OCR-face_recognition.py

''''
Real Time Face Recogition
	==> Each face stored on dataset/ dir, should have a unique numeric integer ID as 1, 2, 3, etc                       
	==> LBPH computed model (trained faces) should be on trainer/ dir
Based on original code by Anirban Kar: https://github.com/thecodacus/Face-Recognition    
Developed by Marcelo Rovai - MJRoBot.org @ 21Feb18  
'''

import cv2
import numpy as np
import os
import imutils 
from gtts import gTTS
import pyttsx3
import datetime
from PIL import Image
from picamera import PiCamera

def faceRecog():
    # Face recog
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    recognizer.read('/home/pi/Desktop/switch/FacialRecognitionProject/trainer/trainer.yml')
    cascadePath = "/home/pi/Desktop/switch/FacialRecognitionProject/haarcascade_frontalface_default.xml"
    faceCascade = cv2.CascadeClassifier(cascadePath);

    font = cv2.FONT_HERSHEY_SIMPLEX

    #iniciate id counter
    id = 0

    # names related to ids: example ==> Marcelo: id=1,  etc
    names = ['None', 'ahmed', 'ibrahim', 'safi', 'najmul', 'Sakib'] 

    # Initialize and start realtime video capture
    #cam = cv2.VideoCapture(0)


    # initialize the camera and grab a reference to the raw camera capture
    cam = PiCamera()
    cam.resolution = (1080, 720)
    cam.framerate = 32
    cam.rotation = 90
    cam.brightness=60

    #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*640
    minH = 0.1*480

    def face_recognition():
        while True:
            
            cam.capture("/home/pi/Desktop/switch/FacialRecognitionProject/face1.png")
            #img = np.asarray(Image.open("/home/pi/Desktop/switch/FacialRecognitionProject/face1.png"))
            #img =cam.read()
            img = cv2.imread("/home/pi/Desktop/switch/FacialRecognitionProject/face1.png")
            img = cv2.flip(img, -1) # Flip vertically
            img=imutils.rotate(img,angle=180)

            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 ((100 - confidence)>40):
                    id = names[id]
                    confidence = "  {0}%".format(round(100 - confidence))
                    #file1 = open("MyFile.txt","a")
                    #file1.write("\n"+str(id)+ " at: "+str(datetime.datetime.now()))
                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)
                #text2speech=gTTS(str(id),lang="en")
                #text2speech.save("sample.mp3")
                #os.system("mpg321 sample.mp3")
                #engine = pyttsx3.init()
                #engine.say("it's "+str(id))
                #engine.runAndWait()
                #print(str(id))
            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()
    face_recognition()
# End face recognition function

faceRecog()