recognize face by dnn openface

face to vector by dnn openface

detect_face_by_dnn_ssd.php

@@ -35,7 +35,4 @@ for ($i = 0; $i < $r->shape[2]; $i++) {
     }
 }
 
-
-$data = [];
-
 imwrite("results/_detect_face_by_dnn_ssd.jpg", $src);

face_to_vector_by_dnn_openface.php

@@ -0,0 +1,47 @@
+<?php
+
+use CV\Scalar, CV\Size;
+use function CV\{imread, imwrite};
+
+$netDet = \CV\DNN\readNetFromCaffe('models/ssd/res10_300x300_ssd_deploy.prototxt', 'models/ssd/res10_300x300_ssd_iter_140000.caffemodel');
+$netRecogn = \CV\DNN\readNetFromTorch('models/openface/openface.nn4.small2.v1.t7');
+
+$src = imread("images/faces.jpg");
+$size = $src->size(); // 2000x500
+
+$minSide = min($size->width, $size->height);
+$divider = $minSide / 300;
+\CV\resize($src, $resized, new Size($size->width / $divider, $size->height / $divider)); // 1200x300
+
+//var_export($resized);
+
+$blob = \CV\DNN\blobFromImage($resized, 1, new Size(), new Scalar(104, 177, 123), true, false);
+
+$netDet->setInput($blob);
+
+$r = $netDet->forward();
+
+//var_export($r->shape);
+
+$faces = [];
+$scalar = new Scalar(0, 0, 255);
+for ($i = 0; $i < $r->shape[2]; $i++) {
+    $confidence = $r->atIdx([0,0,$i,2]);
+    if ($confidence > 0.9) {
+        var_export($confidence);echo "\n";
+        $startX = $r->atIdx([0,0,$i,3]) * $src->cols;
+        $startY = $r->atIdx([0,0,$i,4]) * $src->rows;
+        $endX = $r->atIdx([0,0,$i,5]) * $src->cols;
+        $endY = $r->atIdx([0,0,$i,6]) * $src->rows;
+
+        $face = $src->getImageROI(new \CV\Rect($startX, $startY, $endX - $startX, $endY - $startY));
+        //imwrite("results/_face.jpg", $face);
+
+        $blob = \CV\DNN\blobFromImage($face, 1.0 / 255, new Size(96, 96), new Scalar(), true, false);
+        $netRecogn->setInput($blob);
+        $vec = $netRecogn->forward();
+
+        //$vec->print();
+        var_export($vec->data());
+    }
+}

phpdoc.php

@@ -54,6 +54,13 @@ class Mat {
         return null;
     }
 
+    /**
+     * @return array
+     */
+    public function data() {
+        return [];
+    }
+
     /**
      * @return int
      */
@@ -172,7 +179,7 @@ class Mat {
      * @param null $value
      * @return int|null
      */
-    public function at(int $row, int $col, int $channel, $value = null) {
+    public function at(int $row, int $col, int $channel = 0, $value = null) {
 
     }
 
@@ -1374,7 +1381,7 @@ class Net {
      * @param string $name
      * @return null
      */
-    public function setInput(Mat $blob, string $name) {
+    public function setInput(Mat $blob, string $name = '') {
         return null;
     }
 

recognize_face_by_dnn_openface.php

@@ -0,0 +1,102 @@
+<?php
+
+use CV\Scalar, CV\Size;
+use function CV\{imread, imwrite, rectangle};
+
+$netDet = \CV\DNN\readNetFromCaffe('models/ssd/res10_300x300_ssd_deploy.prototxt', 'models/ssd/res10_300x300_ssd_iter_140000.caffemodel');
+$netRecogn = \CV\DNN\readNetFromTorch('models/openface/openface.nn4.small2.v1.t7');
+
+$image2faces = function ($src) use ($netDet) {
+    $size = $src->size(); // 2000x500
+
+    $minSide = min($size->width, $size->height);
+    $divider = $minSide / 300;
+    \CV\resize($src, $resized, new Size($size->width / $divider, $size->height / $divider)); // 1200x300
+    //var_export($resized);
+    $blob = \CV\DNN\blobFromImage($resized, 1, new Size(), new Scalar(104, 177, 123), true, false);
+
+    $netDet->setInput($blob);
+    $r = $netDet->forward();
+
+    //var_export($r->shape);
+
+    $faces = [];
+
+    for ($i = 0; $i < $r->shape[2]; $i++) {
+        $confidence = $r->atIdx([0,0,$i,2]);
+        if ($confidence > 0.9) {
+            //var_export($confidence);echo "\n";
+            $startX = $r->atIdx([0,0,$i,3]) * $src->cols;
+            $startY = $r->atIdx([0,0,$i,4]) * $src->rows;
+            $endX = $r->atIdx([0,0,$i,5]) * $src->cols;
+            $endY = $r->atIdx([0,0,$i,6]) * $src->rows;
+
+            $faces[] = $src->getImageROI(new \CV\Rect($startX, $startY, $endX - $startX, $endY - $startY));
+        }
+    }
+
+    return $faces;
+};
+
+$face2vec = function ($face) use ($netRecogn) {
+    $blob = \CV\DNN\blobFromImage($face, 1.0 / 255, new Size(96, 96), new Scalar(), true, false);
+    $netRecogn->setInput($blob);
+    return $netRecogn->forward();
+};
+
+function faceDistance($face1, $face2) {
+    $distance = 0;
+    foreach ($face1 as $i => $v) {
+        $distance += ($face1[$i] - $face2[$i])**2;
+    }
+    return sqrt($distance);
+}
+
+$src = imread("images/faces.jpg");
+
+$faces = $image2faces($src);
+
+$faceVectors = [];
+foreach ($faces as $i => $face) {
+    $vec = $face2vec($face);
+    //imwrite("results/_face.jpg", $face);
+
+    //$vec->print();
+    //var_export($vec->data());
+    $faceVectors["me$i"] = $vec->data();
+}
+
+$src = imread("images/angelina_faces.png");
+
+$faces = $image2faces($src);
+
+foreach ($faces as $i => $face) {
+    $vec = $face2vec($face);
+    //imwrite("results/_face.jpg", $face);
+
+    //$vec->print();
+    //var_export($vec->data());
+    $faceVectors["angelina$i"] = $vec->data();
+}
+
+//var_export($faceVectors);
+
+$src = imread("images/angelina_and_me.png");
+
+$faces = $image2faces($src);
+
+foreach ($faces as $i => $face) {
+    $vec = $face2vec($face);
+    $minDistance = 0;
+    $faceLabel = '';
+    foreach ($faceVectors as $label => $faceVector) {
+        $distance = faceDistance($vec->data(), $faceVector);
+
+        if (!$minDistance || $distance < $minDistance) {
+            $minDistance = $distance;
+            $faceLabel = $label;
+        }
+    }
+
+    echo "face$i $faceLabel $minDistance\n";
+}