定义一个HandDetector类
import cv2
import mediapipe as mp
import math
class HandDetector:
"""
Finds Hands using the mediapipe library. Exports the landmarks
in pixel format. Adds extra functionalities like finding how
many fingers are up or the distance between two fingers. Also
provides bounding box info of the hand found.
"""
def __init__(self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5):
"""
:param mode: In static mode, detection is done on each image: slower
:param maxHands: Maximum number of hands to detect
:param detectionCon: Minimum Detection Confidence Threshold
:param minTrackCon: Minimum Tracking Confidence Threshold
"""
self.mode = mode
self.maxHands = maxHands
self.detectionCon = detectionCon
self.minTrackCon = minTrackCon
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(static_image_mode=self.mode, max_num_hands=self.maxHands,
min_detection_confidence=self.detectionCon, min_tracking_confidence = self.minTrackCon)
self.mpDraw = mp.solutions.drawing_utils
self.tipIds = [4, 8, 12, 16, 20]
self.fingers = []
self.lmList = []
def findPosition(self, img, draw=True):
self.lmList = []
if self.results.multi_hand_landmarks:
for handLms in self.results.multi_hand_landmarks:
for id, lm in enumerate(handLms.landmark):
h, w, c = img.shape
cx, cy = int(lm.x * w), int(lm.y * h)
# print(id, cx, cy)
self.lmList.append([id, cx, cy])
if draw:
cv2.circle(img, (cx, cy), 12, (255, 0, 255), cv2.FILLED)
return self.lmList
def findHands(self, img, draw=True, flipType=True):
"""
Finds hands in a BGR image.
:param img: Image to find the hands in.
:param draw: Flag to draw the output on the image.
:return: Image with or without drawings
"""
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.results = self.hands.process(imgRGB)
allHands = []
h, w, c = img.shape
if self.results.multi_hand_landmarks:
for handType,handLms in zip(self.results.multi_handedness,self.results.multi_hand_landmarks):
myHand={}
## lmList
mylmList = []
xList = []
yList = []
for id, lm in enumerate(handLms.landmark):
px, py = int(lm.x * w), int(lm.y * h)
mylmList.append([px, py])
xList.append(px)
yList.append(py)
## bbox
xmin, xmax = min(xList), max(xList)
ymin, ymax = min(yList), max(yList)
boxW, boxH = xmax - xmin, ymax - ymin
bbox = xmin, ymin, boxW, boxH
cx, cy = bbox[0] + (bbox[2] // 2), \
bbox[1] + (bbox[3] // 2)
myHand["lmList"] = mylmList
myHand["bbox"] = bbox
myHand["center"] = (cx, cy)
if flipType:
if handType.classification[0].label =="Right":
myHand["type"] = "Left"
else:
myHand["type"] = "Right"
else:myHand["type"] = handType.classification[0].label
allHands.append(myHand)
## draw
if draw:
self.mpDraw.draw_landmarks(img, handLms,
self.mpHands.HAND_CONNECTIONS)
cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
(255, 0, 255), 2)
cv2.putText(img,myHand["type"],(bbox[0] - 30, bbox[1] - 30),cv2.FONT_HERSHEY_PLAIN,
2,(255, 0, 255),2)
if draw:
return allHands,img
else:
return allHands
def fingersUp(self,myHand):
"""
Finds how many fingers are open and returns in a list.
Considers left and right hands separately
:return: List of which fingers are up
"""
myHandType =myHand["type"]
myLmList = myHand["lmList"]
if self.results.multi_hand_landmarks:
fingers = []
# Thumb
if myHandType == "Right":
if myLmList[self.tipIds[0]][0] > myLmList[self.tipIds[0] - 1][0]:
fingers.append(1)
else:
fingers.append(0)
else:
if myLmList[self.tipIds[0]][0] < myLmList[self.tipIds[0] - 1][0]:
fingers.append(1)
else:
fingers.append(0)
# 4 Fingers
for id in range(1, 5):
if myLmList[self.tipIds[id]][1] < myLmList[self.tipIds[id] - 2][1]:
fingers.append(1)
else:
fingers.append(0)
return fingers
def findDistance(self,p1, p2, img=None):
"""
Find the distance between two landmarks based on their
index numbers.
:param p1: Point1
:param p2: Point2
:param img: Image to draw on.
:param draw: Flag to draw the output on the image.
:return: Distance between the points
Image with output drawn
Line information
"""
x1, y1 = p1
x2, y2 = p2
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
length = math.hypot(x2 - x1, y2 - y1)
info = (x1, y1, x2, y2, cx, cy)
if img is not None:
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
return length,info, img
else:
return length, info
def main():
cap = cv2.VideoCapture(0)
detector = HandDetector(detectionCon=0.8, maxHands=2)
while True:
# Get image frame
success, img = cap.read()
# Find the hand and its landmarks
hands, img = detector.findHands(img) # with draw
# hands = detector.findHands(img, draw=False) # without draw
if hands:
# Hand 1
hand1 = hands[0]
lmList1 = hand1["lmList"] # List of 21 Landmark points
bbox1 = hand1["bbox"] # Bounding box info x,y,w,h
centerPoint1 = hand1['center'] # center of the hand cx,cy
handType1 = hand1["type"] # Handtype Left or Right
fingers1 = detector.fingersUp(hand1)
if len(hands) == 2:
# Hand 2
hand2 = hands[1]
lmList2 = hand2["lmList"] # List of 21 Landmark points
bbox2 = hand2["bbox"] # Bounding box info x,y,w,h
centerPoint2 = hand2['center'] # center of the hand cx,cy
handType2 = hand2["type"] # Hand Type "Left" or "Right"
fingers2 = detector.fingersUp(hand2)
# Find Distance between two Landmarks. Could be same hand or different hands
length, info, img = detector.findDistance(lmList1[8], lmList2[8], img) # with draw
# length, info = detector.findDistance(lmList1[8], lmList2[8]) # with draw
# Display
cv2.imshow("Image", img)
cv2.waitKey(1)
if __name__ == "__main__":
main()
虚拟输入
import cv2
from cvzone.HandTrackingModule import HandDetector
from time import sleep
import pyautogui
import cvzone
from pynput.keyboard import Key,Controller
cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
# 识别手势
detector = HandDetector(detectionCon=0.8)
keyboard = Controller()
# 键盘关键字
keys = [['Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P'],
['A', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', ';'],
['Z', 'X', 'C', 'V', 'B', 'N', 'M', ',', '.', '/']]
class Button():
def __init__(self, pos, text, size = [50, 50]):
self.pos = pos
self.text = text
self.size = size
buttonList = []
finalText = ''
for j in range(len(keys)):
for x, key in enumerate(keys[j]):
# 循环创建buttonList对象列表
buttonList.append(Button([60*x+20,100+j*60],key))
def drawAll(img,buttonList):
for button in buttonList:
x, y = button.pos
w, h = button.size
cvzone.cornerRect(img, (x, y, w, h),20,rt = 0)
cv2.rectangle(img, button.pos, (x + w, y + h), (255, 0, 255), cv2.FILLED)
cv2.putText(img, button.text, (x + 10, y + 40),
cv2.FONT_HERSHEY_PLAIN, 3, (255, 255, 255), 2)
return img
while True:
success, img = cap.read()
# 识别手势
hand = detector.findHands(img)
lmList = detector.findPosition(img, True)
# img = mybutton.draw(img)
img = drawAll(img,buttonList )
if lmList:
for button in buttonList:
x,y = button.pos
w,h = button.size
if x<lmList[8][1]<x+w and y<lmList[8][2]<y+h:
cv2.rectangle(img, (x-5,y-5), (x + w + 5, y + h + 5), (175, 0, 175), cv2.FILLED)
cv2.putText(img, button.text, (x + 10, y + 40),
cv2.FONT_HERSHEY_PLAIN, 3, (255, 255, 255), 2)
l,_,_ = detector.findDistance(lmList[8][1:],lmList[12][1:],img)
print('中指(12)和食指(8)之间的距离：',l)
if l < 120:
keyboard.press(button.text)
pyautogui.typewrite(f"{button.text}", 0.5)
cv2.rectangle(img, button.pos, (x + w, y + h), (0, 255, 0), cv2.FILLED)
cv2.putText(img, button.text, (x + 10, y + 40),
cv2.FONT_HERSHEY_PLAIN, 3, (255, 255, 255), 2)
finalText += button.text
print('当前选中的是：', button.text)
sleep(0.2)
cv2.rectangle(img, (20,350), (600, 400), (175, 0, 175), cv2.FILLED)
cv2.putText(img, finalText, (20, 390),
cv2.FONT_HERSHEY_PLAIN, 3, (255, 255, 255), 4)
cv2.imshow("Image",img)
cv2.waitKey(1)
最后：代码我完全开源可用，需要安装必要的python包。
