Opencv完成手势识别

• 根据坐标识别
• • 寻找最低点
  • 计算其他点与最低点的距离
  • 通过距离阈值判断手指根数和手势
• 效果展现
• 完整代码

根据坐标识别

寻找最低点

所谓寻找最低点，就是寻找手掌最下面点的坐标

比如这幅图，最左下角的点就是手掌的最低点，获得的方法是使用numpy的np.argmax函数，将维度设置为图片高的维度，获得最大的高就是获得最低的点。

maxindex = np.argmax(allpoint)

计算其他点与最低点的距离

获取成功之后就将最低点与其他点的距离计算出来：

#pt1点一
#pt2点二
def get_distance(pt1, pt2):distance = ((pt2[0] - pt1[0]) ** 2 + (pt2[1] - pt1[1]) ** 2) ** (0.5)return distance

for point in allpoint:distance = self.get_distance(self.allpoint[maxindex [1], :], point)if distance > 300:cv2.line(img,all_point[maxindex [1],:],point,bgr,4,16)

通过距离阈值判断手指根数和手势

然后通过这个距离阈值排除两根手指中间的点:

可以发现左边的图片线的数量比右边少了几根双指中间的。
最后我们只需要通过手指线的根数来判断手势就行了。

效果展现

完整代码

import cv2
import numpy as np
import randomclass Img_handle:def __init__(self,area_min,lenth_min,distance,drawtype=0,area_max=100000,lenth_max=100000):#default area_min=20000 lenth_min=1000 distance=300#writer:csdn月明Mo#area_min 轮廓面积最小值#area_max 轮廓面积最大值#lenth_min 轮廓周长最小值#lenth_max 轮廓周长最大值#distance 距离阈值#drawtype 手势描绘类型，0为近似描绘 1为直线描绘self.area_min=area_minself.area_max=area_maxself.lenth_min=lenth_minself.lenth_max=lenth_maxself.distance=distanceself.allpoint = []self.highHSV = np.array([15, 255, 255])self.lowHSV = np.array([0, 50, 50])self.drawtype=drawtype#writer:csdn月明Mo#Hsv阈值改变函数def change_Hsv(self,lowHSV,highHSV):self.highHSV=highHSVself.lowHSV=lowHSV#writer:csdn月明Mo#图片不失真resize函数def resize_img(self,size=list,img=None):size = [size[1], size[0], size[2]]mask = np.zeros(size, dtype=np.uint8)h, w = self.img.shape[0:2]dwh = min([size[0] / h, size[1] / w])self.img = cv2.resize(self.img, None, fx=dwh, fy=dwh)if h > w:dxy = int((size[1] - self.img.shape[1]) / 2)mask[:, dxy:self.img.shape[1] + dxy, :] = self.imgelse:dxy = int((size[0] - self.img.shape[0]) / 2)mask[dxy:self.img.shape[0] + dxy, :, :] = self.imgreturn mask#writer:csdn月明Mo#图片处理函数def img_handle(self):self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2HSV)cv2.GaussianBlur(self.img, [5, 5], 0)self.img = cv2.inRange(self.img, self.lowHSV, self.highHSV)kernel = np.ones([3, 3], dtype=np.uint8)self.img = cv2.morphologyEx(self.img, cv2.MORPH_CLOSE, kernel, iterations=1)kernel = np.ones([3, 3], dtype=np.uint8)self.img = cv2.morphologyEx(self.img, cv2.MORPH_DILATE, kernel, iterations=1)contours, num = cv2.findContours(self.img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)for contour in contours:area = cv2.contourArea(contour)lenth = cv2.arcLength(contour, True)if self.area_max >area > self.area_min and self.lenth_max >lenth > self.lenth_min:epsilon = 0.02 * cv2.arcLength(contour, True)self.allpoint = cv2.approxPolyDP(contour, epsilon, True)self.allpoint = self.allpoint.reshape(len(self.allpoint), 2)self.allpoint = np.array(self.allpoint, dtype=np.int32)if self.drawtype==0:b = random.randint(0, 255)g = random.randint(0, 255)r = random.randint(0, 255)cv2.polylines(self.output_img, [self.allpoint], True, [b, g, r], 4, 16)#writer:csdn月明Mo#距离计算函数def get_distance(self, pt1, pt2):distance = ((pt2[0] - pt1[0]) ** 2 + (pt2[1] - pt1[1]) ** 2) ** (0.5)return distance#writer:csdn月明Mo#手势判断函数def detect(self):num = 0if np.any(self.allpoint):maxindex  = np.argmax(self.allpoint, axis=0)for point in self.allpoint:distance = self.get_distance(self.allpoint[maxindex [1], :], point)if distance > self.distance:if self.drawtype ==1:b = random.randint(0, 255)g = random.randint(0, 255)r = random.randint(0, 255)cv2.line(self.output_img,self.allpoint[maxindex [1],:],point,[b, g, r],4,16)num += 1if num == 1:cv2.putText(self.output_img, 'one', [10, 50], cv2.FONT_HERSHEY_SIMPLEX, 2, [0, 0, 255], thickness=4)elif num == 2:cv2.putText(self.output_img, 'two', [10, 50], cv2.FONT_HERSHEY_SIMPLEX, 2, [0, 0, 255], thickness=4)elif num == 3:cv2.putText(self.output_img, 'there', [10, 50], cv2.FONT_HERSHEY_SIMPLEX, 2, [0, 0, 255], thickness=4)elif num == 4:cv2.putText(self.output_img, 'four', [10, 50], cv2.FONT_HERSHEY_SIMPLEX, 2, [0, 0, 255], thickness=4)elif num == 5:cv2.putText(self.output_img, 'five', [10, 50], cv2.FONT_HERSHEY_SIMPLEX, 2, [0, 0, 255], thickness=4)#writer:csdn月明Mo#获取手势函数def get_hand(self,img):self.img = imgif self.img.shape[0] != 480 and self.img.shape[1] != 640:self.img = self.resize_img([640,480,3])self.output_img=np.copy(self.img)self.img_handle()self.detect()return self.output_imgdef main():video=cv2.VideoCapture(0)hand=Img_handle(20000,1000,280)while video.isOpened():res,img=video.read()if res== True:newimg=hand.get_hand(img)cv2.imshow('frams1', newimg)if cv2.waitKey(1)==ord('q'):breakcv2.destroyAllWindows()video.release()if __name__=='__main__':main()