Djeex
d67a2569f4
Reworked static/style.css to follow the same visual language as lumeex's back office (near-black background, cyan accent, gradient pill buttons, translucent bordered panels) instead of the previous generic dark-teal theme. Dropzones now use a dashed border while empty, turning solid once a file is picked. Also fixes a stale CSS selector (.dropzone label.tag) that never matched the actual markup (a <span>), so the SDR/HDR tag labels were unstyled before. Adds Gitea and GitHub repo links in the footer, using the same icons and link pattern as lumeex.
71 lines
3.1 KiB
Markdown
71 lines
3.1 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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## 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. |