关键点说明
mediapipe手势识别可以识别出手部的21个关键点
获取关键点坐标
模型给出的结果是归一化的坐标比例,要获取在图像中的坐标需要乘以图像的宽高
x = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width
y = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height
示例代码
import cv2
import mediapipe as mp
import numpy as np
Key_Esc = 27
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
# 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:
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
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
print('Handedness:', results.multi_handedness)
# print(results.multi_hand_landmarks)
# 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,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', np.rot90(
cv2.resize(cv2.flip(image, 1), (320, 240))))
if cv2.waitKey(5) == Key_Esc:
break
cap.release()