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Final bit of simplification and fixing an issue with RNet and ONet.

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This commit is contained in:
Tadas Baltrusaitis
2017-08-15 21:53:25 +01:00
parent f4e73cc23f
commit d062ab690d
2 changed files with 59 additions and 45 deletions
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98
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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@@ -136,8 +136,43 @@ CNN::CNN(const CNN& other) : cnn_layer_types(other.cnn_layer_types), cnn_max_poo
}
}
void PReLU(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, cv::Mat_<float> prelu_weights)
{
outputs.clear();
if (input_maps.size() > 1)
{
for (size_t k = 0; k < input_maps.size(); ++k)
{
// Apply the PReLU
cv::Mat_<float> pos;
cv::threshold(input_maps[k], pos, 0, 0, cv::THRESH_TOZERO);
cv::Mat_<float> neg;
cv::threshold(input_maps[k], neg, 0, 0, cv::THRESH_TOZERO_INV);
outputs.push_back(pos + neg * prelu_weights.at<float>(k));
}
}
else
{
cv::Mat_<float> pos(input_maps[0].size(), 0.0);
cv::Mat_<float> neg(input_maps[0].size(), 0.0);
for (size_t k = 0; k < prelu_weights.rows; ++k)
{
// Apply the PReLU
cv::threshold(input_maps[0].row(k), pos.row(k), 0, 0, cv::THRESH_TOZERO);
cv::threshold(input_maps[0].row(k), neg.row(k), 0, 0, cv::THRESH_TOZERO_INV);
neg.row(k) = neg.row(k) * prelu_weights.at<float>(k);
}
outputs.push_back(pos + neg);
}
}
void fully_connected(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, cv::Mat_<float> weights, cv::Mat_<float> biases)
{
outputs.clear();
if (input_maps.size() > 1)
{
// Concatenate all the maps
@@ -335,7 +370,6 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
// Convolutional layer
if (layer_type == 0)
{
outputs.clear();
convolution_fft(outputs, input_maps, cnn_convolutional_layers[cnn_layer], cnn_convolutional_layers_bias[cnn_layer], cnn_convolutional_layers_dft[cnn_layer]);
cnn_layer++;
@@ -354,48 +388,12 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
}
if (layer_type == 2)
{
fully_connected(outputs, input_maps, cnn_fully_connected_layers_weights[fully_connected_layer], cnn_fully_connected_layers_biases[fully_connected_layer]);
fully_connected_layer++;
}
if (layer_type == 3) // PReLU
{
outputs.clear();
if(input_maps.size() > 1)
{
for (size_t k = 0; k < input_maps.size(); ++k)
{
// Apply the PReLU
cv::Mat_<float> pos;
cv::threshold(input_maps[k], pos, 0, 0, cv::THRESH_TOZERO);
cv::Mat_<float> neg;
cv::threshold(input_maps[k], neg, 0, 0, cv::THRESH_TOZERO_INV);
outputs.push_back(pos + neg * cnn_prelu_layer_weights[prelu_layer].at<float>(k));
}
}
else
{
cv::Mat_<float> pos(input_maps[0].size(), 0.0);
cv::Mat_<float> neg(input_maps[0].size(), 0.0);
for (size_t k = 0; k < cnn_prelu_layer_weights[prelu_layer].rows; ++k)
{
// Apply the PReLU
cv::threshold(input_maps[0].row(k), pos.row(k), 0, 0, cv::THRESH_TOZERO);
cv::threshold(input_maps[0].row(k), neg.row(k), 0, 0, cv::THRESH_TOZERO_INV);
neg.row(k) = neg.row(k) * cnn_prelu_layer_weights[prelu_layer].at<float>(k);
}
outputs.push_back(pos + neg);
}
//float diff = 0.0;
//for (size_t k = 0; k < outs.size(); ++k)
//{
// diff += cv::mean(cv::abs(outputs[k] - outs[k]))[0];
//}
//cout << diff << endl;
PReLU(outputs, input_maps, cnn_prelu_layer_weights[prelu_layer]);
prelu_layer++;
}
if (layer_type == 4)
@@ -668,7 +666,7 @@ void select_subset(const vector<int>& to_keep, vector<cv::Rect_<float> >& boundi
scores_tmp.push_back(scores[to_keep[i]]);
corrections_tmp.push_back(corrections[to_keep[i]]);
}
bounding_boxes = bounding_boxes_tmp;
scores = scores_tmp;
corrections = corrections_tmp;
@@ -805,7 +803,19 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
// Actual PNet CNN step
std::vector<cv::Mat_<float> > pnet_out = PNet.Inference(normalised_img);
// Clear the precomputations, as the image sizes will be different (TODO could be useful for videos)
for (size_t k1 = 0; k1 < PNet.cnn_convolutional_layers_dft.size(); ++k1)
{
for (size_t k2 = 0; k2 < PNet.cnn_convolutional_layers_dft[k1].size(); ++k2)
{
for (size_t k3 = 0; k3 < PNet.cnn_convolutional_layers_dft[k1][k2].size(); ++k3)
{
PNet.cnn_convolutional_layers_dft[k1][k2][k3].second = cv::Mat_<double>(0, 0, 0.0);
}
}
}
// Extract the probabilities from PNet response
cv::Mat_<float> prob_heatmap;
cv::exp(pnet_out[0]- pnet_out[1], prob_heatmap);
@@ -860,7 +870,7 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
int end_x_out = cv::min(width_target - (proposal_boxes_all[k].x + proposal_boxes_all[k].width - width_orig), width_target);
int end_y_out = cv::min(height_target - (proposal_boxes_all[k].y + proposal_boxes_all[k].height - height_orig), height_target);
cv::Mat tmp(height_target, width_target, CV_32FC3);
cv::Mat tmp(height_target, width_target, CV_32FC3, cv::Scalar(0.0f,0.0f,0.0f));
img_float(cv::Rect(start_x_in, start_y_in, end_x_in - start_x_in, end_y_in - start_y_in)).copyTo(
tmp(cv::Rect(start_x_out, start_y_out, end_x_out - start_x_out, end_y_out - start_y_out)));
@@ -883,6 +893,7 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
{
to_keep.push_back(k);
}
}
// Pick only the bounding boxes above the threshold
@@ -899,6 +910,7 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
// Preparing for the ONet stage
to_keep.clear();
for (size_t k = 0; k < proposal_boxes_all.size(); ++k)
{
float width_target = proposal_boxes_all[k].width + 1;
@@ -916,7 +928,7 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
int end_x_out = cv::min(width_target - (proposal_boxes_all[k].x + proposal_boxes_all[k].width - width_orig), width_target);
int end_y_out = cv::min(height_target - (proposal_boxes_all[k].y + proposal_boxes_all[k].height - height_orig), height_target);
cv::Mat tmp(height_target, width_target, CV_32FC3);
cv::Mat tmp(height_target, width_target, CV_32FC3, cv::Scalar(0.0f, 0.0f, 0.0f));
img_float(cv::Rect(start_x_in, start_y_in, end_x_in - start_x_in, end_y_in - start_y_in)).copyTo(
tmp(cv::Rect(start_x_out, start_y_out, end_x_out - start_x_out, end_y_out - start_y_out)));
@@ -966,7 +978,7 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
cv::rectangle(disp_img, proposal_boxes_all[k], cv::Scalar(255, 0, 0), 3);
}
cv::imshow("detections", disp_img);
cv::waitKey(5);
cv::waitKey(20);
if(o_regions.size() > 0)
{