HOG detector

Contours are closed regions within an image that has a similar shape. In this section, we will use Contours to classify and detect simple objects within an image. The image we will use consists of apples and oranges and we will use the Contour and the Canny edge detection method to detect the object and write the image class name on the bounding box. The code for this section can be found at https://github.com/PacktPublishing/Mastering-Computer-Vision-with-TensorFlow-2.0/blob/master/Chapter01/Chapter1_contours_opencv_object_detection_HOG.ipynb.

The methodology is described in the following subsections.

Contour detection

We first need to import the image and then use the...

An overview of TensorFlow, its ecosystem, and installation

In the previous sections, we covered the basics of computer vision techniques, such as image conversion, image filtering, convolution using a kernel, edge detection, histograms, and feature matching. This understanding and its various applications should develop a solid foundation for the advanced concept of deep learning, which will be introduced later on in this book.

Deep learning in computer vision is the cumulative learning of many different image features (such as edges, colors, boundaries, shapes, and so on) through a convolution operation of many intermediate (hidden) layers to gain a complete understanding of the image type. Deep learning augments computer vision techniques because it stacks many layers of calculations about how neurons behave. This is done by combining various inputs to produce outputs based...

Summary

In this chapter, we learned how image filtering modifies the input image through a convolution operation to produce an output that detects a portion of a feature called an edge. This is fundamental to computer vision. As you will learn in the following chapters, subsequent application of image filtering will transform the edges to a higher-level pattern, such as features.

We also learned how to calculate an image histogram, perform image matching using SIFT, and use contour and the HOG detector to draw a bounding box. We learned how to use OpenCV's bounding box color and size method to segregate one class from another. The chapter concluded with an introduction to TensorFlow, which will provide a foundation for the remaining chapters of this book.

In the next chapter, we will learn about a different type of computer vision technique, called pattern recognition, and...

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Gain a fundamental understanding of advanced computer vision and neural network models in use today
  • Cover tasks such as low-level vision, image classification, and object detection
  • Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit

Description

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.

Who is this book for?

This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

What you will learn

  • Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
  • Use TensorFlow for various visual search methods for real-world scenarios
  • Build neural networks or adjust parameters to optimize the performance of models
  • Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
  • Evaluate your model and optimize and integrate it into your application to operate at scale
  • Get up to speed with techniques for performing manual and automated image annotation

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : May 15, 2020
Length: 430 pages
Edition : 1st
Language : English
ISBN-13 : 9781838827069
Vendor :
Google
Category :
Languages :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : May 15, 2020
Length: 430 pages
Edition : 1st
Language : English
ISBN-13 : 9781838827069
Vendor :
Google
Category :
Languages :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just Can$6 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just Can$6 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total Can$ 201.97
Modern Computer Vision with PyTorch
Can$83.99
Mastering Computer Vision with TensorFlow 2.x
Can$61.99
Advanced Deep Learning with TensorFlow 2 and Keras
Can$55.99
Total Can$ 201.97 Stars icon

Table of Contents

17 Chapters
Section 1: Introduction to Computer Vision and Neural Networks Chevron down icon Chevron up icon
Computer Vision and TensorFlow Fundamentals Chevron down icon Chevron up icon
Content Recognition Using Local Binary Patterns Chevron down icon Chevron up icon
Facial Detection Using OpenCV and CNN Chevron down icon Chevron up icon
Deep Learning on Images Chevron down icon Chevron up icon
Section 2: Advanced Concepts of Computer Vision with TensorFlow Chevron down icon Chevron up icon
Neural Network Architecture and Models Chevron down icon Chevron up icon
Visual Search Using Transfer Learning Chevron down icon Chevron up icon
Object Detection Using YOLO Chevron down icon Chevron up icon
Semantic Segmentation and Neural Style Transfer Chevron down icon Chevron up icon
Section 3: Advanced Implementation of Computer Vision with TensorFlow Chevron down icon Chevron up icon
Action Recognition Using Multitask Deep Learning Chevron down icon Chevron up icon
Object Detection Using R-CNN, SSD, and R-FCN Chevron down icon Chevron up icon
Section 4: TensorFlow Implementation at the Edge and on the Cloud Chevron down icon Chevron up icon
Deep Learning on Edge Devices with CPU/GPU Optimization Chevron down icon Chevron up icon
Cloud Computing Platform for Computer Vision Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(4 Ratings)
5 star 50%
4 star 25%
3 star 0%
2 star 0%
1 star 25%
c Jun 24, 2022
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Says it teaches image in painting but the linked notebook is just a GAN to generate MNIST digits
Amazon Verified review Amazon
Kindle Customer Dec 29, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good variety of examples to work through. The examples and concepts go beyond the typical introductory book. The code is generic enough to run locally or on AWS SageMaker. The author provides great support when problems were encountered. I'm glad I purchased this book.Would love to see another advanced book that covers TensorFlow model zoo, model/research, object detection API, TensorRT and deploying models to IoT edge devices (e.g. NVIDIA Jetsons)
Amazon Verified review Amazon
maninblack Jul 22, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a good starting point for computer science professionals who are new to the field of Computer Vision. I personally have not worked with CV a lot, but have the general prerequisite knowledge of machine learning concepts, and found this book to be a good starting point. This book had a good balance between theoretical concepts and working code snippets that can be tried out. The GitHub repo linked in the book was very helpful to replicate the code snippets as well, and I have not come across many books with this kind of functionality. All the code snippets I tried out worked well without any problems. From a practical implementation point of view, the section on deploying the model on a phone was quite interesting and fun to explore. Each concept mentioned in the book was very well illustrated and supported with thorough mathematical explanations as well.On the downside, I felt the book was focused mostly on image applications and could have also included more applications with videos as input in addition to images. I would have also liked a little more intuition when explaining certain concepts, in addition to the mathematical formulations already mentioned in the book. That really helps readers grasp the concept well.Overall, this was a well-written and instructive book for people wanting to gain knowledge in the CV domain!
Amazon Verified review Amazon
MIke R Jul 04, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
There are many books out there / but this book stands out - very clear explanation of codes and contents, lots of detailed explanations for object detection, classification, visual search, matching and training in cloud. All the codes work and and their screen video has been really helpful. I find deployment of model in phone and Raspberry PI example has many practical usage. +4minus point: some sections like action recognition and semantic segmentation could be expanded more with training a custom model. -1bonus point: the author has been very helpful to connect and answer questions through LinkedIn +1
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.