python based gesture test

In computer science, gesture recognition is an issue of identifying human gestures through mathematical algorithms . Gesture recognition can come from the movement of various parts of a person's body, but generally refers to the movement of the face and hands. Users can use simple gestures to control or interact with devices, allowing computers to understand human behavior. Its core technologies are gesture segmentation, gesture analysis and gesture recognition.

Future availability is bound to be great

Realize the effect:

get webcam

cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件
#cap = cv2.VideoCapture(0)#读取摄像头
while(True):
    ret, frame = cap.read()    key = cv2.waitKey(50) & 0xFF
    if key == ord('q'):
      break
cap.release()
cv2.destroyAllWindows()

Skin color detection

This part is more important and mainly detects the range of the human body

def A(img):
    YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间
    (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处理
    res = cv2.bitwise_and(img,img, mask = skin)
    return res

Contour processing

def B(img):
    #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测
    h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓
    contour = h[0]
    contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序
    #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标
    bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布
    ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓
    return ret

Full code:

""" 从视频读取帧保存为图片"""
import cv2
import numpy as np
#cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件
cap = cv2.VideoCapture(0)#读取摄像头

#皮肤检测
def A(img):

    YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间
    (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处理
    res = cv2.bitwise_and(img,img, mask = skin)
    return res

def B(img):

    #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测
    h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓
    contour = h[0]
    contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序
    #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标
    bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布
    ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓
    return ret


while(True):

    ret, frame = cap.read()
    #下面三行可以根据自己的电脑进行调节
    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]  # 获取手势框图

    res = A(roi)  # 进行肤色检测
    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)#轮廓处理
    cv2.imshow("2",contour)

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