scripts: figure generators + frame-regen script for the opencv5 report

make_figures.py gains the DE-landscape, calibration, and per-film leave-one-out
holdout figures used by the experiment log. regen_frame_examples.sh replays all
nine films with the shipped learned-boundary flood config and draws GT-aware
TP/FP/FN frames, so every annotated image in the docs is reproducible.

scripts/scene_detector/make_figures.py

@@ -68,5 +68,51 @@ def fig_evolution():
     ax.set_ylim(0,18)
     fig.tight_layout(); fig.savefig(OUT/"scene_detector_evolution.png"); plt.close(fig)
 
-fig_presence(); fig_macro(); fig_evolution()
-print("wrote:", *(p.name for p in sorted(OUT.glob("scene_*.png"))))
+import csv as _csv
+
+# ── Figure 4: DE convergence (the 10-knob presence sweep) ────────────────────
+def fig_de():
+    import json
+    rows=[json.loads(l) for l in open("experiments/trajectories/lvface_opencv5_10knob.FINAL.jsonl")]
+    f1=[r["f1"]*100 for r in rows]
+    run_best=np.maximum.accumulate(f1)
+    fig,ax=plt.subplots(figsize=(8,4.5))
+    ax.scatter(range(len(f1)),f1,s=8,alpha=0.35,color="#9aa7b4",label="candidate")
+    ax.plot(run_best,color="#3d7ea6",lw=2,label="best so far")
+    ax.set_xlabel("DE evaluation"); ax.set_ylabel("macro presence F1 (%)")
+    ax.set_title("10-knob presence sweep (Differential Evolution)")
+    ax.legend(loc="lower right"); ax.set_ylim(0, max(f1)+8)
+    ax.text(0.02,0.95,f"optimum {max(f1):.1f}%",transform=ax.transAxes,va="top",
+            fontsize=10,bbox=dict(boxstyle="round",fc="#f4f4f4",ec="#ccc"))
+    fig.tight_layout(); fig.savefig(OUT/"de_search_landscape.png"); plt.close(fig)
+
+# ── Figure 5: calibration curve (similarity → P(match)) ──────────────────────
+def fig_calibration():
+    sims,ps=[],[]
+    with open("experiments/galleries/gallery_LVFace-B_Glint360K.h5.calib_cache.csv") as f:
+        for r in _csv.DictReader(f):
+            sims.append(float(r["similarity"])); ps.append(float(r["p_match"]))
+    fig,ax=plt.subplots(figsize=(6.5,4.5))
+    ax.plot(sims,ps,color="#3d7ea6",lw=2)
+    ax.axhline(0.485,ls="--",color="#e07a5f",lw=1,label="shipped threshold 0.485")
+    ax.set_xlabel("cosine similarity"); ax.set_ylabel("calibrated P(match)")
+    ax.set_title("LVFace-B Glint360K calibration"); ax.set_xlim(-1,1); ax.legend()
+    fig.tight_layout(); fig.savefig(OUT/"calibration_curves.png"); plt.close(fig)
+
+# ── Figure 6: holdout F1 by film (learned detector, LOO) ─────────────────────
+def fig_holdout():
+    order=np.argsort(FL)
+    fig,ax=plt.subplots(figsize=(8,4.5))
+    y=np.arange(len(FILMS))
+    ax.barh(y,[FL[i] for i in order],color="#3d7ea6")
+    ax.set_yticks(y); ax.set_yticklabels([FILMS[i] for i in order])
+    ax.set_xlabel("presence F1 (%), learned detector (LOO)")
+    ax.set_title("Per-film presence F1 — leave-one-out")
+    ax.axvline(np.mean(FL),ls="--",color="#333",lw=1)
+    ax.text(np.mean(FL)+1,0.2,f"macro {np.mean(FL):.1f}%",fontsize=9)
+    for i,idx in enumerate(order): ax.text(FL[idx]+0.5,i,f"{FL[idx]:.0f}",va="center",fontsize=8)
+    ax.set_xlim(0,100)
+    fig.tight_layout(); fig.savefig(OUT/"holdout_f1_by_film.png"); plt.close(fig)
+
+fig_presence(); fig_macro(); fig_evolution(); fig_de(); fig_calibration(); fig_holdout()
+print("wrote:", *(p.name for p in sorted(OUT.glob("*.png"))))