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Rider Pi Robot📖
0.Quick Start
  • 0. Notes (Must Read)new
  • 1.APP control experience
  • 2.Web page image transmission controlnew
  • 3.Manual
1.RPi Basic course
  • 1.SSH remote login
  • 2.VNC remote login desktop
  • 3. Remote file transfer
  • 4. JupyterLab environment construction and use
  • 5. Write system image
  • 6. Connect to WiFi
  • 7. Close startup programs(MustRead)
  • 8. Raspberry Pi system expansion
2.Car basic control course
  • 1. Car screen display control
  • 2. Car recording audio
  • 3. Car plays audio
  • 4. Car recording video
  • 5. Car plays video
  • 6. RGB light control
  • 7. Car attitude angle reading
  • 8. Read the battery level of the car
3.Car motion control
  • 1. Car forward and backward
  • 2. Car rotation movement
  • 3. Adjust car height
  • 4. Adjust car posture
  • 5. Car self-stabilization mode
  • 6. Car squats and shakes
4.Open Source CV
A. Getting started with OpenCV
  • 1. Introduction to OpenCV
  • 2. OpenCV image reading and display
  • 3. OpenCV image writing
  • 4. OpenCV Image Quality
  • 5.OpenCV pixel operations
B. OpenCV geometric transformation
  • 1. Image scaling
  • 2. Image cutting
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine Transformation
  • 6. Image Rotation
  • 7. Perspective Transformation
C. OpenCV image processing and drawing text segments
  • 1. Grayscale processing
  • 2. Image Binarization
  • 3. Edge Detection
  • 4. Line drawing
  • 5. Rectangle and circle drawing
  • 6. Text and picture drawing
D. Image beautification
  • 1. Color Image Histogram
  • 2. Histogram Equalization
  • 3. Image repair
  • 4. Brightness enhancement
  • 5. Skin whitening
  • 6. Gaussian mean filtering
  • 7. Median Filter
5.AI Visual recognition course
  • 1. Camera driver tutorial
  • 2. Color recognition
  • 3. HSV value test
  • 4. Object Recognition
  • 5. QR code recognition
  • 6. Human face mask
  • 7. License Plate Recognition
  • 8. Human Pose Estimation
  • 9. Object Detection
  • 10.Yolo detection
  • 11. Face Detection
  • 12. Face effects
  • 13. 3D Object Recognition
  • 14. Paintbrush
  • 15. Finger recognition
  • 16. Gesture Recognition
  • 17.Facial Recognitionnew
  • 18.Emotion Recognitionnew
  • 19.Motion Detectionnew
6.AI Visual interaction course
  • 1. Color tracking
  • 2. Color follows
  • 3. QR code recognition action
  • 4. Face tracking
  • 5. Finger control
  • 6. Gesture following
  • 7. Gesture control car
  • 8. Human Body Followingnew
7. AI Large Model Interaction
  • 0.Principles of Large AI Models
  • 1.Large model premise use configuration
  • 2.Q&A(For cm5)
  • 3.Q&A and ChatGPT(For cm4)
  • 4.Speech recognition
  • 5.Picture to Text
  • 6.Embodied intelligence
Download
  • APP(iOS/Android)
  • System_File
  • Code
  • Hareware_Info
  • AI_Model_File
  • 3D_Model_File
  • Instruction_Manual
Download ZIP

Welcome to Rider Pi Robot repository

1. Image scaling

1. Image scaling

1. Image scaling

1. Image scaling1.1. OpenCV image scaling1.2. Actual effect display1.3.matplotlib: Python's 2D plotting library.

1.1. OpenCV image scaling

cv2.resize(InputArray src, OutputArray dst, Size, fx, fy, interpolation)

Parameter meaning:

InputArray src: input image

OutputArray ds: output image

Size: output image size

fx, fy: scaling coefficients along the x-axis and y-axis

interpolation: interpolation method, you can choose INTER_NEAREST (nearest neighbor interpolation), INTER_LINEAR (bilinear interpolation (default setting)), INTER_AREA (resample using pixel area relationship), INTER_CUBIC (bicubic interpolation of 4x4 pixel neighborhood), INTER_LANCZOS4 (Lanczos interpolation of 8x8 pixel neighborhood)

Note:

  1. The output size format is (width, height)
  2. The default interpolation method is: bilinear interpolation

1.2. Actual effect display

Code path:

/home/pi/Rider-pi_class/4.Open Source CV/B.Geometric_Transformations/01_Image_Scaling.ipynb

​x
import cv2
import matplotlib.pyplot as plt # Python 2D drawing library
​
# Read the original image
img = cv2.imread('yahboom.jpg')
# Print the image size
print(img.shape)
# Assign the image height and width to x, y respectively
x, y = img.shape[0:2]
​
# Display the original image
#cv.imshow('OriginalPicture', img)
​
# Scale to half of the original size, output size format is (width, height)
img_test1 = cv2.resize(img, (int(y / 2), int(x / 2)))
# cv2.imshow('resize0', img_test1)
# cv2.waitKey()
​
# Nearest neighbor interpolation method scaling
# Scale to one-fourth of the original size
img_test2 = cv2.resize(img, (0, 0), fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST) 
# cv.imshow('resize1', img_test2) 
#cv.waitKey() 
#cv.destroyAllWindows() 
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
dst1 = cv2.cvtColor(img_test1, cv2.COLOR_BGR2RGB)
dst2 = cv2.cvtColor(img_test2, cv2.COLOR_BGR2RGB) 
# show original image
plt.imshow(
OpenCV pixel operations
Image cutting

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