車道線檢測(cè)是自動(dòng)駕駛汽車以及一般計(jì)算機(jī)視覺(jué)的關(guān)鍵組件。這個(gè)概念用于描述自動(dòng)駕駛汽車的路徑并避免進(jìn)入另一條車道的風(fēng)險(xiǎn)。

在本文中，我們將構(gòu)建一個(gè)機(jī)器學(xué)習(xí)項(xiàng)目來(lái)實(shí)時(shí)檢測(cè)車道線。我們將使用 OpenCV 庫(kù)使用計(jì)算機(jī)視覺(jué)的概念來(lái)做到這一點(diǎn)。為了檢測(cè)車道，我們必須檢測(cè)車道兩側(cè)的白色標(biāo)記。

使用 Python 和 OpenCV 進(jìn)行道路車道線檢測(cè)使用 Python 中的計(jì)算機(jī)視覺(jué)技術(shù)，我們將識(shí)別自動(dòng)駕駛汽車必須行駛的道路車道線。這將是自動(dòng)駕駛汽車的關(guān)鍵部分，因?yàn)樽詣?dòng)駕駛汽車不應(yīng)該越過(guò)它的車道，也不應(yīng)該進(jìn)入對(duì)面車道以避免事故。

幀掩碼和霍夫線變換要檢測(cè)車道中的白色標(biāo)記，首先，我們需要屏蔽幀的其余部分。我們使用幀屏蔽來(lái)做到這一點(diǎn)。該幀只不過(guò)是圖像像素值的 NumPy 數(shù)組。為了掩蓋幀中不必要的像素，我們只需將 NumPy 數(shù)組中的這些像素值更新為 0。

制作后我們需要檢測(cè)車道線。用于檢測(cè)此類數(shù)學(xué)形狀的技術(shù)稱為霍夫變換?；舴蜃儞Q可以檢測(cè)矩形、圓形、三角形和直線等形狀。

代碼下載源碼請(qǐng)下載：車道線檢測(cè)項(xiàng)目代碼

按照以下步驟在 Python 中進(jìn)行車道線檢測(cè)：

1.導(dǎo)入包

import matplotlib.pyplot as pltimport numpy as npimport cv2import osimport matplotlib.image as mpimgfrom moviepy.editor import VideoFileClipimport math

2. 應(yīng)用幀屏蔽并找到感興趣的區(qū)域：

def interested_region(img, vertices): if len(img.shape) > 2: mask_color_ignore = (255,) * img.shape[2] else:mask_color_ignore = 255 cv2.fillPoly(np.zeros_like(img), vertices, mask_color_ignore) return cv2.bitwise_and(img, np.zeros_like(img))

3.霍夫變換空間中像素到線的轉(zhuǎn)換：

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) line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) lines_drawn(line_img,lines) return line_img

4. 霍夫變換后在每一幀中創(chuàng)建兩條線：

def lines_drawn(img, lines, color=[255, 0, 0], thickness=6): global cache global first_frame slope_l, slope_r = [],[] lane_l,lane_r = [],[] α =0.2 for line in lines:for x1,y1,x2,y2 in line: slope = (y2-y1)/(x2-x1) if slope > 0.4:slope_r.append(slope)lane_r.append(line) elif slope < -0.4:slope_l.append(slope)lane_l.append(line)img.shape[0] = min(y1,y2,img.shape[0]) if((len(lane_l) == 0) or (len(lane_r) == 0)):print (’no lane detected’)return 1 slope_mean_l = np.mean(slope_l,axis =0) slope_mean_r = np.mean(slope_r,axis =0) mean_l = np.mean(np.array(lane_l),axis=0) mean_r = np.mean(np.array(lane_r),axis=0)if ((slope_mean_r == 0) or (slope_mean_l == 0 )):print(’dividing by zero’)return 1x1_l = int((img.shape[0] - mean_l[0][1] - (slope_mean_l * mean_l[0][0]))/slope_mean_l) x2_l = int((img.shape[0] - mean_l[0][1] - (slope_mean_l * mean_l[0][0]))/slope_mean_l) x1_r = int((img.shape[0] - mean_r[0][1] - (slope_mean_r * mean_r[0][0]))/slope_mean_r) x2_r = int((img.shape[0] - mean_r[0][1] - (slope_mean_r * mean_r[0][0]))/slope_mean_r) if x1_l > x1_r:x1_l = int((x1_l+x1_r)/2)x1_r = x1_ly1_l = int((slope_mean_l * x1_l ) + mean_l[0][1] - (slope_mean_l * mean_l[0][0]))y1_r = int((slope_mean_r * x1_r ) + mean_r[0][1] - (slope_mean_r * mean_r[0][0]))y2_l = int((slope_mean_l * x2_l ) + mean_l[0][1] - (slope_mean_l * mean_l[0][0]))y2_r = int((slope_mean_r * x2_r ) + mean_r[0][1] - (slope_mean_r * mean_r[0][0])) else:y1_l = img.shape[0]y2_l = img.shape[0]y1_r = img.shape[0]y2_r = img.shape[0] present_frame = np.array([x1_l,y1_l,x2_l,y2_l,x1_r,y1_r,x2_r,y2_r],dtype ='float32')if first_frame == 1:next_frame = present_framefirst_frame = 0 else :prev_frame = cachenext_frame = (1-α)*prev_frame+α*present_frame cv2.line(img, (int(next_frame[0]), int(next_frame[1])), (int(next_frame[2]),int(next_frame[3])), color, thickness) cv2.line(img, (int(next_frame[4]), int(next_frame[5])), (int(next_frame[6]),int(next_frame[7])), color, thickness)cache = next_frame

5.處理每一幀視頻以檢測(cè)車道：

def weighted_img(img, initial_img, α=0.8, β=1., λ=0.): return cv2.addWeighted(initial_img, α, img, β, λ)def process_image(image): global first_frame gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) img_hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV) lower_yellow = np.array([20, 100, 100], dtype = 'uint8') upper_yellow = np.array([30, 255, 255], dtype='uint8') mask_yellow = cv2.inRange(img_hsv, lower_yellow, upper_yellow) mask_white = cv2.inRange(gray_image, 200, 255) mask_yw = cv2.bitwise_or(mask_white, mask_yellow) mask_yw_image = cv2.bitwise_and(gray_image, mask_yw) gauss_gray= cv2.GaussianBlur(mask_yw_image, (5, 5), 0) canny_edges=cv2.Canny(gauss_gray, 50, 150) imshape = image.shape lower_left = [imshape[1]/9,imshape[0]] lower_right = [imshape[1]-imshape[1]/9,imshape[0]] top_left = [imshape[1]/2-imshape[1]/8,imshape[0]/2+imshape[0]/10] top_right = [imshape[1]/2+imshape[1]/8,imshape[0]/2+imshape[0]/10] vertices = [np.array([lower_left,top_left,top_right,lower_right],dtype=np.int32)] roi_image = interested_region(canny_edges, vertices) theta = np.pi/180 line_image = hough_lines(roi_image, 4, theta, 30, 100, 180) result = weighted_img(line_image, image, α=0.8, β=1., λ=0.) return result

6. 將輸入視頻剪輯成幀并得到結(jié)果輸出視頻文件：

first_frame = 1white_output = ’__path_to_output_file__’clip1 = VideoFileClip('__path_to_input_file__')white_clip = clip1.fl_image(process_image)white_clip.write_videofile(white_output, audio=False)

車道線檢測(cè)項(xiàng)目 GUI 代碼：

import tkinter as tkfrom tkinter import *import cv2from PIL import Image, ImageTkimport osimport numpy as npglobal last_frame1 last_frame1 = np.zeros((480, 640, 3), dtype=np.uint8)global last_frame2 last_frame2 = np.zeros((480, 640, 3), dtype=np.uint8)global cap1global cap2cap1 = cv2.VideoCapture('path_to_input_test_video')cap2 = cv2.VideoCapture('path_to_resultant_lane_detected_video')def show_vid(): if not cap1.isOpened(): print('cant open the camera1') flag1, frame1 = cap1.read() frame1 = cv2.resize(frame1,(400,500)) if flag1 is None:print ('Major error!') elif flag1:global last_frame1last_frame1 = frame1.copy()pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB) img = Image.fromarray(pic)imgtk = ImageTk.PhotoImage(image=img)lmain.imgtk = imgtklmain.configure(image=imgtk)lmain.after(10, show_vid)def show_vid2(): if not cap2.isOpened(): print('cant open the camera2') flag2, frame2 = cap2.read() frame2 = cv2.resize(frame2,(400,500)) if flag2 is None:print ('Major error2!') elif flag2:global last_frame2last_frame2 = frame2.copy()pic2 = cv2.cvtColor(last_frame2, cv2.COLOR_BGR2RGB)img2 = Image.fromarray(pic2)img2tk = ImageTk.PhotoImage(image=img2)lmain2.img2tk = img2tklmain2.configure(image=img2tk)lmain2.after(10, show_vid2)if __name__ == ’__main__’: root=tk.Tk() lmain = tk.Label(master=root) lmain2 = tk.Label(master=root) lmain.pack(side = LEFT) lmain2.pack(side = RIGHT) root.title('Lane-line detection')root.geometry('900x700+100+10') exitbutton = Button(root, text=’Quit’,fg='red',command= root.destroy).pack(side = BOTTOM,) show_vid() show_vid2() root.mainloop() cap.release()

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