Python如何使用opencv進行手勢識別詳解

pip install opencv-python

pip install mediapipe

import cv2
import mediapipe as mp

cap = cv2.VideoCapture(0)

while True:
    ret, img = cap.read()#讀取當前數據
    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

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

'''
@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)  # 已輪廓區域面積進行排序
    # contourmax = contour[0][:, 0, :]#保留區域面積最大的輪廓點坐標
    bg = np.ones(dst.shape, np.uint8) * 255  # 創建白色幕佈
    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()
    # 下面三行可以根據自己的電腦進行調節
    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()