1157 lines
47 KiB
Python
Executable File
1157 lines
47 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Fixed-dialogue field deployment wizard for scan calibration."""
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from __future__ import annotations
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import argparse
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from contextlib import redirect_stdout
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from copy import deepcopy
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from datetime import datetime
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import io
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import json
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import math
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from pathlib import Path
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import re
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import select
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import subprocess
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import sys
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import tempfile
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import time
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import scan_calibrator as core
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ROOT = Path(__file__).resolve().parent
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HISTORY_DIR = ROOT / "history"
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CONFIG_PATH = "/smart-cam/config/config.json"
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# The grid contact sheet is the final calibration check. Horizontal seams on
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# the tested PTZ camera retain more real-world overlap than the center-shift
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# model predicts, while vertical seams need a small safety margin. Keep these
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# axis-specific targets within the generator's accepted 0%..5% range.
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TARGET_OVERLAPS = {"pan": 0.045, "tilt": 0.02}
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RED = "\033[31m"
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GREEN = "\033[32m"
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YELLOW = "\033[33m"
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RESET = "\033[0m"
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ACTIVE_HISTORY_FILE: Path | None = None
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class Cancelled(Exception):
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pass
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def say(message: str = "", *, color: str = "") -> None:
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print(f"{color}{message}{RESET if color else ''}", flush=True)
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def choose(question: str, choices: set[str]) -> str:
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while True:
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answer = input(f"\n{question}\n> ").strip()
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if answer == "3":
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raise Cancelled
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if answer in choices:
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return answer
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say(f"请输入 {' / '.join(sorted(choices | {'3'}))}。", color=YELLOW)
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def valid_ptz(ptz: object) -> bool:
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if not isinstance(ptz, dict):
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return False
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try:
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pan = int(ptz["pan"])
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tilt = int(ptz["tilt"])
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zoom = int(ptz["zoom"])
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except (KeyError, TypeError, ValueError):
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return False
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return 0 <= pan <= 36000 and -18000 <= tilt <= 18000 and zoom > 0
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def parse_ptz_input(value: str) -> dict[str, int]:
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"""Parse PTZ copied from this tool, with or without display annotations."""
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result: dict[str, int] = {}
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for name in ("pan", "tilt", "zoom"):
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match = re.search(
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rf"(?:^|[,，;；\s]){name}\s*=\s*(-?\d+)",
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value,
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flags=re.IGNORECASE,
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)
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if match:
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result[name] = int(match.group(1))
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if not valid_ptz(result):
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raise ValueError(
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"格式应为 pan=21720, tilt=1268, zoom=4580；"
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"也可以直接粘贴带角度和倍数的完整输出"
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)
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return result
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def capture_position(question: str) -> dict[str, int]:
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while True:
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answer = input(
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f"\n{question}\n"
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"摄像头到位后回复 1 读取当前位置；也可以直接输入 "
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"pan=21720 (217.20°), tilt=1268 (12.68°), zoom=4580 (45.80×)；"
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"回复 3 随时退出。\n> "
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).strip()
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if answer == "3":
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raise Cancelled
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if answer == "1":
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return camera_ptz()
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try:
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ptz = parse_ptz_input(answer)
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except ValueError as exc:
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say(f"输入无效：{exc}", color=YELLOW)
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continue
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core.print_ptz(ptz, "已采用手动输入")
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return ptz
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def confirm_position(question: str) -> None:
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choose(f"{question}\n到位后回复 1；回复 3 随时退出。", {"1"})
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def camera_ptz() -> dict[str, int]:
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ptz = core.get_ptz()
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core.print_ptz(ptz, "已读取")
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return ptz
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def substantive_state(state: dict) -> dict:
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"""Return reusable calibration data, excluding per-session metadata."""
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return {
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key: deepcopy(state[key])
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for key in ("points", "steps", "scan_zoom", "scan_zoom_average", "mount")
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if key in state
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}
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def state_fingerprint(state: dict) -> str:
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return json.dumps(substantive_state(state), ensure_ascii=False, sort_keys=True)
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def write_json_atomic(path: Path, value: dict) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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temporary = path.with_name(f".{path.name}.tmp")
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temporary.write_text(
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json.dumps(value, ensure_ascii=False, indent=2), encoding="utf-8"
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)
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temporary.replace(path)
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def load_history_records() -> list[tuple[Path, dict]]:
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"""Load sessions and import a pre-upgrade calibration.json when needed."""
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HISTORY_DIR.mkdir(parents=True, exist_ok=True)
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records: list[tuple[Path, dict]] = []
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fingerprints: set[str] = set()
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for path in sorted(HISTORY_DIR.glob("session_*.json")):
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try:
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state = json.loads(path.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError) as exc:
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say(f"忽略无法读取的历史记录 {path.name}：{exc}", color=YELLOW)
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continue
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if not isinstance(state, dict):
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continue
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records.append((path, state))
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fingerprints.add(state_fingerprint(state))
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if core.STATE_FILE.exists():
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try:
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current = json.loads(core.STATE_FILE.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError):
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current = None
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if (
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isinstance(current, dict)
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and any(substantive_state(current).get(key) for key in ("points", "steps"))
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and state_fingerprint(current) not in fingerprints
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):
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stamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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recovered = deepcopy(current)
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recovered.setdefault("_session", {})
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recovered["_session"].update({
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"id": f"recovered_{stamp}",
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"created_at": recovered.get("updated_at")
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or datetime.now().astimezone().isoformat(timespec="seconds"),
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"source": "calibration.json升级前记录",
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})
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path = HISTORY_DIR / f"session_{stamp}_recovered.json"
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write_json_atomic(path, recovered)
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records.append((path, recovered))
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return sorted(
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records,
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key=lambda item: str(
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item[1].get("_session", {}).get("created_at")
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or item[1].get("updated_at")
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or item[0].name
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),
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reverse=True,
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)
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def state_item_exists(state: dict, section: str, name: str) -> bool:
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value = state.get(section, {}).get(name)
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if section == "points":
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return valid_ptz(value)
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return isinstance(value, dict) and bool(value)
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def select_history_record(
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records: list[tuple[Path, dict]],
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) -> tuple[Path, dict] | None:
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if not records:
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say("\n未发现可复用的历史标定记录，将开始一条新记录。")
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return None
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say("\n在第②步之前发现以下历史标定记录（最新的排在前面）：")
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for index, (path, state) in enumerate(records, start=1):
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session = state.get("_session", {})
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when = session.get("created_at") or state.get("updated_at") or path.stem
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mount = {"hanging": "野外倒挂", "desktop": "办公室正放"}.get(
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state.get("mount"), "未记录"
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)
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say(
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f"{index}. {when}，安装方式={mount}，"
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f"左上角={'有' if state_item_exists(state, 'points', 'top-left') else '无'}，"
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f"右下角={'有' if state_item_exists(state, 'points', 'bottom-right') else '无'}，"
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f"pan_step={'有' if state_item_exists(state, 'steps', 'pan') else '无'}，"
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f"tilt_step={'有' if state_item_exists(state, 'steps', 'tilt') else '无'}"
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)
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while True:
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answer = input(
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"\n输入对应序号复用；回复 0 不复用并新建；回复 q 退出。"
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"此处数字 3 表示第3条记录（如果存在）。\n> "
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).strip().lower()
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if answer in {"q", "quit", "退出"}:
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raise Cancelled
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if answer == "0":
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return None
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if answer.isdigit() and 1 <= int(answer) <= len(records):
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return records[int(answer) - 1]
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say(f"请输入 0、1～{len(records)} 或 q。", color=YELLOW)
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def start_history_session(
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mount: str, selected: tuple[Path, dict] | None
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) -> dict:
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global ACTIVE_HISTORY_FILE
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stamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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source_path, source_state = selected if selected else (None, {})
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state = substantive_state(source_state)
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state.setdefault("points", {})
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state.setdefault("steps", {})
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state["mount"] = mount
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state["_session"] = {
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"id": stamp,
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"created_at": datetime.now().astimezone().isoformat(timespec="seconds"),
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**({"reused_from": source_path.name} if source_path else {}),
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}
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ACTIVE_HISTORY_FILE = HISTORY_DIR / f"session_{stamp}.json"
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save_working_state(state)
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return state
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def save_working_state(state: dict) -> None:
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session = state.setdefault("_session", {})
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session["updated_at"] = datetime.now().astimezone().isoformat(timespec="seconds")
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core.save_state(state)
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if ACTIVE_HISTORY_FILE is not None:
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write_json_atomic(ACTIVE_HISTORY_FILE, state)
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def remove_session_snapshots(paths: set[str]) -> None:
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for path in sorted(paths):
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if not path.startswith((
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"/smart-cam/data/scan/manual/",
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"/smart-cam/data/scan/calibration/",
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)):
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continue
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subprocess.run(
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["docker", "exec", "smart-cam", "rm", "-f", "--", path],
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stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=False,
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)
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def candidate_deltas(axis: str) -> list[int]:
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"""Coarse probes from narrow to wide FOV.
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Once any probe provides a usable FOV estimate, estimate_axis replaces the
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wide tail with scene/camera-specific deltas. Dense small values are
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important at high optical zoom, where a jump from 150 to 250 PTZ units can
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go directly from useful overlap to no common image.
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"""
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if axis == "pan":
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return [50, 75, 110, 150, 220, 320, 470, 700, 1050, 1500, 2100]
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return [25, 40, 60, 90, 130, 190, 280, 420, 620, 850, 1150]
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def fov_guided_deltas(delta: int, overlap: float, axis: str) -> list[int]:
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"""Choose probes that retain enough shared image for robust matching."""
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shift = 1.0 - overlap
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if not 0.04 <= shift <= 0.92:
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return []
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estimated_fov = delta / shift
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minimum = 20 if axis == "pan" else 12
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# Fractions are image displacement/FOV, hence target overlaps are roughly
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# 82%, 72%, 60%, 48% and 35%. The middle probes provide the strongest
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# cross-check, while the last one confirms the wider displacement model.
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return sorted({
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max(minimum, round(estimated_fov * fraction))
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for fraction in (0.18, 0.28, 0.40, 0.52, 0.65)
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})
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def choose_direction(axis: str, origin: dict[str, int], points: dict) -> int:
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if axis == "pan":
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# Pan is circular. The direction changes only the sign, not the FOV estimate.
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return 1
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low = min(int(points["top-left"]["tilt"]), int(points["bottom-right"]["tilt"]))
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high = max(int(points["top-left"]["tilt"]), int(points["bottom-right"]["tilt"]))
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return 1 if high - origin["tilt"] >= origin["tilt"] - low else -1
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def consistent_samples(reliable: list[tuple[int, float, float, dict]],
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maximum_spread: float = 0.15) -> list:
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"""Return the largest group with a bounded end-to-end FOV spread.
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The previous seed-radius clustering allowed one sample at -12% and
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another at +12% into the same cluster. That cluster could therefore have
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nearly 24% end-to-end spread and was immediately rejected by the later
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15% check. Use sorted contiguous windows so membership and validation
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apply the same definition of consistency.
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"""
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if not reliable:
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return []
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ordered = sorted(reliable, key=lambda sample: sample[2])
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candidates = []
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for begin in range(len(ordered)):
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for end in range(begin, len(ordered)):
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window = ordered[begin:end + 1]
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_, spread = fov_statistics(window)
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if spread <= maximum_spread:
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candidates.append(window)
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return max(candidates, key=lambda group: (
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len(group),
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-fov_statistics(group)[1],
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sum(int(sample[3].get("inliers") or 0) for sample in group),
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))
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def fov_statistics(samples: list[tuple[int, float, float, dict]]) -> tuple[float, float]:
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fovs = sorted(sample[2] for sample in samples)
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estimated_fov = (
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fovs[len(fovs) // 2] if len(fovs) % 2
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else (fovs[len(fovs) // 2 - 1] + fovs[len(fovs) // 2]) / 2
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)
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return estimated_fov, (max(fovs) - min(fovs)) / estimated_fov
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def horizontal_fov_from_vertical(vertical_fov: float, aspect_ratio: float) -> float:
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"""Convert a rectilinear vertical FOV to horizontal FOV in PTZ units."""
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vertical_radians = math.radians(vertical_fov / 100.0)
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horizontal_radians = 2.0 * math.atan(
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aspect_ratio * math.tan(vertical_radians / 2.0)
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)
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return math.degrees(horizontal_radians) * 100.0
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def constrain_pan_with_tilt(pan_info: dict, tilt_info: dict,
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aspect_ratio: float) -> tuple[dict, str]:
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measured_pan_fov = float(pan_info["derived_from"]["estimated_fov"])
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measured_tilt_fov = float(tilt_info["derived_from"]["estimated_fov"])
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optical_pan_fov = horizontal_fov_from_vertical(
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measured_tilt_fov, aspect_ratio
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)
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agreement = measured_pan_fov / optical_pan_fov
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diagnostic = {
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"image_aspect_ratio": round(aspect_ratio, 6),
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"measured_pan_fov": round(measured_pan_fov, 4),
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"measured_tilt_fov": round(measured_tilt_fov, 4),
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"tilt_predicted_pan_fov": round(optical_pan_fov, 4),
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"agreement_ratio": round(agreement, 6),
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}
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if 0.85 <= agreement <= 1.15:
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tilt_target_overlap = TARGET_OVERLAPS["tilt"]
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tilt_predicted_step = max(
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1, round(optical_pan_fov * (1.0 - tilt_target_overlap))
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)
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direct_pan_step = int(pan_info["value"])
|
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# Neither estimate is authoritative on a PTZ dome: direct horizontal
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# matching is affected by distortion, while the aspect-ratio prediction
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# assumes an ideal rectilinear lens. Fuse them instead of replacing one
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# with the other. The geometric mean stays between both measurements.
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constrained_step = max(
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1, round(math.sqrt(direct_pan_step * tilt_predicted_step))
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)
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effective_overlap = max(
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0.0, min(core.MAX_GENERATED_OVERLAP,
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1.0 - constrained_step / measured_pan_fov)
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)
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return {
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**pan_info,
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"value": constrained_step,
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"overlap": effective_overlap,
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"target_overlap": effective_overlap,
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"overlap_method": "axis_shift_v1_pan_tilt_fused",
|
||
"cross_axis_check": {
|
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**diagnostic,
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"applied": True,
|
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"direct_pan_step": direct_pan_step,
|
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"tilt_predicted_pan_step": tilt_predicted_step,
|
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"fused_pan_step": constrained_step,
|
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},
|
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}, (
|
||
f"pan/tilt视场比例符合普通透视模型（一致率{agreement*100:.1f}%）；"
|
||
f"融合pan实测={direct_pan_step}与tilt推导={tilt_predicted_step}"
|
||
f"后pan_step={constrained_step}"
|
||
)
|
||
return {
|
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**pan_info,
|
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"cross_axis_check": {
|
||
**diagnostic,
|
||
"applied": False,
|
||
"reason": "non_rectilinear_or_axis_scale_mismatch",
|
||
},
|
||
}, (
|
||
f"pan/tilt视场比例仅为普通透视预测的{agreement*100:.1f}%，"
|
||
"疑似镜头畸变或不同tilt行的水平覆盖不一致；"
|
||
"不能用tilt比例替代pan实测，固定pan_step应以不缺图为优先"
|
||
)
|
||
|
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|
||
def configured_image_aspect_ratio() -> float:
|
||
try:
|
||
raw = subprocess.run(
|
||
["docker", "exec", "smart-cam", "cat", CONFIG_PATH],
|
||
text=True, capture_output=True, check=True,
|
||
).stdout
|
||
image = json.loads(raw).get("scan", {}).get("image", {})
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width, height = int(image.get("width", 1280)), int(image.get("height", 720))
|
||
if width > 0 and height > 0:
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||
return width / height
|
||
except (OSError, ValueError, KeyError, json.JSONDecodeError,
|
||
subprocess.SubprocessError):
|
||
pass
|
||
return 16.0 / 9.0
|
||
|
||
|
||
def failure_detail(result: dict) -> str:
|
||
reason = str(result.get("reason") or "unknown")
|
||
labels = {
|
||
"ptz_motion_shortfall": "云台受限，实际移动量不足",
|
||
"not_enough_features": "两张图片可检测特征不足",
|
||
"not_enough_matches": "两张图片的公共特征不足",
|
||
"homography_failed": "公共特征无法形成可靠几何变换",
|
||
"axis_motion_too_small": "匹配到的主轴画面位移过小",
|
||
"axis_motion_inconsistent": "匹配位移方向异常或串轴过大",
|
||
}
|
||
fields = []
|
||
if result.get("actual_axis_motion") is not None:
|
||
fields.append(
|
||
f"实际移动={result['actual_axis_motion']}/"
|
||
f"{result.get('requested_axis_motion', result.get('delta', '?'))}"
|
||
)
|
||
if result.get("good_matches") is not None:
|
||
fields.append(f"公共特征={result['good_matches']}")
|
||
if result.get("inliers") is not None:
|
||
fields.append(f"几何内点={result['inliers']}")
|
||
if result.get("axis_shift") is not None:
|
||
fields.append(f"主轴位移={float(result['axis_shift'])*100:.1f}%")
|
||
if result.get("cross_axis_shift") is not None:
|
||
fields.append(f"串轴位移={float(result['cross_axis_shift'])*100:.1f}%")
|
||
if result.get("phase_response") is not None:
|
||
fields.append(f"相位响应={float(result['phase_response']):.2f}")
|
||
suffix = f"（{'，'.join(fields)}）" if fields else ""
|
||
return f"{labels.get(reason, reason)}{suffix}"
|
||
|
||
|
||
def estimate_axis(axis: str, origin: dict[str, int], points: dict,
|
||
snapshots: set[str]) -> dict:
|
||
direction = choose_direction(axis, origin, points)
|
||
baseline = core.snapshot()
|
||
snapshots.add(baseline)
|
||
reliable: list[tuple[int, float, float, dict]] = []
|
||
failure_reasons: dict[str, int] = {}
|
||
say(f"\n正在标定 {axis}_step，摄像头会自动移动并回到起点……")
|
||
|
||
pending = candidate_deltas(axis)
|
||
tested: set[int] = set()
|
||
adaptive_seeded = False
|
||
while pending:
|
||
delta = pending.pop(0)
|
||
if delta in tested:
|
||
continue
|
||
tested.add(delta)
|
||
result = core.measure_delta(
|
||
axis, delta, direction, 1.0, baseline=baseline, origin=origin
|
||
)
|
||
snapshots.add(str(result.get("candidate_file", "")))
|
||
if (
|
||
axis == "tilt"
|
||
and not reliable
|
||
and result.get("reason") == "ptz_motion_shortfall"
|
||
):
|
||
direction *= -1
|
||
say(
|
||
f" step={delta}: 原方向受云台限位，自动改用相反方向继续标定"
|
||
)
|
||
result = core.measure_delta(
|
||
axis, delta, direction, 1.0, baseline=baseline, origin=origin
|
||
)
|
||
snapshots.add(str(result.get("candidate_file", "")))
|
||
if not result.get("valid"):
|
||
reason = str(result.get("reason") or "unknown")
|
||
failure_reasons[reason] = failure_reasons.get(reason, 0) + 1
|
||
say(f" step={delta}: {failure_detail(result)}，继续尝试")
|
||
continue
|
||
overlap = float(result["overlap"])
|
||
inliers = int(result.get("inliers") or 0)
|
||
ratio = float(result.get("inlier_ratio") or 0)
|
||
if result.get("match_method") == "phase_correlation":
|
||
quality = f"整图相位响应={float(result.get('phase_response') or 0):.2f}"
|
||
else:
|
||
quality = f"有效特征={inliers}，内点率={ratio*100:.1f}%"
|
||
say(f" step={delta}: 重叠约 {overlap*100:.1f}%，{quality}")
|
||
if not adaptive_seeded:
|
||
guided = fov_guided_deltas(delta, overlap, axis)
|
||
if guided:
|
||
# Keep only a small safety tail beyond the guided range. Very
|
||
# large fixed probes cannot match once the current optical FOV
|
||
# has been measured and only waste time.
|
||
cap = round(max(guided) * 1.15)
|
||
pending = sorted({
|
||
value for value in guided + pending
|
||
if value not in tested and value <= cap
|
||
})
|
||
adaptive_seeded = True
|
||
say(
|
||
f" 已根据首个有效样本自适应生成后续step："
|
||
f"{', '.join(str(value) for value in pending)}"
|
||
)
|
||
feature_reliable = inliers >= 50 and ratio >= 0.45
|
||
phase_reliable = (
|
||
result.get("match_method") == "phase_correlation"
|
||
and float(result.get("phase_response") or 0) >= core.PHASE_MIN_RESPONSE
|
||
)
|
||
if (feature_reliable or phase_reliable) and 0.08 <= overlap <= 0.90:
|
||
# Hikvision absolute PTZ movement is quantized and can also have
|
||
# backlash. At high optical zoom, using the requested delta
|
||
# instead of the SDK-reported actual movement creates a large,
|
||
# systematic FOV error. Shortfall samples have already been
|
||
# rejected above, so the actual movement is authoritative here.
|
||
actual_delta = int(result.get("actual_axis_motion") or delta)
|
||
reliable.append((
|
||
actual_delta, overlap,
|
||
actual_delta / (1.0 - overlap), result,
|
||
))
|
||
if len(reliable) >= 4:
|
||
provisional = consistent_samples(reliable)
|
||
if len(provisional) >= 4:
|
||
_, provisional_spread = fov_statistics(provisional)
|
||
if provisional_spread <= 0.08 and min(
|
||
sample[1] for sample in provisional
|
||
) <= 0.55:
|
||
say(
|
||
f" 已获得{len(provisional)}个覆盖充分且一致的样本，"
|
||
"停止测试更大的step"
|
||
)
|
||
break
|
||
|
||
if len(reliable) < 2:
|
||
core.move(origin)
|
||
summary = "、".join(
|
||
f"{reason}={count}次"
|
||
for reason, count in sorted(failure_reasons.items())
|
||
)
|
||
core.fail(
|
||
f"{axis}方向只得到{len(reliable)}个可靠样本，至少需要2个不同移动量互相验证。"
|
||
f"失败统计：{summary or '无'}。请根据上方每个step的具体原因处理后重试"
|
||
)
|
||
|
||
# Fixed OSD text and repeated scenery can yield a convincing false
|
||
# homography. A real optical FOV must agree across different PTZ deltas.
|
||
consistent = consistent_samples(reliable)
|
||
if len(consistent) < 2:
|
||
core.move(origin)
|
||
values = ", ".join(f"{sample[2]:.0f}" for sample in reliable)
|
||
core.fail(
|
||
f"{axis}方向不同移动量反算的视场不一致（{values}），疑似重复图案误匹配；"
|
||
"本次结果不写入配置，请更换标定画面"
|
||
)
|
||
|
||
estimated_fov, spread = fov_statistics(consistent)
|
||
if len(consistent) == 2:
|
||
strong_pair = all(
|
||
(
|
||
int(sample[3].get("inliers") or 0) >= 100
|
||
and float(sample[3].get("inlier_ratio") or 0) >= 0.65
|
||
)
|
||
or (
|
||
sample[3].get("match_method") == "phase_correlation"
|
||
and float(sample[3].get("phase_response") or 0) >= 0.35
|
||
)
|
||
for sample in consistent
|
||
)
|
||
allowed_spread = 0.08
|
||
# Two independently measured FOVs that agree within 5% are already a
|
||
# strong geometric cross-check. Do not reject such a pair merely
|
||
# because a natural scene supplied 74 rather than 100 ORB inliers.
|
||
if not strong_pair and spread > 0.05:
|
||
core.move(origin)
|
||
core.fail(
|
||
f"{axis}方向目前只有2个一致样本，但特征质量不足以使用双样本规则；"
|
||
"需要第3个可靠样本，请更换标定画面"
|
||
)
|
||
else:
|
||
allowed_spread = 0.15
|
||
if spread > allowed_spread:
|
||
core.move(origin)
|
||
core.fail(
|
||
f"{axis}方向视场样本离散度为{spread*100:.1f}%，"
|
||
f"超过{allowed_spread*100:.0f}%；"
|
||
"本次结果不写入配置，请更换标定画面"
|
||
)
|
||
|
||
target_overlap = TARGET_OVERLAPS[axis]
|
||
step = max(1, round(estimated_fov * (1.0 - target_overlap)))
|
||
visual_direction = None
|
||
if axis == "pan":
|
||
# Interactive pan probes always use a positive PTZ delta. The signed
|
||
# image displacement reveals the real pan direction of this exact
|
||
# camera/firmware; mount style is not a reliable substitute.
|
||
signed_shifts = [
|
||
float(sample[3].get("center_dx", 0.0))
|
||
for sample in consistent
|
||
if abs(float(sample[3].get("center_dx", 0.0))) >= 0.02
|
||
]
|
||
if len(signed_shifts) < 2:
|
||
core.move(origin)
|
||
core.fail(
|
||
"pan方向缺少至少2个可靠的带符号位移样本；"
|
||
"请更换纹理分布更均匀的位置重新执行第四步"
|
||
)
|
||
positive = sum(shift > 0 for shift in signed_shifts)
|
||
negative = sum(shift < 0 for shift in signed_shifts)
|
||
agreement = max(positive, negative) / len(signed_shifts)
|
||
if agreement < 0.75:
|
||
core.move(origin)
|
||
core.fail(
|
||
f"pan方向样本符号不一致（一致率{agreement*100:.1f}%），"
|
||
"疑似重复图案或近距离视差；本次不生成扫描弧段"
|
||
)
|
||
# center_dx < 0 means positive numeric pan advances the camera view
|
||
# toward visual right, so left-to-right is numerically increasing.
|
||
visual_direction = "increasing" if negative > positive else "decreasing"
|
||
measured_step, measured_overlap, _, detail = max(
|
||
consistent, key=lambda sample: int(sample[3].get("inliers") or 0)
|
||
)
|
||
core.move(origin)
|
||
say(
|
||
f" {axis}视场一致性通过：{len(consistent)}个样本，"
|
||
f"估算视场={estimated_fov:.0f}，离散度={spread*100:.1f}%"
|
||
)
|
||
if visual_direction is not None:
|
||
say(
|
||
f" 已自动识别该摄像头：画面左→右时pan数值"
|
||
f"{'递增' if visual_direction == 'increasing' else '递减'}"
|
||
)
|
||
return {
|
||
"value": step,
|
||
"direction": direction,
|
||
"overlap": target_overlap,
|
||
"target_overlap": target_overlap,
|
||
"overlap_method": "axis_shift_v1_retargeted",
|
||
"zoom": origin["zoom"],
|
||
"measured_at": origin,
|
||
**({"visual_direction": visual_direction} if visual_direction else {}),
|
||
"good_matches": detail.get("good_matches"),
|
||
"inliers": detail.get("inliers"),
|
||
"inlier_ratio": detail.get("inlier_ratio"),
|
||
"match_method": detail.get("match_method", "orb_homography"),
|
||
"phase_response": detail.get("phase_response"),
|
||
"derived_from": {
|
||
"value": measured_step,
|
||
"overlap": measured_overlap,
|
||
"estimated_fov": round(estimated_fov, 4),
|
||
"samples": [
|
||
{
|
||
"value": sample[0],
|
||
"overlap": round(sample[1], 6),
|
||
"estimated_fov": round(sample[2], 4),
|
||
"inliers": sample[3].get("inliers"),
|
||
"inlier_ratio": sample[3].get("inlier_ratio"),
|
||
"match_method": sample[3].get("match_method", "orb_homography"),
|
||
"phase_response": sample[3].get("phase_response"),
|
||
}
|
||
for sample in consistent
|
||
],
|
||
"spread": round(spread, 6),
|
||
},
|
||
}
|
||
|
||
|
||
def read_zoom_after_move(origin: dict[str, int], requested_zoom: int) -> dict[str, int]:
|
||
core.move({**origin, "zoom": requested_zoom})
|
||
time.sleep(1.0)
|
||
return core.get_ptz()
|
||
|
||
|
||
def move_to_scan_zoom(points: dict) -> tuple[dict[str, int], float]:
|
||
"""Always move to the smallest reachable zoom at or above the mean."""
|
||
origin = camera_ptz()
|
||
zooms = [int(points[name]["zoom"]) for name in ("top-left", "bottom-right")]
|
||
average_zoom = sum(zooms) / len(zooms)
|
||
minimum_zoom = math.ceil(average_zoom)
|
||
say(
|
||
f"两个角点zoom的平均值为{average_zoom:g}（{average_zoom/100:.3f}×）；"
|
||
"正在自动切换到该zoom。若平均值不可达，将向上取最近的可达档位……"
|
||
)
|
||
|
||
# Sending the command is intentional even when the current zoom appears
|
||
# equal: Step 4 must start from a confirmed camera-reported scan zoom.
|
||
actual = read_zoom_after_move(origin, minimum_zoom)
|
||
if int(actual["zoom"]) < minimum_zoom:
|
||
low_request = minimum_zoom
|
||
high_request = max(zooms)
|
||
if high_request <= low_request:
|
||
core.fail(
|
||
f"平均zoom={average_zoom:g}不可达，且没有更大的角点zoom可用于查找"
|
||
)
|
||
|
||
high_actual = read_zoom_after_move(origin, high_request)
|
||
if int(high_actual["zoom"]) < minimum_zoom:
|
||
core.fail(
|
||
f"无法找到大于平均值的可达zoom：平均={average_zoom:g}，"
|
||
f"请求上界={high_request}，实际={high_actual['zoom']}"
|
||
)
|
||
|
||
# Absolute zoom response is monotonic. Find the first request that
|
||
# crosses the mean, while retaining the smallest camera-reported zoom.
|
||
best_zoom = int(high_actual["zoom"])
|
||
while low_request + 1 < high_request:
|
||
probe = (low_request + high_request) // 2
|
||
probe_actual = read_zoom_after_move(origin, probe)
|
||
observed = int(probe_actual["zoom"])
|
||
if observed >= minimum_zoom:
|
||
high_request = probe
|
||
best_zoom = min(best_zoom, observed)
|
||
else:
|
||
low_request = probe
|
||
actual = read_zoom_after_move(origin, best_zoom)
|
||
|
||
selected_zoom = int(actual["zoom"])
|
||
if selected_zoom < minimum_zoom:
|
||
core.fail(
|
||
f"摄像头回读zoom={selected_zoom}，小于要求的平均值{average_zoom:g}"
|
||
)
|
||
core.print_ptz(actual, "扫图zoom到位")
|
||
if selected_zoom != average_zoom:
|
||
say(
|
||
f"平均zoom={average_zoom:g}不是可达档位；"
|
||
f"已向上采用最近可达zoom={selected_zoom}（{selected_zoom/100:.2f}×）。"
|
||
)
|
||
return actual, average_zoom
|
||
|
||
|
||
def calculate_steps(
|
||
points: dict,
|
||
snapshots: set[str],
|
||
mount: str,
|
||
axes: set[str] | None = None,
|
||
) -> None:
|
||
requested_axes = set(axes or {"pan", "tilt"})
|
||
origin, average_zoom = move_to_scan_zoom(points)
|
||
top_left = points["top-left"]
|
||
bottom_right = points["bottom-right"]
|
||
# This check is only advisory and runs before signed pan direction has
|
||
# been measured. Use the shorter arc solely to decide whether the chosen
|
||
# calibration scene is plausibly between the two recorded corners.
|
||
top_left_pan = int(top_left["pan"]) % 36000
|
||
bottom_right_pan = int(bottom_right["pan"]) % 36000
|
||
increasing_span = (bottom_right_pan - top_left_pan) % 36000
|
||
if increasing_span <= 18000:
|
||
pan_start, pan_span = top_left_pan, increasing_span
|
||
else:
|
||
pan_start, pan_span = bottom_right_pan, 36000 - increasing_span
|
||
pan_inside = (origin["pan"] - pan_start) % 36000 <= pan_span
|
||
tilt_low = min(int(top_left["tilt"]), int(bottom_right["tilt"]))
|
||
tilt_high = max(int(top_left["tilt"]), int(bottom_right["tilt"]))
|
||
tilt_inside = tilt_low <= origin["tilt"] <= tilt_high
|
||
if not pan_inside or not tilt_inside:
|
||
say(
|
||
"警告：当前标定位置不在刚才选择的扫描区域内。"
|
||
f"当前pan={origin['pan']}、tilt={origin['tilt']}；"
|
||
f"扫描区域tilt={tilt_low}～{tilt_high}。"
|
||
"同zoom下仍可尝试，但优先选择扫描区域内部的远处纹理。",
|
||
color=YELLOW,
|
||
)
|
||
state = core.load_state()
|
||
state["scan_zoom"] = int(origin["zoom"])
|
||
state["scan_zoom_average"] = average_zoom
|
||
for axis in ("pan", "tilt"):
|
||
existing_zoom = state.get("steps", {}).get(axis, {}).get("zoom")
|
||
if axis not in requested_axes and (
|
||
existing_zoom is None or int(existing_zoom) != int(origin["zoom"])
|
||
):
|
||
say(
|
||
f"历史{axis}_step是在zoom={existing_zoom or '未知'}下测得，"
|
||
f"与本次扫图zoom={origin['zoom']}不一致，将重新标定。",
|
||
color=YELLOW,
|
||
)
|
||
requested_axes.add(axis)
|
||
save_working_state(state)
|
||
|
||
if "pan" in requested_axes:
|
||
state["steps"]["pan"] = estimate_axis("pan", origin, points, snapshots)
|
||
save_working_state(state)
|
||
if "tilt" in requested_axes:
|
||
state["steps"]["tilt"] = estimate_axis("tilt", origin, points, snapshots)
|
||
save_working_state(state)
|
||
|
||
pan_info = state["steps"]["pan"]
|
||
tilt_info = state["steps"]["tilt"]
|
||
pan_info, cross_axis_message = constrain_pan_with_tilt(
|
||
pan_info, tilt_info, configured_image_aspect_ratio()
|
||
)
|
||
say(f" {cross_axis_message}", color=(
|
||
GREEN if pan_info["cross_axis_check"]["applied"] else YELLOW
|
||
))
|
||
state["steps"]["pan"] = pan_info
|
||
state["steps"]["tilt"] = tilt_info
|
||
save_working_state(state)
|
||
|
||
|
||
def generate(mount: str) -> tuple[dict, Path, str]:
|
||
before = set((ROOT / "result").glob("scan_config_*.json"))
|
||
output = io.StringIO()
|
||
with redirect_stdout(output):
|
||
core.command_generate(argparse.Namespace(mount=mount))
|
||
after = set((ROOT / "result").glob("scan_config_*.json"))
|
||
created = sorted(after - before, key=lambda path: path.stat().st_mtime)
|
||
if not created:
|
||
core.fail("没有找到本次生成的配置文件")
|
||
result_file = created[-1]
|
||
scan = json.loads(result_file.read_text(encoding="utf-8"))["scan"]
|
||
return scan, result_file, output.getvalue()
|
||
|
||
|
||
def deploy_config(scan: dict) -> None:
|
||
raw = subprocess.run(
|
||
["docker", "exec", "smart-cam", "cat", CONFIG_PATH],
|
||
text=True, capture_output=True, check=True,
|
||
).stdout
|
||
config = json.loads(raw)
|
||
if not isinstance(config.get("scan"), dict):
|
||
raise RuntimeError(f"{CONFIG_PATH}缺少scan对象")
|
||
|
||
config["scan"].update(scan)
|
||
|
||
with tempfile.NamedTemporaryFile(
|
||
mode="w", encoding="utf-8", suffix=".json", delete=False
|
||
) as temporary:
|
||
json.dump(config, temporary, ensure_ascii=False, indent=2)
|
||
temporary.write("\n")
|
||
local_temp = Path(temporary.name)
|
||
try:
|
||
subprocess.run(
|
||
["docker", "cp", str(local_temp), f"smart-cam:{CONFIG_PATH}.new"],
|
||
check=True,
|
||
)
|
||
subprocess.run(
|
||
["docker", "exec", "smart-cam", "python3", "-m", "json.tool",
|
||
f"{CONFIG_PATH}.new"],
|
||
stdout=subprocess.DEVNULL, check=True,
|
||
)
|
||
subprocess.run(
|
||
["docker", "exec", "smart-cam", "mv", f"{CONFIG_PATH}.new", CONFIG_PATH],
|
||
check=True,
|
||
)
|
||
finally:
|
||
local_temp.unlink(missing_ok=True)
|
||
|
||
subprocess.run(["docker", "restart", "smart-cam"], check=True,
|
||
stdout=subprocess.DEVNULL)
|
||
deadline = time.monotonic() + 90
|
||
while time.monotonic() < deadline:
|
||
try:
|
||
core.api("GET", "/api/v1/scan/status", timeout=5)
|
||
return
|
||
except (SystemExit, subprocess.SubprocessError):
|
||
time.sleep(2)
|
||
raise RuntimeError("smart-cam重启后90秒内scan HTTP服务未就绪")
|
||
|
||
|
||
def run_test_scan() -> dict:
|
||
core.api("POST", "/api/v1/scan/start", timeout=20)
|
||
say("已启动测试扫描。", color=GREEN)
|
||
say("\n扫图日志：")
|
||
since = datetime.now().astimezone().isoformat(timespec="seconds")
|
||
saved_pattern = re.compile(r"\[scan\]\s+saved\b.*\bprogress=(\d+)/(\d+)\b")
|
||
last_progress: tuple[int, int] | None = None
|
||
process = subprocess.Popen(
|
||
["docker", "logs", "-f", "--since", since, "smart-cam"],
|
||
stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True,
|
||
)
|
||
try:
|
||
while True:
|
||
line = ""
|
||
if process.stdout:
|
||
readable, _, _ = select.select([process.stdout], [], [], 1.0)
|
||
if readable:
|
||
line = process.stdout.readline()
|
||
match = saved_pattern.search(line)
|
||
if match:
|
||
progress = (int(match.group(1)), int(match.group(2)))
|
||
if progress != last_progress:
|
||
say(f"已保存第 {progress[0]}/{progress[1]} 张图")
|
||
last_progress = progress
|
||
status = core.api("GET", "/api/v1/scan/status", timeout=5).get("scan", {})
|
||
if status.get("state") in {"completed", "failed", "stopped"}:
|
||
if status.get("state") == "completed":
|
||
say(
|
||
f"\n扫描完成，共保存 {status.get('completed', status.get('total', 0))} 张图。"
|
||
f"\n保存目录：{status.get('output_dir', '')}",
|
||
color=GREEN,
|
||
)
|
||
else:
|
||
message = (
|
||
"扫描已停止。"
|
||
if status.get("state") == "stopped"
|
||
else f"扫描失败：{status.get('error') or '未知错误'}"
|
||
)
|
||
say(f"\n{message}", color=YELLOW if status.get("state") == "stopped" else RED)
|
||
return status
|
||
except KeyboardInterrupt:
|
||
say("\n收到中断，正在通知smart-cam停止本次扫描……", color=YELLOW)
|
||
try:
|
||
core.api("POST", "/api/v1/scan/stop", timeout=10)
|
||
except (SystemExit, subprocess.SubprocessError):
|
||
say("停止接口调用失败，请检查scan状态。", color=RED)
|
||
raise Cancelled
|
||
finally:
|
||
process.terminate()
|
||
try:
|
||
process.wait(timeout=3)
|
||
except subprocess.TimeoutExpired:
|
||
process.kill()
|
||
|
||
|
||
def run_panorama(scan_status: dict, mount: str, pan_direction: str) -> Path:
|
||
scan_dir = str(scan_status.get("output_dir") or "")
|
||
scan_id = str(scan_status.get("scan_id") or "")
|
||
# stitch_scan.py runs on the host. A scan_id resolves beneath
|
||
# /userdata/data/scan, while output_dir reported by the API is the
|
||
# container-side /smart-cam/data/scan path.
|
||
source = scan_id or scan_dir
|
||
if not source:
|
||
raise RuntimeError("扫描状态中没有output_dir或scan_id，无法确定拼图来源")
|
||
stitch = ROOT / "scan-panorama" / "stitch.sh"
|
||
result = subprocess.run(
|
||
[
|
||
str(stitch), source,
|
||
"--mount", mount,
|
||
"--pan-direction", pan_direction,
|
||
],
|
||
text=True, capture_output=True,
|
||
)
|
||
if result.returncode:
|
||
raise RuntimeError(result.stderr.strip() or result.stdout.strip() or "拼图失败")
|
||
match = re.search(r"^结果：(.+)$", result.stdout, flags=re.MULTILINE)
|
||
if not match:
|
||
raise RuntimeError(f"拼图完成但没有返回结果路径：\n{result.stdout}")
|
||
output = Path(match.group(1).strip())
|
||
if not output.is_file() or output.stat().st_size == 0:
|
||
raise RuntimeError(f"拼图结果不存在或为空：{output}")
|
||
return output
|
||
|
||
|
||
def reusable_step_axes(state: dict, points: dict) -> set[str]:
|
||
average_zoom = sum(
|
||
int(points[name]["zoom"]) for name in ("top-left", "bottom-right")
|
||
) / 2
|
||
minimum_zoom = math.ceil(average_zoom)
|
||
scan_zoom = state.get("scan_zoom")
|
||
if scan_zoom is None:
|
||
measured_zooms: set[int] = set()
|
||
for info in state.get("steps", {}).values():
|
||
if not isinstance(info, dict) or info.get("zoom") is None:
|
||
continue
|
||
try:
|
||
measured_zooms.add(int(info["zoom"]))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
if len(measured_zooms) == 1 and next(iter(measured_zooms)) >= minimum_zoom:
|
||
scan_zoom = next(iter(measured_zooms))
|
||
state["scan_zoom"] = scan_zoom
|
||
state["scan_zoom_average"] = average_zoom
|
||
try:
|
||
scan_zoom = int(scan_zoom)
|
||
except (TypeError, ValueError):
|
||
return set()
|
||
if scan_zoom < minimum_zoom:
|
||
return set()
|
||
reusable: set[str] = set()
|
||
for axis in ("pan", "tilt"):
|
||
info = state.get("steps", {}).get(axis)
|
||
if not isinstance(info, dict):
|
||
continue
|
||
try:
|
||
measured_zoom = int(info.get("zoom", -1))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
if measured_zoom == scan_zoom:
|
||
reusable.add(axis)
|
||
return reusable
|
||
|
||
|
||
def main() -> None:
|
||
snapshots: set[str] = set()
|
||
try:
|
||
say("您好！我们现在开始配置扫图参数！", color=GREEN)
|
||
say(
|
||
"标定进度会逐步保存；中途回复 3 或按 Ctrl+C 退出后，"
|
||
"下次运行仍可选择本次记录继续。"
|
||
)
|
||
history_records = load_history_records()
|
||
|
||
stage = choose(
|
||
"① 当前是【野外部署阶段】（回复 1）还是"
|
||
"【办公室测试阶段】（回复 2）？",
|
||
{"1", "2"},
|
||
)
|
||
mount = "hanging" if stage == "1" else "desktop"
|
||
selected = select_history_record(history_records)
|
||
state = start_history_session(mount, selected)
|
||
if selected:
|
||
say(f"已复用历史记录 {selected[0].name}；本次进度另存为一条新记录。")
|
||
|
||
if valid_ptz(state["points"].get("top-left")):
|
||
top_left = state["points"]["top-left"]
|
||
core.print_ptz(top_left, "② 已复用左上角")
|
||
else:
|
||
top_left = capture_position(
|
||
"② 请在Web界面中设置左上角需要的zoom，"
|
||
"再手动把摄像头移到扫描区域的【左上角】。"
|
||
)
|
||
state["points"]["top-left"] = top_left
|
||
state["steps"] = {}
|
||
state.pop("scan_zoom", None)
|
||
state.pop("scan_zoom_average", None)
|
||
save_working_state(state)
|
||
|
||
if valid_ptz(state["points"].get("bottom-right")):
|
||
bottom_right = state["points"]["bottom-right"]
|
||
core.print_ptz(bottom_right, "③ 已复用右下角")
|
||
else:
|
||
bottom_right = capture_position(
|
||
"③ 请手动把摄像头移到扫描区域的【右下角】。\n"
|
||
"右下角可以使用不同zoom；第四步会取两个角点zoom的平均值，"
|
||
"若平均值不可达则向上取最近可达档位。"
|
||
)
|
||
state["points"]["bottom-right"] = bottom_right
|
||
state["steps"] = {}
|
||
state.pop("scan_zoom", None)
|
||
state.pop("scan_zoom_average", None)
|
||
save_working_state(state)
|
||
|
||
average_zoom = (top_left["zoom"] + bottom_right["zoom"]) / 2
|
||
say(
|
||
f"两个角点zoom：左上={top_left['zoom']/100:.2f}×，"
|
||
f"右下={bottom_right['zoom']/100:.2f}×；"
|
||
f"算术平均={average_zoom/100:.3f}×。"
|
||
)
|
||
points = state["points"]
|
||
available_axes = reusable_step_axes(state, points)
|
||
if available_axes == {"pan", "tilt"}:
|
||
save_working_state(state)
|
||
use_steps = choose(
|
||
"④ 该历史记录已有同一扫图zoom下的pan_step和tilt_step。\n"
|
||
"直接复用回复 1；在当前画面重新测量回复 2；回复 3 退出。",
|
||
{"1", "2"},
|
||
)
|
||
if use_steps == "2":
|
||
state["steps"] = {}
|
||
state.pop("scan_zoom", None)
|
||
state.pop("scan_zoom_average", None)
|
||
save_working_state(state)
|
||
available_axes = set()
|
||
|
||
while available_axes != {"pan", "tilt"}:
|
||
missing_axes = {"pan", "tilt"} - available_axes
|
||
confirm_position(
|
||
"④ 这一步用于测量当前扫图zoom下的水平/垂直视场。\n"
|
||
"请优先把摄像头移到扫描区域内部、"
|
||
"距离较远且纹理分布在整幅画面的位置。\n"
|
||
"请避开纯天空、纯墙面、纯水面、玻璃、近距离物体和重复图案；"
|
||
"水面场景需要让岸线、浮标或其他固定物体横向分布在画面中；"
|
||
"回复1后不要再操作Web云台。无论当前zoom是多少，程序都会先切换到"
|
||
"两个角点的平均zoom；若平均值不可达就向上取最近可达档位，"
|
||
f"然后测量{'+'.join(sorted(missing_axes))}并返回起点。"
|
||
)
|
||
say("\n正在计算，请不要操作摄像头或Web控制页面……")
|
||
try:
|
||
calculate_steps(points, snapshots, mount, missing_axes)
|
||
state = core.load_state()
|
||
available_axes = reusable_step_axes(state, points)
|
||
except SystemExit as exc:
|
||
say(f"\n本次视场标定没有通过：{exc}", color=RED)
|
||
choose(
|
||
"请根据上方逐项失败原因调整位置后重试第四步，两个角点无需重录。\n"
|
||
"已成功完成的单轴结果已经保存。重新尝试回复 1；结束对话回复 3。",
|
||
{"1"},
|
||
)
|
||
state = core.load_state()
|
||
available_axes = reusable_step_axes(state, points)
|
||
scan, generated, detail = generate(mount)
|
||
say("\n⑤ 本次得到的扫图参数：", color=GREEN)
|
||
say(json.dumps(scan, ensure_ascii=False, indent=2))
|
||
say(detail.strip())
|
||
|
||
install = choose(
|
||
"⑥ 是否替换 smart-cam 容器内的"
|
||
"/smart-cam/config/config.json？\n"
|
||
"替换并重启回复 1；不替换并结束回复 3。",
|
||
{"1"},
|
||
)
|
||
if install == "1":
|
||
deploy_config(scan)
|
||
say("替换完成，smart-cam已重启。", color=GREEN)
|
||
|
||
test = choose(
|
||
"⑦ 是否现在进行一次完整扫图测试？\n"
|
||
"开始测试回复 1；不测试并结束回复 3。",
|
||
{"1"},
|
||
)
|
||
if test == "1":
|
||
scan_status = run_test_scan()
|
||
if scan_status.get("state") != "completed":
|
||
raise RuntimeError("本次扫描没有成功完成，因此不进入拼图测试")
|
||
stitch = choose(
|
||
"⑧ 本次扫描已完成，是否立即进行拼图测试？\n"
|
||
"开始拼图回复 1；不拼图并结束回复 3。",
|
||
{"1"},
|
||
)
|
||
if stitch == "1":
|
||
pan_direction = str(
|
||
core.load_state()
|
||
.get("steps", {})
|
||
.get("pan", {})
|
||
.get("visual_direction", "")
|
||
)
|
||
if pan_direction not in {"increasing", "decreasing"}:
|
||
raise RuntimeError("缺少本次实测pan方向，无法可靠排列拼图左右顺序")
|
||
panorama = run_panorama(scan_status, mount, pan_direction)
|
||
say(f"\n拼图完成。\n大图存储位置：{panorama}", color=GREEN)
|
||
say("\n配置向导已完成，对话结束。", color=GREEN)
|
||
except Cancelled:
|
||
say("\n已结束对话；当前标定进度和已生成结果均已保留，后续可以复用。", color=YELLOW)
|
||
except (KeyboardInterrupt, EOFError):
|
||
say("\n对话被中断；当前标定进度和已生成结果均已保留，后续可以复用。", color=YELLOW)
|
||
except Exception as exc:
|
||
say(f"\n向导失败：{exc}", color=RED)
|
||
finally:
|
||
remove_session_snapshots(snapshots)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|