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OpenCV實戰(zhàn)之實現(xiàn)手勢虛擬縮放效果

更新時間：2022年11月07日 08:38:46 作者：夏天是冰紅茶

本篇將會以HandTrackingModule為模塊，實現(xiàn)通過手勢對本人的博客海報進(jìn)行縮放。文中的示例代碼講解詳細(xì)，具有一定的借鑒價值，需要的可以參考一下

0、項目介紹

本篇將會以HandTrackingModule為模塊，這里的模塊與之前的有所不同，請按照本篇為準(zhǔn)，前面的HandTrackingModule不足以完成本項目，本篇將會通過手勢對本人的博客海報進(jìn)行縮放，具體效果可以看下面的效果展示。

1、項目展示

2、項目搭建

首先在一個文件夾下建立HandTrackingModule.py文件以及gesture_zoom.py，以及一張圖片，你可以按照你的喜好選擇，建議尺寸不要過大。

在這里用到了食指的索引8，可以完成左右手食指的手勢進(jìn)行縮放。

3、項目的代碼與講解

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
    #################打印幀率#####################
    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

前面的類模塊，我不做過多的講解，它的新添加功能，我會在講解主文件的時候提到。

1.首先，導(dǎo)入我們需要的模塊，第一步先編寫打開攝像頭的代碼，確保攝像頭的正常，并調(diào)節(jié)好窗口的設(shè)置——長、寬、亮度，并且用htm(HandTrackingModule的縮寫，后面都是此意)handDetector調(diào)整置信度，讓我們檢測到手更準(zhǔn)確。

2.其次，用findHands的得到手的landmark，我所設(shè)定的手勢是左右手的大拇指、食指、中指高于其他四指，也就是這六根手指豎起，我們按照[1, 1, 1, 0, 0]，[1, 1, 1, 0, 0]來設(shè)定，如果你不能確定，請解除這里的代碼;

#print(detector.fingersUp(handsimformation[0]),detector.fingersUp(handsimformation[1]))

3.然后，在這里有兩個handsimformation[0]['lmList']，handsimformation[0]["center"]，分別代表我要取食指，和手掌中心點，那么展示的時候是用的中心點，可以按照個人的喜好去選擇手掌的索引，startDist=None表示為沒有檢測到的手時的起始長度，而經(jīng)過每次迭代后，獲得的距離length-起始長度，如果我增大手的距離，我就能得到一個較大的scale，由于打印的scale太大，我不希望它變化太快，所以做了二分后取整，如果得到的是一個負(fù)值，那么就縮小圖片，那么我們沒有檢測到手時，就要令startDist=None。

4.之后來看，info = (x1, y1, x2, y2, cx, cy)，根據(jù)索引得到中心值，然后，我們來獲取現(xiàn)在海報的大小，然后加上我們scale，實現(xiàn)動態(tài)的縮放，但在這里要注意，這里進(jìn)行了整出2，在乘以2的操作，如果是參數(shù)是偶數(shù)，我們無需理會，但如果遇到了奇數(shù)就會出現(xiàn)少一個像素點的問題，比如，值為9，整除2后得到的為4，4+4=8<9，所以為了確保正確，加了這一步。加入try...except語句是因為圖像超出窗口時發(fā)出會發(fā)出警告，起到超出時此代碼將不起作用，回到窗口時，可以繼續(xù)操作。

5.最后，打印出我們的幀率

到此這篇關(guān)于OpenCV實戰(zhàn)之實現(xiàn)手勢虛擬縮放效果的文章就介紹到這了,更多相關(guān)OpenCV手勢虛擬縮放內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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