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Some more work on MTCNN.

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Tadas Baltrusaitis
2017-08-09 16:16:31 -04:00
parent 7fe7bb4904
commit 13e42a70cf
2 changed files with 35 additions and 3 deletions
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34
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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@@ -136,7 +136,7 @@ CNN::CNN(const CNN& other) : cnn_layer_types(other.cnn_layer_types), cnn_max_poo
}
}
cv::Mat_<double> CNN::Inference(const cv::Mat_<uchar>& input_img)
cv::Mat_<double> CNN::Inference(const cv::Mat_<float>& input_img)
{
if (input_img.channels() == 1)
{
@@ -490,4 +490,36 @@ void FaceDetectorMTCNN::Read(string location)
}
}
// The actual MTCNN face detection step
bool DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<float>& input_img, std::vector<double>& o_confidences, int min_face_size = 30, double t1 = 0.6, double t2 = 0.7, double t3 = 0.7)
{
int height_orig = input_img.rows;
int width_orig = input_img.cols;
// Size ratio of image pyramids
double pyramid_factor = 0.709;
// Face support region is 12x12 px, so from that can work out the largest
// scale(which is 12 / min), and work down from there to smallest scale(no smaller than 12x12px)
int min_dim = std::min(height_orig, width_orig);
int face_support = 12;
int num_scales = floor(log(min_face_size / min_dim) / log(pyramid_factor)) + 1;
for (int i = 0; i < num_scales; ++i)
{
double scale = (face_support / min_face_size)*cv::pow(pyramid_factor, i);
int h_pyr = ceil(height_orig * scale);
int w_pyr = ceil(width_orig * scale);
cv::Mat_<float> normalised_img;
cv::resize(input_img, normalised_img, cv::Size(w_pyr, h_pyr));
normalised_img = (normalised_img - 127.5) * 0.0078125;
}
}