test_face_landmark_detection.py

# STEP 1: Import the necessary modules.
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np
import matplotlib.pyplot as plt
import cv2


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

  for idx in range(len(face_landmarks_list)):
    face_landmarks = face_landmarks_list[idx]

    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):
  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]
  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()

  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.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)

image = mp.Image.create_from_file("test.png")

detection_result = detector.detect(image)

plot_face_blendshapes_bar_graph(detection_result.face_blendshapes[0])

print(detection_result.facial_transformation_matrixes)