Lane Detection Optimized with GPU Coder
This example shows how to generate CUDA® code from a deep learning network, represented by a SeriesNetwork object. In this example, the series network is a convolutional neural network that can detect and output lane marker boundaries from an image.
Prerequisites
CUDA enabled NVIDIA® GPU.
NVIDIA CUDA toolkit and driver.
NVIDIA cuDNN library.
OpenCV libraries for video read and image display operations.
Environment variables for the compilers and libraries. For information on the supported versions of the compilers and libraries, see Third-Party Hardware. For setting up the environment variables, see Setting Up the Prerequisite Products.
Verify GPU Environment
Use the coder.checkGpuInstall function to verify that the compilers and libraries necessary for running this example are set up correctly.
envCfg = coder.gpuEnvConfig('host'); envCfg.DeepLibTarget = 'cudnn'; envCfg.DeepCodegen = 1; envCfg.Quiet = 1; coder.checkGpuInstall(envCfg);
Get Pretrained SeriesNetwork
[laneNet, coeffMeans, coeffStds] = getLaneDetectionNetworkGPU();
This network takes an image as an input and outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle. Each lane boundary is represented by the parabolic equation: 
, where y is the lateral offset and x is the longitudinal distance from the vehicle. The network outputs the three parameters a, b, and c per lane. The network architecture is similar to AlexNet except that the last few layers are replaced by a smaller fully connected layer and regression output layer.
laneNet.Layers
ans =
23×1 Layer array with layers:
1 'data' Image Input 227×227×3 images with 'zerocenter' normalization
2 'conv1' Convolution 96 11×11×3 convolutions with stride [4 4] and padding [0 0 0 0]
3 'relu1' ReLU ReLU
4 'norm1' Cross Channel Normalization cross channel normalization with 5 channels per element
5 'pool1' Max Pooling 3×3 max pooling with stride [2 2] and padding [0 0 0 0]
6 'conv2' Convolution 256 5×5×48 convolutions with stride [1 1] and padding [2 2 2 2]
7 'relu2' ReLU ReLU
8 'norm2' Cross Channel Normalization cross channel normalization with 5 channels per element
9 'pool2' Max Pooling 3×3 max pooling with stride [2 2] and padding [0 0 0 0]
10 'conv3' Convolution 384 3×3×256 convolutions with stride [1 1] and padding [1 1 1 1]
11 'relu3' ReLU ReLU
12 'conv4' Convolution 384 3×3×192 convolutions with stride [1 1] and padding [1 1 1 1]
13 'relu4' ReLU ReLU
14 'conv5' Convolution 256 3×3×192 convolutions with stride [1 1] and padding [1 1 1 1]
15 'relu5' ReLU ReLU
16 'pool5' Max Pooling 3×3 max pooling with stride [2 2] and padding [0 0 0 0]
17 'fc6' Fully Connected 4096 fully connected layer
18 'relu6' ReLU ReLU
19 'drop6' Dropout 50% dropout
20 'fcLane1' Fully Connected 16 fully connected layer
21 'fcLane1Relu' ReLU ReLU
22 'fcLane2' Fully Connected 6 fully connected layer
23 'output' Regression Output mean-squared-error with 'leftLane_a', 'leftLane_b', and 4 other responses
Examine Main Entry-Point Function
type detect_lane.m
function [laneFound, ltPts, rtPts] = detect_lane(frame, laneCoeffMeans, laneCoeffStds)
% From the networks output, compute left and right lane points in the
% image coordinates. The camera coordinates are described by the caltech
% mono camera model.
%#codegen
% A persistent object mynet is used to load the series 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('laneNet.mat', 'lanenet');
end
lanecoeffsNetworkOutput = lanenet.predict(permute(frame, [2 1 3]));
% Recover original coeffs by reversing the normalization steps
params = lanecoeffsNetworkOutput .* laneCoeffStds + laneCoeffMeans;
isRightLaneFound = abs(params(6)) > 0.5; %c should be more than 0.5 for it to be a right lane
isLeftLaneFound = abs(params(3)) > 0.5;
vehicleXPoints = 3:30; %meters, ahead of the sensor
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
function yWorld = computeBoundaryModel(model, xWorld)
yWorld = polyval(model, xWorld);
end
function tform = get_tformToImage
% Compute extrinsics based on camera setup
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
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
function intrinsicMat = intrinsicMatrix(FocalLength, Skew, PrincipalPoint)
intrinsicMat = ...
[FocalLength(1) , 0 , 0; ...
Skew , FocalLength(2) , 0; ...
PrincipalPoint(1), PrincipalPoint(2), 1];
end
Generate Code for Network and Post-Processing Code
The network computes parameters a, b, and c that describe the parabolic equation for the left and right lane boundaries.
From these parameters, compute the x and y coordinates corresponding to the lane positions. The coordinates must be mapped to image coordinates. The function detect_lane.m performs all these computations. Generate CUDA code for this function by creating a GPU code configuration object for a 'lib' target and set the target language to C++. Use the coder.DeepLearningConfig function to create a CuDNN deep learning configuration object and assign it to the DeepLearningConfig property of the GPU code configuration object. Run the codegen command.
cfg = coder.gpuConfig('lib'); cfg.DeepLearningConfig = coder.DeepLearningConfig('cudnn'); cfg.GenerateReport = true; cfg.TargetLang = 'C++'; codegen -args {ones(227,227,3,'single'),ones(1,6,'double'),ones(1,6,'double')} -config cfg detect_lane
Code generation successful: To view the report, open('codegen/lib/detect_lane/html/report.mldatx').
Generated Code Description
The series network is generated as a C++ class containing an array of 23 layer classes.
class c_lanenet { public: int32_T batchSize; int32_T numLayers; real32_T *inputData; real32_T *outputData; MWCNNLayer *layers[23]; public: c_lanenet(void); void setup(void); void predict(void); void cleanup(void); ~c_lanenet(void); };
The setup() method of the class sets up handles and allocates memory for each layer object. The predict() method invokes prediction for each of the 23 layers in the network.
The cnn_lanenet_conv*_w and cnn_lanenet_conv*_b files are the binary weights and bias file for convolution layer in the network. The cnn_lanenet_fc*_w and cnn_lanenet_fc*_b files are the binary weights and bias file for fully connected layer in the network.
codegendir = fullfile('codegen', 'lib', 'detect_lane'); dir(codegendir)
. cnn_lanenet0_0_conv4_w.bin .. cnn_lanenet0_0_conv5_b.bin .gitignore cnn_lanenet0_0_conv5_w.bin DeepLearningNetwork.cu cnn_lanenet0_0_data_offset.bin DeepLearningNetwork.h cnn_lanenet0_0_data_scale.bin DeepLearningNetwork.o cnn_lanenet0_0_fc6_b.bin MWCNNLayerImpl.cu cnn_lanenet0_0_fc6_w.bin MWCNNLayerImpl.hpp cnn_lanenet0_0_fcLane1_b.bin MWCNNLayerImpl.o cnn_lanenet0_0_fcLane1_w.bin MWCudaDimUtility.cu cnn_lanenet0_0_fcLane2_b.bin MWCudaDimUtility.hpp cnn_lanenet0_0_fcLane2_w.bin MWCustomLayerForCuDNN.cpp cnn_lanenet0_0_responseNames.txt MWCustomLayerForCuDNN.hpp codeInfo.mat MWCustomLayerForCuDNN.o codedescriptor.dmr MWElementwiseAffineLayer.cpp compileInfo.mat MWElementwiseAffineLayer.hpp defines.txt MWElementwiseAffineLayer.o detect_lane.a MWElementwiseAffineLayerImpl.cu detect_lane.cu MWElementwiseAffineLayerImpl.hpp detect_lane.h MWElementwiseAffineLayerImpl.o detect_lane.o MWElementwiseAffineLayerImplKernel.cu detect_lane_data.cu MWElementwiseAffineLayerImplKernel.o detect_lane_data.h MWFusedConvReLULayer.cpp detect_lane_data.o MWFusedConvReLULayer.hpp detect_lane_initialize.cu MWFusedConvReLULayer.o detect_lane_initialize.h MWFusedConvReLULayerImpl.cu detect_lane_initialize.o MWFusedConvReLULayerImpl.hpp detect_lane_ref.rsp MWFusedConvReLULayerImpl.o detect_lane_rtw.mk MWKernelHeaders.hpp detect_lane_terminate.cu MWTargetNetworkImpl.cu detect_lane_terminate.h MWTargetNetworkImpl.hpp detect_lane_terminate.o MWTargetNetworkImpl.o detect_lane_types.h buildInfo.mat examples cnn_api.cpp gpu_codegen_info.mat cnn_api.hpp html cnn_api.o interface cnn_lanenet0_0_conv1_b.bin mean.bin cnn_lanenet0_0_conv1_w.bin predict.cu cnn_lanenet0_0_conv2_b.bin predict.h cnn_lanenet0_0_conv2_w.bin predict.o cnn_lanenet0_0_conv3_b.bin rtw_proj.tmw cnn_lanenet0_0_conv3_w.bin rtwtypes.h cnn_lanenet0_0_conv4_b.bin
Generate Additional Files for Post-Processing the Output
Export mean and std values from the trained network for use during execution.
codegendir = fullfile(pwd, 'codegen', 'lib','detect_lane'); fid = fopen(fullfile(codegendir,'mean.bin'), 'w'); A = [coeffMeans coeffStds]; fwrite(fid, A, 'double'); fclose(fid);
Main File
Compile the network code by using a main file. The main file uses the OpenCV VideoCapture method to read frames from the input video. Each frame is processed and classified until no more frames are read. Before displaying the output for each frame, the outputs are post-processed by using the detect_lane function generated in detect_lane.cu.
type main_lanenet.cu
/* Copyright 2016 The MathWorks, Inc. */
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/core/types.hpp>
#include <opencv2/highgui.hpp>
#include <list>
#include <cmath>
#include "detect_lane.h"
using namespace cv;
void readData(float *input, Mat& orig, Mat & im)
{
Size size(227,227);
resize(orig,im,size,0,0,INTER_LINEAR);
for(int j=0;j<227*227;j++)
{
//BGR to RGB
input[2*227*227+j]=(float)(im.data[j*3+0]);
input[1*227*227+j]=(float)(im.data[j*3+1]);
input[0*227*227+j]=(float)(im.data[j*3+2]);
}
}
void addLane(float pts[28][2], Mat & im, int numPts)
{
std::vector<Point2f> iArray;
for(int k=0; k<numPts; k++)
{
iArray.push_back(Point2f(pts[k][0],pts[k][1]));
}
Mat curve(iArray, true);
curve.convertTo(curve, CV_32S); //adapt type for polylines
polylines(im, curve, false, CV_RGB(255,255,0), 2, LINE_AA);
}
void writeData(float *outputBuffer, Mat & im, int N, double means[6], double stds[6])
{
// get lane coordinates
boolean_T laneFound = 0;
float ltPts[56];
float rtPts[56];
detect_lane(outputBuffer, means, stds, &laneFound, ltPts, rtPts);
if (!laneFound)
{
return;
}
float ltPtsM[28][2];
float rtPtsM[28][2];
for(int k=0; k<28; k++)
{
ltPtsM[k][0] = ltPts[k];
ltPtsM[k][1] = ltPts[k+28];
rtPtsM[k][0] = rtPts[k];
rtPtsM[k][1] = rtPts[k+28];
}
addLane(ltPtsM, im, 28);
addLane(rtPtsM, im, 28);
}
void readMeanAndStds(const char* filename, double means[6], double stds[6])
{
FILE* pFile = fopen(filename, "rb");
if (pFile==NULL)
{
fputs ("File error",stderr);
return;
}
// obtain file size
fseek (pFile , 0 , SEEK_END);
long lSize = ftell(pFile);
rewind(pFile);
double* buffer = (double*)malloc(lSize);
size_t result = fread(buffer,sizeof(double),lSize,pFile);
if (result*sizeof(double) != lSize) {
fputs ("Reading error",stderr);
return;
}
for (int k = 0 ; k < 6; k++)
{
means[k] = buffer[k];
stds[k] = buffer[k+6];
}
free(buffer);
}
// Main function
int main(int argc, char* argv[])
{
float *inputBuffer = (float*)calloc(sizeof(float),227*227*3);
float *outputBuffer = (float*)calloc(sizeof(float),6);
if ((inputBuffer == NULL) || (outputBuffer == NULL)) {
printf("ERROR: Input/Output buffers could not be allocated!\n");
exit(-1);
}
// get ground truth mean and std
double means[6];
double stds[6];
readMeanAndStds("mean.bin", means, stds);
if (argc < 2)
{
printf("Pass in input video file name as argument\n");
return -1;
}
VideoCapture cap(argv[1]);
if (!cap.isOpened()) {
printf("Could not open the video capture device.\n");
return -1;
}
cudaEvent_t start, stop;
float fps = 0;
cudaEventCreate(&start);
cudaEventCreate(&stop);
Mat orig, im;
namedWindow("Lane detection demo",WINDOW_NORMAL);
while(true)
{
cudaEventRecord(start);
cap >> orig;
if (orig.empty()) break;
readData(inputBuffer, orig, im);
writeData(inputBuffer, orig, 6, means, stds);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
char strbuf[50];
float milliseconds = -1.0;
cudaEventElapsedTime(&milliseconds, start, stop);
fps = fps*.9+1000.0/milliseconds*.1;
sprintf (strbuf, "%.2f FPS", fps);
putText(orig, strbuf, Point(200,30), FONT_HERSHEY_DUPLEX, 1, CV_RGB(0,0,0), 2);
imshow("Lane detection demo", orig);
if( waitKey(50)%256 == 27 ) break; // stop capturing by pressing ESC */
}
destroyWindow("Lane detection demo");
free(inputBuffer);
free(outputBuffer);
return 0;
}
Download Example Video
if ~exist('./caltech_cordova1.avi', 'file') url = 'https://www.mathworks.com/supportfiles/gpucoder/media/caltech_cordova1.avi'; websave('caltech_cordova1.avi', url); end
Build Executable
if ispc setenv('MATLAB_ROOT', matlabroot); vcvarsall = mex.getCompilerConfigurations('C++').Details.CommandLineShell; setenv('VCVARSALL', vcvarsall); system('make_win_lane_detection.bat'); cd(codegendir); system('lanenet.exe ..\..\..\caltech_cordova1.avi'); else setenv('MATLAB_ROOT', matlabroot); system('make -f Makefile_lane_detection.mk'); cd(codegendir); system('./lanenet ../../../caltech_cordova1.avi'); end
Input Screenshot
Output Screenshot
See Also
Functions
Objects
coder.CodeConfig|coder.CuDNNConfig|coder.EmbeddedCodeConfig|coder.gpuConfig|coder.gpuEnvConfig