MediaPipe 0.8 : Colab : MediaPipe 全身用 (Holistic) (翻訳/解説)

翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 03/10/2021 (0.8.3)

* 本ページは、MediaPipe の以下のドキュメントを翻訳した上で適宜、補足説明したものです：

• MediaPipe Hands

* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。

MediaPipe 0.8 : Colab : MediaPipe 全身用 (Holistic)

Python の MediaPipe 全身用 (= Holistic) ソリューション API の使用サンプルです (http://solutions.mediapipe.dev/holistic もまた見てください)。

!pip install mediapipe

可視な上半身を持つ人物を持つ任意の画像を Colab にアップロードします。web: https://unsplash.com/photos/4jqfc2vbHJQ と https://unsplash.com/photos/72zsd_fnxYc から 2 つのサンプル画像を取ります。

from google.colab import files

uploaded = files.upload()

import cv2
from google.colab.patches import cv2_imshow

# Read images with OpenCV.
images = {name: cv2.imread(name) for name in uploaded.keys()}

# Preview the images.
for name, image in images.items():
  print(name)   
  cv2_imshow(image)

総ての MediaPipe ソリューション Python API サンプルは mp.solutions 下にあります。

MediaPipe 全身用ソリューションについては、mp_holistic = mp.solutions.holistic としてこのモジュールにアクセスできます。

初期化の間に static_image_mode と min_detection_confidence のようなパラメータを変更しても良いです。パラメータについてのより多くの情報を得るためには help(mp_holistic.Holistic) を実行します。

import mediapipe as mp
mp_holistic = mp.solutions.holistic

help(mp_holistic.Holistic)

Help on class Holistic in module mediapipe.python.solutions.holistic:

class Holistic(mediapipe.python.solution_base.SolutionBase)
 |  Holistic(static_image_mode=False, upper_body_only=False, smooth_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5)
 |  
 |  MediaPipe Holistic.
 |  
 |  MediaPipe Holistic processes an RGB image and returns pose landmarks, left and
 |  right hand landmarks, and face mesh landmarks on the most prominent person
 |  detected.
 |  
 |  Please refer to https://solutions.mediapipe.dev/holistic#python-solution-api
 |  for usage examples.
 |  
 |  Method resolution order:
 |      Holistic
 |      mediapipe.python.solution_base.SolutionBase
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, static_image_mode=False, upper_body_only=False, smooth_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5)
 |      Initializes a MediaPipe Holistic object.
 |      
 |      Args:
 |        static_image_mode: Whether to treat the input images as a batch of static
 |          and possibly unrelated images, or a video stream. See details in
 |          https://solutions.mediapipe.dev/holistic#static_image_mode.
 |        upper_body_only: Whether to track the full set of 33 pose landmarks or
 |          only the 25 upper-body pose landmarks. See details in
 |          https://solutions.mediapipe.dev/holistic#upper_body_only.
 |        smooth_landmarks: Whether to filter landmarks across different input
 |          images to reduce jitter. See details in
 |          https://solutions.mediapipe.dev/holistic#smooth_landmarks.
 |        min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person
 |          detection to be considered successful. See details in
 |          https://solutions.mediapipe.dev/holistic#min_detection_confidence.
 |        min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
 |          pose landmarks to be considered tracked successfully. See details in
 |          https://solutions.mediapipe.dev/holistic#min_tracking_confidence.
 |  
 |  process(self, image: numpy.ndarray) -> 
 |      Processes an RGB image and returns the pose landmarks, left and right hand landmarks, and face landmarks on the most prominent person detected.
 |      
 |      Args:
 |        image: An RGB image represented as a numpy ndarray.
 |      
 |      Raises:
 |        RuntimeError: If the underlying graph throws any error.
 |        ValueError: If the input image is not three channel RGB.
 |      
 |      Returns:
 |        A NamedTuple that has four fields:
 |          1) "pose_landmarks" field that contains the pose landmarks on the most
 |          prominent person detected.
 |          2) "left_hand_landmarks" and "right_hand_landmarks" fields that contain
 |          the left and right hand landmarks of the most prominent person detected.
 |          3) "face_landmarks" field that contains the face landmarks of the most
 |          prominent person detected.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from mediapipe.python.solution_base.SolutionBase:
 |  
 |  __enter__(self)
 |      A "with" statement support.
 |  
 |  __exit__(self, exc_type, exc_val, exc_tb)
 |      Closes all the input sources and the graph.
 |  
 |  close(self) -> None
 |      Closes all the input sources and the graph.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from mediapipe.python.solution_base.SolutionBase:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

# Prepare DrawingSpec for drawing the face landmarks later.
mp_drawing = mp.solutions.drawing_utils 
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

# Initialize MediaPipe Holistic.
with mp_holistic.Holistic(
    static_image_mode=True, min_detection_confidence=0.5) as holistic:
  for name, image in images.items():
    # Convert the BGR image to RGB and process it with MediaPipe Pose.
    results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Print nose coordinates.
    image_hight, image_width, _ = image.shape
    if results.pose_landmarks:
      print(
        f'Nose coordinates: ('
        f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
        f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
      )
    # Draw pose landmarks.
    print(f'Pose landmarks of {name}:')
    annotated_image = image.copy()
    mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
    mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
    mp_drawing.draw_landmarks(
        image=annotated_image, 
        landmark_list=results.face_landmarks, 
        connections=mp_holistic.FACE_CONNECTIONS,
        landmark_drawing_spec=drawing_spec,
        connection_drawing_spec=drawing_spec)
    mp_drawing.draw_landmarks(
        image=annotated_image, 
        landmark_list=results.pose_landmarks, 
        connections=mp_holistic.POSE_CONNECTIONS,
        landmark_drawing_spec=drawing_spec,
        connection_drawing_spec=drawing_spec)
    cv2_imshow(annotated_image)

以上