362 lines
12 KiB
C++
362 lines
12 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.
|
||
//
|
||
///////////////////////////////////////////////////////////////////////////////
|
||
|
||
#include "stdafx.h"
|
||
|
||
#include "SVR_patch_expert.h"
|
||
|
||
// OpenCV include
|
||
#include <opencv2/core/core.hpp>
|
||
#include <opencv2/imgproc.hpp>
|
||
|
||
#include "LandmarkDetectorUtils.h"
|
||
|
||
using namespace LandmarkDetector;
|
||
|
||
//===========================================================================
|
||
// Computing the image gradient
|
||
void Grad(const cv::Mat& im, cv::Mat& grad)
|
||
{
|
||
|
||
/*float filter[3] = {1, 0, -1};
|
||
float dfilter[1] = {1};
|
||
cv::Mat filterX = cv::Mat(1,3,CV_32F, filter).clone();
|
||
cv::Mat filterY = cv::Mat(1,1,CV_32F, dfilter).clone();
|
||
|
||
cv::Mat gradX;
|
||
cv::Mat gradY;
|
||
cv::sepFilter2D(im, gradX, CV_32F, filterY, filterX, cv::Point(-1,-1), 0);
|
||
cv::sepFilter2D(im, gradY, CV_32F, filterX.t(), filterY, cv::Point(-1,-1), 0);
|
||
cv::pow(gradX,2, gradX);
|
||
cv::pow(gradY,2, gradY);
|
||
grad = gradX + gradY;
|
||
|
||
grad.row(0).setTo(0);
|
||
grad.col(0).setTo(0);
|
||
grad.col(grad.cols-1).setTo(0);
|
||
grad.row(grad.rows-1).setTo(0); */
|
||
|
||
// A quicker alternative
|
||
int x,y,h = im.rows,w = im.cols;
|
||
float vx,vy;
|
||
|
||
// Initialise the gradient
|
||
grad.create(im.size(), CV_32F);
|
||
grad.setTo(0.0f);
|
||
|
||
cv::MatIterator_<float> gp = grad.begin<float>() + w+1;
|
||
cv::MatConstIterator_<float> px1 = im.begin<float>() + w+2;
|
||
cv::MatConstIterator_<float> px2 = im.begin<float>() + w;
|
||
cv::MatConstIterator_<float> py1 = im.begin<float>() + 2*w+1;
|
||
cv::MatConstIterator_<float> py2 = im.begin<float>() + 1;
|
||
|
||
for(y = 1; y < h-1; y++)
|
||
{
|
||
for(x = 1; x < w-1; x++)
|
||
{
|
||
vx = *px1++ - *px2++;
|
||
vy = *py1++ - *py2++;
|
||
*gp++ = vx*vx + vy*vy;
|
||
}
|
||
px1 += 2;
|
||
px2 += 2;
|
||
py1 += 2;
|
||
py2 += 2;
|
||
gp += 2;
|
||
}
|
||
|
||
}
|
||
|
||
// A copy constructor
|
||
SVR_patch_expert::SVR_patch_expert(const SVR_patch_expert& other) : weights(other.weights.clone())
|
||
{
|
||
this->type = other.type;
|
||
this->scaling = other.scaling;
|
||
this->bias = other.bias;
|
||
this->confidence = other.confidence;
|
||
|
||
for (std::map<int, cv::Mat_<double> >::const_iterator it = other.weights_dfts.begin(); it != other.weights_dfts.end(); it++)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->weights_dfts.insert(std::pair<int, cv::Mat>(it->first, it->second.clone()));
|
||
}
|
||
}
|
||
|
||
//===========================================================================
|
||
void SVR_patch_expert::Read(ifstream &stream)
|
||
{
|
||
|
||
// A sanity check when reading patch experts
|
||
int read_type;
|
||
stream >> read_type;
|
||
assert(read_type == 2);
|
||
|
||
stream >> type >> confidence >> scaling >> bias;
|
||
LandmarkDetector::ReadMat(stream, weights);
|
||
|
||
// OpenCV and Matlab matrix cardinality is different, hence the transpose
|
||
weights = weights.t();
|
||
|
||
}
|
||
|
||
//===========================================================================
|
||
void SVR_patch_expert::Response(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
|
||
{
|
||
|
||
int response_height = area_of_interest.rows - weights.rows + 1;
|
||
int response_width = area_of_interest.cols - weights.cols + 1;
|
||
|
||
// the patch area on which we will calculate reponses
|
||
cv::Mat_<float> normalised_area_of_interest;
|
||
|
||
if(response.rows != response_height || response.cols != response_width)
|
||
{
|
||
response.create(response_height, response_width);
|
||
}
|
||
|
||
// If type is raw just normalise mean and standard deviation
|
||
if(type == 0)
|
||
{
|
||
// Perform normalisation across whole patch
|
||
cv::Scalar mean;
|
||
cv::Scalar std;
|
||
|
||
cv::meanStdDev(area_of_interest, mean, std);
|
||
// Avoid division by zero
|
||
if(std[0] == 0)
|
||
{
|
||
std[0] = 1;
|
||
}
|
||
normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
|
||
}
|
||
// If type is gradient, perform the image gradient computation
|
||
else if(type == 1)
|
||
{
|
||
Grad(area_of_interest, normalised_area_of_interest);
|
||
}
|
||
else
|
||
{
|
||
printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__, type);
|
||
abort();
|
||
}
|
||
|
||
cv::Mat_<float> svr_response;
|
||
|
||
// The empty matrix as we don't pass precomputed dft's of image
|
||
cv::Mat_<double> empty_matrix_0(0,0,0.0);
|
||
cv::Mat_<float> empty_matrix_1(0,0,0.0);
|
||
cv::Mat_<float> empty_matrix_2(0,0,0.0);
|
||
|
||
// Efficient calc of patch expert SVR response across the area of interest
|
||
matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF_NORMED);
|
||
|
||
response.create(svr_response.size());
|
||
cv::MatIterator_<float> p = response.begin();
|
||
|
||
cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
|
||
cv::MatIterator_<float> q2 = svr_response.end();
|
||
|
||
while(q1 != q2)
|
||
{
|
||
// the SVR response passed into logistic regressor
|
||
*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
|
||
}
|
||
|
||
}
|
||
|
||
void SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float> &response)
|
||
{
|
||
|
||
// How big the response map will be
|
||
int response_height = area_of_interest.rows - weights.rows + 1;
|
||
int response_width = area_of_interest.cols - weights.cols + 1;
|
||
|
||
// the patch area on which we will calculate reponses
|
||
cv::Mat_<float> normalised_area_of_interest;
|
||
|
||
if(response.rows != response_height || response.cols != response_width)
|
||
{
|
||
response.create(response_height, response_width);
|
||
}
|
||
|
||
if(type == 0)
|
||
{
|
||
// Perform normalisation across whole patch
|
||
cv::Scalar mean;
|
||
cv::Scalar std;
|
||
|
||
// ignore missing values
|
||
cv::Mat_<uchar> mask = area_of_interest > 0;
|
||
cv::meanStdDev(area_of_interest, mean, std, mask);
|
||
|
||
// if all values the same don't divide by 0
|
||
if(std[0] == 0)
|
||
{
|
||
std[0] = 1;
|
||
}
|
||
|
||
normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
|
||
|
||
// Set the invalid pixels to 0
|
||
normalised_area_of_interest.setTo(0, mask == 0);
|
||
}
|
||
else
|
||
{
|
||
printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__,type);
|
||
abort();
|
||
}
|
||
|
||
cv::Mat_<float> svr_response;
|
||
|
||
// The empty matrix as we don't pass precomputed dft's of image
|
||
cv::Mat_<double> empty_matrix_0(0,0,0.0);
|
||
cv::Mat_<float> empty_matrix_1(0,0,0.0);
|
||
cv::Mat_<float> empty_matrix_2(0,0,0.0);
|
||
|
||
// Efficient calc of patch expert response across the area of interest
|
||
|
||
matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF);
|
||
|
||
response.create(svr_response.size());
|
||
cv::MatIterator_<float> p = response.begin();
|
||
|
||
cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
|
||
cv::MatIterator_<float> q2 = svr_response.end();
|
||
|
||
while(q1 != q2)
|
||
{
|
||
// the SVR response passed through a logistic regressor
|
||
*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
|
||
}
|
||
}
|
||
|
||
// Copy constructor
|
||
Multi_SVR_patch_expert::Multi_SVR_patch_expert(const Multi_SVR_patch_expert& other) : svr_patch_experts(other.svr_patch_experts)
|
||
{
|
||
this->width = other.width;
|
||
this->height = other.height;
|
||
}
|
||
|
||
//===========================================================================
|
||
void Multi_SVR_patch_expert::Read(ifstream &stream)
|
||
{
|
||
// A sanity check when reading patch experts
|
||
int type;
|
||
stream >> type;
|
||
assert(type == 3);
|
||
|
||
// The number of patch experts for this view (with different modalities)
|
||
int number_modalities;
|
||
|
||
stream >> width >> height >> number_modalities;
|
||
|
||
svr_patch_experts.resize(number_modalities);
|
||
for(int i = 0; i < number_modalities; i++)
|
||
svr_patch_experts[i].Read(stream);
|
||
|
||
}
|
||
//===========================================================================
|
||
void Multi_SVR_patch_expert::Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response)
|
||
{
|
||
|
||
int response_height = area_of_interest.rows - height + 1;
|
||
int response_width = area_of_interest.cols - width + 1;
|
||
|
||
if(response.rows != response_height || response.cols != response_width)
|
||
{
|
||
response.create(response_height, response_width);
|
||
}
|
||
|
||
// For the purposes of the experiment only use the response of normal intensity, for fair comparison
|
||
|
||
if(svr_patch_experts.size() == 1)
|
||
{
|
||
svr_patch_experts[0].Response(area_of_interest, response);
|
||
}
|
||
else
|
||
{
|
||
// responses from multiple patch experts these can be gradients, LBPs etc.
|
||
response.setTo(1.0);
|
||
|
||
cv::Mat_<float> modality_resp(response_height, response_width);
|
||
|
||
for(size_t i = 0; i < svr_patch_experts.size(); i++)
|
||
{
|
||
svr_patch_experts[i].Response(area_of_interest, modality_resp);
|
||
response = response.mul(modality_resp);
|
||
}
|
||
|
||
}
|
||
|
||
}
|
||
|
||
void Multi_SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
|
||
{
|
||
int response_height = area_of_interest.rows - height + 1;
|
||
int response_width = area_of_interest.cols - width + 1;
|
||
|
||
if(response.rows != response_height || response.cols != response_width)
|
||
{
|
||
response.create(response_height, response_width);
|
||
}
|
||
|
||
// With depth patch experts only do raw data modality
|
||
svr_patch_experts[0].ResponseDepth(area_of_interest, response);
|
||
}
|
||
//===========================================================================
|