手势识别Python代码

### 手势识别 Python 示例代码

手势识别是一个复杂的领域，涉及到计算机视觉和机器学习等多个方面。下面提供了一个基于 OpenCV 的简单手势识别程序示例[^1]。

此代码会打开摄像头捕获视频流，并尝试检测手部区域：

import cv2
import numpy as np

def main():
    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # 创建一个矩形框来限定手的位置
        roi = gray[100:300, 100:300]
        cv2.rectangle(frame, (100, 100), (300, 300), (0, 255, 0), 0)
        
        blur = cv2.GaussianBlur(roi, (35, 35), 0)
        
        _, thresh1 = cv2.threshold(blur, 70, 255,
                                   cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
        
        contours, hierarchy = cv2.findContours(thresh1.copy(), \
                                               cv2.RETR_TREE, \
                                               cv2.CHAIN_APPROX_NONE)
        
        max_area = -1
        ci = None
        for i in range(len(contours)):
            cnt = contours[i]
            area = cv2.contourArea(cnt)
            if(area > max_area):
                max_area = area
                ci = i
                
        if ci is not None and max_area != -1:
            cnt = contours[ci]

            hull = cv2.convexHull(cnt)
            
            drawing = np.zeros(frame.shape,np.uint8)
            cv2.drawContours(drawing,[cnt],0,(0,255,0),0)
            cv2.drawContours(drawing,[hull],0,(0,0,255),0)
            
            hull = cv2.convexHull(cnt, returnPoints=False)
            defects = cv2.convexityDefects(cnt,hull)
            
            count_defects = 0
            
            try:
                for i in range(defects.shape[0]):
                    s,e,f,d = defects[i,0]
                    
                    start = tuple(cnt[s][0])
                    end = tuple(cnt[e][0])
                    far = tuple(cnt[f][0])

                    a = math.sqrt((end[0]-start[0])**2+(end[1]-start[1])**2)
                    b = math.sqrt((far[0]-start[0])**2+(far[1]-start[1])**2)
                    c = math.sqrt((end[0]-far[0])**2+(end[1]-far[1])**2)
                    
                    angle = math.acos((b**2+c**2-a**2)/(2*b*c)) * 57
                    
                    if angle <= 90:
                        count_defects += 1
                        cv2.circle(drawing,far,1,[0,0,255],-1)
                        
                    cv2.line(drawing,start,end,[0,255,0],2)
                
                font = cv2.FONT_HERSHEY_SIMPLEX
                if count_defects == 1:
                    cv2.putText(frame,'2',(11,450),font,2,(255,0,0),5,cv2.LINE_AA)
                elif count_defects == 2:
                    str = "3"
                    cv2.putText(frame,str ,(10,450),font,2,(255,0,0),5,cv2.LINE_AA)
                elif count_defects == 3:
                    cv2.putText(frame,"4",(10,450),font,2,(255,0,0),5,cv2.LINE_AA)
                elif count_defects == 4:
                    cv2.putText(frame,"5",(10,450),font,2,(255,0,0),5,cv2.LINE_AA)
                else:
                    cv2.putText(frame,"1",(10,450),font,2,(255,0,0),5,cv2.LINE_AA)
            except AttributeError:
                pass

        all_image = np.hstack((drawing,frame))
        cv2.imshow('Gesture',all_image)

        k = cv2.waitKey(50) & 0xFF
        if k == 27:
            break
    
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

这段代码实现了通过摄像头捕捉图像并对手指数量进行计数的功能。它利用了轮廓查找、凸包计算以及缺陷检测等技术来判断手指的数量，并显示相应的数字。