Add stylization

Author: Sopamo

example-style.php

@@ -0,0 +1,39 @@
+<?php
+require __DIR__.'/vendor/autoload.php';
+
+/**
+ * Configuration
+ * We need some files (tensorflow itself, and the tensorflow models) to be able
+ * to use FuncAI. Downloading these files happens automatically, but you need
+ * to provide folders where we can store those files.
+ *
+ * These folders will have to be available on your production server
+ * and we will store about 300 Mb of data in them.
+ */
+
+// Specify where the tensorflow models should be stored
+\FuncAI\Config::setModelBasePath(realpath(dirname(__FILE__) . '/models/'));
+// Specify where tensorflow should be downloaded to
+\FuncAI\Config::setLibPath(dirname(__FILE__) . '/tensorflow/');
+
+
+/**
+ * Installation
+ * This downloads all necessary files and makes sure they are set up correctly.
+ */
+$tfi = new \FuncAI\Install\TensorflowInstaller();
+$tfi->install();
+
+
+/**
+ * Prediction
+ * This is a sample prediction which uses the efficientNet model to determine the contents
+ * of an image.
+ */
+$model = new \FuncAI\Models\Stylization();
+#$model->printGraph();
+$output = $model->predict([
+    __DIR__ . '/sample_data/mona-lisa.jpg',
+    __DIR__ . '/sample_data/style5.jpg',
+]);
+# var_dump($output);

src/Models/Stylization.php

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+<?php
+namespace FuncAI\Models;
+
+use FuncAI\Config;
+use FuncAI\Tensorflow\TensorFlow;
+
+class Stylization extends AbstractModel
+{
+    /**
+     * @var int
+     */
+    private $width;
+    /**
+     * @var int
+     */
+    private $height;
+
+    public function getModelPath()
+    {
+        return Config::getModelBasePath() . '/arbitrary-image-stylization';
+    }
+
+    public function getOutputTensor()
+    {
+        $output = $this->tf->getDefaultGraph()->operation('StatefulPartitionedCall')->output(0);
+
+        return $output;
+    }
+
+    public function getInputData($input)
+    {
+        return [
+            'serving_default_placeholder' => $this->toImageTensor($input[0], true),
+            'serving_default_placeholder_1' => $this->toImageTensor($input[1], true),
+        ];
+    }
+
+    private function toImageTensor($path, $resize = false)
+    {
+        $img = imagecreatefromjpeg($path);
+        if($resize) {
+            $img = imagescale($img, 256, 256);
+        }
+        $this->width = imagesx($img);
+        $this->height = imagesy($img);
+        $ret = $this->tf->tensor(null, TensorFlow::FLOAT, [1, $this->width, $this->height, 3]);
+        $data = $ret->data();
+
+        // Convert the image data into a flat array
+        for ($y = 0; $y < $this->height; $y++) {
+            for ($x = 0; $x < $this->width; $x++) {
+                $rgb = imagecolorat($img, $x, $y);
+                $r = ($rgb >> 16) & 0xFF;
+                $g = ($rgb >> 8) & 0xFF;
+                $b = $rgb & 0xFF;
+                $idx = ($y * $this->width * 3) + ($x * 3);
+                $data[$idx] = (float)($r/255);
+                $data[$idx + 1] = (float)($g/255);
+                $data[$idx + 2] = (float)($b/255);
+            }
+        }
+        return $ret;
+    }
+
+    public function getInputLayer()
+    {
+        return 'serving_default_input_0';
+    }
+
+    protected function transformResult($result)
+    {
+        return $this->saveImage($result);
+    }
+
+    private function saveImage($imageData)
+    {
+        $imageData = $imageData[0];
+        $w = count($imageData);
+        $h = count($imageData[0]);
+        $img = imagecreatetruecolor($w, $h);
+        foreach($imageData as $y => $row) {
+            foreach($row as $x => $color) {
+                $color = array_map(function($c) {
+                    $c *= 255;
+                    return min(255,max(0,$c));
+                }, $color);
+                $c = imagecolorallocate($img, $color[0], $color[1], $color[2]);
+                imagesetpixel($img, $x, $y, $c);
+            }
+        }
+        imagejpeg($img, 'out.jpg');
+    }
+}