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xiaoxia-saas/apps/worker/video_processing/thumbnail_generator.py
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saas-backend-agent d088c80f14
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feat(cover): 默认本地ffmpeg抽帧+cv2打分,砍掉MediaKit封面路径
P1优化:之前封面抽帧走火山MediaKit北京节点(公网拉视频+SceneChange算法+
轮询等待),实测1080p视频耗时86s占总渲染92%。

改动:
1. 默认路径改为本地ffmpeg -ss单帧seek抽取5帧+cv2清晰度/亮度/色彩三维
   评分选最优,预期1-2s完成(本地<100ms/帧×5 + cv2评分0.05s + OSS上传1s)
2. MediaKit路径保留为fallback,配置开关MEDIAKIT_COVER_ENABLED(默认false)
3. 新增clip_boundaries参数:基于clip分段边界取各段中点抽帧,效果优于
   均匀分布;render_adapter自动传入ready_clips的(start_time,duration)
4. 跳过片头8%/片尾8%,避免黑屏/开场logo/片尾字幕区
5. extract_first_frame新增seek_seconds参数支持指定时间点
6. 封面完成日志加elapsed和path字段方便观测耗时
2026-09-28 17:55:39 +08:00

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"""视频封面抽帧工具 — 从视频中抽取帧作为封面,支持标题文字叠加。
统一封面管道(P1 优化后默认本地路径):
- 默认路径:本地 ffmpeg -ss 单帧 seek 抽取 + cv2 质量评分(清晰度/亮度/色彩),1-2s 完成
- 可选 MediaKit 路径:配置 MEDIAKIT_COVER_ENABLED=true 时启用火山 MediaKit SceneChange 抽帧
- 从已渲染视频抽帧:标题已通过 ASS 字幕烧进视频,帧天然带标题,无需再叠加。
- 从源素材抽帧(API E2 兜底):源素材无标题,通过 Pillow 在帧上绘制标题文字。
"""
from __future__ import annotations
import logging
import tempfile
from pathlib import Path
from typing import Optional
logger = logging.getLogger(__name__)
def apply_title_overlay(
image_path: str,
title_text: str,
*,
color: str = "#ffffff",
position: str = "bottom",
font_size: int | None = None,
margin_ratio: float = 0.06,
stroke_width_ratio: float = 0.04,
) -> str:
"""在图片上绘制标题文字(指定颜色 + 黑色描边/阴影)。"""
from packages.shared.title_overlay import apply_title_to_image
if not title_text or not title_text.strip():
return image_path
result = apply_title_to_image(
image_path,
title_text,
color=color,
position=position,
font_size=font_size,
margin_ratio=margin_ratio,
stroke_width_ratio=stroke_width_ratio,
)
return result or image_path
def extract_first_frame(
video_path: str,
output_path: str | None = None,
*,
width: int = -1,
height: int = -1,
timeout: int = 30,
seek_ratio: float = 0.15,
seek_seconds: float | None = None,
min_seek_seconds: float = 1.0,
) -> str:
"""抽取视频封面帧(ffmpeg -ss 单帧 seek,<100ms/帧)。
Args:
video_path: 视频文件路径
output_path: 输出图片路径,不传则用临时文件
width/height: 输出宽高(默认保持原始分辨率)
timeout: 超时(秒)
seek_ratio: 抽帧位置占视频时长的比例
seek_seconds: 指定具体抽帧时间点(秒),优先于 seek_ratio
min_seek_seconds: 最小抽帧时间
"""
from video_processing.ffmpeg_utils import FFMPEG_BIN, probe_duration, run_ffmpeg
_is_temp_output = False
if output_path is None:
tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
tmp.close()
output_path = tmp.name
_is_temp_output = True
try:
if seek_seconds is not None:
seek_time = max(0.0, float(seek_seconds))
else:
try:
duration = probe_duration(video_path)
seek_time = max(min_seek_seconds, duration * seek_ratio)
except Exception:
seek_time = min_seek_seconds
seek_str = _format_seek_time(seek_time)
if width > 0 or height > 0:
w_str = str(width) if width > 0 else "-1"
h_str = str(height) if height > 0 else "-1"
scale_filter = f"scale={w_str}:{h_str}:force_original_aspect_ratio=decrease,format=yuvj420p"
else:
scale_filter = "format=yuvj420p"
# -ss 放在 -i 前面(input seeking,极快),-vframes 1 只取一帧
cmd = [
FFMPEG_BIN,
"-y",
"-ss",
seek_str,
"-i",
video_path,
"-vframes",
"1",
"-vf",
scale_filter,
"-q:v",
"2",
output_path,
]
try:
run_ffmpeg(cmd, capture_output=True, timeout=timeout)
except Exception:
# 失败时退回到第0帧兜底
cmd2 = [
FFMPEG_BIN,
"-y",
"-i",
video_path,
"-ss",
"00:00:00",
"-vframes",
"1",
"-vf",
scale_filter,
"-q:v",
"2",
output_path,
]
run_ffmpeg(cmd2, capture_output=True, timeout=timeout)
if not Path(output_path).exists() or Path(output_path).stat().st_size == 0:
raise RuntimeError(f"Cover frame extraction failed: {output_path}")
return output_path
except Exception:
if _is_temp_output and output_path:
try:
Path(output_path).unlink(missing_ok=True)
except Exception:
pass
raise
def _format_seek_time(seconds: float) -> str:
h = int(seconds // 3600)
m = int((seconds % 3600) // 60)
s = seconds % 60
return f"{h:02d}:{m:02d}:{s:05.2f}"
def generate_and_upload_thumbnail(
video_path: str,
storage_key: str,
*,
seek_ratio: float = 0.15,
) -> str:
"""从视频中提取一帧缩略图并上传到 OSS。"""
from video_processing.oss_helpers import upload_to_oss
tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
tmp.close()
try:
frame_path = extract_first_frame(video_path, output_path=tmp.name, seek_ratio=seek_ratio)
url = upload_to_oss(frame_path, storage_key)
if not url:
raise RuntimeError(f"上传缩略图到 OSS 失败: {storage_key}")
return url
finally:
Path(tmp.name).unlink(missing_ok=True)
def _compute_clip_boundary_seek_points(
duration: float,
clip_boundaries: Optional[list[tuple[float, float]]] = None,
num_frames: int = 5,
head_skip_ratio: float = 0.08,
tail_skip_ratio: float = 0.08,
) -> list[float]:
"""基于clip分段边界计算抽帧时间点(取每段中间帧,效果比均匀抽更好)。
策略:
- 如果传入 clip_boundaries(每个元素是 (clip_start_in_timeline, clip_duration)),
取每个片段的中点作为抽帧候选点
- 候选点不足 num_frames 时,均匀补充
- 跳过片头 head_skip_ratio(8%,避免片头黑屏/开场标题)和片尾 tail_skip_ratio(8%)
- 返回按时间排序的 num_frames 个抽帧点(秒)
"""
if duration <= 0:
# 无法probe,均匀分布兜底
return [max(1.0, duration * (0.1 + 0.8 * i / max(num_frames - 1, 1))) for i in range(num_frames)]
head_skip = duration * head_skip_ratio
tail_skip = duration * tail_skip_ratio
valid_start = head_skip
valid_end = max(valid_start + 1.0, duration - tail_skip)
candidates: list[float] = []
if clip_boundaries:
# 累加timeline start,取每clip中点
cur = 0.0
for _clip_start, clip_dur in clip_boundaries:
if clip_dur <= 0:
continue
mid = cur + clip_dur / 2.0
if valid_start <= mid <= valid_end:
candidates.append(mid)
cur += clip_dur
# 去重+排序
candidates = sorted(set(round(c, 3) for c in candidates))
# 如果候选点不足,均匀补充
if len(candidates) < num_frames:
needed = num_frames - len(candidates)
existing = set(round(c, 1) for c in candidates)
for i in range(needed * 3):
ratio = 0.1 + 0.8 * (i + 0.5) / (needed * 3)
t = valid_start + (valid_end - valid_start) * ratio
if round(t, 1) not in existing:
candidates.append(t)
existing.add(round(t, 1))
if len(candidates) >= num_frames:
break
# 如果还不够,强制均匀
while len(candidates) < num_frames:
idx = len(candidates)
ratio = 0.1 + 0.8 * idx / max(num_frames - 1, 1)
candidates.append(valid_start + (valid_end - valid_start) * ratio)
candidates.sort()
# 如果超过num_frames,均匀选取
if len(candidates) > num_frames:
step = len(candidates) / num_frames
candidates = [candidates[int(i * step)] for i in range(num_frames)]
return [round(t, 3) for t in candidates[:num_frames]]
def _extract_frames_via_mediakit(
video_path: str,
plan_id: str,
num_frames: int,
) -> list[dict] | None:
"""使用 MediaKit 智能抽帧 API 提取封面帧(fallback 路径,默认不启用)。"""
import uuid
from video_processing.oss_helpers import delete_from_oss, get_signed_download_url, upload_to_oss
from packages.shared.mediakit_client import get_mediakit_client
client = get_mediakit_client()
if not client.is_available:
logger.info("[thumbnail] MediaKit 未配置,跳过智能抽帧")
return None
video_storage_key: str = ""
try:
video_storage_key = f"temp/{plan_id}/{uuid.uuid4().hex[:8]}_{Path(video_path).name}"
public_url = upload_to_oss(video_path, video_storage_key)
if not public_url:
logger.warning("[thumbnail] 视频上传 OSS 失败,无法使用 MediaKit")
return None
video_url = get_signed_download_url(video_storage_key, expires_seconds=3600) or public_url
logger.info("[thumbnail] 视频已上传 OSS 并生成签名 URL: key=%s", video_storage_key[:80])
except Exception as e:
logger.warning("[thumbnail] 视频上传 OSS 异常: %s,降级到本地 ffmpeg", e)
return None
try:
frames = client.extract_frames(
video_url=video_url,
strategy="SceneChange",
max_frames=num_frames * 2,
)
if not frames:
logger.warning("[thumbnail] MediaKit 抽帧返回空")
return None
if len(frames) > num_frames:
step = len(frames) // num_frames
frames = [frames[i * step] for i in range(num_frames)]
logger.info("[thumbnail] MediaKit 抽帧成功: %d 帧", len(frames))
return frames
except Exception as e:
logger.warning("[thumbnail] MediaKit 抽帧异常: %s", e)
return None
finally:
try:
delete_from_oss(video_storage_key)
except Exception:
pass
def extract_and_upload_cover_frames(
video_path: str,
plan_id: str,
*,
task_id: str = "",
num_frames: int = 5,
title_text: str = "",
title_color: str = "#ffffff",
title_position: str = "bottom",
title_font_size: int | None = None,
clip_boundaries: Optional[list[tuple[float, float]]] = None,
) -> list[dict]:
"""从视频中抽取多帧作为封面候选,通过质量评分选出最佳帧,上传到 OSS。
默认路径(P1优化):本地 ffmpeg 单帧 seek 抽帧 + cv2 评分,预期 <2s 完成。
- 基于 clip 分段边界取各段中间帧(clip_boundaries 参数),效果优于均匀抽帧
- 无边界信息时均匀分布(10%~90% 之间)
- 所有帧本地 cv2 清晰度/亮度/色彩三维评分,最高分自动选出
Fallback(MEDIAKIT_COVER_ENABLED=true):火山 MediaKit SceneChange 抽帧(~60-90s)。
Args:
clip_boundaries: 片段边界列表 [(clip_start, clip_duration), ...],用于智能取点
"""
import time
import httpx
from video_processing.ffmpeg_utils import probe_duration
from video_processing.oss_helpers import upload_to_oss
from packages.shared.config import get_shared_settings
t0 = time.monotonic()
try:
duration = probe_duration(video_path)
except Exception:
duration = 0.0
candidates: list[dict] = []
_temp_paths: list[str] = []
try:
settings = get_shared_settings()
use_mediakit = getattr(settings, "mediakit_cover_enabled", False)
if use_mediakit:
logger.info("[thumbnail] MEDIAKIT_COVER_ENABLED=true,走 MediaKit 路径")
mediakit_frames = _extract_frames_via_mediakit(video_path, plan_id, num_frames)
if mediakit_frames:
for i, frame in enumerate(mediakit_frames):
frame_url = frame.get("image_url")
if not frame_url:
continue
tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
tmp.close()
_temp_paths.append(tmp.name)
try:
resp = httpx.get(frame_url, timeout=30, follow_redirects=True)
resp.raise_for_status()
with open(tmp.name, "wb") as f:
f.write(resp.content)
if title_text and title_text.strip():
apply_title_overlay(
tmp.name,
title_text,
color=title_color,
position=title_position,
font_size=title_font_size,
)
storage_key = f"covers/{plan_id}/{task_id}/mediakit_frame_{i}.jpg"
url = upload_to_oss(tmp.name, storage_key)
if url:
candidates.append(
{
"url": url,
"position": round(frame.get("timestamp", 0.0), 2),
"image_path": tmp.name,
}
)
except Exception as e:
logger.warning("[thumbnail] MediaKit 帧 %d 处理失败: %s", i, e)
if len(candidates) >= num_frames:
logger.info("[thumbnail] MediaKit 抽帧完成: %d 帧", len(candidates))
# ── 默认路径:本地 ffmpeg 单帧 seek ───────────────────────────
if len(candidates) < num_frames:
if candidates:
logger.info("[thumbnail] MediaKit 不足 %d 帧,本地 ffmpeg 补充", num_frames)
else:
logger.info("[thumbnail] 使用本地 ffmpeg 抽帧(num=%d, duration=%.1fs)", num_frames, duration)
seek_points = _compute_clip_boundary_seek_points(duration, clip_boundaries, num_frames)
for i, seek_t in enumerate(seek_points):
tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
tmp.close()
_temp_paths.append(tmp.name)
try:
frame_path = extract_first_frame(
video_path,
output_path=tmp.name,
seek_seconds=seek_t,
min_seek_seconds=0.5,
)
if title_text and title_text.strip():
apply_title_overlay(
frame_path,
title_text,
color=title_color,
position=title_position,
font_size=title_font_size,
)
storage_key = f"covers/{plan_id}/{task_id}/frame_{i}.jpg"
url = upload_to_oss(frame_path, storage_key)
if url:
candidates.append(
{
"url": url,
"position": seek_t,
"image_path": tmp.name,
}
)
except Exception as e:
logger.warning("[thumbnail] 封面候选帧 %d 提取失败: %s", i, e)
# ── 阶段 2:质量评分 ────────────────────────────────────────────
if len(candidates) > 1:
try:
from packages.shared.cover_frame_scorer import score_frames
candidates = score_frames(candidates)
elapsed = time.monotonic() - t0
logger.info(
"[thumbnail] 封面帧评分完成: plan_id=%s count=%d best_score=%.1f elapsed=%.2fs path=%s",
plan_id,
len(candidates),
candidates[0].get("score", 0.0) if candidates else 0.0,
elapsed,
"mediakit" if use_mediakit else "local",
)
except Exception:
logger.warning("[thumbnail] 封面帧质量评分失败,保持原始顺序", exc_info=True)
for c in candidates:
c.pop("image_path", None)
return candidates
finally:
for path in _temp_paths:
try:
Path(path).unlink(missing_ok=True)
except Exception:
pass