Computer Vision Projects with Arduino

Hi. Welcome to the Advanced Computer Vision Project With Arduino (OpenCV) course.

This course provides you to control an LCD display with your facial movements. With this course, we will learn to reach our facial features with the deep learning method.

Main topics you will learn;

LCD Screen: With this section, you will learn how to use the LCD screen.
Deep Learning: With this section, you will learn to draw your facial features and change the physical features of the landmarks you draw.
Computer Vision Project (Deep Learning): With this section, you will learn to automatically print out on the LCD screen according to our facial expressions.

Who is this course for?

Advanced students who want to build deep learning project with Arduino,
Advanced students who want to build serial communication between Python and Arduino and
Advanced students who want to build lcd screen applications.

"You are never too old to set another goal or to dream a new dream." - C.S.Lewis

"Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles begins with a single step" - Lao Tzu

You get the best information that I have compiled over years of trial and error and experience.

Best wishes,

Yılmaz ALACA

What's inside

Learning objectives

Serial communication between python and arduino,
Face detection module (landmarks),

Lcd application and
Image processing (opencv) project.

Serial communication between python and arduino,
Face detection module (landmarks),
Lcd application and
Image processing (opencv) project.

Syllabus

Introduction

List of Materials of This Course

Getting Started

Installation of Python

Installation of PyCharm

Installation of OpenCV

Installation of Deep Learning Module

Installation of Arduino IDE

Interface of Arduino IDE

LCD Screen

Introduction (About LCD)

i2c

Library of LCD Screen

Circuit

Printing Text

Clear

Creating Custom Character

Deep Learning (Face Model)

Introduction of Deep Leaning Module

Capture Video from Webcam

Landmarks of Face

Changing Color and Thickness

Computer Vision Project 1 (Predicting Facial Expressions)

Installing of Serial Module

Coding Python -1 (Modules and Definitions)

Coding Python -2 (Reaching Landmarks of Face)

Index of Lip Corner Points

Coding Python -3 (Showing Special Landmarks)

Distance Between 2 Points (Facial Expressions)

Coding Python -4 (Adding Math Formula)

Coding Python -5 (Sending Data to Arduino Board)

Coding Arduino

Test and Fix

Servo Motor

What is Servo?

Controlling Servo

Computer Vision Project 2 (Arm Movement)

Project Overview

Creating Arm -1

Creating Arm -2

Creating Arm -3

Creating Mechanism -1

Creating Mechanism -2

Coding Python -2 (Process Model)

Coding Python -3 (Reaching Landmarks of Wrist, Elbow and Sholder)

Coding Python -4 (Designing Arm)

Coding Python -5 (Calculating Arm Movements)

Coding Python -6 (Sending Signals to Arduino)

Coding Arduino and Test

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computer Vision Projects with Arduino | 2 Projects with these activities:

Review Arduino Fundamentals

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Refresh your understanding of Arduino basics to better grasp the microcontroller aspects of the course.

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Review Arduino syntax and basic commands.
Practice simple Arduino projects like blinking an LED.
Familiarize yourself with the Arduino IDE.

Review Python Programming

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Strengthen your Python skills, as Python is used for computer vision and serial communication in this course.

Browse courses on Python Programming

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Review Python syntax and data structures.
Practice writing Python scripts for basic tasks.
Familiarize yourself with relevant Python libraries like NumPy.

Review Learning OpenCV 4 by Adrian Rosebrock

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Deepen your understanding of OpenCV, a crucial library for the computer vision aspects of the course.

View Putting Knowledge to Work on Amazon

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Read the chapters related to image processing and feature detection.
Experiment with the code examples provided in the book.
Try to apply the concepts learned to simple image manipulation tasks.

Four other activities

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Practice Serial Communication Examples

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Reinforce your understanding of serial communication between Python and Arduino through practical exercises.

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Write Python scripts to send data to Arduino.
Write Arduino code to receive and process data from Python.
Test different data types and communication protocols.

Simple Face Tracking Project

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Apply your knowledge by building a basic face tracking application using OpenCV and Arduino.

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Use OpenCV to detect faces in a video stream.
Calculate the coordinates of the detected face.
Send the coordinates to Arduino via serial communication.
Control an LED or servo motor based on the face position.

Document Your Projects

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Improve retention by documenting your projects and sharing your learnings with others.

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Write a blog post or create a video tutorial about your projects.
Explain the challenges you faced and how you overcame them.
Share your code and schematics with the community.

Contribute to an OpenCV Project

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Deepen your understanding of OpenCV by contributing to an open-source project.

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Find an OpenCV project on GitHub.
Identify a bug or feature request that you can work on.
Submit a pull request with your changes.

Career center

Learners who complete Computer Vision Projects with Arduino | 2 Projects will develop knowledge and skills that may be useful to these careers:

AI Engineer

An artificial intelligence engineer develops and implements AI models and systems, including those that involve computer vision. This course may help build a foundation by providing experience in deep learning, image processing, and hardware integration using OpenCV and Arduino. The work with facial recognition would transfer directly to AI systems that use facial analysis. This is an exciting time to become an AI Engineer.

See salaries and explore the career path for AI Engineer

Computer Vision Engineer

A computer vision engineer develops algorithms and systems that enable computers to see and interpret images. The course may provide introductory experience in this field by teaching how to process video feeds using OpenCV and deep learning techniques. The work on face detection, landmark identification, and facial expression recognition would be directly relevant to many computer vision applications. This field typically requires advanced education. The image processing knowledge in this course could be brought into the field of Computer Vision Engineering.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine learning engineers develop and deploy machine learning models for various applications. The course may provide a starting point for building machine learning models for computer vision tasks. The experience with deep learning, image processing, and data analysis can be valuable for developing and deploying these models. The course using Arduino for projects may also prove useful for Machine Learning Engineering.

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Embedded Systems Engineer

An embedded systems engineer designs, develops, and tests software and hardware for embedded systems. These are specialized computer systems within larger devices. This course may help build a foundation by providing experience in interfacing software with hardware using Arduino and Python. Experience with LCD screens and knowledge of serial communication allows engineers to build user interfaces and enable communication between different components of a system. Interfacing with a camera and performing deep learning operations will translate into embedded systems with computer vision capabilities. One who takes this course would be setting themselves up well for a role as Embedded Systems Engineer.

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Firmware Engineer

Firmware engineers develop the low-level software that controls hardware devices. The course may help build skills in interfacing software with hardware using Arduino and Python, which are essential for firmware development. Experience with serial communication and hardware control can be applied to developing firmware for embedded systems. These are skills that would lead to advancement as a Firmware Engineer.

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Robotics Engineer

A robotics engineer designs, builds, and programs robots for various applications. This course may provide foundational skills in computer vision and embedded systems, which are crucial for developing robot perception and control. By learning to process video feeds and control hardware through Arduino, one can gain experience relevant to creating intelligent and responsive robots. The work with LCD screens, deep learning, and serial communication using Python and Arduino, are experiences that would translate to robotics projects involving human-robot interaction or automated tasks. Gaining familiarity with image processing using OpenCV would be advantageous in this career.

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Automation Engineer

Automation engineers design, develop, and implement automated systems for various industries. The course may provide skills in controlling hardware with software and processing visual data, which are essential for automating tasks. The experience in projects involving arm movement control and facial expression recognition can be applied to develop automated systems that respond to human input or environmental changes. Therefore, this course may be useful for an Automation Engineer.

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Mechatronics Engineer

Mechatronics engineers integrate mechanical, electrical, and computer engineering principles to design and develop automated systems. This course may provide foundational skills in controlling hardware with software, using Arduino, and processing visual data with OpenCV. The projects involving facial expression recognition and arm movement control offer valuable experience in integrating sensors, actuators, and control algorithms, which are core competencies of a mechatronics engineer. Therefore, a Mechatronics Engineer may find some learning useful.

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Software Engineer

Software engineers design, develop, and test software systems. The course may help build a foundation by introducing skills in Python programming and interfacing with hardware. The hands-on projects using Arduino may offer valuable experience in software engineering principles. The use of image processing and deep learning will translate directly into roles for Software Engineers.

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Software Developer

Software developers create and maintain software applications. The course may help build skills in Python programming and interfacing with hardware, which are valuable for developing a wide range of applications. Experience with image processing, deep learning, and serial communication can be applied to projects involving computer vision or embedded systems. The course's hands-on projects using Arduino can provide valuable experience in software development. A course like this will help Software Developers grow.

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Computer Programmer

Computer programmers write code to create software applications. This course may help build skills in Python programming and interfacing with hardware, which are valuable for developing a wide range of applications. Experience with image processing, deep learning, and serial communication can be applied to projects involving computer vision or embedded systems. Therefore, learning in this course translates into useful skills as a Computer Programmer.

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Control Systems Engineer

Control systems engineers design and implement systems that control the behavior of dynamic systems. The course may give experience in controlling physical systems using Arduino and processing sensor data with computer vision techniques. The projects involving arm movement control and facial expression recognition offer valuable experience in designing control algorithms and integrating sensors and actuators. One who wishes to become a Control Systems Engineer may find this advantageous.

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Research Scientist

Research scientists conduct research and development in various fields, often requiring a master's or doctoral degree. The course may provide a starting point for research in computer vision, robotics, or human-computer interaction. The work with deep learning, image processing, and hardware control can be applied to developing novel algorithms and systems. The experience gained in facial expression recognition projects may be a launching point for further Research Scientist work.

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Application Developer

Application developers design, develop, and test software applications for computers and mobile devices. This course may help build skills by providing experience in Python programming and interfacing with hardware, which are valuable for developing a wide range of applications. Experience with image processing, deep learning, and serial communication can be applied to projects involving computer vision or embedded systems that have some relation to Application Development.

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Data Scientist

Data scientists analyze large datasets to extract insights and build predictive models. The course may provide practical skills in working with image data and applying deep learning techniques. Although this course focuses on computer vision projects with Arduino, the experience gained in data processing and model building can be valuable for a Data Scientist in image-related projects. Data Scientists can use many of the skills taught in this course.

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Computer Vision Projects with Arduino | 2 Projects

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