Master Computer Vision™ OpenCV4 in Python with Deep Learning


Master Computer Vision™ OpenCV4 in Python with Deep Learning

Learn OpenCV4, Dlib, Keras, TensorFlow & Caffe while completing over 21 projects such as classifiers, detectors & more!

What you’ll learn
  • Understand and use OpenCV4 in Python
  • How to use Deep Learning using Keras & TensorFlow in Python
  • Create Face Detectors & Recognizers and create your own advanced face swaps using DLIB
  • Object Detection, Tracking and Motion Analysis
  • Create Augmented Reality Apps
  • Programming skills such as basic Python and Numpy
  • How to use Computer Vision in executing cool startup ideas
  • Understand Neural and Convolutional Neural Networks
  • Learn to build simple Image Classifiers in Python
  • Learn to build an OCR Reader for Credit Cards
  • Learn to Perform Neural Style Transfer Using OpenCV
  • Learn how to do Multi Object Detection in OpenCV (up to 90 Objects!) using SSDs (Single Shot Detector)
  • Learn how to convert black and white Images to color using Caffe
  • Learn to build an Automatic Number (License) Plate Recognition (ALPR)
  • Learn the Basics of Computer Vision and Image Processing
  • Little to no programming knowledge is needed, but basic programing knowledge will help
  • Windows 10 or Ubuntu or a MacOS system
  • A webcam to implement some of the mini projects


Welcome to one of the most thorough and well taught courses on OpenCV, where you’ll learn how to Master Computer Vision using newest version of OpenCV4 in Python!


You will be learning:

  1. The key concepts of Computer Vision & OpenCV (using the newest version OpenCV 4)
  2. To perform image manipulations such as transformations, cropping, blurring, thresholding, edge detection and cropping.
  3. To segment images by understanding contours, circle, and line detection. You’ll even learn how to approximate contours, do contour filtering and ordering as well as approximations.
  4. Use feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.
  5. Implement Object Detection for faces, people & cars.
  6. Extract facial landmarks for face analysis, applying filters and face swaps.
  7. Implement Machine Learning in Computer Vision for handwritten digit recognition.
  8. Implement Facial Recognition.
  9. Implement and understand Motion Analysis & Object Tracking.
  10. Use basic computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).
  11. How to become a true computer vision expert by getting started in Deep Learning ( 3+ hours of Deep Learning with Keras in Python)
  12. How to develop Computer Vision Product Ideas
  13. How to perform Multi Object Detection (90 Object Types)
  14. How to colorize Black & White Photos and Video
  15. Neural Style Transfers – Apply the artistic style of Van Gogh, Picasso and others to any image even your webcam input
  16. How to make your own Automatic Number-Plate Recognition (ALPR
  17. Credit Card Number Identification (Build your own OCR Classifier with PyTesseract)


You’ll also be implementing 21 awesome projects! 


OpenCV Projects Include:

  1. Live Drawing Sketch using your webcam
  2. Identifying Shapes
  3. Counting Circles and Ellipses
  4. Finding Waldo
  5. Single Object Detectors using OpenCV
  6. Car and Pedestrian Detector using Cascade Classifiers
  7. Live Face Swapper (like MSQRD & Snapchat filters!!!)
  8. Yawn Detector and Counter
  9. Handwritten Digit Classification
  10. Facial Recognition
  11. Ball Tracking
  12. Photo-Restoration
  13. Automatic Number-Plate Recognition (ALPR)
  14. Neural Style Transfer Mini Project
  15. Multi Object Detection in OpenCV (up to 90 Objects!) using SSD (Single Shot Detector)
  16. Colorize Black & White Photos and Video

Deep Learning Projects Include:

  1. Build a Handwritten Digit Classifier
  2. Build a Multi Image Classifier
  3. Build a Cats vs Dogs Classifier
  4. Understand how to boost CNN performance using Data Augmentation
  5. Extract and Classify Credit Card Numbers


What previous students have said: 

“I’m amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing… much more to learn & apply”

“Extremely well taught and informative Computer Vision course! I’ve trawled the web looking for Opencv python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them.”

“Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing.”

“I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I’m a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!”

“Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications.”


Why Learn Computer Vision in Python using OpenCV?

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.

Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older an incompatible libraries or are too theoretical, making it difficult to understand.

This was my problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code I found online proved difficult as libraries and functions were often outdated.

I created this course to teach you all the key concepts without the heavy mathematical theory while using the most up to date methods.

I take a very practical approach, using more than 50 Code Examples.

At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.

I use OpenCV which is the most well supported open source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code.

If you’re an academic or college student I still point you in the right direction if you wish to learn more by linking the research papers of techniques we use.

So if you want to get an excellent foundation in Computer Vision, look no further.

This is the course for you!

In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

You get 3+ Hours of Deep Learning in Computer Vision using Keras, which includes:

  • A free Virtual Machine with all Deep Learning Python Libraries such as Keras and TensorFlow pre-installed
  • Detailed Explanations on Neural Networks and Convolutional Neural Networks
  • Understand how Keras works and how to use and create image datasets
  • Build a Handwritten Digit Classifier
  • Build a Multi Image Classifier
  • Build a Cats vs Dogs Classifier
  • Understand how to boost CNN performance using Data Augmentation
  • Extract and Classify Credit Card Numbers

As for Updates and support:

I will be continuously adding updates, fixes, and new amazing projects every month!

I will be active daily in the ‘questions and answers’ area of the course, so you are never on your own.

So, are you ready to get started? Enroll now and start the process of becoming a master in Computer Vision today!

Who this course is for:
  • Beginners who have an interest in computer vision
  • College students looking to get a head start before starting computer vision research
  • Anyone curious using Deep Learning for Computer Vision
  • Entrepreneurs looking to implement computer vision startup ideas
  • Hobbyists wanting to make a cool computer vision prototype
  • Software Developers and Engineers wanting to develop a computer vision skillset

Created by Rajeev Ratan
Last updated 3/2020
English [Auto-generated]

Size: 3.01 GB


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