智慧交通day03-车道线检测实现09：车道线检测代码汇总（Python3.8）

import cv2
import numpy as np
import matplotlib.pyplot as plt
#遍历文件夹
import glob
from moviepy.editor import VideoFileClip
"""参数设置"""
nx = 9
ny = 6
#获取棋盘格数据
file_paths = glob.glob("./camera_cal/calibration*.jpg")
# # 绘制对比图
# def plot_contrast_image(origin_img, converted_img, origin_img_title="origin_img", converted_img_title="converted_img",
#                         converted_img_gray=False):
#     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 20))
#     ax1.set_title = origin_img_title
#     ax1.imshow(origin_img)
#     ax2.set_title = converted_img_title
#     if converted_img_gray == True:
#         ax2.imshow(converted_img, cmap="gray")
#     else:
#         ax2.imshow(converted_img)
#     plt.show()
#相机矫正使用opencv封装好的api
#目的:得到内参、外参、畸变系数
def cal_calibrate_params(file_paths):
    #存储角点数据的坐标
    object_points = [] #角点在真实三维空间的位置
    image_points = [] #角点在图像空间中的位置
    #生成角点在真实世界中的位置
    objp = np.zeros((nx*ny,3),np.float32)
    #以棋盘格作为坐标，每相邻的黑白棋的相差1
    objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)
    #角点检测
    for file_path in file_paths:
        img = cv2.imread(file_path)
        #将图像灰度化
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        #角点检测
        rect,coners = cv2.findChessboardCorners(gray,(nx,ny),None)
        #若检测到角点，则进行保存 即得到了真实坐标和图像坐标
        if rect == True :
            object_points.append(objp)
            image_points.append(coners)
    # 相机较真
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, gray.shape[::-1], None, None)
    return ret, mtx, dist, rvecs, tvecs
# 图像去畸变：利用相机校正的内参，畸变系数
def img_undistort(img, mtx, dist):
    dis = cv2.undistort(img, mtx, dist, None, mtx)
    return dis
#车道线提取
#颜色空间转换--》边缘检测--》颜色阈值--》并且使用L通道进行白色的区域进行抑制
def pipeline(img,s_thresh = (170,255),sx_thresh=(40,200)):
    # 复制原图像
    img = np.copy(img)
    # 颜色空间转换
    hls = cv2.cvtColor(img,cv2.COLOR_RGB2HLS).astype(np.float)
    l_chanel = hls[:,:,1]
    s_chanel = hls[:,:,2]
    #sobel边缘检测
    sobelx = cv2.Sobel(l_chanel,cv2.CV_64F,1,0)
    #求绝对值
    abs_sobelx = np.absolute(sobelx)
    #将其转换为8bit的整数
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    #对边缘提取的结果进行二值化
    sxbinary = np.zeros_like(scaled_sobel)
    #边缘位置赋值为1，非边缘位置赋值为0
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
    #对S通道进行阈值处理
    s_binary = np.zeros_like(s_chanel)
    s_binary[(s_chanel >= s_thresh[0]) & (s_chanel <= s_thresh[1])] = 1
    # 结合边缘提取结果和颜色通道的结果，
    color_binary = np.zeros_like(sxbinary)
    color_binary[((sxbinary == 1) | (s_binary == 1)) & (l_chanel > 100)] = 1
    return color_binary
#透视变换-->将检测结果转换为俯视图。
#获取透视变换的参数矩阵【二值图的四个点】
def cal_perspective_params(img,points):
    # x与y方向上的偏移
    offset_x = 330
    offset_y = 0
    #转换之后img的大小
    img_size = (img.shape[1],img.shape[0])
    src = np.float32(points)
    #设置俯视图中的对应的四个点 左上角 右上角 左下角 右下角
    dst = np.float32([[offset_x, offset_y], [img_size[0] - offset_x, offset_y],
                      [offset_x, img_size[1] - offset_y], [img_size[0] - offset_x, img_size[1] - offset_y]])
    ## 原图像转换到俯视图
    M = cv2.getPerspectiveTransform(src, dst)
    # 俯视图到原图像
    M_inverse = cv2.getPerspectiveTransform(dst, src)
    return M, M_inverse
#根据透视变化矩阵完成透视变换
def img_perspect_transform(img,M):
    #获取图像大小
    img_size = (img.shape[1],img.shape[0])
    #完成图像的透视变化
    return cv2.warpPerspective(img,M,img_size)
# 精确定位车道线
#传入已经经过边缘检测的图像阈值结果的二值图，再进行透明变换
def cal_line_param(binary_warped):
    #定位车道线的大致位置==计算直方图
    histogram = np.sum(binary_warped[:,:],axis=0) #计算y轴
    # 将直方图一分为二，分别进行左右车道线的定位
    midpoint = np.int(histogram.shape[0]/2)
    #分别统计左右车道的最大值
    midpoint = np.int(histogram.shape[0] / 2)
    leftx_base = np.argmax(histogram[:midpoint]) #左车道
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint #右车道
    #设置滑动窗口
    #对每一个车道线来说 滑动窗口的个数
    nwindows = 9
    #设置滑动窗口的高
    window_height = np.int(binary_warped.shape[0]/nwindows)
    #设置滑动窗口的宽度==x的检测范围,即滑动窗口的一半
    margin = 100
    #统计图像中非0点的个数
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])#非0点的位置-x坐标序列
    nonzerox = np.array(nonzero[1])#非0点的位置-y坐标序列
    #车道检测位置
    leftx_current = leftx_base
    rightx_current = rightx_base
    #设置阈值：表示当前滑动窗口中的非0点的个数
    minpix = 50
    #记录窗口中，非0点的索引
    left_lane_inds = []
    right_lane_inds = []
    #遍历滑动窗口
    for window in range(nwindows):
        # 设置窗口的y的检测范围，因为图像是（行列）,shape[0]表示y方向的结果，上面是0
        win_y_low = binary_warped.shape[0] - (window + 1) * window_height #y的最低点
        win_y_high = binary_warped.shape[0] - window * window_height #y的最高点
        # 左车道x的范围
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        # 右车道x的范围
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        # 确定非零点的位置x,y是否在搜索窗口中，将在搜索窗口内的x,y的索引存入left_lane_inds和right_lane_inds中
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
                          (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
                           (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        # 如果获取的点的个数大于最小个数，则利用其更新滑动窗口在x轴的位置=修正车道线的位置
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
    # 将检测出的左右车道点转换为array
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)
    # 获取检测出的左右车道x与y点在图像中的位置
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    # 3.用曲线拟合检测出的点,二次多项式拟合，返回的结果是系数
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    return left_fit, right_fit
#填充车道线之间的多边形
def fill_lane_poly(img,left_fit,right_fit):
    #行数
    y_max = img.shape[0]
    #设置填充之后的图像的大小 取到0-255之间
    out_img = np.dstack((img,img,img))*255
    #根据拟合结果，获取拟合曲线的车道线像素位置
    left_points = [[left_fit[0] * y ** 2 + left_fit[1] * y + left_fit[2], y] for y in range(y_max)]
    right_points = [[right_fit[0] * y ** 2 + right_fit[1] * y + right_fit[2], y] for y in range(y_max - 1, -1, -1)]
    # 将左右车道的像素点进行合并
    line_points = np.vstack((left_points, right_points))
    # 根据左右车道线的像素位置绘制多边形
    cv2.fillPoly(out_img, np.int_([line_points]), (0, 255, 0))
    return out_img
#计算车道线曲率的方法
def cal_radius(img,left_fit,right_fit):
    # 比例
    ym_per_pix = 30/720
    xm_per_pix = 3.7/700
    # 得到车道线上的每个点
    left_y_axis = np.linspace(0,img.shape[0],img.shape[0]-1) #个数img.shape[0]-1
    left_x_axis = left_fit[0]*left_y_axis**2+left_fit[1]*left_y_axis+left_fit[0]
    right_y_axis = np.linspace(0,img.shape[0],img.shape[0]-1)
    right_x_axis = right_fit[0]*right_y_axis**2+right_fit[1]*right_y_axis+right_fit[2]
    # 把曲线中的点映射真实世界，再计算曲率
    # polyfit(x,y,n)。用多项式求过已知点的表达式，其中x为源数据点对应的横坐标，可为行 向 量、矩阵，
    # y为源数据点对应的纵坐标，可为行向量、矩阵，
    # n为你要拟合的阶数，一阶直线拟合，二阶抛物线拟合，并非阶次越高越好，看拟合情况而定
    left_fit_cr = np.polyfit(left_y_axis * ym_per_pix, left_x_axis * xm_per_pix, 2)
    right_fit_cr = np.polyfit(right_y_axis * ym_per_pix, right_x_axis * xm_per_pix, 2)
    # 计算曲率
    left_curverad = ((1+(2*left_fit_cr[0]*left_y_axis*ym_per_pix+left_fit_cr[1])**2)**1.5)/np.absolute(2*left_fit_cr[0])
    right_curverad = ((1+(2*right_fit_cr[0]*right_y_axis*ym_per_pix *right_fit_cr[1])**2)**1.5)/np.absolute((2*right_fit_cr[0]))
    # 将曲率半径渲染在图像上 写什么
    cv2.putText(img,'Radius of Curvature = {}(m)'.format(np.mean(left_curverad)),(20,50),cv2.FONT_ITALIC,1,(255,255,255),5)
    return img
# 计算车道线中心的位置
def cal_line_center(img):
    #去畸变
    undistort_img = img_undistort(img,mtx,dist)
    #提取车道线
    rigin_pipeline_img = pipeline(undistort_img)
    #透视变换
    trasform_img = img_perspect_transform(rigin_pipeline_img,M)
    #精确定位
    left_fit,right_fit = cal_line_param(trasform_img)
    #当前图像的shape[0]
    y_max = img.shape[0]
    #左车道线
    left_x = left_fit[0]*y_max**2+left_fit[1]*y_max+left_fit[2]
    #右车道线
    right_x = right_fit[0]*y_max**2+right_fit[1]*y_max+right_fit[2]
    #返回车道中心点
    return (left_x+right_x)/2
# 计算中心点
def cal_center_departure(img,left_fit,right_fit):
    y_max = img.shape[0]
    left_x = left_fit[0]*y_max**2 + left_fit[1]*y_max +left_fit[2]
    right_x = right_fit[0]*y_max**2 +right_fit[1]*y_max +right_fit[2]
    xm_per_pix = 3.7/700
    center_depart = ((left_x+right_x)/2-lane_center)*xm_per_pix
    # 渲染
    if center_depart>0:
        cv2.putText(img,'Vehicle is {}m right of center'.format(center_depart), (20, 100), cv2.FONT_ITALIC, 1,
                    (255, 255, 255), 5)
    elif center_depart<0:
        cv2.putText(img, 'Vehicle is {}m left of center'.format(-center_depart), (20, 100), cv2.FONT_ITALIC, 1,
                    (255, 255, 255), 5)
    else:
        cv2.putText(img, 'Vehicle is in the center', (20, 100), cv2.FONT_ITALIC, 1, (255, 255, 255), 5)
    return img
#计算车辆偏离中心点的距离
def cal_center_departure(img,left_fit,right_fit):
    # 计算中心点
    y_max = img.shape[0]
    #左车道线
    left_x = left_fit[0]*y_max**2 + left_fit[1]*y_max +left_fit[2]
    #右车道线
    right_x = right_fit[0]*y_max**2 +right_fit[1]*y_max +right_fit[2]
    #x方向上每个像素点代表的距离大小
    xm_per_pix = 3.7/700
    #计算偏移距离 像素距离 × xm_per_pix = 实际距离
    center_depart = ((left_x+right_x)/2-lane_center)*xm_per_pix
    # 渲染
    if center_depart>0:
        cv2.putText(img,'Vehicle is {}m right of center'.format(center_depart), (20, 100), cv2.FONT_ITALIC, 1,
                    (255, 255, 255), 5)
    elif center_depart<0:
        cv2.putText(img, 'Vehicle is {}m left of center'.format(-center_depart), (20, 100), cv2.FONT_ITALIC, 1,
                    (255, 255, 255), 5)
    else:
        cv2.putText(img, 'Vehicle is in the center', (20, 100), cv2.FONT_ITALIC, 1, (255, 255, 255), 5)
    return img
#图片处理流程汇总 方便视频调用
def process_image(img):
    # 图像去畸变
    undistort_img = img_undistort(img,mtx,dist)
    # 车道线检测
    rigin_pipline_img = pipeline(undistort_img)
    # 透视变换
    transform_img = img_perspect_transform(rigin_pipline_img,M)
    # 拟合车道线
    left_fit,right_fit = cal_line_param(transform_img)
    # 绘制安全区域
    result = fill_lane_poly(transform_img,left_fit,right_fit)
    #转换回原来的视角
    transform_img_inv = img_perspect_transform(result,M_inverse)
    # 曲率和偏离距离
    transform_img_inv = cal_radius(transform_img_inv,left_fit,right_fit)
    #偏离距离
    transform_img_inv = cal_center_departure(transform_img_inv,left_fit,right_fit)
    #附加到原图上
    transform_img_inv = cv2.addWeighted(undistort_img,1,transform_img_inv,0.5,0)
    #返回处理好的图像
    return transform_img_inv
if __name__ == "__main__":
    ret, mtx, dist, rvecs, tvecs = cal_calibrate_params(file_paths)
    #透视变换
    #获取原图的四个点
    img = cv2.imread('./test/straight_lines2.jpg')
    points = [[601, 448], [683, 448], [230, 717], [1097, 717]]
    #将四个点绘制到图像上 (文件，坐标起点，坐标终点，颜色，连接起来)
    img = cv2.line(img, (601, 448), (683, 448), (0, 0, 255), 3)
    img = cv2.line(img, (683, 448), (1097, 717), (0, 0, 255), 3)
    img = cv2.line(img, (1097, 717), (230, 717), (0, 0, 255), 3)
    img = cv2.line(img, (230, 717), (601, 448), (0, 0, 255), 3)
    #透视变换的矩阵
    M,M_inverse = cal_perspective_params(img,points)
    #计算车道线的中心距离
    lane_center = cal_line_center(img)
    # 视频处理
    clip1 = VideoFileClip("./project_video.mp4")
    white_clip = clip1.fl_image(process_image)
    white_clip.write_videofile("./output.mp4", audio=False)