#!/usr/bin/env python3
# -*- coding: utf-8 -*-

'''
Источник: https://developers.google.com/mediapipe/solutions/vision/face_landmarker/python

Установка пререквизитов:
wget -O face_landmarker_v2_with_blendshapes.task -q https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task
pip install mediapipe
'''

from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np
import matplotlib.pyplot as plt
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
import cv2

def draw_landmarks_on_image(rgb_image, detection_result):
  face_landmarks_list = detection_result.face_landmarks
  annotated_image = np.copy(rgb_image)

  # Loop through the detected faces to visualize.
  for idx in range(len(face_landmarks_list)):
    face_landmarks = face_landmarks_list[idx]

    # Draw the face landmarks.
    face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
    face_landmarks_proto.landmark.extend([
      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
    ])

    solutions.drawing_utils.draw_landmarks(
        image=annotated_image,
        landmark_list=face_landmarks_proto,
        connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
        landmark_drawing_spec=None,
        connection_drawing_spec=mp.solutions.drawing_styles
        .get_default_face_mesh_tesselation_style())
    solutions.drawing_utils.draw_landmarks(
        image=annotated_image,
        landmark_list=face_landmarks_proto,
        connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
        landmark_drawing_spec=None,
        connection_drawing_spec=mp.solutions.drawing_styles
        .get_default_face_mesh_contours_style())
    solutions.drawing_utils.draw_landmarks(
        image=annotated_image,
        landmark_list=face_landmarks_proto,
        connections=mp.solutions.face_mesh.FACEMESH_IRISES,
          landmark_drawing_spec=None,
          connection_drawing_spec=mp.solutions.drawing_styles
          .get_default_face_mesh_iris_connections_style())

  return annotated_image

def plot_face_blendshapes_bar_graph(face_blendshapes):
  # Extract the face blendshapes category names and scores.
  face_blendshapes_names = [face_blendshapes_category.category_name for face_blendshapes_category in face_blendshapes]
  face_blendshapes_scores = [face_blendshapes_category.score for face_blendshapes_category in face_blendshapes]
  # The blendshapes are ordered in decreasing score value.
  face_blendshapes_ranks = range(len(face_blendshapes_names))

  fig, ax = plt.subplots(figsize=(12, 12))
  bar = ax.barh(face_blendshapes_ranks, face_blendshapes_scores, label=[str(x) for x in face_blendshapes_ranks])
  ax.set_yticks(face_blendshapes_ranks, face_blendshapes_names)
  ax.invert_yaxis()

  # Label each bar with values
  for score, patch in zip(face_blendshapes_scores, bar.patches):
    plt.text(patch.get_x() + patch.get_width(), patch.get_y(), f"{score:.4f}", va="top")

  ax.set_xlabel('Score')
  ax.set_title("Face Blendshapes")
  plt.tight_layout()
  plt.show()

base_options = python.BaseOptions(model_asset_path='face_landmarker_v2_with_blendshapes.task')
options = vision.FaceLandmarkerOptions(base_options=base_options,
                                       output_face_blendshapes=True,
                                       output_facial_transformation_matrixes=True,
                                       num_faces=1)
detector = vision.FaceLandmarker.create_from_options(options)


cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FORMAT, -1)

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret!=True: continue

    image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
    detection_result = detector.detect(image)
    annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)
    cv2.imshow('annotated', cv2.flip(annotated_image, 1))
    if cv2.waitKey(1) & 0xFF == ord('q'): break

cap.release()
cv2.destroyAllWindows()