挑战任务: 车道检测

import cv2
import numpy as np

# 高斯滤波核大小
blur_ksize = 5
# Canny边缘检测高低阈值
canny_lth = 50
canny_hth = 150

def process_an_image(img):
    # 1. 灰度化、滤波和Canny
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    blur_gray = cv2.GaussianBlur(gray, (blur_ksize, blur_ksize), 1)
    edges = cv2.Canny(blur_gray, canny_lth, canny_hth)

if __name__ == "__main__":
    img = cv2.imread('test_pictures/lane.jpg')
    result = process_an_image(img)
    cv2.imshow("lane", np.hstack((img, result)))
    cv2.waitKey(0)Copy to clipboardErrorCopied

def process_an_image(img):
    # 1. 灰度化、滤波和Canny

    # 2. 标记四个坐标点用于ROI截取
    rows, cols = edges.shape
    points = np.array([[(0, rows), (460, 325), (520, 325), (cols, rows)]])
    # [[[0 540], [460 325], [520 325], [960 540]]]
    roi_edges = roi_mask(edges, points)

def roi_mask(img, corner_points):
    # 创建掩膜
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, corner_points, 255)

    masked_img = cv2.bitwise_and(img, mask)
    return masked_imgCopy to clipboardErrorCopied

# 霍夫变换参数
rho = 1
theta = np.pi / 180
threshold = 15
min_line_len = 40
max_line_gap = 20

def process_an_image(img):
    # 1. 灰度化、滤波和Canny

    # 2. 标记四个坐标点用于ROI截取

    # 3. 霍夫直线提取
    drawing, lines = hough_lines(roi_edges, rho, theta, threshold, min_line_len, max_line_gap)

def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    # 统计概率霍夫直线变换
    lines = cv2.HoughLinesP(img, rho, theta, threshold, minLineLength=min_line_len, maxLineGap=max_line_gap)

    # 新建一副空白画布
    drawing = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    # draw_lines(drawing, lines)     # 画出直线检测结果

    return drawing, lines

def draw_lines(img, lines, color=[0, 0, 255], thickness=1):
    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)Copy to clipboardErrorCopied

def process_an_image(img):
    # 1. 灰度化、滤波和Canny

    # 2. 标记四个坐标点用于ROI截取

    # 3. 霍夫直线提取

    # 4. 车道拟合计算
    draw_lanes(drawing, lines)

    # 5. 最终将结果合在原图上
    result = cv2.addWeighted(img, 0.9, drawing, 0.2, 0)

    return result

def draw_lanes(img, lines, color=[255, 0, 0], thickness=8):
    # a. 划分左右车道
    left_lines, right_lines = [], []
    for line in lines:
        for x1, y1, x2, y2 in line:
            k = (y2 - y1) / (x2 - x1)
            if k < 0:
                left_lines.append(line)
            else:
                right_lines.append(line)

    if (len(left_lines) <= 0 or len(right_lines) <= 0):
        return

    # b. 清理异常数据
    clean_lines(left_lines, 0.1)
    clean_lines(right_lines, 0.1)

    # c. 得到左右车道线点的集合，拟合直线
    left_points = [(x1, y1) for line in left_lines for x1, y1, x2, y2 in line]
    left_points = left_points + [(x2, y2) for line in left_lines for x1, y1, x2, y2 in line]
    right_points = [(x1, y1) for line in right_lines for x1, y1, x2, y2 in line]
    right_points = right_points + [(x2, y2) for line in right_lines for x1, y1, x2, y2 in line]

    left_results = least_squares_fit(left_points, 325, img.shape[0])
    right_results = least_squares_fit(right_points, 325, img.shape[0])

    # 注意这里点的顺序
    vtxs = np.array([[left_results[1], left_results[0], right_results[0], right_results[1]]])
    # d. 填充车道区域
    cv2.fillPoly(img, vtxs, (0, 255, 0))

    # 或者只画车道线
    # cv2.line(img, left_results[0], left_results[1], (0, 255, 0), thickness)
    # cv2.line(img, right_results[0], right_results[1], (0, 255, 0), thickness)

def clean_lines(lines, threshold):
    # 迭代计算斜率均值，排除掉与差值差异较大的数据
    slope = [(y2 - y1) / (x2 - x1) for line in lines for x1, y1, x2, y2 in line]
    while len(lines) > 0:
        mean = np.mean(slope)
        diff = [abs(s - mean) for s in slope]
        idx = np.argmax(diff)
        if diff[idx] > threshold:
            slope.pop(idx)
            lines.pop(idx)
        else:
            break

def least_squares_fit(point_list, ymin, ymax):
    # 最小二乘法拟合
    x = [p[0] for p in point_list]
    y = [p[1] for p in point_list]

    # polyfit第三个参数为拟合多项式的阶数，所以1代表线性
    fit = np.polyfit(y, x, 1)
    fit_fn = np.poly1d(fit)  # 获取拟合的结果

    xmin = int(fit_fn(ymin))
    xmax = int(fit_fn(ymax))

    return [(xmin, ymin), (xmax, ymax)]Copy to clipboardErrorCopied

pip install moviepyCopy to clipboardErrorCopied

# 开头导入moviepy
from moviepy.editor import VideoFileClip

# 主函数更改为：
if __name__ == "__main__":
    output = 'test_videos/output.mp4'
    clip = VideoFileClip("test_videos/cv2_white_lane.mp4")
    out_clip = clip.fl_image(process_an_image)
    out_clip.write_videofile(output, audio=False)Copy to clipboardErrorCopied