imgkitHigh-Performance Image Processing

import { metadata, resize, crop, transform, thumbhash, toTensor } from 'imgkit';

// Read image
const buffer = Buffer.from(await Bun.file('photo.jpg').arrayBuffer());

// Get metadata (header-only, ultra-fast)
const info = await metadata(buffer);
console.log(`${info.width}x${info.height} ${info.format}`);

// Crop to aspect ratio (zero-copy, ultra-fast)
const squared = await crop(buffer, { aspectRatio: "1:1" });

// Resize with shrink-on-decode optimization
const thumbnail = await resize(buffer, { width: 200 });

// Full transform pipeline with crop + resize
const result = await transform(buffer, {
  crop: { aspectRatio: "16:9" },
  resize: { width: 1280 },
  sharpen: 5,
  output: { format: 'WebP', webp: { quality: 85 } }
});

// Generate thumbhash placeholder (better than blurhash)
const { dataUrl } = await thumbhash(buffer);
// Use directly: <img src={dataUrl} />

// ML tensor conversion (first JS package with native SIMD!)
const tensor = await toTensor(buffer, {
  width: 224, height: 224,
  normalization: 'Imagenet',
  layout: 'Chw', batch: true
});
// Shape: [1, 3, 224, 224] - Ready for PyTorch/ONNX!