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
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.
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
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
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);
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
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 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
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 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
Fig 9: Hough peaks found for yellow lines
Fig 10: Hough peaks found for white lines
Fig 11: Hough lines found in image
In this section the lines that are found in hough lines are extrapolated on the main filtered image.
%-----------------Extract start and end points of lines----------------
leftp1 = [lines_Y(1).point1; lines_Y(1).point2];
leftp2 = [lines_Y(2).point1; lines_Y(2).point2];
rightp1 = [lines_W(1).point1; lines_W(1).point2];
rightp2 = [lines_W(2).point1; lines_W(2).point2];
if leftp1(1,1) < leftp2(1,1)
left_plot(1,:) = leftp1(1,:);
else
left_plot(1,:) = leftp2(1,:);
end
if leftp1(2,2) < leftp2(2,2)
left_plot(2,:) = leftp1(2,:);
else
left_plot(2,:) = leftp2(2,:);
end
if rightp1(1,2) < rightp2(1,2)
right_plot(1,:) = rightp1(1,:);
else
right_plot(1,:) = rightp2(1,:);
end
if rightp1(2,1) > rightp2(2,2)
right_plot(2,:) = rightp1(2,:);
else
right_plot(2,:) = rightp2(2,:);
end
% right_plot = rightp1;
% left_plot = leftp1;
%----------------Calculate slope of left and right lines---------------
slopeL = (left_plot(2,2)-left_plot(1,2))/(left_plot(2,1)-left_plot(1,1));
slopeR = (right_plot(2,2)-right_plot(1,2))/(right_plot(2,1)-right_plot(1,1));
%------Make equations of left and right lines to extrapolate them------
xLeftY = 1; % x is on the left edge
yLeftY = slopeL * (xLeftY - left_plot(1,1)) + left_plot(1,2);
xRightY = 550; % x is on the reight edge.
yRightY = slopeL * (xRightY - left_plot(2,1)) + left_plot(2,2);
xLeftW = 750; % x is on the left edge
yLeftW = slopeR * (xLeftW - right_plot(1,1)) + right_plot(1,2);
xRightW = 1300; % x is on the reight edge.
yRightW = slopeR * (xRightW - right_plot(2,1)) + right_plot(2,2);
%------Making a transparent Trapezoid between 4 poits of 2 lines-------
points = [xLeftY yLeftY; xRightY yRightY ;xLeftW yLeftW; xRightW yRightW ];
number = [1 2 3 4];
In this section, I have predicted where to turn by looking at the vanishing point found from the extrapolated lines.
%------------------Turn Prediction---------------
Yellow_dir = cross([left_plot(1,1), left_plot(1,2), 1], [left_plot(2,1), left_plot(2,2), 1]);
Yellow_dir = Yellow_dir ./ sqrt(Yellow_dir(1)^2 + Yellow_dir(2)^2);
theta_y = atan2(Yellow_dir(2), Yellow_dir(1));
rho_y = Yellow_dir(3);
yellow_line = [cos(theta_y), sin(theta_y), rho_y];
%-------------Finding vanishing point using cross poduct---------------
white_dir = cross([right_plot(1,1),right_plot(1,2),1], [right_plot(2,1),right_plot(2,2),1]);
white_dir = white_dir ./ (sqrt(white_dir(1)^2 + white_dir(2)^2));
theta_w = atan2(white_dir(2),white_dir(1));
rho_w = white_dir(3);
white_line = [cos(theta_w), sin(theta_w), rho_w];
line1 = [0, 1, -left_plot(2,1)];
point_on_w_lane = cross(line1,white_line);
point_on_w_lane = point_on_w_lane ./ point_on_w_lane(3);
line2 = [0, 1, -left_plot(2,2)];
point_on_w_lane_2 = cross(line2,white_line);
point_on_w_lane_2 = point_on_w_lane_2 ./ point_on_w_lane_2(3);
vanishing_point = cross(yellow_line, white_line);
vanishing_point = vanishing_point ./ vanishing_point(3);
vanishing_ratio = vanishing_point(1) / size(frame, 2);
if vanishing_ratio > 0.47 && vanishing_ratio < 0.49
direction = 'Turn Left';
elseif vanishing_ratio >= 0.49 && vanishing_ratio <= 0.51
direction = 'Go Straight';
else
direction = 'Turn Right';
end
%--Plot the extrapolated lines, Trapezoid and direction on each frame--
figure('Name','Final Output')
imshow(frame);
hold on
plot([xLeftY, xRightY], [yLeftY, yRightY], 'LineWidth',8,'Color','red');
plot([xLeftW, xRightW], [yLeftW, yRightW], 'LineWidth',8,'Color','red');
text(650, 65, direction,'horizontalAlignment', 'center', 'Color','red','FontSize',20)
patch('Faces', number, 'Vertices', points, 'FaceColor','green','Edgecolor','green','FaceAlpha',0.4)
hold off
%------------------Save each frame to the Output File------------------
writeVideo(Output_Video,getframe);
end
%---------------------Closing Save Video File Variable---------------------
close(Output_Video)
Fig12 : Final Image