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DOGZILLA-Lite📖
1. Get started quickly
  • 0. Notes (Must Read)new
  • 1. Main interface function experience (cm5)new
  • 1. Main interface function experience(cm4)
  • 2. APP control experience
  • 3. Web page image transmission control
  • 4. LLM-Embodied Intelligence Capabilities
  • 5. LLM-Smart Dog Capabilities
2. Basic operations of Raspberry Pi system
  • 1.SSH remote login
  • 2.VNC remote login desktop
  • 3.Remote file transfer
  • 4.JupyterLab environment construction and use
  • 5.Burn the system image
  • 6.Connect to WiFi
  • 7.Close large programs
  • 8.Raspberry Pi system expansion
3. Basic control of the dogzilla
  • 0. Introduction to DOGZILLA-Lite
  • 1. Screen display control
  • 2. Record Audio
  • 3. Play Audio
  • 4. Text to audio
  • 5. Voice to text
  • 6. Record Video
  • 7. Play Video
  • 8. Video recording function
  • 9. Read the posture angle
  • 10. Battery level reading
  • 11. Read the servo angle
  • 12. Screen display of expressions
  • 13. Calibration of the robot dog
  • 14. Burn the firmware
  • 15. Analysis of common problems
4. Motion control of the dogzilla
  • 1. The dog's forward, backward, left, and right movements and gait
  • 2. Dog Spin
  • 3. The dog adjusts its height and posture
  • 4. Dog periodic translation and rotation
  • 5. Self-balancing mode
  • 6. The dog performs preset actions
  • 7. Single Leg Control
  • 8. Robotic arm control
  • 9. Teaching mode
  • 10. Group Control Performance
  • 11. Teaching demonstration onlinenew
5. 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
6. AI recognition of the dogzilla
  • 1. Camera driver tutorial
  • 2. Color recognition
  • 3. HSV value test
  • 4. Object Recognition
  • 5. QR code recognition
  • 6. License Plate Recognition
  • 7. Human Pose Estimation
  • 8. Yolo detection
  • 9. Face Detection
  • 10. Human face mask
  • 11. Face effects
  • 12. 3D Object Recognition
  • 13. Paintbrush
  • 14. Finger recognition
  • 15. Gesture Recognition
  • 16. Age and gender identification
  • 17. Background Segmentation
  • 18. Facial Recognitionnew
  • 19. Emotion Recognitionnew
  • 20. Motion Detectionnew
7. Dogzilla AI Interaction
  • 1. Color patrol
  • 2. Line patrol and obstacle removal
  • 3. Color tracking
  • 4. Ball tracking
  • 5. The ball follows
  • 6. QR code recognition exercise
  • 7. Face tracking
  • 8. Remote control
  • 9. Gesture tracking
  • 10. Gesture-controlled movement
  • 11. You Drop, I Pick
  • 12. Independent football exercise
  • 13. Human Body Followingnew
8. AI large model interaction
  • 0.AI large model principle foundation
  • 1.Large model premise use configuration
  • 2.Q&A
  • 3.Embodied intelligence
  • 4.Smart dog
  • 5.Picture to text
  • 6.Intelligent interaction
  • 7.Voice intelligent vision
  • 8.Playing football
  • 9.Block gripping
  • 10.Line tracking and obstacle removal
  • 11.Control object tracking
Download
  • System_File
  • Code
  • Firmware
  • Communication interface
  • Instruction Manual
  • Appendix
Download ZIP

Welcome to DOGZILLA-Lite repository

4. Brightness enhancement

4. Brightness Enhancement

4. Brightness Enhancement4.1. Brightness Enhancement4.2 Actual effect display

4.1. Brightness Enhancement

Implementation process: Synchronously amplify the three-channel values of each pixel point, while keeping the channel value between 0-255. In fact, it is to traverse each pixel point, add or subtract values to them, and then determine whether the three channels RGB are in the range of 0-255. If it is greater or less than, it will take the value of 255 or 0.

4.2 Actual effect display

Source code path:

/home/pi/DOGZILLA_Lite_class/4.Open Source CV/D.Image_Enhancement/04_Brightness_Enhancement.ipynb

​x
​
import cv2
import numpy as np
import matplotlib.pyplot as plt
​
img = cv2.imread('yahboom.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#cv2.imshow('src',img)
dst = np.zeros((height, width, 3),np.uint8)
for i in range(0, height):
    for j in range(0, width):
        (b,g,r) = img[i,j]
        bb = int(b) + 40
        gg = int(g) + 40
        rr = int(r) + 40
        if bb>255:
            bb = 255
        if gg>255:
            gg = 255
        if rr>255:
            rr = 255
        dst[i,j] = (bb,gg,rr)
# cv2.imshow('dst',dst)
# cv2.waitKey(0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(14, 6), dpi=100) #设置绘图区域的大小和像素 Set the size and pixels of the drawing area
plt.subplot(121)  # 一行二列第一个 The first row and second column
plt.imshow(img)
plt.subplot(122)  # 一行二列第二个 The second row, second column
plt.imshow(dst)
plt.show()

Image repair
Skin whitening

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