基于OpenCV的手势跟踪与识别项目代码

以下是一个基于OpenCV的手势跟踪与识别项目的代码示例，供参考：

import cv2
import numpy as np
from sklearn.neighbors import KNeighborsClassifier

# 初始化摄像头
cap = cv2.VideoCapture(0)

# 定义分类器
knn = KNeighborsClassifier(n_neighbors=3)

# 训练手势数据
gestures = ['fist', 'palm', 'peace', 'thumbs_up']
train_data = []
train_labels = []
for gesture in gestures:
    for i in range(1, 6):
        img = cv2.imread('train_data/' + gesture + '/' + str(i) + '.jpg', 0)
        _, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
        img = cv2.resize(img, (50, 50))
        img_vec = np.reshape(img, (1, 2500))
        train_data.append(img_vec)
        train_labels.append(gestures.index(gesture))
knn.fit(train_data, train_labels)

# 定义颜色范围
lower_range = np.array([0, 20, 70], dtype=np.uint8)
upper_range = np.array([20, 255, 255], dtype=np.uint8)

while True:
    # 读取摄像头数据
    ret, frame = cap.read()

    # 镜像翻转
    frame = cv2.flip(frame, 1)

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

    # 提取手部区域
    mask = cv2.inRange(hsv, lower_range, upper_range)

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

    # 轮廓检测
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 处理轮廓
    if contours:
        max_contour = max(contours, key=cv2.contourArea)
        if cv2.contourArea(max_contour) > 500:
            moments = cv2.moments(max_contour)
            if moments['m00'] != 0:
                cx = int(moments['m10'] / moments['m00'])
                cy = int(moments['m01'] / moments['m00'])

                # 绘制中心点
                cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1)

                # 裁剪手部区域
                x, y, w, h = cv2.boundingRect(max_contour)
                hand_roi = mask[y:y+h, x:x+w]
                hand_roi = cv2.resize(hand_roi, (50, 50))
                hand_vec = np.reshape(hand_roi, (1, 2500))

                # 预测手势
                pred = knn.predict(hand_vec)
                cv2.putText(frame, gestures[pred[0]], (cx-50, cy-50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    # 显示结果
    cv2.imshow('frame', frame)

    # 按q键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头并关闭所有窗口
cap.release()
cv2.destroyAllWindows()

在上述代码中，我们使用KNN算法来训练手势数据，并在摄像头画面中提取手部区域，预测手势并在画面中显示结果。需要注意的是，这只是一个简单的示例，实际应用中需要根据具体情况进行优化和改进。