import face_recognition
import cv2
import lip_finder

# This is a demo of running face recognition on a video file and saving the results to a new video file.
#
# 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.

# Open the input movie file

def get_lips(top_lip, bottom_lip):

    return top_lip[0:7] + bottom_lip[1:6] + top_lip[7:][::-1] + bottom_lip[8:11][::-1]


input_movie = cv2.VideoCapture("test.mp4")
length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(input_movie.get(cv2.CAP_PROP_FRAME_WIDTH))
heights = int(input_movie.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(input_movie.get(cv2.CAP_PROP_FPS))
print ("h {}, w {} , f {}".format(heights, width, fps))

# Create an output movie file (make sure resolution/frame rate matches input video!)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
output_movie = cv2.VideoWriter('output.avi', fourcc, fps, (width, heights))

# Load some sample pictures and learn how to recognize them.
lmm_image = face_recognition.load_image_file("efim.jpg")
lmm_face_encoding = face_recognition.face_encodings(lmm_image)[0]


known_faces = [
    lmm_face_encoding,
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
frame_number = 0
i = 0
while True:
    # Grab a single frame of video
    ret, frame = input_movie.read()
    frame_number += 1
    #frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    # Quit when the input video file ends
    if not ret:
        break

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

    face_names = []
    for face_encoding in face_encodings:
        # See if the face is a match for the known face(s)
        #match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)
        match = face_recognition.compare_faces([lmm_face_encoding], face_encoding)

        # If you had more than 2 faces, you could make this logic a lot prettier
        # but I kept it simple for the demo
        name = None
        if match[0]:
            i+=1
            name = "efim"
            print ("found face {}".format(i))
            face_landmarks = face_recognition.face_landmarks(frame, face_locations)
            #lips_alt = lip_finder.LipFinder().find_lips(frame)
            lips = get_lips(face_landmarks[0]["top_lip"], face_landmarks[0]["bottom_lip"])
            lips = lip_finder.LipFinder().find_lips(frame, lips)

            for x, y in lips:
                cv2.circle(frame, (x, y), 2, (255, 0, 0), -1)
            cv2.imshow("", frame)
            cv2.waitKey(0)
        elif match[1]:
            name = "artem"

        face_names.append(name)

    # Label the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        if not name:
            continue

        # 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 - 5), (right, bottom), (0, 0, 255), cv2.FILLED)
        # font = cv2.FONT_HERSHEY_DUPLEX
        # cv2.putText(frame, name, (left + 6, bottom + 6), font, 0.5, (255, 255, 255), 1)

    # Write the resulting image to the output video file
    print("Writing frame {} / {}".format(frame_number, length))
    output_movie.write(frame)

# All done!
input_movie.release()
cv2.destroyAllWindows()
