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mediapipe_demo.py
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import sys
from cv2 import cv2
import mediapipe as mp
import math
def getAngle( firstPoint, midPoint, lastPoint):
front=math.atan2(lastPoint.getPosition().y - midPoint.getPosition().y,lastPoint.getPosition().x - midPoint.getPosition().x)
back=math.atan2(firstPoint.getPosition().y - midPoint.getPosition().y,firstPoint.getPosition().x - midPoint.getPosition().x)
result =math.degrees(front-back)
result = math.abs(result) # Angle should never be negative
if (result > 180):
result = (360.0 - result)
# Always get the acute representation of the angle
return result
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# For webcam input:
cap = cv2.VideoCapture(0)
def getAngleOfLandmarks(landmarks):
for i in landmarks:
print(i)
# if len(landmarks)>3:
# angle=getAngle(landmarks[0],landmarks[1],landmarks[2])
# print("angle",angle)
# https://blog.csdn.net/menglir/article/details/116137112
def Normalize_landmarks(image, hand_landmarks):
new_landmarks = []
for i in range(0,len(hand_landmarks.landmark)):
float_x = hand_landmarks.landmark[i].x
float_y = hand_landmarks.landmark[i].y
# Z坐标靠近屏幕增大，远离屏幕减小
float_z = hand_landmarks.landmark[i].z
# print(float_z)
width = image.shape[1]
height = image.shape[0]
pt = mp_drawing._normalized_to_pixel_coordinates(float_x,float_y,width,height)
new_landmarks.append(pt)
return new_landmarks
# 好稳定
# 也没有装什么特别的东西 怎么就可以用啊 ，那个代码就不行
with mp_hands.Hands(
min_detection_confidence=0.9,
min_tracking_confidence=0.9) 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.
# 水平翻转图像，以便稍后显示自拍视图，然后转换
#将BGR图像转换为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.
# 在图像上绘制手部注释。
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image_hight, image_width, _ = image.shape
# 陆标，地标 multi_hand_landmarks
marks=results.multi_hand_landmarks
if results.multi_hand_landmarks:
# print("results.multi_hand_landmarks",results.multi_hand_landmarks)
# print(len(marks))
# 长度 1
# if len(marks)>3:
# angle=getAngle(marks[0],marks[1],marks[2])
# print("angle",angle)
# hand_landmarks_lst=[]
print("=================")
for hand_landmarks in results.multi_hand_landmarks:
# print("hand_landmarks:",hand_landmarks)
# hand_landmarks_lst.append(hand_landmarks)
# print(hand_landmarks)
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
# getAngleOfLandmarks(hand_landmarks)
# 这是哪几个点
new_landmarks=Normalize_landmarks(image,hand_landmarks)
print(new_landmarks)
# print("mp_hands.HandLandmark.INDEX_FINGER_TIP",mp_hands.HandLandmark.INDEX_FINGER_TIP)
# =================
# mp_hands.HandLandmark.INDEX_FINGER_TIP HandLandmark.INDEX_FINGER_TIP
# Index finger tip coordinates: ( 180.18707275390625, 374.5967102050781)
# 这就是个字符串
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})'
)
# if len(hand_landmarks_lst)>3:
# angle=getAngle(hand_landmarks_lst[0],hand_landmarks_lst[1],hand_landmarks_lst[2])
# print("angle",angle)
cv2.imshow('MediaPipe Hands', image)
# 等待的 esc
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()