import face_recognition
import cv2
import numpy as np

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
pic1 = face_recognition.load_image_file("pic 1.jpg")
pic1_face_encoding = face_recognition.face_encodings(pic1)[0]

# Load a second sample picture and learn how to recognize it.
pic2 = face_recognition.load_image_file("pic 2.jpg")
pic2_face_encoding = face_recognition.face_encodings(pic2)[0]

pic3 = face_recognition.load_image_file("pic 3.jpg")
pic3_face_encoding = face_recognition.face_encodings(pic3)[0]

pic4 = face_recognition.load_image_file("pic 4.jpg")
pic4_face_encoding = face_recognition.face_encodings(pic4)[0]

pic5 = face_recognition.load_image_file("pic 5.jpg")
pic5_face_encoding = face_recognition.face_encodings(pic5)[0]

pic6 = face_recognition.load_image_file("pic 6.jpg")
pic6_face_encoding = face_recognition.face_encodings(pic6)[0]

pic7 = face_recognition.load_image_file("pic 7.jpg")
pic7_face_encoding = face_recognition.face_encodings(pic7)[0]

pic8 = face_recognition.load_image_file("pic 8.jpg")
pic8_face_encoding = face_recognition.face_encodings(pic8)[0]

pic9 = face_recognition.load_image_file("pic 9.jpg")
pic9_face_encoding = face_recognition.face_encodings(pic9)[0]

pic10 = face_recognition.load_image_file("pic 10.jpg")
pic10_face_encoding = face_recognition.face_encodings(pic10)[0]

pic11 = face_recognition.load_image_file("pic 11.jpg")
pic11_face_encoding = face_recognition.face_encodings(pic11)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    pic1_face_encoding,
    pic2_face_encoding,
    pic3_face_encoding,
    pic4_face_encoding,
    pic5_face_encoding,
    pic6_face_encoding,
    pic7_face_encoding,
    pic8_face_encoding,
    pic9_face_encoding,
    pic10_face_encoding,
    pic11_face_encoding   
]
known_face_names = [
    "pic 1",
    "pic 2",
    "pic 3",
    "pic 4",
    "pic 5",
    "pic 6",
    "pic 7",
    "pic 8",
    "pic 9",
    "pic 10",
    "pic 11",
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

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

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

    # Find all the faces and face enqcodings in the frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop through each face in this frame of video
    for (top, right, bottom, left), face_encoding in zip(face_locations, 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]

        # Or instead, use the known face with the smallest distance to the new face
        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
        best_match_index = np.argmin(face_distances)
        if matches[best_match_index]:
            name = known_face_names[best_match_index]

        # 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()