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OpenCV Python For Beginners

Hands on Computer Vision

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Based on 219 ratings
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Yogesh Patel

Welcome to this on OpenCV Python Tutorial For Beginners. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install.

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Welcome to this on OpenCV Python Tutorial For Beginners. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install.

The goal of this course is to provide you with a working knowledge of OpenCV with Python. We'll start with the basics, starting from installing OpenCV with Python on Windows, Mac, and Ubuntu. Then we will see how to create your first OpenCV python script. Then we will dive deep into the amazing world of computer vision Using OpenCV and learn the most important concepts about computer vision using OpenCV .

So Let's get started .

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What's inside

Learning objectives

  • Basics of numpy
  • Face detection with opencv
  • Draw shapes on images and videos with opencv
  • Smoothing, blurring, thresholding, and morphological operations with opencv
  • Objects tracking with opencv
  • Histograms with opencv
  • Find car lane lines with opencv

Syllabus

Install and Setup Python
How to Install Python 3 on Windows 10
How to install Python 3 on Mac
How To Install PyCharm In Windows 10
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Begins with installing OpenCV on various operating systems, which is helpful for those new to the library
Covers fundamental image processing techniques like thresholding, blurring, and morphological operations, which are essential for computer vision tasks
Includes practical projects like lane line detection and face detection, allowing learners to apply their knowledge to real-world scenarios
Teaches object tracking using different methods, such as Meanshift and Camshift, providing a comprehensive overview of tracking algorithms
Uses Python 3, which is actively supported, but some learners may need to upgrade from older versions of Python
Employs PyCharm, which is a popular IDE, but learners may need to install it separately, which may be a barrier for some

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Reviews summary

Hands-on opencv with python basics

According to learners, this course is a largely positive introduction to OpenCV with Python, particularly suitable for beginners. Students praise the hands-on projects and practical examples, finding them helpful for understanding fundamental computer vision concepts. The course provides a solid foundation covering essential operations and techniques like face detection and road lane detection. While the explanations are generally clear, some learners mention potential difficulties with the installation process and note that some methods covered, like Haar Cascades, might feel slightly dated compared to more modern approaches.
Requires some prior familiarity with Python.
"It's helpful to have a decent grasp of Python before starting this course."
"While NumPy is covered, prior general Python knowledge helps immensely with the examples."
"Recommended having Python basics down to fully benefit from the course."
Provides a solid foundation for new learners.
"This course gave me a strong base to start working with OpenCV."
"An excellent starting point for anyone new to computer vision with Python."
"Covers all the essential basics you need to get started."
Explains fundamental concepts clearly.
"Great for beginners, breaks down complex computer vision ideas into manageable steps."
"The instructor explained the concepts in a simple and easy-to-follow manner."
"Made OpenCV accessible even with limited prior computer vision knowledge."
Focuses on practical examples and projects.
"The hands-on examples were really helpful for applying what I learned."
"Loved the lane detection project, it was a very practical application of the concepts."
"Being able to immediately apply the concepts in the labs made it much easier to understand."
Some methods covered are less common now.
"Haar Cascades for face detection feel a bit outdated compared to modern deep learning methods."
"The course content could be updated to include more current techniques."
"Useful for understanding the history of CV, but maybe not for cutting-edge projects."
Some learners faced difficulties with setup.
"Struggled a bit with the installation process depending on my operating system."
"Needed to look up external guides to get everything set up correctly."
"The installation part could be smoother for complete beginners."

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 OpenCV Python For Beginners | Hands on Computer Vision with these activities:
Review Linear Algebra Fundamentals
Reinforce your understanding of linear algebra concepts, as they are foundational for many computer vision algorithms used in OpenCV.
Browse courses on Linear Algebra
Show steps
  • Review matrix operations such as addition, multiplication, and transposition.
  • Practice solving systems of linear equations.
  • Understand vector spaces and linear transformations.
Read 'Learning OpenCV 3' by Adrian Kaehler and Gary Bradski
Deepen your understanding of OpenCV concepts and techniques by studying a comprehensive guide.
View Learning OpenCV 3: Computer Vision in C++ with... on Amazon
Show steps
  • Read the chapters relevant to the course syllabus.
  • Work through the examples provided in the book.
  • Experiment with different parameters and settings to see how they affect the results.
Implement Basic Image Processing Functions
Solidify your understanding of image processing by implementing basic functions like blurring, thresholding, and edge detection from scratch using NumPy and OpenCV.
Show steps
  • Implement a function to blur an image using a Gaussian filter.
  • Implement a function to perform thresholding on an image.
  • Implement a function to detect edges in an image using the Sobel operator.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Blog Post on Road Lane Line Detection
Reinforce your understanding of road lane line detection by writing a blog post explaining the concepts and implementation details.
Show steps
  • Research the different techniques used for road lane line detection.
  • Write a clear and concise explanation of the concepts.
  • Include code examples and visualizations to illustrate the implementation.
Build a Simple Object Tracking System
Apply your knowledge of OpenCV to build a practical object tracking system that can track objects in real-time video.
Show steps
  • Choose an object tracking algorithm (e.g., Meanshift, Camshift).
  • Implement the chosen algorithm using OpenCV functions.
  • Test the system on different video sequences and evaluate its performance.
Read 'Computer Vision: Algorithms and Applications' by Richard Szeliski
Expand your knowledge of computer vision algorithms and applications by studying a comprehensive textbook.
View Computer Vision on Amazon
Show steps
  • Read the chapters relevant to the course syllabus.
  • Focus on the theoretical concepts and mathematical foundations.
  • Relate the concepts to the practical applications covered in the course.
Contribute to an OpenCV Project
Deepen your understanding of OpenCV by contributing to an open-source project, such as reporting bugs, writing documentation, or contributing code.
Show steps
  • Find an OpenCV project on GitHub or GitLab.
  • Identify a bug or feature that you can contribute to.
  • Submit a pull request with your changes.

Career center

Learners who complete OpenCV Python For Beginners | Hands on Computer Vision will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
A Computer Vision Engineer develops algorithms and systems that enable machines to "see" and interpret images, much like the capabilities explored in this course. This role involves designing, developing, and testing computer vision software for various applications such as autonomous vehicles, medical imaging, and security systems. The basics of NumPy, face detection with OpenCV, object tracking, and other topics covered in this course help build a solid foundation for a computer vision engineer. The work with OpenCV, image processing techniques, and object detection that this course facilitates may be especially relevant to this career.
See salaries and explore the career path for Computer Vision Engineer
Image Processing Specialist
Image Processing Specialists manipulate and analyze digital images to extract useful information or improve image quality. This often involves using software libraries like OpenCV, which is a major focus of this course. The skills developed in this course, such as smoothing, blurring, thresholding, morphological operations, and histogram equalization, all help in this role. This course provides experience with image processing techniques using OpenCV, which is directly applicable for someone pursuing a career as an Image Processing Specialist.
See salaries and explore the career path for Image Processing Specialist
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers develop the software and systems that allow vehicles to operate without human input. Computer vision is a critical component of autonomous navigation, allowing vehicles to perceive their surroundings. This course helps one build a foundation in this area, particularly with the sections on lane line detection, object tracking, and road lane line detection. Someone aspiring to be an Autonomous Vehicle Engineer may find that this course’s deep dive into OpenCV is particularly valuable.
See salaries and explore the career path for Autonomous Vehicle Engineer
Robotics Engineer
Robotics Engineers design, build, test, and program robots for various applications, often incorporating computer vision. Understanding how to process images and detect objects, as taught in this course, is crucial for enabling robots to interact with their environment intelligently. The material on object tracking, lane line detection, and face detection covered in this course helps robotics engineers develop these capabilities for robots. Someone interested in becoming a Robotics Engineer may find that this course’s introduction to OpenCV is particularly helpful if they wish to build robots that perceive their environments.
See salaries and explore the career path for Robotics Engineer
Law Enforcement Analyst
Law Enforcement Analysts leverage data and technology to support crime prevention and investigation efforts. This role requires the analyst to examine and interpret data to detect trends, patterns, and anomalies. This course may be helpful by providing the skills to process and analyze images using OpenCV. The study of face detection and background subtraction may be particularly useful for someone aspiring to be a Law Enforcement Analyst.
See salaries and explore the career path for Law Enforcement Analyst
Surveillance System Developer
Surveillance System Developers create and maintain systems that monitor and analyze video feeds for security and safety purposes. The skills taught in this course, particularly face detection, object tracking, and motion detection, are directly applicable to this field. The emphasis on OpenCV in this course provides practical experience useful for a Surveillance System Developer; the work with background subtraction methods and object detection is especially relevant.
See salaries and explore the career path for Surveillance System Developer
Augmented Reality Developer
Augmented Reality Developers create applications that overlay digital information onto the real world, requiring skills in computer vision and image processing. This course may be helpful because it introduces the basics of OpenCV. The course is particularly useful for learning object tracking, image blending, and other techniques needed to create compelling AR experiences. One who wishes to become an Augmented Reality Developer may apply the techniques studied in this course to this career.
See salaries and explore the career path for Augmented Reality Developer
Data Scientist
Data Scientists analyze large datasets to extract insights and build predictive models. While this role often focuses on numerical data, image data is becoming increasingly important. This course may be useful by equipping a Data Scientist with the skills to process and analyze images using OpenCV. The work with histograms and image processing techniques that this course facilitates is particularly relevant.
See salaries and explore the career path for Data Scientist
Biomedical Engineer
Biomedical Engineers apply engineering principles to healthcare, often using image processing techniques for medical imaging analysis. The skills learned in this course, such as image smoothing, edge detection, and morphological transformations, may equip a Biomedical Engineer with the tools to analyze medical images for diagnostic purposes. The grounding this course provides with OpenCV is helpful for any Biomedical Engineer working with image data.
See salaries and explore the career path for Biomedical Engineer
Quality Assurance Engineer
Quality Assurance Engineers use computer vision to automate quality control processes in manufacturing or other industries. This course may be helpful, as it introduces OpenCV and covers topics like object detection and image processing. These skills are useful for developing systems that can automatically inspect products for defects or inconsistencies. The opportunity to study blurring or smoothing images in this course may be particularly useful.
See salaries and explore the career path for Quality Assurance Engineer
Game Developer
Game Developers use computer vision techniques to create realistic and interactive gaming experiences. This course may be useful in this field, as it covers the basics of OpenCV. The course modules on object tracking, face detection, and handling mouse events help in designing games that respond to player actions and environments. The skills this course teaches helps those who wish to become a Game Developer.
See salaries and explore the career path for Game Developer
Geospatial Analyst
Geospatial Analysts analyze geographic data, often using satellite imagery or aerial photographs. This course may be helpful with the basics of OpenCV. Moreover, acquiring the skills to process and interpret images is highly beneficial. Specific skills, such as edge detection and object recognition, help with identifying features and patterns in geospatial data. A Geospatial Analyst can benefit from the work done with image processing techniques in this course.
See salaries and explore the career path for Geospatial Analyst
User Interface Designer
User Interface Designers create intuitive and visually appealing interfaces for software and applications. Although not directly related to UI design, the ability to process and understand images, which one can learn in this course, may be helpful for designing interfaces that incorporate visual elements effectively. If one wishes to become a User Interface Designer, they may find the skills taught in this course useful.
See salaries and explore the career path for User Interface Designer
Technical Writer
Technical Writers create documentation for software and hardware products. While not directly related, a basic understanding of computer vision, as provided by this course, may be helpful for documenting computer vision systems or applications. This course helps those who wish to enter this field.
See salaries and explore the career path for Technical Writer
Digital Marketing Specialist
Digital Marketing Specialists develop and implement online marketing campaigns. A basic understanding of image processing, as introduced in this course, may be helpful for optimizing visual content for marketing purposes. The skills with image processing learned in this course may be useful for someone interested in entering this field.
See salaries and explore the career path for Digital Marketing Specialist

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in OpenCV Python For Beginners | Hands on Computer Vision.
Cover image
Learning OpenCV 3
Save
Provides a comprehensive guide to OpenCV, covering a wide range of topics from basic image processing to advanced computer vision techniques. It serves as an excellent reference for understanding the underlying principles and practical applications of OpenCV functions. This book is commonly used as a textbook at academic institutions. It adds more depth to the course by providing detailed explanations and examples.
Learning OpenCV 3: Computer Vision in C++ with the...
Paperback
$$$
Learning OpenCV 3: Computer Vision in C++ with the...
Kindle Edition
$$$
Cover image
Computer Vision
Save
Provides a comprehensive overview of computer vision algorithms and their applications. It covers a wide range of topics, including image formation, feature extraction, and object recognition. This book is more valuable as additional reading than it is as a current reference. It adds more breadth to the course by providing a broader perspective on computer vision.
Computer Vision
Paperback
Check
Computer Vision
Kindle Edition
Check

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Rating
4.3 Filled star Filled star Filled star Filled star Empty star
Effort
12 total hours
Via
Udemy
Instructor
Yogesh Patel
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Begins with installing OpenCV on various operating systems, which is helpful for those new to the library
Covers fundamental image processing techniques like thresholding, blurring, and morphological operations, which are essential for computer vision tasks
Includes practical projects like lane line detection and face detection, allowing learners to apply their knowledge to real-world scenarios
Teaches object tracking using different methods, such as Meanshift and Camshift, providing a comprehensive overview of tracking algorithms
Uses Python 3, which is actively supported, but some learners may need to upgrade from older versions of Python
Employs PyCharm, which is a popular IDE, but learners may need to install it separately, which may be a barrier for some
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