Vladimir Goncharov
49 linhas
1.5 KiB
PHP
49 linhas
1.5 KiB
PHP
<?php
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use CV\Scalar, CV\Size;
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use function CV\{imread, imwrite};
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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\readNetFromONNX('models/insightface/arcface_mobilefacenet.onnx');
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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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//imwrite("results/_face.jpg", $face);
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$blob = \CV\DNN\blobFromImage($face, 1.0 / 255, new Size(112, 112), new Scalar(), true, false);
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$netRecogn->setInput($blob);
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$vec = $netRecogn->forward();
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\CV\normalize($r, $r);
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//$vec->print();
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var_export($vec->data());
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}
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}
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