红绿灯检测，人脸识别，盲道识别小车代码

以下是一个简单的红绿灯检测、人脸识别、盲道识别小车的代码示例：

import cv2
import numpy as np

# 加载红绿灯检测模型
red_light_cascade = cv2.CascadeClassifier('red_light_cascade.xml')
green_light_cascade = cv2.CascadeClassifier('green_light_cascade.xml')

# 加载人脸识别模型
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 加载盲道识别模型
blind_path_cascade = cv2.CascadeClassifier('blind_path_cascade.xml')

# 初始化摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取视频帧
    ret, frame = cap.read()

    # 红绿灯检测
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    red_lights = red_light_cascade.detectMultiScale(gray, 1.3, 5)
    green_lights = green_light_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in red_lights:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
        cv2.putText(frame, 'Red Light', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
    for (x, y, w, h) in green_lights:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        cv2.putText(frame, 'Green Light', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

    # 人脸识别
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        cv2.putText(frame, 'Face', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)

    # 盲道识别
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blind_paths = blind_path_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in blind_paths:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 0), 2)
        cv2.putText(frame, 'Blind Path', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)

    # 显示视频帧和检测结果
    cv2.imshow('frame', frame)

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

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

这个代码会打开摄像头，并实时检测视频帧中的红绿灯、人脸和盲道。如果检测到目标，会在目标周围绘制一个矩形，并在矩形上绘制一个文本标签，以指示检测到的目标类型。你需要提前准备好训练好的模型文件，即红绿灯检测模型、人脸识别模型和盲道识别模型。