while True:
    ret, img = cap.read()#讀取當前數(shù)據(jù)
    if ret:
        cv2.imshow('img',img)#顯示當前讀取到的畫面
    if cv2.waitKey(1) == ord('q'):#按q鍵退出程序
        break

全部函數(shù)

import cv2
import mediapipe as mp
import time

cap = cv2.VideoCapture(1)
mpHands = mp.solutions.hands
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils
handLmsStyle = mpDraw.DrawingSpec(color=(0, 0, 255), thickness=3)
handConStyle = mpDraw.DrawingSpec(color=(0, 255, 0), thickness=5)
pTime = 0
cTime = 0

while True:
    ret, img = cap.read()
    if ret:
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        result = hands.process(imgRGB)

        # print(result.multi_hand_landmarks)
        imgHeight = img.shape[0]
        imgWidth = img.shape[1]

        if result.multi_hand_landmarks:
            for handLms in result.multi_hand_landmarks:
                mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS, handLmsStyle, handConStyle)
                for i, lm in enumerate(handLms.landmark):
                    xPos = int(lm.x * imgWidth)
                    yPos = int(lm.y * imgHeight)

                    # cv2.putText(img, str(i), (xPos-25, yPos+5), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 2)

                    # if i == 4:
                    #     cv2.circle(img, (xPos, yPos), 20, (166, 56, 56), cv2.FILLED)
                    # print(i, xPos, yPos)

        cTime = time.time()
        fps = 1/(cTime-pTime)
        pTime = cTime
        cv2.putText(img, f"FPS : {int(fps)}", (30, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3)

        cv2.imshow('img', img)

    if cv2.waitKey(1) == ord('q'):
        break

這樣我們就能再電腦上顯示我們的手部關鍵點和坐標了，對于手勢識別或者別的操作就可以通過獲取到的關鍵點的坐標進行判斷了。

附另一個手勢識別實例

'''
@Time : 2021/2/6 15:41 
@Author : WGS
@remarks : 
'''
""" 從視頻讀取幀保存為圖片"""
import cv2
import numpy as np

# cap = cv2.VideoCapture("C:/Users/lenovo/Videos/wgs.mp4") #讀取文件
cap = cv2.VideoCapture(0)  # 讀取攝像頭


# 皮膚檢測
def A(img):
    YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)  # 轉(zhuǎn)換至YCrCb空間
    (y, cr, cb) = cv2.split(YCrCb)  # 拆分出Y,Cr,Cb值
    cr1 = cv2.GaussianBlur(cr, (5, 5), 0)
    _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # Ostu處理
    res = cv2.bitwise_and(img, img, mask=skin)
    return res


def B(img):
    # binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny檢測
    h = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  # 尋找輪廓
    contour = h[0]
    contour = sorted(contour, key=cv2.contourArea, reverse=True)  # 已輪廓區(qū)域面積進行排序
    # contourmax = contour[0][:, 0, :]#保留區(qū)域面積最大的輪廓點坐標
    bg = np.ones(dst.shape, np.uint8) * 255  # 創(chuàng)建白色幕布
    ret = cv2.drawContours(bg, contour[0], -1, (0, 0, 0), 3)  # 繪制黑色輪廓
    return ret


while (True):

    ret, frame = cap.read()
    # 下面三行可以根據(jù)自己的電腦進行調(diào)節(jié)
    src = cv2.resize(frame, (400, 350), interpolation=cv2.INTER_CUBIC)  # 窗口大小
    cv2.rectangle(src, (90, 60), (300, 300), (0, 255, 0))  # 框出截取位置
    roi = src[60:300, 90:300]  # 獲取手勢框圖

    res = A(roi)  # 進行膚色檢測
    cv2.imshow("0", roi)

    gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
    dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
    Laplacian = cv2.convertScaleAbs(dst)

    contour = B(Laplacian)  # 輪廓處理
    cv2.imshow("2", contour)

    key = cv2.waitKey(50) & 0xFF
    if key == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()