| |
| """RipBench semantic-segmentation evaluator (framework-agnostic). |
| |
| HOW TO USE (no CLI args - edit CONFIG below, then: python evaluate_semantic.py |
| in any env with numpy). Consumes the <stem>_confmat_<split>.npz files that |
| predict_semantic.py writes (per-image 4x4 GT-x-pred pixel confusion |
| matrices), sums them per split, and appends one TSV row per model x split to |
| eval/results/semantic_segmentation_eval.tsv. |
| |
| No threshold protocol - semantic predictions are argmax maps, so every |
| metric is threshold-free: per-class IoU (rip_current first - the headline), |
| mIoU, pixel accuracy, mean per-class accuracy (macc). Per-video breakdowns |
| for the insights analysis aggregate the SAME npz files by video id. |
| """ |
| import glob |
| import os |
| _RIPBENCH_ROOT = os.environ.get("RIPBENCH_ROOT", os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".."))) |
| from datetime import datetime |
|
|
| import numpy as np |
|
|
| |
| PRED_DIR = _RIPBENCH_ROOT + "/models/semantic_segmentation/best_models/predictions" |
| SPLITS = ("val", "test") |
| OUT_TSV = os.path.join(os.path.dirname(os.path.abspath(__file__)), "results", |
| "semantic_segmentation_eval.tsv") |
| |
|
|
| CLASSES = ("rip_current", "water", "land", "sky") |
|
|
| HEADER = ("timestamp\tmodel\ttask\tsplit\tn_images" |
| "\tmIoU\tIoU_rip_current\tIoU_water\tIoU_land\tIoU_sky" |
| "\tpixel_acc\tmacc\n") |
|
|
|
|
| def metrics(conf): |
| """conf: [4,4] float64, rows GT, cols pred.""" |
| tp = np.diag(conf) |
| fp = conf.sum(0) - tp |
| fn = conf.sum(1) - tp |
| denom = tp + fp + fn |
| iou = np.where(denom > 0, tp / np.maximum(denom, 1), np.nan) |
| acc = np.where(conf.sum(1) > 0, tp / np.maximum(conf.sum(1), 1), np.nan) |
| return {"iou": iou, "miou": float(np.nanmean(iou)), |
| "pixel_acc": float(tp.sum() / conf.sum()), |
| "macc": float(np.nanmean(acc))} |
|
|
|
|
| def main(): |
| os.makedirs(os.path.dirname(OUT_TSV), exist_ok=True) |
| write_header = not os.path.isfile(OUT_TSV) |
| pairs = {} |
| for f in sorted(glob.glob(os.path.join(PRED_DIR, "*_confmat_*.npz"))): |
| base = os.path.basename(f) |
| stem, split = base.rsplit("_confmat_", 1) |
| pairs.setdefault(stem, {})[split.removesuffix(".npz")] = f |
| print(f"{len(pairs)} model(s) in {PRED_DIR}") |
|
|
| with open(OUT_TSV, "a") as out: |
| if write_header: |
| out.write(HEADER) |
| for stem, files in pairs.items(): |
| for split in SPLITS: |
| if split not in files: |
| print(f"SKIP {stem}: no {split} confmat") |
| continue |
| data = np.load(files[split], allow_pickle=False) |
| conf = data["confmat"].sum(0).astype(np.float64) |
| m = metrics(conf) |
| row = [datetime.now().strftime("%Y-%m-%d %H:%M"), stem, |
| "semantic_segmentation", split, len(data["stems"]), |
| f"{m['miou']:.6f}"] + [f"{v:.6f}" for v in m["iou"]] + [ |
| f"{m['pixel_acc']:.6f}", f"{m['macc']:.6f}"] |
| out.write("\t".join(str(x) for x in row) + "\n") |
| print(f"[{split}] {stem}: mIoU={m['miou']:.4f} " |
| f"rip-IoU={m['iou'][0]:.4f} pixAcc={m['pixel_acc']:.4f}") |
| print(f"rows appended to {OUT_TSV}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
