静态手势识别优惠活动

import cv2
import numpy as np
from keras.models import load_model

# 加载预训练模型
model = load_model('gesture_recognition_model.h5')

# 定义手势类别
gestures = ['none', 'thumbs_up', 'peace', 'stop']

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # 应用高斯模糊
    blurred = cv2.GaussianBlur(gray, (11, 11), 0)
    
    # 边缘检测
    edged = cv2.Canny(blurred, 35, 125)
    
    # 查找轮廓
    contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    if len(contours) > 0:
        c = max(contours, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        
        if radius > 10:
            cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)
            roi = gray[y-radius:y+radius, x-radius:x+radius]
            roi = cv2.resize(roi, (64, 64))
            roi = np.expand_dims(roi, axis=-1)
            roi = np.expand_dims(roi, axis=0)
            prediction = model.predict(roi)
            gesture = gestures[np.argmax(prediction)]
            cv2.putText(frame, gesture, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    cv2.imshow('Gesture Recognition', frame)

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

cap.release()
cv2.destroyAllWindows()