test-hands.py

import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands

"""
# For static images:
IMAGE_FILES = []
with mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.5) as hands:
  for idx, file in enumerate(IMAGE_FILES):
    # 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,
          mp_drawing_styles.get_default_hand_landmarks_style(),
          mp_drawing_styles.get_default_hand_connections_style())
    cv2.imwrite(
        '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
    # Draw hand world landmarks.
    if not results.multi_hand_world_landmarks:
      continue
    for hand_world_landmarks in results.multi_hand_world_landmarks:
      mp_drawing.plot_landmarks(
        hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)
"""

# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as hands: # sets up the model
  while cap.isOpened(): # while the capture is active
    success, image = cap.read() # reads the capture
    if not success: # if the capture doesn't work
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False # Sets it in read mode only?
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Converts to RGB
    results = hands.process(image) # Uses mediapipe to process the image
    print(results.multi_hand_landmarks) # Prints the results

    # Draw the hand annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # Converts the image back
    if results.multi_hand_landmarks: # If there are landmarks, then draw
      for hand_landmarks in results.multi_hand_landmarks: # iterating through each hand landmarks
        mp_drawing.draw_landmarks(
            image,
            hand_landmarks,
            mp_hands.HAND_CONNECTIONS,
            mp_drawing_styles.get_default_hand_landmarks_style(),
            mp_drawing_styles.get_default_hand_connections_style()) # Draws the landmarks on the image
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Hands', cv2.flip(image, 1)) # Shows the image
    if cv2.waitKey(5) & 0xFF == 27: # Allows the user to end the capture
      break
cap.release() # Ends the openCV capture