Add segmentation post-processing: dark water fix + wetland reclassify

Two heuristic rules applied after SegEarth-OV3 inference:

1. Dark water: if background pixels have mean_rgb < 0.24 and std < 0.08,
   reclassify as water. Fixes GTA-UAV satellite dark ocean (57% → ~15% bg).

2. Wetland reclassify (GTA-UAV only): split false-positive wetland pixels
   by color — green-dominant → vegetation, else → bare soil. Fixes 14.3%
   muddy/wetland false positives on GTA-V hillside terrain.

Config flags: seg_fix_dark_water (default True), seg_reclassify_wetland
(default False, enabled in run_gta_uav.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/tests/test_inference.py

@@ -13,6 +13,7 @@ from src.augmentor.inference import (
     infer_chmv2_batch,
     infer_depth_batch,
     infer_segmentation_batch,
+    postprocess_segmentation,
     _SOBEL_X,
     _SOBEL_Y,
 )
@@ -220,3 +221,59 @@ class TestInferSegmentationBatch:
         result = infer_segmentation_batch(pipeline, seg_config, sample_batch, torch.device("cpu"))
         assert result.shape == (B, 1, H, W)
         assert (result == 0).all()
+
+
+# ---------------------------------------------------------------------------
+# Post-processing heuristics
+# ---------------------------------------------------------------------------
+
+class TestPostprocessSegmentation:
+    def test_dark_water_reclassified(self) -> None:
+        """Dark uniform background → water."""
+        # Dark image (mean ~0.15, std ~0.02)
+        rgb = torch.full((1, 3, 32, 32), 0.15)
+        rgb += torch.randn_like(rgb) * 0.02
+        rgb.clamp_(0, 1)
+        seg = torch.zeros(1, 1, 32, 32, dtype=torch.uint8)  # all background
+        result = postprocess_segmentation(seg, rgb)
+        assert (result == 4).all()  # water
+
+    def test_bright_background_unchanged(self) -> None:
+        """Bright background should NOT become water."""
+        rgb = torch.full((1, 3, 32, 32), 0.6)
+        seg = torch.zeros(1, 1, 32, 32, dtype=torch.uint8)
+        result = postprocess_segmentation(seg, rgb)
+        assert (result == 0).all()  # stays background
+
+    def test_non_background_unchanged(self) -> None:
+        """Non-background classes untouched even if dark."""
+        rgb = torch.full((1, 3, 32, 32), 0.1)
+        seg = torch.full((1, 1, 32, 32), 3, dtype=torch.uint8)  # vegetation
+        result = postprocess_segmentation(seg, rgb)
+        assert (result == 3).all()
+
+    def test_wetland_reclassify_green(self) -> None:
+        """Green wetland → vegetation when reclassify_wetland=True."""
+        rgb = torch.zeros(1, 3, 32, 32)
+        rgb[0, 1] = 0.6  # green dominant
+        rgb[0, 0] = 0.2  # low red
+        seg = torch.full((1, 1, 32, 32), 14, dtype=torch.uint8)  # wetland
+        result = postprocess_segmentation(seg, rgb, reclassify_wetland=True)
+        assert (result == 3).all()  # vegetation
+
+    def test_wetland_reclassify_brown(self) -> None:
+        """Brown wetland → bare soil when reclassify_wetland=True."""
+        rgb = torch.zeros(1, 3, 32, 32)
+        rgb[0, 0] = 0.5  # red dominant
+        rgb[0, 1] = 0.3  # low green
+        seg = torch.full((1, 1, 32, 32), 14, dtype=torch.uint8)
+        result = postprocess_segmentation(seg, rgb, reclassify_wetland=True)
+        assert (result == 11).all()  # bare soil
+
+    def test_wetland_unchanged_without_flag(self) -> None:
+        """Wetland stays if reclassify_wetland=False."""
+        rgb = torch.zeros(1, 3, 32, 32)
+        rgb[0, 1] = 0.6
+        seg = torch.full((1, 1, 32, 32), 14, dtype=torch.uint8)
+        result = postprocess_segmentation(seg, rgb, reclassify_wetland=False)
+        assert (result == 14).all()