Add optical flow Auto Segment button

Annotators can now press Auto Segment to replace the current mask with
an automatic river segmentation based on dense optical flow magnitude
and frame brightness. The result is pushed onto the undo stack, so it
can be refined or reverted like any other mask operation.

Parameters (norm_squared_threshold, gaussian_kernel, brightness_range)
live in a separate config/optical_flow_config.yaml; the button is only
enabled when optical_flow_config_file is set in config.yaml.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -95,6 +95,7 @@ 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
@@ -108,6 +109,23 @@ questions:
 
 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).
 
+### Optical flow segmentation (optional)
+
+Set `optical_flow_config_file` in `config.yaml` to point to a YAML file that enables 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.
@@ -176,6 +194,7 @@ Polygons are drawn as overlays and do not affect the mask until you use **Fill**
 | Toggle mask overlay | **Hide Mask / Show Mask** — button turns red when hidden; does not affect mask data |
 | Mask transparency | **Mask Alpha** slider; click **↺** to reset |
 | Load mask from previous clip | **Load Prev Mask** — copies the saved mask of the previous clip; undoable with **Undo** |
+| Optical flow first guess | **Auto Segment** — runs automatic river segmentation and replaces the current mask; undoable with **Undo**. Only enabled when `optical_flow_config_file` is set in `config.yaml`. |
 
 ### Image display adjustments
 
@@ -267,12 +286,14 @@ config/
     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

config/config.example.yaml

@@ -19,6 +19,7 @@ max_frames: 100
 data_dir: data/filtered_data
 out_dir: data/annotation_results
 clips_file: config/clips.txt
+# optical_flow_config_file: config/optical_flow_config.yaml
 
 questions:
   - section: River

config/optical_flow_config.yaml

@@ -0,0 +1,4 @@
+enabled: true
+norm_squared_threshold: 0.06
+gaussian_kernel: [5, 5]
+brightness_range: [2, 253]

src/river_annotation_tool/annotator.py

@@ -20,7 +20,8 @@ from PySide6.QtWidgets import (
 )
 
 from .clip_selector import ClipSelector
-from .config import AppConfig
+from .compute_optical_flow import compute_optical_flow_mask
+from .config import AppConfig, load_optical_flow_config
 from .mask_canvas import MaskCanvas
 from .video_loader import load_frames
 
@@ -38,6 +39,11 @@ class Annotator(QMainWindow):
         self.cfg = config
         self.out_dir = Path(config.out_dir)
         self.extras = extras
+        self.of_cfg = (
+            load_optical_flow_config(Path(config.optical_flow_config_file))
+            if config.optical_flow_config_file
+            else None
+        )
 
         self.selector = ClipSelector(
             data_dir=Path(config.data_dir),
@@ -114,9 +120,17 @@ class Annotator(QMainWindow):
         btn_undo10 = QPushButton("Undo×10")
         btn_redo = QPushButton("Redo")
         btn_load_prev_mask = QPushButton("Load Prev Mask")
+        btn_auto_segment = QPushButton("Auto Segment")
+        btn_auto_segment.setEnabled(self.of_cfg is not None and self.of_cfg.enabled)
 
         row1 = QHBoxLayout()
-        for b in [self.btn_prev, btn_next, btn_skip, btn_load_prev_mask]:
+        for b in [
+            self.btn_prev,
+            btn_next,
+            btn_skip,
+            btn_load_prev_mask,
+            btn_auto_segment,
+        ]:
             row1.addWidget(b)
 
         row_tools = QHBoxLayout()
@@ -198,6 +212,7 @@ class Annotator(QMainWindow):
         btn_undo10.clicked.connect(self.mc.undo10)
         btn_redo.clicked.connect(self.mc.redo)
         btn_load_prev_mask.clicked.connect(self.load_prev_mask)
+        btn_auto_segment.clicked.connect(self.run_optical_flow)
 
         if self._pending_answers:
             self._set_answers(self._pending_answers)
@@ -406,3 +421,13 @@ class Annotator(QMainWindow):
             interpolation=cv2.INTER_NEAREST,
         )
         self.mc.set_mask(mask)
+
+    def run_optical_flow(self):
+        mask = compute_optical_flow_mask(
+            self.frames,
+            self.fps,
+            self.of_cfg.norm_squared_threshold,
+            self.of_cfg.gaussian_kernel,
+            self.of_cfg.brightness_range,
+        )
+        self.mc.set_mask(mask)

src/river_annotation_tool/compute_optical_flow.py

@@ -0,0 +1,49 @@
+import cv2
+import numpy as np
+
+
+def compute_optical_flow_mask(
+    frames: list[np.ndarray],
+    fps: float,
+    norm_squared_threshold: float,
+    gaussian_kernel: tuple[int, int],
+    brightness_range: tuple[int, int],
+) -> np.ndarray:
+    """Return a binary mask (uint8, values 0/1) from optical flow + brightness."""
+    if len(frames) < 2:
+        return np.zeros(frames[0].shape[:2], dtype=np.uint8)
+
+    frames_arr = np.stack(frames).astype(np.float64)
+    frames_sub_mean = frames_arr - np.mean(frames_arr, axis=0)
+    mn, mx = frames_sub_mean.min(), frames_sub_mean.max()
+    if mx > mn:
+        standardized = ((frames_sub_mean - mn) / (mx - mn) * 255).astype(np.uint8)
+    else:
+        standardized = np.zeros_like(frames_arr, dtype=np.uint8)
+
+    N = len(standardized)
+    gray = np.stack([cv2.cvtColor(f, cv2.COLOR_RGB2GRAY) for f in standardized])
+
+    flow_data = np.zeros((N - 1,) + gray.shape[1:] + (2,))
+    for i in range(N - 1):
+        flow_data[i] = fps * cv2.optflow.calcOpticalFlowSparseToDense(
+            gray[i], gray[i + 1]
+        )
+
+    optical_flow = np.median(flow_data, axis=0)
+
+    flow_norm_sq = np.sum(optical_flow**2, axis=-1)
+    max_norm = np.max(flow_norm_sq)
+    if max_norm > 0:
+        flow_mask = flow_norm_sq >= max_norm * norm_squared_threshold**2
+    else:
+        flow_mask = np.zeros(flow_norm_sq.shape, dtype=bool)
+
+    reference_frame = frames[len(frames) // 2]
+    smoothed = cv2.GaussianBlur(reference_frame, gaussian_kernel, 0)
+    gray_ref = cv2.cvtColor(smoothed, cv2.COLOR_RGB2GRAY)
+    brightness_mask = (gray_ref > brightness_range[0]) & (
+        gray_ref < brightness_range[1]
+    )
+
+    return np.logical_and(brightness_mask, flow_mask).astype(np.uint8)

src/river_annotation_tool/config.py

@@ -28,6 +28,7 @@ class AppConfig:
     data_dir: str = "data/clips"
     out_dir: str = "data/annotation_results"
     clips_file: str = "config/clips.txt"
+    optical_flow_config_file: str = ""
     questions: list = field(default_factory=list)
     filenames: FilenameConfig = field(default_factory=FilenameConfig)
 
@@ -51,6 +52,22 @@ class AppConfig:
         ]
 
 
+@dataclass
+class OpticalFlowConfig:
+    enabled: bool = False
+    norm_squared_threshold: float = 0.3
+    gaussian_kernel: tuple[int, int] = (5, 5)
+    brightness_range: tuple[int, int] = (20, 235)
+
+
+def load_optical_flow_config(path: Path) -> OpticalFlowConfig:
+    with open(path) as f:
+        data = yaml.safe_load(f)
+    data["gaussian_kernel"] = tuple(data["gaussian_kernel"])
+    data["brightness_range"] = tuple(data["brightness_range"])
+    return OpticalFlowConfig(**data)
+
+
 def load_config(path: Path) -> AppConfig:
     with open(path) as f:
         data = yaml.safe_load(f)