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11 | 11 |
|
12 | 12 | \CV\resize($src, $resised, new Size(300, 300)); |
13 | 13 |
|
14 | | -$blob = \CV\DNN\Net::blobFromImage($resised, 1, new Size(300, 300), new Scalar(104, 177, 123)); |
| 14 | +$blob = \CV\DNN\blobFromImage($resised, 1, new Size(300, 300), new Scalar(104, 177, 123)); |
15 | 15 |
|
16 | | -$net = \CV\DNN\Net::readNetFromCaffe('models/ssd/res10_300x300_ssd_deploy.prototxt', 'models/ssd/res10_300x300_ssd_iter_140000.caffemodel'); |
| 16 | +$net = \CV\DNN\readNetFromCaffe('models/ssd/res10_300x300_ssd_deploy.prototxt', 'models/ssd/res10_300x300_ssd_iter_140000.caffemodel'); |
17 | 17 |
|
18 | 18 | $net->setInput($blob, ""); |
19 | 19 |
|
20 | 20 | $r = $net->forward(); |
21 | 21 |
|
22 | 22 | $scalar = new Scalar(0, 0, 255); |
23 | 23 | for ($i = 0; $i < $r->shape[2]; $i++) { |
24 | | - $confidence = $r->atIdx([0,0,$i,2], 1); |
| 24 | + $confidence = $r->atIdx([0,0,$i,2]); |
25 | 25 | //var_export($confidence);echo "\n"; |
26 | 26 | if ($confidence > 0.5) { |
27 | | - rectangle($src, $r->atIdx([0,0,$i,3], 1)*$src->cols, $r->atIdx([0,0,$i,4], 1)*$src->rows, $r->atIdx([0,0,$i,5], 1)*$src->cols, $r->atIdx([0,0,$i,6], 1)*$src->rows, $scalar, 3); |
| 27 | + rectangle($src, $r->atIdx([0,0,$i,3])*$src->cols, $r->atIdx([0,0,$i,4])*$src->rows, $r->atIdx([0,0,$i,5])*$src->cols, $r->atIdx([0,0,$i,6])*$src->rows, $scalar, 3); |
28 | 28 | } |
29 | 29 | } |
30 | 30 |
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