Add S3 storage support via s3fs; make storage field required

- New filesystem.py: make_fs() factory (returns s3fs.S3FileSystem or None),
  plus fsjoin/fsstem/fsname path helpers
- config.py: storage field is now required ('local' or 's3'); load_config
  raises a clear ValueError when it is missing
- video_loader, clip_selector, annotator: thread fs through all file I/O;
  local paths unchanged, S3 paths use fs.open/fs.exists/fs.pipe
- annotation_script: load .env via python-dotenv at startup, create fs from
  config and pass to Annotator
- Add .env.example with SwitchEngines endpoint and AWS checksum env vars
- pyproject.toml: add s3fs and python-dotenv dependencies
- Reduce default mask alpha from 40% to 15%
- Update example clip names to colon-separated timestamps

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-05-20 16:15:38 +02:00
parent 8579bad2e2
commit dc59b8affb
15 changed files with 1539 additions and 106 deletions

View File

@@ -35,6 +35,33 @@ cp config/clips.example.txt config/clips.txt
Edit `config/config.yaml` to set your `data_dir` and `out_dir`, then edit `config/clips.txt` to list the clips you want to annotate.
### S3 storage (optional)
By default the tool reads clips from and writes annotations to the local filesystem (`storage: local`). To use an S3-compatible object store instead, set `storage: s3` in `config/config.yaml` and give `data_dir` / `out_dir` as `bucket/prefix` paths:
```yaml
storage: s3
data_dir: my-bucket/clips
out_dir: my-bucket/annotation_results
```
Copy `.env.example` to `.env` and fill in your credentials — the app loads this file automatically at startup:
```sh
cp .env.example .env
# edit .env with your credentials
```
| Variable | Description |
|---|---|
| `S3_ACCESS_KEY` | Access key ID |
| `S3_SECRET_ACCESS_KEY` | Secret access key |
| `S3_ENDPOINT_URL` | Endpoint URL (defaults to `https://os.zhdk.cloud.switch.ch` if not set) |
| `AWS_REQUEST_CHECKSUM_CALCULATION` | Set to `when_required` to avoid checksum errors on SwitchEngines/Ceph |
| `AWS_RESPONSE_CHECKSUM_VALIDATION` | Set to `when_required` to avoid checksum errors on SwitchEngines/Ceph |
The `clips_file` (the list of clip filenames to annotate) is always read from the local filesystem even when `storage: s3`.
## Usage
```sh
@@ -74,6 +101,26 @@ python -m river_annotation_tool.annotation_script --clip left_20230615T120000
All settings live in `config/config.yaml`. Copy `config/config.example.yaml` to get started.
```yaml
storage: local # required: 'local' or 's3'
data_dir: data/clips # directory containing ZIP archives (local path or bucket/prefix for S3)
out_dir: data/annotation_results
clips_file: config/clips.txt
# optical_flow_config_file: config/optical_flow_config.yaml # optional, enables Auto Segment
display_max: 720 # longest side in pixels for display
fps_fallback: 25 # FPS to use if the video header is missing
max_frames: 100 # max frames to extract per clip
questions:
- section: River
items:
- key: flow
label: "Flow Regime"
options: [Turbulent, Laminar, Uncertain]
default: Laminar
# add more items or sections as needed
filenames:
video_in_zip: left.mp4 # video filename inside each ZIP archive
video_tmp_suffix: .mp4 # suffix for the extraction temp file
@@ -87,24 +134,6 @@ filenames:
gif_original_lowres: video_original_lowres.gif
gif_overlay_hires: video_overlay_hires.gif
gif_overlay_lowres: video_overlay_lowres.gif
display_max: 720 # longest side in pixels for display
fps_fallback: 25 # FPS to use if the video header is missing
max_frames: 100 # max frames to extract per clip
data_dir: data/clips # directory containing ZIP archives
out_dir: data/annotation_results
clips_file: config/clips.txt
# optical_flow_config_file: config/optical_flow_config.yaml # optional, enables Auto Segment
questions:
- section: River
items:
- key: flow
label: "Flow Regime"
options: [Turbulent, Laminar, Uncertain]
default: Laminar
# add more items or sections as needed
```
Add, remove, or reorder questions directly in the YAML — the UI rebuilds automatically. `key` is what gets saved in `metadata.json`; `default` selects the pre-checked option (omit or set to `null` to leave unselected).
@@ -192,7 +221,7 @@ Polygons are drawn as overlays and do not affect the mask until you use **Fill**
| Redo | **Redo** |
| Clear entire mask | **Clear** |
| Toggle mask overlay | **Hide Mask / Show Mask** — button turns red when hidden; does not affect mask data |
| Mask transparency | **Mask Alpha** slider (0100%, default 40%); click **↺** to reset |
| Mask transparency | **Mask Alpha** slider (0100%, default 15%); click **↺** to reset |
### Starting-point shortcuts
@@ -280,22 +309,24 @@ When a clip is loaded that already has a saved `mask.png` and `metadata.json`, t
## Repository structure
```
.env.example # S3 credential template (copy to .env and fill in)
config/
config.yaml # Your local config (git-ignored, copy from example)
config.example.yaml # Example config to copy and edit
clips.txt # Your clip list (git-ignored, copy from example)
clips.example.txt # Example clip list
optical_flow_config.yaml # Optional optical flow parameters (enable via config.yaml)
config.yaml # Your local config (git-ignored, copy from example)
config.example.yaml # Example config to copy and edit
clips.txt # Your clip list (git-ignored, copy from example)
clips.example.txt # Example clip list
optical_flow_config.yaml # Optional optical flow parameters (enable via config.yaml)
src/river_annotation_tool/
annotation_script.py # Entry point — argument parsing and app launch
annotator.py # Main QMainWindow — orchestrates all components
clip_selector.py # Reads the clip list and picks the next clip
mask_canvas.py # Drawing widget — brush, undo, erase, mouse events
video_loader.py # ZIP extraction and frame resizing
compute_optical_flow.py # Optical flow river segmentation (Auto Segment button)
config.py # AppConfig dataclass and YAML loader
__init__.py # Package version
pyproject.toml # Project metadata and dependencies
annotation_script.py # Entry point — argument parsing and app launch
annotator.py # Main QMainWindow — orchestrates all components
clip_selector.py # Reads the clip list and picks the next clip
filesystem.py # Storage backend — local passthrough or S3 via s3fs
mask_canvas.py # Drawing widget — brush, undo, erase, mouse events
video_loader.py # ZIP extraction and frame resizing
compute_optical_flow.py # Optical flow river segmentation (Auto Segment button)
config.py # AppConfig dataclass and YAML loader
__init__.py # Package version
pyproject.toml # Project metadata and dependencies
```
## Development