MediaPipe 0.8 : Colab : MediaPipe ハンド (Hands) (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 : 03/08/2021 (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 ハンド (Hands)

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

!pip install mediapipe

ハンド (手) を含む任意の画像を Colab にアップロードします。web: https://unsplash.com/photos/QyCH5jwrD_A と https://unsplash.com/photos/tSePVHkxUCk 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 ハンド・ソリューションについては、mp_hands = mp.solutions.hands 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, max_num_hands 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_hands.Hands) を実行します。

import mediapipe as mp
mp_hands = mp.solutions.hands

help(mp_hands.Hands)

Help on class Hands in module mediapipe.python.solutions.hands:

class Hands(mediapipe.python.solution_base.SolutionBase)
 |  Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5)
 |  
 |  MediaPipe Hands.
 |  
 |  MediaPipe Hands processes an RGB image and returns the hand landmarks and
 |  handedness (left v.s. right hand) of each detected hand.
 |  
 |  Note that it determines handedness assuming the input image is mirrored,
 |  i.e., taken with a front-facing/selfie camera (
 |  https://en.wikipedia.org/wiki/Front-facing_camera) with images flipped
 |  horizontally. If that is not the case, use, for instance, cv2.flip(image, 1)
 |  to flip the image first for a correct handedness output.
 |  
 |  Please refer to https://solutions.mediapipe.dev/hands#python-solution-api for
 |  usage examples.
 |  
 |  Method resolution order:
 |      Hands
 |      mediapipe.python.solution_base.SolutionBase
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5)
 |      Initializes a MediaPipe Hand 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/hands#static_image_mode.
 |        max_num_hands: Maximum number of hands to detect. See details in
 |          https://solutions.mediapipe.dev/hands#max_num_hands.
 |        min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for hand
 |          detection to be considered successful. See details in
 |          https://solutions.mediapipe.dev/hands#min_detection_confidence.
 |        min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
 |          hand landmarks to be considered tracked successfully. See details in
 |          https://solutions.mediapipe.dev/hands#min_tracking_confidence.
 |  
 |  process(self, image: numpy.ndarray) -> 
 |      Processes an RGB image and returns the hand landmarks and handedness of each detected hand.
 |      
 |      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 object with two fields: a "multi_hand_landmarks" field that
 |        contains the hand landmarks on each detected hand and a "multi_handedness"
 |        field that contains the handedness (left v.s. right hand) of the detected
 |        hand.
 |  
 |  ----------------------------------------------------------------------
 |  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)

mp_drawing = mp.solutions.drawing_utils 

with mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.7) as hands:
  for name, image in images.items():
    # Convert the BGR image to RGB, flip the image around y-axis for correct 
    # handedness output and process it with MediaPipe Hands.
    results = hands.process(cv2.flip(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), 1))
    image_hight, image_width, _ = image.shape
    # Print handedness (left v.s. right hand).
    print(f'Handedness of {name}:')
    print(results.multi_handedness)

    # Draw hand landmarks of each hand.
    print(f'Hand landmarks of {name}:')
    if not results.multi_hand_landmarks:
      continue
    annotated_image = cv2.flip(image.copy(), 1)
    for hand_landmarks in results.multi_hand_landmarks:
      # Print index finger tip coordinates.
      print(
          f'Index finger tip coordinate: (',
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_hight})'
      )
      mp_drawing.draw_landmarks(
          annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
    cv2_imshow(cv2.flip(annotated_image, 1))

以上