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trackuplift_v2_for_angle.m
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trackuplift_v2_for_angle.m
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% trackuplift
% Applies tracking methods to image data from uplift rig
% EJR 2016
% License: CC-BY
%
% Notes
% 1. Try to get uniform illumination - non-flat brightness affects pkfnd
%
% 2. The camera pointing direction seems to drift during aquisition,
% which introduced a constant translation. Needs immobilising.
% Or use a fiducial mark to counter drift - prefer immobilising.
%
% 3. Remove red lines on glass. These create detections at thfrac=0.4
%
% 4. Note vertical stretch in image - measure graph paper to identify
% this ratio (1.29 in some sample data), due to non-horizontal optic
% axis, most likely.
% 2016/11/7: included YXratio to 'correct for' this - calculated
% displacements in listDisps are now in 'horizontal pixel widths'
%
% 5. Co-ordinates
% X is horizontal position (column number: leftmost =1)
% Y is vertical position (row number: topmost = 1)
%
% 6. This script currently does not check whether the
% Time ranges defined under 0. (PARTICLE TRACK ANALYSIS) actually exist
% in the MP4 video image data. If they don't, it will fail.
%
% Method
% 0. This script uses a CONTROL / INPUT / ANALYIS / OUTPUT (visualisation)
% layout
% 1. Set scale manually for each video (in case camera angle shifts)
% Use graph paper in image to check scale is uniform.
% Try to align camera so that one camera pixel width corresponds to
% the same distance horizontally and vertically on the testing rig.
% If this is not the case, then make sure you know the calibration.
% 2.
%
% ADD PATH FOR TRACKING FUNCTIONS WRITTEN BY JCC/DLB
addpath([pwd,'/tracking']);
% 0. CONTROL PARAMETERS FOR THIS SCRIPT
filenameMP4 = [pwd,'\input\Nov 22 Exp 7 H180 D45.MP4']; % Name of file to process
thfrac = 0.95; % Fractional value to set threshold for particle finding
% Note 'median' might be more robust than 0.25*(max-bg)
scaleX = 30/108 ; % mm distance per horizontal pixel width
scaleY = 30/144 ; % mm distance per vertical pixel width.
YXratio = scaleX/scaleY; % How many vertical pixel widths equal one
% horizontal width. Should be 1.00 if camera axis
% horizontal, but in practice it may vary! Calibrate.
% REGION OF INTEREST SELECTION
flagROIauto = 0; % Set to 1 to bypass manual roi selection
roiBorder = 50; % Width of border (pixels) where we will not try to interpolate displacement
flagShowAllTracks = 0;
% PARTICLE IDENTIFICATION
szPKFND = 11; % Set to bigger than particle diameter.
szCNTRD = 11; % Diameter of window for centroid finding. Avoid other particles.
lobjectBPASS = 5;% object diameter for bandpass filter. Try 5~diameter
flagBGgs = 1; % Set to 1 to apply gaussian-blurred background subtration
radgauss = 20; % Std deviation value for background subtraction
% PARTICLE TRACK IDENTIFICATION
maxdisp = 4; % Maximum particle displacement per frame
% (distance is in pixel widths - somewhat wrongly assumed identical in X and Y)
% PARTICLE TRACK ANALYSIS
tInit = 12.75; % Timestamp in MP4 data (seconds) for first frame to analyse
tStep = 5; % Time step to get next frame to evaluate particle position
nSteps = 7; % Number of steps to consider
% MOVEMENT / STATIC region analysis
threshMove = 0.025*nSteps; % Threshold for identifying movement.
% Is applied to dispacements in millimetres
% Seems sensible to define as (speed X nSteps)
% 1. INPUT
v = VideoReader(filenameMP4);
numFrames = v.Duration.*v.FrameRate;
pos = []; % Empty array to store identified positions
v.CurrentTime = tInit;
imDat = readFrame(v); % skip a few frames ahead
if(flagROIauto)
roiRect = [480, 356, 550, 618]; % xmin, ymin, width, height
else
figure(1)
imagesc(imDat)
title('Please select rectangular region of interest')
roiRect = floor(getrect(1));
end
% 2. ANALYSIS
lpIm = 0;
for lpIm =1:nSteps % For the required number of frames of MP4 data
v.CurrentTime = tInit + (lpIm-1)*tStep; % Find frame at desired timepoint
imDat = readFrame(v); % Read this frame
% The region of interest, imROI, should only contain material to be tracked
% Try finding mean RGB value level (could use just red, or rgb2gray)
imROI = mean(imcrop(imDat,roiRect) ,3);
% figure(2)
% imagesc(imROI)
% colormap(gray)
if(flagBGgs)
imBG = imgaussfilt(imROI, radgauss);
im1 = double(imBG) - double(imROI);
else
im1 = 255-imROI;
end
figure(4)
imagesc(im1)
colormap(gray)
title('Unfiltered region of interest')
im2 = bpass(im1, 1, lobjectBPASS);
% figure(3)
% imagesc(im2)
% colormap(gray)
% title('Filtered region of interest')
if lpIm == 1 % set threshold using data in frame 1
% thresh = min(im2(:)) + thfrac*(max(im2(:)) - min(im2(:)));
thresh = quantile(im2(:), thfrac);
end
% APPLY JCC / DLB peak- and centroid-finding
pk = pkfnd(im2,thresh,11);
cnt = cntrd(im2,pk,szCNTRD);
figure(5)
imagesc(im2)
colormap('gray')
hold on
scatter(cnt(:,1),cnt(:,2), 'r')
hold off
pause(0.1)
% listThresh = [listThresh;thresh];
newpos = [cnt(:,1), cnt(:,2), lpIm*ones(size(cnt,1),1)];
pos = [pos;newpos];
end
%% Apply tracking method of JCC/DLB
res = track(pos,maxdisp);
% Plot all identified tracks
if(flagShowAllTracks)
figure(3)
imagesc(imROI)
colormap('gray')
hold on
for lpT = 1:res(end,4)
resA = res(res(:,4)==lpT,:);
plot(resA(:,1),resA(:,2) ,'r');
scatter(resA(1,1), resA(1,2), 'c');
scatter(resA(end,1), resA(end,2), 'r');
end
legend('track','start','end')
hold off
end
%% 3. OUTPUT / VISUALISATION
% --------------------------------------------
% POST PROCESSING FOR VISUALISATION
% % Now find initial and displaced positions,
% Find particles that were tracked from frame 1 up to nSteps (all frames)
numTracks = (res(end,4));
posInit = -ones(numTracks,2);
posDisp = -ones(numTracks,2);
for lpTrack = 1:numTracks
myCoords = res((res(:,4)==lpTrack),[1:4]);
if( ((sum(myCoords(:,3)==1))+(sum(myCoords(:,3)==nSteps)))==2 )
posInit(lpTrack,:) = myCoords((myCoords(:,3)==1),1:2);
posDisp(lpTrack,:) = myCoords((myCoords(:,3)==nSteps),1:2);
end
end
invalid = (posInit(:,1)==-1) | (posDisp(:,1)==-1);
posInit(invalid,:) = [];
posDisp(invalid,:) = [];
figure(10)
imagesc(imROI)
colormap(gray)
hold on
scatter(posInit(:,1), posInit(:,2), 'c');
scatter(posDisp(:,1), posDisp(:,2), 'r');
hold off
title('Identified positions of real particles');
legend('frame 1', ['frame', int2str(nSteps)])
axis equal
xlabel('X-position, pixels')
ylabel('Y-position, pixels')
set(gca, 'fontSize', 14)
% ---------------------------- CP 2 TFORM
mytform = cp2tform(posDisp, posInit, 'lwm');
% 'piecewise linear' is stricter than local weighted mean 'lwm'
[XX,YY] = meshgrid([roiBorder:25:(roiRect(3)-roiBorder)], ...
[roiBorder:25:(roiRect(4)-roiBorder)]);
listXinit = XX(:);
listYinit = YY(:);
[listXfinal, listYfinal] = tforminv(mytform, listXinit, listYinit);
listDisps = sqrt((listXfinal - listXinit).^2 +...
((listYfinal - listYinit)/YXratio).^2); % YXratio for vstretch
matrDisps = reshape(listDisps, size(XX));
% % Scatterplot just showing initial and displaced positions, no image
% figure(11)
% scatter(listXinit, -listYinit, 'r')
% hold on
% scatter(listXfinal, -listYfinal, 'c')
% hold off
figure(12)
imagesc(imROI)
colormap(gray)
hold on
scatter(listXinit, listYinit, 'r')
scatter(listXfinal, listYfinal, 'c')
hold off
axis equal
legend('frame 1', ['frame', int2str(nSteps)])
title('Interpolated positions');
xlabel('X-position, pixels')
ylabel('Y-position, pixels')
set(gca, 'fontSize', 14)
figure(13)
imagesc(imROI)
colormap(gray)
hold on
for lpPt = 1:size(listXinit,1)
plot([listXinit(lpPt);listXfinal(lpPt)],...
[listYinit(lpPt);listYfinal(lpPt)], 'r', 'lineWidth', 1)
end
hold off
axis equal
title(['Displacement during ', int2str(nSteps), ' frames']);
xlabel('X-position, pixels')
ylabel('Y-position, pixels')
set(gca, 'fontSize', 14)
figure(14)
mesh(XX, YY, matrDisps)
colormap(jet)
xlabel('X position, pixels', 'fontSize', 14)
ylabel('Y position, pixels', 'fontSize', 14)
zlabel('speed, pixel per 9 frames', 'fontSize', 14)
set(gca, 'fontSize', 12)
% IDENTIFY MOVEMENT / NON-MOVED REGIONS
% % Next section of analysis - threshold the displacement field.
% Try overlaying thresholded slip field to identiy regions where
% speed exceeds some specific value
% First calculate displacement field at 1:1 scale
[XX,YY] = meshgrid([roiBorder:1:(roiRect(3)-roiBorder)], ...
[roiBorder:1:(roiRect(4)-roiBorder)]);
listXinit = XX(:);
listYinit = YY(:);
[listXfinal, listYfinal] = tforminv(mytform, listXinit, listYinit);
listDisps = sqrt( (scaleX*(listXfinal - listXinit)).^2 +...
(scaleY*(listYfinal - listYinit)).^2); % In millimetres
matrDisps = reshape(listDisps, size(XX));
% Get displacements separately in X- and Y-
listDispX = listXfinal - listXinit;
listDispY = (listYfinal - listYinit);
maskSlip = (matrDisps>threshMove);
figure(15)
imagesc(maskSlip)
title(['Region of movement > ', num2str(threshMove), ' mm'])
xlabel('X position, pixels', 'fontSize', 14)
ylabel('Y position, pixels', 'fontSize', 14)
imOverlay = double(imDat)/255;
imOverlay( roiRect(2)-0+[roiBorder:1:(roiRect(4)-roiBorder)], ...
roiRect(1)-0+[roiBorder:1:(roiRect(3)-roiBorder)],2) = ...
0.5*imOverlay( roiRect(2)-0+[roiBorder:1:(roiRect(4)-roiBorder)], ...
roiRect(1)-0+[roiBorder:1:(roiRect(3)-roiBorder)],2)+...
0.5*double(maskSlip);
figure(16)
imagesc(imOverlay)
% NEED TO FIX PROFILE PLOTS:
% figure(17)
% plot(listXinit(listYinit==500)*scaleX, listDisps(listYinit==500)*scaleX,...
% 'b','lineWidth', 2);
% hold on
% plot(listXinit(listYinit==100)*scaleX, listDisps(listYinit==100)*scaleX,...
% 'r', 'lineWidth', 2);
%
% plot([roiXplate(1), roiXplate(1)]*scaleX, [0,4], 'k--', 'lineWidth', 1)
% plot([roiXplate(2), roiXplate(2)]*scaleX, [0,4], 'k--', 'lineWidth', 1)
% hold off
% legend('Near base','Near top', 'Plate edges')
% set(gca,'fontSize', 14)
% xlabel('Horizontal position / mm')
% ylabel('Displacement during 18 seconds / mm')
% grid on
% % Try integrating speedss - change this to integrating vertical velocities
% figure(18)
% matrDispY = reshape(listDispY, size(YY));
% plot(-sum(matrDispY,2))