The Best Opencv Online Courses

Banner Image The Best Opencv Online Courses

Hey there, fellow tech-enthusiast! So you’ve decided to embark on the exciting journey of mastering OpenCV, huh? Excellent choice, my friend! OpenCV (Open Source Computer Vision) is a powerful and highly sought-after skill in today’s world of computer vision, artificial intelligence, and machine learning. With numerous applications spanning across industries, your decision to up your game by learning OpenCV is sure to open a world of possibilities for your career.

Now, you might be thinking (like anyone dipping their toes into a new skill), where do I begin? Fear not, because we’ve got you covered. In today’s blog post, we’ll be going through some of the best OpenCV online courses available to jumpstart your learning experience. Whether you’re a complete beginner or someone looking to refresh your skills, these courses will meet you right where you are and help propel you into the realm of computer vision expertise. So, grab that coffee, sit back, and let’s dive into the world of OpenCV!

Opencv Courses – Table of Contents

  1. Python for Computer Vision with OpenCV and Deep Learning
  2. Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4
  3. Object Tracking using Python and OpenCV
  4. Computer Vision Masterclass
  5. Computer Vision in Python for Beginners (Theory & Projects)
  6. OpenCV with Python (Computer Vision)
  7. Computer Vision Fundamentals with OpenCV and C#
  8. Motion Detection using Python and OpenCV

Disclosure: This post contains affiliate links, meaning at no additional cost for you, we may earn a commission if you click the link and purchase.

Python for Computer Vision with OpenCV and Deep Learning

Course Preview Python for Computer Vision with OpenCV and Deep Learning


4.6 out of 5

Welcome to an amazing online course on Python for Computer Vision! This course aims to provide you with the necessary skills to use the Python programming language along with the OpenCV (Open Computer Vision) library to analyze and work with image and video data. With the ever-growing popularity of Python, it’s the perfect language to exploit the power of existing computer vision libraries and learn from the massive amount of image and video data that’s generated daily.

Throughout the course, you’ll learn about numerical processing with the NumPy library, as well as how to open, manipulate, and work with images using NumPy and OpenCV. You’ll also explore video processing, object detection, face detection, and object tracking with OpenCV. The course even covers the latest deep learning topics, including image recognition, custom image classification, and the state-of-the-art YOLO (you only look once) deep learning network. With an array of topics to cover, ranging from NumPy basics to deep learning with Keras, this course is designed to help you become an expert in computer vision and gain valuable skillsets for an industry set to be worth $20 billion globally! So, join in and prepare for an exciting learning journey!

Skills you’ll learn in this course:

  1. Image and video processing with OpenCV
  2. Color mappings and blending techniques
  3. Image thresholding and smoothing
  4. Morphological operations and gradients
  5. Object and face detection
  6. Object tracking and optical flow
  7. Deep learning with Keras and convolutional networks
  8. Customized deep learning networks and YOLO (You Only Look Once)

Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4

Course Preview Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4


4.4 out of 5

Welcome to the Modern Computer Vision™ Tensorflow, Keras & PyTorch course! This comprehensive course dives into the fascinating world of computer vision, a rapidly growing field with numerous real-world applications such as medical imaging, self-driving cars, military technology, security monitoring, and so much more. As the demand for computer vision experts skyrockets, this course aims to equip learners with the necessary skills and knowledge to excel in this field. The course is designed with beginners in mind, providing 27+ hours of relevant content on computer vision theory, example code, and detailed explanations.

The course is split into two main sections: the first part focuses on Classical Computer Vision using OpenCV, while the second section dives into Deep Learning. Taught through Google Colab Notebooks for easy accessibility, learners will engage in exciting projects, including facial landmark detection, perspective transforms, object detection, GANs, Autoencoders, and much more. Throughout the course, students will gain experience in working with both PyTorch and Tensorflow Keras. So, whether you’re looking for an entry point into the intriguing world of computer vision or wanting to level up your skills, this course is an excellent resource for you.

Skills you’ll learn in this course:

  1. Image operations and manipulations using OpenCV
  2. Deep learning with CNNs for image classification and analysis
  3. Implementation of facial recognition, age, gender, emotion, ethnicity analysis
  4. Object detection using YOLOvvEfficientDetect, SSDs, Faster R-CNNs
  5. Deep segmentation techniques with MaskCNN, U-NET, SegNET, and DeepLabV3
  6. Video classification and manipulation techniques
  7. computer vision with point cloud data analysis
  8. Medical imaging processing, including X-ray analysis and CT scans

Object Tracking using Python and OpenCV

Course Preview Object Tracking using Python and OpenCV


4.5 out of 5

Dive into the fascinating world of object tracking with this comprehensive online course on the main object tracking algorithms using Python and the OpenCV library. Object tracking, a subarea of Computer Vision, has a variety of applications such as video surveillance, traffic monitoring, and analyzing player movements in sports. By the end of this course, you’ll have a solid understanding of 12 key algorithms and will be well-prepared to implement them in your own projects.

This practical, hands-on course takes a step-by-step approach as you learn the basics of 12 different algorithms, including Boosting, MIL, KCF, CSRT, MedianFlow, TLD, MOSSE, Goturn, Meanshift, CAMShift, Optical Flow Sparse, and Optical Flow Dense. Through the use of PyCharm IDE, you’ll implement and test these algorithms, deepening your understanding of how they work. While this course focuses on practical implementation, it also provides an overview of the fundamental aspects of each algorithm, allowing you to make informed decisions when choosing which one best suits your unique application. So, come on and jump into this exciting journey of object tracking with this informative and engaging course!

Skills you’ll learn in this course:

  1. Understand the main object tracking algorithms using Python and OpenCV library
  2. Implement and test different tracking algorithms
  3. Develop and apply tracking algorithms to real-life video scenarios
  4. Gain practical experience with PyCharm IDE
  5. Develop a basic understanding of the theory behind each algorithm
  6. Choose the best tracking algorithm for specific applications
  7. Analyze videos in fields such as traffic monitoring and sports analytics
  8. Create your own projects using object tracking techniques

Computer Vision Masterclass

Course Preview Computer Vision Masterclass


4.5 out of 5

Dive into the fascinating world of Computer Vision with this comprehensive online course designed to teach you everything you need to know. As a subarea of Artificial Intelligence, you’ll discover how systems process, analyze, and identify visual data, similar to the human eye, and see firsthand how this technology is applied in industries like security, marketing, and production. The course aims to provide a practical overview of all areas, even for those new to computer vision, by teaching the step-by-step implementation of the 14 main computer vision techniques.

Throughout this course, you’ll get hands-on experience working with popular tools and libraries, such as OpenCV, Dlib, TensorFlow, and Google Colab. The curriculum covers topics such as detecting faces in images and videos, training algorithms to recognize faces, tracking objects using KCF and CSRT algorithms, and exploring artificial neural networks and convolutional neural networks for image classification. You’ll also delve into more advanced concepts like emotion detection, image compression, object detection with YOLO, gesture recognition, image segmentation, and even the creation of hallucinogenic images using Deep Dream techniques. With a combination of theory and practical implementation using Python language, this course will equip you with a solid foundation in the world of computer vision.

Skills you’ll learn in this course:

  1. Detect and recognize faces in images and videos using OpenCV and Dlib libraries.
  2. Track objects in videos using KCF and CSRT algorithms.
  3. Understand and implement artificial neural networks for image classification.
  4. Implement and optimize convolutional neural networks with transfer learning and fine-tuning.
  5. Detect emotions in images and videos using neural networks.
  6. Compress images using autoencoders and TensorFlow.
  7. Recognize gestures and actions in videos using OpenCV.
  8. Create and manipulate images using Deep Dream, style transfer, and GANs (Generative Adversarial Networks).

Computer Vision in Python for Beginners (Theory & Projects)

Course Preview Computer Vision in Python for Beginners (Theory & Projects)


4.4 out of 5

Dive into the exciting world of computer vision with the “Mastering Computer Vision from the Absolute Beginning Using Python” course! This comprehensive and descriptive course is designed for absolute beginners, and you’ll learn core concepts in the field of computer vision along with digital imaging processes and applications of CV. The course is presented with an easy-to-understand, descriptive, and practical approach that includes live coding examples and updated knowledge in the field.

The course consists of 320+ HD video tutorials with over 27 hours of runtime, providing an in-depth understanding of theoretical concepts and practical implementation. The learning process is complemented with hands-on projects such as “Change Detection in CCTV Cameras (Real-time)” and “Smart DVRs (Real-time)”, end-of-section quizzes, and activities focused on coding. By successfully completing this course, you’ll be able to implement any project from scratch that requires computer vision knowledge and relate CV concepts to real-world problems. So, if you’re passionate about learning computer vision with Python and implementing it in realistic projects, this course is the perfect fit for you!

Skills you’ll learn in this course:

  1. Understand the core concepts of computer vision and digital imaging.
  2. Apply image transformations and geometric transformation estimation techniques.
  3. Utilize image filtering, morphology, and shape detection methods.
  4. Implement key point detection and matching, as well as corner and feature detection algorithms.
  5. Analyze optical flow, global flow, and various object tracking techniques.
  6. Leverage deep learning approaches for object detection in computer vision.
  7. Gain knowledge in computer vision and applications like mocap and animations.
  8. Design and execute real-world computer vision projects, like Change Detection in CCTV cameras and Smart DVRs.

OpenCV with Python (Computer Vision)

Course Preview OpenCV with Python (Computer Vision)


4.5 out of 5

If you’re looking to jump into the exciting world of Computer Vision or Deep Learning, then the OpenCV course is perfect for you! The course covers the basic to advanced concepts of image and video processing. With a focus on a practical approach, this course is perfect for students wanting to start a career as a Computer Vision Engineer. From self-driving cars to automation in various domains, this course provides a comprehensive understanding of how computer vision is transforming the world.

The course is organized into chapters, and each one delves deep into the core concepts of Image and Video Processing. You’ll learn a range of topics, including image reading, cropping, resizing, rotating, video processing, drawing shapes on images, adding text messages, arithmetic operations, image blending, threshold and blurring, and many more advanced techniques. So, if you want to learn and excel in Computer Vision, don’t hesitate to join this course! And if you have any questions or concerns, you’re always welcome to reach out through the Udemy Q&A board. We hope to see you in the course soon!

Skills you’ll learn in this course:

  1. Image and video processing techniques
  2. Drawing shapes and adding text to images
  3. Image blending and thresholding
  4. Contour detection and analysis
  5. Applying arithmetic operations to images
  6. Image feature extraction with SIFT
  7. Feature matching for object recognition
  8. Video manipulation and saving

Computer Vision Fundamentals with OpenCV and C#

Course Preview Computer Vision Fundamentals with OpenCV and C#


4.5 out of 5

I’ve got some exciting news about a fantastic new online course that covers Computer Vision Fundamentals with C# programming language and OpenCVSharp, an OpenCV wrapper. This course will teach you how to enter the incredible world of computer vision, using both the widely-used OpenCV library and OpenCVSharp. Once you’ve mastered the essentials of computer vision in this course, you’ll be able to follow more advanced future courses in the computer vision and deep learning fields.

In this course, you’ll learn everything from reading and displaying images to Gray Scale Image conversions. You’ll also explore image thresholding techniques, filters, bitwise operations, and edge detection methods. By the end of the course, you’ll be able to tackle real-life applications like barcode detection, object tracking via webcam, and even text OCR with Tesseract and OpenCV. Plus, there are assignments designed to help you gain advanced skills in computer vision, such as hand gesture detection, coin counting, and textile defect detection. So, are you ready to dive into this amazing world of computer vision? Best regards, Frank Ozz!

Skills you’ll learn in this course:

  1. Fundamentals of image processing in computer vision
  2. Utilizing OpenCVSharp C# wrapper for OpenCV
  3. Gray scale image conversion and thresholding techniques
  4. Edge detection methods like Sobel, Scharr, and Canny
  5. Shape contour detection and repair
  6. Image manipulation: resizing, rotation, and flipping
  7. Morphological operations and histogram plotting
  8. Real-life applications such as barcode detection and OCR techniques

Motion Detection using Python and OpenCV

Course Preview Motion Detection using Python and OpenCV


5 out of 5

If motion detection and computer vision pique your interest, then this online course is perfect for you! In this class, you’ll learn how to use background subtraction algorithms to detect movements in videos, all while using the Python programming language. This skill comes in handy for a wide range of applications like security systems, traffic analysis, people counting, animal tracking, and more. Get ready to dive into the basic theories behind algorithms, such as Temporal Median Filter, MOG, GMG, KNN, and CNT, and compare their quality and performance.

But it’s not just all theory. Throughout the course, you’ll work on three practical projects to help solidify your newfound knowledge. First, you’ll create a motion detector to monitor environments. Then, you’ll learn how to build a social distancing detector to identify possible crowds of people. Finally, you’ll develop a system that counts cars and trucks on highways. By the end of the course, you’ll have a strong foundation in motion detection techniques and be ready to create your very own motion detection projects!

Skills you’ll learn in this course:

  1. Understanding basic background subtraction algorithms
  2. Performance and quality comparison of different algorithms
  3. Implementing a motion detector for environment monitoring
  4. Developing a social distancing detector
  5. Creating a car and truck counter for highways
  6. Utilizing Python programming language for motion detection
  7. Applying motion detection for security and traffic analysis
  8. Building custom motion detection projects

In this rapidly evolving world of technology, staying adaptive and engaged with new tools and languages is essential for professional growth. Opencv online courses provide a fantastic opportunity to learn and immerse yourself in the world of computer vision and image processing from the comfort and convenience of your own space. With a plethora of options available, you can choose a course that caters to your current skill level and preferred learning pace, while also aligning with your individual goals in your career path.

So, now it’s time to pull out your calendar, mark a start date, and embark on your journey towards mastering Opencv. As you begin or continue to grow your computer vision skills, remember that knowledge begets confidence and success. Remember to practice, collaborate, and engage with online communities, forums, and workshops to hone your newfound abilities. The digital world is your oyster, ready for you to make your mark and impact the future of technology with your newfound computer vision expertise. Happy learning!