OpenCV 赛道识别：使用 Python 代码标记远近、倾斜和明暗

以下是一个使用 OpenCV 进行赛道识别并标记远近、倾斜和明暗的 Python 代码示例：

import cv2
import numpy as np

def preprocess_image(image):
    # 转换为灰度图像
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 应用高斯模糊以减少噪声
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    
    return blurred

def detect_edges(image):
    # 使用 Canny 边缘检测算法检测边缘
    edges = cv2.Canny(image, 50, 150)
    
    return edges

def detect_lines(image):
    # 使用霍夫直线变换检测直线
    lines = cv2.HoughLinesP(image, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)
    
    return lines

def process_lines(image, lines):
    # 绘制检测到的直线
    for line in lines:
        x1, y1, x2, y2 = line[0]
        cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
    
    return image

def mark_distance(image, lines):
    # 计算每条直线的长度，然后根据长度标记远近
    for line in lines:
        x1, y1, x2, y2 = line[0]
        distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)
        
        if distance < 100:
            cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(image, 'Far', (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
        else:
            cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
            cv2.putText(image, 'Near', (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
    
    return image

def mark_slope(image, lines):
    # 根据斜率标记倾斜
    for line in lines:
        x1, y1, x2, y2 = line[0]
        
        if x2 - x1 != 0:
            slope = (y2 - y1) / (x2 - x1)
            
            if abs(slope) < 0.5:
                cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
                cv2.putText(image, 'Flat', (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
            elif slope < 0:
                cv2.line(image, (x1, y1), (x2, y2), (0, 255, 255), 2)
                cv2.putText(image, 'Downhill', (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 255), 2)
            else:
                cv2.line(image, (x1, y1), (x2, y2), (255, 255, 0), 2)
                cv2.putText(image, 'Uphill', (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 0), 2)
    
    return image

def mark_brightness(image, edges):
    # 计算灰度图像的平均亮度，然后根据亮度标记明暗
    brightness = np.mean(image)
    
    if brightness < 100:
        cv2.putText(edges, 'Dark', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
    else:
        cv2.putText(edges, 'Bright', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    return edges

# 读取图像
image = cv2.imread('map.jpg')

# 图像预处理
processed_image = preprocess_image(image)

# 边缘检测
edges = detect_edges(processed_image)

# 直线检测
lines = detect_lines(edges)

# 标记直线
image_with_lines = process_lines(image.copy(), lines)

# 标记远近
image_with_distance = mark_distance(image_with_lines.copy(), lines)

# 标记倾斜
image_with_slope = mark_slope(image_with_distance.copy(), lines)

# 标记明暗
final_image = mark_brightness(image_with_slope.copy(), edges)

# 显示结果
cv2.imshow('Result', final_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请确保将代码中的 'map.jpg' 替换为您要处理的地图图像的路径。此代码使用了一系列的图像处理技术，包括灰度转换、高斯模糊、Canny 边缘检测、霍夫直线变换和计算斜率和亮度等。最后，通过在图像上绘制直线和文本来自动标记远近、倾斜和明暗。