revert: remove post-processing, use raw model output — works best for majority of images

src/segmentation.py

@@ -252,7 +252,6 @@ def segment(model: nn.Module, img_bgr: np.ndarray, device: torch.device, use_tta
     probs_np = probs[0].cpu().numpy()
     probs_resized = np.stack([cv2.resize(probs_np[c], (w, h), interpolation=cv2.INTER_LINEAR) for c in range(4)])
     classmap = probs_resized.argmax(axis=0).astype(np.uint8)
-    classmap = postprocess_segmentation(classmap, img_bgr)
     masks = {name: (classmap == cid) for cid, name in CLASS_NAMES.items() if cid > 0}
     return {"classmap": classmap, "masks": masks, "probs": probs_resized}