matlab中拟合中心线的代码-Lane-Detection-using-MATLAB:道路上的车道检测和基于消失点的转弯预测

# Lane Detection In this project, MATLAB is used as an Image Processing Tool to detect Lanes on the road. The following techniques are used for lane detection. • Color Masking • Canny Edge Detection • Region of Interest Selection • Hough Transform Line detection ## Pre-processing the Image The first step is to import the video file and initialize the variables to be use din the code. Some variables are also imported from the .mat file to be used in the code. #### Initializing the loop to take frames one by one First the frame is read and them filtered using a Gaussian Filter. while hasFrame(VideoFile) %------------------Reading each frame from Video File------------------ frame = readFrame(VideoFile); figure('Name','Original Image'), imshow(frame); frame = imgaussfilt3(frame); figure('Name','Filtered Image'), imshow(frame);  Fig 1: Original Image  Fig 2: Filtered Image #### Masking the image for White and Yellow Color The frame is masked with yellow and white color to detect the lane lines perfectly. %--------------Define Thresholds for masking Yellow Color-------------- %----------------------Define thresholds for 'Hue'--------------------- channel1MinY = 130; channel1MaxY = 255; %------------------Define thresholds for 'Saturation'------------------ channel2MinY = 130; channel2MaxY = 255; %---------------------Define thresholds for 'Value'-------------------- channel3MinY = 0; channel3MaxY = 130; %-----------Create mask based on chosen histogram thresholds----------- Yellow=((frame(:,:,1)>=channel1MinY)|(frame(:,:,1)<=channel1MaxY))& ... (frame(:,:,2)>=channel2MinY)&(frame(:,:,2)<=channel2MaxY)&... (frame(:,:,3)>=channel3MinY)&(frame(:,:,3)<=channel3MaxY); figure('Name','Yellow Mask'), imshow(Yellow); %--------------Define Thresholds for masking White Color--------------- %----------------------Define thresholds for 'Hue'--------------------- channel1MinW = 200; channel1MaxW = 255; %------------------Define thresholds for 'Saturation'------------------ channel2MinW = 200; channel2MaxW = 255; %---------------------Define thresholds for 'Value'-------------------- channel3MinW = 200; channel3MaxW = 255; %-----------Create mask based on chosen histogram thresholds----------- White=((frame(:,:,1)>=channel1MinW)|(frame(:,:,1)<=channel1MaxW))&... (frame(:,:,2)>=channel2MinW)&(frame(:,:,2)<=channel2MaxW)& ... (frame(:,:,3)>=channel3MinW)&(frame(:,:,3)<=channel3MaxW); figure('Name','White Mask'), imshow(White);  Fig 3: Yellow Mask  Fig 4: White Mask ## Edge Detection In this section, edges are obtained from the masked image and closed edges with smaller areas are neglected. frameW = edge(White, 'canny', 0.2); frameY = edge(Yellow, 'canny', 0.2); #### Neglecting closed edges in smaller areas frameY = bwareaopen(frameY,15); frameW = bwareaopen(frameW,15); figure('Name','Detecting Edges of Yellow mask'), imshow(frameY); figure('Name','Detecting Edges of White mask'), imshow(frameW);  Fig 5: Detecting Edges of Yellow Mask  Fig 6: Detecting Edges of White Mask ## Extraction of Region of Interest As guided in the pipeline for the implementation of the project 1 the region of interest is extracted using the 'roipoly' function and selecting the points from the frame. #### Deciding ROI Points and Extracting ROI %--------------Deciding ROI points by plotting it on image------------- % figure(1) % imshow(frame); % [r c] = ginput(10); %---------Extracting Region of Interest from Yellow Edge Frame--------- roiY = roipoly(frameY, r, c); [R , C] = size(roiY); for i = 1:R for j = 1:C if roiY(i,j) == 1 frame_roiY(i,j) = frameY(i,j); else frame_roiY(i,j) = 0; end end end figure('Name','Filtering ROI from Yellow mask'), imshow(frame_roiY); %---------Extracting Region of Interest from White Edge Frame---------- roiW = roipoly(frameW, r, c); [R , C] = size(roiW); for i = 1:R for j = 1:C if roiW(i,j) == 1 frame_roiW(i,j) = frameW(i,j); else frame_roiW(i,j) = 0; end end end figure('Name','Filtering ROI from White mask'), imshow(frame_roiW);  Fig 7: Filtering ROI from Yellow Mask  Fig 8: Filtering ROI from White Mask ## Hough Transform In this section I have used the hough function to get the hough transfrom of the binary edge detected image, which gives us the hough values and then I have plotted the hough plot as shown in the figure below. #### Applying Hough Tansform to get straight lines from Image %----------Applying Hough Transform to White and Yellow Frames--------- [H_Y,theta_Y,rho_Y] = hough(frame_roiY); [H_W,theta_W,rho_W] = hough(frame_roiW); %--------Extracting Hough Peaks from Hough Transform of frames--------- P_Y = houghpeaks(H_Y,2,'threshold',2); P_W = houghpeaks(H_W,2,'threshold',2); %----------Plotting Hough Transform and detecting Hough Peaks---------- figure('Name','Hough Peaks for White Line') imshow(imadjust(rescale(H_W)),[],'XData',theta_W,'YData',rho_W,'InitialMagnification','fit'); xlabel('\theta (degrees)') ylabel('\rho') axis on axis normal hold on colormap(gca,hot) x = theta_W(P_W(:,2)); y = rho_W(P_W(:,1)); plot(x,y,'s','color','blue'); hold off figure('Name','Hough Peaks for Yellow Line') imshow(imadjust(rescale(H_Y)),[],'XData',theta_Y,'YData',rho_Y,'InitialMagnification','fit'); xlabel('\theta (degrees)') ylabel('\rho') axis on axis normal hold on colormap(gca,hot) x = theta_W(P_Y(:,2)); y = rho_W(P_Y(:,1)); plot(x,y,'s','color','blue'); hold off %--------------Extracting Lines from Detected Hough Peaks-------------- lines_Y = houghlines(frame_roiY,theta_Y,rho_Y,P_Y,'FillGap',3000,'MinLength',20); figure('Name','Hough Lines found in image'), imshow(frame), hold on max_len = 0; for k = 1:length(lines_Y) xy = [lines_Y(k).point1; lines_Y(k).point2]; plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green'); % Plot beginnings and ends of lines plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow'); plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red'); end lines_W = houghlines(frame_roiW,theta_W,rho_W,P_W,'FillGap',3000,'MinLength',20); max_len = 0; for k = 1:2 xy = [lines_W(k).point1; lines_W(k).point2]; plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green'); % Plot beginnings and ends of lines plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow'); plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red'); end hold off ![third_main_09](https://use