Python OpenCV 赛道图片识别：灰度识别、距离、倾斜度和亮度分析

下面是基于灰度识别的代码示例，用于识别赛道图片并分别显示距离数据、倾斜度数据和明暗亮度数据：

import cv2
import numpy as np

# 加载赛道图片
image = cv2.imread('track_image.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 进行边缘检测
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

# 进行霍夫直线检测
lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)

# 初始化三个屏幕的数据
distance_screen = np.zeros_like(image)
tilt_screen = np.zeros_like(image)
brightness_screen = np.zeros_like(image)

# 分析检测到的直线
if lines is not None:
    for line in lines:
        rho, theta = line[0]
        a = np.cos(theta)
        b = np.sin(theta)
        x0 = a * rho
        y0 = b * rho

        # 计算直线的倾斜度
        tilt = np.rad2deg(theta) - 90

        # 计算直线的距离
        distance = abs(y0 - image.shape[0] / 2)

        # 计算直线的明暗亮度
        brightness = np.mean(gray)

        # 在对应的屏幕上绘制数据
        if distance < 100:
            cv2.putText(distance_screen, f'Distance: {distance}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
        if tilt < -10 or tilt > 10:
            cv2.putText(tilt_screen, f'Tilt: {tilt}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
        if brightness < 150:
            cv2.putText(brightness_screen, f'Brightness: {brightness}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

# 显示三个屏幕
cv2.imshow('Distance Screen', distance_screen)
cv2.imshow('Tilt Screen', tilt_screen)
cv2.imshow('Brightness Screen', brightness_screen)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个简单的示例，并且可能需要根据实际情况进行调整和优化。