730 lines
30 KiB
Python
Executable File
730 lines
30 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 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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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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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 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 remove_session_snapshots(paths: set[str]) -> None:
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for path in sorted(paths):
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if not path.startswith("/smart-cam/data/scan/calibration/"):
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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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if axis == "pan":
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return [150, 250, 350, 450, 600, 800, 1100, 1500, 2100]
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return [80, 120, 170, 220, 280, 350, 450, 600, 850, 1150]
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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]]) -> list:
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"""Return the largest group that estimates one mutually consistent FOV."""
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if not reliable:
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return []
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clusters = [
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[
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sample for sample in reliable
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if abs(sample[2] - seed[2]) / max(seed[2], 1.0) <= 0.12
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]
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for seed in reliable
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]
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return max(
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clusters, key=lambda cluster: (len(cluster), -max(x[2] for x in cluster))
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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",
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"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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}, (
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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}%）；"
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f"融合pan实测={direct_pan_step}与tilt推导={tilt_predicted_step}"
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f"后pan_step={constrained_step}"
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)
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return {
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**pan_info,
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"cross_axis_check": {
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**diagnostic,
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"applied": False,
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"reason": "non_rectilinear_or_axis_scale_mismatch",
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},
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}, (
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f"pan/tilt视场比例仅为普通透视预测的{agreement*100:.1f}%，"
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"疑似镜头畸变或不同tilt行的水平覆盖不一致；"
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"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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)
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def configured_image_aspect_ratio() -> float:
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try:
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raw = subprocess.run(
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["docker", "exec", "smart-cam", "cat", CONFIG_PATH],
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text=True, capture_output=True, check=True,
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).stdout
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image = json.loads(raw).get("scan", {}).get("image", {})
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width, height = int(image.get("width", 1280)), int(image.get("height", 720))
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if width > 0 and height > 0:
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return width / height
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except (OSError, ValueError, KeyError, json.JSONDecodeError,
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subprocess.SubprocessError):
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pass
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return 16.0 / 9.0
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def failure_detail(result: dict) -> str:
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reason = str(result.get("reason") or "unknown")
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labels = {
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"ptz_motion_shortfall": "云台受限，实际移动量不足",
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"not_enough_features": "两张图片可检测特征不足",
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"not_enough_matches": "两张图片的公共特征不足",
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"homography_failed": "公共特征无法形成可靠几何变换",
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"axis_motion_too_small": "匹配到的主轴画面位移过小",
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"axis_motion_inconsistent": "匹配位移方向异常或串轴过大",
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}
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fields = []
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if result.get("actual_axis_motion") is not None:
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fields.append(
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f"实际移动={result['actual_axis_motion']}/"
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f"{result.get('requested_axis_motion', result.get('delta', '?'))}"
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)
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if result.get("good_matches") is not None:
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fields.append(f"公共特征={result['good_matches']}")
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if result.get("inliers") is not None:
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fields.append(f"几何内点={result['inliers']}")
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if result.get("axis_shift") is not None:
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fields.append(f"主轴位移={float(result['axis_shift'])*100:.1f}%")
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if result.get("cross_axis_shift") is not None:
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fields.append(f"串轴位移={float(result['cross_axis_shift'])*100:.1f}%")
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if result.get("phase_response") is not None:
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fields.append(f"相位响应={float(result['phase_response']):.2f}")
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suffix = f"（{'，'.join(fields)}）" if fields else ""
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return f"{labels.get(reason, reason)}{suffix}"
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def estimate_axis(axis: str, origin: dict[str, int], points: dict,
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snapshots: set[str]) -> dict:
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direction = choose_direction(axis, origin, points)
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baseline = core.snapshot()
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snapshots.add(baseline)
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reliable: list[tuple[int, float, float, dict]] = []
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failure_reasons: dict[str, int] = {}
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say(f"\n正在标定 {axis}_step，摄像头会自动移动并回到起点……")
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for delta in candidate_deltas(axis):
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result = core.measure_delta(
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axis, delta, direction, 1.0, baseline=baseline, origin=origin
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)
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snapshots.add(str(result.get("candidate_file", "")))
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if (
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axis == "tilt"
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and not reliable
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and result.get("reason") == "ptz_motion_shortfall"
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):
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direction *= -1
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say(
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f" step={delta}: 原方向受云台限位，自动改用相反方向继续标定"
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)
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result = core.measure_delta(
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axis, delta, direction, 1.0, baseline=baseline, origin=origin
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)
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snapshots.add(str(result.get("candidate_file", "")))
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if not result.get("valid"):
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reason = str(result.get("reason") or "unknown")
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failure_reasons[reason] = failure_reasons.get(reason, 0) + 1
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say(f" step={delta}: {failure_detail(result)}，继续尝试")
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continue
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overlap = float(result["overlap"])
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inliers = int(result.get("inliers") or 0)
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ratio = float(result.get("inlier_ratio") or 0)
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if result.get("match_method") == "phase_correlation":
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quality = f"整图相位响应={float(result.get('phase_response') or 0):.2f}"
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else:
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quality = f"有效特征={inliers}，内点率={ratio*100:.1f}%"
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say(f" step={delta}: 重叠约 {overlap*100:.1f}%，{quality}")
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feature_reliable = inliers >= 50 and ratio >= 0.45
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phase_reliable = (
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result.get("match_method") == "phase_correlation"
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and float(result.get("phase_response") or 0) >= core.PHASE_MIN_RESPONSE
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)
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if (feature_reliable or phase_reliable) and 0.08 <= overlap <= 0.80:
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reliable.append((delta, overlap, delta / (1.0 - overlap), result))
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if len(reliable) >= 4:
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provisional = consistent_samples(reliable)
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if len(provisional) >= 4:
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_, provisional_spread = fov_statistics(provisional)
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if provisional_spread <= 0.08 and min(
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sample[1] for sample in provisional
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) <= 0.55:
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say(
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f" 已获得{len(provisional)}个覆盖充分且一致的样本，"
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"停止测试更大的step"
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)
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break
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if len(reliable) < 2:
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core.move(origin)
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summary = "、".join(
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f"{reason}={count}次"
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for reason, count in sorted(failure_reasons.items())
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)
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core.fail(
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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。"
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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"
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)
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# Fixed OSD text and repeated scenery can yield a convincing false
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# homography. A real optical FOV must agree across different PTZ deltas.
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consistent = consistent_samples(reliable)
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if len(consistent) < 2:
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core.move(origin)
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values = ", ".join(f"{sample[2]:.0f}" for sample in reliable)
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core.fail(
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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；"
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"本次结果不写入配置，请更换标定画面"
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)
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estimated_fov, spread = fov_statistics(consistent)
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if len(consistent) == 2:
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strong_pair = all(
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(
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int(sample[3].get("inliers") or 0) >= 100
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and float(sample[3].get("inlier_ratio") or 0) >= 0.65
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)
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or (
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sample[3].get("match_method") == "phase_correlation"
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and float(sample[3].get("phase_response") or 0) >= 0.35
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)
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for sample in consistent
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)
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allowed_spread = 0.08
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if not strong_pair:
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core.move(origin)
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core.fail(
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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；"
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"需要第3个可靠样本，请更换标定画面"
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)
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else:
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allowed_spread = 0.15
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if spread > allowed_spread:
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core.move(origin)
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core.fail(
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f"{axis}方向视场样本离散度为{spread*100:.1f}%，"
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f"超过{allowed_spread*100:.0f}%；"
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"本次结果不写入配置，请更换标定画面"
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)
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target_overlap = TARGET_OVERLAPS[axis]
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step = max(1, round(estimated_fov * (1.0 - target_overlap)))
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visual_direction = None
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if axis == "pan":
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# Interactive pan probes always use a positive PTZ delta. The signed
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# image displacement reveals the real pan direction of this exact
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# camera/firmware; mount style is not a reliable substitute.
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signed_shifts = [
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float(sample[3].get("center_dx", 0.0))
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for sample in consistent
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if abs(float(sample[3].get("center_dx", 0.0))) >= 0.02
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]
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if len(signed_shifts) < 2:
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core.move(origin)
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core.fail(
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"pan方向缺少至少2个可靠的带符号位移样本；"
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"请更换纹理分布更均匀的位置重新执行第四步"
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)
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positive = sum(shift > 0 for shift in signed_shifts)
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negative = sum(shift < 0 for shift in signed_shifts)
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agreement = max(positive, negative) / len(signed_shifts)
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if agreement < 0.75:
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core.move(origin)
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core.fail(
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f"pan方向样本符号不一致（一致率{agreement*100:.1f}%），"
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"疑似重复图案或近距离视差；本次不生成扫描弧段"
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)
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# center_dx < 0 means positive numeric pan advances the camera view
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# toward visual right, so left-to-right is numerically increasing.
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visual_direction = "increasing" if negative > positive else "decreasing"
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measured_step, measured_overlap, _, detail = max(
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consistent, key=lambda sample: int(sample[3].get("inliers") or 0)
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)
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core.move(origin)
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say(
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f" {axis}视场一致性通过：{len(consistent)}个样本，"
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f"估算视场={estimated_fov:.0f}，离散度={spread*100:.1f}%"
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)
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if visual_direction is not None:
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say(
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f" 已自动识别该摄像头：画面左→右时pan数值"
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f"{'递增' if visual_direction == 'increasing' else '递减'}"
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)
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return {
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"value": step,
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"direction": direction,
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"overlap": target_overlap,
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"target_overlap": target_overlap,
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"overlap_method": "axis_shift_v1_retargeted",
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"zoom": origin["zoom"],
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"measured_at": origin,
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**({"visual_direction": visual_direction} if visual_direction else {}),
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"good_matches": detail.get("good_matches"),
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"inliers": detail.get("inliers"),
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"inlier_ratio": detail.get("inlier_ratio"),
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"match_method": detail.get("match_method", "orb_homography"),
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"phase_response": detail.get("phase_response"),
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"derived_from": {
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"value": measured_step,
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"overlap": measured_overlap,
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"estimated_fov": round(estimated_fov, 4),
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"samples": [
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{
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"value": sample[0],
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"overlap": round(sample[1], 6),
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"estimated_fov": round(sample[2], 4),
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"inliers": sample[3].get("inliers"),
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"inlier_ratio": sample[3].get("inlier_ratio"),
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"match_method": sample[3].get("match_method", "orb_homography"),
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"phase_response": sample[3].get("phase_response"),
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}
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for sample in consistent
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],
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"spread": round(spread, 6),
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},
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}
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def calculate_steps(points: dict, snapshots: set[str], mount: str) -> None:
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origin = camera_ptz()
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zooms = [int(points[name]["zoom"]) for name in ("top-left", "bottom-right")]
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if max(zooms + [origin["zoom"]]) - min(zooms + [origin["zoom"]]) > 10:
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core.fail("三个位置的zoom不一致；请固定焦段后重新开始")
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top_left = points["top-left"]
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bottom_right = points["bottom-right"]
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# 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()
|
||
pan_info = estimate_axis("pan", origin, points, snapshots)
|
||
tilt_info = estimate_axis("tilt", origin, points, snapshots)
|
||
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
|
||
core.save_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 main() -> None:
|
||
original_state = core.STATE_FILE.read_bytes() if core.STATE_FILE.exists() else None
|
||
snapshots: set[str] = set()
|
||
generated: Path | None = None
|
||
success = False
|
||
try:
|
||
say("您好！我们现在开始配置扫图参数！", color=GREEN)
|
||
say("在接下来的对话中，回复 3 会立即结束，并清理本次向导产生的标定痕迹。")
|
||
|
||
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"
|
||
|
||
confirm_position(
|
||
"② 请在Web界面中固定本次扫描使用的zoom，"
|
||
"再手动把摄像头移到扫描区域的【左上角】。"
|
||
)
|
||
top_left = camera_ptz()
|
||
state = core.load_state()
|
||
state["points"]["top-left"] = top_left
|
||
core.save_state(state)
|
||
|
||
confirm_position(
|
||
"③ 请保持zoom不变，手动把摄像头移到扫描区域的【右下角】。"
|
||
)
|
||
bottom_right = camera_ptz()
|
||
if abs(top_left["zoom"] - bottom_right["zoom"]) > 10:
|
||
core.fail("左右角的zoom不一致，请固定焦段后重新运行")
|
||
state = core.load_state()
|
||
state["points"]["bottom-right"] = bottom_right
|
||
core.save_state(state)
|
||
|
||
points = core.load_state()["points"]
|
||
while True:
|
||
confirm_position(
|
||
"④ 这一步用于测量当前zoom下的水平/垂直视场。\n"
|
||
"请保持zoom不变，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、水面、玻璃、近距离物体和重复图案；"
|
||
"回复1后不要再操作Web云台，程序会自动移动、拍照并返回起点。"
|
||
)
|
||
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)
|
||
break
|
||
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"
|
||
"重新尝试回复 1；结束对话回复 3。",
|
||
{"1"},
|
||
)
|
||
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)
|
||
success = True
|
||
say("\n配置向导已完成，对话结束。", color=GREEN)
|
||
except Cancelled:
|
||
say("\n已结束对话，正在清理本次向导痕迹……", color=YELLOW)
|
||
except (KeyboardInterrupt, EOFError):
|
||
say("\n对话被中断，正在清理本次向导痕迹……", color=YELLOW)
|
||
except Exception as exc:
|
||
say(f"\n向导失败：{exc}", color=RED)
|
||
finally:
|
||
remove_session_snapshots(snapshots)
|
||
if not success:
|
||
if original_state is None:
|
||
core.STATE_FILE.unlink(missing_ok=True)
|
||
else:
|
||
current_state = (
|
||
core.STATE_FILE.read_bytes() if core.STATE_FILE.exists() else None
|
||
)
|
||
if current_state != original_state:
|
||
core.STATE_FILE.write_bytes(original_state)
|
||
if generated is not None:
|
||
generated.unlink(missing_ok=True)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|