使用OpenCV和Arduino实现计算机视觉

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"Arduino Computer Vision Programming" 本书"Arduino Computer Vision Programming"由Giray Yıllıkçı和Özen Özkaya撰写，由Packt Publishing出版，发布于2015年8月，ISBN号为9781783552627，主要聚焦在使用Arduino和OpenCV进行计算机视觉编程。本书是针对已经掌握基本Arduino知识，并希望进一步学习如何使用Arduino构建计算机视觉应用的消费者和爱好者。无需具备计算机视觉编程的先验知识。通过本书，读者将能够： 1. 学习到计算机视觉系统的构建模块和通用架构，掌握一种有效的方法来建模计算机视觉系统。这包括理解数据采集、预处理、图像处理、后置过滤、识别（检测）和驱动等关键步骤，这些都是构建一个完整的计算机视觉系统所必需的。 2. 掌握使用OpenCV设计计算机视觉系统的基础知识。OpenCV是一个强大的开源库，广泛用于实时图像处理和计算机视觉项目。读者将学习如何利用OpenCV来实现各种功能，例如图像捕获、图像处理算法以及对象检测和识别。 3. 探索计算机视觉开发的最佳实践，包括最新的算法和技术。书中包含的实际示例项目将帮助读者深入理解这些概念，从而能够设计和实施自己的检测、分类和识别算法。 4. 学习如何选择合适的相机，理解相机参数对计算机视觉系统性能的影响，以及如何优化图像质量以提高系统性能。 5. 了解如何在Arduino平台上运行和调试计算机视觉应用程序，从而创建智能系统。这涉及到硬件接口设计、代码编写和调试技巧，使读者能够将理论知识应用于实际项目。 6. 逐步引导读者从基础到高级，逐步提升计算机视觉技能，最终能够独立设计和开发具有实际应用价值的计算机视觉系统。 "Arduino Computer Vision Programming"是一本全面的指南，旨在帮助读者利用Arduino和OpenCV的组合，进入计算机视觉的世界，开发出具有创新性和实用性的智能系统。无论是对于业余爱好者还是专业开发者，这本书都提供了丰富的知识和实践经验，有助于提升在这一领域的技能水平。

Mat image_frame;

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

#include <opencv2/highgui/highgui.hpp>

#include <iostream>

using namespace cv;

using namespace std;

int main( int argc, char** argv )

{

Mat image_frame;

Any command-line input or output is written as follows:

sudo mkdir build

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear

in the text like this: "Then, click on Build Phases | Link Binary with Librariesand click (+) to add the two required frameworks."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Downloading the example code

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you.

Chapter 1. General Overview of Computer Vision Systems

In this chapter, you will learn about the fundamentals and the general scheme of a computer vision system. The chapter will enable

you to take a wide perspective when approaching computer vision problems.

Introducing computer vision systems

We use our five senses to observe everything around us—touch, taste, smell, hearing, and vision. Although all of these five senses are

crucial, there is a sense which creates the biggest impact on perception. It is the main topic of this book and, undoubtedly, it is vision.

When looking at a scene, we understand and interpret the details within a meaningful context. This seems easy but it is a very complex

process which is really hard to model. What makes vision easy for human eyes and hard for devices? The answer is hidden in the

difference between human and machine perception. Many researchers are trying to go even further.

One of the most important milestones on the journey is the invention of the camera. Even though a camera is a good tool to save

vision-based memories of scenes, it can lead to much more than just saving scenes. Just as with the invention of the camera, man has

always tried to build devices to make life better. As the current trend is to develop intelligent devices, being aware of the environment

around us is surely a crucial step in this. It is more or less the same for us; vision makes the biggest difference to the game. Thanks to

technology, it is possible to mimic the human visual system and implement it on various types of devices. In the process we are able to

build vision-enabled devices.

Images and timed series of images can be called video, in other words the computed representations of the real world. Any

vision-enabled device recreates real scenes via images. Because extracting interpretations and hidden knowledge from images via

devices is complex, computers are generally used for this purpose. The term, computer vision, comes from the modern approach of

enabling machines to understand the real world in a human-like way. Since computer vision is necessary to automate daily tasks with

devices or machines, it is growing quickly, and lots of frameworks, tools and libraries have already been developed.

Open Source Computer Vision Library (OpenCV) changed the game in computer vision and lots of people contributed to it to make

it even better. Now it is a mature library which provides state-of-the-art design blocks which are handled in subsequent sections of this

book. Because it is an easy-to-use library, you don't need to know the complex calculations under-the-hood to achieve vision tasks.

This simplicity makes sophisticated tasks easy, but even so you should know how to approach problems and how to use design tools

in harmony.

Approaching computer vision problems

To be able to solve any kind of complex problem such as a computer vision problem, it is crucial to divide it into simple and realizable

substeps by understanding the purpose of each step. This chapter aims to show you how to approach any computer vision problem

and how to model the problem by using a generic model template.

A practical computer vision architecture, explained in this book, consists of the combination of an Arduino system and an OpenCV

system, as shown in the following diagram:

Arduino is solely responsible for collecting the sensory information—such as temperature, or humidity—from the environment and

sending this information to the vision controller OpenCV system. The communication between the vision controller system and the

Arduino system can be both wired or wireless as Arduino can handle both easily. After the vision system processes the data from

Arduino and the webcam, it comes to a detection (or recognition) conclusion. For example, it can even recognize your face. The next

step is acting on this conclusion by sending commands to the Arduino system and taking the appropriate actions. These actions might

be driving a fan to make the environment cooler, moving a robotic arm to pass your coffee, and so on!

Note

A vision controller can be a desktop computer, laptop, mobile phone or even a microcomputer such as Raspberry Pi, or Beaglebone! OpenCV

works on all of these platforms, so the principles are valid for all of these platforms. Microcomputers are also able to do some of the work otherwise

done by Arduino.

Any computer vision system consists of well-defined design blocks ordered by data acquisition, preprocessing, image processing, post

filtering, recognition (or detection) and actuation. This book will handle all of these steps in detail with a practical approach. We can

draw a generic diagram of a computer vision system by mapping the steps to the related implementation platforms. In the following

diagram, you can find a generic process view of a computer vision system:

Data acquisition

As can be seen, the first step is data acquisition, which normally collects the sensory information from the environment. Within the

perspective of the vision controller, there are two main data sources—the camera, and the Arduino system.

The camera is the ultimate sensor to mimic the human vision system and it is directly connected to the vision controller in our scheme.

By using OpenCV's data acquisition capabilities, the vision controller reads the vision data from the camera. This data is either an

image snapshot or a video created from the timed series of image frames. The camera can be of various types and categories.

In the most basic categorization, a camera can give out analog or digital data. All of the cameras used in the examples in this book are

digital because the processing environment and processing operation itself are also digital. Each element of the picture is referred to

as a pixel. In digital imaging, a pixel, pel, or picture element is a physical point in a raster image or the smallest addressable element in

an all-points-addressable display device; so it is the smallest controllable element of a picture represented on the screen. You can find

more information on this at http://en.wikipedia.org/wiki/Pixel.

Cameras can also be classified by their color sensing capabilities. RGB cameras are able to sense both main color components and a

huge amount of combinations of these colors. Grayscale cameras are able to detect the scene only in terms of shades of gray. Hence,

rather than color information, these cameras provide shape information. Lastly, binary cameras sense the scene only in black or white.

By the way, a pixel in a binary camera can have only two values—black and white.

Another classification for cameras is their communication interface. Some examples are a USB camera, IP camera, wireless camera,

and so on. The communication interface of the camera also directly affects the usability and capability of that camera. At home

generally we have web cameras with USB interfaces. When using USB web cameras, generally you don't need external power

sources or the external stuff that makes using the camera harder, so it is really easy to use a USB webcam for image processing tasks.

Cameras also have properties such as resolution but we'll handle camera properties in forthcoming chapters.

Regular USB cameras, most often deployed as webcams, offer a 2D image. In addition to 2D camera systems, we now have 3D

camera systems which can detect the depth of each element in the scene. The best known example of 3D camera systems is probably

Kinect, which is shown here:

OpenCV supports various types of cameras, and it is possible to read the vision information from all these cameras by using simple

interfaces, as this issue is handled by examples in the forthcoming chapters. Please keep in mind that image acquisition is the

fundamental step of the vision process and we have lots of options.

Generally, we need information in addition to that from the camera to analyze the environment around us. Some of this information is

related to our other four senses. Moreover, sometimes we need additional information beyond human capabilities. We can capture this

information by using the Arduino sensors.

Imagine that you want to build a face-recognizing automatic door lock project. The system will probably be triggered by a door knock or

a bell. You need a sound sensor to react when the door is knocked or the bell is rung. All of this information can be easily collected by

Arduino. Let's add a fingerprint sensor to make it doubly safe! In this way, you can combine the data from the Arduino and the camera

to reach a conclusion about the scene by running the vision system.

In conclusion, both the camera and the Arduino system (with sensors) can be used by the vision controller to capture the environment

in detail!

Preprocessing

Preprocessing means getting something ready for processing. It can include various types of substeps but the principle is always

the same. We will now explain preprocessing and why it is important in a vision system.

Firstly, let's make something clear. This step aims to make the collected vision data ready for processing. Preprocessing is required in

computer vision systems since raw data is generally noisy. In the image data we get from the camera, we have lots of unneeded

regions and sometimes we have a blurry image because of vibration, movement, and so on. In any case, it is better to filter the image

to make it more useful for our system. For example, if you want to detect a big red ball in the image, you can just remove small dots, or

you can even remove those parts which are not red. All of these kinds of filtering operations will make our life easy.

Generally, filtering is also done in data acquisition by the cameras, but every camera has different preprocessing capabilities and some

of them even have vibration isolation. But, when built-in capabilities increase, cost is increased in parallel. So we'll handle how to do

the filtering inside of our design via OpenCV. By the way, it is possible to design robust vision systems even with cheap equipment

such as a webcam.

The same is valid for the sensor data. We always get noisy data in real life cases so noise should be filtered to get the actual

information from the sensor. Some of these noises come from the environment and some of them come from the internal structure of

the sensor. In any case, data should be made ready for processing; this book will give practical ways to achieve that end.

It should be understood that the complexity of image data is generally much greater than with any regular sensor such as a

temperature sensor or a humidity sensor. The dimensions of the data which represents the information are also different. RGB images

include three color components per pixel; red, green and blue. To represent a scene with a resolution of 640x480, a RGB camera

needs 640x480x3 = 921600 bytes. Multiplication by three comes from the dimension of each pixel. Each pixel holds 3 bytes of data in

total, 1 byte for each color. To represent the temperature of a room, we generally need 4 bytes of data. This also explains why we need

highly capable devices to work on images. Moreover, the complexity of image filters is different from simple sensor filters.

But it doesn't mean that we cannot use complex filters in a simple way. If we know the purpose of the filter and the meanings of filter

parameters, we can use them easily. This book aims to make you aware of the filtering process and how to apply advanced filtering

techniques in an easy way.

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