257 lines
8.7 KiB
Python
257 lines
8.7 KiB
Python
from __future__ import annotations
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import json
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import os
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import sys
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import tempfile
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import List, Optional, Tuple
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from app.logging_config import format_json_pretty, new_run_id, stage
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from app.services.video_slice import get_video_duration, slice_video
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from app.settings import Settings
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from app.state import HealthSnapshot
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from app.subprocess_run import run_subprocess_with_log
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from loguru import logger
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def _action_subprocess_dump_dir(settings: Settings) -> Path:
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"""``{LOG_SUBPROCESS_DIR}/action/subprocess`` —— 行为识别子进程整段输出目录。"""
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return Path(settings.log_subprocess_dir) / "action" / "subprocess"
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# 视频切片配置
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DEFAULT_SLICE_DURATION = 10.0 # 每10秒切一个片段
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DEFAULT_MIN_DURATION_FOR_SLICE = 15.0 # 视频超过15秒才切片
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BEHAVIOR_EN_TO_ZH = {
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"feeding": "吃饵",
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"normal": "正常游行",
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"scared": "惊吓",
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}
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def _py_exe(settings: Settings) -> str:
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return settings.python_fish_action or sys.executable
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def behavior_to_health(behavior_en: str) -> str:
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if behavior_en == "scared":
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return "不健康"
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return "健康"
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def run_action_subprocess(
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mp4_path: Path, settings: Settings, *, run_id: Optional[str] = None,
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) -> str:
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script = settings.fish_action_root / "predict_video_x3d_3class.py"
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if not script.is_file():
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raise FileNotFoundError(f"Missing FishAction script: {script}")
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mp4_path = mp4_path.resolve()
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path_prefix = str(mp4_path.parent)
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rel_name = mp4_path.name
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with tempfile.NamedTemporaryFile(
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mode="w", suffix=".csv", delete=False, encoding="utf-8"
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) as tmp:
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tmp.write(f"{rel_name} 0\n")
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csv_path = tmp.name
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out_fd, out_json = tempfile.mkstemp(suffix="_pred.json")
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os.close(out_fd)
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rid = run_id or new_run_id("action")
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try:
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#: 使用脚本默认参数（clips_per_video=10, batch_size=8, num_workers=4）
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#: 如需调整，请修改 FishAction/predict_video_x3d_3class.py 中的默认值
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cmd = [
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_py_exe(settings),
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str(script),
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"--checkpoint",
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settings.action_checkpoint,
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"--csv",
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csv_path,
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"--path_prefix",
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path_prefix,
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"--output_json",
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out_json,
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"--log_interval",
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"0",
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]
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with stage(
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f"行为识别子进程 X3D（{mp4_path.name}）",
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pipeline="action",
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step="action_subprocess",
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run_id=rid,
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source=mp4_path.name,
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metrics={"checkpoint": settings.action_checkpoint},
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):
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proc = run_subprocess_with_log(
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cmd,
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cwd=str(settings.fish_action_root),
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env=os.environ.copy(),
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log_name="FishAction",
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stream_to_logger=False,
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dump_dir=_action_subprocess_dump_dir(settings),
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run_id=rid,
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source_label=mp4_path.stem,
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kind="predict_video_x3d_3class",
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pipeline="action",
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step="action_subprocess",
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summary_tail_lines=settings.log_subprocess_tail_lines,
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)
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if proc.returncode != 0:
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err = proc.stdout or ""
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raise RuntimeError(
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f"predict_video_x3d_3class.py failed ({proc.returncode}): {err[-4000:]}"
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)
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if not Path(out_json).is_file():
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raise RuntimeError(f"No output_json written: {out_json}")
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with open(out_json, encoding="utf-8") as f:
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rows = json.load(f)
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if not rows:
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raise RuntimeError("Empty prediction JSON")
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pred_en = str(rows[0].get("pred_3class", "")).strip().lower()
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logger.bind(pipeline="action", run_id=rid).debug(
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"[FishAction] 预测原始行：\n{}",
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format_json_pretty(rows[0]),
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)
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if pred_en not in BEHAVIOR_EN_TO_ZH:
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raise RuntimeError(f"Unexpected pred_3class: {pred_en!r}")
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return pred_en
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finally:
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Path(csv_path).unlink(missing_ok=True)
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Path(out_json).unlink(missing_ok=True)
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def prepare_action_slices(
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mp4_path: Path, settings: Settings, *, run_id: Optional[str] = None,
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) -> Tuple[List[Path], float]:
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"""Check video duration and create slices if needed.
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Returns ``(slice_files, duration)``. If the video is short enough,
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returns ``([mp4_path], duration)`` without slicing.
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"""
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rid = run_id or new_run_id("action")
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log = logger.bind(pipeline="action", run_id=rid, source=mp4_path.name)
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log.info("[FishAction] 开始处理视频 | mp4={}", mp4_path.resolve())
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with stage(
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f"视频切片预处理（{mp4_path.name}）",
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pipeline="action",
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step="prepare_slices",
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run_id=rid,
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source=mp4_path.name,
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metrics={"min_duration_for_slice": DEFAULT_MIN_DURATION_FOR_SLICE,
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"slice_duration": DEFAULT_SLICE_DURATION},
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):
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duration = get_video_duration(mp4_path)
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if duration > DEFAULT_MIN_DURATION_FOR_SLICE:
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log.info(
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"[FishAction] 视频时长 {:.1f}s > {}s，切分为 {}s 一段",
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duration, DEFAULT_MIN_DURATION_FOR_SLICE, DEFAULT_SLICE_DURATION,
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)
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slice_files, _slice_dir = slice_video(mp4_path, DEFAULT_SLICE_DURATION)
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if len(slice_files) > 1:
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log.info(
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"[FishAction] {} 将处理 {} 段切片",
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mp4_path.name, len(slice_files),
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)
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return slice_files, duration
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return [mp4_path], duration
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def run_single_slice_inference(
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slice_file: Path,
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slice_index: int,
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total_slices: int,
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duration: float,
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mp4_name: str,
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settings: Settings,
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*,
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run_id: Optional[str] = None,
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) -> HealthSnapshot:
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"""Run FishAction inference on a single video file / slice.
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Returns a ``HealthSnapshot`` (may contain error info if inference fails).
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"""
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start_time = slice_index * DEFAULT_SLICE_DURATION
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end_time = min(start_time + DEFAULT_SLICE_DURATION, duration)
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rid = run_id or new_run_id("action")
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log = logger.bind(pipeline="action", run_id=rid, source=slice_file.name)
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try:
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with stage(
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f"切片识别 #{slice_index + 1}/{total_slices}（{slice_file.name}）",
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pipeline="action",
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step="slice_inference",
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run_id=rid,
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source=slice_file.name,
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metrics={
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"slice_index": slice_index,
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"total_slices": total_slices,
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"start_sec": round(start_time, 2),
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"end_sec": round(end_time, 2),
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},
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):
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pred_en = run_action_subprocess(slice_file, settings, run_id=rid)
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zh = BEHAVIOR_EN_TO_ZH[pred_en]
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health = behavior_to_health(pred_en)
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snap = HealthSnapshot(
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behavior_result=zh,
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health_result=health,
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updated_at=datetime.now(timezone.utc),
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raw_class_en=pred_en,
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)
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# 中文识别结果事件（用于离线分析）
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logger.bind(
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event=True,
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pipeline="action",
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step="slice_result",
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run_id=rid,
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source=slice_file.name,
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status="info",
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metrics={
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"slice_index": slice_index,
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"total_slices": total_slices,
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"start_sec": round(start_time, 2),
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"end_sec": round(end_time, 2),
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"pred_3class": pred_en,
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"behavior_zh": zh,
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"health_zh": health,
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"mp4_name": mp4_name,
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},
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).info(
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"[FishAction] 切片识别结果 | {} | {} | 段 {}/{} | {:.1f}~{:.1f}s | 行为={} | 健康={}",
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mp4_name,
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slice_file.name,
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slice_index + 1,
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total_slices,
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start_time,
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end_time,
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zh,
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health,
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)
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if total_slices == 1:
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log.info(
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"[FishAction] 视频识别完成 | mp4={} | 行为={} | 健康={}",
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mp4_name, zh, health,
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)
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return snap
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except Exception as e:
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log.error(
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"[FishAction] 切片处理失败 | slice={}/{} | {}: {}",
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slice_index + 1, total_slices, type(e).__name__, e,
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)
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return HealthSnapshot(
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behavior_result="处理失败",
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health_result="未知",
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updated_at=datetime.now(timezone.utc),
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raw_class_en="error",
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error=str(e),
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)
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