OpenCV / Computer Vision Learning Guide¶

Introduction¶

This folder contains systematic learning materials for computer vision using OpenCV. You can learn step-by-step from the basics of image processing to deep learning-based object detection.

Target Audience: Developers with Python basics, computer vision beginners, those preparing for video processing projects

Learning Roadmap¶

[Basics]                 [Intermediate]             [Advanced]
  │                         │                         │
  ▼                         ▼                         ▼
Setup (01) ─────────▶ Filtering (05) ──────▶ Feature Detection (13)
  │                         │                         │
  ▼                         ▼                         ▼
Image Basics (02) ──▶ Morphology (06) ─────▶ Feature Matching (14)
  │                         │                         │
  ▼                         ▼                         ▼
Color Spaces (03) ──▶ Thresholding (07) ───▶ Object Detection (15)
  │                         │                         │
  ▼                         ▼                         ▼
Geometric Transforms (04) ─▶ Edge Detection (08) ──▶ Face Detection (16)
                            │                         │
                            ▼                         ▼
                     Contours (09) ─────────▶ Video Processing (17)
                            │                         │
                            ▼                         ▼
                     Shape Analysis (10) ───▶ Calibration (18)
                            │                         │
                     ┌──────┴──────┐                  ▼
                     ▼             ▼           DNN Module (19)
               Hough Transform (11)  Histogram (12)        │
                                                      ▼
                                              Practical Projects (20)

Prerequisites¶

Required¶

Python basics (variables, control flow, functions, classes)
NumPy basics (ndarray, indexing, slicing, broadcasting)
File I/O, exception handling

Recommended¶

Linear algebra basics (matrix operations)
Probability/statistics basics
Machine learning concepts (classification, training)

File List¶

Basics (01-04)¶

File	Difficulty	Key Topics
01_Environment_Setup.md	⭐	OpenCV installation, opencv-python vs contrib, version check
02_Image_Basics.md	⭐	imread, imshow, imwrite, pixel access, ROI
03_Color_Spaces.md	⭐⭐	BGR/RGB, HSV, LAB, cvtColor, channel separation
04_Geometric_Transforms.md	⭐⭐	resize, rotate, flip, warpAffine, warpPerspective

Image Processing (05-08)¶

File	Difficulty	Key Topics
05_Image_Filtering.md	⭐⭐	blur, GaussianBlur, medianBlur, bilateralFilter
06_Morphology.md	⭐⭐	erode, dilate, opening, closing, gradient
07_Thresholding.md	⭐⭐	threshold, OTSU, adaptiveThreshold
08_Edge_Detection.md	⭐⭐⭐	Sobel, Scharr, Laplacian, Canny

Object Analysis (09-12)¶

File	Difficulty	Key Topics
09_Contours.md	⭐⭐⭐	findContours, drawContours, hierarchy, approxPolyDP
10_Shape_Analysis.md	⭐⭐⭐	moments, boundingRect, convexHull, matchShapes
11_Hough_Transform.md	⭐⭐⭐	HoughLines, HoughLinesP, HoughCircles
12_Histogram_Analysis.md	⭐⭐	calcHist, equalizeHist, CLAHE, backprojection

Features and Detection (13-15)¶

File	Difficulty	Key Topics
13_Feature_Detection.md	⭐⭐⭐	Harris, FAST, SIFT, ORB, keypoints/descriptors
14_Feature_Matching.md	⭐⭐⭐	BFMatcher, FLANN, ratio test, homography
15_Object_Detection_Basics.md	⭐⭐⭐	template matching, Haar cascade, HOG+SVM

Advanced Topics (16-18)¶

File	Difficulty	Key Topics
16_Face_Detection.md	⭐⭐⭐⭐	Haar/dlib face detection, landmarks, LBPH, face_recognition
17_Video_Processing.md	⭐⭐⭐	VideoCapture, VideoWriter, background subtraction, optical flow
18_Camera_Calibration.md	⭐⭐⭐⭐	camera matrix, distortion correction, chessboard calibration

DNN and Practice (19-20)¶

File	Difficulty	Key Topics
19_DNN_Module.md	⭐⭐⭐⭐	cv2.dnn, readNet, blobFromImage, YOLO, SSD
20_Practical_Projects.md	⭐⭐⭐⭐	document scanner, lane detection, AR marker, face filter

3D Vision (21-23)¶

File	Difficulty	Key Topics
21_3D_Vision_Basics.md	⭐⭐⭐	stereo vision, depth map, point cloud, 3D reconstruction
22_Depth_Estimation.md	⭐⭐⭐⭐	monocular depth estimation, MiDaS, DPT, Structure from Motion
23_SLAM_Introduction.md	⭐⭐⭐⭐	Visual SLAM, ORB-SLAM, LiDAR SLAM, Loop Closure

Recommended Learning Sequence¶

Quick Start (1 week)¶

01 → 02 → 03 → 05 → 07 → 08 → 09

Basic Completion (2-3 weeks)¶

01 → 02 → 03 → 04 → 05 → 06 → 07 → 08 → 09 → 10 → 11 → 12

Intermediate Completion (4-5 weeks)¶

Basic Completion + 13 → 14 → 15 → 16 → 17

Full Mastery (6-8 weeks)¶

Intermediate Completion + 18 → 19 → 20

3D Vision Deep Dive (2 weeks)¶

Full Mastery + 21 → 22 → 23

Practice Environment¶

Installation¶

# Basic installation (most features)
pip install opencv-python numpy matplotlib

# Extended installation (additional features like SIFT, SURF)
pip install opencv-contrib-python

# For face recognition
pip install dlib face_recognition

# Version check
python -c "import cv2; print(cv2.__version__)"

Recommended Environment¶

- Python 3.8 or higher
- OpenCV 4.x
- IDE: VSCode, PyCharm, Jupyter Notebook
- OS: Windows, macOS, Linux all supported

Example Project Structure¶

my_cv_project/
├── images/           # Input images
├── output/           # Output results
├── models/           # Trained models (Haar, DNN, etc.)
├── src/              # Source code
│   ├── preprocessing.py
│   ├── detection.py
│   └── utils.py
├── notebooks/        # Jupyter experiments
└── requirements.txt

OpenCV Quick Function Reference¶

Image I/O¶

Function	Description	Example
`cv2.imread()`	Read image	`img = cv2.imread('image.jpg')`
`cv2.imshow()`	Display image	`cv2.imshow('Window', img)`
`cv2.imwrite()`	Save image	`cv2.imwrite('out.jpg', img)`

Color Conversion¶

Function	Description	Example
`cv2.cvtColor()`	Color space conversion	`gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)`
`cv2.split()`	Split channels	`b, g, r = cv2.split(img)`
`cv2.merge()`	Merge channels	`img = cv2.merge([b, g, r])`

Geometric Transforms¶

Function	Description	Example
`cv2.resize()`	Resize	`resized = cv2.resize(img, (w, h))`
`cv2.rotate()`	Rotate	`rotated = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)`
`cv2.warpAffine()`	Affine transform	`dst = cv2.warpAffine(img, M, (w, h))`

Filtering¶

Function	Description	Example
`cv2.GaussianBlur()`	Gaussian blur	`blur = cv2.GaussianBlur(img, (5, 5), 0)`
`cv2.Canny()`	Canny edge detection	`edges = cv2.Canny(img, 100, 200)`
`cv2.threshold()`	Thresholding	`_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)`

Contours/Shapes¶

Function	Description	Example
`cv2.findContours()`	Find contours	`contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)`
`cv2.drawContours()`	Draw contours	`cv2.drawContours(img, contours, -1, (0, 255, 0), 2)`
`cv2.boundingRect()`	Bounding rectangle	`x, y, w, h = cv2.boundingRect(contour)`

Links to Other Folders¶

Folder	Related Content
Python/	Advanced Python syntax, testing, packaging
Algorithm/	Algorithms for image processing (graphs, DP)
Linux/	Development environment, file handling

External Resources¶

Learning Tips¶

Practice-oriented: Execute code directly and check results
Visualization: Visualize intermediate results with matplotlib
Parameter tuning: Real-time adjustment with trackbars (createTrackbar)
Step-by-step processing: Break down complex tasks into pipelines
Debugging: Check images at each step with imshow

Interview Preparation Key Topics¶

Topic	Key Questions
Color spaces	RGB vs HSV - When to use HSV?
Filtering	Gaussian vs Bilateral - What's the difference?
Thresholding	What is the principle of Otsu's method?
Edge detection	What are the steps of Canny algorithm?
Features	SIFT vs ORB - Compare pros and cons
Object detection	Haar cascade vs HOG+SVM differences
DNN	YOLO vs SSD - Speed and accuracy trade-offs