import cv2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
cap = cv2.VideoCapture('X:/video.mp4')

R = 0
G = 0
B = 0
red = green = blue = 0
n = 0
i = 0

with mp_face_mesh.FaceMesh(
    max_num_faces=1,
    refine_landmarks=True,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as face_mesh:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue
    i += 1
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    img = cv2.resize(image, (640, 480))
    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = face_mesh.process(image)

    # Draw the face mesh annotations on the image.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.multi_face_landmarks:
        for face_landmarks in results.multi_face_landmarks:
            shape = image.shape
            for landmark in face_landmarks.landmark:
                n += 1
                x = landmark.x
                y = landmark.y
                relative_x = int(x * shape[1])
                relative_y = int(y * shape[0])
                #cv2.circle()

                if relative_x >= shape[0]:
                    relative_x = shape[0]-1
                if relative_y >= shape[1]:
                    relative_y = shape[1]-1
                B, G, R = img[relative_x, relative_y]
                #cv2.circle(image, [relative_x, relative_y], 3, [int(B), int(G), int(R)], 3)
                red += R
                blue += B
                green += G
            color = [int(blue/n), int(green/n), int(red/n)]
            print(color)
            red = green = blue = n = 0
            #cv2.rectangle(image, [400, 450], [450, 450], [255,255,255], 10)
            #color_hex = '#'+str(hex(color[2])[2:])+str(hex(color[1])[2:])+str(hex(color[0])[2:])
            #cv2.putText(img,str(i), [400,450], cv2.FONT_HERSHEY_PLAIN, 1, color, 2)
            #cv2.text(image, [600, 400], )
        mp_drawing.draw_landmarks(
                image=image,
                landmark_list=face_landmarks,
                connections=mp_face_mesh.FACEMESH_TESSELATION,
                landmark_drawing_spec=None,
                connection_drawing_spec=mp_drawing_styles
                .get_default_face_mesh_tesselation_style())
        '''mp_drawing.draw_landmarks(
            image=image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_CONTOURS,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_contours_style())
        mp_drawing.draw_landmarks(
            image=image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_IRISES,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_iris_connections_style())'''
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Face Mesh', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()