Allowing to control the size of aligned output image

This commit is contained in:
Tadas Baltrusaitis 2016-12-05 17:28:39 -05:00
parent 984cfb58e7
commit 0befd5f756
15 changed files with 276 additions and 108 deletions

View file

@ -300,7 +300,7 @@ int main (int argc, char **argv)
vector<string> output_similarity_align; vector<string> output_similarity_align;
vector<string> output_hog_align_files; vector<string> output_hog_align_files;
double sim_scale = 0.7; double sim_scale = -1;
int sim_size = 112; int sim_size = 112;
bool grayscale = false; bool grayscale = false;
bool video_output = false; bool video_output = false;
@ -320,6 +320,7 @@ int main (int argc, char **argv)
get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic, get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic,
output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments); output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
// Used for image masking // Used for image masking
string tri_loc; string tri_loc;
@ -339,11 +340,6 @@ int main (int argc, char **argv)
} }
} }
// Will warp to scaled mean shape
cv::Mat_<double> similarity_normalised_shape = face_model.pdm.mean_shape * sim_scale;
// Discard the z component
similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
// If multiple video files are tracked, use this to indicate if we are done // If multiple video files are tracked, use this to indicate if we are done
bool done = false; bool done = false;
int f_n = -1; int f_n = -1;
@ -381,7 +377,11 @@ int main (int argc, char **argv)
} }
// Creating a face analyser that will be used for AU extraction // Creating a face analyser that will be used for AU extraction
FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
// Make sure sim_scale is proportional to sim_size if not set
if (sim_scale == -1) sim_scale = sim_size * (0.7 / 112.0);
FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), sim_scale, sim_size, sim_size, au_loc, tri_loc);
while(!done) // this is not a for loop as we might also be reading from a webcam while(!done) // this is not a for loop as we might also be reading from a webcam
{ {
@ -588,7 +588,7 @@ int main (int argc, char **argv)
} }
if(hog_output_file.is_open()) if(hog_output_file.is_open())
{ {
FaceAnalysis::Extract_FHOG_descriptor(hog_descriptor, sim_warped_img, num_hog_rows, num_hog_cols); face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
if(visualise_hog && !det_parameters.quiet_mode) if(visualise_hog && !det_parameters.quiet_mode)
{ {

View file

@ -88,7 +88,7 @@ namespace OpenFaceDemo
clnf_params = new FaceModelParameters(root, true); clnf_params = new FaceModelParameters(root, true);
clnf_model = new CLNF(clnf_params); clnf_model = new CLNF(clnf_params);
face_analyser = new FaceAnalyserManaged(root, true); face_analyser = new FaceAnalyserManaged(root, true, 112);
Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() => Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() =>
{ {

View file

@ -29,7 +29,7 @@
<MenuItem Header="Open image sequence" Click="imageSequenceFileOpenClick"> <MenuItem Header="Open image sequence" Click="imageSequenceFileOpenClick">
</MenuItem> </MenuItem>
</MenuItem> </MenuItem>
<MenuItem Name="RecordingMenu" Header="Recording settings"> <MenuItem Name="RecordingMenu" Header="Record">
<MenuItem Header="Set Location"></MenuItem> <MenuItem Header="Set Location"></MenuItem>
<MenuItem Name="RecordAUCheckBox" IsCheckable="True" Header="Record AUs" Click="recordCheckBox_click"></MenuItem> <MenuItem Name="RecordAUCheckBox" IsCheckable="True" Header="Record AUs" Click="recordCheckBox_click"></MenuItem>
<MenuItem Name="RecordPoseCheckBox" IsCheckable="True" Header="Record pose" Click="recordCheckBox_click"></MenuItem> <MenuItem Name="RecordPoseCheckBox" IsCheckable="True" Header="Record pose" Click="recordCheckBox_click"></MenuItem>
@ -41,6 +41,10 @@
<MenuItem Name="RecordAlignedCheckBox" IsCheckable="True" Header="Record aligned faces" Click="recordCheckBox_click"></MenuItem> <MenuItem Name="RecordAlignedCheckBox" IsCheckable="True" Header="Record aligned faces" Click="recordCheckBox_click"></MenuItem>
<MenuItem Name="RecordTrackedVidCheckBox" IsCheckable="True" Header="Record tracked video" Click="recordCheckBox_click"></MenuItem> <MenuItem Name="RecordTrackedVidCheckBox" IsCheckable="True" Header="Record tracked video" Click="recordCheckBox_click"></MenuItem>
</MenuItem> </MenuItem>
<MenuItem Name="SettingsMenu" Header="Recording settings">
<MenuItem Header="Set output location..."></MenuItem>
<MenuItem Header="Set output image size..." Click="setOutputImageSize_Click"></MenuItem>
</MenuItem>
<MenuItem Header="AU settings"> <MenuItem Header="AU settings">
<MenuItem Name="UseDynamicModelsCheckBox" IsChecked="True" IsCheckable="True" Header="Use dynamic models" Click="UseDynamicModelsCheckBox_Click"></MenuItem> <MenuItem Name="UseDynamicModelsCheckBox" IsChecked="True" IsCheckable="True" Header="Use dynamic models" Click="UseDynamicModelsCheckBox_Click"></MenuItem>
<MenuItem Name="UseDynamicShiftingCheckBox" IsCheckable="True" Header="Use dynamic shifting" Click="UseDynamicModelsCheckBox_Click"></MenuItem> <MenuItem Name="UseDynamicShiftingCheckBox" IsCheckable="True" Header="Use dynamic shifting" Click="UseDynamicModelsCheckBox_Click"></MenuItem>

View file

@ -148,6 +148,8 @@ namespace OpenFaceOffline
bool show_geometry = true; bool show_geometry = true;
bool show_aus = true; bool show_aus = true;
int image_output_size = 112;
// TODO classifiers converted to regressors // TODO classifiers converted to regressors
// TODO indication that track is done // TODO indication that track is done
@ -196,7 +198,7 @@ namespace OpenFaceOffline
clnf_params = new FaceModelParameters(root, false); clnf_params = new FaceModelParameters(root, false);
clnf_model = new CLNF(clnf_params); clnf_model = new CLNF(clnf_params);
face_analyser = new FaceAnalyserManaged(root, use_dynamic_models); face_analyser = new FaceAnalyserManaged(root, use_dynamic_models, image_output_size);
} }
@ -505,7 +507,7 @@ namespace OpenFaceOffline
List<Tuple<double, double>> landmarks = null; List<Tuple<double, double>> landmarks = null;
List<Tuple<double, double>> eye_landmarks = null; List<Tuple<double, double>> eye_landmarks = null;
List<Tuple<Point, Point>> gaze_lines = null; List<Tuple<Point, Point>> gaze_lines = null;
Tuple<double, double> gaze_angle = new Tuple<double, double>(0,0); Tuple<double, double> gaze_angle = new Tuple<double, double>(0, 0);
if (detectionSucceeding) if (detectionSucceeding)
{ {
@ -556,7 +558,7 @@ namespace OpenFaceOffline
nonRigidGraph.Update(non_rigid_params); nonRigidGraph.Update(non_rigid_params);
// Update eye gaze // Update eye gaze
GazeXLabel.Content = gaze_angle.Item1 * (180.0/ Math.PI); GazeXLabel.Content = gaze_angle.Item1 * (180.0 / Math.PI);
GazeYLabel.Content = gaze_angle.Item2 * (180.0 / Math.PI); GazeYLabel.Content = gaze_angle.Item2 * (180.0 / Math.PI);
} }
@ -625,7 +627,7 @@ namespace OpenFaceOffline
})); }));
// Recording the tracked model // Recording the tracked model
RecordFrame(clnf_model, detectionSucceeding, frame_id + 1, frame, grayFrame, ((double)frame_id)/fps, RecordFrame(clnf_model, detectionSucceeding, frame_id + 1, frame, grayFrame, ((double)frame_id) / fps,
record_2D_landmarks, record_2D_landmarks, record_model_params, record_pose, record_AUs, record_gaze, fx, fy, cx, cy); record_2D_landmarks, record_2D_landmarks, record_model_params, record_pose, record_AUs, record_gaze, fx, fy, cx, cy);
if (reset) if (reset)
@ -1206,10 +1208,26 @@ namespace OpenFaceOffline
{ {
// Change the face analyser, this should be safe as the model is only allowed to change when not running // Change the face analyser, this should be safe as the model is only allowed to change when not running
String root = AppDomain.CurrentDomain.BaseDirectory; String root = AppDomain.CurrentDomain.BaseDirectory;
face_analyser = new FaceAnalyserManaged(root, UseDynamicModelsCheckBox.IsChecked); face_analyser = new FaceAnalyserManaged(root, UseDynamicModelsCheckBox.IsChecked, image_output_size);
} }
use_dynamic_models = UseDynamicModelsCheckBox.IsChecked; use_dynamic_models = UseDynamicModelsCheckBox.IsChecked;
} }
private void setOutputImageSize_Click(object sender, RoutedEventArgs e)
{
NumberEntryWindow number_entry_window = new NumberEntryWindow();
number_entry_window.Icon = this.Icon;
number_entry_window.WindowStartupLocation = WindowStartupLocation.CenterScreen;
if (number_entry_window.ShowDialog() == true)
{
image_output_size = number_entry_window.OutputInt;
String root = AppDomain.CurrentDomain.BaseDirectory;
face_analyser = new FaceAnalyserManaged(root, use_dynamic_models, image_output_size);
}
}
} }
} }

View file

@ -91,6 +91,9 @@
<Compile Include="UI_items\MultiBarGraphHorz.xaml.cs"> <Compile Include="UI_items\MultiBarGraphHorz.xaml.cs">
<DependentUpon>MultiBarGraphHorz.xaml</DependentUpon> <DependentUpon>MultiBarGraphHorz.xaml</DependentUpon>
</Compile> </Compile>
<Compile Include="UI_items\NumberEntryWindow.xaml.cs">
<DependentUpon>NumberEntryWindow.xaml</DependentUpon>
</Compile>
<Compile Include="UI_items\OverlayImage.xaml.cs"> <Compile Include="UI_items\OverlayImage.xaml.cs">
<DependentUpon>OverlayImage.xaml</DependentUpon> <DependentUpon>OverlayImage.xaml</DependentUpon>
</Compile> </Compile>
@ -125,6 +128,10 @@
<SubType>Designer</SubType> <SubType>Designer</SubType>
<Generator>MSBuild:Compile</Generator> <Generator>MSBuild:Compile</Generator>
</Page> </Page>
<Page Include="UI_items\NumberEntryWindow.xaml">
<SubType>Designer</SubType>
<Generator>MSBuild:Compile</Generator>
</Page>
<Page Include="UI_items\OverlayImage.xaml"> <Page Include="UI_items\OverlayImage.xaml">
<SubType>Designer</SubType> <SubType>Designer</SubType>
<Generator>MSBuild:Compile</Generator> <Generator>MSBuild:Compile</Generator>

View file

@ -0,0 +1,17 @@
<Window x:Class="OpenFaceOffline.NumberEntryWindow"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
xmlns:d="http://schemas.microsoft.com/expression/blend/2008"
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
mc:Ignorable="d"
Title="NumberEntryWindow" Height="160" Width="300">
<Grid>
<StackPanel FocusManager.FocusedElement="{Binding ElementName=ResponseTextBox}">
<TextBlock HorizontalAlignment="Center" Text="Enter new output image size" FontSize="20"/>
<TextBox Margin="0,4,0,0" x:Name="ResponseTextBox" FontSize="20" Width="120" TextChanged="ResponseTextBox_TextChanged" />
<Label Name="warningLabel" Visibility="Collapsed" FontStyle="Italic" Foreground="Red" HorizontalAlignment="Center">Has to be a non negative integer</Label>
<Button Margin="0,8,0,0" Content="OK" Click="OKButton_Click" Width="100" VerticalAlignment="Bottom"/>
</StackPanel>
</Grid>
</Window>

View file

@ -0,0 +1,142 @@
///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called “open source” software licenses (“Open Source
// Components”), which means any software licenses approved as open source licenses by the
// Open Source Initiative or any substantially similar licenses, including without limitation any
// license that, as a condition of distribution of the software licensed under such license,
// requires that the distributor make the software available in source code format. Licensor shall
// provide a list of Open Source Components for a particular version of the Software upon
// Licensees request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.
// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
//
// OpenFace: an open source facial behavior analysis toolkit
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
// in IEEE Winter Conference on Applications of Computer Vision, 2016
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
using System.Windows;
using System.Windows.Controls;
using System.Windows.Data;
using System.Windows.Documents;
using System.Windows.Input;
using System.Windows.Media;
using System.Windows.Media.Imaging;
using System.Windows.Shapes;
namespace OpenFaceOffline
{
/// <summary>
/// Interaction logic for TextEntryWindow.xaml
/// </summary>
public partial class NumberEntryWindow : Window
{
public NumberEntryWindow()
{
InitializeComponent();
ResponseTextBox.Text = "112";
OutputInt = 112;
this.KeyDown += new KeyEventHandler(TextEntry_KeyDown);
}
private string ResponseText
{
get { return ResponseTextBox.Text; }
set { ResponseTextBox.Text = value; }
}
public int OutputInt;
private void OKButton_Click(object sender, System.Windows.RoutedEventArgs e)
{
DialogResult = true;
}
private void TextEntry_KeyDown(object sender, KeyEventArgs e)
{
if (e.Key == Key.Enter)
{
DialogResult = true;
}
}
// Do not allow illegal characters like
private void ResponseTextBox_TextChanged(object sender, TextChangedEventArgs e)
{
try
{
OutputInt = Int32.Parse(ResponseTextBox.Text);
if(OutputInt > 0)
{
warningLabel.Visibility = System.Windows.Visibility.Collapsed;
}
else
{
warningLabel.Visibility = System.Windows.Visibility.Visible;
OutputInt = 112;
ResponseTextBox.Text = "112";
ResponseTextBox.SelectionStart = ResponseTextBox.Text.Length;
}
}
catch (FormatException except)
{
OutputInt = 112;
ResponseTextBox.Text = "112";
ResponseTextBox.SelectionStart = ResponseTextBox.Text.Length;
warningLabel.Visibility = System.Windows.Visibility.Visible;
}
}
}
}

View file

@ -133,14 +133,16 @@ private:
public: public:
FaceAnalyserManaged(System::String^ root, bool dynamic) FaceAnalyserManaged(System::String^ root, bool dynamic, int output_width)
{ {
vector<cv::Vec3d> orientation_bins; vector<cv::Vec3d> orientation_bins;
orientation_bins.push_back(cv::Vec3d(0,0,0)); orientation_bins.push_back(cv::Vec3d(0,0,0));
double scale = 0.7;
int width = 112; int width = output_width;
int height = 112; int height = output_width;
double scale = width * (0.7 / 112.0);
string root_std = msclr::interop::marshal_as<std::string>(root); string root_std = msclr::interop::marshal_as<std::string>(root);

View file

@ -112,12 +112,8 @@ public:
cv::Mat_<int> GetTriangulation(); cv::Mat_<int> GetTriangulation();
cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
void GetGeomDescriptor(cv::Mat_<double>& geom_desc); void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
void ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations);
// Grab the names of AUs being predicted // Grab the names of AUs being predicted
std::vector<std::string> GetAUClassNames() const; // Presence std::vector<std::string> GetAUClassNames() const; // Presence
std::vector<std::string> GetAURegNames() const; // Intensity std::vector<std::string> GetAURegNames() const; // Intensity
@ -148,8 +144,8 @@ private:
int frames_tracking; int frames_tracking;
// Cache of intermediate images // Cache of intermediate images
cv::Mat_<uchar> aligned_face_grayscale; cv::Mat aligned_face_for_au;
cv::Mat aligned_face; cv::Mat aligned_face_for_output;
cv::Mat hog_descriptor_visualisation; cv::Mat hog_descriptor_visualisation;
// Private members to be used for predictions // Private members to be used for predictions

View file

@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image) void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
{ {
image = this->aligned_face.clone(); image = this->aligned_face_for_output.clone();
} }
void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols) void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
@ -267,50 +267,15 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
} }
void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
{
orientations = this->head_orientations;
for(size_t i = 0; i < orientations.size(); ++i)
{
cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);
ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
// Add the HOG sample
hog_medians.push_back(median_hog.clone());
// For the face image need to convert it to suitable format
cv::Mat_<uchar> aligned_face_cols_uchar;
median_face.convertTo(aligned_face_cols_uchar, CV_8U);
cv::Mat aligned_face_uchar;
if(aligned_face.channels() == 1)
{
aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
}
else
{
aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
}
face_image_medians.push_back(aligned_face_uchar.clone());
}
}
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) 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)
{ {
// First align the face // First align the face
AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height); AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
// Extract HOG descriptor from the frame and convert it to a useable format // Extract HOG descriptor from the frame and convert it to a useable format
cv::Mat_<double> hog_descriptor; 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 // Store the descriptor
hog_desc_frame = hog_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); cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
// First convert the face image to double representation as a row vector // 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_<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; //cv::Mat_<double> aligned_face_cols_double;
aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F); //aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
// Visualising the median HOG // Visualising the median HOG
if (visualise) if (visualise)
@ -363,26 +328,31 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
// First align the face if tracking was successfull // First align the face if tracking was successfull
if(clnf_model.detection_success) 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);
cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
// 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)
{
aligned_face_for_output = aligned_face_for_au.clone();
}
else
{
AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
}
} }
else else
{ {
aligned_face_grayscale = aligned_face.clone(); 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 // Extract HOG descriptor from the frame and convert it to a useable format
cv::Mat_<double> hog_descriptor; 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 // Store the descriptor
hog_desc_frame = hog_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); 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 // 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_<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; //cv::Mat_<double> aligned_face_cols_double;
aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F); //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);
// Visualising the median HOG // Visualising the median HOG
if(visualise) if(visualise)
@ -1097,12 +1064,6 @@ vector<pair<string, double>> FaceAnalyser::PredictCurrentAUsClass(int view)
return predictions; return predictions;
} }
cv::Mat_<uchar> FaceAnalyser::GetLatestAlignedFaceGrayscale()
{
return aligned_face_grayscale.clone();
}
cv::Mat FaceAnalyser::GetLatestHOGDescriptorVisualisation() cv::Mat FaceAnalyser::GetLatestHOGDescriptorVisualisation()
{ {
return hog_descriptor_visualisation; return hog_descriptor_visualisation;

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@ -125,6 +125,8 @@ end
%% %%
f = fopen('results/BP4D_valid_res_class.txt', 'w'); f = fopen('results/BP4D_valid_res_class.txt', 'w');
f1s_class = zeros(1, numel(aus_BP4D));
for au = 1:numel(aus_BP4D) for au = 1:numel(aus_BP4D)
if(inds_au_class(au) ~= 0) if(inds_au_class(au) ~= 0)
@ -137,7 +139,7 @@ for au = 1:numel(aus_BP4D)
recall = tp./(tp+fn); recall = tp./(tp+fn);
f1 = 2 * precision .* recall ./ (precision + recall); 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); fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', aus_BP4D(au), precision, recall, f1);
end end
@ -195,8 +197,10 @@ end
%% %%
f = fopen('results/BP4D_valid_res_int.txt', 'w'); f = fopen('results/BP4D_valid_res_int.txt', 'w');
ints_cccs = zeros(1, numel(aus_BP4D));
for au = 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)); [ 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); fprintf(f, 'AU%d results - rms %.3f, corr %.3f, ccc - %.3f\n', aus_BP4D(au), rms, corrs, ccc);
end end
fclose(f); fclose(f);

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@ -100,6 +100,7 @@ end
f = fopen('results/Bosphorus_res_class.txt', 'w'); f = fopen('results/Bosphorus_res_class.txt', 'w');
labels_gt_bin = labels_gt; labels_gt_bin = labels_gt;
labels_gt_bin(labels_gt_bin > 1) = 1; labels_gt_bin(labels_gt_bin > 1) = 1;
f1s_class = zeros(1, numel(aus_Bosph));
for au = 1:numel(aus_Bosph) for au = 1:numel(aus_Bosph)
tp = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 1); 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); recall = tp./(tp+fn);
f1 = 2 * precision .* recall ./ (precision + recall); 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); 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'); f = fopen('results/Bosphorus_res_int.txt', 'w');
cccs_reg = zeros(1, numel(aus_Bosph));
for au = 1:numel(aus_Bosph) for au = 1:numel(aus_Bosph)
[ ~, ~, corrs, ccc, rms, ~ ] = evaluate_regression_results( labels_pred(:, au), labels_gt(:, au)); [ ~, ~, 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); fprintf(f, 'AU%d intensity, Corr - %.3f, RMS - %.3f, CCC - %.3f\n', aus_Bosph(au), corrs, rms, ccc);
end end

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@ -98,10 +98,11 @@ end
%% %%
f = fopen('results/UNBC_valid_res_int.txt', 'w'); f = fopen('results/UNBC_valid_res_int.txt', 'w');
ints_cccs = zeros(1, numel(aus_UNBC);
for au = 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)); [ 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); fprintf(f, 'AU%d results - rms %.3f, corr %.3f, ccc - %.3f\n', aus_UNBC(au), rms, corrs, ccc);
ints_cccs(au) = ccc;
end end
fclose(f); fclose(f);

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@ -36,7 +36,7 @@ for i=1:numel(in_files)
output_shape_params = [output name '.params.txt']; output_shape_params = [output name '.params.txt'];
command = cat(2, command, [' -f "' inputFile '" -of "' outputFile '"']); 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 end
@ -153,7 +153,7 @@ hold off;
[hog_data, valid_inds, vid_id] = Read_HOG_files({name}, output); [hog_data, valid_inds, vid_id] = Read_HOG_files({name}, output);
%% Output aligned images %% Output aligned images
img_files = dir([outputDir_aligned, '/*.png']); img_files = dir([outputDir_aligned, '/*.bmp']);
imgs = cell(numel(img_files, 1)); imgs = cell(numel(img_files, 1));
for i=1:numel(img_files) for i=1:numel(img_files)
imgs{i} = imread([ outputDir_aligned, '/', img_files(i).name]); imgs{i} = imread([ outputDir_aligned, '/', img_files(i).name]);

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@ -21,6 +21,17 @@ cd('../');
%% AUs %% AUs
cd('Action Unit Experiments'); 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 run_AU_prediction_DISFA
assert(mean(au_res) > 0.7); assert(mean(au_res) > 0.7);