PHP Conference Nagoya 2025

ImagickKernel::addUnityKernel

(PECL imagick >= 3.3.0)

ImagickKernel::addUnityKernelAdds a Unity Kernel to the kernel list

Опис

public ImagickKernel::addUnityKernel(float $scale): void

Adds a given amount of the 'Unity' Convolution Kernel to the given pre-scaled and normalized Kernel. This in effect adds that amount of the original image into the resulting convolution kernel. The resulting effect is to convert the defined kernels into blended soft-blurs, unsharp kernels or into sharpening kernels.

Параметри

У цієї функції немає параметрів.

Значення, що повертаються

Приклади

Приклад #1 ImagickKernel::addUnityKernel()

<?php



function renderKernelTable($matrix) {
$output = "<table class='infoTable'>";

foreach (
$matrix as $row) {
$output .= "<tr>";
foreach (
$row as $cell) {
$output .= "<td style='text-align:left'>";
if (
$cell === false) {
$output .= "false";
}
else {
$output .= round($cell, 3);
}
$output .= "</td>";
}
$output .= "</tr>";
}

$output .= "</table>";

return
$output;
}

$matrix = [
[-
1, 0, -1],
[
0, 4, 0],
[-
1, 0, -1],
];

$kernel = \ImagickKernel::fromMatrix($matrix);
$kernel->scale(1, \Imagick::NORMALIZE_KERNEL_VALUE);
$output = "Before adding unity kernel: <br/>";
$output .= renderKernelTable($kernel->getMatrix());
$kernel->addUnityKernel(0.5);
$output .= "After adding unity kernel: <br/>";
$output .= renderKernelTable($kernel->getMatrix());


$kernel->scale(1, \Imagick::NORMALIZE_KERNEL_VALUE);
$output .= "After renormalizing kernel: <br/>";
$output .= renderKernelTable($kernel->getMatrix());

echo
$output;

?>

Приклад #2 ImagickKernel::addUnityKernel()

<?php
function addUnityKernel($imagePath) {

$matrix = [
[-
1, 0, -1],
[
0, 4, 0],
[-
1, 0, -1],
];

$kernel = ImagickKernel::fromMatrix($matrix);

$kernel->scale(4, \Imagick::NORMALIZE_KERNEL_VALUE);
$kernel->addUnityKernel(0.5);


$imagick = new \Imagick(realpath($imagePath));
$imagick->filter($kernel);
header("Content-Type: image/jpg");
echo
$imagick->getImageBlob();

}

?>

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