"""
For finding the face and face landmarks for further manipulication
"""

import cv2
import mediapipe as mp
import numpy as np

class FaceMeshDetector:
    def __init__(self):
        # 使用Mediapipe提供的绘图方法来绘制面部标识
        self.mp_drawing = mp.solutions.drawing_utils
        self.drawing_spec = self.mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
    # def __init__(self,
    #             model_complexity=1,
    #             static_image_mode=False,
    #             # enable_segmentation=False,
    #             refine_face_landmarks=True,
    #             min_detection_confidence=0.5,
    #             min_tracking_confidence=0.5):
    #
    #     self.model_complexity=model_complexity
    #     self.static_image_mode = static_image_mode
    #     self.refine_face_landmarks = refine_face_landmarks
    #     self.min_detection_confidence=min_detection_confidence
    #     self.min_tracking_confidence=min_tracking_confidence
    #
    #     self.mp_holistic = mp.solutions.holistic
    #     # The object to do the stuffs
    #     self.holistic = self.mp_holistic.Holistic(
    #         self.model_complexity,
    #         self.static_image_mode,
    #         self.refine_face_landmarks,)
    #         # self.min_detection_confidence,
    #         # self.min_tracking_confidence)
    #
    #     self.mp_drawing = mp.solutions.drawing_utils
    #     self.drawing_spec = self.mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

    # def findFaceMesh(self, img, draw=True):
    #     # convert the img from BRG to RGB 并镜像翻转图像
    #     # img = cv2.cvtColor(cv2.flip(img, 1), cv2.COLOR_BGR2RGB)
    #     #
    #     # # To improve performance, optionally mark the image as not writeable to
    #     # # pass by reference.
    #     # img.flags.writeable = False
    #     # # self.results = self.face_mesh.process(img)
    #     #
    #     # # Draw the face mesh annotations on the image.
    #     # img.flags.writeable = True
    #     # img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    #
    #
    #     # 若有多张脸 则将每个人脸图像的468个像素坐标传入矩阵中
    #     # if self.results.multi_face_landmarks:
    #     #     for face_landmarks in self.results.multi_face_landmarks:
    #     # self.results = self.face_mesh.process(img)
    #     # face_landmarks = self.results.face_landmarks
    #
    #     img = cv2.cvtColor(cv2.flip(img, 1), cv2.COLOR_BGR2RGB)
    #     img.flags.writeable = False
    #     self.results = self.holistic.process(img)
    #     img.flags.writeable = True
    #     img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    #
    #     self.imgH, self.imgW, self.imgC = img.shape
    #     self.faces = []
    #
    #     face_landmarks = self.results.face_landmarks
    #     if face_landmarks:
    #         # print(face_landmarks)
    #         if draw:
    #             self.mp_drawing.draw_landmarks(
    #                 image = img,
    #                 landmark_list = face_landmarks,
    #                 # connections = self.mp_face_mesh.FACEMESH_TESSELATION,
    #                 landmark_drawing_spec = self.drawing_spec,
    #                 connection_drawing_spec = self.drawing_spec)
    #
    #     # 一个人脸图像的468个像素坐标传入face矩阵之中
    #     #     print(len(face_landmarks.landmark))
    #         face = []
    #         for id, lmk in enumerate(face_landmarks.landmark):
    #             x, y = int(lmk.x * self.imgW), int(lmk.y * self.imgH)
    #             face.append([x, y])
    #
    #         # show the id of each point on the image
    #         # cv2.putText(img, str(id), (x-4, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)
    #
    #         self.faces.append(face)
    #
    #     return img, self.faces

    def findFaceMesh(self, img,face_landmarks, draw=True):
        self.imgH, self.imgW, self.imgC = img.shape
        self.faces = []
        if face_landmarks:
            # print(face_landmarks)
            if draw:
                self.mp_drawing.draw_landmarks(
                    image = img,
                    landmark_list = face_landmarks,
                    # connections = self.mp_face_mesh.FACEMESH_TESSELATION,
                    landmark_drawing_spec = self.drawing_spec,
                    connection_drawing_spec = self.drawing_spec)
        # 一个人脸图像的468个像素坐标传入face矩阵之中
            face = []
            for id, lmk in enumerate(face_landmarks.landmark):
                x, y = int(lmk.x * self.imgW), int(lmk.y * self.imgH)
                face.append([x, y])

            # show the id of each point on the image
            # cv2.putText(img, str(id), (x-4, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)

            self.faces.append(face)

        return img, self.faces


# sample run of the module
def main():

    detector = FaceMeshDetector()

    cap = cv2.VideoCapture(0)

    while cap.isOpened():
        success, img = cap.read()

        if not success:
            print("Ignoring empty camera frame.")
            continue

        img, faces = detector.findFaceMesh(img)

        # if faces:
        #     print(faces[0])

        cv2.imshow('MediaPipe FaceMesh', img)

        # press "q" to leave
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()


if __name__ == "__main__":
    # demo code
    main()