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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

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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...

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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

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Publication date : May 15, 2020
Length: 430 pages
Edition : 1st
Language : English
ISBN-13 : 9781838827069
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Product Details

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

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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%
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
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
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