Speeding up fancy face validation.

lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h

@@ -162,6 +162,9 @@ private:
 	// Feed-forward Neural Network
 	double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
 
+	// Convolutional Neural Network
+	double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
+
 	// Convolutional Neural Network
 	double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
 

lib/local/LandmarkDetector/include/LandmarkDetectorModel.h

@@ -124,6 +124,7 @@ public:
 	// Keeping track of how many frames the tracker has failed in so far when tracking in videos
 	// This is useful for knowing when to initialise and reinitialise tracking
 	int failures_in_a_row;
+	int success_in_a_row;
 
 	// A template of a face that last succeeded with tracking (useful for large motions in video)
 	cv::Mat_<uchar> face_template;

lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp

@@ -64,6 +64,9 @@
 #include <opencv2/core/core.hpp>
 #include <opencv2/imgproc.hpp>
 
+// TBB includes
+#include <tbb/tbb.h>
+
 // System includes
 #include <fstream>
 
@@ -481,7 +484,9 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uc
 	}
 	else if (validator_type == 3)
 	{
-		dec = CheckCNN(warped, id);
+		// On some machines the non-TBB version may be faster
+		//dec = CheckCNN(warped, id);
+		dec = CheckCNN_tbb(warped, id);
 	}
 	return dec;
 }
@@ -789,6 +794,293 @@ double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int
 	return dec;
 }
 
+// Convolutional Neural Network
+double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id)
+{
+
+	cv::Mat_<double> feature_vec;
+	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
+
+	// Create a normalised image from the crop vector
+	cv::Mat_<float> img(warped_img.size(), 0.0);
+	img = img.t();
+
+	cv::Mat mask = paws[view_id].pixel_mask.t();
+	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();
+
+	cv::MatIterator_<double> feature_it = feature_vec.begin();
+	cv::MatIterator_<float> img_it = img.begin();
+
+	int wInt = img.cols;
+	int hInt = img.rows;
+
+	for (int i = 0; i < wInt; ++i)
+	{
+		for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
+		{
+			// if is within mask
+			if (*mask_it)
+			{
+				// assign the feature to image if it is within the mask
+				*img_it = (float)*feature_it++;
+			}
+		}
+	}
+	img = img.t();
+
+	int cnn_layer = 0;
+	int fully_connected_layer = 0;
+
+	vector<cv::Mat_<float> > input_maps;
+	input_maps.push_back(img);
+
+	vector<cv::Mat_<float> > outputs;
+
+	for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
+	{
+		// Determine layer type
+		int layer_type = cnn_layer_types[view_id][layer];
+
+		// Convolutional layer
+		if (layer_type == 0)
+		{
+			outputs.clear();
+			// Pre-allocate the output feature maps
+			outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size());
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> input_image = input_maps[in];
+
+				// Useful precomputed data placeholders for quick correlation (convolution)
+				cv::Mat_<double> input_image_dft;
+				cv::Mat integral_image;
+				cv::Mat integral_image_sq;
+
+				// To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed
+				cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];
+
+				// The convolution (with precomputation)
+				cv::Mat_<float> output;
+				if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass
+				{
+					std::map<int, cv::Mat_<double> > precomputed_dft;
+
+					LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first;
+					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second;
+				}
+				else
+				{
+					std::map<int, cv::Mat_<double> > precomputed_dft;
+					precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second;
+					LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+				}
+
+				// Combining the maps
+				if (in == 0)
+				{
+					outputs[0] = output;
+				}
+				else
+				{
+					outputs[0] = outputs[0] + output;
+				}
+
+				if(cnn_convolutional_layers[view_id][cnn_layer][0].size() > 20)
+				{
+					// TBB pass for the remaining kernels, empirically helps with layers with more kernels
+					tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
+					{
+						cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
+
+						// The convolution (with precomputation)
+						cv::Mat_<float> output;
+						if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
+						{
+							std::map<int, cv::Mat_<double> > precomputed_dft;
+
+							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
+							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
+						}
+						else
+						{
+							std::map<int, cv::Mat_<double> > precomputed_dft;
+							precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
+							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+						}
+
+						// Combining the maps
+						if (in == 0)
+						{
+							outputs[k] = output;
+						}
+						else
+						{
+							outputs[k] = outputs[k] + output;
+						}
+					}
+					});
+				}
+				else
+				{
+					for (size_t k = 1; 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()) // This will only be needed during the first pass
+						{
+							std::map<int, cv::Mat_<double> > precomputed_dft;
+
+							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
+							cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
+						}
+						else
+						{
+							std::map<int, cv::Mat_<double> > precomputed_dft;
+							precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
+							LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+						}
+
+						// Combining the maps
+						if (in == 0)
+						{
+							outputs[k] = output;
+						}
+						else
+						{
+							outputs[k] = outputs[k] + output;
+						}
+					}
+				}
+			}
+
+			for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
+			{
+				outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
+			}
+			cnn_layer++;
+		}
+		if (layer_type == 1)
+		{
+			vector<cv::Mat_<float>> outputs_sub;
+
+			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
+			int stride_x = 2;
+			int stride_y = 2;
+
+			int pool_x = 2;
+			int pool_y = 2;
+
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				int out_x = input_maps[in].cols / stride_x;
+				int out_y = input_maps[in].rows / stride_y;
+
+				cv::Mat_<float> sub_out(out_y, out_x, 0.0);
+				cv::Mat_<float> in_map = input_maps[in];
+
+				for (int x = 0; x < input_maps[in].cols; x += stride_x)
+				{
+					for (int y = 0; y < input_maps[in].rows; y += stride_y)
+					{
+						float curr_max = -FLT_MAX;
+						for (int x_in = x; x_in < x + pool_x; ++x_in)
+						{
+							for (int y_in = y; y_in < y + pool_y; ++y_in)
+							{
+								float curr_val = in_map.at<float>(y_in, x_in);
+								if (curr_val > curr_max)
+								{
+									curr_max = curr_val;
+								}
+							}
+						}
+						int x_in_out = x / stride_x;
+						int y_in_out = y / stride_y;
+						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
+					}
+				}
+
+				outputs_sub.push_back(sub_out);
+
+			}
+			outputs = outputs_sub;
+		}
+		if (layer_type == 2)
+		{
+			// Concatenate all the maps
+			cv::Mat_<float> input_concat = input_maps[0].t();
+			input_concat = input_concat.reshape(0, 1);
+
+			for (size_t in = 1; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> add = input_maps[in].t();
+				add = add.reshape(0, 1);
+				cv::hconcat(input_concat, add, input_concat);
+			}
+
+			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
+			input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
+
+			outputs.clear();
+			outputs.push_back(input_concat);
+
+			fully_connected_layer++;
+		}
+		if (layer_type == 3) // ReLU
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the ReLU
+				cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		if (layer_type == 4)
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the sigmoid
+				cv::exp(-input_maps[k], input_maps[k]);
+				input_maps[k] = 1.0 / (1.0 + input_maps[k]);
+
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		// Set the outputs of this layer to inputs of the next
+		input_maps = outputs;
+
+	}
+
+	// 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;
+}
+
 // Convolutional Neural Network
 double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
 {
@@ -849,6 +1141,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
 				cv::Mat integral_image;
 				cv::Mat integral_image_sq;
 
+				// TODO can TBB-ify this
 				for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
 				{
 					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];

lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp

@@ -288,16 +288,36 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
 		}
 
+
+		// If we are performing face validation, do it every 3 frames due to performance
+		bool reset_to_true = false;
+		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);
+
+		if (reset_to_true)
+		{
+			params.validate_detections = true;
+			clnf_model.detection_certainty = old_certainty;
+		}
+
 		if(!track_success)
 		{
 			// Make a record that tracking failed
 			clnf_model.failures_in_a_row++;
+			clnf_model.success_in_a_row = 0;
 		}
 		else
 		{
 			// indicate that tracking is a success
 			clnf_model.failures_in_a_row = -1;			
+			clnf_model.success_in_a_row++;
 			UpdateTemplate(grayscale_image, clnf_model);
 		}
 	}
@@ -377,7 +397,8 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			}
 			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);
 				return true;
 			}
@@ -388,12 +409,14 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 	if(!clnf_model.tracking_initialised)
 	{
 		clnf_model.failures_in_a_row++;
+		clnf_model.success_in_a_row = 0;
 	}
 
 	// un-initialise the tracking
 	if(	clnf_model.failures_in_a_row > 100)
 	{
 		clnf_model.tracking_initialised = false;
+		clnf_model.success_in_a_row = 0;
 	}
 
 	return clnf_model.detection_success;

lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp

@@ -76,7 +76,8 @@ CLNF::CLNF(const CLNF& other): pdm(other.pdm), params_local(other.params_local.c
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	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)
 	if(!face_detector_location.empty())
 	{
@@ -121,6 +122,7 @@ CLNF & CLNF::operator= (const CLNF& other)
 		this->detection_certainty = other.detection_certainty;
 		this->model_likelihood = other.model_likelihood;
 		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;
 
@@ -164,6 +166,7 @@ CLNF::CLNF(const CLNF&& other)
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	this->failures_in_a_row = other.failures_in_a_row;
+	this->success_in_a_row = other.success_in_a_row;
 
 	pdm = other.pdm;
 	params_local = other.params_local;
@@ -199,6 +202,7 @@ CLNF & CLNF::operator= (const CLNF&& other)
 	this->detection_certainty = other.detection_certainty;
 	this->model_likelihood = other.model_likelihood;
 	this->failures_in_a_row = other.failures_in_a_row;
+	this->success_in_a_row = other.success_in_a_row;
 
 	pdm = other.pdm;
 	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);
 
 	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);
 
 	failures_in_a_row = -1;
+	success_in_a_row = 0;
 	face_template = cv::Mat_<uchar>();
 }