使用 GPU Coder 优化车道检测

  SeriesNetwork with properties:

         Layers: [23×1 nnet.cnn.layer.Layer]
     InputNames: {'data'}
    OutputNames: {'output'}

function detectLanesInVideo(videoFile,net,laneCoeffMeans,laneCoeffsStds)
% detectLanesInVideo Entry-point function for the Lane Detection Optimized
% with GPU Coder example
%  
% detectLanesInVideo(videoFile,net,laneCoeffMeans,laneCoeffsStds) uses the
% VideoFileReader system object to read frames from the input video, calls
% the predict method of the LaneNet network object, and draws the detected
% lanes on the input video. A DeployableVideoPlayer system object is used
% to display the lane detected video output.

%   Copyright 2022 The MathWorks, Inc.

%#codegen

%% Create Video Reader and Video Player Object 
videoFReader   = vision.VideoFileReader(videoFile);
depVideoPlayer = vision.DeployableVideoPlayer(Name='Lane Detection on GPU');

%% Video Frame Processing Loop
while ~isDone(videoFReader)
    videoFrame = videoFReader();
    scaledFrame = 255.*(imresize(videoFrame,[227 227]));

    [laneFound,ltPts,rtPts] = laneNetPredict(net,scaledFrame, ...
        laneCoeffMeans,laneCoeffsStds);
    if(laneFound)
        pts = [reshape(ltPts',1,[]);reshape(rtPts',1,[])];
        videoFrame = insertShape(videoFrame, 'Line', pts, 'LineWidth', 4);
    end
    depVideoPlayer(videoFrame);
end
end

function [laneFound,ltPts,rtPts] = laneNetPredict(net,frame,means,stds) 
% laneNetPredict Predict lane markers on the input image frame using the
% lane detection network
%

%   Copyright 2017-2022 The MathWorks, Inc.

%#codegen

% A persistent object lanenet is used to load the network object. At the
% first call to this function, the persistent object is constructed and
% setup. When the function is called subsequent times, the same object is
% reused to call predict on inputs, thus avoiding reconstructing and
% reloading the network object.
persistent lanenet;
if isempty(lanenet)
    lanenet = coder.loadDeepLearningNetwork(net, 'lanenet');
end

lanecoeffsNetworkOutput = predict(lanenet,frame);

% Recover original coeffs by reversing the normalization steps.
params = lanecoeffsNetworkOutput .* stds + means;

% 'c' should be more than 0.5 for it to be a lane.
isRightLaneFound = abs(params(6)) > 0.5;
isLeftLaneFound =  abs(params(3)) > 0.5;

% From the networks output, compute left and right lane points in the image
% coordinates.
vehicleXPoints = 3:30;
ltPts = coder.nullcopy(zeros(28,2,'single'));
rtPts = coder.nullcopy(zeros(28,2,'single'));

if isRightLaneFound && isLeftLaneFound
    rtBoundary = params(4:6);
    rt_y = computeBoundaryModel(rtBoundary, vehicleXPoints);
    
    ltBoundary = params(1:3);
    lt_y = computeBoundaryModel(ltBoundary, vehicleXPoints);

    % Visualize lane boundaries of the ego vehicle.
    tform = get_tformToImage;

    % Map vehicle to image coordinates.
    ltPts =  tform.transformPointsInverse([vehicleXPoints', lt_y']);
    rtPts =  tform.transformPointsInverse([vehicleXPoints', rt_y']);
    laneFound = true;
else
    laneFound = false;
end
end

%% Helper Functions

% Compute boundary model.
function yWorld = computeBoundaryModel(model, xWorld)
yWorld = polyval(model, xWorld);
end

% Compute extrinsics.
function tform = get_tformToImage

%The camera coordinates are described by the caltech mono
% camera model.
yaw = 0;
pitch = 14; % Pitch of the camera in degrees
roll = 0;

translation = translationVector(yaw, pitch, roll);
rotation    = rotationMatrix(yaw, pitch, roll);

% Construct a camera matrix.
focalLength    = [309.4362, 344.2161];
principalPoint = [318.9034, 257.5352];
Skew = 0;

camMatrix = [rotation; translation] * intrinsicMatrix(focalLength, ...
    Skew, principalPoint);

% Turn camMatrix into 2-D homography.
tform2D = [camMatrix(1,:); camMatrix(2,:); camMatrix(4,:)]; % drop Z

tform = projective2d(tform2D);
tform = tform.invert();
end

% Translate to image co-ordinates.
function translation = translationVector(yaw, pitch, roll)
SensorLocation = [0 0];
Height = 2.1798;    % mounting height in meters from the ground
rotationMatrix = (...
    rotZ(yaw)*... % last rotation
    rotX(90-pitch)*...
    rotZ(roll)... % first rotation
    );


% Adjust for the SensorLocation by adding a translation.
sl = SensorLocation;

translationInWorldUnits = [sl(2), sl(1), Height];
translation = translationInWorldUnits*rotationMatrix;
end

% Rotation around X-axis.
function R = rotX(a)
a = deg2rad(a);
R = [...
    1   0        0;
    0   cos(a)  -sin(a);
    0   sin(a)   cos(a)];

end

% Rotation around Y-axis.
function R = rotY(a)
a = deg2rad(a);
R = [...
    cos(a)  0 sin(a);
    0       1 0;
    -sin(a) 0 cos(a)];

end

% Rotation around Z-axis.
function R = rotZ(a)
a = deg2rad(a);
R = [...
    cos(a) -sin(a) 0;
    sin(a)  cos(a) 0;
    0       0      1];
end

% Given the Yaw, Pitch, and Roll, determine the appropriate Euler angles
% and the sequence in which they are applied to align the camera's
% coordinate system with the vehicle coordinate system. The resulting
% matrix is a Rotation matrix that together with the Translation vector
% defines the extrinsic parameters of the camera.
function rotation = rotationMatrix(yaw, pitch, roll)
rotation = (...
    rotY(180)*...            % last rotation: point Z up
    rotZ(-90)*...            % X-Y swap
    rotZ(yaw)*...            % point the camera forward
    rotX(90-pitch)*...       % "un-pitch"
    rotZ(roll)...            % 1st rotation: "un-roll"
    );
end

% Intrinsic matrix computation.
function intrinsicMat = intrinsicMatrix(FocalLength, Skew, PrincipalPoint)
intrinsicMat = ...
    [FocalLength(1)  , 0                     , 0; ...
    Skew             , FocalLength(2)   , 0; ...
    PrincipalPoint(1), PrincipalPoint(2), 1];
end

Code generation successful: View report

cnn_lanenet0_0_conv1_b.bin        cnn_lanenet0_0_conv3_b.bin        cnn_lanenet0_0_conv5_b.bin        cnn_lanenet0_0_fc6_b.bin          cnn_lanenet0_0_fcLane2_b.bin      
cnn_lanenet0_0_conv1_w.bin        cnn_lanenet0_0_conv3_w.bin        cnn_lanenet0_0_conv5_w.bin        cnn_lanenet0_0_fc6_w.bin          cnn_lanenet0_0_fcLane2_w.bin      
cnn_lanenet0_0_conv2_b.bin        cnn_lanenet0_0_conv4_b.bin        cnn_lanenet0_0_data_offset.bin    cnn_lanenet0_0_fcLane1_b.bin      networkParamsInfo_lanenet0_0.bin  
cnn_lanenet0_0_conv2_w.bin        cnn_lanenet0_0_conv4_w.bin        cnn_lanenet0_0_data_scale.bin     cnn_lanenet0_0_fcLane1_w.bin