OpalImage Processing for PHP

use PhpMlKit\Opal\Image;
use PhpMlKit\Opal\Color;
use PhpMlKit\Opal\ColorSpace;
use PhpMlKit\Opal\Kernel;
use PhpMlKit\Opal\SaveOptions;

// Load an image
$img = Image::fromFile('photo.jpg');

// Chain transforms (nothing executes until you save or export)
$result = $img
    ->resize(1920, 1080, Kernel::Lanczos3)
    ->sharpen(sigma: 1.5)
    ->saturation(1.1);

// Draw overlays
$result = $result
    ->drawRect(50, 50, 200, 100, Color::red(), fill: false)
    ->drawText('Label', 60, 70, color: Color::white());

// Save with format options
$result->toFile('output.jpg', SaveOptions::jpeg(quality: 90, strip: true));

// Export as NDArray for ML pipelines
$array = $result->toArray(ChannelFormat::CHW);

// Nothing happens here — just building a pipeline graph
$pipeline = $img
    ->resize(800, 600)
    ->sharpen(sigma: 2.0)
    ->toGrayscale();

// NOW it executes — libvips processes everything in one pass
$pipeline->toFile('output.jpg');

// PyTorch format (CHW)
$tensor = $image->toArray(ChannelFormat::CHW);

// TensorFlow format (HWC)
$tensor = $image->toArray(ChannelFormat::HWC);

// Import back from NDArray
$image = Image::fromArray($tensor, ColorSpace::RGB, ChannelFormat::HWC);