148 lines
6.4 KiB
C++
148 lines
6.4 KiB
C++
///////////////////////////////////////////////////////////////////////////////
|
||
// 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
|
||
// Licensee’s 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.
|
||
//
|
||
///////////////////////////////////////////////////////////////////////////////
|
||
|
||
#ifndef __CCNF_PATCH_EXPERT_h_
|
||
#define __CCNF_PATCH_EXPERT_h_
|
||
|
||
#include <opencv2/core/core.hpp>
|
||
|
||
#include <map>
|
||
#include <vector>
|
||
|
||
namespace LandmarkDetector
|
||
{
|
||
|
||
//===========================================================================
|
||
/**
|
||
A single Neuron response
|
||
*/
|
||
class CCNF_neuron{
|
||
|
||
public:
|
||
|
||
// Type of patch (0=raw,1=grad,3=depth, other types besides raw are not actually used now)
|
||
int neuron_type;
|
||
|
||
// scaling of weights (needed as the energy of neuron might not be 1)
|
||
double norm_weights;
|
||
|
||
// Weight bias
|
||
double bias;
|
||
|
||
// Neural weights
|
||
cv::Mat_<float> weights;
|
||
|
||
// can have neural weight dfts that are calculated on the go as needed, this allows us not to recompute
|
||
// the dft of the template each time, improving the speed of tracking
|
||
std::map<int, cv::Mat_<double> > weights_dfts;
|
||
|
||
// the alpha associated with the neuron
|
||
double alpha;
|
||
|
||
// Default constructor
|
||
CCNF_neuron(){;}
|
||
|
||
// Copy constructor
|
||
CCNF_neuron(const CCNF_neuron& other);
|
||
|
||
void Read(std::ifstream &stream);
|
||
// The im_dft, integral_img, and integral_img_sq are precomputed images for convolution speedups (they get set if passed in empty values)
|
||
void Response(cv::Mat_<float> &im, cv::Mat_<double> &im_dft, cv::Mat &integral_img, cv::Mat &integral_img_sq, cv::Mat_<float> &resp);
|
||
|
||
};
|
||
|
||
//===========================================================================
|
||
/**
|
||
A CCNF patch expert
|
||
*/
|
||
class CCNF_patch_expert{
|
||
public:
|
||
|
||
// Width and height of the patch expert support region
|
||
int width;
|
||
int height;
|
||
|
||
// Collection of neurons for this patch expert
|
||
std::vector<CCNF_neuron> neurons;
|
||
|
||
// Information about the vertex features (association potentials)
|
||
std::vector<int> window_sizes;
|
||
std::vector<cv::Mat_<float> > Sigmas;
|
||
std::vector<double> betas;
|
||
|
||
// How confident we are in the patch
|
||
double patch_confidence;
|
||
|
||
// Default constructor
|
||
CCNF_patch_expert(){;}
|
||
|
||
// Copy constructor
|
||
CCNF_patch_expert(const CCNF_patch_expert& other);
|
||
|
||
void Read(std::ifstream &stream, std::vector<int> window_sizes, std::vector<std::vector<cv::Mat_<float> > > sigma_components);
|
||
|
||
// actual work (can pass in an image and a potential depth image, if the CCNF is trained with depth)
|
||
void Response(cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
|
||
|
||
// Helper function to compute relevant sigmas
|
||
void ComputeSigmas(std::vector<cv::Mat_<float> > sigma_components, int window_size);
|
||
|
||
};
|
||
//===========================================================================
|
||
}
|
||
#endif
|