OpenCV4 entry to advanced
Chapter 1 Introduction and Learning Guide
Chapter 2 OpenCV Development Environment Construction
Chapter 3 Image & Video Loading and Display
Chapter 4 OpenCV Must Know the Basics
Chapter 5 OpenCV Realizes Graphics Drawing
Chapter 6 OpenCV Arithmetic and Bit Operations
Chapter 7 Basic Image Transformation
Chapter 8 Filters in OpenCV
Chapter 9 Morphology in OpenCV
Chapter 10 Object Recognition - Vehicle Statistics Project
Chapter 11 Feature Point Detection and Matching - Image Stitching Project
Chapter 12 Image Segmentation and Integration Repair
Chapter 13 Machine Learning - Face Recognition Project
Chapter 14 Course Summary
Chapter 1 Course Introduction and Study Guide
foreword
With the continuous development of artificial intelligence, the technology of machine learning is becoming more and more important. Machine learning is mostly based on vision, so let's introduce the most important module in vision: OpenCV.
提示：以下是本篇文章正文内容，下面案例仅供参考
1. What is OpenCV, why learn OpenCV?
1. OpenCV application scenarios
Target Recognition
For example, target recognition is a large category that can be subdivided into multiple branches.
For example, face recognition can solve problems such as reputation. Now the financial system basically adds face recognition modules.
vehicle tracking
animal classification, object classification;
Autopilot
Autonomous driving: Baidu, Google, Tesla, etc. are all vigorously developing;
For autonomous driving, when there were not so many applications of vision algorithms before, autonomous driving used laser radar to collect road information, which required a lot of transformation of road test information.
Distance detection:
Medical Image Analysis
Video content understanding and analysis
2. The relationship between OpenCV and graphics
The basic relationship between OpenCV and graphics:
The relationship between OpenCV and FFmpeg:
OpenCV calls FFmpeg mainly to process multimedia files. OpenCV generally needs to get the original video frame data, which is yuv and rgb data, but the data that users often give to OpenCV is basically png, jpg, or a video. When you need to call the decoding module of FFmpeg, after decoding the encoded data, it can be converted into yuv and rgb data required by OpenCV for processing.
1. The various industries introduced above are inseparable from the support of OpenCV;
2. It can be applied to platforms such as live broadcast, and can be used for Beijing replacement;
3. Change life.
The overall content is detailed in the catalog:
Two, OpenCV entry and actual combat
OpenCV can be divided into two parts: Open+CV.
CV: Compute Vision computer vision. Plus open means open source, it is an open source computer vision library.
1. Introduction to OpenCV:
2. What can I get?
3. Build OpenCV environment under ubuntu
1. Steps to install OpenCV environment under Ubuntu
For example:
apt install python-numpy
1. Install python3 first
Check the system:
uname -a
# 查看版本：
apt-cache search search python | grep python3
# 安装：
sudo apt install python3.9
# 卸载：
sudo remove python3.8
2. Install CV related libraries
Method 1: Install numpy:
Method 2: Install numpy:
Install OpenCV:
Install matplotlib:
