使用机器视觉的红绿灯检测的树莓派智能小车python代码

下面是一个使用机器视觉进行红绿灯检测的树莓派智能小车的简单示例代码：

import cv2
import numpy as np
from picamera.array import PiRGBArray
from picamera import PiCamera
import RPi.GPIO as GPIO
import time

# 设置红灯和绿灯引脚
RED_PIN = 11
GREEN_PIN = 13

# 设置红绿灯颜色的范围
lower_red = np.array([0, 100, 100])
upper_red = np.array([10, 255, 255])
lower_green = np.array([50, 100, 100])
upper_green = np.array([70, 255, 255])

# 初始化树莓派摄像头
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 30
rawCapture = PiRGBArray(camera, size=(640, 480))

# 设置GPIO引脚
GPIO.setmode(GPIO.BOARD)
GPIO.setup(RED_PIN, GPIO.OUT)  # 红灯引脚
GPIO.setup(GREEN_PIN, GPIO.OUT)  # 绿灯引脚

# 等待摄像头预热
time.sleep(0.1)

for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # 获取图像帧
    image = frame.array

    # 转换颜色空间
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # 创建红绿灯掩膜
    mask_red = cv2.inRange(hsv, lower_red, upper_red)
    mask_green = cv2.inRange(hsv, lower_green, upper_green)

    # 对掩膜进行形态学处理以去除噪声
    kernel = np.ones((5, 5), np.uint8)
    mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_OPEN, kernel)
    mask_green = cv2.morphologyEx(mask_green, cv2.MORPH_OPEN, kernel)

    # 检测红绿灯区域
    contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours_green, _ = cv2.findContours(mask_green, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 判断红灯状态
    if len(contours_red) > 0:
        GPIO.output(RED_PIN, GPIO.HIGH)  # 点亮红灯
        GPIO.output(GREEN_PIN, GPIO.LOW)  # 熄灭绿灯
    else:
        GPIO.output(RED_PIN, GPIO.LOW)  # 熄灭红灯
        GPIO.output(GREEN_PIN, GPIO.HIGH)  # 点亮绿灯

    # 显示结果图像
    cv2.imshow('Traffic Light Detection', image)

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

    # 清空缓冲区
    rawCapture.truncate(0)

# 清理GPIO引脚和关闭摄像头
GPIO.cleanup()
camera.close()
cv2.destroyAllWindows()

请注意，上述代码假设你已经连接了红灯和绿灯的LED到树莓派的GPIO引脚，并正确设置了引脚编号。此外，还可能需要根据你的硬件和实际情况进行适当的调整。