webcam_recognize.py

import face_recognition
import cv2
import pickle
import os

def train(image_path):
    print ('[INFO]: Looking for known faces in', image_path)
    # Create arrays of known face encodings and their names
    known_face_encodings = []
    known_face_names = []

    for image in os.listdir(image_path):
        location = os.path.join(image_path, image)
        if( os.path.isfile(location) ):
            sample_image = face_recognition.load_image_file(os.path.join(image_path, image))
            sample_encoding = face_recognition.face_encodings(sample_image)[0]
            known_face_encodings.append(sample_encoding)
            name = image.split('.')[0].split('_')[0]
            known_face_names.append(name)
    return known_face_encodings, known_face_names

def get_webcam():
    # Get a reference to webcam #0 (the default one)
    return cv2.VideoCapture(0)

def recognize_faces(known_face_encodings, known_face_names, webcam):
##    known_face_encodings = ['encodings']
##    known_face_names = data['names']
##    print (known_face_names)
    # Initialize some variables
    face_locations = []
    face_encodings = []
    face_names = []
    
    video_capture = webcam
    
    process_this_frame = True

    while True:
        # Grab a single frame of video
        ret, frame = video_capture.read()

        # Resize frame of video to 1/4 size for faster face recognition processing
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
        rgb_small_frame = small_frame[:, :, ::-1]

        # Only process every other frame of video to save time
        if process_this_frame:
            # Find all the faces and face encodings in the current frame of video
            face_locations = face_recognition.face_locations(rgb_small_frame, number_of_times_to_upsample = 3)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

            face_names = []
            for face_encoding in face_encodings:
                # See if the face is a match for the known face(s)
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"

                # If a match was found in known_face_encodings, just use the first one.
                if True in matches:
                    first_match_index = matches.index(True)
                    name = known_face_names[first_match_index]

                face_names.append(name)

        process_this_frame = not process_this_frame


        # Display the results
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            # Scale back up face locations since the frame we detected in was scaled to 1/4 size
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4

            # Draw a box around the face
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            # Draw a label with a name below the face
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

        # Display the resulting image
        cv2.imshow('Video', frame)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release handle to the webcam
    video_capture.release()
    cv2.destroyAllWindows()


#training_mode = False

Base_Path = os.getcwd()

if __name__ == "__main__":
    print ("[INFO]: Extracting encodings from test images")
    encodings, names = train(os.path.join(os.getcwd(), "test_images/"))
    print ("[INFO]: Accessing WebCam")
    webcam = get_webcam()
    print ("[INFO]: Initiating Face Recognition")
    recognize_faces(encodings, names, webcam)