sustaining_gazes/lib/local/LandmarkDetector/src/SVR_patch_expert.cpp

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2016-04-28 19:40:36 +00:00
///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
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//
// * 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 )));
}
}
// 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);
}
}
}