Computer vision + deep learning + machine learning + opencv + target detection and tracking + one-stop learning (code + video + PPT)

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Chapter 1: Visual project material introduction and study guide

Related knowledge: Introduction to computer vision, OpenCV library, and the overall structure of the course.
Learning Summary: Understand the objectives and learning paths of the course and prepare for subsequent chapters.
Importance: Provide students with an overall understanding of the entire course, providing a framework and context for learning.

The picture shows the complete information of computer vision opencv:

include

Computer vision/opencv video
PPT corresponding to the video.
Each module code
Self-study pdf materials
Includes image processing, target detection, and computer vision tasks

Chapter 2: OpenCV development environment setup

Related knowledge: Learn how to install and configure the OpenCV development environment.
Learning summary: Understand the steps to build an OpenCV environment and solutions to common problems.
Importance: Provides the necessary foundation for actual programming in subsequent chapters.
The development environment of OpenCV can be set up on different operating systems. The following are the basic steps on some common operating systems. Please note that this is a general approach and the specific steps may vary based on different systems and needs.

Build OpenCV in Windows environment:

Install Python:
- Download Python: https://www.python.org/downloads/
- Install Python, remember to check the “Add Python to PATH” option.
Install CMake:
- Download CMake: https://cmake.org/download/
- Install CMake, remember to select the “Add CMake to the system PATH for all users” option.
Install Visual Studio:
- Download and install Visual Studio (Visual Studio 2019 Community version recommended): https://visualstudio.microsoft.com/visual-cpp-build-tools/
- When installing, select the "Desktop development with C++" workload.
Install NumPy:
- Open the command line (CMD) and run the following command:pip install numpy
Download OpenCV:
- Visit the OpenCV official website: https://opencv.org/releases/
- Download the latest version of OpenCV source code.
Compile OpenCV:
- Configure OpenCV using CMake.
- Open the CMake GUI, set the source code path and build path, and click "Configure".
- Adjust the configuration as needed and click Generate.
- Open Visual Studio, open the generated solution file, compile and build OpenCV.
Install OpenCV:
- Locate the generated OpenCV installation file (.exe or .msi) in the generated directory, run it and follow the prompts to install OpenCV.

Build OpenCV in macOS environment:

Install Homebrew:

Open a terminal and run the following command:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Install Python:
- Run the following command:
```
brew install python
```
Install NumPy:
- Run the following command:
```
pip install numpy
```
Install OpenCV:
- Run the following command:
```
brew install opencv
```

Build OpenCV in Linux environment:

Install Python:
- Install Python using your system package manager (e.g., apt, yum).
- For example, on Ubuntu run:
```
sudo apt-get update
sudo apt-get install python3
```
Install NumPy:
- Run the following command:
```
pip install numpy
```
Install OpenCV:
- Install OpenCV using your system package manager.
- Running on Ubuntu:
```
sudo apt-get install libopencv-dev python3-opencv
```

The above are the basic steps. The specific environment setup may vary depending on the operating system version, package manager version, etc. It is recommended to check the relevant documentation for more detailed and up-to-date information. When setting up the environment, be sure to follow the instructions in the official documentation to ensure correct configuration and dependencies.

Chapter 3: Loading and display of images & videos

Related knowledge: Learn how to use OpenCV to load and display images and videos.
Learning Summary: Master the basic operations of image and video processing.
Importance: This is an introduction to computer vision applications and lays the foundation for subsequent operations.

import cv2

# 读取图像
img = cv2.imread('image.jpg')

# 显示图像
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Chapter 4: You must know the basics of OpenCV

Related knowledge: Learn the basic concepts, data types, functions, etc. in the OpenCV library.
Learning summary: Understand the basic structure and usage of OpenCV and lay the foundation for further image processing.
Importance: Basic knowledge of OpenCV is the key to mastering computer vision.

import cv2

# 读取图像
img = cv2.imread('image.jpg')

# 转换为灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 显示原图和灰度图
cv2.imshow('Original Image', img)
cv2.imshow('Gray Image', gray_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Chapter 5: OpenCV implements graphics drawing

Related knowledge: Learn how to use OpenCV to draw lines, graphics, etc. on images.
Learning Summary: Master the skills of drawing graphics to provide a foundation for image annotation and analysis.
Importance: In image processing, drawing graphics is a common visualization method.

import cv2
import numpy as np

# 创建一张空白图像
img = np.zeros((300, 300, 3), dtype=np.uint8)

# 画线
cv2.line(img, (0, 0), (300, 300), (0, 255, 0), 2)

# 画矩形
cv2.rectangle(img, (50, 50), (250, 250), (0, 0, 255), 2)

# 显示图像
cv2.imshow('Drawing Example', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Chapter 6: Arithmetic and Bit Operations of OpenCV

Related knowledge: Learn arithmetic and bit operations in OpenCV, such as addition, subtraction, logical operations, etc.
Learning Summary: Understand basic mathematical operations on images and provide tools for image processing and analysis.
Importance: Image processing often involves pixel-level operations that are critical to many tasks.

Chapter 7: Basic Image Transformation

Related knowledge: Learn basic transformations such as image translation, rotation, and scaling in OpenCV.
Learning Summary: Master the basic geometric transformation of images to provide a foundation for image registration and enhancement.
Importance: Image transformation is a prerequisite for many computer vision tasks.
Basic image transformations are operations commonly used in image processing to adjust the geometry and appearance of images. Here are some common basic image transformations:

Translation:
- Panning is the operation of moving an image in both horizontal and vertical directions.
- Translation transformations can be represented by matrix multiplication.
Rotation:
- Rotation is performed at a certain angle around the center of the image or at a specified point.
- Rotation transformation can be represented by a rotation matrix.
Scaling:
- Scaling is the resizing of an image, either proportionally smaller or larger.
- The scaling transformation can be represented by a scaling matrix.
Flip:
- Flip is to flip the image horizontally or vertically.
- Horizontal flipping can be achieved by swapping the columns of the image, and vertical flipping can be achieved by swapping the rows of the image.
Affine Transformation:
- Affine transformations are linear transformations including translation, rotation, scaling and shearing.
- Affine transformation can be implemented through matrix transformation.
Perspective Transformation:
- Perspective transformation is a nonlinear transformation used to process the deformation of images under perspective projection.
- Perspective transformation can be represented by a perspective transformation matrix.

These basic transformations are widely used in computer vision and image processing to correct image distortion, adjust image angle, change image size, etc. In practical applications, various tools and libraries (such as OpenCV) can be used to implement these transformations, simplifying complex mathematical operations.
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Chapter 8: Filters in OpenCV

Related knowledge: Learn the use of filters such as image smoothing, sharpening, and edge detection.
Learning Summary: Understand the principles and applications of different filters and provide more advanced skills for image processing.
Importance: Filtering is a common operation in image processing and can be used for denoising, enhancement, etc.

Chapter 9: Morphology in OpenCV

Related knowledge: Learn morphological operations such as dilation, erosion, opening and closing operations.
Learning Summary: Master the basic concepts of morphological operations and provide tools for image analysis and object detection.
Importance: Morphological operations are very important for processing binary images and changes in object structure.

Chapter 10: Target Recognition

Related knowledge: Learn how to use OpenCV for target detection and recognition.
Learning Summary: Understand the basic processes and algorithms of target recognition, providing a basis for practical applications.
Importance: Object recognition is a key task in computer vision and is widely used.

import cv2

# 加载分类器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 读取图像
img = cv2.imread('faces.jpg')

# 转换为灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 进行人脸检测
faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5)

# 在图像上标记人脸
for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

# 显示图像
cv2.imshow('Face Detection', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Chapter 11: Feature Point Detection and Matching

Related knowledge: Learn feature point detection and matching algorithms, such as SIFT, SURF, ORB, etc.
Learning summary: Master the application of feature points in image registration and stitching.
Importance: Feature points are a commonly used information representation method in image processing.

Chapter 12: Image Segmentation and Repair

Related knowledge: Learn the basic concepts and methods of image segmentation and restoration.
Learning summary: Understand the application of image segmentation and repair in medical images, non-destructive testing and other fields.
Importance: Image segmentation and inpainting are prerequisites for many image analysis tasks.

Chapter 13: Machine Learning

Related knowledge: Introduce the basic concepts of machine learning in computer vision, such as supervised learning, unsupervised learning, etc.

Learning summary: Understand the application of machine learning in computer vision tasks, including image classification, target detection, etc.
Importance: Machine learning provides computer vision with powerful tools that can be used to learn complex patterns from data.

Chapter 14: Course Summary

Related knowledge: Review the knowledge points and skills covered throughout the course.
Learning summary: Summarize what you have learned, emphasize important concepts, and consolidate learning results.
Importance: Provide a framework to help students integrate the knowledge they have learned and provide directions for further learning or practice
Includes the following figure
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Data acquisition

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