Learn Practical Approach to OpenCV using Kotlin

Learn Practical Approach to OpenCV using Kotlin, available at $19.99, has an average rating of 4.5, with 138 lectures, based on 1 reviews, and has 19 subscribers.

You will learn about Become proficient enough to do complex opencv operation on the image Able to understand and use core API of the OpenCV library Apply image processing concepts to create better image using OpenCV Sharing OpenCV code across different platform as library dependency Learn TDD and clean coding approach in OpenCV application Applying design pattern to known coding problems while building apps Practical examples of the commonly used features in popular apps mobile or desktop Developing complex feature in Android app that uses OpenCV This course is ideal for individuals who are Someone who has an interest in learning about Image processing. or Someone who wants to explore the power of the OpenCV library. or Someone who wants to learn and build OpenCV complex functions. It is particularly useful for Someone who has an interest in learning about Image processing. or Someone who wants to explore the power of the OpenCV library. or Someone who wants to learn and build OpenCV complex functions.

Enroll now: Learn Practical Approach to OpenCV using Kotlin

Summary

Title: Learn Practical Approach to OpenCV using Kotlin

Price: $19.99

Average Rating: 4.5

Number of Lectures: 138

Number of Published Lectures: 138

Number of Curriculum Items: 138

Number of Published Curriculum Objects: 138

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Become proficient enough to do complex opencv operation on the image
Able to understand and use core API of the OpenCV library
Apply image processing concepts to create better image using OpenCV
Sharing OpenCV code across different platform as library dependency
Learn TDD and clean coding approach in OpenCV application
Applying design pattern to known coding problems while building apps
Practical examples of the commonly used features in popular apps mobile or desktop
Developing complex feature in Android app that uses OpenCV

Who Should Attend

Someone who has an interest in learning about Image processing.
Someone who wants to explore the power of the OpenCV library.
Someone who wants to learn and build OpenCV complex functions.

Target Audiences

Someone who has an interest in learning about Image processing.
Someone who wants to explore the power of the OpenCV library.
Someone who wants to learn and build OpenCV complex functions.

OpenCV is a widely used library for developing complex applications on various devices like Mobile, Desktop, Raspberry Pi, backend applications, etc.

There are many courses and books available that provide a good theoretical understanding of image processing concepts. If you are someone who has no understanding of image processing, I would suggest you take some basic level course or book to gain a high-level understanding to better understand this course.

This course is primarily designed for someone who wants to see the power of the OpenCV library and also to learn by coding. In this course, we will first learn the basic concept and later implement the code to visually understand the same using code.

If you are a beginner or intermediate-level learner, you will learn a lot for sure. There are many skills you will learn as you progress with the course. Skills like TDD, clean coding, identifying the problem, and using appropriate design patterns to simplify the code which is something many developers in big MNCs give very less emphasis on.

If you are an expert-level learner, It will be a refreshing course for you.

In this course, we have covered complex-level application that uses the advanced-level concept to achieve interesting results.

Happy learning !!

Course Curriculum

Chapter 1: Introduction to the course

Lecture 1: Introduction to course – 1

Lecture 2: Introduction to course – 2

Lecture 3: Requirement to develop in this course

Chapter 2: Setup your machine for OpenCV

Lecture 1: Project: GitHub project link for the course

Lecture 2: Windows: OpenCV installation – 1

Lecture 3: Windows: OpenCV installation – 2

Lecture 4: Windows: OpenCV installation – 3

Lecture 5: Linux: OpenCV installation from official documentation

Lecture 6: Linux: OpenCV installation – 1

Lecture 7: Linux: OpenCV installation – 2

Lecture 8: Mac: OpenCV installation

Lecture 9: Project: Modification in build.gralde file of the project

Lecture 10: Project: Take checkout of the project and start playing on it

Chapter 3: Image Filters

Lecture 1: Introduction

Lecture 2: Negative: change image to negative image – 1

Lecture 3: Negative: change image to negative image – 2

Lecture 4: Negative: change image to negative image – 3

Lecture 5: Thresholding: Introduction of Threshold API

Lecture 6: Thresholding: Binary Thresholding (convert image to black and white)

Lecture 7: Thresholding: OTSU thresholding (convert image to black and white)

Lecture 8: Thresholding: Fixing image for thresholding

Lecture 9: Thresholding: trying different parameters and code refactoring

Lecture 10: Grayscale Filter

Lecture 11: Denoising: FastNL Mean Denoise API (remove noise from image) – 1

Lecture 12: Denoising: FastNL Mean Denoise API (remove noise from image) – 2

Lecture 13: Denoising: FastNL Mean Denoise API (remove noise from image) – 3

Lecture 14: Sharpening: difference between gaussian blur and Laplacian filter – 1

Lecture 15: Sharpening: implementation using Laplacian filter – 2

Lecture 16: Sharpening: sharpen color image using Laplacian and Gaussian in YUV – 3.1

Lecture 17: Sharpening: sharpen color image using Laplacian and Gaussian in YUV – 3.2

Lecture 18: Sharpening: Code refactoring – 5

Lecture 19: Smoothing: Introduction

Lecture 20: Smoothing: Gaussian filter implementation

Lecture 21: Smoothing: Bilateral filter implementation

Lecture 22: Smoothing: smoothing to specific region in the image

Lecture 23: Smoothing: Test code refactoring

Lecture 24: Crisper Image: Intro to unsharp masking

Lecture 25: Crisper Image: unsharp masking implementation

Lecture 26: Section Summary

Chapter 4: Image Transformations

Lecture 1: Introduction of Transformations

Lecture 2: Rotation: Introduction of rotation API

Lecture 3: Rotation: Implementation of rotation

Lecture 4: Rotation: Refactoring code

Lecture 5: Rotation: Understanding rotation matrix 2d and warp affine

Lecture 6: Rotation: Enhancement of rotation code for any angle rotation

Lecture 7: Scaling: Understanding scaling transformation

Lecture 8: Scaling: Scaleing up image using inter_linear and inter cubic

Lecture 9: Scaling: Scaling down

Lecture 10: Scaling: scaling differently in x and y direction

Lecture 11: Translation: Understanding translation

Lecture 12: Translation: Implementation of translation

Lecture 13: Perspective: Understanding perspective transformation

Lecture 14: Perspective: Implementation of perspective transformation

Lecture 15: Perspective: Refactoring and enhancing perspective transform

Lecture 16: Section Summary

Chapter 5: Contrast Enhancements

Lecture 1: Histogram: Introduction to histogram equalization

Lecture 2: Histogram: Histogram equalization for gray image

Lecture 3: Histogram: Histogram equalization for color image

Lecture 4: Gamma Correction: Introduction

Lecture 5: Gamma Correction: Gamma correction implementation grayscale image – 1

Lecture 6: Gamma Correction: Gamma correction implementation grayscale image – 2

Lecture 7: Gamma Correction: Gamma correction on color image

Lecture 8: Adaptive Histogram: Histogram CLAHE introduction

Lecture 9: Adaptive Histogram: Applying Histogram CLAHE on grayscale image

Lecture 10: Adaptive Histogram: Applying Histogram CLAHE on color image

Lecture 11: Saturation correction: Adjusting Contrast and brightness

Lecture 12: Saturation correction: Applying on gray and color image

Lecture 13: Comparison of different algorithms: Introduction

Lecture 14: Comparison of different algorithms with imadjust algorithm (from matlab)

Lecture 15: Custom enhancement: Introduction

Lecture 16: Custom enhancement: algorithm explaination

Lecture 17: Custom enhancement: imadjust for gray image implementation

Lecture 18: Custom enhancement: code refactoring

Lecture 19: Custom enhancement: Add test for color image before coding

Lecture 20: Custom enhancement: 4. run test 1 continue and implement for color image

Lecture 21: Custom enhancement: Refactoring and testing color image

Lecture 22: Section Summary

Chapter 6: Text and Complex Operations

Lecture 1: Introduction

Lecture 2: Text Operation: Add text task to do

Lecture 3: Text Operation: OpenCV documentation walkthrough

Lecture 4: Text Operation: add text to image

Lecture 5: Text Operation: add rectangle enclosing text

Lecture 6: Text Operation: add rotated text in image

Lecture 7: Text Operation: fix text intensity issue for rotated text added

Lecture 8: Advanced Text Operation: explanation of refactored code

Lecture 9: Advanced Text Operation: Implementation for add text with perspective transform

Lecture 10: Advanced Text Operation: refactoring code

Lecture 11: Section Summary

Chapter 7: [Advanced] Building image editor application using learnt concepts

Lecture 1: Intro

Lecture 2: design explanation

Lecture 3: Snapshot(memento design pattern)

Lecture 4: Command (command design pattern)

Lecture 5: Code refactoring

Instructors

Pravin Tripathi
Software Engineer

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