main.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import glob
from moviepy.editor import VideoFileClip

"参数设置"
nx = 9
ny = 6
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()


# 相机校正：外参，内参，畸变系数
def cal_calibrate_params(file_paths):
    # 存储角点数据的坐标
    object_points = []  # 角点在三维空间的对未知
    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)
    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)
        # 灰度化
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 角点检测
        rect, coners = cv2.findChessboardCorners(gray, (nx, ny), None)
        # imgcopy = img.copy()
        # cv2.drawChessboardCorners(imgcopy,(nx,ny),coners,rect)
        # plot_contrast_image(img,imgcopy)
        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-》合并并且使用L通道进行白的区域的抑制
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)
    # 将其装换为8bit的整数
    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
    # 对边缘提取结果进行二值化
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
    # plt.figure()
    # plt.imshow(sxbinary, cmap='gray')
    # plt.title("sobel")
    # plt.show()

    # s通道阈值处理
    s_binary = np.zeros_like(s_chanel)
    s_binary[(s_chanel >= s_thresh[0]) & (s_chanel <= s_thresh[1])] = 1
    # plt.figure()
    # plt.imshow(s_binary, cmap='gray')
    # plt.title("schanel")
    # plt.show()
    # 结合边缘提取结果和颜色的结果，
    color_binary = np.zeros_like(sxbinary)
    color_binary[((sxbinary == 1) | (s_binary == 1)) & (l_chanel > 100)] = 1
    return color_binary


# 透视变换
# 获取透视变换的参数矩阵
def cal_perspective_params(img, points):
    offset_x = 330
    offset_y = 0
    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
    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


# 根据参数矩阵完成透视变换
def img_perspect_transform(img, M):
    img_size = (img.shape[1], img.shape[0])
    return cv2.warpPerspective(img, M, img_size)


# 精确定位车道线
def cal_line_param(binary_warped):
    # 1.确定左右车道线的位置
    # 统计直方图
    histogram = np.sum(binary_warped[:, :], axis=0)
    # 在统计结果中找到左右最大的点的位置，作为左右车道检测的开始点
    # 将统计结果一分为二，划分为左右两个部分，分别定位峰值位置，即为两条车道的搜索位置
    midpoint = np.int(histogram.shape[0] / 2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    # 2.滑动窗口检测车道线
    # 设置滑动窗口的数量，计算每一个窗口的高度
    nwindows = 9
    window_height = np.int(binary_warped.shape[0] / nwindows)
    # 获取图像中不为0的点
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # 车道检测的当前位置
    leftx_current = leftx_base
    rightx_current = rightx_base
    # 设置x的检测范围，滑动窗口的宽度的一半，手动指定
    margin = 100
    # 设置最小像素点，阈值用于统计滑动窗口区域内的非零像素个数，小于50的窗口不对x的中心值进行更新
    minpix = 50
    # 用来记录搜索窗口中非零点在nonzeroy和nonzerox中的索引
    left_lane_inds = []
    right_lane_inds = []

    # 遍历该副图像中的每一个窗口
    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
        win_y_high = binary_warped.shape[0] - window * window_height
        # 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
        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):
    # 获取图像的行数
    y_max = img.shape[0]
    # 设置输出图像的大小，并将白色位置设为255
    out_img = np.dstack((img, img, img)) * 255
    # 在拟合曲线中获取左右车道线的像素位置
    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
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)
    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，在计算曲率
    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
    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
def cal_line_center(img):
    undistort_img = img_undistort(img,mtx,dist)
    rigin_pipeline_img = pipeline(undistort_img)
    trasform_img = img_perspect_transform(rigin_pipeline_img,M)
    left_fit,right_fit = cal_line_param(trasform_img)
    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]
    return (left_x+right_x)/2

def cal_center_departure(img,left_fit,right_fit):
    # 计算中心点
    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 



if __name__ == "__main__":
    ret, mtx, dist, rvecs, tvecs = cal_calibrate_params(file_paths)
    # if np.all(mtx != None):
    #     img = cv2.imread("./test/test1.jpg")
    #     undistort_img = img_undistort(img, mtx, dist)
    #     plot_contrast_image(img, undistort_img)
    #     print("done")
    # else:
    #     print("failed")

    # 测试车道线提取
    # img = cv2.imread('./test/frame45.jpg')
    # result = pipeline(img)
    # plot_contrast_image(img, result, converted_img_gray=True)

    # 测试透视变换
    img = cv2.imread('./test/straight_lines2.jpg')
    points = [[601, 448], [683, 448], [230, 717], [1097, 717]]
    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)
    # plt.figure()
    # plt.imshow(img[:, :, ::-1])
    # plt.title("原图")
    # plt.show()
    M, M_inverse = cal_perspective_params(img, points)
    # if np.all(M != None):
    #     trasform_img = img_perspect_transform(img, M)
    #     plt.figure()
    #     plt.imshow(trasform_img[:, :, ::-1])
    #     plt.title("俯视图")
    #     plt.show()
    # else:
    #     print("failed")

    img = cv2.imread('./test/straight_lines2.jpg')
    # undistort_img = img_undistort(img,mtx,dist)
    # pipeline_img = pipeline(undistort_img)
    # trasform_img = img_perspect_transform(pipeline_img,M)
    # left_fit,right_fit = cal_line_param(trasform_img)
    # result = fill_lane_poly(trasform_img,left_fit,right_fit)
    # plt.figure()
    # plt.imshow(result[:,:,::-1])
    # plt.title("俯视图：填充结果")
    # plt.show()
    # trasform_img_inv = img_perspect_transform(result,M_inverse)
    # plt.figure()
    # plt.imshow(trasform_img_inv[:, :, ::-1])
    # plt.title("填充结果")
    # plt.show()
    # res = cv2.addWeighted(img,1,trasform_img_inv,0.5,0)
    # plt.figure()
    # plt.imshow(res[:, :, ::-1])
    # plt.title("安全区域")
    # plt.show()
    lane_center = cal_line_center(img)
    print("中心点位置：{}".format(lane_center))

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)
    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)
    transform_img_inv = cal_center_departure(transform_img_inv,left_fit,right_fit)
    transform_img_inv = cv2.addWeighted(undistort_img,1,transform_img_inv,0.5,0)
    return transform_img_inv

# 视频处理
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_image)
# white_clip.ipython_display()
white_clip.write_videofile("output.mp4",audio=False)