基于计算机视觉的车道线检测（完整源码）

from moviepy.editor import VideoFileClip import matplotlib.pyplot as plt import matplotlib.image as mplimg import numpy as np import cv2 blur_ksize = 5 # Gaussian blur kernel size canny_lthreshold = 50 # Canny edge detection low threshold canny_hthreshold = 150 # Canny edge detection high threshold # Hough transform parameters rho = 1 # rho的步长，即直线到图像原点(0,0)点的距离 theta = np.pi / 180 # theta的范围 threshold = 15 # 累加器中的值高于它时才认为是一条直线 min_line_length = 40 # 线的最短长度，比这个短的都被忽略 max_line_gap = 20 # 两条直线之间的最大间隔，小于此值，认为是一条直线 def roi_mask(img, vertices): # img是输入的图像，verticess是兴趣区的四个点的坐标（三维的数组） mask = np.zeros_like(img) # 生成与输入图像相同大小的图像，并使用0填充,图像为黑色 # defining a 3 channel or 1 channel color to fill the mask with depending on the input image if len(img.shape) > 2: channel_count = img.shape[2] # i.e. 3 or 4 depending on your image mask_color = (255,) * channel_count # 如果 channel_count=3,则为(255,255,255) else: mask_color = 255 cv2.fillPoly(mask, vertices, mask_color) # 使用白色填充多边形，形成蒙板 masked_img = cv2.bitwise_and(img, mask) # img&mask，经过此操作后，兴趣区域以外的部分被蒙住了，只留下兴趣区域的图像 return masked_img def draw_roi(img, vertices): cv2.polylines(img, vertices, True, [255, 0, 0], thickness=2) def draw_lines(img, lines, color=[255, 0, 0], thickness=2): for line in lines: for x1, y1, x2, y2 in line: cv2.line(img, (x1, y1), (x2, y2), color, thickness) def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap) # 函数输出的直接就是一组直线点的坐标位置（每条直线用两个点表示[x1,y1],[x2,y2]） line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) # 生成绘制直线的绘图板，黑底 # draw_lines(line_img, lines) draw_lanes(line_img, lines) return line_img def draw_lanes(img, lines, color=[255, 0, 0], thickness=8): left_lines, right_lines = [], [] # 用于存储左边和右边的直线 for line in lines: # 对直线进行分类 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 img clean_lines(left_lines, 0.1) # 弹出左侧不满足斜率要求的直线 clean_lines(right_lines, 0.1) # 弹出右侧不满足斜率要求的直线 left_points = [(x1, y1) for line in left_lines for x1, y1, x2, y2 in line] # 提取左侧直线族中的所有的第一个点 left_points = left_points + [(x2, y2) for line in left_lines for x1, y1, x2, y2 in line] # 提取左侧直线族中的所有的第二个点 right_points = [(x1, y1) for line in right_lines for x1, y1, x2, y2 in line] # 提取右侧直线族中的所有的第一个点 right_points = right_points + [(x2, y2) for line in right_lines for x1, y1, x2, y2 in line] # 提取右侧侧直线族中的所有的第二个点 left_vtx = calc_lane_vertices(left_points, 325, img.shape[0]) # 拟合点集，生成直线表达式，并计算左侧直线在图像中的两个端点的坐标 right_vtx = calc_lane_vertices(right_points, 325, img.shape[0]) # 拟合点集，生成直线表达式，并计算右侧直线在图像中的两个端点的坐标 cv2.line(img, left_vtx[0], left_vtx[1], color, thickness) # 画出直线 cv2.line(img, right_vtx[0], right_vtx[1], color, thickness) # 画出直线 # 将不满足斜率要求的直线弹出 def clean_lines(lines, threshold): slope = [] for line in lines: for x1, y1, x2, y2 in line: k = (y2 - y1) / (x2 - x1) slope.append(k) # slope = [(y2 - y1) / (x2 - x1) for line in lines for x1, y1, x2, y2 in line] while len(lines) > 0: mean = np.mean(slope) # 计算斜率的平均值，因为后面会将直线和斜率值弹出 diff = [abs(s - mean) for s in slope] # 计算每条直线斜率与平均值的差值 idx = np.argmax(diff) # 计算差值的最大值的下标 if diff[idx] > threshold: # 将差值大于阈值的直线弹出 slope.pop(idx) # 弹出斜率 lines.pop(idx) # 弹出直线 else: break # 拟合点集，生成直线表达式，并计算直线在图像中的两个端点的坐标 def calc_lane_vertices(point_list, ymin, ymax): x = [p[0] for p in point_list] # 提取x y = [p[1] for p in point_list] # 提取y fit = np.polyfit(y, x, 1) # 用一次多项式x=a*y+b拟合这些点，fit是(a,b) fit_fn = np.poly1d(fit) # 生成多项式对象a*y+b xmin = int(fit_fn(ymin)) # 计算这条直线在图像中最左侧的横坐标 xmax = int(fit_fn(ymax)) # 计算这条直线在图像中最右侧的横坐标 return [(xmin, ymin), (xmax, ymax)] def process_an_image(img): roi_vtx = np.array( [[(0, img.shape[0]), (460, 325), (520, 325), (img.shape[1], img.shape[0])]]) # 目标区域的四个点坐标，roi_vtx是一个三维的数组 gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # 图像转换为灰度图 blur_gray = cv2.GaussianBlur(gray, (blur_ksize, blur_ksize), 0, 0) # 使用高斯模糊去噪声 edges = cv2.Canny(blur_gray, canny_lthreshold, canny_hthreshold) # 使用Canny进行边缘检测 roi_edges = roi_mask(edges, roi_vtx) # 对边缘检测的图像生成图像蒙板，去掉不感兴趣的区域，保留兴趣区 line_img = hough_lines(roi_edges, rho, theta, threshold, min_line_length, max_line_gap) # 使用霍夫直线检测，并且绘制直线 res_img = cv2.addWeighted(img, 0.8, line_img, 1, 0) # 将处理后的图像与原图做融合 return res_img # 图片处理 img = cv2.imread("C:\\Users\\chu\\Desktop\\pythonProject\\input_image\\solidWhiteRight_before.jpg") # res_img = cv2.imread('C:\\Users\\chu\\Desktop\\pythonProject\\input_image\\solidWhiteRight_before.jpg') print("start to process the image....") after_img = process_an_image(img) # res_img = process_an_image(res_img) # cv2.imwrite('solidWhiteRight_after1.jpg', res_img) cv2.imwrite('solidWhiteRight_after3.jpg', after_img) print("show you the image....") img =after_img[:, :, ::-1] plt.imshow(img) plt.show() # cv2.imshow('solidWhiteRight_after3.jpg', after_img) # cv2.imshow('solidWhiteRight_after1.jpg', res_img) # cv2.waitKey(0)#等待键盘触发 # cv2.destroyAllWindows()#释放窗口 # 处理视频 print("start to process the video....") output = 'C:\\Users\\chu\\Desktop\\pythonProject\\output_video\\solidWhiteRight_video_out1.mp4' # ouput video clip = VideoFileClip('C:\\Users\\chu\\Desktop\\pythonProject\\input_video\\solidWhiteRight.mp4') # input video out_clip = clip.fl_image(process_an_image) # 对视频的每一帧进行处理 out_clip.write_videofile(output, audio=True) # 将处理后的视频写入新的视频文件 # 展示视频 cap = cv2.VideoCapture('C:\\Users\\chu\\Desktop\\pythonProject\\output_video\\solidWhiteRight_video_out1.mp4') ret = True while (ret): ret, frame = cap.read() # 按帧读取视频，返回值有两ret,frame，其中ret是布尔值，如果读取的帧是正确的那么则返回True，如果文件读取到末尾，他的返回值为false。frame就是每一个帧的图像，是一个三维矩阵 if ret