【 DigiKey DIY原创大赛】-宠物进出自动门2

Python程序

face_dataset_01.py

import cv2
import os
 
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
  
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# For each person, enter one numeric face id
face_id = input('\n enter user id end press <return> ==>  ')
  
print("\n [INFO] Initializing face capture. Look the camera and wait ...")
# Initialize individual sampling face count
count = 0
  
while(True):
     ret, img = cam.read()
     img = cv2.flip(img, -1) # flip video image vertically
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     faces = face_detector.detectMultiScale(gray, 1.3, 5)
  
     for (x,y,w,h) in faces:
         cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)     
         count += 1
  
         # Save the captured image into the datasets folder
         cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg?x-oss-process=image/watermark,g_center,image_YXJ0aWNsZS9wdWJsaWMvd2F0ZXJtYXJrLnBuZz94LW9zcy1wcm9jZXNzPWltYWdlL3Jlc2l6ZSxQXzQwCg,t_20", gray[y:y+h,x:x+w])
  
         cv2.imshow('image', img)
  
     k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video
     if k == 27:
         break
     elif count >= 30: # Take 30 face sample and stop video
          break
  
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

face_training_02.py

import numpy as np
from PIL import Image
import os
import cv2
 
# Path for face image database
path = 'dataset'
 
recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");
 
# function to get the images and label data
def getImagesAndLabels(path):
     imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
     faceSamples=[]
     ids = []
     for imagePath in imagePaths:
         PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
         img_numpy = np.array(PIL_img,'uint8')
         id = int(os.path.split(imagePath)[-1].split(".")[1])
         faces = detector.detectMultiScale(img_numpy)
         for (x,y,w,h) in faces:
             faceSamples.append(img_numpy[y:y+h,x:x+w])
             ids.append(id)
     return faceSamples,ids
  
print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
faces,ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))
  
# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
  
# Print the numer of faces trained and end program
print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))

face_recognition_03.py

import cv2
import numpy as np
import os
from gpiozero import LED
from time import sleep

# 定义引脚编号
pin = 17
 
# 创建LED对象
led = LED(pin)

 
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
  
font = cv2.FONT_HERSHEY_SIMPLEX
  
#iniciate id counter
id = 0
  
# names related to ids: example ==> 1: id=1,  etc
names = ['None', '1', '2', '3', '4', '5'] 
  
# Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height
  
# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

#开门
def open_door():
    print("开锁...")
    #开锁三秒
    led.on()
    sleep(3)
    led.off()
    # 清理资源
    led.close()
  
while True:
     ret, img =cam.read()
     img = cv2.flip(img, -1) # Flip vertically
     gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
      
     faces = faceCascade.detectMultiScale( 
         gray,
         scaleFactor = 1.2,
         minNeighbors = 5,
         minSize = (int(minW), int(minH)),
        )
  
     for(x,y,w,h) in faces:
         cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
         id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
  
         # Check if confidence is less them 100 ==> "0" is perfect match 
         if (confidence < 60):
             id = names[id]
             confidence = "  {0}%".format(round(100 - confidence))
             
             #cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
             cv2.putText(img, str("OPEN DOOR"), (x+5,y-5), font, 1, (255,255,255), 2)
             #cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
             cv2.imshow('camera',img)
             open_door()
         else:
             id = "unknown"
             confidence = "  {0}%".format(round(100 - confidence))          
             cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
             cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
             cv2.imshow('camera',img) 
  
     k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
     if k == 27:
         break
  
 # Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

servo1.py

from gpiozero import Servo
from time import sleep
 
myGPIO=17
 
myCorrection=0.45
maxPW=(2.0+myCorrection)/1000
minPW=(1.0-myCorrection)/1000
 
servo = Servo(myGPIO,min_pulse_width=minPW,max_pulse_width=maxPW)
 
while True:
    servo.mid()
    print("mid")
    sleep(10)
    servo.min()
    print("min")
    sleep(10)
    servo.mid()
    print("mid")
    sleep(10)
    servo.max()
    print("max")
    sleep(10)