211 lines
No EOL
7.1 KiB
Matlab
211 lines
No EOL
7.1 KiB
Matlab
clear
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addpath('../PDM_helpers/');
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addpath(genpath('../fitting/'));
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addpath('../models/');
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addpath(genpath('../face_detection'));
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addpath('../CCNF/');
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%%
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vid_dir = '../../samples/';
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vids = cat(1, dir([vid_dir, '*.avi']), dir([vid_dir, '*.wmv']));
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%%
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verbose = true;
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record = true;
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%% loading the patch experts
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[clmParams, pdm] = Load_CLM_params_vid();
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% An accurate CCNF (or CLNF) model
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[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
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% A simpler (but less accurate SVR)
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% [patches] = Load_Patch_Experts( '../models/wild/', 'svr_patches_*_wild.mat', [], [], clmParams);
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% A general SVR
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% [patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
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clmParams.multi_modal_types = patches(1).multi_modal_types;
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% load the face validator and add its dependency
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load('../face_validation/trained/faceCheckers.mat', 'faceCheckers');
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addpath(genpath('../face_validation'));
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od = cd('../face_validation/');
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setup;
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cd(od);
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%%
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for v=1:numel(vids)
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% load the video
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vr = VideoReader([vid_dir, vids(v).name]);
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[~,fname,~] = fileparts(vids(v).name);
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if(record)
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if(~exist('./tracked_vids', 'file'))
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mkdir('tracked_vids');
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end
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writerObj = VideoWriter(sprintf('./tracked_vids/%s.avi', fname));
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open(writerObj);
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end
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det = false;
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initialised = false;
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nFrames = vr.NumberOfFrames;
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% Read one frame at a time.
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all_local_params = zeros(nFrames, numel(pdm.E));
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all_global_params = zeros(nFrames,6);
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for i = 1 : nFrames
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% if this version throws a "Dot name reference on non-scalar structure"
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% error change obj.NumberOfFrames to obj(1).NumberOfFrames (in two
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% places in read function) or surround it with an empty try catch
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% statement
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image_orig = read(vr, i);
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if((~det && mod(i,4) == 0) || ~initialised)
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% First attempt to use the Matlab one (fastest but not as accurate, if not present use yu et al.)
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[bboxs, det_shapes] = detect_faces(image_orig, {'cascade', 'yu'});
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% Zhu and Ramanan and Yu et al. are slower, but also more accurate
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% and can be used when vision toolbox is unavailable
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% [bboxs, det_shapes] = detect_faces(image_orig, {'yu', 'zhu'});
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if(~isempty(bboxs))
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% Pick the biggest face for tracking
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[~,ind] = max(bboxs(3,:) - bboxs(1,:));
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bbox = bboxs(:,ind);
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% Discard overly small detections
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if(bbox(3) - bbox(1) > 40)
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% Either infer the local and global shape parameters
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% from the detected landmarks or just using the
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% bounding box
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if(~isempty(det_shapes))
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shape = det_shapes(:,:,ind);
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inds = [1:60,62:64,66:68];
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M = pdm.M([inds, inds+68, inds+68*2]);
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E = pdm.E;
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V = pdm.V([inds, inds+68, inds+68*2],:);
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[ a, R, T, ~, params, err] = fit_PDM_ortho_proj_to_2D(M, E, V, shape);
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g_param_n = [a; Rot2Euler(R)'; T];
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l_param_n = params;
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else
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num_points = numel(pdm.M) / 3;
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M = reshape(pdm.M, num_points, 3);
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width_model = max(M(:,1)) - min(M(:,1));
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height_model = max(M(:,2)) - min(M(:,2));
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a = (((bbox(3) - bbox(1)) / width_model) + ((bbox(4) - bbox(2))/ height_model)) / 2;
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tx = (bbox(3) + bbox(1))/2;
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ty = (bbox(4) + bbox(2))/2;
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% correct it so that the bounding box is just around the minimum
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% and maximum point in the initialised face
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tx = tx - a*(min(M(:,1)) + max(M(:,1)))/2;
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ty = ty + a*(min(M(:,2)) + max(M(:,2)))/2;
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% visualisation
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g_param_n = [a, 0, 0, 0, tx, ty]';
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l_param_n = zeros(size(pdm.E));
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end
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% If tracking has not started trust the detection
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if(~initialised)
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g_param = g_param_n;
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l_param = l_param_n;
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det = true;
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initialised = true;
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else
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% If tracking has already started double check the
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% detection
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shape_new = GetShapeOrtho(pdm.M, pdm.V, params, g_param_n);
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dec = face_check_cnn(image, shape_new, g_param, faceCheckers);
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if(dec < 0.5)
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det = true;
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g_param = g_param_n;
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l_param = l_param_n;
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else
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det = false;
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end
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end
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end
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end
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end
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if(size(image_orig,3) == 3)
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image = rgb2gray(image_orig);
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else
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image = image_orig;
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end
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d_image = [];
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if(initialised)
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[shape,g_param,l_param,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, d_image, bbox, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
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all_local_params(i,:) = l_param;
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all_global_params(i,:) = g_param;
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dec = face_check_cnn(image, shape, g_param, faceCheckers);
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if(dec < 0.5)
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clmParams.window_size = [19,19; 17,17;];
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clmParams.numPatchIters = 2;
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det = true;
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else
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clmParams.window_size = [21,21; 19,19; 17,17;];
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clmParams.numPatchIters = 3;
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det = false;
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end
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end
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if(verbose)
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try
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if(max(image_orig(:)) > 1)
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imshow(double(image_orig)/255, 'Border', 'tight');
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else
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imshow(double(image_orig), 'Border', 'tight');
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end
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axis equal;
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hold on;
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if(initialised)
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plot(shape(:,1), shape(:,2),'.r','MarkerSize',20);
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plot(shape(:,1), shape(:,2),'.b','MarkerSize',10);
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end
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hold off;
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drawnow expose;
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pause(0.05);
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if(record)
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frame = getframe;
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writeVideo(writerObj,frame);
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end
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catch warn
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fprintf('%s', warn.message);
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end
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end
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end
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if(record)
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close(writerObj);
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end
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close all;
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experiments.local_params = all_local_params;
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experiments.global_params = all_global_params;
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end |