| |
| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
| from _common import ( |
| cell_set, |
| ensure_dir, |
| read_jsonl, |
| safe_div, |
| universe_cells, |
| valid_candidates, |
| write_json, |
| write_jsonl, |
| ) |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description="Run deterministic greedy CM-EVS view selection.") |
| parser.add_argument("--candidates", type=Path, required=True, help="Candidate JSONL.") |
| parser.add_argument("--output-dir", type=Path, default=Path("outputs/tiny"), help="Run output directory.") |
| parser.add_argument("--budget", type=int, default=4, help="Maximum number of selected views.") |
| parser.add_argument("--lambda-conflict", type=float, default=0.35, help="Conflict-prior penalty weight.") |
| parser.add_argument("--min-gain", type=float, default=0.01, help="Stop when marginal coverage falls below this value.") |
| return parser.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| rows = read_jsonl(args.candidates) |
| candidates = valid_candidates(rows) |
| universe = universe_cells(candidates) |
| universe_size = max(1, len(universe)) |
|
|
| covered: set[str] = set() |
| selected: list[dict] = [] |
| log_rows: list[dict] = [] |
| used: set[str] = set() |
|
|
| for step in range(args.budget): |
| best = None |
| best_tuple = None |
| for candidate in candidates: |
| cid = str(candidate["candidate_id"]) |
| if cid in used: |
| continue |
| cells = cell_set(candidate) |
| new_cells = cells - covered |
| marginal_gain = safe_div(len(new_cells), universe_size) |
| probe = float(candidate.get("single_view_probe_coverage", 0.0)) |
| conflict = float(candidate.get("conflict_prior", 0.0)) |
| score = marginal_gain + 0.15 * probe - args.lambda_conflict * conflict |
| score_tuple = (score, marginal_gain, -conflict, cid) |
| if best_tuple is None or score_tuple > best_tuple: |
| best_tuple = score_tuple |
| best = (candidate, new_cells, score, marginal_gain) |
|
|
| if best is None: |
| break |
|
|
| candidate, new_cells, score, marginal_gain = best |
| if selected and marginal_gain < args.min_gain: |
| break |
|
|
| cid = str(candidate["candidate_id"]) |
| used.add(cid) |
| covered.update(new_cells) |
| rank = len(selected) |
| selected.append( |
| { |
| "candidate_id": cid, |
| "rank": rank, |
| "position": candidate.get("position", [0.0, 0.0, 0.0]), |
| "yaw_deg": float(candidate.get("yaw_deg", 0.0)), |
| "score": round(float(score), 6), |
| "marginal_gain": round(float(marginal_gain), 6), |
| } |
| ) |
| log_rows.append( |
| { |
| "step": step, |
| "candidate_id": cid, |
| "score": round(float(score), 6), |
| "marginal_gain": round(float(marginal_gain), 6), |
| "coverage_after": round(safe_div(len(covered), universe_size), 6), |
| "conflict_prior": float(candidate.get("conflict_prior", 0.0)), |
| } |
| ) |
|
|
| scene_id = str(candidates[0].get("scene_id", "unknown")) if candidates else "unknown" |
| metadata_dir = ensure_dir(args.output_dir / "metadata") |
| write_json( |
| metadata_dir / "selected_viewpoints.json", |
| { |
| "scene_id": scene_id, |
| "method": "cmevs_greedy_conflict_minimized", |
| "selected_viewpoints": selected, |
| "summary": { |
| "budget": args.budget, |
| "lambda_conflict": args.lambda_conflict, |
| "min_gain": args.min_gain, |
| "num_candidates": len(rows), |
| "num_valid_candidates": len(candidates), |
| "num_selected": len(selected), |
| "coverage": round(safe_div(len(covered), universe_size), 6), |
| "universe_cells": len(universe), |
| }, |
| }, |
| ) |
| write_jsonl(metadata_dir / "per_step_log.jsonl", log_rows) |
| print(f"Selected {len(selected)} viewpoints; final coverage={safe_div(len(covered), universe_size):.3f}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
