Bagaimana untuk melakukan pemprosesan imej dan pembelajaran mendalam dalam PHP?

<?php
$width = 400;
$height = 200;
$image = imagecreate($width, $height);
$red = imagecolorallocate($image, 255, 0, 0);
imagefilledrectangle($image, 0, 0, $width, $height, $red);
header('Content-Type: image/png');
imagepng($image);
imagedestroy($image);
?>

<?php
$image = new Imagick('image.jpg');
$image->resizeImage(800, 600, Imagick::FILTER_LANCZOS, 1);
$image->writeImage('image_resized.jpg');
?>

<?php
$graph = new TensorFlowGraph();
$session = new TensorFlowSession($graph);
$saver = new TensorFlowSaver($graph);
$saver->restore($session, '/tmp/model.ckpt');
$tensor = $graph->operation('input')->output(0);
$result = $session->run([$graph->operation('output')->output(0)], [$tensor->shape()]);
print_r($result);
?>

<?php
use PhpmlDatasetObjectCollection;
use PhpmlDatasetDemoImagesDataset;
use PhpmlFeatureExtractionStopWordsEnglish;
use PhpmlFeatureExtractionTokenCountVectorizer;
use PhpmlFeatureExtractionTfIdfTransformer;
use PhpmlCrossValidationStratifiedRandomSplit;
use PhpmlMetricAccuracy;
use PhpmlNeuralNetworkLayer;
use PhpmlNeuralNetworkActivationFunctionSigmoid;
use PhpmlNeuralNetworkActivationFunctionReLU;
use PhpmlNeuralNetworkNetworkMultilayerPerceptron;
use PhpmlPreprocessingImputerMeanImputer;
use PhpmlPreprocessingStandardScaler;
use PhpmlSupportVectorMachineKernel;

$dataset = new ImagesDataset();
$vectorizer = new TokenCountVectorizer(new English());
$tfIdfTransformer = new TfIdfTransformer();
$stopWords = new English();

$vectorizer->fit($dataset->getSamples());
$vectorizer->transform($dataset->getSamples());
$tfIdfTransformer->fit($dataset->getSamples());
$tfIdfTransformer->transform($dataset->getSamples());
$stopWords->removeStopWords($dataset->getSamples());

$split = new StratifiedRandomSplit($dataset->getTargets(), 0.3);
$trainSamples = $split->getTrainSamples();
$trainLabels = $split->getTrainLabels();
$testSamples = $split->getTestSamples();
$testLabels = $split->getTestLabels();

$imputer = new MeanImputer();
$scaler = new StandardScaler();

$imputer->fit($trainSamples);
$scaler->fit($trainSamples);

$trainSamples = $imputer->transform($trainSamples);
$testSamples = $imputer->transform($testSamples);

$trainSamples = $scaler->transform($trainSamples);
$testSamples = $scaler->transform($testSamples);

$mlp = new MultilayerPerceptron(
    [count($trainSamples[0]), 100, 50, count(array_unique($trainLabels))],
    [new Sigmoid(), new ReLU(), new ReLU()]
);
$mlp->train($trainSamples, $trainLabels);

$predictedLabels = $mlp->predict($testSamples);

echo 'Accuracy: '.Accuracy::score($testLabels, $predictedLabels);
?>