Opencv项目实战：15 手势缩放图片
目录
0、项目介绍
1、项目展示
2、项目搭建
3、项目的代码与讲解
4、项目资源
5、项目总结
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
GitHub:Opencv-project-training/Opencv project training/15 Gesture Zoom Picture at main · Auorui/Opencv-project-training · GitHub
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。