feat/ sync weight fitting logic and confidence level marker *

FishMeasure/predict_weigth_from_svo2.py

@@ -102,26 +102,6 @@ def _run_fish_video_evaluation_subprocess(args: argparse.Namespace, *, batch_fol
         cmd.append("--use-flatness-filter")
         cmd.extend(["--flatness-threshold", str(args.flatness_threshold)])
 
-    # 始终在 fish 内跑 DGCNN，生成 weight_estimation_results.json，预览视频才能叠加 weight/length；
-    # predict 后续会合并该 JSON，避免重复跑 test_dgcnn。
-    wck = Path(args.weight_checkpoint).expanduser().resolve()
-    if wck.is_file():
-        cmd.extend(
-            [
-                "--run-weight-estimation",
-                "--weight-estimator-checkpoint",
-                str(wck),
-            ]
-        )
-    if getattr(args, "fish_video_weight_overlay", False):
-        cmd.extend(
-            [
-                "--weight-overlay-video",
-                "--minute-interval-sec",
-                str(getattr(args, "minute_interval_sec", 60.0)),
-            ]
-        )
-
     print(f"Invoking fish_video_weight_evaluation.py:\n  {' '.join(cmd)}")
     proc = subprocess.run(cmd, cwd=str(REPO_ROOT))
     if proc.returncode != 0:
@@ -144,6 +124,13 @@ def _run_test_dgcnn_weight_estimator_subprocess(
     remove_outliers: bool,
     outlier_method: str,
     labels_json: Optional[str],
+    weight_max_length_mm: float = 400.0,
+    weight_min_length_width_ratio: float = 1.5,
+    weight_length_quality_cv_threshold_pct: float = 15.0,
+    weight_length_quality_max_span_mm: float = 130.0,
+    weight_average_all_after_filter: bool = False,
+    weight_average_all_fallback_max_if_mean_over_g: float = 400.0,
+    weight_mean_pool_fallback_max_if_over_g: float = 440.0,
 ) -> Tuple[Path, Dict[str, Any]]:
     if (cloud_dir is None) == (ply_list_file is None):
         raise ValueError("Exactly one of cloud_dir or ply_list_file must be set")
@@ -187,6 +174,22 @@ def _run_test_dgcnn_weight_estimator_subprocess(
     if remove_outliers:
         cmd.append("--remove-outliers")
         cmd.extend(["--outlier-method", outlier_method])
+    cmd.extend(["--max-length-mm", str(weight_max_length_mm)])
+    cmd.extend(["--min-length-width-ratio", str(weight_min_length_width_ratio)])
+    cmd.extend(["--length-quality-cv-threshold-pct", str(weight_length_quality_cv_threshold_pct)])
+    cmd.extend(["--length-quality-max-span-mm", str(weight_length_quality_max_span_mm)])
+    if weight_average_all_after_filter:
+        cmd.append("--average-all-after-filter")
+    fb_thr = weight_average_all_fallback_max_if_mean_over_g
+    if fb_thr is not None and float(fb_thr) > 0:
+        cmd.extend(
+            ["--average-all-fallback-max-if-mean-over-g", str(float(fb_thr))]
+        )
+    mp_fb = weight_mean_pool_fallback_max_if_over_g
+    if mp_fb is not None and float(mp_fb) > 0:
+        cmd.extend(
+            ["--mean-pool-fallback-max-if-over-g", str(float(mp_fb))]
+        )
 
     print(f"Invoking test_dgcnn_weight_estimator.py:\n  {' '.join(cmd)}")
     proc = subprocess.run(cmd, cwd=str(REPO_ROOT))
@@ -215,6 +218,9 @@ def _merge_weight_prediction_json(
     skipped = bool(summary.get("skipped"))
     avg_kg = summary.get("avg_predicted_weight_kg")
     avg_g = summary.get("avg_predicted_weight_g")
+    pred_kg = summary.get("pred_weight_kg")
+    pred_g = summary.get("pred_weight_g")
+    pred_rule = summary.get("pred_weight_rule")
     return {
         "svo": str(svo_path),
         "svo_name": svo_name,
@@ -228,6 +234,9 @@ def _merge_weight_prediction_json(
         "num_ply_predicted": summary.get("num_files_predicted"),
         "avg_predicted_weight_kg": None if skipped else avg_kg,
         "avg_predicted_weight_g": None if skipped else avg_g,
+        "pred_weight_g": None if skipped else pred_g,
+        "pred_weight_kg": None if skipped else pred_kg,
+        "pred_weight_rule": None if skipped else pred_rule,
         "dgcnn_meta": dgcnn_data.get("meta"),
         "dgcnn_summary": summary,
         "weight_summary": summary,
@@ -248,6 +257,9 @@ def _merge_weight_prediction_json_combined(
     skipped = bool(summary.get("skipped"))
     avg_kg = summary.get("avg_predicted_weight_kg")
     avg_g = summary.get("avg_predicted_weight_g")
+    pred_kg = summary.get("pred_weight_kg")
+    pred_g = summary.get("pred_weight_g")
+    pred_rule = summary.get("pred_weight_rule")
     cloud_dirs = [output_base / p.stem / "cloud" for p in svo_paths]
     n_ply = sum(len(_collect_existing_clouds(d)) for d in cloud_dirs)
     return {
@@ -265,6 +277,9 @@ def _merge_weight_prediction_json_combined(
         "num_ply_predicted": summary.get("num_files_predicted"),
         "avg_predicted_weight_kg": None if skipped else avg_kg,
         "avg_predicted_weight_g": None if skipped else avg_g,
+        "pred_weight_g": None if skipped else pred_g,
+        "pred_weight_kg": None if skipped else pred_kg,
+        "pred_weight_rule": None if skipped else pred_rule,
         "dgcnn_meta": dgcnn_data.get("meta"),
         "dgcnn_summary": summary,
         "weight_summary": summary,
@@ -297,7 +312,13 @@ def run_weight_prediction_for_svo(
     weight_outlier_method: str,
     weight_xyz_scale: float,
     weight_labels_json: Optional[str],
-    force_dgcnn_subprocess: bool = False,
+    weight_max_length_mm: float = 400.0,
+    weight_min_length_width_ratio: float = 1.5,
+    weight_length_quality_cv_threshold_pct: float = 15.0,
+    weight_length_quality_max_span_mm: float = 130.0,
+    weight_average_all_after_filter: bool = False,
+    weight_average_all_fallback_max_if_mean_over_g: float = 400.0,
+    weight_mean_pool_fallback_max_if_over_g: float = 440.0,
 ) -> Dict[str, Any]:
     svo_path = svo_path.expanduser().resolve()
     if not svo_path.exists():
@@ -314,28 +335,6 @@ def run_weight_prediction_for_svo(
             f"fish_video_weight_evaluation.py first."
         )
 
-    fish_wj = out_dir / "weight_estimation" / "weight_estimation_results.json"
-    if not force_dgcnn_subprocess and fish_wj.is_file():
-        try:
-            dgcnn_data = json.loads(fish_wj.read_text(encoding="utf-8"))
-            if dgcnn_data.get("summary") is not None or dgcnn_data.get("per_file"):
-                print(f"Using existing DGCNN results from fish_video: {fish_wj}")
-                result = _merge_weight_prediction_json(
-                    svo_path=svo_path,
-                    svo_name=svo_name,
-                    out_dir=out_dir,
-                    cloud_dir=cloud_dir,
-                    dgcnn_json_path=fish_wj,
-                    dgcnn_data=dgcnn_data,
-                )
-                (out_dir / "weight_prediction.json").write_text(
-                    json.dumps(_sanitize_for_json(result), indent=2, ensure_ascii=False),
-                    encoding="utf-8",
-                )
-                return result
-        except Exception as e:
-            print(f"WARNING: Could not merge {fish_wj}, falling back to test_dgcnn subprocess: {e}")
-
     dgcnn_path, dgcnn_data = _run_test_dgcnn_weight_estimator_subprocess(
         cloud_dir=cloud_dir,
         ply_list_file=None,
@@ -351,6 +350,13 @@ def run_weight_prediction_for_svo(
         remove_outliers=weight_remove_outliers,
         outlier_method=weight_outlier_method,
         labels_json=weight_labels_json,
+        weight_max_length_mm=weight_max_length_mm,
+        weight_min_length_width_ratio=weight_min_length_width_ratio,
+        weight_length_quality_cv_threshold_pct=weight_length_quality_cv_threshold_pct,
+        weight_length_quality_max_span_mm=weight_length_quality_max_span_mm,
+        weight_average_all_after_filter=weight_average_all_after_filter,
+        weight_average_all_fallback_max_if_mean_over_g=weight_average_all_fallback_max_if_mean_over_g,
+        weight_mean_pool_fallback_max_if_over_g=weight_mean_pool_fallback_max_if_over_g,
     )
     result = _merge_weight_prediction_json(
         svo_path=svo_path,
@@ -381,6 +387,13 @@ def run_weight_prediction_combined_svos(
     weight_outlier_method: str,
     weight_xyz_scale: float,
     weight_labels_json: Optional[str],
+    weight_max_length_mm: float = 400.0,
+    weight_min_length_width_ratio: float = 1.5,
+    weight_length_quality_cv_threshold_pct: float = 15.0,
+    weight_length_quality_max_span_mm: float = 130.0,
+    weight_average_all_after_filter: bool = False,
+    weight_average_all_fallback_max_if_mean_over_g: float = 400.0,
+    weight_mean_pool_fallback_max_if_over_g: float = 440.0,
 ) -> Dict[str, Any]:
     """One DGCNN run over all ``<output_base>/<svo_stem>/cloud/*.ply`` (top-K / by-length applies to the union)."""
     output_base = output_base.expanduser().resolve()
@@ -411,6 +424,13 @@ def run_weight_prediction_combined_svos(
         remove_outliers=weight_remove_outliers,
         outlier_method=weight_outlier_method,
         labels_json=weight_labels_json,
+        weight_max_length_mm=weight_max_length_mm,
+        weight_min_length_width_ratio=weight_min_length_width_ratio,
+        weight_length_quality_cv_threshold_pct=weight_length_quality_cv_threshold_pct,
+        weight_length_quality_max_span_mm=weight_length_quality_max_span_mm,
+        weight_average_all_after_filter=weight_average_all_after_filter,
+        weight_average_all_fallback_max_if_mean_over_g=weight_average_all_fallback_max_if_mean_over_g,
+        weight_mean_pool_fallback_max_if_over_g=weight_mean_pool_fallback_max_if_over_g,
     )
     result = _merge_weight_prediction_json_combined(
         svo_paths=svo_paths,
@@ -502,6 +522,48 @@ def main() -> None:
     )
     parser.add_argument("--weight-xyz-scale", type=float, default=0.001)
     parser.add_argument("--weight-labels-json", type=str, default=None)
+    parser.add_argument(
+        "--weight-max-length-mm",
+        type=float,
+        default=400.0,
+        help="Passed to test_dgcnn --max-length-mm: exclude length > this from aggregation (0 = off).",
+    )
+    parser.add_argument(
+        "--weight-min-length-width-ratio",
+        type=float,
+        default=1.5,
+        help="Passed to test_dgcnn --min-length-width-ratio; 0 disables.",
+    )
+    parser.add_argument(
+        "--weight-average-all-after-filter",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Passed to test_dgcnn --average-all-after-filter: mean all PLYs after filters (no top-K).",
+    )
+    parser.add_argument(
+        "--weight-average-all-fallback-max-if-mean-over-g",
+        type=float,
+        default=400.0,
+        help="Passed to test_dgcnn --average-all-fallback-max-if-mean-over-g; 0 disables (default: 400).",
+    )
+    parser.add_argument(
+        "--weight-mean-pool-fallback-max-if-over-g",
+        type=float,
+        default=440.0,
+        help="Passed to test_dgcnn --mean-pool-fallback-max-if-over-g; 0 disables (default: 440).",
+    )
+    parser.add_argument(
+        "--weight-length-quality-cv-threshold-pct",
+        type=float,
+        default=15.0,
+        help="Passed to test_dgcnn --length-quality-cv-threshold-pct (length variance hint).",
+    )
+    parser.add_argument(
+        "--weight-length-quality-max-span-mm",
+        type=float,
+        default=130.0,
+        help="Passed to test_dgcnn --length-quality-max-span-mm; 0 disables span rule.",
+    )
 
     parser.add_argument(
         "--reuse-existing-clouds",
@@ -511,24 +573,6 @@ def main() -> None:
     )
     parser.add_argument("--no-reuse-existing-clouds", action="store_false", dest="reuse_existing_clouds")
 
-    parser.add_argument(
-        "--fish-video-weight-overlay",
-        action="store_true",
-        help="Extra on-video header lines (top-5 / per-minute bucket). "
-        "DGCNN in fish + preview weight/length labels are already enabled when weight checkpoint exists.",
-    )
-    parser.add_argument(
-        "--minute-interval-sec",
-        type=float,
-        default=60.0,
-        help="Passed to fish_video --minute-interval-sec for on-video bucket stats (default: 60).",
-    )
-    parser.add_argument(
-        "--force-dgcnn-subprocess",
-        action="store_true",
-        help="Always run test_dgcnn_weight_estimator.py subprocess even if fish_video left weight_estimation_results.json.",
-    )
-
     args = parser.parse_args()
     if args.frame_stride < 1:
         raise SystemExit("--frame-stride must be >= 1")
@@ -586,6 +630,13 @@ def main() -> None:
                     weight_outlier_method=args.weight_outlier_method,
                     weight_xyz_scale=args.weight_xyz_scale,
                     weight_labels_json=args.weight_labels_json,
+                    weight_max_length_mm=args.weight_max_length_mm,
+                    weight_min_length_width_ratio=args.weight_min_length_width_ratio,
+                    weight_length_quality_cv_threshold_pct=args.weight_length_quality_cv_threshold_pct,
+                    weight_length_quality_max_span_mm=args.weight_length_quality_max_span_mm,
+                    weight_average_all_after_filter=args.weight_average_all_after_filter,
+                    weight_average_all_fallback_max_if_mean_over_g=args.weight_average_all_fallback_max_if_mean_over_g,
+                    weight_mean_pool_fallback_max_if_over_g=args.weight_mean_pool_fallback_max_if_over_g,
                 )
             )
         except Exception as e:
@@ -612,7 +663,13 @@ def main() -> None:
                         weight_outlier_method=args.weight_outlier_method,
                         weight_xyz_scale=args.weight_xyz_scale,
                         weight_labels_json=args.weight_labels_json,
-                        force_dgcnn_subprocess=args.force_dgcnn_subprocess,
+                        weight_max_length_mm=args.weight_max_length_mm,
+                        weight_min_length_width_ratio=args.weight_min_length_width_ratio,
+                        weight_length_quality_cv_threshold_pct=args.weight_length_quality_cv_threshold_pct,
+                        weight_length_quality_max_span_mm=args.weight_length_quality_max_span_mm,
+                        weight_average_all_after_filter=args.weight_average_all_after_filter,
+                        weight_average_all_fallback_max_if_mean_over_g=args.weight_average_all_fallback_max_if_mean_over_g,
+                        weight_mean_pool_fallback_max_if_over_g=args.weight_mean_pool_fallback_max_if_over_g,
                     )
                 )
             except Exception as e:
@@ -628,8 +685,11 @@ def main() -> None:
     if len(results) == 1 and "error" not in results[0]:
         r0 = results[0]
         avg_g = r0.get("avg_predicted_weight_g")
-        if avg_g is not None:
-            print(f"Final predicted weight (test_dgcnn): {avg_g:.2f} g")
+        pred_g = r0.get("pred_weight_g")
+        out_g = pred_g if pred_g is not None else avg_g
+        if out_g is not None:
+            label = "pred_weight" if pred_g is not None else "avg_predicted_weight"
+            print(f"Final predicted weight (test_dgcnn, {label}): {float(out_g):.2f} g")
         else:
             print("Final predicted weight: N/A (skipped or no valid point clouds)")