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Bug fixes with multi-threaded sequence capture and recording. Fix with multi-hypothesis visualization in matlab.

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Tadas Baltrusaitis
2018-04-29 18:49:32 +01:00
parent 1387710f39
commit 165fa2e306
5 changed files with 36 additions and 156 deletions
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101
matlab_version/experiments_menpo/Script_CLNF_cross_data_general.m
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@@ -3,51 +3,24 @@ function Script_CLNF_cross_data_general()
addpath(genpath('../'));
% Replace this with the location of the Menpo dataset
[images, detections, labels] = Collect_menpo_imgs('D:\Datasets\menpo/');
[images, detections, labels] = Collect_menpo_imgs('G:\Datasets\menpo/');
%% loading the patch experts
clmParams = struct;
[ patches, pdm, clmParams ] = Load_CLNF_general();
views = [0,0,0; 0,-30,0; 0,30,0; 0,-55,0; 0,55,0; 0,0,30; 0,0,-30; 0,-90,0; 0,90,0; 0,-70,40; 0,70,-40];
views = views * pi/180;
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);
[ patches_51, pdm_51, clmParams_51, inds_full, inds_inner ] = Load_CLNF_inner();
[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
%% Fitting the model to the provided image
% the default PDM to use
pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
load(pdmLoc);
pdm = struct;
pdm.M = double(M);
pdm.E = double(E);
pdm.V = double(V);
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
% Loading the final scale
[clmParams_inner, pdm_inner] = Load_CLM_params_inner();
clmParams_inner.window_size = [17,17;19,19;21,21;23,23];
inds_inner = 18:68;
[patches_inner] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*general_no_out.mat', [], [], clmParams_inner);
clmParams_inner.multi_modal_types = patches_inner(1).multi_modal_types;
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
lhoods = zeros(numel(images),1);
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
num_points = numel(pdm.M)/3;
shapes_all = cell(numel(images), 1);
labels_all = cell(numel(images), 1);
@@ -55,9 +28,6 @@ lhoods = zeros(numel(images),1);
all_lmark_lhoods = zeros(num_points, numel(images));
all_views_used = zeros(numel(images),1);
% Use the multi-hypothesis model, as bounding box tells nothing about
% orientation
multi_view = true;
verbose = false; % set to true to visualise the fitting
tic
@@ -72,55 +42,10 @@ for i=1:numel(images)
bbox = detections(i,:);
% have a multi-view version
if(multi_view)
views = [0,0,0; 0,-30,0; 0,30,0; 0,-55,0; 0,55,0; 0,0,30; 0,0,-30; 0,-90,0; 0,90,0; 0,-70,40; 0,70,-40];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(size(views,1),1);
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v_ind);
else
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
end
% Perform inner face fitting
shape_inner = shape(inds_inner,:);
[ a, R, T, ~, l_params, err] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_inner.M, pdm_inner.E, pdm_inner.V, shape_inner);
if(a > 0.9)
g_param = [a; Rot2Euler(R)'; T];
bbox_2 = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
[shape_inner] = Fitting_from_bb(image, [], bbox_2, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
% Now after detections incorporate the eyes back
% into the face model
shape(inds_inner, :) = shape_inner;
[ ~, ~, ~, ~, ~, ~, shape_fit] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, shape);
all_lmark_lhoods(:,i) = lmark_lhood;
all_views_used(i) = view_used;
shape = shape_fit;
end
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb_multi_hyp(image, [], bbox, pdm, patches, clmParams, views);
% Perform inner landmark fitting now
[shape, shape_inner] = Fitting_from_bb_hierarch(image, pdm, pdm_51, patches_51, clmParams_51, shape, inds_full, inds_inner);
all_lmark_lhoods(:,i) = lmark_lhood;
all_views_used(i) = view_used;