• 1.1 Installation video
• 1.2 First Trial

• 2.1 APP control car
• 2.2 IR control car

• 3.1 Preparation
• 3.2 Run a code on JupyterLab

A.Getting started with OpenCV

• 1.Introduction of OpenCV
• 2.OpenCV image read and display
• 3.OpenCV image write
• 4.OpenCV image quality
• 5.OpenCV pixel operation

B.OpenCV Geometric transformat

• 1.Picture Zoom
• 2.Picture Cut
• 3.Picture Shift
• 4.Picture Mirroring
• 5.Affine Map
• 6.Picture Rotation
• 7.Perspective Transformation

C.OpenCV picture processing

• 1.Grayscale processing
• 2.Image Binarization
• 3.Edge detection
• 4.Drawing line segment
• 5.Drawing rectangle circle
• 6.Drawing text picture

D.OpenCV image beautification

• 1.Histogram of color pictures
• 2.Histogram equalization
• 3.Image restoration
• 4.Brightness enhancement
• 5.Skin whitening
• 6.Gaussian mean filtering
• 7.Median filtering

E.OpenCV machine learning

• 1.KNN recognition of handwritten numbers
• 2.CNN recognition of handwritten numbers
• 3.SVM recognition of handwritten numbers

• 5.1 Drive buzzer
• 5.2 Drive motor
• 5.3 Drive servo
• 5.4 Ultrasonic ranging
• 5.5 Ultrasonic avoid
• 5.6 Ultrasonic+IR avoid
• 5.7 Tracking
• 5.8 Restricting
• 5.9 Drive LED
• 5.10 IR control test
• 5.11 IR control car

• 6.1 Driver camera
• 6.2 Color recognition
• 6.3 HSV value test
• 6.4 Camera color tracking
• 6.5 Car color tracking
• 6.6 Tensorflow object recognition
• 6.7 QR code recognition
• 6.8 QR code control
• 6.9 Face detection
• 6.10 Face tracking
• 6.11 Autopilot

• Battery and charging