import cv2 import numpy as np import face_recognition import os from datetime import datetime import streamlit as st

st.title("STOP CHILD-TRAFFICKING") run = st.checkbox('RUN') FRAME_WINDOW = st.image([])

path = 'images_database' images = [] classNames = [] myList = os.listdir(path)

with st.sidebar: st.subheader("JOIN HANDS TO") st.header("STOP TRAFFICKING") st.write("Ever seen a child lost and scared on the streets?") st.write("Here's a database of lost children!") st.subheader("POLICE : 100")

for cl in myList: # read images in myList curImg = cv2.imread(f'{path}/{cl}') # append image in images directory images.append(curImg) # .jpg removed from name and name stored classNames.append(os.path.splitext(cl)[0])

def findEncodings(images): # empty list created encodeList = [] for img in images: # image is converted to rgb img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # finding the encodings using face_encoding function of imported face_recognition module encode = face_recognition.face_encodings(img)[0] # append encodings to encodeList encodeList.append(encode) return encodeList

def markAttendance(name): with open('Attendance.csv', 'r+') as f: myDataList = f.readlines() nameList = [] for line in myDataList: entry = line.split(',') nameList.append(entry[0]) if name not in nameList: now = datetime.now() dtString = now.strftime('%H:%M:%S') f.writelines(f'\n{name},{dtString}')

markAttendance('a')

encodeListKnown = findEncodings(images) print('Encoding Completed')

cap = cv2.VideoCapture(0) while run: # object read from camera goes to img variable ret, img = cap.read() # img is converted from bgr to rgb and stored in img img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # faces img resized and stored in imgS imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25) # face image is converted from bgr to rgb and stored in imgS imgS = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# find face location in current camera frame
facesCurFrame = face_recognition.face_locations(imgS)
# current frame encoded using face_encoding function of imported face_recognition module
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
    # matches camera image to list image
    matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
    # distance between 2 images compared
    faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)

    # finds minimum distance list image compared to camera image
    matchIndex = np.argmin(faceDis)

    if matches[matchIndex]:
        # name of person's face which matches the camera face is converted to upper case and stored in name variable
        name = classNames[matchIndex].upper()
        print(name)
        # rectangle created around camera face
        y1, x2, y2, x1 = faceLoc
        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
        cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2)
        with st.sidebar:
            st.warning(name)
            st.warning(" is VICTIM OF HUMAN TRAFFICKING, Please immediately report to police")
    else:
        # rectangle created around camera face with name UNKNOWN
        y1, x2, y2, x1 = faceLoc
        cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
        name = 'UNKNOWN'
        cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2)
        with st.sidebar:
            st.info("The child is not a victim of human trafficking")

    markAttendance(name)

FRAME_WINDOW.image(img)