% Download the pretrained network.
pretrainedURL = 'https://www.mathworks.com/supportfiles/vision/data/segnetVGG16CamVid.mat';
pretrainedFolder = fullfile(tempdir,'pretrainedSegNet');
pretrainedSegNet = fullfile(pretrainedFolder,'segnetVGG16CamVid.mat');
if ~exist(pretrainedFolder,'dir')
    mkdir(pretrainedFolder);
    disp('Downloading pretrained SegNet (107 MB)...');
    websave(pretrainedSegNet,pretrainedURL);
    disp('Download complete.');
end

% % Load the network.
data = load(pretrainedSegNet);
net = data.net;

% Read the image.
I = imread('seq05vd_snap_shot.jpg');

% Segment the image.
[C,scores,allScores] = semanticseg(I,net);

% Overlay free space onto the image.
B = labeloverlay(I,C,'IncludedLabels',"Road");

% Display free space and image.
figure
imshow(B)

% Use the network's output score for Road as the free space confidence.
roadClassIdx = 4;
freeSpaceConfidence = allScores(:,:,roadClassIdx);
    
% Display the free space confidence.
figure
imagesc(freeSpaceConfidence)
title('Free Space Confidence Scores')
colorbar

% Create monoCamera for CamVid data.
sensor = camvidMonoCameraSensor();

% Define bird's-eye-view transformation parameters.
distAheadOfSensor = 20; % in meters, as previously specified in monoCamera height input
spaceToOneSide    = 3;  % look 3 meters to the right and left
bottomOffset      = 0;  
outView = [bottomOffset, distAheadOfSensor, -spaceToOneSide, spaceToOneSide];

outImageSize = [NaN, 256]; % output image width in pixels; height is chosen automatically to preserve units per pixel ratio

birdsEyeConfig = birdsEyeView(sensor,outView,outImageSize);

% Resize image and free space estimate to size of CamVid sensor. 
imageSize = sensor.Intrinsics.ImageSize;
I = imresize(I,imageSize);
freeSpaceConfidence = imresize(freeSpaceConfidence,imageSize);

% Transform image and free space confidence scores into bird's-eye view.
imageBEV = transformImage(birdsEyeConfig,I);
freeSpaceBEV = transformImage(birdsEyeConfig,freeSpaceConfidence); 

% Display image frame in bird's-eye view.
figure
imshow(imageBEV)

figure
imagesc(freeSpaceBEV)
title('Free Space Confidence')

% Define dimensions and resolution of the occupancy grid.
gridX = distAheadOfSensor;
gridY = 2 * spaceToOneSide;
cellSize = 0.25; % in meters to match units used by CamVid sensor

% Create the occupancy grid from the free space estimate.
occupancyGrid = createOccupancyGridFromFreeSpaceEstimate(...
    freeSpaceBEV, birdsEyeConfig, gridX, gridY, cellSize);

% Create bird's-eye plot.
bep = birdsEyePlot('XLimits',[0 distAheadOfSensor],'YLimits', [-5 5]);

% Add occupancy grid to bird's-eye plot.
hold on
[numCellsY,numCellsX] = size(occupancyGrid);
X = linspace(0, gridX, numCellsX);
Y = linspace(-gridY/2, gridY/2, numCellsY);
h = pcolor(X,Y,occupancyGrid);
title('Occupancy Grid (probability)')
colorbar
delete(legend)

% Make the occupancy grid visualization transparent and remove grid lines.
h.FaceAlpha = 0.5;
h.LineStyle = 'none';

% Add coverage area to plot.
caPlotter = coverageAreaPlotter(bep, 'DisplayName', 'Coverage Area');

% Update it with a field of view of 35 degrees and a range of 60 meters
mountPosition = [0 0];
range = 15;
orientation = 0;
fieldOfView = 35;
plotCoverageArea(caPlotter, mountPosition, range, orientation, fieldOfView);
hold off

% Create the costmap.
costmap = vehicleCostmap(flipud(occupancyGrid), ...
    'CellSize',cellSize, ...
    'MapLocation',[0,-spaceToOneSide]);
costmap.CollisionChecker.InflationRadius = 0;

% Display the costmap.
figure
plot(costmap,'Inflation','off')
colormap(parula)
colorbar
title('Vehicle Costmap')

% Orient the costmap so that it lines up with the vehicle coordinate
% system, where the X-axis points in front of the ego vehicle and the
% Y-axis points to the left.
view(gca,-90,90)

% Create a set of locations in vehicle coordinates.
candidateLocations = [
    8 0.375
    10 0.375
    12 2
    14 0.375   
    ];

% Check if locations are occupied.
isOccupied = checkOccupied(costmap,candidateLocations);

% Partition locations into free and occupied for visualization purposes.
occupiedLocations = candidateLocations(isOccupied,:);
freeLocations = candidateLocations(~isOccupied,:);

% Display free and occupied points on top of costmap.
hold on
markerSize = 100;
scatter(freeLocations(:,1),freeLocations(:,2),markerSize,'g','filled')
scatter(occupiedLocations(:,1),occupiedLocations(:,2),markerSize,'r','filled');
legend(["Free" "Occupied"])
hold off

function sensor = camvidMonoCameraSensor()
% Return a monoCamera camera configuration based on data from the CamVid 
% data set[1].
%
% The cameraCalibrator app was used to calibrate the camera using the
% calibration images provided in CamVid:
%
% http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/data/CalibrationSeq_and_Files_0010YU.zip
%
% Calibration pattern grid size is 28 mm. 
%
% Camera pitch is computed from camera pose matrices [R t] stored here:
%
% http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/data/EgoBoost_trax_matFiles.zip

% References
% ----------
% [1] Brostow, Gabriel J., Julien Fauqueur, and Roberto Cipolla. "Semantic Object 
% Classes in Video: A high-definition ground truth database." _Pattern Recognition 
% Letters_. Vol. 30, Issue 2, 2009, pp. 88-97.

calibrationData = load('camera_params_camvid.mat');

% Describe camera configuration.
focalLength    = calibrationData.cameraParams.FocalLength;
principalPoint = calibrationData.cameraParams.PrincipalPoint;
imageSize      = calibrationData.cameraParams.ImageSize;

% Camera height estimated based on camera setup pictured in [1].
height = 0.5;  % height in meters from the ground

% Camera pitch was computed using camera extrinsics provided in data set.
pitch = 0;  % pitch of the camera, towards the ground, in degrees

camIntrinsics = cameraIntrinsics(focalLength,principalPoint,imageSize);
sensor = monoCamera(camIntrinsics,height,'Pitch',pitch);
end

function occupancyGrid = createOccupancyGridFromFreeSpaceEstimate(...
    freeSpaceBEV,birdsEyeConfig,gridX,gridY,cellSize)
% Return an occupancy grid that contains the occupancy probability over
% a uniform 2-D grid.

% Number of cells in occupancy grid.
numCellsX = ceil(gridX / cellSize);
numCellsY = ceil(gridY / cellSize);

% Generate a set of (X,Y) points for each grid cell. These points are in
% the vehicle's coordinate system. Start by defining the edges of each grid
% cell.

% Define the edges of each grid cell in vehicle coordinates.
XEdges = linspace(0,gridX,numCellsX);
YEdges = linspace(-gridY/2,gridY/2,numCellsY);

% Next, specify the number of sample points to generate along each
% dimension within a grid cell. Use these to compute the step size in the
% X and Y direction. The step size will be used to shift the edge values of
% each grid to produce points that cover the entire area of a grid cell at
% the desired resolution.

% Sample 20 points from each grid cell. Sampling more points may produce
% smoother estimates at the cost of additional computation.
numSamplePoints = 20;

% Step size needed to sample number of desired points.
XStep = (XEdges(2)-XEdges(1)) / (numSamplePoints-1);
YStep = (YEdges(2)-YEdges(1)) / (numSamplePoints-1);

% Finally, slide the set of points across both dimensions of the grid
% cells. Sample the occupancy probability along the way using
% griddedInterpolant.

% Create griddedInterpolant for sampling occupancy probability. Use 1
% minus the free space confidence to represent the probability of occupancy.
occupancyProb = 1 - freeSpaceBEV;
sz = size(occupancyProb);
[y,x] = ndgrid(1:sz(1),1:sz(2));
F = griddedInterpolant(y,x,occupancyProb);

% Initialize the occupancy grid to zero.
occupancyGrid = zeros(numCellsY*numCellsX,1);

% Slide the set of points XEdges and YEdges across both dimensions of the
% grid cell. 
for j = 1:numSamplePoints
    
    % Increment sample points in the X-direction
    X = XEdges + (j-1)*XStep;
   
    for i = 1:numSamplePoints
        
        % Increment sample points in the Y-direction
        Y = YEdges + (i-1)*YStep;
        
        % Generate a grid of sample points in bird's-eye-view vehicle coordinates
        [XGrid,YGrid] = meshgrid(X,Y);
        
        % Transform grid of sample points to image coordinates
        xy = vehicleToImage(birdsEyeConfig,[XGrid(:) YGrid(:)]);
        
        % Clip sample points to lie within image boundaries
        xy = max(xy,1);
        xq = min(xy(:,1),sz(2));        
        yq = min(xy(:,2),sz(1));
        
        % Sample occupancy probabilities using griddedInterpolant and keep
        % a running sum.
        occupancyGrid = occupancyGrid + F(yq,xq);  
    end
    
end

% Determine mean occupancy probability.
occupancyGrid = occupancyGrid / numSamplePoints^2;
occupancyGrid = reshape(occupancyGrid,numCellsY,numCellsX);
end