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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

2. Color recognition

2. Color recognition

1. Experimental purpose

Drive the car to do color recognition

2. Experimental path source code

Enter the car system, end the car program, enter "ip (ip is the car's ip): 8888" in the browser, enter the password "yahboom" image-2024112100001 Then log in Enter the path of Rider-pi_class/5.AI Visual Recognition Course/2. Color recognition and run color_detection.ipynb.

3. Experimental phenomenon

After running the source code, the car can recognize red, yellow, blue, and green at the same time. When a certain color is recognized, the RGB light will also light up the corresponding color. The car cannot be in a stopped state (that is, not in a standing balance state), otherwise it cannot move. image-2024112100002

4. Analysis of main source code parameters

​x
def TEST():
detected_colors = [] # Used to store the detected color names
unique_colorstemp=None
global color_hsv
display(image_widget)
while True:
ret, frame = image.read() #usb camera usb camera
frame, binary,hsvname=update_hsv.get_contours(frame,color_hsv)
unique_colors = list(dict.fromkeys(hsvname))
# Determine how to split the string based on the length of the list
num_colors = len(unique_colors)
# If there are 4 elements in the list
if num_colors == 4:
first_line = ', '.join(unique_colors[:2])
second_line = ', '.join(unique_colors[2:])
elif num_colors == 3:
first_line = ', '.join(unique_colors[:2])
second_line = unique_colors[2]
elif num_colors == 2:
first_line = ', '.join(unique_colors)
second_line = ""
elif num_colors == 1:
first_line = unique_colors[0]
second_line = ""
else:
first_line = ""
second_line = ""
if(unique_colors!=unique_colorstemp):
light_leds(unique_colors)
unique_colorstemp=unique_colors.copy()
​
# Real-time image data transmission for display
image_widget.value = bgr8_to_jpeg(frame)
​
# Display on the LCD screen of the car
b,g,r = cv2.split(frame)
img = cv2.merge((r,g,b))
imgok = Image.fromarray(img)
mydisplay.ShowImage(imgok)

From the code, we can get: by turning on the camera of the car, we can do the corresponding color recognition; then the recognized color, RGB light corresponding color. And the result is displayed on the computer screen and the car screen.

Camera driver tutorial
HSV value test

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