English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 31m | 885 MB
Dive right into the world of Computer Vision and AI by making five awesome, real-life applications with this hands-on course
The scope of computer vision has been booming in the past few years and it has become a highly sought-after skill. There are tons of real-life problems just waiting to be solved with computer vision. If you want to get your hands dirty with this technology and use it to craft your own, unique solutions, then look no further because this course is made for you!
The course is designed so you’ll learn as you develop interesting apps. First you’ll learn to set up your environment, before building five exciting applications. We’ll introduce you to all necessary concepts and slowly transition into the field of Artificial Intelligence (AI) and deep learning such as classification and object detection.
This course will not only help you use OpenCV 4 but also apply your computer vision and AI skills to your projects. All you need is a basic working knowledge of Python and you’re good to go!
The best way to learn something new in programming is to start building as soon as possible and learning the topic as you develop. This is the exact approach we follow in this course. A hands-on, practical approach ensures that the course is never boring and you learn through interactive examples and apps. All the necessary information is conveyed as and when required, with an emphasis on where to apply it in real life.
What You Will Learn
- Get a solid introduction and foundation to Computer Vision
- Build real-time apps that deal with image and video processing
- Extract information from images and videos
- Explore the vastness of deep learning with images and videos
- Build an Optical Character Recognition (OCR) engine from scratch
- Train your own face recognition system
- Create your own real-time object classifier
Table of Contents
Getting the Environment Set Up
1 The Course Overview
2 Computer Vision with OpenCV 4
3 Setting Up the Environment
Building a Motion Detector
4 Preprocessing Video Input, Thresholding, and Blurring
5 Calculating Image Differences
6 Visualizing and Triggering Actions
Building a Hand Detector
7 Understanding Histograms and Back Projection
8 Implementing the Histogram Capture for Skin
9 Implementing Back Projection on Input Video Feed
10 Bounding the Hand – Contour Extraction
11 Extracting Fingertips – Convexity Defects
12 Air Writing – Translating Gestures to Controls
Building a Smart Video Player
13 Using Haar Cascades – Eye and Face Detection
14 Extending Haar Cascades for Eye Detection
15 GUI Automation – Interfacing the App with a Media Player
Building an Object Detector with Deep Learning
16 Deep Learning – What and Why
17 Using the DNN Module with a Pre-Trained Model
18 Digging Deeper – Feeding the Input Image to the Neural Network
19 Running Object Detection on Videos
Building an OCR App
20 Optical Character Recognition –What, Why, and How
21 Training a Digit Classifier on the MNIST Dataset
22 Developing the OCR Engine Functions
23 Developing the OCR Engine Functions (Continued)
24 OCR Square Calculator
Resolve the captcha to access the links!