Speeding up fancy face validation.
This commit is contained in:
parent
8cc98be724
commit
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5 changed files with 329 additions and 3 deletions
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@ -162,6 +162,9 @@ private:
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// Feed-forward Neural Network
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// Feed-forward Neural Network
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double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
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double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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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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double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
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@ -124,6 +124,7 @@ public:
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// Keeping track of how many frames the tracker has failed in so far when tracking in videos
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// Keeping track of how many frames the tracker has failed in so far when tracking in videos
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// This is useful for knowing when to initialise and reinitialise tracking
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// This is useful for knowing when to initialise and reinitialise tracking
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int failures_in_a_row;
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int failures_in_a_row;
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int success_in_a_row;
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// A template of a face that last succeeded with tracking (useful for large motions in video)
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// A template of a face that last succeeded with tracking (useful for large motions in video)
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cv::Mat_<uchar> face_template;
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cv::Mat_<uchar> face_template;
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@ -64,6 +64,9 @@
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#include <opencv2/core/core.hpp>
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/imgproc.hpp>
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// TBB includes
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#include <tbb/tbb.h>
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// System includes
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// System includes
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#include <fstream>
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#include <fstream>
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@ -481,7 +484,9 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uc
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}
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}
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else if (validator_type == 3)
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else if (validator_type == 3)
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{
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{
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dec = CheckCNN(warped, id);
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// On some machines the non-TBB version may be faster
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//dec = CheckCNN(warped, id);
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dec = CheckCNN_tbb(warped, id);
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}
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}
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return dec;
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return dec;
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}
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}
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@ -789,6 +794,293 @@ double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int
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return dec;
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return dec;
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}
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}
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// Convolutional Neural Network
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double DetectionValidator::CheckCNN_tbb(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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NormaliseWarpedToVector(warped_img, feature_vec, view_id);
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// Create a normalised image from the crop vector
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cv::Mat_<float> img(warped_img.size(), 0.0);
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img = img.t();
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cv::Mat mask = paws[view_id].pixel_mask.t();
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cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
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cv::MatIterator_<double> feature_it = feature_vec.begin();
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cv::MatIterator_<float> img_it = img.begin();
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int wInt = img.cols;
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int hInt = img.rows;
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for (int i = 0; i < wInt; ++i)
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{
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for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
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{
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// if is within mask
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if (*mask_it)
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{
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// assign the feature to image if it is within the mask
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*img_it = (float)*feature_it++;
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}
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}
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}
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img = img.t();
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int cnn_layer = 0;
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int fully_connected_layer = 0;
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vector<cv::Mat_<float> > input_maps;
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input_maps.push_back(img);
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vector<cv::Mat_<float> > outputs;
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for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
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{
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// Determine layer type
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int layer_type = cnn_layer_types[view_id][layer];
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// Convolutional layer
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if (layer_type == 0)
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{
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outputs.clear();
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// Pre-allocate the output feature maps
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outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size());
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for (size_t in = 0; in < input_maps.size(); ++in)
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{
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cv::Mat_<float> input_image = input_maps[in];
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// Useful precomputed data placeholders for quick correlation (convolution)
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cv::Mat_<double> input_image_dft;
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cv::Mat integral_image;
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cv::Mat integral_image_sq;
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// To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed
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cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];
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// The convolution (with precomputation)
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cv::Mat_<float> output;
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if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first;
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second;
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}
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else
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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}
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// Combining the maps
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if (in == 0)
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{
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outputs[0] = output;
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}
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else
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{
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outputs[0] = outputs[0] + output;
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}
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if(cnn_convolutional_layers[view_id][cnn_layer][0].size() > 20)
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{
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// TBB pass for the remaining kernels, empirically helps with layers with more kernels
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tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
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{
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cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
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// The convolution (with precomputation)
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cv::Mat_<float> output;
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if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
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}
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else
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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}
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// Combining the maps
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if (in == 0)
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{
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outputs[k] = output;
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}
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else
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{
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outputs[k] = outputs[k] + output;
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}
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}
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});
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}
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else
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{
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for (size_t k = 1; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
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{
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cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
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// The convolution (with precomputation)
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cv::Mat_<float> output;
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if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
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cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
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}
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else
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{
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std::map<int, cv::Mat_<double> > precomputed_dft;
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precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
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LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
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}
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// Combining the maps
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if (in == 0)
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{
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outputs[k] = output;
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}
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else
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{
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outputs[k] = outputs[k] + output;
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}
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}
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}
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}
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for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
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{
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outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
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}
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cnn_layer++;
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}
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if (layer_type == 1)
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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;
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}
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if (layer_type == 2)
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{
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// Concatenate all the maps
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cv::Mat_<float> input_concat = input_maps[0].t();
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input_concat = input_concat.reshape(0, 1);
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for (size_t in = 1; in < input_maps.size(); ++in)
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{
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cv::Mat_<float> add = input_maps[in].t();
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add = add.reshape(0, 1);
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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_weights[view_id][fully_connected_layer];
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input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
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outputs.clear();
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outputs.push_back(input_concat);
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fully_connected_layer++;
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}
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if (layer_type == 3) // ReLU
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{
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outputs.clear();
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for (size_t k = 0; k < input_maps.size(); ++k)
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{
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// Apply the ReLU
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cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
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outputs.push_back(input_maps[k]);
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}
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}
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if (layer_type == 4)
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{
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outputs.clear();
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for (size_t k = 0; k < input_maps.size(); ++k)
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{
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// Apply the sigmoid
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cv::exp(-input_maps[k], input_maps[k]);
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input_maps[k] = 1.0 / (1.0 + input_maps[k]);
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outputs.push_back(input_maps[k]);
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}
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}
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// Set the outputs of this layer to inputs of the next
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input_maps = outputs;
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}
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// First turn to the 0-3 range
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double max_val = 0;
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cv::Point max_loc;
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cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
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int max_idx = max_loc.y;
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double max = 3;
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double min = 0;
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double bins = (double)outputs[0].cols;
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// Unquantizing the softmax layer to continuous value
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double step_size = (max - min) / bins; // This should be saved somewhere
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double unquantized = min + step_size / 2.0 + max_idx * step_size;
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// Turn it to -1, 1 range
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double dec = (unquantized - 1.5) / 1.5;
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return dec;
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}
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// Convolutional Neural Network
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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(const cv::Mat_<double>& warped_img, int view_id)
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{
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{
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@ -849,6 +1141,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
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cv::Mat integral_image;
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cv::Mat integral_image;
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cv::Mat integral_image_sq;
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cv::Mat integral_image_sq;
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// TODO can TBB-ify this
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for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
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for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
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{
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{
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cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
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cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
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||||||
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@ -288,16 +288,36 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
|
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CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
|
CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
|
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}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// If we are performing face validation, do it every 3 frames due to performance
|
||||||
|
bool reset_to_true = false;
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||||||
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double old_certainty = 0;
|
||||||
|
if (params.validate_detections == true && clnf_model.success_in_a_row % 3 != 0)
|
||||||
|
{
|
||||||
|
params.validate_detections = false;
|
||||||
|
reset_to_true = true;
|
||||||
|
old_certainty = clnf_model.detection_certainty;
|
||||||
|
}
|
||||||
|
|
||||||
bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
|
bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
|
||||||
|
|
||||||
|
if (reset_to_true)
|
||||||
|
{
|
||||||
|
params.validate_detections = true;
|
||||||
|
clnf_model.detection_certainty = old_certainty;
|
||||||
|
}
|
||||||
|
|
||||||
if(!track_success)
|
if(!track_success)
|
||||||
{
|
{
|
||||||
// Make a record that tracking failed
|
// Make a record that tracking failed
|
||||||
clnf_model.failures_in_a_row++;
|
clnf_model.failures_in_a_row++;
|
||||||
|
clnf_model.success_in_a_row = 0;
|
||||||
}
|
}
|
||||||
else
|
else
|
||||||
{
|
{
|
||||||
// indicate that tracking is a success
|
// indicate that tracking is a success
|
||||||
clnf_model.failures_in_a_row = -1;
|
clnf_model.failures_in_a_row = -1;
|
||||||
|
clnf_model.success_in_a_row++;
|
||||||
UpdateTemplate(grayscale_image, clnf_model);
|
UpdateTemplate(grayscale_image, clnf_model);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
@ -378,6 +398,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
|
||||||
else
|
else
|
||||||
{
|
{
|
||||||
clnf_model.failures_in_a_row = -1;
|
clnf_model.failures_in_a_row = -1;
|
||||||
|
clnf_model.success_in_a_row++;
|
||||||
UpdateTemplate(grayscale_image, clnf_model);
|
UpdateTemplate(grayscale_image, clnf_model);
|
||||||
return true;
|
return true;
|
||||||
}
|
}
|
||||||
|
@ -388,12 +409,14 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
|
||||||
if(!clnf_model.tracking_initialised)
|
if(!clnf_model.tracking_initialised)
|
||||||
{
|
{
|
||||||
clnf_model.failures_in_a_row++;
|
clnf_model.failures_in_a_row++;
|
||||||
|
clnf_model.success_in_a_row = 0;
|
||||||
}
|
}
|
||||||
|
|
||||||
// un-initialise the tracking
|
// un-initialise the tracking
|
||||||
if( clnf_model.failures_in_a_row > 100)
|
if( clnf_model.failures_in_a_row > 100)
|
||||||
{
|
{
|
||||||
clnf_model.tracking_initialised = false;
|
clnf_model.tracking_initialised = false;
|
||||||
|
clnf_model.success_in_a_row = 0;
|
||||||
}
|
}
|
||||||
|
|
||||||
return clnf_model.detection_success;
|
return clnf_model.detection_success;
|
||||||
|
|
|
@ -76,6 +76,7 @@ CLNF::CLNF(const CLNF& other): pdm(other.pdm), params_local(other.params_local.c
|
||||||
this->detection_certainty = other.detection_certainty;
|
this->detection_certainty = other.detection_certainty;
|
||||||
this->model_likelihood = other.model_likelihood;
|
this->model_likelihood = other.model_likelihood;
|
||||||
this->failures_in_a_row = other.failures_in_a_row;
|
this->failures_in_a_row = other.failures_in_a_row;
|
||||||
|
this->success_in_a_row = other.success_in_a_row;
|
||||||
|
|
||||||
// Load the CascadeClassifier (as it does not have a proper copy constructor)
|
// Load the CascadeClassifier (as it does not have a proper copy constructor)
|
||||||
if(!face_detector_location.empty())
|
if(!face_detector_location.empty())
|
||||||
|
@ -121,6 +122,7 @@ CLNF & CLNF::operator= (const CLNF& other)
|
||||||
this->detection_certainty = other.detection_certainty;
|
this->detection_certainty = other.detection_certainty;
|
||||||
this->model_likelihood = other.model_likelihood;
|
this->model_likelihood = other.model_likelihood;
|
||||||
this->failures_in_a_row = other.failures_in_a_row;
|
this->failures_in_a_row = other.failures_in_a_row;
|
||||||
|
this->success_in_a_row = other.success_in_a_row;
|
||||||
|
|
||||||
this->eye_model = other.eye_model;
|
this->eye_model = other.eye_model;
|
||||||
|
|
||||||
|
@ -164,6 +166,7 @@ CLNF::CLNF(const CLNF&& other)
|
||||||
this->detection_certainty = other.detection_certainty;
|
this->detection_certainty = other.detection_certainty;
|
||||||
this->model_likelihood = other.model_likelihood;
|
this->model_likelihood = other.model_likelihood;
|
||||||
this->failures_in_a_row = other.failures_in_a_row;
|
this->failures_in_a_row = other.failures_in_a_row;
|
||||||
|
this->success_in_a_row = other.success_in_a_row;
|
||||||
|
|
||||||
pdm = other.pdm;
|
pdm = other.pdm;
|
||||||
params_local = other.params_local;
|
params_local = other.params_local;
|
||||||
|
@ -199,6 +202,7 @@ CLNF & CLNF::operator= (const CLNF&& other)
|
||||||
this->detection_certainty = other.detection_certainty;
|
this->detection_certainty = other.detection_certainty;
|
||||||
this->model_likelihood = other.model_likelihood;
|
this->model_likelihood = other.model_likelihood;
|
||||||
this->failures_in_a_row = other.failures_in_a_row;
|
this->failures_in_a_row = other.failures_in_a_row;
|
||||||
|
this->success_in_a_row = other.success_in_a_row;
|
||||||
|
|
||||||
pdm = other.pdm;
|
pdm = other.pdm;
|
||||||
params_local = other.params_local;
|
params_local = other.params_local;
|
||||||
|
@ -527,6 +531,7 @@ void CLNF::Read(string main_location)
|
||||||
params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
|
params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
|
||||||
|
|
||||||
failures_in_a_row = -1;
|
failures_in_a_row = -1;
|
||||||
|
success_in_a_row = 0;
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
@ -547,6 +552,7 @@ void CLNF::Reset()
|
||||||
params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
|
params_global = cv::Vec6d(1, 0, 0, 0, 0, 0);
|
||||||
|
|
||||||
failures_in_a_row = -1;
|
failures_in_a_row = -1;
|
||||||
|
success_in_a_row = 0;
|
||||||
face_template = cv::Mat_<uchar>();
|
face_template = cv::Mat_<uchar>();
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
Loading…
Reference in a new issue