目录
0、项目介绍
本篇将会以HandTrackingModule为模块，这里的模块与之前的有所不同，请按照本篇为准，前面的HandTrackingModule不足以完成本项目，本篇将会通过手势对本人的博客海报进行缩放，具体效果可以看下面的效果展示。
1、项目展示
2、项目搭建
首先在一个文件夹下建立HandTrackingModule.py文件以及gesture_zoom.py，以及一张图片，你可以按照你的喜好选择，建议尺寸不要过大。
在这里用到了食指的索引8，可以完成左右手食指的手势进行缩放。
3、项目的代码与讲解
HandTrackingModule.py：
-
import cv2
-
import mediapipe
as mp
-
import math
-
-
-
class
handDetector:
-
-
def
__init__(
self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5):
-
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
findHands(
self, img, draw=True, flipType=True):
-
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, pz =
int(lm.x * w),
int(lm.y * h),
int(lm.z * w)
-
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):
-
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):
-
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][
0:
2], lmList2[
8][
0:
2], 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()
gesture_zoom.py
-
import cv2
-
import mediapipe
as mp
-
import time
-
import HandTrackingModule
as htm
-
-
startDist =
None
-
scale =
0
-
cx, cy =
500,
200
-
wCam, hCam =
1280,
720
-
pTime =
0
-
-
cap = cv2.VideoCapture(
0)
-
cap.
set(
3, wCam)
-
cap.
set(
4, hCam)
-
cap.
set(
10,
150)
-
-
detector = htm.handDetector(detectionCon=
0.75)
-
-
while
1:
-
success, img = cap.read()
-
handsimformation,img=detector.findHands(img)
-
-
img1 = cv2.imread(
"1.png")
-
# img[0:360, 0:260] = img1
-
if
len(handsimformation)==
2:
-
-
# print(detector.fingersUp(handsimformation[0]),detector.fingersUp(handsimformation[1]))
-
#detector.fingersUp(handimformation[0]右手
-
if detector.fingersUp(handsimformation[
0]) == [
1,
1,
1,
0,
0]
and \
-
detector.fingersUp(handsimformation[
1]) == [
1,
1,
1 ,
0,
0]:
-
lmList1 = handsimformation[
0][
'lmList']
-
lmList2 = handsimformation[
1][
'lmList']
-
if startDist
is
None:
-
#lmList1[8],lmList2[8]右、左手指尖
-
-
# length,info,img=detector.findDistance(lmList1[8],lmList2[8], img)
-
length, info, img = detector.findDistance(handsimformation[
0][
"center"], handsimformation[
1][
"center"], img)
-
startDist=length
-
length, info, img = detector.findDistance(handsimformation[
0][
"center"], handsimformation[
1][
"center"], img)
-
# length, info, img = detector.findDistance(lmList1[8], lmList2[8], img)
-
scale=
int((length-startDist)//
2)
-
cx, cy=info[
4:]
-
print(scale)
-
else:
-
startDist=
None
-
try:
-
h1, w1, _ = img1.shape
-
newH, newW = ((h1 + scale) //
2) *
2, ((w1 + scale) //
2) *
2
-
img1 = cv2.resize(img1, (newW, newH))
-
-
img[cy-newH//
2:cy+ newH//
2, cx-newW//
2:cx+newW//
2] = img1
-
except:
-
pass
-
#################打印帧率#####################
-
cTime = time.time()
-
fps =
1 / (cTime - pTime)
-
pTime = cTime
-
cv2.putText(img,
f'FPS: {int(fps)}', (
40,
50), cv2.FONT_HERSHEY_COMPLEX,
-
1, (
100,
0,
255),
3)
-
-
cv2.imshow(
"image",img)
-
k=cv2.waitKey(
1)
-
if k==
27:
-
break
-
前面的类模块，我不做过多的讲解，它的新添加功能，我会在讲解主文件的时候提到。
- 首先，导入我们需要的模块，第一步先编写打开摄像头的代码，确保摄像头的正常，并调节好窗口的设置——长、宽、亮度，并且用htm(HandTrackingModule的缩写，后面都是此意)handDetector调整置信度，让我们检测到手更准确。
- 其次，用findHands的得到手的landmark，我所设定的手势是左右手的大拇指、食指、中指高于其他四指，也就是这六根手指竖起，我们按照[1, 1, 1, 0, 0]，[1, 1, 1, 0, 0]来设定，如果你不能确定，请解除这里的代码;
#print(detector.fingersUp(handsimformation[0]),detector.fingersUp(handsimformation[1]))- 然后，在这里有两个handsimformation[0]['lmList']，handsimformation[0]["center"]，分别代表我要取食指，和手掌中心点，那么展示的时候是用的中心点，可以按照个人的喜好去选择手掌的索引，startDist=None表示为没有检测到的手时的起始长度，而经过每次迭代后，获得的距离length-起始长度，如果我增大手的距离，我就能得到一个较大的scale，由于打印的scale太大，我不希望它变化太快，所以做了二分后取整，如果得到的是一个负值，那么就缩小图片，那么我们没有检测到手时，就要令startDist=None。
- 之后来看，info = (x1, y1, x2, y2, cx, cy)，根据索引得到中心值，然后，我们来获取现在海报的大小，然后加上我们scale，实现动态的缩放，但在这里要注意，这里进行了整出2，在乘以2的操作，如果是参数是偶数，我们无需理会，但如果遇到了奇数就会出现少一个像素点的问题，比如，值为9，整除2后得到的为4，4+4=8<9，所以为了确保正确，加了这一步。加入try...except语句是因为图像超出窗口时发出会发出警告，起到超出时此代码将不起作用，回到窗口时，可以继续操作。
- 最后，打印出我们的帧率
4、项目资源
5、项目总结
本次项目完成了手势图片的虚拟缩放，如果你喜欢的话可以关注点赞加收藏。如果你们对于其他项目感兴趣，可以进入GitHub中，点击收藏。
感谢大家的关注，如果你对于本项目较为喜欢，那么我会在评论中看到你哦。
转载：https://blog.csdn.net/m0_62919535/article/details/127714724
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