OpenCV (Open Source Computer Vision) is a widely-used open-source computer vision and machine learning software library. It has been the go-to tool for developers and researchers looking to build vision-based applications. As computer vision continues to gain popularity in various industries, the demand for OpenCV skills has also increased. With the abundance of online courses available, it may be difficult to choose the most appropriate one. This article provides a comprehensive overview of some of the best OpenCV courses available online.
Here’s a look at the Best Opencv Courses and Certifications Online and what they have to offer for you!
10 Best Opencv Courses and Certifications Online
- 10 Best Opencv Courses and Certifications Online
- 1. Master Computer Vision™ OpenCV4 in Python with Deep Learning by Rajeev D. Ratan (Udemy) (Our Best Pick)
- 2. OpenCV Python For Beginners | Hands on Computer Vision by Yogesh Patel (Udemy)
- 3. Hands on Computer Vision with OpenCV & Python by Shrobon Biswas (Udemy)
- 4. Start OpenCV with Python: Real-time Processing with Webcam by Rune Thomsen (Udemy)
- 5. Learn Computer Vision with OpenCV and Python by Ibrahim Delibasoglu (Udemy)
- 6. Facial recognition using Raspberry Pi and OpenCV by Venkatesh Varadachari, Satyajeet Sah (Udemy)
- 7. Learning Path: OpenCV: Master Image Processing with OpenCV 3 by Packt Publishing (Udemy)
- 8. Develop Opencv based Facial recognition system using c# by Maria Khalid (Udemy)
- 9. Computer Vision Fundamentals with OpenCV and C# by F. Frank Ozz (Udemy)
- 10. Basics of Python and Image Processing With Python (OpenCV) by Yılmaz Alaca (Udemy)
1. Master Computer Vision™ OpenCV4 in Python with Deep Learning by Rajeev D. Ratan (Udemy) (Our Best Pick)
The Master Computer Vision™ OpenCV4 in Python with Deep Learning course is designed to teach students how to master computer vision using the newest version of OpenCV4 in Python. This comprehensive course covers key concepts of Computer Vision & OpenCV, image manipulations, segmentation of images, feature detection, object detection for faces, people & cars, facial recognition, motion analysis & object tracking, computational photography techniques, deep learning with Keras in Python, and computer vision product and startup ideas. With 21 projects included, students will have the opportunity to apply their knowledge and build their skills.
The course instructor, Rajeev D. Ratan, has updated the course material as of early 2019 to address previous concerns about outdated code and broken examples. The course is updated as of August 2019 and provides students with the most up-to-date methods and tools.
As computer vision applications and technology continue to grow, the demand for computer vision expertise is also increasing. However, learning computer vision can be challenging due to outdated resources or theoretical approaches. This course takes a practical approach and includes more than 50 code examples to help students grasp the key concepts.
The course is designed to be accessible to both academic and non-academic students, and includes links to research papers for further exploration. Students will learn how to use OpenCV in Python, the most well-supported open-source computer vision library available.
In addition to the comprehensive curriculum, students also get 3+ hours of deep learning in computer vision using Keras. The instructor provides updates, fixes, and new projects on a regular basis, and is available to answer questions and provide additional support to students.
Overall, the Master Computer Vision™ OpenCV4 in Python with Deep Learning course offers an excellent foundation in computer vision and provides students with the skills and knowledge to become a master in the field. The course is well-taught, informative, and includes real-world projects to help students apply their knowledge.
This course, titled “OpenCV Python For Beginners | Hands on Computer Vision Course,” is instructed by Yogesh Patel and focuses on practical examples with OpenCV and Python. OpenCV is an image processing library that was created by Intel and is now maintained by Itseez, with support from Willow Garage. It is available on Mac, Windows, and Linux, and works in C, C++, and Python. OpenCV is free and open source, and easy to use and install.
The course begins with a focus on installing OpenCV with Python on Windows, Mac, and Ubuntu, and then moves on to creating a first OpenCV Python script. The course provides a comprehensive introduction to computer vision using OpenCV, covering the most important concepts.
The course is divided into several sections. The first section covers the installation and setup of Python, followed by an introduction to NumPy. The next section introduces OpenCV and covers the installation process. The course then moves on to the basics of OpenCV and core operations, followed by image processing in OpenCV. The Hough Transform is then covered, followed by a mini project on road lane line detection. The course then covers face and eye detection, corner detection, background subtraction methods, object tracking, and a stand alone executable.
Overall, this course aims to provide a working knowledge of OpenCV with Python, starting from the basics and progressing to more advanced concepts.
Hands on Computer Vision with OpenCV & Python is a comprehensive and cost-effective video course designed to provide a strong foundation in Computer Vision. The course is tailor-made for individuals who wish to transition quickly from being an absolute beginner to an OpenCV expert in just three weeks. The instructor assures students that by using a step-by-step approach and breaking down difficult concepts in a simple manner, students will be able to learn OpenCV easily. The course covers topics such as Image Basics, Histograms, Pixel Manipulation, Filtering, Blurring, Noise Removal, Thresholding, Working with Videos, and Mastering Contours.
The course is suitable for individuals who are enthusiastic about learning OpenCV but do not know where to start, or those who are interested in adding Computer Vision algorithms to their current software projects. Students who want to build a portfolio with Computer Vision and Image Processing Projects will also benefit from this course. The instructor guarantees fast, friendly, and responsive support by email and on Udemy.
The instructor’s approach is simple – students are encouraged to understand concepts rather than parrot rote code. The instructor assures students that this is the number one course for anyone interested in learning OpenCV. The course is updated regularly to add new and exciting content. The instructor offers a full money-back guarantee if students request it within 30 days of purchasing the course.
The course is divided into several sections, including an introduction and installation guide. The other sections cover Image Basics, Histograms, Pixel Manipulation, Filtering, Blurring, Noise Removal, Thresholding, Working with Videos, and Mastering Contours. The instructor also includes three projects to help students apply the concepts they have learned. The projects include making an Image Snipping utility using Mouse EVENTS, creating a custom Glitter Filter of an Image, and Vehicle Detection from Traffic Videos.
Overall, Hands on Computer Vision with OpenCV & Python is an excellent course for individuals who are interested in learning Computer Vision.
The course “Start OpenCV with Python: Real-time Processing with Webcam” is offered by Rune Thomsen and provides an introduction to Computer Vision. The course is designed to help students learn how to extract high-level understanding from a video using hands-on projects. The course focuses on learning the concepts required to create interactive Computer Vision games, with an emphasis on practical applications.
The course uses OpenCV and Python, two widely used tools in the field of Computer Vision. OpenCV is highly optimized for real-time applications and supports multiple languages and platforms. Python is easy to learn and leaves heavy processing to libraries like OpenCV. The course teaches students these tools and their interactions, with the goal of providing practical experience.
The course covers all the necessary concepts for real-time application, including noise-tolerant motion detection, inserting objects, and interacting with objects in the frames. The course is structured in an easy-to-understand way, with coding examples and visual explanations. Students code along with the instructor in 40 coding lectures.
The course is designed for individuals who want to learn Computer Vision in a fun way, with a focus on learning concepts through projects. The course assumes a basic understanding of Python and an idea of Object Oriented Programming concepts. All questions are answered within a day, and the course comes with a 30-day money-back guarantee.
The course is divided into several sections: Introduction, Python and PyCham (Installation on Mac and Windows), OpenCV – Let’s get started, Understand Frames-per-Second and how the webcam limits it, How to configure your webcam with OpenCV and the limitations, Webcam Flow of Processing and NumPy, First project: Insert logo in live webcam stream, Project: Motion Detection from live Webcam stream, Motion Detection: Noise Tolerant, Project: Create Your First Game, and Project: A More Advanced Game.
The Learn Computer Vision with OpenCV and Python course is designed to teach students computer vision and image processing from scratch. The course instructor, Ibrahim Delibasoglu, provides easy-to-understand explanations of key concepts without the heavily mathematical theory. The course focuses on using OpenCV, an open-source computer vision library, with Python due to its ease of use and ability to focus on the problem at hand. The course includes real-world examples and new chapters will be added over time.
The course covers various topics, including basic operations such as histogram equalization, edge detection, and morphological operations. Keypoints and keypoint matching are also covered, along with image segmentation, contour properties, and blob detection. Object tracking APIs and filtering by color are also discussed, as well as object detection with haarcascade face and eye detection and HOG pedestrian detection.
The course also includes several special applications, including a mini-game using keypoint detection, people counter, and tracking of moving objects. The course also covers how to prepare a dataset and train a deep learning model using YOLO. The course also includes chapters on facial landmarks and special applications, such as real-time sleep and smile detection.
The course is structured to provide students with a clear understanding of computer vision and image processing, along with practical examples that can be used in real-world applications. The course also includes assignments to reinforce learning and a “questions and answers” area to share information.
The course “Facial recognition using Raspberry Pi and OpenCV” is designed for individuals with an interest in digital image processing using Raspberry Pi and OpenCV. The course requires a basic knowledge of Python programming and Linux commands. The course includes an exploration of facial recognition features using OpenCV libraries. OpenCV is an open source C++ library for image processing and computer vision, originally developed by Intel and now supported by Willow Garage. It is a library of many inbuilt functions mainly aimed at real-time image processing. The course will cover how to track faces in the image captured using Webcam or any other device and how to locate and count the faces present in the image. Additionally, the course will cover how to program in Python and OpenCV to detect and highlight the eyes of individuals in pictures, and create image puzzles using machine learning-based projects. The course instructor will provide step-by-step guidance and source code for replicating the projects.
The Learning Path titled “OpenCV: Master Image Processing with OpenCV 3” by Packt Publishing provides a comprehensive guide to developing interactive computer vision applications using OpenCV 3’s C++ libraries. The course is designed to equip learners with the fundamental concepts of computer vision and image processing necessary for building computer vision applications. The Learning Path consists of a series of individual video products arranged in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The Learning Path’s highlights include diving into the essentials of OpenCV, building one’s own projects, applying complex visual effects to images, reconstructing a 3D scene from images, and mastering the fundamental concepts of computer vision and image processing.
The Learning Path helps learners get started with the OpenCV library, learn how to install and deploy it to develop effective computer vision applications following good programming practices, read and display images, and understand the basic OpenCV data structures. Through the Learning Path, learners will start a new project, learn how to load an image file, handle keyboard events in the display window, interactively adjust image brightness, add a miniaturizing tilt-shift effect, blur images, and apply Instagram-like color ambiance filters to images.
At the end of the Learning Path, learners will have the skills and knowledge necessary to build computer vision applications that make the most of OpenCV 3.
The Learning Path is taught by two renowned experts, Robert Laganiere and AdiShavit. Robert is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000.
Course Title: Develop Opencv based Facial recognition system using c#
Course Instructors: Maria Khalid
Course Short Description: This course aims to teach face detection and recognition through real-time implementation.
Course Long Description: The Develop Opencv based Facial recognition system using c# course is designed to provide students with a strong foundation in opencv face recognition through step by step real-time implementation. The coding files and other necessary resources will be provided to facilitate learning and practical experience with face detection and face recognition. Upon completion, students will be equipped to build their own industrial-level facial recognition applications, saving them the expense of outsourcing such services.
This course, titled “Computer Vision Fundamentals with OpenCV and C#,” is the first video course to cover Computer Vision Fundamentals using the C# programming language and the OpenCV wrapper, OpenCVSharp. The course focuses on fundamental image processing techniques, which will enable students to tackle problems such as Barcode Recognition, Webcam programming, Text Segmentation, and OCR techniques to read text from scanned documents. The course is taught by F. Frank Ozz, and it is designed to be an introductory course for students who want to learn Computer Vision using OpenCV.
The course covers a range of topics, such as reading images from disk and saving images to disk, Mat type object of OpenCV, image pixel manipulation, drawing on images, locating a Region of Interest area and cropping, gray scale image conversion, image thresholding techniques, image binarization, image bitwise operations, image filters, image convolution, Gaussian blur, median blur, high-pass filters, Sobel, Scharr Edge Detection methods, Canny Edge Detection, OpenCV Trackbar Programming, shape contour detection, contour repair, image resizing, image rotation, image flipping, morphological operations, contour smoothing, convex hull, non-convex defects, shape matching, image masking, image histograms, and histogram plotting.
The course includes real-life applications, such as Barcode detection and decoding from a food package, Object Tracking via its color using a webcam, text OCR with tesseract plus OpenCV. Additionally, there are assignments designed to help students develop advanced skills in computer vision, such as Hand Gesture Detection, Color image channel histograms, Coin counting, Textile Defect Detection.
The Basics of Python and Image Processing with Python (OpenCV) course offers an introduction to the Python programming language and image processing, along with the opportunity to build deep learning projects. The course aims to make participants experts in Python and Image Processing. The course covers a variety of topics, including variables, data types, standard input and output functions, lists, tuples, and dictionaries, methods, arithmetic operators, type conversions, logical conjunctions, conditional structures, loops, functions, modules, and more.
The Basics of OpenCV section covers reading and showing an image, reaching height, width, and channels of an image, resizing an image, reaching pixel density of color image, cropping a certain area, saving an image, texting, video and webcam operations, color transformations, copy, rotation, and filters. The Machine Learning section covers face and eye detection. The Deep Learning section covers creating face and hand models. The course culminates in the Virtual Button project.
The course does not require any previous programming knowledge. It offers a friendly and encouraging learning environment, as reflected in quotes by C.S. Lewis and Lao. The course instructor, Yılmaz Alaca, has compiled the best information he has acquired through years of trial and error and experience. The course consists of Introduction, Installations, Basics of Python, Conditional Structures, Loops, Functions, Useful Functions and Parameters, Modules, Installation of OpenCV and IDE, Basics of OpenCV, Color Transformations, Copy, Rotation, Filters, Machine Learning, Deep Learning (Face and Hand Model), and the Computer Vision Project: Virtual Button. The course also offers information about other courses.