#from __future__ import division

import cv2

import time

from skimage import exposure

import threading

from scipy.misc import imsave

from urllib.request import urlopen

import cv2

import numpy as np

import tensorflow as tf

url='http://192.168.0.101:8080/shot.jpg'

def gen(frame):

import cv2

import math

import argparse

def highlightFace(net, frame, conf_threshold=0.7):

frameOpencvDnn=frame.copy()

frameHeight=frameOpencvDnn.shape[0]

frameWidth=frameOpencvDnn.shape[1]

blob=cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)

net.setInput(blob)

detections=net.forward()

faceBoxes=[]

for i in range(detections.shape[2]):

confidence=detections[0,0,i,2]

if confidence>conf_threshold:

x1=int(detections[0,0,i,3]*frameWidth)

y1=int(detections[0,0,i,4]*frameHeight)

x2=int(detections[0,0,i,5]*frameWidth)

y2=int(detections[0,0,i,6]*frameHeight)

faceBoxes.append([x1,y1,x2,y2])

cv2.rectangle(frameOpencvDnn, (x1,y1), (x2,y2), (0,255,0), int(round(frameHeight/150)), 8)

return frameOpencvDnn,faceBoxes

faceProto="opencv_face_detector.pbtxt"

faceModel="opencv_face_detector_uint8.pb"

ageProto="age_deploy.prototxt"

ageModel="age_net.caffemodel"

genderProto="gender_deploy.prototxt"

genderModel="gender_net.caffemodel"

MODEL_MEAN_VALUES=(78.4263377603, 87.7689143744, 114.895847746)

ageList=['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']

genderList=['Male','Female']

faceNet=cv2.dnn.readNet(faceModel,faceProto)

ageNet=cv2.dnn.readNet(ageModel,ageProto)

genderNet=cv2.dnn.readNet(genderModel,genderProto)

padding=20

#while cv2.waitKey(1)<0 :

#hasFrame,frame=video.read()

resultImg,faceBoxes=highlightFace(faceNet,frame)

for faceBox in faceBoxes:

face=frame[max(0,faceBox[1]-padding):

min(faceBox[3]+padding,frame.shape[0]-1),max(0,faceBox[0]-padding)

:min(faceBox[2]+padding, frame.shape[1]-1)]

blob=cv2.dnn.blobFromImage(face, 1.0, (227,227), MODEL_MEAN_VALUES, swapRB=False)

genderNet.setInput(blob)

genderPreds=genderNet.forward()

gender=genderList[genderPreds[0].argmax()]

print(f'Gender: {gender}')

#print(type(f'{gender}'))

ageNet.setInput(blob)

agePreds=ageNet.forward()

age=ageList[agePreds[0].argmax()]

print(f'Age: {age[1:-1]} years')

#print(type(f'{age}'))

break

#cv2.imshow(frame)

#cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,50,50), 2, cv2.LINE_AA)

#cv2.imshow("Detecting age and gender",resultImg)

def violence(s):

import boto3

from botocore.client import Config

ACCESS_KEY_ID = ''

ACCESS_SECRET_KEY = ''

BUCKET_NAME = 'intelbucket'

data = open(s, 'rb')

s3 = boto3.resource(

's3',

aws_access_key_id=ACCESS_KEY_ID,

aws_secret_access_key=ACCESS_SECRET_KEY,

config=Config(signature_version='s3v4')

)

s3.Bucket(BUCKET_NAME).put_object(Key=s, Body=data)

def moderate_image(photo, bucket):

client=boto3.client('rekognition')

response = client.detect_moderation_labels(Image={'S3Object':{'Bucket':bucket,'Name':photo}})

#print(response)

print('Detected labels for ' + photo)

for label in response['ModerationLabels']:

print (label['Name'] + ' : ' + str(label['Confidence']))

#print (label['ParentName'])

return len(response['ModerationLabels'])

photo=s

bucket='intelbucket'

label_count=moderate_image(photo, bucket)

print("Labels detected: " + str(label_count))

if str(label_count)=='0':

print('No violence detected')

def show_webcam(vs) :

import datetime

import numpy as np

import pandas as pd

from time import time

from time import sleep

import re

import os

import math

import argparse

from collections import OrderedDict

### Image processing ###

from scipy.ndimage import zoom

from scipy.spatial import distance

import imutils

from scipy import ndimage

import dlib

from tensorflow.keras.models import load_model

from imutils import face_utils

import requests

global shape_x

global shape_y

global input_shape

global nClasses

from imutils import face_utils

from threading import Thread

import numpy as np

import playsound

import argparse

import imutils

import dlib

import simpleaudio as sa

shape_x = 48

shape_y = 48

input_shape = (shape_x, shape_y, 1)

nClasses = 7

thresh = 0.25

frame_check = 20

def prepare(filepath):

IMG_SIZE=150

img_array=cv2.imread(filepath,cv2.IMREAD_GRAYSCALE)

new_array=cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))

return new_array.reshape(-1,IMG_SIZE,IMG_SIZE,1)

def sound_alarm():

filename = 'alarm.wav'

wave_obj = sa.WaveObject.from_wave_file(filename)

play_obj = wave_obj.play()

def eye_aspect_ratio(eye):

A = distance.euclidean(eye[1], eye[5])

B = distance.euclidean(eye[2], eye[4])

C = distance.euclidean(eye[0], eye[3])

ear = (A + B) / (2.0 * C)

return ear

def detect_face(frame):

#Cascade classifier pre-trained model

cascPath = 'face_landmarks.dat'

faceCascade = cv2.CascadeClassifier(cascPath)

#BGR -> Gray conversion

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

#Cascade MultiScale classifier

detected_faces = faceCascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=6,

minSize=(shape_x, shape_y),

flags=cv2.CASCADE_SCALE_IMAGE)

coord = []

for x, y, w, h in detected_faces :

if w > 100 :

sub_img=frame[y:y+h,x:x+w]

cv2.rectangle(frame,(x,y),(x+w,y+h),(0, 255,255),1)

coord.append([x,y,w,h])

return gray, detected_faces, coord

def extract_face_features(faces, offset_coefficients=(0.075, 0.05)):

gray = faces[0]

detected_face = faces[1]

new_face = []

for det in detected_face :

#Region dans laquelle la face est détectée

x, y, w, h = det

#X et y correspondent à la conversion en gris par gray, et w, h correspondent à la hauteur/largeur

#Offset coefficient, np.floor takes the lowest integer (delete border of the image)

horizontal_offset = np.int(np.floor(offset_coefficients[0] * w))

vertical_offset = np.int(np.floor(offset_coefficients[1] * h))

#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

#gray transforme l'image

extracted_face = gray[y+vertical_offset:y+h, x+horizontal_offset:x-horizontal_offset+w]

#Zoom sur la face extraite

new_extracted_face = zoom(extracted_face, (shape_x / extracted_face.shape[0],shape_y / extracted_face.shape[1]))

#cast type float

new_extracted_face = new_extracted_face.astype(np.float32)

#scale

new_extracted_face /= float(new_extracted_face.max())

#print(new_extracted_face)

new_face.append(new_extracted_face)

return new_face

EYE_AR_THRESH = 0.24

EYE_AR_CONSEC_FRAMES = 6

# initialize the frame counter as well as a boolean used to

# indicate if the alarm is going off

COUNTER = 0

ALARM_ON = False

# initialize dlib's face detector (HOG-based) and then create

# the facial landmark predictor

print("[INFO] loading facial landmark predictor...")

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# grab the indexes of the facial landmarks for the left and

# right eye, respectively

(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]

(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]

(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]

(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]

(nStart, nEnd) = face_utils.FACIAL_LANDMARKS_IDXS["nose"]

(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"]

(jStart, jEnd) = face_utils.FACIAL_LANDMARKS_IDXS["jaw"]

(eblStart, eblEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]

(ebrStart, ebrEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]

model = load_model('video.h5')

face_detect = dlib.get_frontal_face_detector()

predictor_landmarks = dlib.shape_predictor("face_landmarks.dat")

nag=1

count=0

#Lancer la capture video

nav=0

prev=0

while True:

while True:

# Capture frame-by-frame

'''imageResp=urlopen(url)

imgNp=np.array(bytearray(imageResp.read()),dtype=np.uint8)

frame1=cv2.imdecode(imgNp,-1)'''

ret, frame1 = vs.read()

count=count+1

#frame = imutils.resize(frame1, width=450)

if int(str(datetime.datetime.now())[11]+str(datetime.datetime.now())[12])>=18:

frame = exposure.equalize_hist(frame1)

imsave('test2.jpg',frame1)

else:

cv2.imwrite('test2.jpg',frame1)

img1=cv2.imread('test2.jpg')

'''if nav==1:

gen(img1)

nav=0'''

gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

if count==1 or count==300:

violence('test2.jpg')

count=2

face_index = 0

#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

rects = face_detect(gray, 1)

#gray, detected_faces, coord = detect_face(frame)

'''pres=len(rects)

if pres!=prev:

prev=pres

nav=1'''

# detect faces in the grayscale frame

#rects = detector(gray, 0)

for (i, rect) in enumerate(rects):

try:

shape = predictor_landmarks(gray, rect)

shape = face_utils.shape_to_np(shape)

# Identify face coordinates

(x, y, w, h) = face_utils.rect_to_bb(rect)

face = gray[y:y+h,x:x+w]

#Zoom on extracted face

face = zoom(face, (shape_x / face.shape[0],shape_y / face.shape[1]))

#Cast type float

face = face.astype(np.float32)

#Scale

face /= float(face.max())

face = np.reshape(face.flatten(), (1, 48, 48, 1))

#Make Prediction

prediction = model.predict(face)

prediction_result = np.argmax(prediction)

'''print("Angry : " + str(round(prediction[0][0],3)))

print("Disgust : " + str(round(prediction[0][1],3)))

print("Fear : " + str(round(prediction[0][2],3)))

print("Happy : " + str(round(prediction[0][3],3)))

print("Sad : " + str(round(prediction[0][4],3)))

print("Surprise : " + str(round(prediction[0][5],3)))

print("Neutral : " + str(round(prediction[0][6],3)))'''

# Rectangle around the face

'''cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, "Face #{}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

for (j, k) in shape:

cv2.circle(frame, (j, k), 1, (0, 0, 255), -1)'''

shape1 = predictor(gray, rect)

shape1 = face_utils.shape_to_np(shape1)

# extract the left and right eye coordinates, then use the

# coordinates to compute the eye aspect ratio for both eyes

leftEye = shape[lStart:lEnd]

rightEye = shape[rStart:rEnd]

leftEAR = eye_aspect_ratio(leftEye)

rightEAR = eye_aspect_ratio(rightEye)

# average the eye aspect ratio together for both eyes

ear = (leftEAR + rightEAR) / 2.0

#print(1)

# compute the convex hull for the left and right eye, then

# visualize each of the eyes

leftEyeHull = cv2.convexHull(leftEye)

rightEyeHull = cv2.convexHull(rightEye)

cv2.drawContours(img1, [leftEyeHull], -1, (0, 255, 0), 1)

cv2.drawContours(img1, [rightEyeHull], -1, (0, 255, 0), 1)

# check to see if the eye aspect ratio is below the blink

# threshold, and if so, increment the blink frame counter

if ear < EYE_AR_THRESH:

COUNTER += 1

#print(1)

# if the eyes were closed for a sufficient number of

# then sound the alarm

if COUNTER >= EYE_AR_CONSEC_FRAMES:

cv2.putText(img1, "DROWSINESS ALERT!", (10, 80),

cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

#print('drowsiness alert')

# if the alarm is not on, turn it on

if not ALARM_ON:

ALARM_ON = True

# check to see if an alarm file was supplied,

# and if so, start a thread to have the alarm

# sound played in the background

t = Thread(target=sound_alarm)

t.deamon = True

t.start()

#nag=1

# draw an alarm on the frame

# otherwise, the eye aspect ratio is not below the blink

# threshold, so reset the counter and alarm

else:

COUNTER = 0

ALARM_ON = False

'''

# 1. Add prediction probabilities

cv2.putText(frame, "----------------",(40,100 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)

cv2.putText(frame, "Emotional report : Face #" + str(i+1),(40,120 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)

cv2.putText(frame, "Angry : " + str(round(prediction[0][0],3)),(40,140 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)

cv2.putText(frame, "Disgust : " + str(round(prediction[0][1],3)),(40,160 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)

cv2.putText(frame, "Fear : " + str(round(prediction[0][2],3)),(40,180 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)

cv2.putText(frame, "Happy : " + str(round(prediction[0][3],3)),(40,200 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)

cv2.putText(frame, "Sad : " + str(round(prediction[0][4],3)),(40,220 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)

cv2.putText(frame, "Surprise : " + str(round(prediction[0][5],3)),(40,240 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)

cv2.putText(frame, "Neutral : " + str(round(prediction[0][6],3)),(40,260 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)'''

# 2. Annotate main image with a label

if prediction_result == 0 :

cv2.putText(img1, "Angry",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

elif prediction_result == 1 :

cv2.putText(img1, "Disgust",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

elif prediction_result == 2 :

cv2.putText(img1, "Fear",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

elif prediction_result == 3 :

cv2.putText(img1, "Happy",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

elif prediction_result == 4 :

cv2.putText(img1, "Sad",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

elif prediction_result == 5 :

cv2.putText(img1, "Surprise",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

else :

cv2.putText(img1, "Neutral",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

# 3. Eye Detection and Blink Count

leftEye = shape[lStart:lEnd]

rightEye = shape[rStart:rEnd]

# Compute Eye Aspect Ratio

leftEAR = eye_aspect_ratio(leftEye)

rightEAR = eye_aspect_ratio(rightEye)

ear = (leftEAR + rightEAR) / 2.0

# And plot its contours

leftEyeHull = cv2.convexHull(leftEye)

rightEyeHull = cv2.convexHull(rightEye)

cv2.drawContours(img1, [leftEyeHull], -1, (0, 255, 0), 1)

cv2.drawContours(img1, [rightEyeHull], -1, (0, 255, 0), 1)

# 4. Detect Nose

nose = shape[nStart:nEnd]

noseHull = cv2.convexHull(nose)

cv2.drawContours(img1, [noseHull], -1, (0, 255, 0), 1)

# 5. Detect Mouth

mouth = shape[mStart:mEnd]

mouthHull = cv2.convexHull(mouth)

cv2.drawContours(img1, [mouthHull], -1, (0, 255, 0), 1)

# 6. Detect Jaw

jaw = shape[jStart:jEnd]

jawHull = cv2.convexHull(jaw)

cv2.drawContours(img1, [jawHull], -1, (0, 255, 0), 1)

# 7. Detect Eyebrows

ebr = shape[ebrStart:ebrEnd]

ebrHull = cv2.convexHull(ebr)

cv2.drawContours(img1, [ebrHull], -1, (0, 255, 0), 1)

ebl = shape[eblStart:eblEnd]

eblHull = cv2.convexHull(ebl)

cv2.drawContours(img1, [eblHull], -1, (0, 255, 0), 1)

'''modelx=tf.keras.models.load_model("64x3-CNN.model")

prediction=modelx.predict([prepare('test2.jpg')])

print(prediction)'''

except:

pass

cv2.putText(img1,'Number of Faces : ' + str(len(rects)),(40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, 155, 1)

cv2.imshow('Video', img1)

if cv2.waitKey(1) & 0xFF == ord('q'):

cv2.destroyAllWindows()

vs.release()

break

#cv2.imshow('Video', img1)

# When everything is done, release the capture

#video_capture.release()

vs=cv2.VideoCapture(0)

from PIL import ImageTk, Image

import webbrowser

def callback(url):

webbrowser.open_new(url)

newWindow = Toplevel(window)

newWindow.title("boys")

newWindow.geometry("300x300")

newWindow.configure(background='grey')

head=Label(newWindow,text='Top picks for you')

head.configure(font=('Courier',30))

head.place(x=800,y=0)

head2=Label(newWindow,text='Gender = '+gender)

head2.configure(font=('Courier',20))

head2.place(x=1350,y=720)

head3=Label(newWindow,text='Approximate age = '+age)

head3.configure(font=('Courier',20))

head3.place(x=1300,y=760)

ur_pic = s

ur_pic_img = Image.open(ur_pic)

ur_pic_img = ur_pic_img.resize((400,300), Image.ANTIALIAS)

ur_pic_photoImg = ImageTk.PhotoImage(ur_pic_img)

ur_pic_panel = Label(newWindow, image = ur_pic_photoImg)

ur_pic_panel.place(x=1250,y=400)

path = 'IMG-3630.jpg'

img = Image.open(path)

img = img.resize((400,300), Image.ANTIALIAS)

photoImg = ImageTk.PhotoImage(img)

panel = Label(newWindow, image = photoImg)

panel.place(x=100,y=50)

link = Label(newWindow, text="look out for more clothes", fg="blue", cursor="hand2")

link.place(x=550,y=200)

link.config(font=('Courier',20))

link.bind("<Button-1>", lambda e: callback("https://www.amazon.in/s?k=clothes+for+kids+boys&i=apparel&crid=34FBULXMLNYCS&sprefix=clothes+for+kids%2Capparel%2C292&ref=nb_sb_ss_ac-a-p_2_16"))

path1 = 'IMG_3644.jpg'

img1 = Image.open(path1)

img1 = img1.resize((400,300), Image.ANTIALIAS)

photoImg1= ImageTk.PhotoImage(img1)

panel1 = Label(newWindow, image = photoImg1)

panel1.place(x=100,y=410)

link1 = Label(newWindow, text="look out for more toys", fg="blue", cursor="hand2")

link1.place(x=550,y=550)

link1.config(font=('Courier',20))

link1.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=toys+for+kids+boys&ref=nb_sb_noss_1"))

path2 = 'IMG_3645.jpg'

img2 = Image.open(path2)

img2 = img2.resize((400,300), Image.ANTIALIAS)

photoImg2= ImageTk.PhotoImage(img2)

panel2 = Label(newWindow, image = photoImg2)

panel2.place(x=100,y=770)

link2 = Label(newWindow, text="look out for more watches", fg="blue", cursor="hand2")

link2.place(x=550,y=920)

link2.config(font=('Courier',20))

link2.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=watches+for+kids+boys&ref=nb_sb_noss_1"))

kill_button=Button(newWindow,text="Go back",command=newWindow.destroy)

kill_button.config(font=('Courier',20))

kill_button.place(x=1600,y=10)

from PIL import ImageTk, Image

import webbrowser

def callback(url):

webbrowser.open_new(url)

newWindow = Toplevel(window)

newWindow.title("girls")

newWindow.geometry("300x300")

newWindow.configure(background='grey')

head=Label(newWindow,text='Top picks for you')

head.configure(font=('Courier',30))

head.place(x=800,y=0)

head2=Label(newWindow,text='Gender = '+gender)

head2.configure(font=('Courier',20))

head2.place(x=1350,y=720)

head3=Label(newWindow,text='Approximate age = '+age)

head3.configure(font=('Courier',20))

head3.place(x=1300,y=760)

ur_pic = s

ur_pic_img = Image.open(ur_pic)

ur_pic_img = ur_pic_img.resize((400,300), Image.ANTIALIAS)

ur_pic_photoImg = ImageTk.PhotoImage(ur_pic_img)

ur_pic_panel = Label(newWindow, image = ur_pic_photoImg)

ur_pic_panel.place(x=1250,y=400)

path = 'IMG_3636.jpg'

img = Image.open(path)

img = img.resize((400,300), Image.ANTIALIAS)

photoImg = ImageTk.PhotoImage(img)

panel = Label(newWindow, image = photoImg)

panel.place(x=100,y=50)

link = Label(newWindow, text="look out for more clothes", fg="blue", cursor="hand2")

link.place(x=550,y=200)

link.config(font=('Courier',20))

link.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=clothes+for+girls+6-7+years+old&crid=1KH05VWM85166&sprefix=clothes+for+girls%2Caps%2C353&ref=nb_sb_ss_i_3_17"))

path1 = 'Kids-Makeup-Toys-Girls-Games-Baby-Cosmetics-Pretend-Play-Set-Hairdressing-Make-Up-Beauty-Toy-For.jpg'

img1 = Image.open(path1)

img1 = img1.resize((400,300), Image.ANTIALIAS)

photoImg1= ImageTk.PhotoImage(img1)

panel1 = Label(newWindow, image = photoImg1)

panel1.place(x=100,y=410)

link1 = Label(newWindow, text="look out for more toys", fg="blue", cursor="hand2")

link1.place(x=550,y=550)

link1.config(font=('Courier',20))

link1.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=toys+for+girls+6-7+years+old&ref=nb_sb_noss_2"))

path2 = 'paris-new-stylish-watch-for-girls-500x500.jpg'

img2 = Image.open(path2)

img2 = img2.resize((400,300), Image.ANTIALIAS)

photoImg2= ImageTk.PhotoImage(img2)

panel2 = Label(newWindow, image = photoImg2)

panel2.place(x=100,y=770)

link2 = Label(newWindow, text="look out for more watches", fg="blue", cursor="hand2")

link2.place(x=550,y=920)

link2.config(font=('Courier',20))

link2.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=watches+for+little+girls&crid=353KIC68L1ACS&sprefix=watches+for+%2Caps%2C351&ref=nb_sb_ss_i_4_12"))

kill_button=Button(newWindow,text="Go back",command=newWindow.destroy)

kill_button.config(font=('Courier',20))

kill_button.place(x=1600,y=10)

from PIL import ImageTk, Image

import webbrowser

def callback(url):

webbrowser.open_new(url)

newWindow = Toplevel(window)

newWindow.title("Men")

newWindow.geometry("300x300")

newWindow.configure(background='grey')

head=Label(newWindow,text='Top picks for you')

head.configure(font=('Courier',30))

head.place(x=800,y=0)

head2=Label(newWindow,text='Gender = '+gender)

head2.configure(font=('Courier',20))

head2.place(x=1350,y=720)

head3=Label(newWindow,text='Approximate age = '+age)

head3.configure(font=('Courier',20))

head3.place(x=1300,y=760)

ur_pic = s

ur_pic_img = Image.open(ur_pic)

ur_pic_img = ur_pic_img.resize((400,300), Image.ANTIALIAS)

ur_pic_photoImg = ImageTk.PhotoImage(ur_pic_img)

ur_pic_panel = Label(newWindow, image = ur_pic_photoImg)

ur_pic_panel.place(x=1250,y=400)

path = 'IMG_3641.jpg'

img = Image.open(path)

img = img.resize((400,300), Image.ANTIALIAS)

photoImg = ImageTk.PhotoImage(img)

panel = Label(newWindow, image = photoImg)

panel.place(x=100,y=50)

link = Label(newWindow, text="look out for more clothes", fg="blue", cursor="hand2")

link.place(x=550,y=200)

link.config(font=('Courier',20))

link.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=clothes+for+men&ref=nb_sb_noss_1"))

path1 = 'watch.png'

img1 = Image.open(path1)

img1 = img1.resize((400,300), Image.ANTIALIAS)

photoImg1= ImageTk.PhotoImage(img1)

panel1 = Label(newWindow, image = photoImg1)

panel1.place(x=100,y=410)

link1 = Label(newWindow, text="look out for more watches", fg="blue", cursor="hand2")

link1.place(x=550,y=550)

link1.config(font=('Courier',20))

link1.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=watches+for+men&crid=27TRXYHRP4RH3&sprefix=watches+%2Caps%2C346&ref=nb_sb_ss_i_1_8"))

path2 = 'men-s-sport-shoes-500x500.jpg'

img2 = Image.open(path2)

img2 = img2.resize((400,300), Image.ANTIALIAS)

photoImg2= ImageTk.PhotoImage(img2)

panel2 = Label(newWindow, image = photoImg2)

panel2.place(x=100,y=770)

link2 = Label(newWindow, text="look out for more shoes", fg="blue", cursor="hand2")

link2.place(x=550,y=920)

link2.config(font=('Courier',20))

link2.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=shoes+for+men&ref=nb_sb_noss_1"))

kill_button=Button(newWindow,text="Go back",command=newWindow.destroy)

kill_button.config(font=('Courier',20))

kill_button.place(x=1600,y=10)

from PIL import ImageTk, Image

import webbrowser

def callback(url):

webbrowser.open_new(url)

newWindow = Toplevel(window)

newWindow.title("women")

newWindow.geometry("300x300")

newWindow.configure(background='grey')

head=Label(newWindow,text='Top picks for you')

head.configure(font=('Courier',30))

head.place(x=800,y=0)

head2=Label(newWindow,text='Gender = '+gender)

head2.configure(font=('Courier',20))

head2.place(x=1350,y=720)

head3=Label(newWindow,text='Approximate age = '+age)

head3.configure(font=('Courier',20))

head3.place(x=1300,y=760)

ur_pic = s

ur_pic_img = Image.open(ur_pic)

ur_pic_img = ur_pic_img.resize((400,300), Image.ANTIALIAS)

ur_pic_photoImg = ImageTk.PhotoImage(ur_pic_img)

ur_pic_panel = Label(newWindow, image = ur_pic_photoImg)

ur_pic_panel.place(x=1250,y=400)

path = '2.jpg'

img = Image.open(path)

img = img.resize((400,300), Image.ANTIALIAS)

photoImg = ImageTk.PhotoImage(img)

panel = Label(newWindow, image = photoImg)

panel.place(x=100,y=50)

link = Label(newWindow, text="look out for more clothes", fg="blue", cursor="hand2")

link.place(x=550,y=200)

link.config(font=('Courier',20))

link.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=traditional+dresses+for+women&ref=nb_sb_noss_1"))

path1 = '2017_10$blog_Global Women Cosmetics Market.jpg_17_Oct_2017_071911923.jpg'

img1 = Image.open(path1)

img1 = img1.resize((400,300), Image.ANTIALIAS)

photoImg1= ImageTk.PhotoImage(img1)

panel1 = Label(newWindow, image = photoImg1)

panel1.place(x=100,y=410)

link1 = Label(newWindow, text="look out for more cosmetics", fg="blue", cursor="hand2")

link1.place(x=550,y=550)

link1.config(font=('Courier',20))

link1.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=cosmeticsfor+women&ref=nb_sb_noss_2"))

path2 = '712LwwAdKSL._AC_UL1500_.jpg'

img2 = Image.open(path2)

img2 = img2.resize((400,300), Image.ANTIALIAS)

photoImg2= ImageTk.PhotoImage(img2)

panel2 = Label(newWindow, image = photoImg2)

panel2.place(x=100,y=770)

link2 = Label(newWindow, text="look out for more heels", fg="blue", cursor="hand2")

link2.place(x=550,y=920)

link2.config(font=('Courier',20))

link2.bind("<Button-1>", lambda e: callback("https://www.amazon.com/s?k=heelsfor+women&ref=nb_sb_noss_2"))

kill_button=Button(newWindow,text="Go back",command=newWindow.destroy)

kill_button.config(font=('Courier',20))

kill_button.place(x=1600,y=10)

cap = cv2.VideoCapture(0)

ret, frame = cap.read()

cap.release()

cv2.imwrite('gen.jpg',frame)

import test

gender,age=test.suggest('gen.jpg')

al=age.split('-')

ax=int(al[1])

if gender=='Male':

if ax<=10:

child_male('gen.jpg',gender,age)

else:

ad_male('gen.jpg',gender,age)

elif gender=='Female':

if ax<=10:

child_female('gen.jpg',gender,age)

else:

b= geek.load('geekfile1.npy')

l=[]

for i in b:

l.append(i)

def recognize(s,leap):

unknown_picture = face_recognition.load_image_file(s)

unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]

results = face_recognition.api.face_distance(leap,unknown_face_encoding)

r=list(results)

q=min(r)

if q<=0.45:

return 1

else :

return 2

cond=0

faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

while cond==0:

cap = cv2.VideoCapture(0)

ret, frame = cap.read()

cap.release()

img_counter=0

if ret:

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(

gray,

scaleFactor=1.1,

minNeighbors=5, minSize=(30, 30)

#flags = cv2.CV_HAAR_SCALE_IMAGE

)

if (len(faces)>0):

#ser.write(b'done')

#time.sleep(1.25)

print('done')

print("Found {0} faces".format(len(faces)))

img_name = "opencv_frame_{}.png".format(img_counter)

cv2.imwrite(img_name, frame)

#img_counter += 1

qs=recognize(img_name,l)

if qs==1:

label=Label(window,text='Access granted')

a=1

label.config(font=('Courier',64))

label.place(x=600,y=500)

label.after(10000 , lambda: label.destroy())

btn1=Button(window, text = 'Activate system', bd = '5',

command = work)

btn1.config(font=('Courier',35),fg='red')

btn1.place(x=200,y=300)

btn2=Button(window, text='Need any shopping suggestions?',bd=5,

command=amazon)

btn2.config(font=('Courier',35),fg='red')

btn2.place(x=1000,y=300)

btn3=Button(window, text='Exit',bd=5,

command=window.destroy)

btn3.config(font=('Courier',25),fg='blue')

btn3.place(x=1650,y=900)

# Set the position of button on the top of window.

#btn1.place(x=600,y=300)

print('yes')

cond=1

elif qs==2:

label=Label(window,text="Access denied")

#label.insert(INSERT, "ACCESS DENIED")

label.config(font=('Times New Roman',64))

label.place(x=600,y=500)

print('no')