[!NOTE] This is one of 190 standalone projects, maintained as part of the @thi.ng/umbrella monorepo and anti-framework.
🚀 Please help me to work full-time on these projects by sponsoring me on GitHub. Thank you! ❤️
Typedarray integer & float pixel buffers w/ customizable formats, blitting, drawing, convolution.
ImageData
utilitiesAll integer formats use the canvas native ABGR 32bit format as common intermediate for conversions. During conversion to ABGR, channels with sizes smaller than 8 bits will be scaled appropriately to ensure an as full-range and as linear as possible mapping. E.g. a 4 bit channel will be scaled by 255 / 15 = 17.
Format specs can freely control channel layout within current limits:
New formats can be defined via defIntFormat()
.
Format ID | Bits per pixel | Description |
---|---|---|
ALPHA8 | 8 | 8 bit channel (alpha only) |
GRAY8 | 8 | 8 bit single channel (grayscale conv) |
GRAY_ALPHA8 | 16 | 8 bit single channel (grayscale conv), 8 bit alpha |
GRAY16 | 16 | 16 bit single channel (grayscale conv) |
GRAY_ALPHA16 | 32 | 16 bit single channel (grayscale conv), 16 bit alpha |
ARGB4444 | 16 | 4 channels @ 4 bits each |
ARGB1555 | 16 | 5 bits each for RGB, 1 bit alpha |
RGB565 | 16 | 5 bits red, 6 bits green, 5 bits blue |
RGB888 | 32 (24 effective) | 3 channels @ 8 bits each |
ARGB8888 | 32 | 4 channels @ 8 bits each |
BGR888 | 32 (24 effective) | 3 channels @ 8 bits each |
ABGR8888 | 32 | 4 channels @ 8 bits each |
ALPHA8
is mapped from/to ABGR alpha channelGRAY8/16
, GRAY_ALPHA8/16
compute grayscale/luminance when converting from ABGR and in return produce grayscale ABGRStrided floating point format presets for use with floatBuffer()
. New formats can be defined via defFloatFormat()
.
Format ID | Channel count | Description |
---|---|---|
FLOAT_GRAY | 1 | Single channel / grayscale |
FLOAT_GRAY_ALPHA | 2 | Grayscale and alpha channel |
FLOAT_GRAY_RANGE | 1 | Grayscale (user defined value range) |
FLOAT_NORMAL | 3 | Normal map (signed values) |
FLOAT_RGB | 3 | Red, Green, Blue |
FLOAT_RGBA | 4 | Red, Green, Blue, Alpha |
buf.clamp()
). For conversion to packed int formats assumed to contain normalized data (i.e. [0..1] interval, with exception of FLOAT_NORMAL
which uses [-1..1] range)Available (and optimized) for both integer & floating point formats, image samplers can be created with the following filters & wrap modes:
"nearest"
- nearest neighbor"linear"
- bilinear interpolation"cubic"
- bicubic interpolation"clamp"
- outside values return 0"wrap"
- infinite tiling"repeat"
- edge pixels are repeatedimport { intBuffer, defSampler, ABGR8888 } from "@thi.ng/pixel";
const src = intBuffer(4, 4, ABGR8888);
// fill w/ random colors
src.forEach((_,i) => 0xff000000 | Math.random() * 0xffffff);
// create bilinear sampler w/ repeated edge pixels
const sampler = defSampler(src, "linear", "repeat");
// sample at fractional positions (even outside image)
sampler(-1.1, 0.5).toString(16)
// 'ff79643a'
// resize image to 1024x256 using bicubic sampling
const img = src.resize(1024, 256, "cubic");
Filter | |
---|---|
"nearest" | |
"linear" | |
"cubic" |
Floating point buffers can be processed using arbitrary convolution kernels. The following convolution kernel presets are provided for convenience:
Kernel | Size |
---|---|
BOX_BLUR3 | 3x3 |
BOX_BLUR5 | 5x5 |
GAUSSIAN_BLUR3 | 3x3 |
GAUSSIAN_BLUR5 | 5x5 |
GAUSSIAN(n) | 2n+1 x 2n+1 |
HIGHPASS3 | 3x3 |
LANCZOS(a,s) | as+1 x as+1 |
SHARPEN3 | 3x3 |
SOBEL_X | 3x3 |
SOBEL_Y | 3x3 |
UNSHARP_MASK5 | 5x5 |
Custom kernels can be defined (and code generated) using an array of coefficients and a given kernel size. See above presets and defKernel()
for reference.
Furthermore, convolution supports striding (i.e. only processing & keeping every nth pixel column/row, aka downscaling) and pixel pooling (e.g. for ML applications). Available pooling kernel presets (kernel sizes must be configured independently):
Kernel | Description |
---|---|
POOL_MEAN | Moving average |
POOL_MAX | Local maximum |
POOL_MIN | Local minimum |
POOL_NEAREST | Nearest neighbor |
POOL_THRESHOLD(bias) | Adaptive threshold |
Convolution can be applied to single, multiple or all channels of a FloatBuffer
. See convolveChannel()
and convolveImage()
.
See ConvolveOpts for config options.
// convolutions are only available for float buffers (for now)
src = floatBuffer(read("test.ppm"), FLOAT_RGB);
// apply horizontal Sobel kernel preset to all channels
// downscale image by factor 2 (must be integer)
// scale kernel result values by factor 4
convolveImage(src, { kernel: SOBEL_X, stride: 2, scale: 4 });
Normal maps can be created via normalMap()
. This function uses an adjustable convolution kernel size to control gradient smoothness & details. Result X/Y gradients can also be scaled (uniform or anisotropic) and the Z component can be customized to (default: 1.0). The resulting image is in FLOAT_NORMAL
format, using signed channel values. This channel format is auto-translating these into unsigned values when the image is converted into an integer format.
Step | Scale = 1 | Scale = 2 | Scale = 4 | Scale = 8 |
---|---|---|---|---|
0 | ||||
1 | ||||
2 | ||||
3 |
import { floatBuffer, normalMap, FLOAT_GRAY, RGB888 } from "@thi.ng/pixel";
import { asPPM, read } from "@thi.ng/pixel-io-netpbm";
// read source image into a single channel floating point buffer
const src = floatBuffer(read(readFileSync("noise.pgm")), FLOAT_GRAY);
// create normal map (w/ default options)
const nmap = normalMap(src, { step: 0, scale: 1 });
// pixel lookup (vectors are stored _un_normalized)
nmap.getAt(10, 10);
// Float32Array(3) [ -0.019607841968536377, -0.04313725233078003, 1 ]
// save as 24bit PBM, conversion to RGB int format first
writeFileSync("noise-normal.ppm", asPPM(nmap.as(RGB888)));
The dominantColors()
function applies k-means clustering to extract the dominant colors from the given image. The clustering can be configured. The function returns an array of { color, area }
objects (sorted by descending area), where color
is a cluster's dominant color (in the format of the source image) and area
the normalized cluster size (number of selected pixels over total number of pixels in the image).
Also see the dominant colors example project & online tool based on this function. Furthermore, the thi.ng/color-palettes package provides 200+ curated color themes extracted from images using this function...
Picture credit: /u/kristophershinn
import { floatBuffer, dominantColors, FLOAT_RGB } from "@thi.ng/pixel";
import { read } from "@thi.ng/pixel-io-netpbm";
import { readFileSync } from "fs";
// read test PPM image and convert into float RGB format
const img = floatBuffer(read(readFileSync(`test.ppm`)), FLOAT_RGB);
// extract 5 dominant color clusters
const clusters = dominantColors(img, 5);
console.log(clusters);
// [
// {
// color: [ 0.4000000059604645, 0.30980393290519714, 0.21176470816135406 ],
// area: 0.3141084558823529
// },
// {
// color: [ 0.21960784494876862, 0.19607843458652496, 0.1411764770746231 ],
// area: 0.2780330882352941
// },
// {
// color: [ 0.4156862795352936, 0.4745098054409027, 0.5647059082984924 ],
// area: 0.16620710784313725
// },
// {
// color: [ 0.6666666865348816, 0.7568627595901489, 0.9254902005195618 ],
// area: 0.12385110294117647
// },
// {
// color: [ 0.7176470756530762, 0.4745098054409027, 0.12941177189350128 ],
// area: 0.11780024509803921
// }
// ]
STABLE - used in production
Search or submit any issues for this package
yarn add @thi.ng/pixel
ES module import:
<script type="module" src="https://cdn.skypack.dev/@thi.ng/pixel"></script>
For Node.js REPL:
const pixel = await import("@thi.ng/pixel");
Package sizes (brotli'd, pre-treeshake): ESM: 9.43 KB
Several projects in this repo's /examples directory are using this package:
Screenshot | Description | Live demo | Source |
---|---|---|---|
Interactive image processing (adaptive threshold) | Demo | Source | |
ASCII art raymarching with thi.ng/shader-ast & thi.ng/text-canvas | Demo | Source | |
Interactive & reactive image blurhash generator | Demo | Source | |
Color palette generation via dominant color extraction from uploaded images | Demo | Source | |
2.5D hidden line visualization of digital elevation files (DEM) | Demo | Source | |
Barnsley fern IFS fractal renderer | Demo | Source | |
Pixel buffer manipulations | Demo | Source | |
Matrix-based image color adjustments | Demo | Source | |
Showcase of various dithering algorithms | Demo | Source | |
Randomized 4-point 2D color gradient image generator | Demo | Source | |
Image dithering and remapping using indexed palettes | Demo | Source | |
Normal map creation/conversion basics | Demo | Source | |
Interactive pixel sorting tool using thi.ng/color & thi.ng/pixel | Demo | Source | |
RGB waveform image analysis | Demo | Source | |
Port-Duff image compositing / alpha blending | Demo | Source | |
Steering behavior drawing with alpha-blended shapes | Demo | Source | |
Basic usage of the declarative rdom-forms generator | Demo | Source | |
Responsive image gallery with tag-based Jaccard similarity ranking | Demo | Source | |
2D scenegraph & image map based geometry manipulation | Demo | Source | |
WebGL & Canvas2D textured tunnel shader | Demo | Source | |
Fork-join worker-based raymarch renderer (JS/CPU only) | Demo | Source | |
Textmode image warping w/ 16bit color output | Demo | Source | |
Multi-layer vectorization & dithering of bitmap images | Demo | Source | |
Visual comparison of biased vs. unbiased normal vectors projected on the surface of a sphere | Demo | Source | |
Minimal multi-pass / GPGPU example | Demo | Source |
import * as pix from "@thi.ng/pixel";
import { SRC_OVER_I } from "@thi.ng/porter-duff";
import { pixelCanvas2d } from "@thi.ng/canvas";
import IMG from "../assets/haystack.jpg";
import LOGO from "../assets/logo-64.png";
const [img, logo] = await Promise.all([IMG, LOGO].map((x) => imageFromURL(x)));
// init 16bit int RGB pixel buffer from image (resized to 256x256)
const buf = intBufferFromImage(img, RGB565, 256, 256);
// create grayscale buffer for logo and use Porter-Duff operator to
// composite with main image. Since the logo has transparency, we
// need to premultiply alpha first...
intBufferFromImage(logo, GRAY_ALPHA8).premultiply().blend(SRC_OVER_I, buf, {
dx: 10,
dy: 10,
});
// extract sub-image
// (method returns undefined if result region is < 1 pixel)
const region = buf.getRegion(32, 96, 128, 64)!;
// copy region back at new position
region.blit(buf, { dx: 96, dy: 32 });
// or alternatively blit buf into itself
// buf.blit(buf, { dx: 96, dy: 32, sx: 32, sy: 96, w: 128, h: 64 });
// create html canvas
// (returns obj of canvas & 2d context)
const { canvas } = pixelCanvas2d(buf.width, buf.height * 3, document.body);
// write pixel buffer to canvas
buf.blitCanvas(canvas);
// manipulate single color channel
const id = 0;
const ch = buf.getChannel(id).invert();
for (let y = 0; y < ch.height; y += 2) {
for (let x = (y >> 1) & 1; x < ch.width; x += 2) {
ch.setAt(x, y, 0xff);
}
}
// replace original channel
buf.setChannel(id, ch);
// write pixel buffer to new position
buf.blitCanvas(canvas, { y: buf.height });
// create & write grayscale version
buf.as(GRAY8).blitCanvas(canvas, { y: buf.height * 2 });
TODO see examples & source comments for now
If this project contributes to an academic publication, please cite it as:
@misc{thing-pixel,
title = "@thi.ng/pixel",
author = "Karsten Schmidt",
note = "https://thi.ng/pixel",
year = 2019
}
© 2019 - 2024 Karsten Schmidt // Apache License 2.0
Generated using TypeDoc