streming.py

from flask import Flask, Response
from threading import Thread
import cv2
import os
import csv
import time
from datetime import datetime
from sklearn.neighbors import KNeighborsClassifier
from win32com.client import Dispatch
import pickle
import numpy as np

app = Flask(__name__)
camera = cv2.VideoCapture(0)

def generate_frames():
    while True:
        success, frame = camera.read()
        if not success:
            break
        else:
            ret, buffer = cv2.imencode('.jpg', frame)
            frame = buffer.tobytes()
            yield (b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

@app.route('/')
def index():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

def run_flask_server():
    app.run(host='0.0.0.0', port=5000, debug=False)

def speak(str1):
    speak = Dispatch(("SAPI.SpVoice"))
    speak.Speak(str1)

def face_recognition():
    facedetect = cv2.CascadeClassifier('data/haarcascade_frontalface_default.xml')

    with open('data/names.pkl', 'rb') as w:
        LABELS = pickle.load(w)
    with open('data/faces_data.pkl', 'rb') as f:
        FACES = pickle.load(f)

    print('Shape of Faces matrix --> ', FACES.shape)

    knn = KNeighborsClassifier(n_neighbors=5)
    knn.fit(FACES, LABELS)

    imgBackground = cv2.imread("background.png")

    COL_NAMES = ['NAME', 'TIME']

    while True:
        ret, frame = camera.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = facedetect.detectMultiScale(gray, 1.3, 5)
        for (x, y, w, h) in faces:
            crop_img = frame[y:y+h, x:x+w, :]
            resized_img = cv2.resize(crop_img, (50,50)).flatten().reshape(1,-1)
            output = knn.predict(resized_img)
            ts = time.time()
            date = datetime.fromtimestamp(ts).strftime("%d-%m-%Y")
            timestamp = datetime.fromtimestamp(ts).strftime("%H:%M-%S")
            exist = os.path.isfile("Recognize/Recognize_" + date + ".csv")
            cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 1)
            cv2.rectangle(frame, (x,y), (x+w,y+h), (50,50,255), 2)
            cv2.rectangle(frame, (x,y-40), (x+w,y), (50,50,255), -1)
            cv2.putText(frame, str(output[0]), (x,y-15), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1)
            cv2.rectangle(frame, (x,y), (x+w, y+h), (50,50,255), 1)
            attendance = [str(output[0]), str(timestamp)]
        imgBackground[162:162 + 480, 55:55 + 640] = frame
        cv2.imshow("Frame",imgBackground)
        k = cv2.waitKey(1)
        if k == ord('o'):
            time.sleep(5)
            if exist:
                speak("Person Recognized..")
                with open("Recognize/Recognize_" + date + ".csv", "+a") as csvfile:
                    writer = csv.writer(csvfile)
                    writer.writerow(attendance)
                csvfile.close()
            else:
                speak("Intruder Alert initiating weapon system")
                with open("Recognize/Recognize_" + date + ".csv", "+a") as csvfile:
                    writer = csv.writer(csvfile)
                    writer.writerow(COL_NAMES)
                    writer.writerow(attendance)
                csvfile.close()
        if k == ord('q'):
            break

    camera.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    flask_thread = Thread(target=run_flask_server)
    flask_thread.daemon = True
    flask_thread.start()

    # Run face recognition in the main thread
    face_recognition()