Currencydetect

import RPi.GPIO as GPIO
import time
from utils import cv2, read_img
from gtts import gTTS
import os
import pygame
import time
from gtts import gTTS
from mutagen.mp3 import MP3
import time

GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)

IR = 17
SW = 23

GPIO.setup(IR, GPIO.IN)
GPIO.setup(SW, GPIO.IN)

def Play(text):
    myobj = gTTS(text=text, lang='en', slow =False)
    myobj.save("voice.mp3")
    print('\n--------------Playing------------\n')
    song = MP3("voice.mp3")
    pygame.mixer.init()
    pygame.mixer.music.load('voice.mp3')
    pygame.mixer.music.play()
    time.sleep(song.info.length)
    pygame.quit()
    
def helper(filename):
    # from matplotlib import pyplot as plt
        max_val = 8
        max_pt = -1
        max_kp = 0

        orb = cv2.ORB_create(nfeatures=2500)
        # orb is an alternative to SIFT

    #test_img = read_img('files/test_100_2.jpg')
    #test_img = read_img('files/test_50_2.jpg')
    #test_img = read_img('files/500test.jpg')
    #test_img = read_img('files/test_100_3.jpg')
    #test_img = read_img('files/10test.jpg')
        test_img = read_img(filename)
    # resizing must be dynamic
    # original = resize_img(test_img, 0.4)
    # display('original', original)
    # keypoints and descriptors
    # (kp1, des1) = orb.detectAndCompute(test_img, None)

        (kp1, des1) = orb.detectAndCompute(test_img, None)
        
        training_set = os.listdir('currency')
        
        for i in range(0, len(training_set)):
        # train image
                try:
                    print(i)
                        
                    train_img = cv2.imread('currency/'+training_set[i])

                    (kp2, des2) = orb.detectAndCompute(train_img, None)

                   # brute force matcher
                    bf = cv2.BFMatcher()
                    all_matches = bf.knnMatch(des1, des2, k=2)

                    good = []
                    # give an arbitrary number -> 0.789
                    # if good -> append to list of good matches
                    for (m, n) in all_matches:
                        if m.distance < 0.80 * n.distance:
                            good.append([m])

                    if len(good) > max_val:
                        max_val = len(good)
                        max_pt = i
                        max_kp = kp2

                    print(i, ' ', 'currency/'+training_set[i], ' ', len(good))
                except:
                        continue
        print(max_val)
        if max_val > 50:
            print('currency/'+training_set[max_pt])
            print('good matches ', max_val)

            train_img = cv2.imread('currency/'+training_set[max_pt])
            img3 = cv2.drawMatchesKnn(test_img, kp1, train_img, max_kp, good, 4)

            note = training_set[max_pt].split('(')[0]
            note = int(note.replace(' ', ''))
            print('\nDetected denomination: Rs. ', note)

            return note
        else:
            print('No Matches')
            return "-1"
        
import cv2
amount = 0
print('Starting...')
while True:
    if GPIO.input(IR) == False:
        print("Inserting")
        time.sleep(5)
        vs = cv2.VideoCapture(0)
        print("Capturing")
        counting = 0
        while True:
            ret, img = vs.read()
            counting +=1
            cv2.imshow('img', img)
            if cv2.waitKey(100) & 0xFF == ord('q'):
                vs.release()
                cv2.destroyAllWindows()
                break
            if counting > 50:
                cv2.imwrite('frame.jpg', img)
                amt = helper('frame.jpg')
                if  amt != '-1':
                    amount += amt
                    Play(f'detected amount is {amt}')
                vs.release()
                cv2.destroyAllWindows()
                break
        print('Amount ', amount)
    
    if GPIO.input(SW) == False:
        print("Total amount is ", amount)
        Play(f'total amount is {amount} rupees')
        amount = 0
    time.sleep(1)

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