Allowing to control the size of aligned output image
This commit is contained in:
Tadas Baltrusaitis
parent
984cfb58e7
commit
0befd5f756
15 changed files with 276 additions and 108 deletions
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@ -300,7 +300,7 @@ int main (int argc, char **argv)
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vector<string> output_similarity_align;
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vector<string> output_hog_align_files;
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double sim_scale = 0.7;
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double sim_scale = -1;
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int sim_size = 112;
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bool grayscale = false;
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bool video_output = false;
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@ -320,6 +320,7 @@ int main (int argc, char **argv)
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get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic,
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output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
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// Used for image masking
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string tri_loc;
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@ -339,11 +340,6 @@ int main (int argc, char **argv)
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}
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}
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// Will warp to scaled mean shape
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cv::Mat_<double> similarity_normalised_shape = face_model.pdm.mean_shape * sim_scale;
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// Discard the z component
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similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
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// If multiple video files are tracked, use this to indicate if we are done
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bool done = false;
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int f_n = -1;
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@ -381,7 +377,11 @@ int main (int argc, char **argv)
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}
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// Creating a face analyser that will be used for AU extraction
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
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// Make sure sim_scale is proportional to sim_size if not set
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if (sim_scale == -1) sim_scale = sim_size * (0.7 / 112.0);
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), sim_scale, sim_size, sim_size, au_loc, tri_loc);
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while(!done) // this is not a for loop as we might also be reading from a webcam
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{
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@ -588,7 +588,7 @@ int main (int argc, char **argv)
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}
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if(hog_output_file.is_open())
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{
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FaceAnalysis::Extract_FHOG_descriptor(hog_descriptor, sim_warped_img, num_hog_rows, num_hog_cols);
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face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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if(visualise_hog && !det_parameters.quiet_mode)
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{
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@ -88,7 +88,7 @@ namespace OpenFaceDemo
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clnf_params = new FaceModelParameters(root, true);
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clnf_model = new CLNF(clnf_params);
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face_analyser = new FaceAnalyserManaged(root, true);
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face_analyser = new FaceAnalyserManaged(root, true, 112);
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Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() =>
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{
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@ -29,7 +29,7 @@
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<MenuItem Header="Open image sequence" Click="imageSequenceFileOpenClick">
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</MenuItem>
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</MenuItem>
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<MenuItem Name="RecordingMenu" Header="Recording settings">
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<MenuItem Name="RecordingMenu" Header="Record">
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<MenuItem Header="Set Location"></MenuItem>
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<MenuItem Name="RecordAUCheckBox" IsCheckable="True" Header="Record AUs" Click="recordCheckBox_click"></MenuItem>
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<MenuItem Name="RecordPoseCheckBox" IsCheckable="True" Header="Record pose" Click="recordCheckBox_click"></MenuItem>
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@ -41,6 +41,10 @@
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<MenuItem Name="RecordAlignedCheckBox" IsCheckable="True" Header="Record aligned faces" Click="recordCheckBox_click"></MenuItem>
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<MenuItem Name="RecordTrackedVidCheckBox" IsCheckable="True" Header="Record tracked video" Click="recordCheckBox_click"></MenuItem>
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</MenuItem>
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<MenuItem Name="SettingsMenu" Header="Recording settings">
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<MenuItem Header="Set output location..."></MenuItem>
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<MenuItem Header="Set output image size..." Click="setOutputImageSize_Click"></MenuItem>
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</MenuItem>
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<MenuItem Header="AU settings">
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<MenuItem Name="UseDynamicModelsCheckBox" IsChecked="True" IsCheckable="True" Header="Use dynamic models" Click="UseDynamicModelsCheckBox_Click"></MenuItem>
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<MenuItem Name="UseDynamicShiftingCheckBox" IsCheckable="True" Header="Use dynamic shifting" Click="UseDynamicModelsCheckBox_Click"></MenuItem>
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@ -148,6 +148,8 @@ namespace OpenFaceOffline
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bool show_geometry = true;
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bool show_aus = true;
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int image_output_size = 112;
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// TODO classifiers converted to regressors
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// TODO indication that track is done
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@ -196,7 +198,7 @@ namespace OpenFaceOffline
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clnf_params = new FaceModelParameters(root, false);
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clnf_model = new CLNF(clnf_params);
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face_analyser = new FaceAnalyserManaged(root, use_dynamic_models);
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face_analyser = new FaceAnalyserManaged(root, use_dynamic_models, image_output_size);
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}
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@ -1206,10 +1208,26 @@ namespace OpenFaceOffline
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{
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// Change the face analyser, this should be safe as the model is only allowed to change when not running
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String root = AppDomain.CurrentDomain.BaseDirectory;
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face_analyser = new FaceAnalyserManaged(root, UseDynamicModelsCheckBox.IsChecked);
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face_analyser = new FaceAnalyserManaged(root, UseDynamicModelsCheckBox.IsChecked, image_output_size);
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}
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use_dynamic_models = UseDynamicModelsCheckBox.IsChecked;
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}
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private void setOutputImageSize_Click(object sender, RoutedEventArgs e)
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{
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NumberEntryWindow number_entry_window = new NumberEntryWindow();
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number_entry_window.Icon = this.Icon;
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number_entry_window.WindowStartupLocation = WindowStartupLocation.CenterScreen;
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if (number_entry_window.ShowDialog() == true)
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{
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image_output_size = number_entry_window.OutputInt;
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String root = AppDomain.CurrentDomain.BaseDirectory;
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face_analyser = new FaceAnalyserManaged(root, use_dynamic_models, image_output_size);
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}
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}
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}
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}
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@ -91,6 +91,9 @@
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<Compile Include="UI_items\MultiBarGraphHorz.xaml.cs">
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<DependentUpon>MultiBarGraphHorz.xaml</DependentUpon>
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</Compile>
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<Compile Include="UI_items\NumberEntryWindow.xaml.cs">
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<DependentUpon>NumberEntryWindow.xaml</DependentUpon>
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</Compile>
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<Compile Include="UI_items\OverlayImage.xaml.cs">
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<DependentUpon>OverlayImage.xaml</DependentUpon>
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</Compile>
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@ -125,6 +128,10 @@
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<SubType>Designer</SubType>
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<Generator>MSBuild:Compile</Generator>
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</Page>
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<Page Include="UI_items\NumberEntryWindow.xaml">
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<SubType>Designer</SubType>
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<Generator>MSBuild:Compile</Generator>
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</Page>
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<Page Include="UI_items\OverlayImage.xaml">
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<SubType>Designer</SubType>
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<Generator>MSBuild:Compile</Generator>
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17
gui/OpenFaceOffline/UI_items/NumberEntryWindow.xaml
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17
gui/OpenFaceOffline/UI_items/NumberEntryWindow.xaml
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@ -0,0 +1,17 @@
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<Window x:Class="OpenFaceOffline.NumberEntryWindow"
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xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
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xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
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xmlns:d="http://schemas.microsoft.com/expression/blend/2008"
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xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
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mc:Ignorable="d"
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Title="NumberEntryWindow" Height="160" Width="300">
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<Grid>
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<StackPanel FocusManager.FocusedElement="{Binding ElementName=ResponseTextBox}">
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<TextBlock HorizontalAlignment="Center" Text="Enter new output image size" FontSize="20"/>
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<TextBox Margin="0,4,0,0" x:Name="ResponseTextBox" FontSize="20" Width="120" TextChanged="ResponseTextBox_TextChanged" />
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<Label Name="warningLabel" Visibility="Collapsed" FontStyle="Italic" Foreground="Red" HorizontalAlignment="Center">Has to be a non negative integer</Label>
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<Button Margin="0,8,0,0" Content="OK" Click="OKButton_Click" Width="100" VerticalAlignment="Bottom"/>
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</StackPanel>
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</Grid>
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</Window>
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142
gui/OpenFaceOffline/UI_items/NumberEntryWindow.xaml.cs
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142
gui/OpenFaceOffline/UI_items/NumberEntryWindow.xaml.cs
Normal file
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@ -0,0 +1,142 @@
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///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called “open source” software licenses (“Open Source
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// Components”), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Text.RegularExpressions;
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using System.Threading.Tasks;
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using System.Windows;
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using System.Windows.Controls;
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using System.Windows.Data;
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using System.Windows.Documents;
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using System.Windows.Input;
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using System.Windows.Media;
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using System.Windows.Media.Imaging;
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using System.Windows.Shapes;
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namespace OpenFaceOffline
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{
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/// <summary>
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/// Interaction logic for TextEntryWindow.xaml
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/// </summary>
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public partial class NumberEntryWindow : Window
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{
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public NumberEntryWindow()
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{
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InitializeComponent();
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ResponseTextBox.Text = "112";
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OutputInt = 112;
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this.KeyDown += new KeyEventHandler(TextEntry_KeyDown);
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}
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private string ResponseText
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{
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get { return ResponseTextBox.Text; }
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set { ResponseTextBox.Text = value; }
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}
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public int OutputInt;
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private void OKButton_Click(object sender, System.Windows.RoutedEventArgs e)
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{
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DialogResult = true;
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}
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private void TextEntry_KeyDown(object sender, KeyEventArgs e)
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{
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if (e.Key == Key.Enter)
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{
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DialogResult = true;
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}
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}
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// Do not allow illegal characters like
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private void ResponseTextBox_TextChanged(object sender, TextChangedEventArgs e)
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{
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try
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{
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OutputInt = Int32.Parse(ResponseTextBox.Text);
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if(OutputInt > 0)
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{
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warningLabel.Visibility = System.Windows.Visibility.Collapsed;
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}
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else
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{
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warningLabel.Visibility = System.Windows.Visibility.Visible;
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OutputInt = 112;
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ResponseTextBox.Text = "112";
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ResponseTextBox.SelectionStart = ResponseTextBox.Text.Length;
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}
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}
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catch (FormatException except)
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{
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OutputInt = 112;
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ResponseTextBox.Text = "112";
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ResponseTextBox.SelectionStart = ResponseTextBox.Text.Length;
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warningLabel.Visibility = System.Windows.Visibility.Visible;
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}
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}
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}
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}
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@ -133,14 +133,16 @@ private:
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public:
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FaceAnalyserManaged(System::String^ root, bool dynamic)
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FaceAnalyserManaged(System::String^ root, bool dynamic, int output_width)
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{
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vector<cv::Vec3d> orientation_bins;
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orientation_bins.push_back(cv::Vec3d(0,0,0));
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double scale = 0.7;
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int width = 112;
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int height = 112;
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int width = output_width;
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int height = output_width;
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double scale = width * (0.7 / 112.0);
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string root_std = msclr::interop::marshal_as<std::string>(root);
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@ -112,12 +112,8 @@ public:
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cv::Mat_<int> GetTriangulation();
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cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
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void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
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void ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations);
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// Grab the names of AUs being predicted
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std::vector<std::string> GetAUClassNames() const; // Presence
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std::vector<std::string> GetAURegNames() const; // Intensity
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@ -148,8 +144,8 @@ private:
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int frames_tracking;
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// Cache of intermediate images
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cv::Mat_<uchar> aligned_face_grayscale;
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cv::Mat aligned_face;
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cv::Mat aligned_face_for_au;
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cv::Mat aligned_face_for_output;
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cv::Mat hog_descriptor_visualisation;
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// Private members to be used for predictions
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@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
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void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
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{
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image = this->aligned_face.clone();
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image = this->aligned_face_for_output.clone();
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}
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void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
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@ -267,50 +267,15 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
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}
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void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
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{
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orientations = this->head_orientations;
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for(size_t i = 0; i < orientations.size(); ++i)
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{
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cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
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cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
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ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);
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ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
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// Add the HOG sample
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hog_medians.push_back(median_hog.clone());
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// For the face image need to convert it to suitable format
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cv::Mat_<uchar> aligned_face_cols_uchar;
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median_face.convertTo(aligned_face_cols_uchar, CV_8U);
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cv::Mat aligned_face_uchar;
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if(aligned_face.channels() == 1)
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
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}
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else
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
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}
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face_image_medians.push_back(aligned_face_uchar.clone());
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}
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}
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std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> FaceAnalyser::PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise)
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{
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// First align the face
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AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height);
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AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
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// Extract HOG descriptor from the frame and convert it to a useable format
|
||||
cv::Mat_<double> hog_descriptor;
|
||||
Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
|
||||
Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
|
||||
|
||||
// Store the descriptor
|
||||
hog_desc_frame = hog_descriptor;
|
||||
|
|
@ -326,10 +291,10 @@ std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string,
|
|||
|
||||
cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
|
||||
|
||||
// First convert the face image to double representation as a row vector
|
||||
cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
|
||||
cv::Mat_<double> aligned_face_cols_double;
|
||||
aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
|
||||
// First convert the face image to double representation as a row vector, TODO rem
|
||||
//cv::Mat_<uchar> aligned_face_cols(1, aligned_face_for_au.cols * aligned_face_for_au.rows * aligned_face_for_au.channels(), aligned_face_for_au.data, 1);
|
||||
//cv::Mat_<double> aligned_face_cols_double;
|
||||
//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
|
||||
|
||||
// Visualising the median HOG
|
||||
if (visualise)
|
||||
|
|
@ -363,26 +328,31 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
// First align the face if tracking was successfull
|
||||
if(clnf_model.detection_success)
|
||||
{
|
||||
AlignFaceMask(aligned_face, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face = cv::Mat(align_height, align_width, CV_8UC3);
|
||||
aligned_face.setTo(0);
|
||||
}
|
||||
|
||||
if(aligned_face.channels() == 3)
|
||||
// The aligned face requirement for AUs
|
||||
AlignFaceMask(aligned_face_for_au, frame, clnf_model, triangulation, true, 0.7, 112, 112);
|
||||
|
||||
// If the output requirement matches use the already computed one, else compute it again
|
||||
if(align_scale == 0.7 && align_width == 112 && align_height == 112)
|
||||
{
|
||||
cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
|
||||
aligned_face_for_output = aligned_face_for_au.clone();
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face_grayscale = aligned_face.clone();
|
||||
AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face_for_output = cv::Mat(align_height, align_width, CV_8UC3);
|
||||
aligned_face_for_au = cv::Mat(112, 112, CV_8UC3);
|
||||
aligned_face_for_output.setTo(0);
|
||||
aligned_face_for_au.setTo(0);
|
||||
}
|
||||
|
||||
// Extract HOG descriptor from the frame and convert it to a useable format
|
||||
cv::Mat_<double> hog_descriptor;
|
||||
Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
|
||||
Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
|
||||
|
||||
// Store the descriptor
|
||||
hog_desc_frame = hog_descriptor;
|
||||
|
|
@ -450,13 +420,10 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
UpdateRunningMedian(this->geom_desc_hist, this->geom_hist_sum, this->geom_descriptor_median, geom_descriptor_frame, update_median, this->num_bins_geom, this->min_val_geom, this->max_val_geom);
|
||||
}
|
||||
|
||||
// First convert the face image to double representation as a row vector
|
||||
cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
|
||||
cv::Mat_<double> aligned_face_cols_double;
|
||||
aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
|
||||
|
||||
// TODO get rid of this completely as it takes too long?
|
||||
//UpdateRunningMedian(this->face_image_hist[orientation_to_use], this->face_image_hist_sum[orientation_to_use], this->face_image_median, aligned_face_cols_double, update_median, 256, 0, 255);
|
||||
// First convert the face image to double representation as a row vector, TODO rem?
|
||||
//cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
|
||||
//cv::Mat_<double> aligned_face_cols_double;
|
||||
//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
|
||||
|
||||
// Visualising the median HOG
|
||||
if(visualise)
|
||||
|
|
@ -1097,12 +1064,6 @@ vector<pair<string, double>> FaceAnalyser::PredictCurrentAUsClass(int view)
|
|||
return predictions;
|
||||
}
|
||||
|
||||
|
||||
cv::Mat_<uchar> FaceAnalyser::GetLatestAlignedFaceGrayscale()
|
||||
{
|
||||
return aligned_face_grayscale.clone();
|
||||
}
|
||||
|
||||
cv::Mat FaceAnalyser::GetLatestHOGDescriptorVisualisation()
|
||||
{
|
||||
return hog_descriptor_visualisation;
|
||||
|
|
|
|||
|
|
@ -125,6 +125,8 @@ end
|
|||
|
||||
%%
|
||||
f = fopen('results/BP4D_valid_res_class.txt', 'w');
|
||||
f1s_class = zeros(1, numel(aus_BP4D));
|
||||
|
||||
for au = 1:numel(aus_BP4D)
|
||||
|
||||
if(inds_au_class(au) ~= 0)
|
||||
|
|
@ -137,7 +139,7 @@ for au = 1:numel(aus_BP4D)
|
|||
recall = tp./(tp+fn);
|
||||
|
||||
f1 = 2 * precision .* recall ./ (precision + recall);
|
||||
|
||||
f1s_class(au) = f1;
|
||||
fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', aus_BP4D(au), precision, recall, f1);
|
||||
end
|
||||
|
||||
|
|
@ -195,8 +197,10 @@ end
|
|||
|
||||
%%
|
||||
f = fopen('results/BP4D_valid_res_int.txt', 'w');
|
||||
ints_cccs = zeros(1, numel(aus_BP4D));
|
||||
for au = 1:numel(aus_BP4D)
|
||||
[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_au_prediction_results( preds_all_int(valid_ids, inds_au_int(au)), labels_gt(valid_ids,au));
|
||||
ints_cccs(au) = ccc;
|
||||
fprintf(f, 'AU%d results - rms %.3f, corr %.3f, ccc - %.3f\n', aus_BP4D(au), rms, corrs, ccc);
|
||||
end
|
||||
fclose(f);
|
||||
|
|
@ -100,6 +100,7 @@ end
|
|||
f = fopen('results/Bosphorus_res_class.txt', 'w');
|
||||
labels_gt_bin = labels_gt;
|
||||
labels_gt_bin(labels_gt_bin > 1) = 1;
|
||||
f1s_class = zeros(1, numel(aus_Bosph));
|
||||
for au = 1:numel(aus_Bosph)
|
||||
|
||||
tp = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 1);
|
||||
|
|
@ -111,6 +112,7 @@ for au = 1:numel(aus_Bosph)
|
|||
recall = tp./(tp+fn);
|
||||
|
||||
f1 = 2 * precision .* recall ./ (precision + recall);
|
||||
f1s_class(au) = f1;
|
||||
|
||||
fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', aus_Bosph(au), precision, recall, f1);
|
||||
|
||||
|
|
@ -180,10 +182,13 @@ end
|
|||
|
||||
%%
|
||||
f = fopen('results/Bosphorus_res_int.txt', 'w');
|
||||
cccs_reg = zeros(1, numel(aus_Bosph));
|
||||
for au = 1:numel(aus_Bosph)
|
||||
|
||||
[ ~, ~, corrs, ccc, rms, ~ ] = evaluate_regression_results( labels_pred(:, au), labels_gt(:, au));
|
||||
|
||||
cccs_reg(au) = ccc;
|
||||
|
||||
fprintf(f, 'AU%d intensity, Corr - %.3f, RMS - %.3f, CCC - %.3f\n', aus_Bosph(au), corrs, rms, ccc);
|
||||
|
||||
end
|
||||
|
|
|
|||
|
|
@ -98,10 +98,11 @@ end
|
|||
|
||||
%%
|
||||
f = fopen('results/UNBC_valid_res_int.txt', 'w');
|
||||
ints_cccs = zeros(1, numel(aus_UNBC);
|
||||
for au = 1:numel(aus_UNBC)
|
||||
|
||||
[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_au_prediction_results( preds_all_int(:, inds_au_int(au)), labels_gt(:,au));
|
||||
fprintf(f, 'AU%d results - rms %.3f, corr %.3f, ccc - %.3f\n', aus_UNBC(au), rms, corrs, ccc);
|
||||
|
||||
ints_cccs(au) = ccc;
|
||||
end
|
||||
fclose(f);
|
||||
|
|
@ -36,7 +36,7 @@ for i=1:numel(in_files)
|
|||
output_shape_params = [output name '.params.txt'];
|
||||
|
||||
command = cat(2, command, [' -f "' inputFile '" -of "' outputFile '"']);
|
||||
command = cat(2, command, [' -simalign "' outputDir_aligned '" -hogalign "' outputHOG_aligned '"' ]);
|
||||
command = cat(2, command, [' -simsize 224 -simalign "' outputDir_aligned '" -hogalign "' outputHOG_aligned '"' ]);
|
||||
|
||||
end
|
||||
|
||||
|
|
@ -153,7 +153,7 @@ hold off;
|
|||
[hog_data, valid_inds, vid_id] = Read_HOG_files({name}, output);
|
||||
|
||||
%% Output aligned images
|
||||
img_files = dir([outputDir_aligned, '/*.png']);
|
||||
img_files = dir([outputDir_aligned, '/*.bmp']);
|
||||
imgs = cell(numel(img_files, 1));
|
||||
for i=1:numel(img_files)
|
||||
imgs{i} = imread([ outputDir_aligned, '/', img_files(i).name]);
|
||||
|
|
|
|||
|
|
@ -21,6 +21,17 @@ cd('../');
|
|||
|
||||
%% AUs
|
||||
cd('Action Unit Experiments');
|
||||
run_AU_prediction_Bosphorus
|
||||
assert(mean(cccs_reg) > 0.56);
|
||||
assert(mean(f1s_class) > 0.46);
|
||||
|
||||
run_AU_prediction_BP4D
|
||||
assert(mean(ints_cccs) > 0.6);
|
||||
assert(mean(f1s_class) > 0.6);
|
||||
|
||||
run_AU_prediction_UNBC
|
||||
assert(mean(ints_cccs) > 0.38);
|
||||
|
||||
run_AU_prediction_DISFA
|
||||
assert(mean(au_res) > 0.7);
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue