First try at a new landmark validator.
This commit is contained in:
Tadas Baltrusaitis
parent
39d0dac347
commit
8cc98be724
8 changed files with 447 additions and 36 deletions
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@ -396,11 +396,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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UpdateRunningMedian(this->geom_desc_hist, this->geom_hist_sum, this->geom_descriptor_median, geom_descriptor_frame, update_median, this->num_bins_geom, this->min_val_geom, this->max_val_geom);
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}
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// First convert the face image to double representation as a row vector, TODO rem?
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//cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
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//cv::Mat_<double> aligned_face_cols_double;
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//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
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// Visualising the median HOG
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if (visualise)
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{
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@ -1,14 +1,38 @@
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///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// Copyright (C) 2016, 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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// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called “open source” software licenses (“Open Source
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// Components”), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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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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@ -59,7 +83,7 @@ class DetectionValidator
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public:
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// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net
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// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net
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int validator_type;
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// The orientations of each of the landmark detection validator
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@ -98,11 +122,15 @@ public:
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vector<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft;
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vector<vector<vector<float > > > cnn_convolutional_layers_bias;
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vector< vector<int> > cnn_subsampling_layers;
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vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers;
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vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_weights;
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vector< vector<float > > cnn_fully_connected_layers_bias;
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// 0 - convolutional, 1 - subsampling, 2 - fully connected
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// OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected
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// NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid
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vector<vector<int> > cnn_layer_types;
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// Extra params for the new CNN
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vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_biases;
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//==========================================
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// Normalisation for face validation
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@ -137,6 +165,9 @@ private:
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// Convolutional Neural Network
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double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id);
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// A normalisation helper
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void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id);
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Binary file not shown.
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@ -1,3 +1,3 @@
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LandmarkDetector clm_general.txt
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FaceDetConversion haarAlign.txt
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DetectionValidator detection_validation/validator_general_68.txt
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DetectionValidator detection_validation/validator_cnn_68.txt
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@ -1,3 +1,3 @@
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LandmarkDetector clm_wild.txt
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FaceDetConversion haarAlign.txt
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DetectionValidator detection_validation/validator_general_68.txt
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DetectionValidator detection_validation/validator_cnn_68.txt
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@ -3,4 +3,4 @@ LandmarkDetector_part model_inner/main_clnf_inner.txt inner 17 0 18 1 19 2 20 3
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LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18
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LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18
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FaceDetConversion haarAlign.txt
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DetectionValidator detection_validation/validator_general_68.txt
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DetectionValidator detection_validation/validator_cnn_68.txt
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@ -2,4 +2,4 @@ LandmarkDetector clnf_wild.txt
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LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18
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LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18
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FaceDetConversion haarAlign.txt
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DetectionValidator detection_validation/validator_general_68.txt
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DetectionValidator detection_validation/validator_cnn_68.txt
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@ -1,14 +1,38 @@
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///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// Copyright (C) 2016, 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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// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
|
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called “open source” software licenses (“Open Source
|
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// Components”), which means any software licenses approved as open source licenses by the
|
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
|
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// requires that the distributor make the software available in source code format. Licensor shall
|
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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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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@ -108,15 +132,27 @@ cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias), cnn_convolut
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}
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}
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this->cnn_fully_connected_layers.resize(other.cnn_fully_connected_layers.size());
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for (size_t v = 0; v < other.cnn_fully_connected_layers.size(); ++v)
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this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size());
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for (size_t v = 0; v < other.cnn_fully_connected_layers_weights.size(); ++v)
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{
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this->cnn_fully_connected_layers[v].resize(other.cnn_fully_connected_layers[v].size());
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this->cnn_fully_connected_layers_weights[v].resize(other.cnn_fully_connected_layers_weights[v].size());
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for (size_t l = 0; l < other.cnn_fully_connected_layers[v].size(); ++l)
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for (size_t l = 0; l < other.cnn_fully_connected_layers_weights[v].size(); ++l)
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{
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// Make sure the matrix is copied.
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this->cnn_fully_connected_layers[v][l] = other.cnn_fully_connected_layers[v][l].clone();
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this->cnn_fully_connected_layers_weights[v][l] = other.cnn_fully_connected_layers_weights[v][l].clone();
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}
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}
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this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size());
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for (size_t v = 0; v < other.cnn_fully_connected_layers_biases.size(); ++v)
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{
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this->cnn_fully_connected_layers_biases[v].resize(other.cnn_fully_connected_layers_biases[v].size());
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for (size_t l = 0; l < other.cnn_fully_connected_layers_biases[v].size(); ++l)
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{
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// Make sure the matrix is copied.
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this->cnn_fully_connected_layers_biases[v][l] = other.cnn_fully_connected_layers_biases[v][l].clone();
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}
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}
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@ -188,11 +224,20 @@ void DetectionValidator::Read(string location)
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cnn_convolutional_layers.resize(n);
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cnn_convolutional_layers_dft.resize(n);
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cnn_subsampling_layers.resize(n);
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cnn_fully_connected_layers.resize(n);
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cnn_fully_connected_layers_weights.resize(n);
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cnn_layer_types.resize(n);
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cnn_fully_connected_layers_bias.resize(n);
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cnn_convolutional_layers_bias.resize(n);
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}
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else if (validator_type == 3)
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{
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cnn_convolutional_layers.resize(n);
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cnn_convolutional_layers_dft.resize(n);
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cnn_fully_connected_layers_weights.resize(n);
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cnn_layer_types.resize(n);
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cnn_fully_connected_layers_biases.resize(n);
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cnn_convolutional_layers_bias.resize(n);
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}
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// Initialise the normalisation terms
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mean_images.resize(n);
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@ -318,11 +363,82 @@ void DetectionValidator::Read(string location)
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// Fully connected layer
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cv::Mat_<float> weights;
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ReadMatBin(detection_validator_stream, weights);
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cnn_fully_connected_layers[i].push_back(weights);
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cnn_fully_connected_layers_weights[i].push_back(weights);
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}
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}
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}
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else if (validator_type == 3)
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{
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int network_depth;
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detection_validator_stream.read((char*)&network_depth, 4);
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cnn_layer_types[i].resize(network_depth);
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for (int layer = 0; layer < network_depth; ++layer)
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{
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int layer_type;
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detection_validator_stream.read((char*)&layer_type, 4);
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cnn_layer_types[i][layer] = layer_type;
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// convolutional
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if (layer_type == 0)
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{
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// Read the number of input maps
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int num_in_maps;
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detection_validator_stream.read((char*)&num_in_maps, 4);
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// Read the number of kernels for each input map
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int num_kernels;
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detection_validator_stream.read((char*)&num_kernels, 4);
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vector<vector<cv::Mat_<float> > > kernels;
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vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
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kernels.resize(num_in_maps);
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kernel_dfts.resize(num_in_maps);
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vector<float> biases;
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for (int k = 0; k < num_kernels; ++k)
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{
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float bias;
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detection_validator_stream.read((char*)&bias, 4);
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biases.push_back(bias);
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}
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cnn_convolutional_layers_bias[i].push_back(biases);
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// For every input map
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for (int in = 0; in < num_in_maps; ++in)
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{
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kernels[in].resize(num_kernels);
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kernel_dfts[in].resize(num_kernels);
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// For every kernel on that input map
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for (int k = 0; k < num_kernels; ++k)
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{
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ReadMatBin(detection_validator_stream, kernels[in][k]);
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}
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}
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cnn_convolutional_layers[i].push_back(kernels);
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cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
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}
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else if (layer_type == 2)
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{
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cv::Mat_<float> biases;
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ReadMatBin(detection_validator_stream, biases);
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cnn_fully_connected_layers_biases[i].push_back(biases);
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// Fully connected layer
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cv::Mat_<float> weights;
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ReadMatBin(detection_validator_stream, weights);
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cnn_fully_connected_layers_weights[i].push_back(weights);
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}
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}
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}
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// Read in the piece-wise affine warps
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paws[i].Read(detection_validator_stream);
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}
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@ -360,6 +476,10 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uc
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dec = CheckNN(warped, id);
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}
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else if(validator_type == 2)
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{
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dec = CheckCNN_old(warped, id);
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}
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else if (validator_type == 3)
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{
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dec = CheckCNN(warped, id);
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}
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@ -433,7 +553,7 @@ double DetectionValidator::CheckSVR(const cv::Mat_<double>& warped_img, int view
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}
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// Convolutional Neural Network
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double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
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double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id)
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{
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cv::Mat_<double> feature_vec;
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@ -599,7 +719,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
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cv::hconcat(input_concat, add, input_concat);
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}
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input_concat = input_concat * cnn_fully_connected_layers[view_id][fully_connected_layer].t();
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input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer].t();
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cv::exp(-input_concat - cnn_fully_connected_layers_bias[view_id][fully_connected_layer], input_concat);
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input_concat = 1.0 /(1.0 + input_concat);
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@ -609,6 +729,55 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
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fully_connected_layer++;
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}
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// Max pooling layer
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if (layer_type == 3)
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{
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vector<cv::Mat_<float>> outputs_sub;
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// Iterate over pool height and width, all the stride is 2x2 and no padding is used
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int stride_x = 2;
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int stride_y = 2;
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int pool_x = 2;
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int pool_y = 2;
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for (size_t in = 0; in < input_maps.size(); ++in)
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{
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int out_x = input_maps[in].cols / stride_x;
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int out_y = input_maps[in].rows / stride_y;
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cv::Mat_<float> sub_out(out_y, out_x, 0.0);
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cv::Mat_<float> in_map = input_maps[in];
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for (int x = 0; x < input_maps[in].cols; x+= stride_x)
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{
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for (int y = 0; y < input_maps[in].rows; y+= stride_y)
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{
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float curr_max = -FLT_MAX;
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for (int x_in = x; x_in < x+pool_x; ++x_in)
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{
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for (int y_in = y; y_in < y + pool_y; ++y_in)
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{
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float curr_val = in_map.at<float>(y_in, x_in);
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if (curr_val > curr_max)
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{
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curr_max = curr_val;
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}
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}
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}
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int x_in_out = x / stride_x;
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int y_in_out = y / stride_y;
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sub_out.at<float>(y_in_out, x_in_out) = curr_max;
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}
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}
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outputs_sub.push_back(sub_out);
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}
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outputs = outputs_sub;
|
||||
subsample_layer++;
|
||||
}
|
||||
|
||||
// Set the outputs of this layer to inputs of the next
|
||||
input_maps = outputs;
|
||||
|
||||
|
|
@ -620,6 +789,222 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
|
|||
return dec;
|
||||
}
|
||||
|
||||
// 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;
|
||||
|
||||
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();
|
||||
|
|
|
|||
Loading…
Reference in a new issue