Djeex
78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
> [!NOTE]
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> ⚠️ _This is an heavily vibe-coded proof of concept — **do not expose it to the internet** — use it at your own risk._
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> _Github repo is a mirror of https://git.djeex.fr/Djeex/insta-hdr-converter. You'll find full package, history and release note there._
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# Instagram HDR Assembler
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A small web front end for assembling Instagram-compatible HDR JPEGs (gain
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map) from an SDR export and an HDR export out of Lightroom or Camera Raw.
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The gain-map assembly logic ([app/assembler.py](app/assembler.py)) is
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adapted from [kostis-kounadis/instagram-hdr-assembler](https://github.com/kostis-kounadis/instagram-hdr-assembler),
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itself based on the reverse-engineering work of
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[karachungen/instagram-hdr-converter](https://github.com/karachungen/instagram-hdr-converter).
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Original MIT license kept in [LICENSE](LICENSE).
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Only the upstream's Method 2 (starting from an HDR JPEG that already
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carries a gain map) is implemented here. Method 1's AVIF decoding and
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its quality/transfer/gamut settings were dropped, but the HDR side can
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still be a 32-bit float linear TIFF (Lightroom's HDR TIFF export)
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instead of a gain-map JPEG for a more precise result. Either way the
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gain map is recomputed rather than reused as-is (see below), since it
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needs to be correct against the SDR file you actually provide.
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## How it works
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1. Upload an SDR file (`.jpg`/`.jpeg`) and an HDR file: either a gain-map
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JPEG (`.jpg`/`.jpeg`) or Lightroom's 32-bit float linear HDR TIFF
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export (`.tif`/`.tiff`).
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2. Both files must have identical dimensions and match one of Instagram's
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feed resolutions exactly: `1080x1080` (1:1), `1080x1350` (4:5), or
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`1080x566` (1.91:1).
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3. The real HDR pixel data is obtained from the HDR file: decoded back
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out of a gain-map JPEG (`ultrahdr_app`, undoing its own embedded base
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image and gain map), or read directly from a linear TIFF and given
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HLG's system-gamma OOTF so it's on the same display-referred scale a
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gain-map JPEG's HDR intent would be. A gain map is only correct
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against the exact base it was computed from, and that's rarely the
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SDR file you upload, so reusing an existing one as-is would quietly
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reconstruct the wrong HDR look. Your SDR file is cleaned up and
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re-encoded at 4:2:0 chroma subsampling (Pillow), then a fresh gain map
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is computed against it and packaged into the final UltraHDR JPEG
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(`ultrahdr_app`). The output's visible image is exactly your SDR file;
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only the gain map is recomputed. The TIFF route skips the gain-map
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JPEG's 8-bit-in-8-bit precision ceiling, giving a more accurate result.
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4. The validation report is shown on the result page, including the
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"GainMapMin is negative" warning when present, with a download link
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for the finished JPEG.
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Uploads and results don't stick around: SDR/HDR originals are deleted
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right after conversion, the result a few seconds after you leave the
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result page (heartbeat + background reaper), and `jobs/` is wiped on every
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app startup.
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## Running locally, without Docker
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```bash
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pip install -r requirements.txt
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# also install exiftool and compile libultrahdr, see docker/Dockerfile
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# for the exact packages and commands (validated on Alpine)
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PORT=5050 python3 main.py # PORT is optional, defaults to 5000
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```
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## Running with Docker
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```bash
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docker compose -f docker/compose.yaml up -d --build
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```
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Opens on `http://<host>:5050` (port 5000 collides with AirPlay Receiver on
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macOS, hence 5050 by default; change it in `docker/compose.yaml` if
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needed).
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## Running this on the open internet
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Don't do it. |