clip-annotator/README.md

# River Annotation Tool

A desktop GUI application for manually annotating river video clips as part of the [HydroScan](https://github.com/HydroScan) project. Annotators draw pixel-level water masks over river footage and answer structured survey questions about flow conditions, lighting, and scene quality.

## Requirements

- Python 3.12
- [uv](https://docs.astral.sh/uv/) (recommended) or pip

## Quick start

```sh
# 1. Clone and install
git clone https://github.com/HydroScan/river-annotation-tool
cd river-annotation-tool
uv sync

# 2. Create config and clip list from examples
cp config/config.example.yaml config/config.yaml
cp config/clips.example.txt config/clips.txt

# 3. Edit config/config.yaml (set data_dir and out_dir)
#    Edit config/clips.txt (list clips to annotate)
#    Edit config/questions.yaml to customise survey questions (optional)

# 4. Run
uv run python -m river_annotation_tool.annotation_script
```

## Installation

```sh
# Install with uv (creates the virtual environment automatically)
uv sync

# Or with pip
python -m venv .venv
.venv\Scripts\activate        # Windows
source .venv/bin/activate     # macOS/Linux
pip install -e .
```

## Setup

Before running, create your config and clip list from the provided examples:

```sh
cp config/config.example.yaml config/config.yaml
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. Survey questions are defined in `config/questions.yaml` (committed to the repo; edit to customise). See the [Configuration](#configuration) section for all available options.

### 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
uv run python -m river_annotation_tool.annotation_script
# or, if you have the venv activated:
python -m river_annotation_tool.annotation_script
```

### Arguments

| Argument | Default | Description |
|---|---|---|
| `--config` | `config/config.yaml` | Path to the config YAML file |
| `--data` | *(from config)* | Override `data_dir` from config |
| `--out` | *(from config)* | Override `out_dir` from config |
| `--clips` | *(from config)* | Override `clips_file` from config |
| `--clip` | *(first unannotated in list)* | Open a specific clip by stem name |
| `--extras` | off | Also save GIFs and extra PNGs (see Output section) |
| `--no-skip` | off | Show already-annotated clips instead of skipping them |

### Typical workflows

```sh
# Annotate clips listed in config/clips.txt (default)
uv run python -m river_annotation_tool.annotation_script

# Use a different config file
uv run python -m river_annotation_tool.annotation_script --config config/my_config.yaml

# Override paths from the command line
uv run python -m river_annotation_tool.annotation_script --data data/clips --out data/out

# Annotate a single specific clip
uv run python -m river_annotation_tool.annotation_script --clip left_20230615T120000
```

## Configuration

Main settings live in `config/config.yaml`. Copy `config/config.example.yaml` to get started.

```yaml
storage: local            # required: 'local' or 's3'

data_dir:                 # required: directory containing ZIP archives (local path or bucket/prefix for S3)
out_dir:                  # required: where to write annotations

clips_file: config/clips.txt
optical_flow_config_file: config/optical_flow_config.yaml
questions_config_file: config/questions.yaml

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

# Override input filenames only if your ZIP archives differ from the defaults
filenames:
  video_in_zip: left.mp4
  video_tmp_suffix: .mp4
  zip_extension: .zip
```

Output filenames (`mask.png`, `metadata.json`, etc.) have sensible defaults and can be overridden in the `filenames:` block — see [`config.py`](src/river_annotation_tool/config.py) for the full list.

### Survey questions

Survey questions are defined in `config/questions.yaml` (committed to the repo). Add, remove, or reorder sections and items — 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).

### Optical flow segmentation

`config/optical_flow_config.yaml` controls the **Auto Segment** button. When pressed, the tool computes a river mask from the loaded frames and replaces the current mask (undoable). The segmentation combines two criteria:

- **Optical flow magnitude** — pixels where the temporal median of frame-to-frame flow (scaled by FPS) exceeds a fraction of the maximum are considered moving water.
- **Brightness** — pixels outside a brightness window are excluded (removes sky, saturated glare, etc.).

```yaml
# config/optical_flow_config.yaml
enabled: true
norm_squared_threshold: 0.06   # fraction of max flow² that counts as moving
gaussian_kernel: [5, 5]        # blur kernel applied to the reference frame before brightness check
brightness_range: [2, 253]     # [min, max] greyscale brightness to keep
```

`enabled: false` disables the button without removing the config file.

## Clip list file

`config/clips.txt` lists the clip filenames to annotate, one per line. Lines starting with `#` are ignored. Clips are processed in order; already-annotated clips (those with an existing `mask.png`) are skipped automatically. Pass `--no-skip` to include them. When the last clip is reached, a dialog appears and the app exits.

```
# Example clips.txt
left_20230501T120000.zip
left_20230502T120000.zip
```

Copy `config/clips.example.txt` as a starting point.

## Controls

The window is split into two panels: the **video canvas** on the left (~70% of the width) and the **survey panel** on the right. The video auto-plays as a looping preview. Drawing tools and mask controls are arranged above and beside the canvas; navigation buttons (**Previous / Next / Skip**) sit at the top.

### Tool modes

Three drawing tools are available in the tool row. The active tool is highlighted in blue.

| Tool | How to activate | Description |
|---|---|---|
| **Brush** | Click **Brush** | Click and drag to paint the mask with a circular brush (default) |
| **Polygon** | Click **Polygon** | Click to place vertices and build closed shapes; use **Fill** mode to commit them |
| **Fill** | Click **Fill** | Click inside a closed polygon to fill it onto the mask |

### Brush tool

| Action | How |
|---|---|
| Draw water mask | Click and drag on the video |
| Erase mask | Toggle **Eraser** button (turns orange when active), then drag |
| Brush preview | A white circle follows the cursor showing the current brush size |
| Adjust brush size | **Brush size** slider (2–50 px, default 5); click **↺** to reset |

### Polygon tool

Polygons are drawn as overlays and do not affect the mask until you use **Fill** mode.

| Action | How |
|---|---|
| Add vertex | Left-click on the canvas |
| Remove last vertex | Right-click |
| Close a shape | Left-click near the first vertex (red dot) when ≥ 3 vertices are placed; completed shapes turn bold cyan |
| Draw multiple shapes | Each closed shape is kept independently; draw as many as needed |
| Cancel in-progress polygon | **Cancel Current Poly** — discards the unfinished polygon, keeps completed shapes |
| Delete last completed shape | **Del Shape** |

### Fill tool

| Action | How |
|---|---|
| Fill a shape | Left-click anywhere inside a closed polygon; that shape's interior is painted onto the mask |
| Nested shapes | If a closed polygon lies entirely inside the target, its interior is left unfilled (acts as a hole) |
| Innermost shape | Clicking inside nested shapes always fills the innermost (smallest) polygon containing the click |
| Undo fill | **Undo** — each fill is a single undoable step |

### Mask editing

| Action | How |
|---|---|
| Undo last action | **Undo** |
| Undo 10 actions | **Undo×10** |
| 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 (0–100%, default 15%); click **↺** to reset |

### Starting-point shortcuts

| Action | How |
|---|---|
| Load mask from previous clip | **Load Prev Mask** — copies the saved mask of the previous clip onto the current one; undoable |
| Optical flow first guess | **Auto Segment** — replaces the current mask with an automatic river segmentation; undoable. Disabled when `enabled: false` in `config/optical_flow_config.yaml`. |

### Image display adjustments

Three vertical sliders sit to the left of the video and affect display only — they do not change what is saved.

| Slider | Effect | Range |
|---|---|---|
| Brightness | Shifts all pixel values up or down | −100 to +100 |
| Contrast | Scales pixel values around the midpoint | −100 to +100 |
| Gamma | Applies a power-law correction (higher = brighter) | 0.1× to 3.0× |

Click **↺** below any slider to restore its default value.

### Navigation

| Action | How |
|---|---|
| Save and continue | **Next** — saves current clip and loads the next one. If the clip already has a saved annotation a dialog asks whether to replace it or keep the existing save. |
| Go back | **Previous** — saves current clip and returns to the previously viewed clip. Disabled on the first clip. |
| Skip without saving | **Skip** — discards any unsaved changes and loads the next clip without writing anything to disk. |

## Output

Each annotated clip produces a folder `<out_dir>/<clip_stem>/` with:

```
mask.png          # Binary water mask at full source resolution (always)
metadata.json     # Survey answers as JSON (always)
frame.png         # Middle frame of the clip (always)
overlay.png       # That frame with the mask blended in green (always)

# Only with --extras:
mask_vis.png               # Mask rendered as a greyscale PNG
video_original_hires.gif   # All frames at display resolution
video_original_lowres.gif  # All frames at 50% of display resolution
video_overlay_hires.gif    # Overlay GIF at display resolution
video_overlay_lowres.gif   # Overlay GIF at 50% of display resolution
```

All output filenames can be overridden via the `filenames:` section in `config/config.yaml`.

### Survey answers (`metadata.json`)

Keys and values are determined by `config/questions.yaml`. With the default questions:

```json
{
  "flow":          "Turbulent | Laminar | Uncertain",
  "shadows":       "Yes | No | Uncertain",
  "artifacts":     "Yes | No | Uncertain",
  "lighting":      "Day | Night | Uncertain",
  "exposure":      "Overexposed | Underexposed | Both | Normal | Uncertain",
  "snowing":       "Yes | No | Uncertain",
  "snow_on_ground":"Yes | No | Uncertain"
}
```

## Internals

### Clip format

Each clip is a ZIP archive containing a video file (default `left.mp4`, configurable via `filenames.video_in_zip`). The filename encodes the recording timestamp (e.g. `left_20230615T120000.zip`).

### Frame loading

Up to `max_frames` frames are extracted from the video and scaled so the longest side is `display_max` px. This display-resolution copy is what the annotator works on; the full-resolution dimensions are remembered separately so the saved mask is upscaled back to the original size on export.

### Mask drawing

The mask is a binary array at display resolution. **Brush** strokes stamp a filled circle (draw or erase). **Polygon** shapes are stored as overlays and don't touch the mask until a **Fill** click rasterises them — the innermost polygon containing the click is filled, and any polygon whose centroid falls inside it is punched out as a hole.

Every mask-changing operation is pushed onto an undo stack before it executes. On save, the mask is upscaled to the original video resolution and written as an 8-bit PNG (0 or 255).

### Resuming

When a clip is loaded that already has a saved `mask.png` and `metadata.json`, the mask is restored at display resolution and the survey answers are pre-filled.

## 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
    questions.yaml                  # Survey question definitions
    optical_flow_config.yaml        # Optical flow parameters (set enabled: false to disable Auto Segment)
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
    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

```sh
# Install pre-commit hooks
uv run pre-commit install
uv run pre-commit run --all-files   # Run manually once

# Add a dependency
uv add <package>
uv add --dev <package>              # Development-only
```