C / C# / C++

Learn With OpenCV Master Image Processing Free Video Course Free Download

Learn With OpenCV Master Image Processing Free Video Course Free Download

OpenCV Master Image Processing Make a complete mobile social media app like Twitter, Instagram or Facebook using Flutter and Firebase!

OpenCV

What you will learn

  • Scan an image with pointers and neighbor access
  • Represent colors with hue, saturation, and brightness
  • Retrieve similar images using the histogram comparison
  • Calibrate the camera from different image observations
  • Detect people and objects in images using machine learning techniques
  • Reconstruct a 3D scene from images
  • Add the miniaturizing tilt-shift effect
  • Load videos and store them

Requirements of OpenCV:

  • Basic programming knowledge on C++ is needed.

Description of OpenCV Master Image Processing:

OpenCV 3 Master Image Processing is a local cross-stage C++ Library for PC vision, AI, and picture handling. PC vision applications are the most recent buzz of late time! Large brands, for example, Microsoft, Apple, Google, Facebook, and Apple are progressively utilizing PC vision for object, example, picture, and face acknowledgment. This has prompted an extremely appeal for PC vision mastery. Thus, in case you’re intrigued to realize how to utilize the OpenCV library to assemble PC vision applications, at that point go for this Learning Path.

Packt’s Video Learning Paths are a progression of individual video items set up in a coherent and stepwise way with the end goal that every video expands on the abilities learned in the video before it.

The features of this Learning Path are:

  • Jump into the basics of OpenCV and fabricate your own ventures
  • Figure out how to apply complex enhanced visualizations to pictures
  • Reproduce a 3D scene from pictures
  • Ace the essential ideas in PC vision and picture preparing

How about we investigate your learning venture. This Learning Path encourages you to begin with the OpenCV library and tells you the best way to introduce and send it to compose powerful PC vision applications following great programming rehearses. You will figure out how to peruse and show pictures. You will at that point be acquainted with the fundamental OpenCV information structures.

Further, you will begin another venture and perceive how to stack a picture record and show it. Next, you’ll discover how to deal with console functions in our showcase window. In the following venture, you will bounce into intelligently changing picture brilliance. You will at that point figure out how to add a scaling down tilt-move impact and how to obscure pictures. In the last task, you will figure out how to apply Instagram-like shading feel channels to pictures.

Before the finish of this Learning Path, you will have the option to manufacture PC vision applications that take advantage of OpenCV 3.

Meet Your Experts:

We have joined the best works of the accompanying regarded creators to guarantee that your learning venture is smooth:

  • Robert Laganiere is an educator at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is additionally an employee of the VIVA research lab and is the co-creator of a few logical distributions and licenses in content-based video examination, visual reconnaissance, driver-help, object location, and following. Robert wrote the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-created Object Oriented Software Development, distributed by McGraw Hill in 2001. He is likewise an expert in PC vision and has expected the part of Chief Scientist in various new businesses organizations, for example, Cognivue Corp, iWatchlife, and Tempo Analytics.
  • AdiShavit is an accomplished programming modeler and has been an OpenCV client since it was in early beta in 2000. From that point forward he has been utilizing it basically ceaselessly to assemble frameworks and items going from installed, vehicle, and portable applications to work areas and huge, circulated cloud-based workers and administrations. His forte is in PC vision, picture handling, and AI with an accentuation on constant applications. He has some expertise in cross-stage, superior programming joined with a high creation quality viable code base. He constructs numerous items, applications, and administrations that influence OpenCV.

Who this course is for:

  • This learning path is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming.

Course content of OpenCV Master Image Processing:

Introduction of OpenCV Master Image Processing:

  • Installing the OpenCV Library
  • Loading, Displaying, and Saving Images
  • Exploring the cv::Mat Data Structure
  • Defining Regions of Interest
  • Accessing Pixel ValuesPreview
  • Scanning an Image with Pointers
  • Scanning an Image with Iterators
  • Writing Efficient Image-Scanning Loops
  • Scanning an Image with Neighbor Access
  • Performing Simple Image Arithmetic
  • Remapping an Image
  • Comparing Colors Using the Strategy Design Pattern
  • Segmenting an Image with the GrabCut Algorithm
  • Converting Color Representations
  • Representing Colors with Hue, Saturation, and Brightness
  • Computing an Image Histogram
  • Applying Look-Up Tables to Modify the Image’s Appearance
  • Equalizing the Image Histogram
  • Backprojecting a Histogram to Detect Specific Image Content
  • Using the Mean Shift Algorithm to Find an Object
  • Retrieving Similar Images Using Histogram Comparison
  • Counting Pixels with Integral Images
  • OpenCV 3 – Getting started with Image processing5 questions

OpenCV 3 – Advanced Image Detection and Reconstruction:

  • The Course OverviewPreview
  • Detecting Corners in an Image
  • Detecting Features Quickly
  • Detecting Scale-Invariant Features
  • Detecting FAST Features at Multiple Scales
  • Matching Local Templates
  • Describing and Matching Local Intensity Patterns
  • Matching Keypoints with Binary Descriptors
  • Computing the Fundamental Matrix of an Image Pair0
  • Matching Images Using Random Sample Consensus
  • Computing a Homography Between Two Images
  • Detecting a Planar Target in Images
  • Recognizing Faces Using Nearest Neighbors
  • Finding Objects and Faces with a Cascade of Haar Features
  • Detecting Objects and People with Support Vector Machines
  • OpenCV 3 – Advanced Image Detection and Reconstruction5 questions

OpenCV 3 Projects for Photo Filtering:

  • The Course OverviewPreview
  • Building OpenCV
  • Creating a New Project
  • Loading Images
  • Showing Images
  • Keyboard Events
  • Understanding Brightness and Contrast
  • Adjusting Brightness and Contrast with OpenCV
  • Interactive Image Adjustment
  • Storing Images
  • Miniature Faking
  • Blurring Images
  • Compositing Images
  • The Miniature Effect via Partial Blurring
  • Handling Mouse Events
  • Color Filters
  • Remapping Colors
  • Processing Video
  • Recoloring Video
  • Saving Video
  • OpenCV 3 Projects for Photo Filtering

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