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Fix with CNN inference sizes.

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Tadas Baltrusaitis
2017-08-11 14:56:45 -04:00
parent abcc91364e
commit 6dbed701bc
1 changed files with 64 additions and 19 deletions
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83
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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@@ -269,27 +269,45 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
}
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)
if(input_maps.size() > 1)
{
cv::Mat_<float> add = input_maps[in].t();
add = add.reshape(0, 1);
cv::vconcat(input_concat, add, input_concat);
// Concatenate all the maps
cv::Size orig_size = input_maps[0].size();
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::vconcat(input_concat, add, input_concat);
}
input_concat = input_concat.t() * cnn_fully_connected_layers_weights[fully_connected_layer];
// Add biases
for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
{
input_concat.col(k) = input_concat.col(k) + cnn_fully_connected_layers_biases[fully_connected_layer].at<float>(k);
}
outputs.clear();
// Resize and add as output
for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
{
cv::Mat_<float> reshaped = input_concat.col(k).clone();
reshaped = reshaped.reshape(1, orig_size.width).t();
outputs.push_back(reshaped);
}
}
input_concat = input_concat.t() * cnn_fully_connected_layers_weights[fully_connected_layer];
for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
else
{
input_concat.col(k) = input_concat.col(k) + cnn_fully_connected_layers_biases[fully_connected_layer].at<float>(k);
cv::Mat out = input_maps[0].t() * cnn_fully_connected_layers_weights[fully_connected_layer] + cnn_fully_connected_layers_biases[fully_connected_layer].t();
outputs.clear();
outputs.push_back(out);
}
outputs.clear();
outputs.push_back(input_concat);
fully_connected_layer++;
}
if (layer_type == 3) // PReLU
@@ -513,6 +531,35 @@ void FaceDetectorMTCNN::Read(string location)
}
}
cv::Mat_<float> generate_bounding_boxes(cv::Mat_<float> heatmap, vector<cv::Mat_<float> > corrections, double scale, double threshold, int face_support)
{
// use heatmap to generate bounding boxes in the original image space
// Correction for the pooling
int stride = 2;
// Offsets for, x, y, width and height
//cv::Mat_<float> dx1 = corrections.col(1);
//cv::Mat_<float> dy1 = corrections.col(2);
//cv::Mat_<float> dx2 = corrections.col(3);
//cv::Mat_<float> dy2 = corrections.col(4);
// Find the parts of a heatmap above the threshold(x, y, and indices)
cv::Mat_<uchar> mask = heatmap >= threshold;
// Find the corresponding scores and bbox corrections
//score = heatmap(inds);
//correction = [dx1(inds) dy1(inds) dx2(inds) dy2(inds)];
// Correcting for Matlab's format
//bboxes = [y - 1 x - 1];
//bboxes = [fix((stride*(bboxes)+1) / scale) fix((stride*(bboxes)+face_support) / scale) score correction];
return cv::Mat_<float>();
}
// The actual MTCNN face detection step
bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat& input_img, std::vector<double>& o_confidences, int min_face_size, double t1, double t2, double t3)
{
@@ -552,14 +599,12 @@ bool FaceDetectorMTCNN::DetectFaces(vector<cv::Rect_<double> >& o_regions, const
std::vector<cv::Mat_<float> > pnet_out = PNet.Inference(normalised_img);
// TODO resize appropriately the output
cv::Mat_<float> out_prob;
cv::exp(pnet_out[0]- pnet_out[1], out_prob);
out_prob = 1.0 / (1.0 + out_prob);
cv::imshow("out_map", out_prob);
cv::waitKey(0);
// Grab the detections
}
return true;