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map","gainmap","three","threejs"],"repository":{"url":"git+ssh://git@github.com/MONOGRID/gainmap-js.git","type":"git"},"description":"A Javascript (TypeScript) Port of Adobe Gainmap Technology for storing HDR Images using an SDR Image + a gain map","maintainers":[{"name":"daniele-pelagatti","email":"daniele@monogrid.com"}],"readme":"# gainmap-js\n[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2FMONOGRID%2Fgainmap-js.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2FMONOGRID%2Fgainmap-js?ref=badge_shield)\n\nA Javascript (TypeScript) Encoder/Decoder Implementation of Adobe's Gain Map Technology for storing HDR Images using an SDR Image + a \"Gain map\"\n\n> :warning: This library is intended for encoding and decoding gain map images for the [three.js](https://github.com/mrdoob/three.js/) 3D Library\n>\n> It can be used for general encode/decode of gain maps but it depends on the three.js library which, in itself, is quite heavy if you only use it to encode/decode gain maps.\n\n## Share Your Project!\nHave you built something awesome using this library? We’d love to see what you’ve created!\nWhether it’s a small demo or a full-scale application, your project can inspire others and help improve the library. Please share your work in one of these ways:\n* GitHub Issues: [Open a new issue](https://github.com/MONOGRID/gainmap-js/issues/new) in this repository with a brief description, screenshots, and links to your project.\n* Email: Send us an email at gainmap@monogrid.com with your project details.\nYour contributions and creative ideas make our community stronger. We can’t wait to see what you build!\n\n## Live Demo\n\n* [WebGPU Version (node loaders)](https://monogrid.github.io/gainmap-js/webgpu-node.html)\n* [Legacy WebGL Version](https://monogrid.github.io/gainmap-js/)\n\nBoth compares loading:\n 1. a `JPEG` file with embedded gain map data\n 2. a `webp` sdr file + a `webp` gain map + metadata JSON\n 3. a comparable size `.hdr` file for comparison\n\n## Benchmark\n\n[Loader benchmark](https://monogrid.github.io/gainmap-js/benchmark.html): compares `HDRJPGNodeLoader` (GPU decode) against three.js `UltraHDRLoader` (CPU decode), loading the same gain map JPEGs in your browser.\n\n## [Free online encoder](https://gainmap-creator.monogrid.com)\n\nUse it to convert `.hdr` and `.exr` files into gain maps. It's free and the whole process happens in your browser.\n\n## Installing\n```bash\n$ npm install @monogrid/gainmap-js three\n```\n\n## What is a Gain map?\n\n[See here](https://gregbenzphotography.com/hdr-images/jpg-hdr-gain-maps-in-adobe-camera-raw/) for a detailed explanation, here are some relevant parts:\n\n> A gain map is a single file with a second pseudo-image embedded in it to create an optimized result for a specific monitor. It can be used to generate the HDR version (which looks dramatically better where supported), the SDR version (without tone mapping to ensures great quality), or anything in between (to better support less capable HDR displays).\n\n> Gain maps are not a new type of file, but rather a technology which can be embedded into a variety of image formats. There are reference specs already for the JPG, AVIF, JXL, and HEIF file formats. JPG is especially notable as it could not properly support HDR without gain maps and it offers a very useful bridge to the future (i.e. highly compatible with today’s software).\n\n> A gain map includes:\n>\n> * A **base (default) image**. This can be an SDR or an HDR image (JPG gain maps are always encoded with SDR as the base). If the browser or viewing software does not understand gain maps, it will just the treat file as if it were just the base image.\n> * The **gain map**. This is a secondary “image” embedded in the file. It is not a real image, but rather contains data to convert each pixel from the base image into the other (SDR or HDR) version of the image.\n>* Gain map **metadata**. This tells the browser how the gain map is encoded as well as critical information to optimize rendering on any display.\n\nPlease note that Google [is adopting the gain map technology in Android 14](https://support.google.com/photos/answer/14159275) but its naming of the technology refers to it as **Ultra HDR Image Format** and a JPEG file with embedded gain map is [apparently called JPEGR](https://github.com/google/libultrahdr/blob/3a3a752a5da0b2304b1b6de0ef383bbe41256a67/lib/jpegr.h#L28C16-L28C21) in their terminology, we call it `HDRJPEG` for the moment.\n\n## API\n\nRefer to the [WIKI](https://github.com/MONOGRID/gainmap-js/wiki) for detailed documentation about the API.\n\n## Examples\n\n### Decoding\n\nThe main use case of this library is to decode a JPEG file that contains gain map data\nand use it instead of a traditional `.exr` or `.hdr` image.\n\nThe recommended way is to use the **node loaders** from the `@monogrid/gainmap-js/webgpu` entry point,\ntogether with three.js `WebGPURenderer`.\n\nNode loaders return a `GainMapNode`: a [TSL](https://github.com/mrdoob/three.js/wiki/Three.js-Shading-Language) texture node holding the decoded HDR image.\n\n* **No renderer needed**: `load()` returns the node synchronously (like `TextureLoader` returns a `Texture`), it can be assigned to materials right away.\n* **Decoded on the GPU** into a HalfFloat render target, and only when something changes: sampling the node costs a single texture read.\n* **Every parameter is a uniform**: changing e.g. `maxDisplayBoost` re-decodes the image (and the environment PMREM) once, on the next frame.\n\n> NOTE: Starting from v3.1.0 Decoding also works in a WebWorker, thanks to [Alejandro Romano](https://github.com/Arecsu) for the [contribution](https://github.com/MONOGRID/gainmap-js/pull/59)\n\n#### Using a single JPEG with embedded Gain map Metadata\n\nThis approach lets you load a single file with an embedded Gain Map.\n\nThe advantage is to have a single file to load.\n\nThe disadvantages are:\n * No WEBP compression\n * Editing software that does not support gain maps will only see (and save) the SDR image, discarding the HDR information.\n   Software support is growing: Adobe Lightroom and Camera Raw and Pixelmator Pro can export gain map JPEGs,\n   while others (e.g. GIMP, Affinity) can't yet, and Photoshop needs Camera Raw or third party plugins.\n   You can also use our [free online encoder](https://gainmap-creator.monogrid.com) to create them from `.hdr` and `.exr` files.\n * Many websites and services (e.g. Facebook, Slack) re-encode uploaded images and strip the gain map, leaving you with only the SDR Representation.\n   Serve the files yourself, unmodified, to keep the HDR information.\n\n```ts\nimport { HDRJPGNodeLoader } from '@monogrid/gainmap-js/webgpu'\nimport { equirectUV } from 'three/tsl'\nimport {\n  EquirectangularReflectionMapping,\n  Mesh,\n  MeshBasicNodeMaterial,\n  PerspectiveCamera,\n  PlaneGeometry,\n  Scene,\n  WebGPURenderer\n} from 'three/webgpu'\n\nconst renderer = new WebGPURenderer()\nawait renderer.init()\n\n// no renderer needed, `hdr` is a TSL texture node which can be used right away\n// the image is decoded on the GPU once loaded\nconst hdr = new HDRJPGNodeLoader().load('gainmap.jpeg', () => {\n  // `hdr.value` is the decoded HDR texture, for APIs requiring a Texture\n  // it's kept up to date as long as the node is used in a rendered node graph\n  // (here: the plane material and the background), otherwise call `hdr.decode(renderer)`\n  // assign it once loaded: the texture gets its final size on load and PMREMs don't follow resizes\n  hdr.value.mapping = EquirectangularReflectionMapping\n  scene.environment = hdr.value\n})\n\nconst scene = new Scene()\nconst material = new MeshBasicNodeMaterial()\nmaterial.colorNode = hdr\nscene.add(new Mesh(new PlaneGeometry(), material))\n\n// sample with different uvs, e.g. as an equirectangular background\nscene.backgroundNode = hdr.sample(equirectUV())\n\n// every parameter is a uniform: changing it re-decodes the image (and the environment PMREM) once\nhdr.maxDisplayBoost.value = 2\n\nrenderer.render(scene, new PerspectiveCamera())\n\n// the node must be manually disposed when you are done using it\nhdr.dispose()\n```\n\n#### Using separate files\n\nUsing separate files will get rid of the limitations of using a single JPEG file but it will force to use three separate files\n\n1. An SDR Representation file\n2. A Gainmap file\n3. A JSON containing the gainmap metadata used for decoding\n\n```ts\nimport { GainMapNodeLoader } from '@monogrid/gainmap-js/webgpu'\nimport {\n  Mesh,\n  MeshBasicNodeMaterial,\n  PerspectiveCamera,\n  PlaneGeometry,\n  Scene,\n  WebGPURenderer\n} from 'three/webgpu'\n\nconst renderer = new WebGPURenderer()\nawait renderer.init()\n\nconst hdr = new GainMapNodeLoader().load([\n  'sdr.webp',\n  'gainmap.webp',\n  'metadata.json'\n])\n\nconst scene = new Scene()\nconst material = new MeshBasicNodeMaterial()\nmaterial.colorNode = hdr\nscene.add(new Mesh(new PlaneGeometry(), material))\n\nrenderer.render(scene, new PerspectiveCamera())\n\n// the node must be manually disposed when you are done using it\nhdr.dispose()\n```\n\n#### Migrating to v4.0.0\n\n> [!WARNING]\n> The render target based decoding APIs of `@monogrid/gainmap-js/webgpu` are deprecated and **will be removed in v4.0.0**:\n> `HDRJPGLoader`, `JPEGRLoader`, `GainMapLoader`, `decode()`, `GainMapDecoderMaterial` and `QuadRenderer`.\n> The loaders and `decode()` log a deprecation warning once when used.\n>\n> The `WebGLRenderer` APIs of the main `@monogrid/gainmap-js` entry point are **not** deprecated.\n>\n> From v4.0.0 the node loaders are the only way to decode gain maps with `WebGPURenderer`.\n\nIf you are using the `WebGPURenderer` render target based APIs, switch to the node loaders. If you are still on `WebGLRenderer` you can keep using `@monogrid/gainmap-js`, or move to `WebGPURenderer` from `three/webgpu`: it falls back to a WebGL2 backend\nwhere WebGPU is not available, so the node loaders work in the same browsers. Use `new WebGPURenderer({ forceWebGL: true })`\nto keep the WebGL2 backend everywhere. Materials need to be node materials (`MeshBasicNodeMaterial`, `MeshStandardNodeMaterial`, ...),\nor regular materials using `hdr.value` as a texture.\n\nThe node loaders are **not a drop-in replacement**: they return a `GainMapNode` instead of a render target.\n\n| Before (deprecated, `@monogrid/gainmap-js/webgpu`) | After |\n| --- | --- |\n| `new HDRJPGLoader(renderer)` | `new HDRJPGNodeLoader()`, no renderer |\n| `new GainMapLoader(renderer)` | `new GainMapNodeLoader()`, no renderer |\n| `decode({ sdr, gainMap, renderer, ...metadata })` | `gainMapNode(sdr, gainMap, metadata)` |\n| `await loader.loadAsync(url)` | `loader.load(url)` returns the node right away, `loadAsync` resolves once loaded |\n| `result.renderTarget.texture` | `hdr` in node materials (`material.colorNode = hdr`), `hdr.value` where a `Texture` is needed |\n| `material.map = result.renderTarget.texture` | `material.colorNode = hdr` (or `hdr.sample(uv)`) |\n| `scene.background = result.renderTarget.texture` | `scene.backgroundNode = hdr.sample(equirectUV())` |\n| `setRenderTargetOptions({ mapping })` | `hdr.value.mapping = ...` once loaded |\n| `result.material.maxDisplayBoost = 2; result.render()` | `hdr.maxDisplayBoost.value = 2`, re-decoded on the next frame |\n| `result.toDataTexture()` / `result.toArray()` | `await renderer.readRenderTargetPixelsAsync(hdr.renderTarget, 0, 0, hdr.width, hdr.height)` |\n| `result.dispose()` | `hdr.dispose()` |\n\nWhen the node is not part of a rendered node graph (e.g. only `scene.environment = hdr.value` is used),\ncall `hdr.decode(renderer)` after load to update its texture.\n\n#### Switching from the WebGL encoder to the node based encoder\n\nThe WebGL encoder is not deprecated, you can keep using it. The node based encoder is available as an alternative with `WebGPURenderer`.\n\nWith `LinearToneMapping` the node based encoder produces the same images as the WebGL encoder (byte for byte on the WebGL2 backend,\nwithin 1/255 on a handful of pixels on the WebGPU backend, where shaders compile differently).\nOther tone mappings use the three.js TSL tone mapping functions, so their SDR rendition differs from the one of the WebGL encoder.\nBrightness, contrast and saturation are applied after tone mapping: `LinearToneMapping` now clamps values above 1 before they are applied.\n\n| WebGL encoder (`@monogrid/gainmap-js/encode`) | Node based encoder (`@monogrid/gainmap-js/encode/webgpu`) |\n| --- | --- |\n| `encode({ image, maxContentBoost, ... })` | `new GainMapEncoder(image, { maxContentBoost, ... })`, no renderer |\n| `findTextureMinMax(image)` | `await findTextureMinMax(image, 'max', renderer)`, or `await encoder.fitMaxContentBoost(renderer)` |\n| `result.sdr.material.exposure = 0.9; result.sdr.render()` | `encoder.sdr.exposure.value = 0.9`, re-rendered when next used |\n| `result.sdr.material.toneMapping = AgXToneMapping` | `encoder.sdr.setToneMapping(AgXToneMapping)` |\n| `result.gainMap.material.gamma = [1.1, 1.1, 1.1]` | `encoder.gainMap.gamma.value.setScalar(1.1)` |\n| `result.getMetadata()` | `encoder.metadata` |\n| `result.sdr.renderTarget.texture` | `encoder.sdr` in node materials, `encoder.sdr.value` where a `Texture` is needed |\n| `decode({ sdr: result.sdr.renderTarget.texture, ... })` to preview | `encoder.createPreviewNode()`, it follows parameter changes |\n| `new ImageData(result.sdr.toArray(), ...)` + `compress({ flipY: true })` | `(await encoder.read(renderer)).sdr`, upright, no `flipY` |\n| `await encodeAndCompress({ ... })` | `await encoder.compress(renderer, { mimeType, quality, withWorker })` |\n| `result.sdr.dispose(); result.gainMap.dispose()` | `encoder.dispose()` |\n\n#### Decoding with WebGLRenderer\n\nThe main `@monogrid/gainmap-js` entry point exposes `HDRJPGLoader` and `GainMapLoader`, which decode into a `WebGLRenderTarget`.\n\n```ts\nimport { HDRJPGLoader } from '@monogrid/gainmap-js'\nimport { EquirectangularReflectionMapping, Scene, WebGLRenderer } from 'three'\n\nconst renderer = new WebGLRenderer()\nconst scene = new Scene()\n\nconst result = await new HDRJPGLoader(renderer)\n  .setRenderTargetOptions({ mapping: EquirectangularReflectionMapping })\n  .loadAsync('gainmap.jpeg')\n// or: await new GainMapLoader(renderer).loadAsync(['sdr.webp', 'gainmap.webp', 'metadata.json'])\n\nscene.background = result.renderTarget.texture\n\n// result must be manually disposed\n// when you are done using it\nresult.dispose()\n```\n\nOther decoding APIs (the `GainMapNode` constructor, `decode()` and the deprecated render target based\n`HDRJPGLoader` / `GainMapLoader` for `WebGPURenderer`) are documented in the [WIKI](https://github.com/MONOGRID/gainmap-js/wiki).\n\n### Encoding\n\nEncoding a Gain map starting from an EXR file.\n\nThis is generally not useful in a `three.js` site but this library exposes methods\nthat allow to encode an `.exr` or `hdr` file into a `jpeg` with an embedded gain map.\n\n`GainMapEncoder` encodes on the GPU with TSL nodes, no renderer is needed until the result is read back.\n`encoder.sdr` (the SDR rendition) and `encoder.gainMap` are live texture nodes: every encoding parameter is a uniform,\nchanging one re-renders only the affected nodes, once, the next time they are used.\nThis makes it suitable for interactive editors as well.\n\n```ts\nimport { GainMapEncoder } from '@monogrid/gainmap-js/encode/webgpu'\nimport { encodeJPEGMetadata } from '@monogrid/gainmap-js/libultrahdr'\nimport { EXRLoader } from 'three/examples/jsm/loaders/EXRLoader.js'\nimport { AgXToneMapping, LinearToneMapping, WebGPURenderer } from 'three/webgpu'\n\nconst renderer = new WebGPURenderer()\nawait renderer.init()\n\n// load an HDR file (raw texture data and TSL nodes are accepted too)\nconst image = await new EXRLoader().loadAsync('image.exr')\n\nconst encoder = new GainMapEncoder(image, { toneMapping: AgXToneMapping })\n// encode the full HDR range\nawait encoder.fitMaxContentBoost(renderer)\n\n// optional: live previews, they follow any parameter change\nsdrMaterial.colorNode = encoder.sdr\ngainMapMaterial.colorNode = encoder.gainMap\nhdrMaterial.colorNode = encoder.createPreviewNode() // a GainMapNode decoding the encoded result\n\n// every parameter is a uniform\nencoder.sdr.exposure.value = 0.9\nencoder.sdr.setToneMapping(LinearToneMapping)\nencoder.gainMap.gamma.value.setScalar(1.1)\n\n// read back and compress, no flipY needed\nconst result = await encoder.compress(renderer, { mimeType: 'image/jpeg', quality: 0.9 })\n\n// embed the compressed images + metadata into a single JPEG file\nconst jpeg = encodeJPEGMetadata(result)\n\n// `jpeg` will be an `Uint8Array` which can be saved somewhere\n\n// the encoder must be manually disposed when no longer needed\nencoder.dispose()\n```\n\nThe raw (uncompressed) images and the metadata are available with `await encoder.read(renderer)`,\n`await findTextureMinMax(image, 'max', renderer)` finds the max (or min) value of each channel of an HDR image.\n\n#### Encoding with WebGLRenderer\n\n\nEncoding a Gain map starting from an EXR file.\n\nThis is generally not useful in a `three.js` site but this library exposes methods\nthat allow to encode an `.exr` or `hdr` file into a `jpeg` with an embedded gain map.\n\n```ts\nimport { compress, encode, findTextureMinMax } from '@monogrid/gainmap-js/encode'\nimport { encodeJPEGMetadata } from '@monogrid/gainmap-js/libultrahdr'\nimport { EXRLoader } from 'three/examples/jsm/loaders/EXRLoader.js'\n\n// load an HDR file\nconst loader = new EXRLoader()\nconst image = await loader.loadAsync('image.exr')\n\n// find RAW RGB Max value of a texture\nconst textureMax = findTextureMinMax(image)\n\n// Encode the gainmap\nconst encodingResult = encode({\n  image,\n  // this will encode the full HDR range\n  maxContentBoost: Math.max.apply(this, textureMax)\n})\n\n// obtain the RAW RGBA SDR buffer and create an ImageData\nconst sdrImageData = new ImageData(encodingResult.sdr.toArray(), encodingResult.sdr.width, encodingResult.sdr.height)\n// obtain the RAW RGBA Gain map buffer and create an ImageData\nconst gainMapImageData = new ImageData(encodingResult.gainMap.toArray(), encodingResult.gainMap.width, encodingResult.gainMap.height)\n\n// parallel compress the RAW buffers into the specified mimeType\nconst mimeType = 'image/jpeg'\nconst quality = 0.9\n\nconst [sdr, gainMap] = await Promise.all([\n  compress({\n    source: sdrImageData,\n    mimeType,\n    quality,\n    flipY: true // output needs to be flipped\n  }),\n  compress({\n    source: gainMapImageData,\n    mimeType,\n    quality,\n    flipY: true // output needs to be flipped\n  })\n])\n\n// obtain the metadata which will be embedded into\n// and XMP tag inside the final JPEG file\nconst metadata = encodingResult.getMetadata()\n\n// embed the compressed images + metadata into a single\n// JPEG file\nconst jpeg = encodeJPEGMetadata({\n  ...encodingResult,\n  ...metadata,\n  sdr,\n  gainMap\n})\n\n// `jpeg` will be an `Uint8Array` which can be saved somewhere\n\n// encoder must be manually disposed\n// when no longer needed\nencodingResult.gainMap.dispose()\nencodingResult.sdr.dispose()\n\n```\n\n## Libultrahdr in Vite\n\nIf you import `@monogrid/gainmap-js/libultrahdr`\nYou will need to exclude it from Vite optimizations.\n\n```js\n// vite.config.js\n\nmodule.exports = defineConfig({\n  ...\n  optimizeDeps: {\n    exclude: ['@monogrid/gainmap-js/libultrahdr']\n  },\n  ...\n})\n```\n\n\n## Building\n\nClone the repository:\n```bash\n$ git clone git@github.com:MONOGRID/gainmap-js.git\n$ cd gainmap-js\n$ npm i\n$ npm run build\n```\n\n\n## References\n\n* [Adobe Gainmap Specification](https://helpx.adobe.com/camera-raw/using/gain-map.html)\n* [Ultra HDR Image Format v1.0](https://developer.android.com/guide/topics/media/platform/hdr-image-format)\n\n\n## License\n[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2FMONOGRID%2Fgainmap-js.svg?type=large)](https://app.fossa.com/projects/git%2Bgithub.com%2FMONOGRID%2Fgainmap-js?ref=badge_large)\n","readmeFilename":"README.md"}