1339 lines
38 KiB
C++
1339 lines
38 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 "LandmarkDetectionValidator.h"
|
||
|
||
// OpenCV includes
|
||
#include <opencv2/core/core.hpp>
|
||
#include <opencv2/imgproc.hpp>
|
||
|
||
// TBB includes
|
||
#include <tbb/tbb.h>
|
||
|
||
// System includes
|
||
#include <fstream>
|
||
|
||
// Math includes
|
||
#define _USE_MATH_DEFINES
|
||
#include <cmath>
|
||
|
||
#ifndef M_PI
|
||
#define M_PI 3.14159265358979323846
|
||
#endif
|
||
// Local includes
|
||
#include "LandmarkDetectorUtils.h"
|
||
|
||
using namespace LandmarkDetector;
|
||
|
||
// Copy constructor
|
||
DetectionValidator::DetectionValidator(const DetectionValidator& other) : orientations(other.orientations), bs(other.bs), paws(other.paws),
|
||
cnn_subsampling_layers(other.cnn_subsampling_layers), cnn_layer_types(other.cnn_layer_types), cnn_fully_connected_layers_bias(other.cnn_fully_connected_layers_bias),
|
||
cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias), cnn_convolutional_layers_dft(other.cnn_convolutional_layers_dft)
|
||
{
|
||
|
||
this->validator_type = other.validator_type;
|
||
|
||
this->activation_fun = other.activation_fun;
|
||
this->output_fun = other.output_fun;
|
||
|
||
this->ws.resize(other.ws.size());
|
||
for (size_t i = 0; i < other.ws.size(); ++i)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->ws[i] = other.ws[i].clone();
|
||
}
|
||
|
||
this->ws_nn.resize(other.ws_nn.size());
|
||
for (size_t i = 0; i < other.ws_nn.size(); ++i)
|
||
{
|
||
this->ws_nn[i].resize(other.ws_nn[i].size());
|
||
|
||
for (size_t k = 0; k < other.ws_nn[i].size(); ++k)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->ws_nn[i][k] = other.ws_nn[i][k].clone();
|
||
}
|
||
}
|
||
|
||
this->cnn_convolutional_layers.resize(other.cnn_convolutional_layers.size());
|
||
for (size_t v = 0; v < other.cnn_convolutional_layers.size(); ++v)
|
||
{
|
||
this->cnn_convolutional_layers[v].resize(other.cnn_convolutional_layers[v].size());
|
||
|
||
for (size_t l = 0; l < other.cnn_convolutional_layers[v].size(); ++l)
|
||
{
|
||
this->cnn_convolutional_layers[v][l].resize(other.cnn_convolutional_layers[v][l].size());
|
||
|
||
for (size_t i = 0; i < other.cnn_convolutional_layers[v][l].size(); ++i)
|
||
{
|
||
this->cnn_convolutional_layers[v][l][i].resize(other.cnn_convolutional_layers[v][l][i].size());
|
||
|
||
for (size_t k = 0; k < other.cnn_convolutional_layers[v][l][i].size(); ++k)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->cnn_convolutional_layers[v][l][i][k] = other.cnn_convolutional_layers[v][l][i][k].clone();
|
||
}
|
||
|
||
}
|
||
}
|
||
}
|
||
|
||
this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size());
|
||
for (size_t v = 0; v < other.cnn_fully_connected_layers_weights.size(); ++v)
|
||
{
|
||
this->cnn_fully_connected_layers_weights[v].resize(other.cnn_fully_connected_layers_weights[v].size());
|
||
|
||
for (size_t l = 0; l < other.cnn_fully_connected_layers_weights[v].size(); ++l)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->cnn_fully_connected_layers_weights[v][l] = other.cnn_fully_connected_layers_weights[v][l].clone();
|
||
}
|
||
}
|
||
|
||
this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size());
|
||
for (size_t v = 0; v < other.cnn_fully_connected_layers_biases.size(); ++v)
|
||
{
|
||
this->cnn_fully_connected_layers_biases[v].resize(other.cnn_fully_connected_layers_biases[v].size());
|
||
|
||
for (size_t l = 0; l < other.cnn_fully_connected_layers_biases[v].size(); ++l)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->cnn_fully_connected_layers_biases[v][l] = other.cnn_fully_connected_layers_biases[v][l].clone();
|
||
}
|
||
}
|
||
|
||
this->mean_images.resize(other.mean_images.size());
|
||
for (size_t i = 0; i < other.mean_images.size(); ++i)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->mean_images[i] = other.mean_images[i].clone();
|
||
}
|
||
|
||
this->standard_deviations.resize(other.standard_deviations.size());
|
||
for (size_t i = 0; i < other.standard_deviations.size(); ++i)
|
||
{
|
||
// Make sure the matrix is copied.
|
||
this->standard_deviations[i] = other.standard_deviations[i].clone();
|
||
}
|
||
|
||
}
|
||
|
||
//===========================================================================
|
||
// Read in the landmark detection validation module
|
||
void DetectionValidator::Read(string location)
|
||
{
|
||
|
||
ifstream detection_validator_stream (location, ios::in|ios::binary);
|
||
if (detection_validator_stream.is_open())
|
||
{
|
||
detection_validator_stream.seekg (0, ios::beg);
|
||
|
||
// Read validator type
|
||
detection_validator_stream.read ((char*)&validator_type, 4);
|
||
|
||
// Read the number of views (orientations) within the validator
|
||
int n;
|
||
detection_validator_stream.read ((char*)&n, 4);
|
||
|
||
orientations.resize(n);
|
||
|
||
for(int i = 0; i < n; i++)
|
||
{
|
||
cv::Mat_<double> orientation_tmp;
|
||
LandmarkDetector::ReadMatBin(detection_validator_stream, orientation_tmp);
|
||
|
||
orientations[i] = cv::Vec3d(orientation_tmp.at<double>(0), orientation_tmp.at<double>(1), orientation_tmp.at<double>(2));
|
||
|
||
// Convert from degrees to radians
|
||
orientations[i] = orientations[i] * M_PI / 180.0;
|
||
}
|
||
|
||
// Initialise the piece-wise affine warps, biases and weights
|
||
paws.resize(n);
|
||
|
||
if( validator_type == 0)
|
||
{
|
||
// Reading in SVRs
|
||
bs.resize(n);
|
||
ws.resize(n);
|
||
}
|
||
else if(validator_type == 1)
|
||
{
|
||
// Reading in NNs
|
||
ws_nn.resize(n);
|
||
|
||
activation_fun.resize(n);
|
||
output_fun.resize(n);
|
||
}
|
||
else if(validator_type == 2)
|
||
{
|
||
cnn_convolutional_layers.resize(n);
|
||
cnn_convolutional_layers_dft.resize(n);
|
||
cnn_subsampling_layers.resize(n);
|
||
cnn_fully_connected_layers_weights.resize(n);
|
||
cnn_layer_types.resize(n);
|
||
cnn_fully_connected_layers_bias.resize(n);
|
||
cnn_convolutional_layers_bias.resize(n);
|
||
}
|
||
else if (validator_type == 3)
|
||
{
|
||
cnn_convolutional_layers.resize(n);
|
||
cnn_convolutional_layers_dft.resize(n);
|
||
cnn_fully_connected_layers_weights.resize(n);
|
||
cnn_layer_types.resize(n);
|
||
cnn_fully_connected_layers_biases.resize(n);
|
||
cnn_convolutional_layers_bias.resize(n);
|
||
}
|
||
|
||
// Initialise the normalisation terms
|
||
mean_images.resize(n);
|
||
standard_deviations.resize(n);
|
||
|
||
// Read in the validators for each of the views
|
||
for(int i = 0; i < n; i++)
|
||
{
|
||
|
||
// Read in the mean images
|
||
LandmarkDetector::ReadMatBin(detection_validator_stream, mean_images[i]);
|
||
mean_images[i] = mean_images[i].t();
|
||
|
||
LandmarkDetector::ReadMatBin(detection_validator_stream, standard_deviations[i]);
|
||
standard_deviations[i] = standard_deviations[i].t();
|
||
|
||
// Model specifics
|
||
if(validator_type == 0)
|
||
{
|
||
// Reading in the biases and weights
|
||
detection_validator_stream.read ((char*)&bs[i], 8);
|
||
LandmarkDetector::ReadMatBin(detection_validator_stream, ws[i]);
|
||
|
||
}
|
||
else if(validator_type == 1)
|
||
{
|
||
|
||
// Reading in the number of layers in the neural net
|
||
int num_depth_layers;
|
||
detection_validator_stream.read ((char*)&num_depth_layers, 4);
|
||
|
||
// Reading in activation and output function types
|
||
detection_validator_stream.read ((char*)&activation_fun[i], 4);
|
||
detection_validator_stream.read ((char*)&output_fun[i], 4);
|
||
|
||
ws_nn[i].resize(num_depth_layers);
|
||
for(int layer = 0; layer < num_depth_layers; layer++)
|
||
{
|
||
LandmarkDetector::ReadMatBin(detection_validator_stream, ws_nn[i][layer]);
|
||
|
||
// Transpose for efficiency during multiplication
|
||
ws_nn[i][layer] = ws_nn[i][layer].t();
|
||
}
|
||
}
|
||
else if(validator_type == 2)
|
||
{
|
||
// Reading in CNNs
|
||
|
||
int network_depth;
|
||
detection_validator_stream.read ((char*)&network_depth, 4);
|
||
|
||
cnn_layer_types[i].resize(network_depth);
|
||
|
||
for(int layer = 0; layer < network_depth; ++layer)
|
||
{
|
||
|
||
int layer_type;
|
||
detection_validator_stream.read ((char*)&layer_type, 4);
|
||
cnn_layer_types[i][layer] = layer_type;
|
||
|
||
// convolutional
|
||
if(layer_type == 0)
|
||
{
|
||
|
||
// Read the number of input maps
|
||
int num_in_maps;
|
||
detection_validator_stream.read ((char*)&num_in_maps, 4);
|
||
|
||
// Read the number of kernels for each input map
|
||
int num_kernels;
|
||
detection_validator_stream.read ((char*)&num_kernels, 4);
|
||
|
||
vector<vector<cv::Mat_<float> > > kernels;
|
||
vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
|
||
|
||
kernels.resize(num_in_maps);
|
||
kernel_dfts.resize(num_in_maps);
|
||
|
||
vector<float> biases;
|
||
for (int k = 0; k < num_kernels; ++k)
|
||
{
|
||
float bias;
|
||
detection_validator_stream.read ((char*)&bias, 4);
|
||
biases.push_back(bias);
|
||
}
|
||
|
||
cnn_convolutional_layers_bias[i].push_back(biases);
|
||
|
||
// For every input map
|
||
for (int in = 0; in < num_in_maps; ++in)
|
||
{
|
||
kernels[in].resize(num_kernels);
|
||
kernel_dfts[in].resize(num_kernels);
|
||
|
||
// For every kernel on that input map
|
||
for (int k = 0; k < num_kernels; ++k)
|
||
{
|
||
ReadMatBin(detection_validator_stream, kernels[in][k]);
|
||
|
||
// Flip the kernel in order to do convolution and not correlation
|
||
cv::flip(kernels[in][k], kernels[in][k], -1);
|
||
}
|
||
}
|
||
|
||
cnn_convolutional_layers[i].push_back(kernels);
|
||
cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
|
||
}
|
||
else if(layer_type == 1)
|
||
{
|
||
// Subsampling layer
|
||
|
||
int scale;
|
||
detection_validator_stream.read ((char*)&scale, 4);
|
||
|
||
cnn_subsampling_layers[i].push_back(scale);
|
||
}
|
||
else if(layer_type == 2)
|
||
{
|
||
float bias;
|
||
detection_validator_stream.read ((char*)&bias, 4);
|
||
cnn_fully_connected_layers_bias[i].push_back(bias);
|
||
|
||
// Fully connected layer
|
||
cv::Mat_<float> weights;
|
||
ReadMatBin(detection_validator_stream, weights);
|
||
cnn_fully_connected_layers_weights[i].push_back(weights);
|
||
}
|
||
}
|
||
}
|
||
else if (validator_type == 3)
|
||
{
|
||
int network_depth;
|
||
detection_validator_stream.read((char*)&network_depth, 4);
|
||
|
||
cnn_layer_types[i].resize(network_depth);
|
||
|
||
for (int layer = 0; layer < network_depth; ++layer)
|
||
{
|
||
|
||
int layer_type;
|
||
detection_validator_stream.read((char*)&layer_type, 4);
|
||
cnn_layer_types[i][layer] = layer_type;
|
||
|
||
// convolutional
|
||
if (layer_type == 0)
|
||
{
|
||
|
||
// Read the number of input maps
|
||
int num_in_maps;
|
||
detection_validator_stream.read((char*)&num_in_maps, 4);
|
||
|
||
// Read the number of kernels for each input map
|
||
int num_kernels;
|
||
detection_validator_stream.read((char*)&num_kernels, 4);
|
||
|
||
vector<vector<cv::Mat_<float> > > kernels;
|
||
vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
|
||
|
||
kernels.resize(num_in_maps);
|
||
kernel_dfts.resize(num_in_maps);
|
||
|
||
vector<float> biases;
|
||
for (int k = 0; k < num_kernels; ++k)
|
||
{
|
||
float bias;
|
||
detection_validator_stream.read((char*)&bias, 4);
|
||
biases.push_back(bias);
|
||
}
|
||
|
||
cnn_convolutional_layers_bias[i].push_back(biases);
|
||
|
||
// For every input map
|
||
for (int in = 0; in < num_in_maps; ++in)
|
||
{
|
||
kernels[in].resize(num_kernels);
|
||
kernel_dfts[in].resize(num_kernels);
|
||
|
||
// For every kernel on that input map
|
||
for (int k = 0; k < num_kernels; ++k)
|
||
{
|
||
ReadMatBin(detection_validator_stream, kernels[in][k]);
|
||
|
||
}
|
||
}
|
||
|
||
cnn_convolutional_layers[i].push_back(kernels);
|
||
cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
|
||
}
|
||
else if (layer_type == 2)
|
||
{
|
||
cv::Mat_<float> biases;
|
||
ReadMatBin(detection_validator_stream, biases);
|
||
cnn_fully_connected_layers_biases[i].push_back(biases);
|
||
|
||
// Fully connected layer
|
||
cv::Mat_<float> weights;
|
||
ReadMatBin(detection_validator_stream, weights);
|
||
cnn_fully_connected_layers_weights[i].push_back(weights);
|
||
}
|
||
}
|
||
}
|
||
// Read in the piece-wise affine warps
|
||
paws[i].Read(detection_validator_stream);
|
||
}
|
||
|
||
}
|
||
else
|
||
{
|
||
cout << "WARNING: Can't find the Face checker location" << endl;
|
||
}
|
||
}
|
||
|
||
//===========================================================================
|
||
// Check if the fitting actually succeeded
|
||
double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uchar>& intensity_img, cv::Mat_<double>& detected_landmarks)
|
||
{
|
||
|
||
int id = GetViewId(orientation);
|
||
|
||
// The warped (cropped) image, corresponding to a face lying withing the detected lanmarks
|
||
cv::Mat_<double> warped;
|
||
|
||
// the piece-wise affine image
|
||
cv::Mat_<double> intensity_img_double;
|
||
intensity_img.convertTo(intensity_img_double, CV_64F);
|
||
|
||
paws[id].Warp(intensity_img_double, warped, detected_landmarks);
|
||
|
||
double dec;
|
||
if(validator_type == 0)
|
||
{
|
||
dec = CheckSVR(warped, id);
|
||
}
|
||
else if(validator_type == 1)
|
||
{
|
||
dec = CheckNN(warped, id);
|
||
}
|
||
else if(validator_type == 2)
|
||
{
|
||
dec = CheckCNN_old(warped, id);
|
||
}
|
||
else if (validator_type == 3)
|
||
{
|
||
// On some machines the non-TBB version may be faster
|
||
//dec = CheckCNN(warped, id);
|
||
dec = CheckCNN_tbb(warped, id);
|
||
}
|
||
return dec;
|
||
}
|
||
|
||
double DetectionValidator::CheckNN(const cv::Mat_<double>& warped_img, int view_id)
|
||
{
|
||
cv::Mat_<double> feature_vec;
|
||
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
|
||
feature_vec = feature_vec.t();
|
||
|
||
for(size_t layer = 0; layer < ws_nn[view_id].size(); ++layer)
|
||
{
|
||
// Add a bias term
|
||
cv::hconcat(cv::Mat_<double>(1,1, 1.0), feature_vec, feature_vec);
|
||
|
||
// Apply the weights
|
||
feature_vec = feature_vec * ws_nn[view_id][layer];
|
||
|
||
// Activation or output
|
||
int fun_type;
|
||
if(layer != ws_nn[view_id].size() - 1)
|
||
{
|
||
fun_type = activation_fun[view_id];
|
||
}
|
||
else
|
||
{
|
||
fun_type = output_fun[view_id];
|
||
}
|
||
|
||
if(fun_type == 0)
|
||
{
|
||
cv::exp(-feature_vec, feature_vec);
|
||
feature_vec = 1.0 /(1.0 + feature_vec);
|
||
}
|
||
else if(fun_type == 1)
|
||
{
|
||
cv::MatIterator_<double> q1 = feature_vec.begin(); // respone for each pixel
|
||
cv::MatIterator_<double> q2 = feature_vec.end();
|
||
|
||
// the logistic function (sigmoid) applied to the response
|
||
while(q1 != q2)
|
||
{
|
||
*q1 = 1.7159 * tanh((2.0/3.0) * (*q1));
|
||
q1++;
|
||
}
|
||
}
|
||
// TODO ReLU
|
||
|
||
}
|
||
|
||
// Turn it to -1, 1 range
|
||
double dec = (feature_vec.at<double>(0) - 0.5) * 2;
|
||
|
||
return dec;
|
||
|
||
}
|
||
|
||
double DetectionValidator::CheckSVR(const cv::Mat_<double>& warped_img, int view_id)
|
||
{
|
||
|
||
cv::Mat_<double> feature_vec;
|
||
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
|
||
|
||
|
||
double dec = (ws[view_id].dot(feature_vec.t()) + bs[view_id]);
|
||
|
||
return dec;
|
||
|
||
}
|
||
|
||
// Convolutional Neural Network
|
||
double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id)
|
||
{
|
||
|
||
cv::Mat_<double> feature_vec;
|
||
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
|
||
|
||
// Create a normalised image from the crop vector
|
||
cv::Mat_<float> img(warped_img.size(), 0.0);
|
||
img = img.t();
|
||
|
||
cv::Mat mask = paws[view_id].pixel_mask.t();
|
||
cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
|
||
|
||
cv::MatIterator_<double> feature_it = feature_vec.begin();
|
||
cv::MatIterator_<float> img_it = img.begin();
|
||
|
||
int wInt = img.cols;
|
||
int hInt = img.rows;
|
||
|
||
for(int i=0; i < wInt; ++i)
|
||
{
|
||
for(int j=0; j < hInt; ++j, ++mask_it, ++img_it)
|
||
{
|
||
// if is within mask
|
||
if(*mask_it)
|
||
{
|
||
// assign the feature to image if it is within the mask
|
||
*img_it = (float)*feature_it++;
|
||
}
|
||
}
|
||
}
|
||
img = img.t();
|
||
|
||
int cnn_layer = 0;
|
||
int subsample_layer = 0;
|
||
int fully_connected_layer = 0;
|
||
|
||
vector<cv::Mat_<float> > input_maps;
|
||
input_maps.push_back(img);
|
||
|
||
vector<cv::Mat_<float> > outputs;
|
||
|
||
for(size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
|
||
{
|
||
// Determine layer type
|
||
int layer_type = cnn_layer_types[view_id][layer];
|
||
|
||
|
||
// Convolutional layer
|
||
if(layer_type == 0)
|
||
{
|
||
vector<cv::Mat_<float> > outputs_kern;
|
||
for(size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> input_image = input_maps[in];
|
||
|
||
// Useful precomputed data placeholders for quick correlation (convolution)
|
||
cv::Mat_<double> input_image_dft;
|
||
cv::Mat integral_image;
|
||
cv::Mat integral_image_sq;
|
||
|
||
for(size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
|
||
{
|
||
cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
|
||
|
||
// The convolution (with precomputation)
|
||
cv::Mat_<float> output;
|
||
if(cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty())
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
|
||
}
|
||
else
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
}
|
||
|
||
// Combining the maps
|
||
if(in == 0)
|
||
{
|
||
outputs_kern.push_back(output);
|
||
}
|
||
else
|
||
{
|
||
outputs_kern[k] = outputs_kern[k] + output;
|
||
}
|
||
|
||
}
|
||
|
||
}
|
||
|
||
outputs.clear();
|
||
for(size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
|
||
{
|
||
// Apply the sigmoid
|
||
cv::exp(-outputs_kern[k] - cnn_convolutional_layers_bias[view_id][cnn_layer][k], outputs_kern[k]);
|
||
outputs_kern[k] = 1.0 /(1.0 + outputs_kern[k]);
|
||
|
||
outputs.push_back(outputs_kern[k]);
|
||
|
||
}
|
||
|
||
cnn_layer++;
|
||
}
|
||
if(layer_type == 1)
|
||
{
|
||
// Subsampling layer
|
||
int scale = cnn_subsampling_layers[view_id][subsample_layer];
|
||
|
||
cv::Mat kx = cv::Mat::ones(2, 1, CV_32F)*1.0f/scale;
|
||
cv::Mat ky = cv::Mat::ones(1, 2, CV_32F)*1.0f/scale;
|
||
|
||
vector<cv::Mat_<float>> outputs_sub;
|
||
for(size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
|
||
cv::Mat_<float> conv_out;
|
||
|
||
cv::sepFilter2D(input_maps[in], conv_out, CV_32F, kx, ky);
|
||
conv_out = conv_out(cv::Rect(1, 1, conv_out.cols - 1, conv_out.rows - 1));
|
||
|
||
int res_rows = conv_out.rows / scale;
|
||
int res_cols = conv_out.cols / scale;
|
||
|
||
if(conv_out.rows % scale != 0)
|
||
{
|
||
res_rows++;
|
||
}
|
||
if(conv_out.cols % scale != 0)
|
||
{
|
||
res_cols++;
|
||
}
|
||
|
||
cv::Mat_<float> sub_out(res_rows, res_cols);
|
||
for(int w = 0; w < conv_out.cols; w+=scale)
|
||
{
|
||
for(int h=0; h < conv_out.rows; h+=scale)
|
||
{
|
||
sub_out.at<float>(h/scale, w/scale) = conv_out(h, w);
|
||
}
|
||
}
|
||
outputs_sub.push_back(sub_out);
|
||
}
|
||
outputs = outputs_sub;
|
||
subsample_layer++;
|
||
|
||
}
|
||
if(layer_type == 2)
|
||
{
|
||
// Concatenate all the maps
|
||
cv::Mat_<float> input_concat = input_maps[0].t();
|
||
input_concat = input_concat.reshape(0, 1);
|
||
|
||
for(size_t in = 1; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> add = input_maps[in].t();
|
||
add = add.reshape(0,1);
|
||
cv::hconcat(input_concat, add, input_concat);
|
||
}
|
||
|
||
input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer].t();
|
||
|
||
cv::exp(-input_concat - cnn_fully_connected_layers_bias[view_id][fully_connected_layer], input_concat);
|
||
input_concat = 1.0 /(1.0 + input_concat);
|
||
|
||
outputs.clear();
|
||
outputs.push_back(input_concat);
|
||
|
||
fully_connected_layer++;
|
||
}
|
||
// Max pooling layer
|
||
if (layer_type == 3)
|
||
{
|
||
|
||
vector<cv::Mat_<float>> outputs_sub;
|
||
|
||
// Iterate over pool height and width, all the stride is 2x2 and no padding is used
|
||
int stride_x = 2;
|
||
int stride_y = 2;
|
||
|
||
int pool_x = 2;
|
||
int pool_y = 2;
|
||
|
||
for (size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
int out_x = input_maps[in].cols / stride_x;
|
||
int out_y = input_maps[in].rows / stride_y;
|
||
|
||
cv::Mat_<float> sub_out(out_y, out_x, 0.0);
|
||
cv::Mat_<float> in_map = input_maps[in];
|
||
|
||
for (int x = 0; x < input_maps[in].cols; x+= stride_x)
|
||
{
|
||
for (int y = 0; y < input_maps[in].rows; y+= stride_y)
|
||
{
|
||
float curr_max = -FLT_MAX;
|
||
for (int x_in = x; x_in < x+pool_x; ++x_in)
|
||
{
|
||
for (int y_in = y; y_in < y + pool_y; ++y_in)
|
||
{
|
||
float curr_val = in_map.at<float>(y_in, x_in);
|
||
if (curr_val > curr_max)
|
||
{
|
||
curr_max = curr_val;
|
||
}
|
||
}
|
||
}
|
||
int x_in_out = x / stride_x;
|
||
int y_in_out = y / stride_y;
|
||
sub_out.at<float>(y_in_out, x_in_out) = curr_max;
|
||
}
|
||
}
|
||
|
||
outputs_sub.push_back(sub_out);
|
||
}
|
||
outputs = outputs_sub;
|
||
subsample_layer++;
|
||
}
|
||
|
||
// Set the outputs of this layer to inputs of the next
|
||
input_maps = outputs;
|
||
|
||
}
|
||
|
||
// Turn it to -1, 1 range
|
||
double dec = (outputs[0].at<float>(0) - 0.5) * 2.0;
|
||
|
||
return dec;
|
||
}
|
||
|
||
// Convolutional Neural Network
|
||
double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id)
|
||
{
|
||
|
||
cv::Mat_<double> feature_vec;
|
||
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
|
||
|
||
// Create a normalised image from the crop vector
|
||
cv::Mat_<float> img(warped_img.size(), 0.0);
|
||
img = img.t();
|
||
|
||
cv::Mat mask = paws[view_id].pixel_mask.t();
|
||
cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
|
||
|
||
cv::MatIterator_<double> feature_it = feature_vec.begin();
|
||
cv::MatIterator_<float> img_it = img.begin();
|
||
|
||
int wInt = img.cols;
|
||
int hInt = img.rows;
|
||
|
||
for (int i = 0; i < wInt; ++i)
|
||
{
|
||
for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
|
||
{
|
||
// if is within mask
|
||
if (*mask_it)
|
||
{
|
||
// assign the feature to image if it is within the mask
|
||
*img_it = (float)*feature_it++;
|
||
}
|
||
}
|
||
}
|
||
img = img.t();
|
||
|
||
int cnn_layer = 0;
|
||
int fully_connected_layer = 0;
|
||
|
||
vector<cv::Mat_<float> > input_maps;
|
||
input_maps.push_back(img);
|
||
|
||
vector<cv::Mat_<float> > outputs;
|
||
|
||
for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
|
||
{
|
||
// Determine layer type
|
||
int layer_type = cnn_layer_types[view_id][layer];
|
||
|
||
// Convolutional layer
|
||
if (layer_type == 0)
|
||
{
|
||
outputs.clear();
|
||
// Pre-allocate the output feature maps
|
||
outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size());
|
||
for (size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> input_image = input_maps[in];
|
||
|
||
// Useful precomputed data placeholders for quick correlation (convolution)
|
||
cv::Mat_<double> input_image_dft;
|
||
cv::Mat integral_image;
|
||
cv::Mat integral_image_sq;
|
||
|
||
// To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed
|
||
cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];
|
||
|
||
// The convolution (with precomputation)
|
||
cv::Mat_<float> output;
|
||
if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first;
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second;
|
||
}
|
||
else
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second;
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
}
|
||
|
||
// Combining the maps
|
||
if (in == 0)
|
||
{
|
||
outputs[0] = output;
|
||
}
|
||
else
|
||
{
|
||
outputs[0] = outputs[0] + output;
|
||
}
|
||
|
||
|
||
// TBB pass for the remaining kernels, empirically helps with layers with more kernels
|
||
tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
|
||
{
|
||
cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
|
||
|
||
// The convolution (with precomputation)
|
||
cv::Mat_<float> output;
|
||
if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
|
||
}
|
||
else
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
}
|
||
|
||
// Combining the maps
|
||
if (in == 0)
|
||
{
|
||
outputs[k] = output;
|
||
}
|
||
else
|
||
{
|
||
outputs[k] = outputs[k] + output;
|
||
}
|
||
}
|
||
});
|
||
|
||
}
|
||
|
||
for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
|
||
{
|
||
outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
|
||
}
|
||
cnn_layer++;
|
||
}
|
||
if (layer_type == 1)
|
||
{
|
||
vector<cv::Mat_<float>> outputs_sub;
|
||
|
||
// Iterate over pool height and width, all the stride is 2x2 and no padding is used
|
||
int stride_x = 2;
|
||
int stride_y = 2;
|
||
|
||
int pool_x = 2;
|
||
int pool_y = 2;
|
||
|
||
for (size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
int out_x = input_maps[in].cols / stride_x;
|
||
int out_y = input_maps[in].rows / stride_y;
|
||
|
||
cv::Mat_<float> sub_out(out_y, out_x, 0.0);
|
||
cv::Mat_<float> in_map = input_maps[in];
|
||
|
||
for (int x = 0; x < input_maps[in].cols; x += stride_x)
|
||
{
|
||
for (int y = 0; y < input_maps[in].rows; y += stride_y)
|
||
{
|
||
float curr_max = -FLT_MAX;
|
||
for (int x_in = x; x_in < x + pool_x; ++x_in)
|
||
{
|
||
for (int y_in = y; y_in < y + pool_y; ++y_in)
|
||
{
|
||
float curr_val = in_map.at<float>(y_in, x_in);
|
||
if (curr_val > curr_max)
|
||
{
|
||
curr_max = curr_val;
|
||
}
|
||
}
|
||
}
|
||
int x_in_out = x / stride_x;
|
||
int y_in_out = y / stride_y;
|
||
sub_out.at<float>(y_in_out, x_in_out) = curr_max;
|
||
}
|
||
}
|
||
|
||
outputs_sub.push_back(sub_out);
|
||
|
||
}
|
||
outputs = outputs_sub;
|
||
}
|
||
if (layer_type == 2)
|
||
{
|
||
// Concatenate all the maps
|
||
cv::Mat_<float> input_concat = input_maps[0].t();
|
||
input_concat = input_concat.reshape(0, 1);
|
||
|
||
for (size_t in = 1; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> add = input_maps[in].t();
|
||
add = add.reshape(0, 1);
|
||
cv::hconcat(input_concat, add, input_concat);
|
||
}
|
||
|
||
input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
|
||
input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
|
||
|
||
outputs.clear();
|
||
outputs.push_back(input_concat);
|
||
|
||
fully_connected_layer++;
|
||
}
|
||
if (layer_type == 3) // ReLU
|
||
{
|
||
outputs.clear();
|
||
for (size_t k = 0; k < input_maps.size(); ++k)
|
||
{
|
||
// Apply the ReLU
|
||
cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
|
||
outputs.push_back(input_maps[k]);
|
||
|
||
}
|
||
}
|
||
if (layer_type == 4)
|
||
{
|
||
outputs.clear();
|
||
for (size_t k = 0; k < input_maps.size(); ++k)
|
||
{
|
||
// Apply the sigmoid
|
||
cv::exp(-input_maps[k], input_maps[k]);
|
||
input_maps[k] = 1.0 / (1.0 + input_maps[k]);
|
||
|
||
outputs.push_back(input_maps[k]);
|
||
|
||
}
|
||
}
|
||
// Set the outputs of this layer to inputs of the next
|
||
input_maps = outputs;
|
||
|
||
}
|
||
|
||
// Convert the class label to a continuous value
|
||
double max_val = 0;
|
||
cv::Point max_loc;
|
||
cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
|
||
int max_idx = max_loc.y;
|
||
double max = 1;
|
||
double min = -1;
|
||
double bins = (double)outputs[0].cols;
|
||
// Unquantizing the softmax layer to continuous value
|
||
double step_size = (max - min) / bins; // This should be saved somewhere
|
||
double unquantized = min + step_size / 2.0 + max_idx * step_size;
|
||
|
||
return unquantized;
|
||
}
|
||
|
||
// Convolutional Neural Network
|
||
double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
|
||
{
|
||
|
||
cv::Mat_<double> feature_vec;
|
||
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
|
||
|
||
// Create a normalised image from the crop vector
|
||
cv::Mat_<float> img(warped_img.size(), 0.0);
|
||
img = img.t();
|
||
|
||
cv::Mat mask = paws[view_id].pixel_mask.t();
|
||
cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
|
||
|
||
cv::MatIterator_<double> feature_it = feature_vec.begin();
|
||
cv::MatIterator_<float> img_it = img.begin();
|
||
|
||
int wInt = img.cols;
|
||
int hInt = img.rows;
|
||
|
||
for (int i = 0; i < wInt; ++i)
|
||
{
|
||
for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
|
||
{
|
||
// if is within mask
|
||
if (*mask_it)
|
||
{
|
||
// assign the feature to image if it is within the mask
|
||
*img_it = (float)*feature_it++;
|
||
}
|
||
}
|
||
}
|
||
img = img.t();
|
||
|
||
int cnn_layer = 0;
|
||
int fully_connected_layer = 0;
|
||
|
||
vector<cv::Mat_<float> > input_maps;
|
||
input_maps.push_back(img);
|
||
|
||
vector<cv::Mat_<float> > outputs;
|
||
|
||
for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
|
||
{
|
||
// Determine layer type
|
||
int layer_type = cnn_layer_types[view_id][layer];
|
||
|
||
// Convolutional layer
|
||
if (layer_type == 0)
|
||
{
|
||
outputs.clear();
|
||
for (size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> input_image = input_maps[in];
|
||
|
||
// Useful precomputed data placeholders for quick correlation (convolution)
|
||
cv::Mat_<double> input_image_dft;
|
||
cv::Mat integral_image;
|
||
cv::Mat integral_image_sq;
|
||
|
||
// TODO can TBB-ify this
|
||
for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
|
||
{
|
||
cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
|
||
|
||
// The convolution (with precomputation)
|
||
cv::Mat_<float> output;
|
||
if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty())
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
|
||
cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
|
||
}
|
||
else
|
||
{
|
||
std::map<int, cv::Mat_<double> > precomputed_dft;
|
||
precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
|
||
LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
|
||
}
|
||
|
||
// Combining the maps
|
||
if (in == 0)
|
||
{
|
||
outputs.push_back(output);
|
||
}
|
||
else
|
||
{
|
||
outputs[k] = outputs[k] + output;
|
||
}
|
||
|
||
}
|
||
|
||
}
|
||
|
||
for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
|
||
{
|
||
outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
|
||
}
|
||
cnn_layer++;
|
||
}
|
||
if (layer_type == 1)
|
||
{
|
||
vector<cv::Mat_<float>> outputs_sub;
|
||
|
||
// Iterate over pool height and width, all the stride is 2x2 and no padding is used
|
||
int stride_x = 2;
|
||
int stride_y = 2;
|
||
|
||
int pool_x = 2;
|
||
int pool_y = 2;
|
||
|
||
for (size_t in = 0; in < input_maps.size(); ++in)
|
||
{
|
||
int out_x = input_maps[in].cols / stride_x;
|
||
int out_y = input_maps[in].rows / stride_y;
|
||
|
||
cv::Mat_<float> sub_out(out_y, out_x, 0.0);
|
||
cv::Mat_<float> in_map = input_maps[in];
|
||
|
||
for (int x = 0; x < input_maps[in].cols; x += stride_x)
|
||
{
|
||
for (int y = 0; y < input_maps[in].rows; y += stride_y)
|
||
{
|
||
float curr_max = -FLT_MAX;
|
||
for (int x_in = x; x_in < x + pool_x; ++x_in)
|
||
{
|
||
for (int y_in = y; y_in < y + pool_y; ++y_in)
|
||
{
|
||
float curr_val = in_map.at<float>(y_in, x_in);
|
||
if (curr_val > curr_max)
|
||
{
|
||
curr_max = curr_val;
|
||
}
|
||
}
|
||
}
|
||
int x_in_out = x / stride_x;
|
||
int y_in_out = y / stride_y;
|
||
sub_out.at<float>(y_in_out, x_in_out) = curr_max;
|
||
}
|
||
}
|
||
|
||
outputs_sub.push_back(sub_out);
|
||
|
||
}
|
||
outputs = outputs_sub;
|
||
}
|
||
if (layer_type == 2)
|
||
{
|
||
// Concatenate all the maps
|
||
cv::Mat_<float> input_concat = input_maps[0].t();
|
||
input_concat = input_concat.reshape(0, 1);
|
||
|
||
for (size_t in = 1; in < input_maps.size(); ++in)
|
||
{
|
||
cv::Mat_<float> add = input_maps[in].t();
|
||
add = add.reshape(0, 1);
|
||
cv::hconcat(input_concat, add, input_concat);
|
||
}
|
||
|
||
input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
|
||
input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
|
||
|
||
outputs.clear();
|
||
outputs.push_back(input_concat);
|
||
|
||
fully_connected_layer++;
|
||
}
|
||
if (layer_type == 3) // ReLU
|
||
{
|
||
outputs.clear();
|
||
for (size_t k = 0; k < input_maps.size(); ++k)
|
||
{
|
||
// Apply the ReLU
|
||
cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
|
||
outputs.push_back(input_maps[k]);
|
||
|
||
}
|
||
}
|
||
if (layer_type == 4)
|
||
{
|
||
outputs.clear();
|
||
for (size_t k = 0; k < input_maps.size(); ++k)
|
||
{
|
||
// Apply the sigmoid
|
||
cv::exp(-input_maps[k], input_maps[k]);
|
||
input_maps[k] = 1.0 / (1.0 + input_maps[k]);
|
||
|
||
outputs.push_back(input_maps[k]);
|
||
|
||
}
|
||
}
|
||
// Set the outputs of this layer to inputs of the next
|
||
input_maps = outputs;
|
||
|
||
}
|
||
|
||
// First turn to the 0-3 range
|
||
double max_val = 0;
|
||
cv::Point max_loc;
|
||
cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
|
||
int max_idx = max_loc.y;
|
||
double max = 3;
|
||
double min = 0;
|
||
double bins = (double)outputs[0].cols;
|
||
// Unquantizing the softmax layer to continuous value
|
||
double step_size = (max - min) / bins; // This should be saved somewhere
|
||
double unquantized = min + step_size / 2.0 + max_idx * step_size;
|
||
|
||
// Turn it to -1, 1 range
|
||
double dec = (unquantized - 1.5) / 1.5;
|
||
|
||
return dec;
|
||
}
|
||
|
||
void DetectionValidator::NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id)
|
||
{
|
||
cv::Mat_<double> warped_t = warped_img.t();
|
||
|
||
// the vector to be filled with paw values
|
||
cv::MatIterator_<double> vp;
|
||
cv::MatIterator_<double> cp;
|
||
|
||
cv::Mat_<double> vec(paws[view_id].number_of_pixels,1);
|
||
vp = vec.begin();
|
||
|
||
cp = warped_t.begin();
|
||
|
||
int wInt = warped_img.cols;
|
||
int hInt = warped_img.rows;
|
||
|
||
// the mask indicating if point is within or outside the face region
|
||
|
||
cv::Mat maskT = paws[view_id].pixel_mask.t();
|
||
|
||
cv::MatIterator_<uchar> mp = maskT.begin<uchar>();
|
||
|
||
for(int i=0; i < wInt; ++i)
|
||
{
|
||
for(int j=0; j < hInt; ++j, ++mp, ++cp)
|
||
{
|
||
// if is within mask
|
||
if(*mp)
|
||
{
|
||
*vp++ = *cp;
|
||
}
|
||
}
|
||
}
|
||
|
||
// Local normalisation
|
||
cv::Scalar mean;
|
||
cv::Scalar std;
|
||
cv::meanStdDev(vec, mean, std);
|
||
|
||
// subtract the mean image
|
||
vec -= mean[0];
|
||
|
||
// Normalise the image
|
||
if(std[0] == 0)
|
||
{
|
||
std[0] = 1;
|
||
}
|
||
|
||
vec /= std[0];
|
||
|
||
// Global normalisation
|
||
feature_vec = (vec - mean_images[view_id]) / standard_deviations[view_id];
|
||
}
|
||
|
||
// Getting the closest view center based on orientation
|
||
int DetectionValidator::GetViewId(const cv::Vec3d& orientation) const
|
||
{
|
||
int id = 0;
|
||
|
||
double dbest = -1.0;
|
||
|
||
for(size_t i = 0; i < this->orientations.size(); i++)
|
||
{
|
||
|
||
// Distance to current view
|
||
double d = cv::norm(orientation, this->orientations[i]);
|
||
|
||
if(i == 0 || d < dbest)
|
||
{
|
||
dbest = d;
|
||
id = i;
|
||
}
|
||
}
|
||
return id;
|
||
|
||
}
|
||
|
||
|