基于python的手势识别项目代码

以下是一个基于Python和OpenCV的手势识别项目代码示例：

import cv2
import numpy as np
import math

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    # 翻转帧以使其更易于处理
    frame = cv2.flip(frame, 1)

    # 定义感兴趣区域(ROI)
    roi = frame[100:400, 100:400]

    # 应用高斯模糊
    blur = cv2.GaussianBlur(roi, (5, 5), 0)

    # 将帧转换为HSV颜色空间
    hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)

    # 创建HSV颜色空间的掩码
    lower_skin = np.array([0, 20, 70], dtype=np.uint8)
    upper_skin = np.array([20, 255, 255], dtype=np.uint8)
    mask = cv2.inRange(hsv, lower_skin, upper_skin)

    # 应用形态学操作
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.dilate(mask, kernel, iterations=1)
    mask = cv2.erode(mask, kernel, iterations=1)

    # 找到轮廓
    contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 获取最大轮廓
    if len(contours) > 0:
        max_contour = max(contours, key=cv2.contourArea)

        # 如果最大轮廓的面积大于一定值，则认为手势识别成功
        if cv2.contourArea(max_contour) > 10000:
            # 找到手指指尖
            hull = cv2.convexHull(max_contour, returnPoints=False)
            defects = cv2.convexityDefects(max_contour, hull)
            count_defects = 0
            for i in range(defects.shape[0]):
                s, e, f, d = defects[i, 0]
                start = tuple(max_contour[s][0])
                end = tuple(max_contour[e][0])
                far = tuple(max_contour[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)) * 180 / math.pi
                if angle <= 90:
                    count_defects += 1
                    cv2.circle(roi, far, 5, [0, 0, 255], -1)
            if count_defects == 1:
                cv2.putText(frame, "I am V", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
            elif count_defects == 2:
                cv2.putText(frame, "This is a bird", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
            elif count_defects == 3:
                cv2.putText(frame, "This is a three", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
            elif count_defects == 4:
                cv2.putText(frame, "This is a four", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
            else:
                cv2.putText(frame, "Hello World!!!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)

            # 绘制轮廓和凸包
            cv2.drawContours(roi, [max_contour], 0, (0, 255, 0), 2)
            cv2.drawContours(roi, [hull], 0, (0, 0, 255), 3)

    # 显示结果
    cv2.imshow("Gesture Recognition", frame)

    # 按下ESC键退出循环
    if cv2.waitKey(1) == 27:
        break

# 释放摄像头并销毁所有窗口
cap.release()
cv2.destroyAllWindows()

这个示例使用的是HSV颜色空间的掩码、轮廓和凸包的概念来识别手势。它首先将感兴趣区域(ROI)转换为HSV颜色空间，并创建一个掩码来过滤出肤色。然后，它对掩码应用形态学操作，并找到最大轮廓。接着，它通过凸包和凸缺陷来找到手指指尖，根据指尖的个数来识别手势。最后，它将结果显示在一个窗口中。