English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 5m | 443 MB
OpenCV is a toolkit for advanced image recognition. It is among the most popular professional tools used for facial recognition and is being used in a wide variety of security, marketing, and photography applications. This course offers Python developers a detailed introduction to OpenCV, starting with installing and configuring your Mac, Windows, or Linux development environment along with Python 3. Learn about the data and image types unique to OpenCV, and find out how to manipulate pixels and images. Instructor Patrick W. Crawford also shows how to read video streams as inputs, and create custom real-time video interfaces. Then comes the real power of OpenCV: object, facial, and feature detection. Learn how to leverage the image-processing power of OpenCV using methods like template matching and pre-train machine learning models to identify and recognize features.
Table of Contents
Introduction
1 Image processing with OpenCV
2 What you should know
3 How to use the exercise files
Install and Configure OpenCV
4 Python and OpenCV
5 Using virtual environments
6 Install on Mac OS
7 Install on Windows
8 Install on Linux Prerequisites
9 Install on Linux Compile OpenCV
10 Using OpenCV with Google Colab
11 Test the install
Basic Image Operations
12 Get started with OpenCV and Python
13 Get started with OpenCV and Python Google Collab
14 Access and understand pixel data
15 Data types and structures
16 Image types and color channels
17 Pixel manipulations and filtering
18 Blur, dilation, and erosion
19 Scale and rotate images
20 Use video inputs
21 Create custom interfaces
22 Challenge Create a simple drawing app
23 Solution Create a simple drawing app
Object Detection
24 Segmentation and binary images
25 Simple thresholding
26 Adaptive thresholding
27 Skin detection
28 Introduction to contours
29 Contour object detection
30 Area, perimeter, center, and curvature
31 Canny edge detection
32 Object detection overview
33 Challenge Assign object ID and attributes
34 Solution Assign object ID and attributes
Face and Feature Detection
35 Overview of face and feature detection
36 Introduction to template matching
37 Application of template matching
38 Haar cascading
39 Face detection
40 Challenge Eye detection
41 Solution Eye detection
Conclusion
42 Additional techniques
43 Next steps
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