自学教程：Python道路车道线检测的实现

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

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))

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

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 1        x1_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_l        y1_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_frame                first_frame = 0            else :        prev_frame = cache        next_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

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

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)

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_frame1        last_frame1 = frame1.copy()        pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB)             img = Image.fromarray(pic)        imgtk = ImageTk.PhotoImage(image=img)        lmain.imgtk = imgtk        lmain.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_frame2        last_frame2 = frame2.copy()        pic2 = cv2.cvtColor(last_frame2, cv2.COLOR_BGR2RGB)        img2 = Image.fromarray(pic2)        img2tk = ImageTk.PhotoImage(image=img2)        lmain2.img2tk = img2tk        lmain2.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()