Master Computer Vision™ OpenCV4 in Python and Machine Learning [Video]
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Free ChapterCourse Introduction and Setup
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Basics of Computer Vision and OpenCV
- What are Images?
- How are Images Formed?
- Storing Images on Computers
- Getting Started with OpenCV - A Brief OpenCV Intro
- Grayscaling - Converting Color Images to Shades of Gray
- Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
- Histogram representation of Images - Visualizing the Components of Images
- Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text
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Image Manipulations - The Many Ways You Can Change Images
- Setup of Virtual Machine - Optional
- Transformations, Affine and Non-Affine - The Many Ways We Can Change Images
- Image Translations - Moving Images Up, Down. Left and Right
- Rotations - How to Spin Your Image Around and Do Horizontal Flipping
- Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
- Image Pyramids - Another Way of Re-Sizing
- Cropping - Cut Out the Image the Regions You Want or Don't Want
- Arithmetic Operations - Brightening and Darkening Images
- Bitwise Operations - How Image Masking Works
- Blurring - The Many Ways We Can Blur Images & Why It's Important
- Sharpening - Reverse Your Images Blurs
- Thresholding (Binarization) - Making Certain Images Areas Black or White
- Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
- Edge Detection using Image Gradients & Canny Edge Detection
- Perspective & Affine Transforms - Take an Off Angle Shot & Make It Look Top Down
- Mini Project 1 - Live Sketch App - Turn your Webcam Feed into a Pencil Drawing
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Image Segmentation - Extract Areas Specific Areas of an Image Automatically
- Segmentation and Contours - Extract Defined Shapes in Your Image
- Sorting Contours - Sort Those Shapes by Size
- Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
- Matching Contour Shapes - Match Shapes in Images Even When Distorted
- Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
- Line Detection - Detect Straight Lines E.g. the Lines on a Sudoku Game
- Blob Detection - Detect the Centre of Flowers
- Mini Project 3 - Counting Circles and Ellipses
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Object Detection - Use Computer Vision to Detect Objects in an Image
- Object Detection Overview
- Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern in an Image)
- Feature Description Theory - How We Digitally Represent Objects
- Finding Corners - Why Corners in Images Are Important to Object Detection
- SIFT, SURF, FAST, BRIEF & ORB - Learn the Different Ways to Get Image Features
- Mini Project 5 - Object Detection - Detect a Specific Object Using Your Webcam
- Histogram of Oriented Gradients - Another Novel Way of Representing Images
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Face, People and Car Detection
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Augmented Reality - Facial Landmark Identification (Filters, Swaps & Analysis)
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Machine Learning in Computer Vision
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Motion Analysis and Object Tracking
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BONUS - Computation Photography
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Conclusion
Computer vision applications and technology are exploding right now, with several apps and industries making amazing use of the technology—ranging from up-and-coming apps such as MSQRD, and PRISMA to billion-dollar apps such as Pokémon GO and Snapchat! Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face and 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 and incompatible libraries, or are too theoretical, making the subject difficult to understand.
This was the author's problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code found online proved difficult as libraries and functions were often outdated. The author created this course to teach you all the key concepts without the heavy mathematical theory—all the while using the most up-to-date methods. At the end of the course, you will be able to build 12 awesome Computer Vision apps using OpenCV (the best supported open-source computer vision library that exists today!) in Python. Using it in Python is just fantastic as Python allows us to focus on the problem at hand without getting bogged down in complex code. If you're an academic or college student but want to learn more, the author still points you in the right direction by linking the research papers for techniques used. So if you want to get an excellent foundation in Computer Vision, look no further. This is the course for you!
The code bundle for this video course is available at https://github.com/PacktPublishing/Master-Computer-Vision-OpenCV3-in-Python-and-Machine-Learning
Style and Approach
Learn Computer Vision using OpenCV in Python, using the latest 2018 concepts, and implement 12 awesome projects! In this course, you will discover the power of OpenCV in Python, and obtain the skills to dramatically increase your career prospects as a Computer Vision developer.
- Publication date:
- October 2018
- Publisher
- Packt
- Duration
- 6 hours 19 minutes
- ISBN
- 9781789616521