338 lines
10 KiB
C++
338 lines
10 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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//
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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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#include "stdafx.h"
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#include "SVR_patch_expert.h"
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// OpenCV include
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include "LandmarkDetectorUtils.h"
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using namespace LandmarkDetector;
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//===========================================================================
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// Computing the image gradient
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void Grad(const cv::Mat& im, cv::Mat& grad)
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{
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/*float filter[3] = {1, 0, -1};
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float dfilter[1] = {1};
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cv::Mat filterX = cv::Mat(1,3,CV_32F, filter).clone();
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cv::Mat filterY = cv::Mat(1,1,CV_32F, dfilter).clone();
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cv::Mat gradX;
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cv::Mat gradY;
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cv::sepFilter2D(im, gradX, CV_32F, filterY, filterX, cv::Point(-1,-1), 0);
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cv::sepFilter2D(im, gradY, CV_32F, filterX.t(), filterY, cv::Point(-1,-1), 0);
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cv::pow(gradX,2, gradX);
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cv::pow(gradY,2, gradY);
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grad = gradX + gradY;
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grad.row(0).setTo(0);
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grad.col(0).setTo(0);
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grad.col(grad.cols-1).setTo(0);
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grad.row(grad.rows-1).setTo(0); */
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// A quicker alternative
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int x,y,h = im.rows,w = im.cols;
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float vx,vy;
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// Initialise the gradient
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grad.create(im.size(), CV_32F);
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grad.setTo(0.0f);
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cv::MatIterator_<float> gp = grad.begin<float>() + w+1;
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cv::MatConstIterator_<float> px1 = im.begin<float>() + w+2;
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cv::MatConstIterator_<float> px2 = im.begin<float>() + w;
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cv::MatConstIterator_<float> py1 = im.begin<float>() + 2*w+1;
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cv::MatConstIterator_<float> py2 = im.begin<float>() + 1;
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for(y = 1; y < h-1; y++)
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{
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for(x = 1; x < w-1; x++)
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{
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vx = *px1++ - *px2++;
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vy = *py1++ - *py2++;
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*gp++ = vx*vx + vy*vy;
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}
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px1 += 2;
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px2 += 2;
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py1 += 2;
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py2 += 2;
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gp += 2;
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}
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}
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// A copy constructor
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SVR_patch_expert::SVR_patch_expert(const SVR_patch_expert& other) : weights(other.weights.clone())
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{
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this->type = other.type;
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this->scaling = other.scaling;
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this->bias = other.bias;
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this->confidence = other.confidence;
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for (std::map<int, cv::Mat_<double> >::const_iterator it = other.weights_dfts.begin(); it != other.weights_dfts.end(); it++)
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{
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// Make sure the matrix is copied.
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this->weights_dfts.insert(std::pair<int, cv::Mat>(it->first, it->second.clone()));
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}
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}
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//===========================================================================
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void SVR_patch_expert::Read(ifstream &stream)
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{
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// A sanity check when reading patch experts
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int read_type;
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stream >> read_type;
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assert(read_type == 2);
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stream >> type >> confidence >> scaling >> bias;
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LandmarkDetector::ReadMat(stream, weights);
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// OpenCV and Matlab matrix cardinality is different, hence the transpose
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weights = weights.t();
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}
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//===========================================================================
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void SVR_patch_expert::Response(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
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{
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int response_height = area_of_interest.rows - weights.rows + 1;
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int response_width = area_of_interest.cols - weights.cols + 1;
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// the patch area on which we will calculate reponses
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cv::Mat_<float> normalised_area_of_interest;
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if(response.rows != response_height || response.cols != response_width)
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{
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response.create(response_height, response_width);
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}
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// If type is raw just normalise mean and standard deviation
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if(type == 0)
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{
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// Perform normalisation across whole patch
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cv::Scalar mean;
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cv::Scalar std;
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cv::meanStdDev(area_of_interest, mean, std);
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// Avoid division by zero
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if(std[0] == 0)
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{
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std[0] = 1;
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}
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normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
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}
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// If type is gradient, perform the image gradient computation
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else if(type == 1)
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{
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Grad(area_of_interest, normalised_area_of_interest);
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}
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else
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{
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printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__, type);
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abort();
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}
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cv::Mat_<float> svr_response;
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// The empty matrix as we don't pass precomputed dft's of image
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cv::Mat_<double> empty_matrix_0(0,0,0.0);
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cv::Mat_<float> empty_matrix_1(0,0,0.0);
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cv::Mat_<float> empty_matrix_2(0,0,0.0);
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// Efficient calc of patch expert SVR response across the area of interest
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matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF_NORMED);
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response.create(svr_response.size());
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cv::MatIterator_<float> p = response.begin();
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cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
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cv::MatIterator_<float> q2 = svr_response.end();
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while(q1 != q2)
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{
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// the SVR response passed into logistic regressor
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*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
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}
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}
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void SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float> &response)
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{
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// How big the response map will be
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int response_height = area_of_interest.rows - weights.rows + 1;
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int response_width = area_of_interest.cols - weights.cols + 1;
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// the patch area on which we will calculate reponses
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cv::Mat_<float> normalised_area_of_interest;
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if(response.rows != response_height || response.cols != response_width)
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{
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response.create(response_height, response_width);
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}
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if(type == 0)
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{
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// Perform normalisation across whole patch
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cv::Scalar mean;
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cv::Scalar std;
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// ignore missing values
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cv::Mat_<uchar> mask = area_of_interest > 0;
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cv::meanStdDev(area_of_interest, mean, std, mask);
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// if all values the same don't divide by 0
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if(std[0] == 0)
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{
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std[0] = 1;
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}
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normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
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// Set the invalid pixels to 0
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normalised_area_of_interest.setTo(0, mask == 0);
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}
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else
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{
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printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__,type);
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abort();
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}
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cv::Mat_<float> svr_response;
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// The empty matrix as we don't pass precomputed dft's of image
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cv::Mat_<double> empty_matrix_0(0,0,0.0);
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cv::Mat_<float> empty_matrix_1(0,0,0.0);
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cv::Mat_<float> empty_matrix_2(0,0,0.0);
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// Efficient calc of patch expert response across the area of interest
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matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF);
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response.create(svr_response.size());
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cv::MatIterator_<float> p = response.begin();
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cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
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cv::MatIterator_<float> q2 = svr_response.end();
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while(q1 != q2)
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{
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// the SVR response passed through a logistic regressor
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*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
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}
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}
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// Copy constructor
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Multi_SVR_patch_expert::Multi_SVR_patch_expert(const Multi_SVR_patch_expert& other) : svr_patch_experts(other.svr_patch_experts)
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{
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this->width = other.width;
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this->height = other.height;
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}
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//===========================================================================
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void Multi_SVR_patch_expert::Read(ifstream &stream)
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{
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// A sanity check when reading patch experts
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int type;
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stream >> type;
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assert(type == 3);
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// The number of patch experts for this view (with different modalities)
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int number_modalities;
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stream >> width >> height >> number_modalities;
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svr_patch_experts.resize(number_modalities);
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for(int i = 0; i < number_modalities; i++)
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svr_patch_experts[i].Read(stream);
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}
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//===========================================================================
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void Multi_SVR_patch_expert::Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response)
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{
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int response_height = area_of_interest.rows - height + 1;
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int response_width = area_of_interest.cols - width + 1;
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if(response.rows != response_height || response.cols != response_width)
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{
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response.create(response_height, response_width);
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}
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// For the purposes of the experiment only use the response of normal intensity, for fair comparison
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if(svr_patch_experts.size() == 1)
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{
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svr_patch_experts[0].Response(area_of_interest, response);
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}
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else
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{
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// responses from multiple patch experts these can be gradients, LBPs etc.
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response.setTo(1.0);
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cv::Mat_<float> modality_resp(response_height, response_width);
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for(size_t i = 0; i < svr_patch_experts.size(); i++)
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{
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svr_patch_experts[i].Response(area_of_interest, modality_resp);
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response = response.mul(modality_resp);
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}
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}
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}
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void Multi_SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
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{
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int response_height = area_of_interest.rows - height + 1;
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int response_width = area_of_interest.cols - width + 1;
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if(response.rows != response_height || response.cols != response_width)
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{
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response.create(response_height, response_width);
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}
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// With depth patch experts only do raw data modality
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svr_patch_experts[0].ResponseDepth(area_of_interest, response);
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}
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//===========================================================================
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