树莓派识别手势：无需RPi库，使用OpenCV实现手势识别

识别手势可以使用OpenCV库，以下是详细过程和代码：

1. 安装OpenCV库

在树莓派上安装OpenCV库：

sudo apt-get install python-opencv

2. 捕获视频

使用树莓派摄像头捕获视频：

import cv2

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

3. 提取手部区域

使用OpenCV库对视频进行处理，提取手部区域：

import cv2

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    if len(contours) > 0:
        max_contour = max(contours, key=cv2.contourArea)
        if cv2.contourArea(max_contour) > 1000:
            x, y, w, h = cv2.boundingRect(max_contour)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

4. 识别手势

使用OpenCV库和numpy库对手部区域进行处理，识别手势：

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    if len(contours) > 0:
        max_contour = max(contours, key=cv2.contourArea)
        if cv2.contourArea(max_contour) > 1000:
            epsilon = 0.01 * cv2.arcLength(max_contour, True)
            approx = cv2.approxPolyDP(max_contour, epsilon, True)
            hull = cv2.convexHull(max_contour)
            areahull = cv2.contourArea(hull)
            areacnt = cv2.contourArea(max_contour)
            arearatio = ((areahull - areacnt) / areacnt) * 100
            drawing = np.zeros(frame.shape, np.uint8)

            if arearatio < 30:
                if len(approx) == 4:
                    cv2.putText(frame, 'Rectangle', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                elif len(approx) == 5:
                    cv2.putText(frame, 'Pentagon', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                elif len(approx) == 6:
                    cv2.putText(frame, 'Hexagon', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                elif len(approx) == 7:
                    cv2.putText(frame, 'Heptagon', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                elif len(approx) == 8:
                    cv2.putText(frame, 'Octagon', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                elif len(approx) == 3:
                    cv2.putText(frame, 'Triangle', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)
                else:
                    cv2.putText(frame, 'Circle', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)

    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

代码解释：

• cv2.GaussianBlur函数用于高斯模糊处理。
• cv2.threshold函数用于图像二值化处理。
• cv2.findContours函数用于查找轮廓。
• cv2.boundingRect函数用于计算轮廓的边框。
• cv2.rectangle函数用于绘制边框。
• cv2.arcLength函数用于计算轮廓周长。
• cv2.approxPolyDP函数用于进行多边形逼近。
• cv2.convexHull函数用于计算凸包。
• cv2.contourArea函数用于计算轮廓面积。
• cv2.putText函数用于绘制文字。