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VanFace
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‎detect_facemarks_by_dnn_vanface.php‎

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<?php
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use CV\Scalar, CV\Size;
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use function CV\{imread, imwrite, cvtColor, circle};
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$netDet = \CV\DNN\readNetFromCaffe('models/ssd/res10_300x300_ssd_deploy.prototxt', 'models/ssd/res10_300x300_ssd_iter_140000.caffemodel');
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$netRecogn = \CV\DNN\readNetFromCaffe('models/VanFace/VanFace.prototxt', 'models/VanFace/VanFace.caffemodel');
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$src = imread("images/faces.jpg");
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$size = $src->size(); // 2000x500
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$minSide = min($size->width, $size->height);
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$divider = $minSide / 300;
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\CV\resize($src, $resized, new Size($size->width / $divider, $size->height / $divider)); // 1200x300
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//var_export($resized);
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$blob = \CV\DNN\blobFromImage($resized, 1, new Size(), new Scalar(104, 177, 123), true, false);
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$netDet->setInput($blob);
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$r = $netDet->forward();
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//var_export($r->shape);
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$faces = [];
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$scalar = new Scalar(0, 0, 255);
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for ($i = 0; $i < $r->shape[2]; $i++) {
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$confidence = $r->atIdx([0,0,$i,2]);
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if ($confidence > 0.9) {
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var_export($confidence);echo "\n";
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$startX = $r->atIdx([0,0,$i,3]) * $src->cols;
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$startY = $r->atIdx([0,0,$i,4]) * $src->rows;
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$endX = $r->atIdx([0,0,$i,5]) * $src->cols;
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$endY = $r->atIdx([0,0,$i,6]) * $src->rows;
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$face = $src->getImageROI(new \CV\Rect($startX, $startY, $endX - $startX, $endY - $startY));
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\CV\resize($face, $resized, new Size(60,60));
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$resized = cvtColor($resized, \CV\COLOR_BGR2GRAY, 2);
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\CV\meanStdDev($resized, $mean, $sdv);
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$m = $mean->data()[0];
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$s = $sdv->data()[0];
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$blob = \CV\DNN\blobFromImage($resized, 1 / (0.000001 + $s), new Size(60,60), new Scalar($m, $m, $m));
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$netRecogn->setInput($blob);
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//var_export($blob);die();
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$out = $netRecogn->forward();
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$data = $out->data();
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for ($j=0;$j<68;$j++) {
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$point = new \CV\Point($startX + $data[$j*2] * $face->cols, $startY + $data[$j*2+1] * $face->rows);
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circle($src, $point, 2, new Scalar(0, 0, 255), 2);
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//var_export($point);
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}
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}
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}
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imwrite("results/_detect_facemarks_by_dnn_vanface.jpg", $src);

‎models/README.md‎

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* ssdlite_mobilenet_v2_coco - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
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* ssd_mobilenet_v2_coco - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
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* insightface - https://github.com/axinc-ai/ailia-models/tree/master/face_identification/insightface
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* VanFace - https://github.com/lsy17096535/face-landmark

‎models/VanFace/VanFace.caffemodel‎

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‎models/VanFace/VanFace.prototxt‎

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name: "landmark"
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input: "data"
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input_dim: 1
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input_dim: 1
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input_dim: 60
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input_dim: 60
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########################################
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# the actual net
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# layer 1
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layer {
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name: "Conv1"
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type: "Convolution"
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bottom: "data"
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top: "Conv1"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 20
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pad: 2
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kernel_size: 5
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stride: 1
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weight_filler {
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type: "xavier"
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std: 0.1
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}
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bias_filler {
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type: "constant"
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value: 0.2
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}
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}
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}
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layer {
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name: "ActivationTangH1"
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bottom: "Conv1"
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top: "ActivationTangH1"
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type: "TanH"
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}
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layer {
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name: "ActivationAbs1"
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bottom: "ActivationTangH1"
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top: "Abs1"
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type: "AbsVal"
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}
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layer {
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name: "Pool1"
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type: "Pooling"
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bottom: "Abs1"
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top: "Pool1"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "Conv2"
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type: "Convolution"
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bottom: "Pool1"
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top: "Conv2"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 48
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pad: 2
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kernel_size: 5
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stride: 1
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weight_filler {
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type: "xavier"
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std: 0.1
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}
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bias_filler {
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type: "constant"
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value: 0.2
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}
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}
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}
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layer {
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name: "ActivationTangH2"
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bottom: "Conv2"
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top: "ActivationTangH2"
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type: "TanH"
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}
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layer {
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name: "ActivationAbs2"
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bottom: "ActivationTangH2"
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top: "Abs2"
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type: "AbsVal"
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}
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layer {
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name: "Pool2"
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type: "Pooling"
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bottom: "Abs2"
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top: "Pool2"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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# layer 3
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layer {
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name: "Conv3"
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type: "Convolution"
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bottom: "Pool2"
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top: "Conv3"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 64
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pad: 0
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kernel_size: 3
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stride: 1
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weight_filler {
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type: "xavier"
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std: 0.1
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}
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bias_filler {
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type: "constant"
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value: 0.2
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}
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}
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}
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layer {
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name: "ActivationTangH3"
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bottom: "Conv3"
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top: "ActivationTangH3"
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type: "TanH"
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}
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layer {
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name: "ActivationAbs3"
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bottom: "ActivationTangH3"
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top: "Abs3"
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type: "AbsVal"
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}
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layer {
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name: "Pool3"
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type: "Pooling"
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bottom: "Abs3"
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top: "Pool3"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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# layer 4
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layer {
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name: "Conv4"
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type: "Convolution"
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bottom: "Pool3"
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top: "Conv4"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 80
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pad: 0
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kernel_size: 3
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stride: 1
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weight_filler {
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type: "xavier"
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std: 0.1
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}
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bias_filler {
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type: "constant"
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value: 0.2
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}
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}
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}
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layer {
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name: "ActivationTangH4"
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bottom: "Conv4"
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top: "ActivationTangH4"
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type: "TanH"
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}
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layer {
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name: "ActivationAbs4"
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bottom: "ActivationTangH4"
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top: "Abs4"
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type: "AbsVal"
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}
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########################################
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layer {
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name: "Dense1"
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type: "InnerProduct"
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bottom: "Abs4"
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top: "Dense1"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 512
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weight_filler {
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type: "xavier"
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "ActivationTangH5"
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bottom: "Dense1"
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top: "ActivationTangH5"
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type: "TanH"
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}
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layer {
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name: "ActivationAbs5"
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bottom: "ActivationTangH5"
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top: "Abs5"
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type: "AbsVal"
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}
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layer {
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name: "Dense3"
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type: "InnerProduct"
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bottom: "Abs5"
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top: "Dense3"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 136
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weight_filler {
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type: "xavier"
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}

‎results/detect_facemarks_by_dnn_vanface.jpg‎

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