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cam recognition optimized
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‎requirements.txt

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cachetools==2.0.1
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certifi==2018.1.18
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chardet==3.0.4
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click==6.7
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coverage==4.4.2
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dlib==19.7.0
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face-recognition==1.0.0
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face-recognition-models==0.3.0
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Flask==0.12.2
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Flask-Cors==3.0.3
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google-api-core==0.1.4
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google-auth==1.3.0
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google-cloud-vision==0.30.0
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googleapis-common-protos==1.5.3
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grpcio==1.9.1
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idna==2.6
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itsdangerous==0.24
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Jinja2==2.10
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MarkupSafe==1.0
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nose==1.3.7
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numpy==1.13.3
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olefile==0.44
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Pillow==4.3.0
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protobuf==3.5.1
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pyasn1==0.4.2
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pyasn1-modules==0.2.1
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pytesseract==0.2.0
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pytz==2017.3
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requests==2.18.4
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rsa==3.4.2
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scipy==1.0.0
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six==1.11.0
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urllib3==1.22
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Werkzeug==0.12.2

‎webcam/Encodings.py

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import os
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import face_recognition
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from flask import Flask, request, redirect
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from werkzeug.utils import secure_filename
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import PIL
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from PIL import Image
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import numpy as np
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ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
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app = Flask(__name__)
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app.config['MAX_CONTENT_LENGTH'] = 3 * 1024 * 1024
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def allowed_file(filename):
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return '.' in filename and \
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filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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@app.route('/', methods=['GET', 'POST'])
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def upload_file():
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if request.method == 'POST':
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# check if the post request has the file part
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if 'file' not in request.files:
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# flash('No file part')
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return redirect(request.url)
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file = request.files['file']
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# if user does not select file, browser also
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# submit a empty part without filename
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if file.filename == '':
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# flash('No selected file')
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return redirect(request.url)
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if file and allowed_file(file.filename):
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filename = secure_filename(file.filename)
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_image = face_recognition.load_image_file(file)
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face_locations = face_recognition.face_locations(_image)
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if len(face_locations) > 0:
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_face_encoding = face_recognition.face_encodings(_image)[0]
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dir = os.path.abspath("Encodings")
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np.save(dir+os.path.abspath(os.sep)+filename[:-4], _face_encoding)
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# file = open(dir+os.path.abspath(os.sep)+filename[:-4]+".txt", "w")
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# file.write(_face_encoding)
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# file.close()
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return '''
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<!doctype html>
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<title>Success Page</title>
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<h1>File Successfully Uploaded</h1>
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'''
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else:
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return '''
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<!doctype html>
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<title>Error Page</title>
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<h1>No face Detected</h1>
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'''
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return '''
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<!doctype html>
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<title>Upload new File</title>
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<h1>Upload new File</h1>
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<form method=post enctype=multipart/form-data>
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<p><input type=file name=file>
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<input type=submit value=Upload>
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</form>
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'''
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def resize_image(img):
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base = 800
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new_image = Image.open(img)
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w_percent = (base / float(new_image.size[0]))
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h_size = int((float(new_image.size[1]) * float(w_percent)))
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new_image = new_image.resize((base, h_size), PIL.Image.ANTIALIAS)
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return new_image
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if __name__ == "__main__":
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app.run(host='0.0.0.0', port=5001, debug=True)

‎webcam/Encodings/Baby_Shantel.npy

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‎webcam/Encodings/Chioma_ECE.npy

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‎webcam/Encodings/Diribe.npy

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‎webcam/Encodings/Doris_Alumona.npy

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‎webcam/Encodings/Gibbs.npy

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‎webcam/Encodings/Jon_snow.npy

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‎webcam/Encodings/Kingsley.npy

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‎webcam/Encodings/Nenye.npy

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‎webcam/Encodings/Njay.npy

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‎webcam/Encodings/Santos.npy

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‎webcam/Encodings/TOC_.npy

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‎webcam/Encodings/WENDY.npy

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‎webcam/Encodings/daenerys.npy

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‎webcam/Encodings/greg.npy

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‎webcam/Encodings/lizzy_nysc.npy

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‎webcam/Encodings/natalie_dormer.npy

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‎webcam/Encodings/obinna.npy

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‎webcam/face_recognision_webcam_optimized.py

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import face_recognition
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import cv2
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import os
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import numpy as np
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# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
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# other example, but it includes some basic performance tweaks to make things run a lot faster:
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# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
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# 2. Only detect faces in every other frame of video.
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# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
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# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
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# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
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# Get a reference to webcam #0 (the default one)
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video_capture = cv2.VideoCapture(0)
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# Load a sample picture and learn how to recognize it.
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encodings = []
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images = []
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names = []
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directory = os.fsencode("Encodings/")
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_dir = os.path.abspath("Encodings")
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def process_encodings():
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list_dir = os.listdir(directory)
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for file in list_dir:
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filename = os.fsdecode(file)
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a = _dir + os.path.abspath(os.sep) + filename
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encoding = np.load(a)
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encodings.append(encoding)
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names.append(filename[:-4])
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# Initialize some variables
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process_encodings()
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encoding_size = len(encodings)
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face_locations = []
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face_encodings = []
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face_names = []
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process_this_frame = True
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while True:
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# Grab a single frame of video
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ret, frame = video_capture.read()
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# Resize frame of video to 1/4 size for faster face recognition processing
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small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
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# Only process every other frame of video to save time
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if process_this_frame:
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# Find all the faces and face encodings in the current frame of video
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face_locations = face_recognition.face_locations(small_frame)
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face_encodings = face_recognition.face_encodings(small_frame, face_locations)
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face_names = []
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for face_encoding in face_encodings:
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# See if the face is a match for the known face(s)
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match = face_recognition.compare_faces(encodings, face_encoding, 0.45)
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name = "Unknown"
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for i in range(len(match)):
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if match[i]:
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name = names[i]
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face_names.append(name)
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process_this_frame = not process_this_frame
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# Display the results
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for (top, right, bottom, left), name in zip(face_locations, face_names):
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# Scale back up face locations since the frame we detected in was scaled to 1/4 size
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top *= 4
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right *= 4
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bottom *= 4
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left *= 4
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# Draw a box around the face
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
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# Draw a label with a name below the face
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cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
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font = cv2.FONT_HERSHEY_DUPLEX
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cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
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# Display the resulting image
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cv2.imshow('Video', frame)
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# Refresh Encodings
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current_encoding_size = len(os.listdir(directory))
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if current_encoding_size != encoding_size:
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encoding_size = current_encoding_size
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process_encodings()
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# Release handle to the webcam
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video_capture.release()
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cv2.destroyAllWindows()

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