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facialRecognition.py
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import cv2
import numpy as np
import os
from db import insertIntoDB
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
# initiate id counter
id = 0
# names related to ids: example ==> Marcelo: id=1, etc
names = ['None', 'Jainam', 'Person2', 'Person3', 'Person4', 'Person5']
# people found will be added in this
peopleFound = []
# 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)
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 then 100 ==> "0" is perfect match
if (confidence < 100):
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
# Add the person found to the found list
if id not in peopleFound:
peopleFound.append(id)
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 Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()
print("People Found: ", peopleFound)
for people in peopleFound:
insertIntoDB(people)