From 8cc98be724cfef1a7ea08528bb841a25db5a8a29 Mon Sep 17 00:00:00 2001 From: Tadas Baltrusaitis Date: Sat, 29 Jul 2017 18:19:41 -0400 Subject: [PATCH] First try at a new landmark validator. --- lib/local/FaceAnalyser/src/FaceAnalyser.cpp | 5 - .../include/LandmarkDetectionValidator.h | 51 ++- .../detection_validation/validator_cnn_68.txt | Bin 0 -> 2328608 bytes .../model/main_clm_general.txt | 2 +- .../LandmarkDetector/model/main_clm_wild.txt | 2 +- .../model/main_clnf_general.txt | 2 +- .../LandmarkDetector/model/main_clnf_wild.txt | 2 +- .../src/LandmarkDetectionValidator.cpp | 419 +++++++++++++++++- 8 files changed, 447 insertions(+), 36 deletions(-) create mode 100644 lib/local/LandmarkDetector/model/detection_validation/validator_cnn_68.txt diff --git a/lib/local/FaceAnalyser/src/FaceAnalyser.cpp b/lib/local/FaceAnalyser/src/FaceAnalyser.cpp index 064ae88..e91afc6 100644 --- a/lib/local/FaceAnalyser/src/FaceAnalyser.cpp +++ b/lib/local/FaceAnalyser/src/FaceAnalyser.cpp @@ -396,11 +396,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL 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); } - // First convert the face image to double representation as a row vector, TODO rem? - //cv::Mat_ aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1); - //cv::Mat_ aligned_face_cols_double; - //aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F); - // Visualising the median HOG if (visualise) { diff --git a/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h b/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h index 2a141a7..4c7c3a2 100644 --- a/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h +++ b/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h @@ -1,14 +1,38 @@ /////////////////////////////////////////////////////////////////////////////// -// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge, +// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge, // all rights reserved. // -// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY -// -// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. -// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE. -// -// License can be found in OpenFace-license.txt +// 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: @@ -59,7 +83,7 @@ class DetectionValidator public: - // What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net + // 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 int validator_type; // The orientations of each of the landmark detection validator @@ -98,11 +122,15 @@ public: vector > > > > > cnn_convolutional_layers_dft; vector > > cnn_convolutional_layers_bias; vector< vector > cnn_subsampling_layers; - vector< vector > > cnn_fully_connected_layers; + vector< vector > > cnn_fully_connected_layers_weights; vector< vector > cnn_fully_connected_layers_bias; - // 0 - convolutional, 1 - subsampling, 2 - fully connected + // OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected + // NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid vector > cnn_layer_types; + // Extra params for the new CNN + vector< vector > > cnn_fully_connected_layers_biases; + //========================================== // Normalisation for face validation @@ -137,6 +165,9 @@ private: // Convolutional Neural Network double CheckCNN(const cv::Mat_& warped_img, int view_id); + // Convolutional Neural Network + double CheckCNN_old(const cv::Mat_& warped_img, int view_id); + // A normalisation helper void NormaliseWarpedToVector(const cv::Mat_& warped_img, cv::Mat_& feature_vec, int view_id); diff --git 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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[v].size(); ++l) + 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[v][l] = other.cnn_fully_connected_layers[v][l].clone(); + 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(); } } @@ -188,11 +224,20 @@ void DetectionValidator::Read(string location) cnn_convolutional_layers.resize(n); cnn_convolutional_layers_dft.resize(n); cnn_subsampling_layers.resize(n); - cnn_fully_connected_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); @@ -318,11 +363,82 @@ void DetectionValidator::Read(string location) // Fully connected layer cv::Mat_ weights; ReadMatBin(detection_validator_stream, weights); - cnn_fully_connected_layers[i].push_back(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 > > kernels; + vector > > > kernel_dfts; + + kernels.resize(num_in_maps); + kernel_dfts.resize(num_in_maps); + + vector 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_ biases; + ReadMatBin(detection_validator_stream, biases); + cnn_fully_connected_layers_biases[i].push_back(biases); + + // Fully connected layer + cv::Mat_ 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); } @@ -360,6 +476,10 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_& warped_img, int view } // Convolutional Neural Network -double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view_id) +double DetectionValidator::CheckCNN_old(const cv::Mat_& warped_img, int view_id) { cv::Mat_ feature_vec; @@ -599,7 +719,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view cv::hconcat(input_concat, add, input_concat); } - input_concat = input_concat * cnn_fully_connected_layers[view_id][fully_connected_layer].t(); + 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); @@ -609,6 +729,55 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view fully_connected_layer++; } + // Max pooling layer + if (layer_type == 3) + { + + vector> 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_ sub_out(out_y, out_x, 0.0); + cv::Mat_ 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(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(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; @@ -620,6 +789,222 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view return dec; } +// Convolutional Neural Network +double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view_id) +{ + + cv::Mat_ feature_vec; + NormaliseWarpedToVector(warped_img, feature_vec, view_id); + + // Create a normalised image from the crop vector + cv::Mat_ img(warped_img.size(), 0.0); + img = img.t(); + + cv::Mat mask = paws[view_id].pixel_mask.t(); + cv::MatIterator_ mask_it = mask.begin(); + + cv::MatIterator_ feature_it = feature_vec.begin(); + cv::MatIterator_ 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 > input_maps; + input_maps.push_back(img); + + vector > 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_ input_image = input_maps[in]; + + // Useful precomputed data placeholders for quick correlation (convolution) + cv::Mat_ 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_ kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k]; + + // The convolution (with precomputation) + cv::Mat_ output; + if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) + { + std::map > 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 > 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> 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_ sub_out(out_y, out_x, 0.0); + cv::Mat_ 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(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(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_ 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_ 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_& warped_img, cv::Mat_& feature_vec, int view_id) { cv::Mat_ warped_t = warped_img.t();

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