"""在 motion_filter 找出的活动窗口内按固定间隔抽帧，调用本地 Ollama VLM
做限定词表活动分类，连续同标签的采样点合并成一条 activity 事件。
"""
import json
from datetime import datetime, timedelta

import cv2

from analysis.config import ACTIVITY_SAMPLE_INTERVAL_SEC, ACTIVITY_MERGE_GAP_SEC, PROJECT_ROOT
from analysis.db import get_all_videos, insert_event, update_video_status
from analysis.ollama_client import classify_frame


def _sample_labels(file_path, windows, fps):
    """在活动窗口内按固定秒数间隔抽帧分类，返回 [(t, label, raw_text), ...]。"""
    cap = cv2.VideoCapture(str(file_path))
    if not cap.isOpened():
        return []

    results = []
    for start_sec, end_sec in windows:
        t = start_sec
        while t <= end_sec:
            cap.set(cv2.CAP_PROP_POS_FRAMES, int(t * fps))
            ret, frame = cap.read()
            if ret:
                label, raw_text = classify_frame(frame)
                results.append((t, label, raw_text))
            t += ACTIVITY_SAMPLE_INTERVAL_SEC

    cap.release()
    return results


def _merge_labeled_samples(samples):
    """连续同标签的采样点合并成一段 {start, end, label, raw_texts}。"""
    segments = []
    for t, label, raw_text in samples:
        if (segments and segments[-1]["label"] == label
                and (t - segments[-1]["end"]) <= ACTIVITY_MERGE_GAP_SEC):
            segments[-1]["end"] = t
            segments[-1]["raw_texts"].append(raw_text)
        else:
            segments.append({"start": t, "end": t, "label": label, "raw_texts": [raw_text]})
    return segments


def process_video(video):
    """返回 (segment_count, failed)。"""
    try:
        windows = json.loads(video["motion_windows_json"] or "[]")
    except (json.JSONDecodeError, TypeError):
        windows = []
    if not windows:
        update_video_status(video["id"], "activity_done")
        return 0, False

    file_path = PROJECT_ROOT / video["file_path"]
    if not file_path.exists():
        update_video_status(video["id"], "activity_failed", error_message="文件不存在")
        return 0, True

    try:
        fps = video["fps"] or 15.0
        samples = _sample_labels(file_path, windows, fps)
    except Exception as e:
        update_video_status(video["id"], "activity_failed", error_message=str(e))
        return 0, True

    segments = _merge_labeled_samples(samples)

    start_time = None
    if video["start_time"]:
        try:
            start_time = datetime.strptime(video["start_time"], "%Y-%m-%d %H:%M:%S")
        except ValueError:
            pass  # 只精确到天的粗粒度值，无法算出绝对时间戳，timestamp_abs留空

    for seg in segments:
        timestamp_abs = (start_time + timedelta(seconds=seg["start"])).isoformat() if start_time else None
        insert_event({
            "video_id": video["id"],
            "event_type": "activity",
            "label": seg["label"],
            "start_offset_sec": seg["start"],
            "end_offset_sec": seg["end"],
            "timestamp_abs": timestamp_abs,
            "confidence": None,
            "track_id": None,
            "thumbnail_path": None,
            "extra_json": json.dumps({"raw_texts": seg["raw_texts"]}, ensure_ascii=False),
            "created_at": datetime.now().isoformat(),
        })

    update_video_status(video["id"], "activity_done")
    return len(segments), False


def process_all(limit=None):
    videos = get_all_videos(status="fall_done")
    if limit:
        videos = videos[:limit]

    processed = 0
    total_segments = 0
    failed = 0
    for video in videos:
        segments, is_failed = process_video(video)
        total_segments += segments
        if is_failed:
            failed += 1
        processed += 1

    return {"processed": processed, "total_segments": total_segments, "failed": failed}


if __name__ == "__main__":
    print("===== 开始活动语义打标 =====")
    result = process_all()
    print(f"→ 处理 {result['processed']} 个视频，共标注 {result['total_segments']} 段活动")
    print("===== 打标完成 =====")