从零开始：Python与CNN实现深度学习计算机视觉实战

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《实用计算机视觉应用：深度学习与卷积神经网络(CNNs)实战》是一本深入讲解如何在多个平台上部署深度学习应用的书籍，作者是Ahmed Fawzy Gad。书中以Python为基础，着重介绍了人工智能领域的核心概念，特别是人工神经网络(ANNs)和卷积神经网络(CNNs)的工作原理。首先，读者将通过传统机器学习管道，分析图像数据集，包括从头构建简单的ANN模型，再到使用遗传算法进行优化。作者强调了传统手工特征提取在计算机视觉中的局限性，并阐述了CNNs作为当前最先进的解决方案，它们能够提供更好的性能和效率，尤其是在处理图像识别任务时。本书的核心内容涵盖了从基础到实践的全过程。通过Python实现CNN，读者将深入了解这种模型的内部工作。随后，利用TensorFlow构建实际的图像识别模型，并将其部署到Web服务器上，利用Flask技术使其在互联网上可访问。为了创建跨平台的数据科学应用，作者还将指导读者使用Kivy和NumPy，这些工具能够降低开发复杂度，提高应用的兼容性。书中提供的详细示例帮助读者一步步地掌握从项目构思到生产环境的全过程，包括理解CNN如何工作、从零开始创建计算机视觉应用以及使用深度学习项目的完整生命周期。无论你是初学者还是经验丰富的开发者，都能从中受益匪浅，提升在计算机视觉和深度学习领域的技能。通过阅读这本书，读者将收获以下知识和技能： 1. 理解人工神经网络和卷积神经网络的基本原理 2. 使用Python从头构建计算机视觉应用和CNN模型 3. 遵循TensorFlow实现的深度学习项目，从概念设计到实际部署 4. 结合NumPy和Kivy构建适应不同平台的数据科学应用《实用计算机视觉应用：深度学习与CNNs实战》是一本适合计算机视觉和深度学习领域的宝贵资源，它不仅提供了理论知识，还提供了实践经验，有助于读者在实际项目中快速应用所学知识。

placeholders, variables, dataflow graphs, and TensorBoard are discussed based on some

examples building a simple linear model and an ANN for simulating the XOR gate. By

the end of this chapter, a CNN is created using tensorflow.nn module for classifying the

CIFAR10 dataset.

Chapter 7 deploys the trained models into a web server for being accessed by

Internet users using a web browser. A web application is created using the Flask

microframework. HTML, CSS, and JavaScript are used to build the front pages for

accessing the web server. The HTML pages send HTTP requests to the server with an

image, and the server responds to such requests with the predicted class.

Chapter 8 builds cross-platform applications using the Kivy open source library.

By linking Kivy to NumPy, it is possible to build data science applications that work

unchanged on different platforms. This removes the overhead of customizing the code

for a specific platform. An Android application is created to read an image and execute

the CNN layers implemented using NumPy in Chapter 5.

To benefit from the projects created, it is preferred to push them online for other

people to use and benefit from. An appendix discusses how to package Python projects

and distribute them into the Python package index (PyPI) repository.

Before starting, let’s take a brief overview of the Python environment used in the

book.

All code in the book is implemented using Python. Because native Python is

complex for handling images, multiple libraries are used to help to produce an efficient

implementation for applications across the chapters.

At first, native Python could be downloaded from this link (www.python.org/

downloads). The book uses Python 3. Just install the version of Python that is suitable

for your system. The next step is to prepare all libraries required across the entire book.

Rather than installing individual libraries, it is recommended to use Anaconda Python

distribution. It is available for download from this link (www.anaconda.com/download).

It supports Windows, Mac, and Linux and packages more than 1,400 data science

libraries. A list of all supported packages can be accessed from this page (https://repo.

anaconda.com/pkgs). By just installing Anaconda on your machine, all of the supported

libraries will be ready for use. This is helpful to avoid the challenges of preparing the

Python environment.

The required libraries in this book are NumPy, SciPy, Matplotlib, scikit-image,

scikit-learn, TensorFlow, Flask, Werkzeug, Jinja, Pickle, Pillow, and Kivy. All of these

libraries, except for Kivy, are supported by Anaconda. Later in this chapter, we will

InTroduCTIon

xxi

see the function of each of them. Note that such libraries can be installed easily. After

installing the native Python, we can use the pip installer to download and install a library

based on this command: “pip install <lib-name>”. Just type the name of the library. Some

installations are not straightforward and might change if the system changes. Thus,

we can’t cover the different installations. For such reasons, Anaconda is better than

installing each library individually. Let’s discuss the libraries needed.

Python supports a number of built-in data structures: list, tuple, dictionary, set, and

string. Unfortunately, no data structure provides flexibility in data science applications.

These data structures support working with different data types at the same time.

The same data structure might contain numbers, characters, objects, and more. String

is an exception, in which only characters are supported. Moreover, string and tuple are

immutable, which means it is impossible to change their values after they are created.

Dictionary adds a key to each item. Saving image pixels using a dictionary requires

adding a key to each pixel which enlarges the amount of data saved. Set is restricted to

just set operations and images are not restricted to just such operations.

Talking about images, which are the main concern of the book, list is the suitable

data structure. It is a mutable data type that is able to hold matrices. Unfortunately,

working with lists makes the process complex. We have to make sure everything is

numeric, of a certain specific type because different numeric data types can be saved

in the same list. To apply a simple operation such as adding a number to the image, we

have to write loops for visiting each element and apply such operations individually.

In data science applications, it is recommended to use the tools that make applying

the operations easier. There are some challenging tasks to conquer when building an

application, and we do not need to add another challenge in programming such tasks.

For such reasons, the NumPy (Numeric Python) library is used. Its basic role is to

support a new data structure in Python, which is array. Working with NumPy arrays is

simpler than working with lists. For example, using just the addition operator (+), we

can add a number to each element in the image after it is converted into a NumPy array.

Many other libraries have their functions accept and return a NumPy array.

Some operations are supported inside the NumPy array, but it is not meant to apply

operations. The SciPy (Scientific Python) library supports the same operations in the

NumPy arrays and more. It also supports working with the n-dimensional NumPy arrays

(e.g., images) using the scipy.ndimage submodule. For more advanced operations on

images, the scikit-image library is used. For example, image features can be extracted

using this library.

InTroduCTIon

xxii

After reading the image and applying some operations, Matplotlib is used for

displaying the images. It is mainly used for 2D visualization, but it also supports some 3D

features.

After reading the images, extracting features, and making visualizations, we can start

building ML models using the scikit-learn library. It supports different types of models

that are ready for use. Just feed it with inputs, outputs, and their parameters to have a

trained model.

After training a ML model, we can save it for later use using the pickle library, which

serializes and deserializes the objects. Up to this point, we can build and save an ML

model. We then move to building and saving a DL model using TensorFlow. It is the

most commonly used DL library, as it supports different APIs that match the needs

of professionals and beginners alike. TensorFlow has its own ways to save the trained

models.

In order to deploy the trained models, Flask is used. It is a microframework for

building web applications. By deploying the trained models to the web server, clients can

access such applications using a web browser. They can upload test images to the server

and receive the classification label. Flask uses the Jinja2 template engine and WSGI for

building the applications. For such reasons, the libraries Jinja and werkzeug must be

installed.

In order to build a data science mobile application that runs on-device, Kivy is used.

It is a Python library that allows the Python code to run cross-platform. In this book, Kivy

is used to build rich data science applications running for Android. The APK generated

by Kivy can be used in the market exactly as if it were created normally using Android

Studio.

Kivy uses the python-for-android packager, which allows adding the required

dependencies in the Android application. Because scikit-image is not supported by

python-for-android, images are read using Pillow, which is supported to run on Android

devices.

Implementations of the projects are available at this GitHub account: github.com/

AhmedFGad

InTroduCTIon

A. F. Gad, Practical Computer Vision Applications Using Deep Learning with CNNs,

https://doi.org/10.1007/978-1-4842-4167-7_1

CHAPTER 1

Recognition inComputer

Vision

Most computer science research tries to build a human-like robot that is able to function

exactly as humans. Even emotional properties are not impossible for such robots. Using

a sensor, the robot feels the temperature in the surrounding environment. Using facial

expressions, it is possible to know whether a person is sad or happy. Even things that

seem impossible might eventually only be challenging.

At the current time, a very challenging application is object recognition. Recognition

can be based on different types of data such as audio, image, and text. But image

recognition is a very efficient way due to the plenitude of information that can be helpful

in the task. Thus, it is regarded as the most popular application in computer vision.

There is a massive number of objects existing in the world, and differentiating them

is a complex task. Different objects might have similar visual appearance except for subtle

details. Moreover, the same object appears differently based on its surrounding environment.

For example, based on the light, viewing angle, distortion, and occlusion, the same object

appears differently in the image. Depending on the native image, pixels may not be a good

option for image recognition. This is because a minor change in each pixel leads to a major

change in the image, and thus the system is unable to recognize the objects correctly. The

target is to find a set of unique properties or features that do not change even with changing

pixel locations or values, as long as the structure of the object appears somewhere in the

image. Manually extracting features from images is a big challenge in image recognition.

This is why automatic approaches to feature extraction are becoming the alternative.

Because recognition of any object in any environment is complex at the current time,

the alternative way is to restrict the environment or the objects targeted. For example,

rather than recognizing all types of animals, we can just target a group of them. Rather than

working indoors and outdoors, we might restrict the environment to just indoor images.

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