Mediapipe (Hands) - cheeseBG/EmergencyResponseSystem GitHub Wiki

Appendix: https://google.github.io/mediapipe/solutions/hands.html

Mediapipe Hands

MediaPipe Hands is a high-fidelity hand and finger tracking solution. It employs machine learning (ML) to infer 21 3D landmarks of a hand from just a single frame. Whereas current state-of-the-art approaches rely primarily on powerful desktop environments for inference, our method achieves real-time performance on a mobile phone, and even scales to multiple hands.

ML Pipeline

MediaPipe Hands utilizes an ML pipeline consisting of multiple models working together. A hand landmark model that operates on the cropped image region defined by the palm detector and returns high-fidelity 3D hand keypoints. Providing the accurately cropped hand image to the hand landmark model drastically reduces the need for data augmentation (e.g. rotations, translation and scale) and instead allows the network to dedicate most of its capacity towards coordinate prediction accuracy.

Hand Landmark Model

After the palm detection over the whole image our subsequent hand landmark model performs precise keypoint localization of 21 3D hand-knuckle coordinates inside the detected hand regions via regression, that is direct coordinate prediction. The model learns a consistent internal hand pose representation and is robust even to partially visible hands and self-occlusions.

To obtain ground truth data, we have manually annotated ~30K real-world images with 21 3D coordinates, as shown below (we take Z-value from image depth map, if it exists per corresponding coordinate). To better cover the possible hand poses and provide additional supervision on the nature of hand geometry, we also render a high-quality synthetic hand model over various backgrounds and map it to the corresponding 3D coordinates.

## Python Solution API 
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands

For static images:

with mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.5) as hands:
   for idx, file in enumerate(file_list):
    # Read an image, flip it around y-axis for correct handedness output (see
    # above).
    image = cv2.flip(cv2.imread(file), 1)
    # Convert the BGR image to RGB before processing.
    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Print handedness and draw hand landmarks on the image.
    print('Handedness:', results.multi_handedness)
    if not results.multi_hand_landmarks:
      continue
    image_height, image_width, _ = image.shape
    annotated_image = image.copy()
    for hand_landmarks in results.multi_hand_landmarks:
      print('hand_landmarks:', hand_landmarks)
      print(
          f'Index finger tip coordinates: (',
          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_height})'
      )
      mp_drawing.draw_landmarks(
          annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
    cv2.imwrite(
        '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))

For webcam input:

cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as hands:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # Flip the image horizontally for a later selfie-view display, and convert
    # the BGR image to RGB.
    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    results = hands.process(image)

    # Draw the hand annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.multi_hand_landmarks:
      for hand_landmarks in results.multi_hand_landmarks:
        mp_drawing.draw_landmarks(
            image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
    cv2.imshow('MediaPipe Hands', image)
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()
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