OpenCV 赛道识别 - 远近、倾斜、明暗识别 Python 代码

以下是一个使用 OpenCV 进行赛道识别、远近、倾斜和明暗识别的 Python 代码示例：

import cv2
import numpy as np

def detect_lane(image):
    # 灰度化图像
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 边缘检测
    edges = cv2.Canny(gray, 50, 150)

    # 霍夫变换检测直线
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=50)

    # 绘制检测到的直线
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 3)

    return image

def detect_distance(image):
    # 计算图像的宽度和高度
    height, width = image.shape[:2]

    # 根据远近关系，可以使用图像的高度或宽度来估计距离
    if height > width:
        distance = '远'
    else:
        distance = '近'

    cv2.putText(image, distance, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

    return image

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

    # 检测图像的倾斜角度
    corners = cv2.goodFeaturesToTrack(gray, 4, 0.01, 10)
    corners = np.int0(corners)

    angles = []
    for corner in corners:
        x, y = corner.ravel()
        cv2.circle(image, (x, y), 3, (255, 0, 0), -1)
        angles.append(np.arctan2(y, x))

    mean_angle = np.mean(angles)
    tilt = np.degrees(mean_angle)

    cv2.putText(image, '{:.2f}'.format(tilt), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

    return image

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

    # 计算图像的平均亮度
    brightness = np.mean(gray)

    cv2.putText(image, '{:.2f}'.format(brightness), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

    return image

# 读取图像
image = cv2.imread('map.jpg')

# 进行赛道识别
image = detect_lane(image)

# 进行远近识别
image = detect_distance(image)

# 进行倾斜识别
image = detect_tilt(image)

# 进行明暗识别
image = detect_brightness(image)

# 显示结果图像
cv2.imshow('Result', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请确保已经安装了 OpenCV 库，并将图像文件命名为 'map.jpg' 并与代码文件放置在同一目录下。该代码将会进行赛道识别、远近识别、倾斜识别和明暗识别，并在图像上绘制相关信息，最后显示结果图像。