test.py

import cv2
import mediapipe as mp
import sys
import os 
import random 
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands

# For static images:
root = 'D:\CVlabortory\hand_detection'
path = os.path.join(root, 'pics')
filenames = os.listdir(path)
file_list = [os.path.join(path, filename) for filename in filenames]
hands = mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.5)
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_hight, image_width, _ = image.shape
  annotated_image = image.copy()
  print(results.multi_hand_landmarks)
#   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_hight})'
#     )
#     mp_drawing.draw_landmarks(
#         annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
#   cv2.imwrite(
#       'annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
hands.close()

# # For webcam input:
# hands = mp_hands.Hands(
#     min_detection_confidence=0.5, min_tracking_confidence=0.5)
# cap = cv2.VideoCapture(0)
# 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
# hands.close()
# cap.release()