test.py

from PIL import Image
from gtts import gTTS

# ocr import
import os
import pytesseract
import subprocess
import picamera
import numpy as np
import cv2
import pyttsx3
import datetime
#switch import
import RPi.GPIO as GPIO
import time
import imutils
from picamera import PiCamera
GPIO.setmode(GPIO.BOARD)
GPIO.setup(11, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)
GPIO.setup(13, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)


# Face recognition function
    
def faceRecog():
    
    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 speechCallName(text_str):
        engine = pyttsx3.init()
        engine.setProperty('voice', 'english+f3')  # changes the voice
        engine.setProperty('rate', 125)  
        engine.say(text_str)     
        engine.runAndWait()
    
    
    def face_recognition():
        
        while not GPIO.input(11):
        #while True:
            print("yes3")        
            cam.capture("/home/pi/Desktop/switch/face1.png")
            #img = np.asarray(Image.open("/home/pi/Desktop/switch/FacialRecognitionProject/face1.png"))
            #img =cam.read()
            img = cv2.imread("/home/pi/Desktop/switch/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)
               
            cv2.imshow('camera',img) 
            print("yes3")
            #if str(id)!= "unknown":
             #   speechCallName(str(id))
            k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
            if k == 27:
                break

        
        
    face_recognition()
    cam.close()
    cam.release()
    cv2.destroyAllWindows()
# End face recognition function






src_path = "/home/pi/Desktop/switch/"

def captureImage():
    print("take picture")
    with picamera.PiCamera() as camera:
        camera.start_preview()
        camera.resolution = (1080,720)
        #camera.framerate=15
        camera.brightness=60
        camera.rotation = 90
        camera.capture('/home/pi/Desktop/switch/p1.png')
        time.sleep(1)
        camera.capture('/home/pi/Desktop/switch/p2.png')
        time.sleep(1)
        camera.capture('/home/pi/Desktop/switch/p3.png')
        camera.start_preview()
    print("taken")


 
def get_string(img_path):
    #print("yes1 "+img_path)
    # Read image with opencv
    img = cv2.imread(img_path)
    print("yes2")
    # Convert to gray
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
    # Apply dilation and erosion to remove some noise
    kernel = np.ones((1, 1), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)
    img = cv2.erode(img, kernel, iterations=1)
 
    # Write image after removed noise
    cv2.imwrite(src_path + "removed_noise.png", img)
 
    #  Apply threshold to get image with only black and white
    #img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
 
    # Write the image after apply opencv to do some ...
    cv2.imwrite(src_path + "thres.png", img)
    # Recognize text with tesseract for python
    result = pytesseract.image_to_string(Image.open(src_path + "thres.png"))
 
    # Remove template file
    #os.remove(temp)
 
    return result
    
    
def speechCall(text_ocr):
    '''text2speech = gTTS(text=text_ocr,lang='en')
    text2speech.save("sample.mp3")
    os.system("mpg321 sample.mp3")'''
    engine = pyttsx3.init()
    engine.setProperty('voice', 'english+f3')  # changes the voice
    engine.setProperty('rate', 125)  
    engine.say(text_ocr)     
    engine.runAndWait()

while True:
    if GPIO.input(11):
        print("Clicked for text to speech")
        try:
            captureImage()
        except:
            print("error occured in image")
            
        try:
            text_ocr1 = get_string(src_path + "p3.png")
            if len(text_ocr1)!=0:
                print(text_ocr1)
                speechCall(text_ocr1)
            else:
                print("No text in image 3")
                text_ocr2 = get_string(src_path + "p2.png")
                print(text_ocr2)
                if len(text_ocr2)!=0:
                    speechCall(text_ocr2)
        except:
            #speechCall("no text in image")
            print("error occured in OCR")
    elif GPIO.input(13):
        print("Clicked for Face recognition")
        try:
            faceRecog()
        except:
            print("error occured in Face recognition")
        
    else:
        print("No click")
    time.sleep(1)