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Raspbot📖
1.First Trial and Installation
  • 1.1 Installation video
  • 1.2 First Trial
2.Remote control course
  • 2.1 APP control car
  • 2.2 IR control car
3.Preparation
  • 3.1 Preparation
  • 3.2 Run a code on JupyterLab
  • 3.5 TF card expand capacity
4.OpenCV Basic course
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.Hardware Control course
  • 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.AI vision course
  • 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
7.Battery and charging
  • Battery and charging
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  • Code
  • Download image
  • Instruction manual
  • More Annex
Download ZIP
FAQ
  • 1、Raspbot-Raspberry Pi 4b version desktop connection WiFi method
  • 2、Can't connect your home WiFi
  • 3、How to check IIC function on Raspbot
  • 4、Gesture recognition cannot work normally.
  • 5、How to write image file into SD card

Welcome to Raspbot repository

6.7 QR code recognition

Introduction to QR Code

QR code uses a certain geometric figure to record data symbol information in a black and white pattern distributed on a plane (two-dimensional direction) according to a certain rule.

Code path:

/home/pi/Yahboom_Project/Raspbot/3.AI Vision course/06.QR code recognition/QR code recognition.ipynb

#bgr8 to jpeg format
import enum
import cv2
 
def bgr8_to_jpeg(value, quality=75):
    return bytes(cv2.imencode('.jpg', value)[1])

· # Import library and show camera display component
# import the necessary packages
#import simple_barcode_detection
import cv2
import numpy as np
import pyzbar.pyzbar as pyzbar
from PIL import Image
import ipywidgets.widgets as widgets
 
image_widget = widgets.Image(format='jpeg', width=320, height=240)
display(image_widget)             # Display camera component

# Define the parse QR code interface
def decodeDisplay(image):
    barcodes = pyzbar.decode(image)
    for barcode in barcodes:
        # Extract the position of the bounding box of the QR code
    # Draw the bounding box of the barcode in the image
        (x, y, w, h) = barcode.rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (225, 225, 225), 2)
        # Extract the QR code data as a byte object, so if we want to output the image, you need to convert it to a string 
        barcodeData = barcode.data.decode("utf-8")
        barcodeType = barcode.type
 
        # Draws the data and barcode type of the barcode on the image
        text = "{} ({})".format(barcodeData, barcodeType)
        cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,0.5, (225, 225, 225), 2)
 
        # Print the data and barcode type of the barcode on the terminal
        print("[INFO] Found {} barcode: {}".format(barcodeType, barcodeData))
    return image
 
def detect():
    camera = cv2.VideoCapture(0)
    camera.set(3, 320)
    camera.set(4, 240)
    camera.set(5, 120)  # Set frame rate
    # fourcc = cv2.VideoWriter_fourcc(*"MPEG")
    camera.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
    camera.set(cv2.CAP_PROP_BRIGHTNESS, 40) #Set brightness -64 - 64  0.0
    camera.set(cv2.CAP_PROP_CONTRAST, 50) #Set contrast -64 - 64  2.0
    camera.set(cv2.CAP_PROP_EXPOSURE, 156) #Set exposure 1.0 - 5000  156.0
    ret, frame = camera.read()
    image_widget.value = bgr8_to_jpeg(frame)
    while True:
        # Read frame currently
        ret, frame = camera.read()
        # To Grayscale image
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        im = decodeDisplay(gray)
 
        cv2.waitKey(5)
        image_widget.value = bgr8_to_jpeg(im)
        # If you press q, you will be out of the loop
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
    camera.release()
    cv2.destroyAllWindows()

while 1:
    detect()

After run above program, put a QR code front the camera, it will be recognized.

image.png 

image.png 

We can click the pause button to stop the process. As shown below.

image.png 


Tensorflow object recognition
QR code control

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