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recognize_face_by_dnn_insightface.php

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Esse commit está contido em:
Vladimir Goncharov
2021-01-30 21:06:56 +02:00
pai 3a27a0006c
commit dda7cd1a3b
7 arquivos alterados com 10005 adições e 2 exclusões
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README.md
+1 -1
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@@ -11,7 +11,7 @@
![detect_facemarks_by_lbf.jpg](https://raw.githubusercontent.com/php-opencv/php-opencv-examples/master/results/detect_facemarks_by_lbf.jpg)
- [recognize face by LBPH algorithm](https://github.com/php-opencv/php-opencv-examples/blob/master/recognize_face_by_lbph.php) / [recognize face by DNN openface](https://github.com/php-opencv/php-opencv-examples/blob/master/recognize_face_by_dnn_openface.php)
- [recognize face by LBPH algorithm](https://github.com/php-opencv/php-opencv-examples/blob/master/recognize_face_by_lbph.php) / [recognize face by DNN openface](https://github.com/php-opencv/php-opencv-examples/blob/master/recognize_face_by_dnn_openface.php) / [recognize face by DNN insightface](https://github.com/php-opencv/php-opencv-examples/blob/master/recognize_face_by_dnn_insightface.php)
![recognize_face_by_lbph.jpg](https://raw.githubusercontent.com/php-opencv/php-opencv-examples/master/results/recognize_face_by_lbph.jpg)
face_to_vector_by_dnn_insightface.php
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<?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\readNetFromONNX('models/insightface/arcface_mobilefacenet.onnx');
$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(112, 112), new Scalar(), true, false);
$netRecogn->setInput($blob);
$vec = $netRecogn->forward();
\CV\normalize($r, $r);
//$vec->print();
var_export($vec->data());
}
}
models/README.md
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@@ -9,3 +9,4 @@ Sources:
* ssd_mobilenet_v1_coco - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
* ssdlite_mobilenet_v2_coco - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
* ssd_mobilenet_v2_coco - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
* insightface - https://github.com/axinc-ai/ailia-models/tree/master/face_identification/insightface
models/insightface/arcface_mobilefacenet.onnx
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models/insightface/arcface_mobilefacenet.onnx.prototxt
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phpdoc.php
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@@ -863,7 +863,16 @@ function log() {}
* @param int $norm_type
* @param int $dtype
*/
function normalize(Mat $src, Mat $dst, int $alpha = 1, int $beta = 0, int $norm_type, $dtype = -1)
function normalize(Mat $src, Mat $dst, int $alpha = 1, int $beta = 0, int $norm_type = NORM_L2, $dtype = -1)
{
}
/**
* @param Mat $src
* @param int $norm_type
*/
function norm(Mat $src, int $norm_type = NORM_L2)
{
}
recognize_face_by_dnn_insightface.php
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<?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\readNetFromONNX('models/insightface/arcface_mobilefacenet.onnx');
$image2faces = function ($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, new Size(112, 112), new Scalar(104, 117, 123), true, false);
$netRecogn->setInput($blob);
$r = $netRecogn->forward();
\CV\normalize($r, $r);
//$norm = \CV\norm($r);
//$r = $r->divide($norm);
return $r->data();
};
function faceDistance($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;
}
$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"] = $vec;
}
//var_export($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, $faceVector);
if (!$minDistance || $distance < $minDistance) {
$minDistance = $distance;
$faceLabel = $label;
$similarity = intval((max(sqrt(2), $minDistance) - $minDistance) / sqrt(2) * 100);
echo "face$i $faceLabel distance: $minDistance, similarity: $similarity%\n";
}
}
}