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Dataset.m
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Dataset.m
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classdef Dataset
properties
logPath
labelPath
videoPath;
segmentPath;
dataID;
logData;
segData;
videoObj;
logTable;
eventLabel;
frameLabel;
startDistance;
timeDelayforVideo;
segWindow;
segStep;
logField;
eventField;
negativeField;
eventAna;
samplingStep;
frameRate;
end
methods(Static)
function time = timeParser(date)
colonIndex = find(date{1}==':');
if length(colonIndex)==1
dateVector = datevec(date, 'MM:SS.FFF');
else
dateVector = datevec(date, 'HH:MM:SS.FFF');
end
time = seconds(duration(dateVector(:,4),dateVector(:,5), dateVector(:,6)));
end
end
methods
function obj = Dataset(dataID,feature_field, event_field)
obj.dataID = dataID;
obj.logData=table();
obj.logPath = sprintf('./input/raw_data/%s/%s_datalog.Csv',dataID,dataID);
obj.labelPath = sprintf('./input/raw_data/%s/%s_labelresult.Csv',dataID,dataID);
obj.videoPath = sprintf('./input/raw_data/%s/%s_video.avi',dataID,dataID);%avi
obj.segmentPath = sprintf('./input/raw_data/%s/%s_labelresult.Csv',dataID,dataID);
obj = obj.readData(obj.logPath , obj.labelPath, obj.segmentPath);
obj.logTable = obj.logTable(:,feature_field);
obj.logField = feature_field;
obj.eventField = event_field;
obj = obj.table2data();
obj.timeDelayforVideo = 0;
obj.negativeField = 'goStraight';
end
function obj = readData(varargin)
obj = varargin{1};
addpath './OBDExtraction'
obj.logTable = TRI_GPS_extract_oneTrip(obj.logPath);%logPath
obj.eventLabel = readtable(obj.labelPath );
obj.videoObj = VideoReader(obj.videoPath);
obj.frameRate = obj.videoObj.FrameRate ;
end
function obj = table2data(varargin)
obj = varargin{1};
for i = 1:size(obj.logTable ,2)
obj.logData(:,i) = table(str2num(char(table2array(obj.logTable(:,i)))));
end
obj.logData.Properties = obj.logTable.Properties;
obj.startDistance = obj.logData.distance(1);
obj.logData.distance = obj.logData.distance - obj.startDistance;
end
function obj = reSync(varargin)
obj = varargin{1};
if numel(varargin)==1
obj.timeDelayforVideo = checkIfSync(obj.logData);
else
obj.timeDelayforVideo = checkIfSync(obj.logData,varargin{2},varargin{3},varargin{4} );
end
end
function obj = appendLabel(varargin)
obj = varargin{1};
eventField = obj.eventField;
segNum = size(obj.segData,2);
% sampleNum = size(obj.segData(1).data,1);
data_time = [obj.segData.minFrame, obj.segData.maxFrame];
data_time = reshape(data_time,[segNum,2]);
event_start = obj.timeParser(obj.eventLabel.StartTime)* obj.frameRate;
event_end = obj.timeParser(obj.eventLabel.EndTime)* obj.frameRate;
event_time =[ event_start, event_end];
eventNum = length(event_start);
for i= 1:segNum
for j = 1:eventNum
if data_time(i,2) < event_time(j,1)
break;
end
start = max(data_time(i,1), event_time(j,1));
last = min(data_time(i,2), event_time(j,2));
duration = last - start;
radio_event = duration /(event_time(j,2) - event_time(j,1));
radio_seg = duration /(data_time(i,2) - data_time(i,1));
radio = max(radio_event,radio_seg);
if radio > 0.7
tLabel = obj.eventLabel(j,eventField);
tLabel = table2array(tLabel);
label_index = find(tLabel==1);
if isempty(label_index)
obj.segData(i).Label = obj.negativeField ;
else
obj.segData(i).Label = eventField{label_index};
break;
end
else
obj.segData(i).Label = obj.negativeField ;
end
end
end
end
function obj = checkLabel(varargin)
obj = varargin{1};
x = obj.logData.GPS_long;
y = obj.logData.GPS_lat;
maxLength = length(x);
obj.frameLabel = zeros(maxLength,1);
obj.frameLabel = categorical(obj.frameLabel);
obj.frameLabel(1:maxLength) = obj.negativeField ;
logRate = 1 / double(obj.logData.time(2));
event_start = round((obj.timeParser(obj.eventLabel.StartTime )+ obj.timeDelayforVideo ) * logRate)+1;
event_end = round((obj.timeParser(obj.eventLabel.EndTime) + obj.timeDelayforVideo )* logRate)+1;
% event_time =[ event_start, event_end];
eventNum = length(event_start);
figure();hold on;
plot(x,y,'.k');
colorBar = ['r', 'g', 'y', 'b'];
for i =1 : eventNum
hold on;
label_index = table2array(obj.eventLabel(i,obj.eventField));
label_index = find(label_index==1);
if ~isempty(label_index)
obj.frameLabel(event_start(i):event_end(i))= obj.eventField{label_index};
plot(x(event_start(i):event_end(i)),y(event_start(i):event_end(i)),'.', ...
'Color', colorBar(label_index), 'MarkerSize', 6);
end
end
end
function obj = checkSeg(varargin)
obj = varargin{1};
color = '.k';
figure;hold on;
numSample = size(obj.segData,2);
x_event = [];
y_event = [];
event_color = color;
colorBar = ['.r'; '.g'; '.y'; '.b'];
for i = 1: numSample
x = obj.segData(i).data.GPS_long;
y = obj.segData(i).data.GPS_lat;
z = obj.segData(i).Label;
if strcmp(color,'.k')
color = '.w';
else
color = '.k';
end
if any(strcmp(z,obj.eventField))
event_index = find(strcmp(z,obj.eventField) == 1);
x_event = x;
y_event = y;
% x_event = [x_event; x];
% y_event = [y_event; y];
event_color = colorBar(event_index,:);
end
plot(x,y,color);
plot(x_event,y_event,event_color);
end
% plot(x_event,y_event,'.y');
% plot_google_map
end
function obj = extractVgg19(varargin)
obj = varargin{1};
segNum = size(obj.segData,2);
numSamples = length( obj.segData(1).frameIndex);
net = vgg19;
sz = net.Layers(1).InputSize;
netInput = zeros(sz(1),sz(2),3, numSamples);
for i = 1:segNum
frameIndex = obj.segData(i).frameIndex;
for j = 1 : numSamples
frame = read(obj.videoObj, frameIndex(j));
frame = imresize(frame,[sz(1),sz(2)]);
netInput(:,:,:,j) = frame;
end
frame_feature = activations(net,netInput,42);
obj.segData(i).vgg19 = frame_feature;
end
end
function obj = extractDrifting(varargin)
obj = varargin{1};
segNum = size(obj.segData,2);
videoObj = VideoReader(obj.videoPath);
maxFrame = videoObj.NumberOfFrames;
startIndex = 1;
lastIndex = maxFrame;
vstep = 10;
numImages = length([startIndex+1 : vstep : lastIndex]);
preFrame = read(videoObj, startIndex);
preFrame = rgb2gray(preFrame);
prePoints = detectSURFFeatures(preFrame);
[preFeatures,prePoints] = extractFeatures(preFrame,prePoints);
tforms(numImages) = projective2d(eye(3));
tIndex = 2;
for index = startIndex+vstep : vstep : lastIndex
curFrame = read(videoObj, index);
curFrame = rgb2gray(curFrame);
curPoints = detectSURFFeatures(curFrame);
[curFeatures,curPoints] = extractFeatures(curFrame,curPoints);
matchedIndex = matchFeatures(curFeatures, preFeatures,'Unique', true);
curMatched = curPoints(matchedIndex(:,1),:);
preMatched = prePoints(matchedIndex(:,2),:);
% Estimate the transformation between I(n) and I(n-1).
tforms(tIndex) = estimateGeometricTransform(curMatched, preMatched,...
'projective', 'Confidence', 99.9, 'MaxNumTrials', 2000);
tforms(tIndex).T = tforms(tIndex).T * tforms(tIndex-1).T;
tIndex = tIndex+1;
prePoints = curPoints;
preFeatures = curFeatures;
end
imageSize = size(preFrame); % all the images are the same size
% Compute the output limits for each transform
for i = 1:numel(tforms)
[xlim(i,:), ylim(i,:)] = outputLimits(tforms(i), [1 imageSize(2)], [1 imageSize(1)]);
end
avgXLim = mean(xlim, 2);
avgYLim = mean(ylim, 2);
% [~, idx] = sort(avgXLim);
% centerIdx = floor((numel(tforms)+1)/2);
% centerImageIdx = idx(centerIdx);
% Tinv = invert(tforms(centerImageIdx));
% for i = 1:numel(tforms)
% tforms(i).T = tforms(i).T * Tinv.T;
% end
% for i = 1:numel(tforms)
% [xlim(i,:), ylim(i,:)] = outputLimits(tforms(i), [1 imageSize(2)], [1 imageSize(1)]);
% end
% % Find the minimum and maximum output limits
% xMin = min([1; xlim(:)]);
% xMax = max([imageSize(2); xlim(:)]);
%
% yMin = min([1; ylim(:)]);
% yMax = max([imageSize(1); ylim(:)]);
%
% % Width and height of panorama.
% width = round(xMax - xMin);
% height = round(yMax - yMin);
%
% % Initialize the "empty" panorama.
% Frame = read(videoObj, 1);
% panorama = zeros([height width 3], 'like', Frame);
% blender = vision.AlphaBlender('Operation', 'Binary mask', ...
% 'MaskSource', 'Input port');
% % Create a 2-D spatial reference object defining the size of the panorama.
% xLimits = [xMin xMax];
% yLimits = [yMin yMax];
% panoramaView = imref2d([height width], xLimits, yLimits);
% % Create the panorama.
% tIndex = 1;
% for i = startIndex : vstep : lastIndex
% I = read(videoObj, i);
% % I = rgb2gray(I);
% % Transform I into the panorama.
% warpedImage = imwarp(I, tforms(tIndex), 'OutputView', panoramaView);
% % Generate a binary mask.
% mask = imwarp(true(size(I,1),size(I,2)), tforms(tIndex), 'OutputView', panoramaView);
% % Overlay the warpedImage onto the panorama.
% panorama = step(blender, panorama, warpedImage, mask);
% tIndex = tIndex + 1;
% end
% figure
% imshow(panorama)
end
function obj = extractCurvature(varargin)
obj = varargin{1};
width = 64;
height = 64;
segNum = size(obj.segData,2);
stride = 5;
for i = 1:segNum
x = obj.segData(i).data.GPS_lat;
y = obj.segData(i).data.GPS_long;
num = length(x);
minx = min(x(:));
maxx = max(x(:));
miny = min(y(:));
maxy = max(y(:));
max_range = max((maxx -minx ),(maxy-miny));
x = stride+(width-2*stride) *(x-minx)/max_range;%(maxx-minx);
y = stride+(height-2*stride) *(y-miny)/max_range;%(maxy-miny);
p = polyfit(x , y, 2);
x_ = [1:width]';
y_= polyval(p,x);
%compute the curvature
pp1 = 2 * p(1) * y_ + p(2);
pp2 = 2 * p(1);
k = abs(pp2) ./ sqrt((1+pp1.^2).^3);
u = minx + ( x - stride ) * max_range /(width - 2 * stride) ;
v = miny + ( y_ - stride) * max_range /(height - 2 * stride) ;
% u = obj.segData(i).data.GPS_long;
% v = obj.segData(i).data.GPS_lat;
% uvp = polyfit(u , v, 2);
% v = polyval(uvp,u);
obj.segData(i).curvature = [u,v,k];
% obj.segData(i).curvatureMean = mean(k);
% obj.segData(i).curvatureVar = var (k);
% obj.segData(i).curvatureMax = max(k);
% image plot
% y_ = polyval(p,x_);
% x_y = [x_, y_];
% x_y(x_y(:,2)>(height-1),:)=[];
% x_y(x_y(:,2)<0,:)=[];
% img = zeros(height, width);
% index = int64(floor(x_y(:,2)) * width + floor(x_y(:,1)) + 1);
% img(index) = 255;
end
end
function obj = checkCurvature(varargin)
obj = varargin{1};
color = '.r';
figure;hold on;
numSample = size(obj.segData,2);
x_event = [];
y_event = [];
for i = 1: numSample
x = obj.segData(i).curvature(:,2);
y = obj.segData(i).curvature(:,1);
z = max(obj.segData(i).curvature(:,3));
if strcmp(color,'.r')
color = '.b';
else
color = '.r';
end
if z > 0.03
x_event = [x_event;x];
y_event = [y_event;y];
end
plot(x,y,color);
end
plot(x_event,y_event,'.g');
end
function obj = segtrip( varargin)
segType = varargin{5};
obj = varargin{1};
obj.samplingStep = varargin{4};
[num_case, num_feature] = size(obj.logData);
segVector = obj.logData.(segType);
max_sample = 1 + floor((segVector(end) - segVector(1)) / obj.samplingStep );
tempData = zeros(max_sample, num_feature);
interValue = [segVector(1) : obj.samplingStep : segVector(end)];
tempData(1,:) = obj.logData{1,:};
index = 1;
for i=2:num_case
current =interValue(index) + obj.samplingStep;%tempData(index, segIndex)
if obj.logData.(segType)(i) >= current
x0 = obj.logData.(segType)(i-1);
x1 = obj.logData.(segType)(i);
x = current;
theta = (x-x0)/(x1-x0);
index = index + 1;
tempData(index,1:end) = obj.logData{i-1,1:end}+ theta * (obj.logData{i,1:end}-obj.logData{i-1,1:end});
end
end
%segment by distance
obj.segWindow = varargin{2};
obj.segWindow = floor(obj.segWindow / obj.samplingStep );
obj.segStep = varargin{3};
obj.segStep = floor(obj.segStep / obj.samplingStep );
fps = obj.frameRate ;
max_length = size(tempData,1);
index = 1;
begin = 1;
last = begin + obj.segWindow;
while(last< max_length)
obj.segData(index).data = [ tempData(begin:last ,:)];
obj.segData(index).data = array2table(obj.segData(index).data);
obj.segData(index).data.Properties = obj.logData.Properties;
% obj.segData(index).data.name = {};
frame = round( (obj.segData(index).data.time - obj.timeDelayforVideo )* fps ) +1;
obj.segData(index).frameIndex = frame;
obj.segData(index).minFrame = min(frame(:));
obj.segData(index).maxFrame = max(frame(:));
begin = begin + obj.segStep ;
last = begin + obj.segWindow;
index = index + 1;
end
end
function obj = eventAnalysis(varargin)
obj = varargin{1};
eventField = obj.eventField;
tLabel = obj.eventLabel(:,eventField);
event_start = obj.timeParser(obj.eventLabel.StartTime) + ...
obj.timeDelayforVideo;
event_end = obj.timeParser(obj.eventLabel.EndTime) + ...
obj.timeDelayforVideo;
logRate = 1 / double(obj.logData.time(2));
event_start_idx = round(event_start * logRate)+1;
event_end_idx = round(event_end * logRate)+1;
event_time = [event_start, event_end];
eventNum = length(event_start);
eventIndex = 0;
obj.eventAna = table();
for i = 1 : eventNum
label_index = table2array(tLabel(i,:));
label_index = find(label_index==1);
if ~isempty(label_index)
obj.eventAna.name(eventIndex + 1,1) = ...
obj.eventField(label_index);
obj.eventAna.duration(eventIndex + 1,1) = event_end(i) - ...
event_start(i);
obj.eventAna.distance(eventIndex + 1,1) = ...
obj.logData.distance(event_end_idx(i)) - ...
obj.logData.distance(event_start_idx(i));
eventIndex = eventIndex + 1;
end
end
end
end
end