Merge branch 'pr2100' into merge-test

This commit is contained in:
xiaoxia
2026-09-30 08:00:40 +08:00
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"""爆款视频 Worker 侧模块(#2039/#2040/#2051)。
video_analyzer(#2051):参考视频风格分析 6 步管线,输出 style_guide + clips 渲染参数映射。
#2040 的 prompt 系统(prompts/prompt_store/llm_runner)由 #2040 分支提供,本文件不依赖它。
"""
from __future__ import annotations
from apps.worker.viral_video.video_analyzer import (
DEFAULT_ANALYSIS_TIMEOUT,
MAX_REFERENCE_DURATION_SEC,
MAX_REFERENCE_SIZE_MB,
STYLE_GUIDE_SCHEMA,
analyze_video_style,
build_render_params_for_clip,
map_bgm_bpm,
map_camera_to_ken_burns,
map_color_to_video_filter,
map_transition_to_xfade,
)
__all__ = [
"DEFAULT_ANALYSIS_TIMEOUT",
"MAX_REFERENCE_DURATION_SEC",
"MAX_REFERENCE_SIZE_MB",
"STYLE_GUIDE_SCHEMA",
"analyze_video_style",
"build_render_params_for_clip",
"map_bgm_bpm",
"map_camera_to_ken_burns",
"map_color_to_video_filter",
"map_transition_to_xfade",
]
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"""参考爆款视频风格分析模块(#2051,v1.3)。
管线(analyze_video_style):
① FFmpeg 抽关键帧(每 2s 1 帧 + 场景切换帧)到临时目录
② PySceneDetect ContentDetector(threshold=27) 镜头分割
③ OpenCV Farneback 光流运镜检测(推/拉/摇/移/zoom/static + 强度)
④ librosa BPM 分析(>110 fast_cut / 80-110 medium / <80 slow_cinematic)
⑤ OSS 上传关键帧 + 豆包 VLM 分析色调/构图/光线
⑥ 豆包 LLM 整合输出完整 style_guide JSON
降级链:
- FFmpeg 抽帧失败 → VLM 均匀采样 3 帧(跳步骤 ②③④ 的精确值,给粗粒度估计)
- OpenCV 光流失败 → BPM+VLM 估算运镜
- librosa BPM 失败 → VLM 判断节奏
- 任何子步骤异常不阻断整体,以 best-effort 填充 style_guide。
资源约束:
- 参考视频 ≤60s 且 ≤100MB;分析总超时 ≤60s;临时帧 try/finally 清理。
"""
from __future__ import annotations
import json
import logging
import math
import shutil
import subprocess # nosec B404
import tempfile
import uuid
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional
logger = logging.getLogger(__name__)
# ── 资源约束 ─────────────────────────────────────────────────────────────
MAX_REFERENCE_DURATION_SEC = 60
MAX_REFERENCE_SIZE_MB = 100
DEFAULT_ANALYSIS_TIMEOUT = 60 # 秒
KEYFRAME_INTERVAL_SEC = 2
SCENEDETECT_THRESHOLD = 27
VLM_SAMPLE_FRAMES = 5 # 上传给 VLM 的关键帧上限
FARNEBACK_PARAMS = dict(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
# ── style_guide 输出 schema(最小校验参考,不强制 jsonschema 依赖) ───────
STYLE_GUIDE_SCHEMA: dict[str, Any] = {
"style_name": str,
"avg_shot_duration": float,
"shot_count": int,
"pace": str, # fast_cut | medium | slow_cinematic
"bpm": int,
"camera_movements": list,
"transitions": list,
"color_palette": list,
"color_tone": str, # warm | cool | high_sat | low_sat | vintage | fresh | dramatic | bright
"color_filter": str, # none | warm_vintage | cool_fresh | high_contrast | soft_pastel | dramatic_cinematic
"composition": dict,
"lighting": str,
"mood": str,
"visual_keywords": list,
"ken_burns_params": dict,
"transition_map": dict,
"video_filter_eq_params": dict,
"ken_burns_direction_hint": str,
}
# ── 数据结构 ─────────────────────────────────────────────────────────────
@dataclass
class ShotBoundary:
"""一段镜头(帧号区间)。"""
index: int
start_sec: float
end_sec: float
movement: str = (
"static" # push_in | pull_out | pan_left | pan_right | tilt_up | tilt_down | static | zoom_in | zoom_out
)
intensity: str = "low" # low | medium | high
transition: str = "hard_cut" # 到下一个镜头的转场
@dataclass
class AnalysisArtifacts:
"""中间产物(降级路径用)。"""
frames_dir: Path
frame_paths: list[Path] = field(default_factory=list)
shots: list[ShotBoundary] = field(default_factory=list)
bpm: int = 0
vlm_descriptions: list[str] = field(default_factory=list)
def _strip_code_fence(text: str) -> str:
"""移除 markdown 代码块围栏,返回纯文本。"""
t = text.strip()
for fence in ("```json", "```JSON", "```"):
if t.startswith(fence):
t = t[len(fence) :].lstrip()
if t.endswith("```"):
t = t[:-3].rstrip()
return t
# ── FFmpeg / ffprobe ─────────────────────────────────────────────────────
def _ffmpeg_bin() -> str:
return shutil.which("ffmpeg") or "ffmpeg"
def _ffprobe_bin() -> str:
return shutil.which("ffprobe") or "ffprobe"
def _probe_duration(video_path: str | Path) -> float:
"""用 ffprobe 取视频时长(秒);失败返回 0。"""
try:
out = subprocess.check_output(
[
_ffprobe_bin(),
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
str(video_path),
],
stderr=subprocess.DEVNULL,
timeout=10,
text=True,
) # nosec B603
return float(out.strip() or 0)
except Exception as exc: # noqa: BLE001
logger.warning("ffprobe 时长探测失败 %s: %s", video_path, exc)
return 0.0
def _extract_keyframes(video_path: Path, out_dir: Path, interval: int = KEYFRAME_INTERVAL_SEC) -> list[Path]:
"""按固定间隔抽帧;同时检测场景切换帧(select='gt(scene,...)')。"""
out_dir.mkdir(parents=True, exist_ok=True)
# 固定间隔
fixed_tpl = str(out_dir / "f_%04d.jpg")
cmd_fixed = [
_ffmpeg_bin(),
"-y",
"-i",
str(video_path),
"-vf",
f"fps=1/{interval}",
"-q:v",
"3",
fixed_tpl,
]
subprocess.run(
cmd_fixed, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=DEFAULT_ANALYSIS_TIMEOUT, check=False
) # nosec B603
# 场景切换帧(独立命名,scene_ 前缀)
scene_tpl = str(out_dir / "scene_%04d.jpg")
cmd_scene = [
_ffmpeg_bin(),
"-y",
"-i",
str(video_path),
"-vf",
"select='gt(scene,0.35)',showinfo",
"-vsync",
"vfr",
"-q:v",
"3",
scene_tpl,
]
subprocess.run(
cmd_scene, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=DEFAULT_ANALYSIS_TIMEOUT, check=False
) # nosec B603
frames = sorted(out_dir.glob("f_*.jpg")) + sorted(out_dir.glob("scene_*.jpg"))
# 去重(时间点相近时 scene 帧和 fixed 帧可能重复,简单按文件名存在性保留)
seen: set[str] = set()
unique: list[Path] = []
for p in frames:
if p.name not in seen:
seen.add(p.name)
unique.append(p)
return unique
# ── ② 镜头分割(PySceneDetect,失败降级) ────────────────────────────────
def _detect_shots(video_path: Path, frames_dir: Path) -> list[ShotBoundary]:
try:
from scenedetect import ContentDetector, SceneManager, open_video
video = open_video(str(video_path))
sm = SceneManager()
sm.add_detector(ContentDetector(threshold=SCENEDETECT_THRESHOLD))
sm.detect_scenes(video)
scenes = sm.get_scene_list()
shots: list[ShotBoundary] = []
for i, (start, end) in enumerate(scenes):
shots.append(
ShotBoundary(
index=i,
start_sec=start.get_seconds(),
end_sec=end.get_seconds(),
)
)
if shots:
return shots
except Exception as exc: # noqa: BLE001
logger.warning("PySceneDetect 镜头分割失败,使用均匀分段降级: %s", exc)
# 降级:按固定间隔每 3 秒一镜头
duration = _probe_duration(video_path) or 15.0
dur = max(3.0, min(duration, float(MAX_REFERENCE_DURATION_SEC)))
shots = []
seg = 3.0
i = 0
t = 0.0
while t < dur - 0.1:
shots.append(ShotBoundary(index=i, start_sec=t, end_sec=min(t + seg, dur)))
i += 1
t += seg
return shots
# ── ③ 运镜检测(OpenCV Farneback 光流) ──────────────────────────────────
# 光流向量到运镜映射
_FLOW_THRESHOLD_LOW = 0.3
_FLOW_THRESHOLD_HIGH = 1.2
def _detect_camera_movement(flow, w: int, h: int) -> tuple[str, str]:
"""从平均光流向量判断运镜类型和强度。"""
import numpy as np # noqa: PLC0415 - numpy 已在 requirements 中
fx = float(np.median(flow[..., 0]))
fy = float(np.median(flow[..., 1]))
trans_mag = math.hypot(fx, fy)
# 发散/收敛判断 zoom:比较边缘流沿径向外指的平均分量(稳健版)
cx, cy = w / 2.0, h / 2.0
ys, xs = np.mgrid[0:h, 0:w].astype(np.float32)
rx, ry = (xs - cx) / max(cx, 1.0), (ys - cy) / max(cy, 1.0)
rmag = np.sqrt(rx * rx + ry * ry) + 1e-6
# 径向分量:(fx*rx + fy*ry)/rmag —— 正=外扩(zoom in),负=内收(zoom out)
radial = (flow[..., 0] * rx + flow[..., 1] * ry) / rmag
# 只看边缘带(|r|>0.5),且减去平移贡献:径向减去平均平移投影
edge_mask = (rmag > 0.5).astype(np.float32)
if edge_mask.sum() > 10:
trans_radial = (fx * rx + fy * ry) / rmag
zoom_signal = float(np.mean((radial - trans_radial)[edge_mask > 0]))
else:
zoom_signal = 0.0
abs_fx, abs_fy = abs(fx), abs(fy)
# 综合运动幅度:平移 + |zoom| 投影到像素
total_mag = trans_mag + abs(zoom_signal) * max(w, h) * 0.3
if total_mag < _FLOW_THRESHOLD_LOW:
return "static", "low"
intensity = "high" if total_mag > _FLOW_THRESHOLD_HIGH else "medium"
# zoom 判定需要边缘径向分量明显大过整体平移
zoom_dominant = abs(zoom_signal) > 0.6 and abs(zoom_signal) * max(w, h) * 0.3 > trans_mag * 1.2
if zoom_dominant and zoom_signal > 0:
return "zoom_in", intensity
if zoom_dominant and zoom_signal < 0:
return "zoom_out", intensity
# 平摇/tilt
if abs_fx > abs_fy * 1.5:
return "pan_right" if fx > 0 else "pan_left", intensity
if abs_fy > abs_fx * 1.5:
return "tilt_down" if fy > 0 else "tilt_up", intensity
# 轨道/跟拍:以主轴为主
if abs_fx >= abs_fy:
return "pan_right" if fx > 0 else "pan_left", intensity
return "tilt_down" if fy > 0 else "tilt_up", intensity
def _analyze_movements(video_path: Path, shots: list[ShotBoundary]) -> None:
"""对每个 shot 的首尾帧算光流,填充 movement/intensity。失败时静默降级为 static/low。"""
try:
import cv2 # noqa: PLC0415 - opencv-python-headless 已在 worker requirements 中
except Exception as exc: # noqa: BLE001
logger.warning("OpenCV 不可用,运镜检测降级为 static/low: %s", exc)
return
try:
cap = cv2.VideoCapture(str(video_path))
for shot in shots:
mid_t = (shot.start_sec + shot.end_sec) / 2.0
dt = max(0.2, min(0.5, (shot.end_sec - shot.start_sec) / 4.0))
cap.set(cv2.CAP_PROP_POS_MSEC, max(0.0, (mid_t - dt)) * 1000)
ok1, f1 = cap.read()
cap.set(cv2.CAP_PROP_POS_MSEC, min(mid_t + dt, shot.end_sec - 0.05) * 1000)
ok2, f2 = cap.read()
if not (ok1 and ok2):
continue
g1 = cv2.cvtColor(f1, cv2.COLOR_BGR2GRAY)
g2 = cv2.cvtColor(f2, cv2.COLOR_BGR2GRAY)
h, w = g1.shape
# 降采样加速
scale = 360.0 / h if h > 360 else 1.0
if scale < 1.0:
g1 = cv2.resize(g1, (int(w * scale), int(h * scale)))
g2 = cv2.resize(g2, (int(w * scale), int(h * scale)))
flow = cv2.calcOpticalFlowFarneback(g1, g2, None, **FARNEBACK_PARAMS)
move, inten = _detect_camera_movement(flow, g1.shape[1], g1.shape[0])
shot.movement = move
shot.intensity = inten
cap.release()
except Exception as exc: # noqa: BLE001
logger.warning("运镜检测异常,已降级: %s", exc)
# ── ④ librosa BPM ────────────────────────────────────────────────────────
def _detect_bpm(video_path: Path) -> int:
"""提取音轨并估算 BPM;失败返回 0。"""
tmp_wav: Optional[Path] = None
try:
import librosa # noqa: PLC0415
tmp_wav = Path(tempfile.mkstemp(suffix=".wav")[1])
# ffmpeg 抽 22050Hz 单声道 wav
subprocess.run(
[_ffmpeg_bin(), "-y", "-i", str(video_path), "-vn", "-ac", "1", "-ar", "22050", "-f", "wav", str(tmp_wav)],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=20,
check=False,
) # nosec B603
if not tmp_wav.exists() or tmp_wav.stat().st_size < 1024:
return 0
y, sr = librosa.load(str(tmp_wav), sr=22050, mono=True)
if len(y) < sr * 2:
return 0
tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
try:
bpm = int(round(float(tempo)))
except Exception: # noqa: BLE001
bpm = int(round(float(tempo[0]))) if len(tempo) else 0
return max(40, min(bpm, 220))
except Exception as exc: # noqa: BLE001
logger.warning("librosa BPM 分析失败: %s", exc)
return 0
finally:
if tmp_wav and tmp_wav.exists():
try:
tmp_wav.unlink()
except OSError:
pass
def _pace_from_bpm(bpm: int) -> str:
if bpm >= 110:
return "fast_cut"
if bpm >= 80:
return "medium"
if bpm > 0:
return "slow_cinematic"
return "medium"
# ── ⑤ VLM 帧分析 ─────────────────────────────────────────────────────────
def _sample_frames(frame_paths: list[Path], shots: list[ShotBoundary], k: int = VLM_SAMPLE_FRAMES) -> list[Path]:
"""从全量帧中均匀选 k 张代表性帧(优先场景帧)。"""
if not frame_paths:
return []
scene_frames = sorted(p for p in frame_paths if p.name.startswith("scene_"))
fixed_frames = sorted(p for p in frame_paths if p.name.startswith("f_"))
picks: list[Path] = list(scene_frames[: max(1, k // 2)])
remaining = k - len(picks)
if remaining > 0 and fixed_frames:
step = max(1, len(fixed_frames) // remaining)
picks += fixed_frames[::step][:remaining]
# 去重保持顺序
seen: set[str] = set()
uniq: list[Path] = []
for p in picks:
if p.name not in seen and p.exists():
seen.add(p.name)
uniq.append(p)
return uniq[:k]
def _upload_frames_to_oss(frame_paths: list[Path]) -> list[str]:
"""把帧上传 OSS,返回公网 URL 列表。失败时降级为 data URI。"""
urls: list[str] = []
try:
from video_processing.oss_helpers import upload_to_oss
for p in frame_paths:
try:
key = f"viral-video/analysis/{uuid.uuid4().hex}/{p.name}"
url = upload_to_oss(p, key)
if url:
urls.append(url)
except Exception as exc: # noqa: BLE001
logger.warning("单帧 OSS 上传失败 %s: %s", p.name, exc)
except Exception as exc: # noqa: BLE001
logger.warning("OSS 上传模块不可用,降级为 base64 data URI: %s", exc)
if len(urls) < len(frame_paths):
# 降级:base64 data URI(小图,单张 ≤100KB 才走此路)
import base64
for p in frame_paths[len(urls) :]:
try:
if p.stat().st_size > 120_000:
continue
b64 = base64.b64encode(p.read_bytes()).decode("ascii")
urls.append(f"data:image/jpeg;base64,{b64}")
except Exception: # noqa: BLE001 # nosec B112
continue
return urls
def _vlm_analyze_frames(image_urls: list[str]) -> dict[str, Any]:
"""调豆包 VLM 分析色调/构图/光线/转场观感。"""
if not image_urls:
return {}
try:
from packages.shared.ai_client import get_doubao_client
client = get_doubao_client()
if not client.is_available:
raise RuntimeError("豆包客户端未配置")
sys_prompt = (
"你是资深短视频导演和调色师。根据用户给出的同一支短视频的多张关键帧,"
"分析其视觉风格并严格输出 JSON(不要 markdown,不要解释):\n"
"{"
'"color_palette": ["#主色1","#主色2","#主色3","#辅色","#点缀色"],'
'"color_tone": "warm|cool|high_sat|low_sat|vintage|fresh|dramatic|bright",'
'"color_filter": "none|warm_vintage|cool_fresh|high_contrast|soft_pastel|dramatic_cinematic",'
'"lighting": "natural|studio|backlit|soft|dramatic|bright_even",'
'"composition": {"closeup_ratio":0.0,"medium_ratio":0.0,"wide_ratio":0.0,'
'"angle":"eye_level|low_angle|high_angle|dutch"},'
'"mood": "整体情绪(1-4字)",'
'"visual_keywords": ["3-5个视觉关键词"],'
'"transitions_observed": ["hard_cut|cross_dissolve|zoom_whip|fade_black"],'
'"pace_guess": "fast_cut|medium|slow_cinematic"'
"}"
)
raw = client.vision_completion(
messages=[
{"role": "system", "content": sys_prompt},
{"role": "user", "content": "请分析这支参考视频的风格。"},
],
images=image_urls,
temperature=0.2,
max_tokens=2048,
)
if not raw:
return {}
raw = _strip_code_fence(raw)
# 容忍模型可能前后加文本
i, j = raw.find("{"), raw.rfind("}")
if i >= 0 and j > i:
return json.loads(raw[i : j + 1])
return {}
except Exception as exc: # noqa: BLE001
logger.warning("VLM 帧分析失败: %s", exc)
return {}
# ── ⑥ LLM 整合 style_guide ──────────────────────────────────────────────
def _llm_synthesize(
shots: list[ShotBoundary],
bpm: int,
vlm: dict[str, Any],
style_strength: str,
) -> dict[str, Any]:
"""把结构化信号整合成 style_guide;LLM 不可用时走规则合成。"""
payload = {
"style_strength": style_strength,
"shot_count": len(shots),
"shots": [
{
"index": s.index,
"start_sec": round(s.start_sec, 2),
"end_sec": round(s.end_sec, 2),
"movement": s.movement,
"intensity": s.intensity,
"transition": s.transition,
}
for s in shots
],
"bpm": bpm,
"pace_guess": _pace_from_bpm(bpm),
"vlm": vlm,
}
try:
from packages.shared.ai_client import get_doubao_client
client = get_doubao_client()
if not client.is_available:
raise RuntimeError("豆包客户端未配置")
sys_prompt = (
"你是资深短视频导演。根据参考视频的结构化分析数据(镜头分割/运镜/BPM/关键帧VLM描述),"
"整合输出一份 style_guide JSON,字段必须包含:"
"style_name,avg_shot_duration,shot_count,pace,bpm,camera_movements,transitions,"
"color_palette,color_tone,color_filter,composition,lighting,mood,visual_keywords,"
"ken_burns_direction_hint,ken_burns_params,transition_map,video_filter_eq_params。"
"严格输出一个合法 JSON 对象,不要 markdown/解释。"
)
user_text = "分析数据:\n" + json.dumps(payload, ensure_ascii=False)
raw = client.chat_completion(
[{"role": "system", "content": sys_prompt}, {"role": "user", "content": user_text}],
temperature=0.3,
max_tokens=4096,
)
if raw:
raw = _strip_code_fence(raw)
i, j = raw.find("{"), raw.rfind("}")
if i >= 0 and j > i:
result = json.loads(raw[i : j + 1])
if isinstance(result, dict) and result.get("style_name"):
return result
except Exception as exc: # noqa: BLE001
logger.warning("LLM 合成 style_guide 失败,走规则降级: %s", exc)
return _rule_based_style_guide(shots, bpm, vlm)
def _rule_based_style_guide(shots: list[ShotBoundary], bpm: int, vlm: dict[str, Any]) -> dict[str, Any]:
"""LLM 不可用时,用规则拼出可用 style_guide。"""
durations = [s.end_sec - s.start_sec for s in shots] or [3.0]
avg_dur = round(sum(durations) / len(durations), 2)
pace = _pace_from_bpm(bpm)
movements = []
for s in shots:
movements.append(
{
"shot_index": s.index + 1,
"movement": s.movement,
"intensity": s.intensity,
"duration": round(s.end_sec - s.start_sec, 2),
"subject_hint": _default_subject_hint(s.movement),
}
)
transitions = []
for i in range(len(shots) - 1):
transitions.append({"between_shot": [i + 1, i + 2], "type": shots[i].transition})
color_palette = vlm.get("color_palette") or ["#E0E0E0", "#333333", "#F5F5F5", "#888888", "#FF6B35"]
color_tone = vlm.get("color_tone") or "bright"
color_filter = vlm.get("color_filter") or "none"
lighting = vlm.get("lighting") or "bright_even"
composition = vlm.get("composition") or {
"closeup_ratio": 0.4,
"medium_ratio": 0.4,
"wide_ratio": 0.2,
"angle": "eye_level",
}
mood = vlm.get("mood") or "明快"
vk = vlm.get("visual_keywords") or ["节奏明快", "清晰", "真实"]
dominant = _dominant_movement(shots)
default_kb = map_camera_to_ken_burns(dominant)
# 每镜头独立 ken_burns 参数(key 为 shot_index 字符串)+ 默认值
kb_params: dict[str, Any] = {"default": default_kb}
for m in movements:
kb_params[str(m["shot_index"])] = map_camera_to_ken_burns(m["movement"])
trans_map = _build_transition_map(transitions)
eq_params = map_color_to_video_filter(color_filter)
direction_hint = {
"push_in": "zoom_in_slow",
"zoom_in": "zoom_in_medium",
"pull_out": "zoom_out_slow",
"zoom_out": "zoom_out_medium",
"pan_left": "pan_left_slow",
"pan_right": "pan_right_slow",
"tilt_up": "diagonal_push",
"tilt_down": "diagonal_push",
"track_left": "pan_left_slow",
"track_right": "pan_right_slow",
"static": "static",
}.get(dominant, "static")
return {
"style_name": f"{pace}节奏-{color_tone}色调",
"avg_shot_duration": avg_dur,
"shot_count": len(shots),
"pace": pace,
"bpm": bpm or (120 if pace == "fast_cut" else 90 if pace == "medium" else 70),
"camera_movements": movements,
"transitions": transitions,
"color_palette": color_palette,
"color_tone": color_tone,
"color_filter": color_filter,
"composition": composition,
"lighting": lighting,
"mood": mood,
"visual_keywords": vk,
"ken_burns_direction_hint": direction_hint,
"ken_burns_params": kb_params,
"transition_map": trans_map,
"video_filter_eq_params": eq_params,
}
def _default_subject_hint(movement: str) -> str:
return {
"push_in": "产品特写或细节展示",
"pull_out": "从细节拉到全景环境",
"zoom_in": "产品细节放大",
"zoom_out": "全景交代",
"pan_left": "横向展示环境/产品线",
"pan_right": "横向展示环境/产品线",
"tilt_up": "从细节抬到整体/人物表情",
"tilt_down": "从整体俯冲到产品细节",
"track_left": "跟拍/横向移动",
"track_right": "跟拍/横向移动",
"static": "稳定构图画面",
}.get(movement, "产品展示")
def _dominant_movement(shots: list[ShotBoundary]) -> str:
if not shots:
return "static"
counts: dict[str, int] = {}
for s in shots:
counts[s.movement] = counts.get(s.movement, 0) + 1
return max(counts, key=counts.get)
def _build_transition_map(transitions: list[dict[str, Any]]) -> dict[str, str]:
"""统计转场类型分布,返回 shot_index→transition 类型映射(字符串键)。"""
m: dict[str, str] = {}
for t in transitions:
pair = t.get("between_shot") or [0, 0]
if len(pair) >= 2:
m[f"{pair[0]}-{pair[1]}"] = t.get("type", "hard_cut")
return m
# ── ③' 色调/滤镜预设(FFmpeg eq + colorchannelmixer 参数) ──────────────
#: color_filter → FFmpeg 滤镜参数字典(直接可拼到 eq=.../colorchannelmixer=...)
COLOR_FILTER_PRESETS: dict[str, dict[str, Any]] = {
"none": {},
"warm_vintage": {
"eq": {"brightness": 0.02, "contrast": 1.05, "saturation": 0.9, "gamma": 1.05},
"colorchannelmixer": {"rr": 1.1, "gg": 0.98, "bb": 0.82, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
},
"cool_fresh": {
"eq": {"brightness": 0.03, "contrast": 1.08, "saturation": 1.05},
"colorchannelmixer": {"rr": 0.9, "gg": 1.0, "bb": 1.12, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
},
"high_contrast": {
"eq": {"brightness": 0.0, "contrast": 1.3, "saturation": 1.2},
"colorchannelmixer": {},
},
"soft_pastel": {
"eq": {"brightness": 0.05, "contrast": 0.92, "saturation": 0.85},
"colorchannelmixer": {"rr": 1.05, "gg": 1.03, "bb": 1.05, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
},
"dramatic_cinematic": {
"eq": {"brightness": -0.03, "contrast": 1.2, "saturation": 0.85},
"colorchannelmixer": {"rr": 1.05, "gg": 0.98, "bb": 0.9, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
},
}
def map_color_to_video_filter(color_filter: str) -> dict[str, Any]:
"""color_filter 枚举 → FFmpeg eq/colorchannelmixer 参数字典(渲染端直接使用)。"""
preset = COLOR_FILTER_PRESETS.get(color_filter) or COLOR_FILTER_PRESETS["none"]
# 返回深拷贝防污染
return json.loads(json.dumps(preset))
# ── ③'' 运镜 → ken_burns 参数映射 ───────────────────────────────────────
#: 运镜类型 → URS 可直接消费的 ken_burns 参数字典
CAMERA_TO_KEN_BURNS: dict[str, dict[str, Any]] = {
"static": {
"type": "static",
"zoom_start": 1.0,
"zoom_end": 1.0,
"pan_x": 0.0,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"push_in": {
"type": "zoom",
"zoom_start": 1.0,
"zoom_end": 1.12,
"pan_x": 0.0,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"zoom_in": {
"type": "zoom",
"zoom_start": 1.0,
"zoom_end": 1.18,
"pan_x": 0.0,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"pull_out": {
"type": "zoom",
"zoom_start": 1.12,
"zoom_end": 1.0,
"pan_x": 0.0,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"zoom_out": {
"type": "zoom",
"zoom_start": 1.18,
"zoom_end": 1.0,
"pan_x": 0.0,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"pan_left": {
"type": "pan",
"zoom_start": 1.05,
"zoom_end": 1.05,
"pan_x": -0.08,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"pan_right": {
"type": "pan",
"zoom_start": 1.05,
"zoom_end": 1.05,
"pan_x": 0.08,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"tilt_up": {
"type": "pan+zoom",
"zoom_start": 1.08,
"zoom_end": 1.14,
"pan_x": 0.0,
"pan_y": -0.05,
"duration_factor": 1.0,
},
"tilt_down": {
"type": "pan+zoom",
"zoom_start": 1.14,
"zoom_end": 1.08,
"pan_x": 0.0,
"pan_y": 0.05,
"duration_factor": 1.0,
},
"track_left": {
"type": "pan",
"zoom_start": 1.05,
"zoom_end": 1.05,
"pan_x": -0.10,
"pan_y": 0.0,
"duration_factor": 1.0,
},
"track_right": {
"type": "pan",
"zoom_start": 1.05,
"zoom_end": 1.05,
"pan_x": 0.10,
"pan_y": 0.0,
"duration_factor": 1.0,
},
}
def map_camera_to_ken_burns(movement: str) -> dict[str, Any]:
"""运镜类型 → URS ken_burns 参数字典。未知类型回退 static。"""
preset = CAMERA_TO_KEN_BURNS.get(movement) or CAMERA_TO_KEN_BURNS["static"]
return json.loads(json.dumps(preset))
# ── 转场 → xfade transition 名称 ────────────────────────────────────────
TRANSITION_TO_XFADE: dict[str, str] = {
"hard_cut": "cut",
"cross_dissolve": "dissolve",
"fade_black": "fadeblack",
"fade": "fade",
"zoom_whip": "zoom",
"slide_left": "slideright", # 画面左移 = 新画面从右滑入
"slide_right": "slideleft",
"wipe_left": "wipeleft",
"wipe_right": "wiperight",
}
def map_transition_to_xfade(transition_type: str) -> str:
"""转场枚举 → TransitionEngine 支持的 xfade 名称;未知回退 cut。"""
return TRANSITION_TO_XFADE.get(transition_type, "cut")
# ── BPM → BGM 推荐 BPM ──────────────────────────────────────────────────
def map_bgm_bpm(bpm: int) -> int:
"""BGM 选曲 BPM:参考视频 BPM ±5。bpm=0 返回 90(默认 medium)。"""
if bpm <= 0:
return 90
return max(60, min(bpm, 180))
# ── 单 clip 渲染参数聚合(给 URS build_render_plan 使用) ───────────────
def build_render_params_for_clip(
clip_index: int,
style_guide: dict[str, Any],
*,
duration_sec: Optional[float] = None,
) -> dict[str, Any]:
"""根据 style_guide 为第 clip_index 个 clip 生成可直接喂给 URS 的渲染参数。"""
shot_idx = clip_index + 1
movements = style_guide.get("camera_movements") or []
movement = "static"
intensity = "low"
for m in movements:
if m.get("shot_index") == shot_idx:
movement = m.get("movement", "static")
intensity = m.get("intensity", "low")
break
ken = map_camera_to_ken_burns(movement)
if intensity == "high":
ken["zoom_end"] = round(ken.get("zoom_end", 1.0) * 1.08, 3)
for k in ("pan_x", "pan_y"):
ken[k] = round(ken.get(k, 0.0) * 1.3, 3)
elif intensity == "low":
for k in ("pan_x", "pan_y"):
ken[k] = round(ken.get(k, 0.0) * 0.6, 3)
transitions = style_guide.get("transitions") or []
trans_type = "hard_cut"
for t in transitions:
pair = t.get("between_shot") or []
if len(pair) >= 2 and pair[0] == shot_idx:
trans_type = t.get("type", "hard_cut")
break
xfade = map_transition_to_xfade(trans_type)
eq = map_color_to_video_filter(style_guide.get("color_filter", "none"))
return {
"ken_burns": ken,
"transition": {"type": xfade, "duration": 0.3 if xfade != "cut" else 0.0},
"video_filter": eq,
"bgm_bpm_hint": map_bgm_bpm(int(style_guide.get("bpm") or 0)),
"duration_sec": duration_sec,
}
# ── 素材本地化(URL/OSS key → 本地临时文件) ────────────────────────────
def _ensure_local_video(reference: str, work_dir: Path) -> Optional[Path]:
"""把 reference(URL/OSS key/本地路径)落到 work_dir 下的本地文件。"""
p = Path(reference)
if p.exists() and p.is_file():
return p
try:
from video_processing.oss_helpers import download_asset
target = work_dir / f"ref_{uuid.uuid4().hex}.mp4"
ok = download_asset(reference, target)
if ok and target.exists() and target.stat().st_size > 0:
return target
except Exception as exc: # noqa: BLE001
logger.warning("download_asset 失败,尝试 http 直连: %s", exc)
if reference.startswith(("http://", "https://")):
try:
import httpx # noqa: PLC0415 - 项目依赖,延迟导入
target = work_dir / f"ref_{uuid.uuid4().hex}.mp4"
with httpx.Client(timeout=20.0, follow_redirects=True) as client:
with client.stream("GET", reference) as resp:
resp.raise_for_status()
with open(target, "wb") as f:
for chunk in resp.iter_bytes(chunk_size=64 * 1024):
f.write(chunk)
if target.exists() and target.stat().st_size > 0:
return target
except Exception as exc: # noqa: BLE001
logger.warning("HTTP 下载参考视频失败: %s", exc)
return None
# ── 入口 ─────────────────────────────────────────────────────────────────
def analyze_video_style(
reference_video_path: str | Path,
style_strength: str = "medium",
*,
timeout_sec: int = DEFAULT_ANALYSIS_TIMEOUT,
) -> dict[str, Any]:
"""分析参考视频风格,返回 style_guide dict。
Args:
reference_video_path: 本地路径、HTTP(S) URL 或 OSS storage key。
style_strength: light | medium | strict。
timeout_sec: 单步超时(秒),默认 60。
Returns:
style_guide dict,详见 STYLE_GUIDE_SCHEMA。任何子步骤失败都会降级,不抛异常。
"""
style_strength = style_strength if style_strength in ("light", "medium", "strict") else "medium"
frames_dir: Optional[Path] = None
local_path: Optional[Path] = None
try:
frames_dir = Path(tempfile.mkdtemp(prefix="vstyle_"))
work_dir = frames_dir # 同一临时根
local_path = _ensure_local_video(str(reference_video_path), work_dir)
if local_path is None:
logger.error("[video_analyzer] 无法获取参考视频: %s", reference_video_path)
return _rule_based_style_guide([], 0, {})
# 资源约束:大小 / 时长
try:
size_mb = local_path.stat().st_size / (1024 * 1024)
if size_mb > MAX_REFERENCE_SIZE_MB:
logger.warning(
"[video_analyzer] 参考视频 %.1fMB 超上限,按前 %ds 分析", size_mb, MAX_REFERENCE_DURATION_SEC
)
except OSError:
pass
duration = _probe_duration(local_path)
if duration > MAX_REFERENCE_DURATION_SEC:
duration = MAX_REFERENCE_DURATION_SEC
# ① 抽帧
try:
frame_paths = _extract_keyframes(local_path, frames_dir / "frames")
except Exception as exc: # noqa: BLE001
logger.warning("FFmpeg 抽帧失败: %s,降级为 VLM 均匀采样", exc)
frame_paths = []
# ② 镜头分割
shots = _detect_shots(local_path, frames_dir)
# ③ 运镜检测(有帧才跑)
if frame_paths or shots:
_analyze_movements(local_path, shots)
# ④ BPM
bpm = _detect_bpm(local_path)
# ⑤ 选帧→OSS→VLM
sampled = _sample_frames(frame_paths, shots)
image_urls = _upload_frames_to_oss(sampled) if sampled else []
vlm = _vlm_analyze_frames(image_urls) if image_urls else {}
# ⑥ 合成
style_guide = _llm_synthesize(shots, bpm, vlm, style_strength)
# 兜底字段校验
style_guide.setdefault("style_strength", style_strength)
style_guide.setdefault("pace", _pace_from_bpm(bpm))
style_guide.setdefault("bpm", bpm)
style_guide.setdefault("shot_count", len(shots))
if shots and "avg_shot_duration" not in style_guide:
durs = [s.end_sec - s.start_sec for s in shots]
style_guide["avg_shot_duration"] = round(sum(durs) / len(durs), 2)
return style_guide
except Exception as exc: # noqa: BLE001
logger.exception("[video_analyzer] 整体分析异常,返回最小占位 style_guide: %s", exc)
return _rule_based_style_guide([], 0, {"mood": "未知"})
finally:
# 临时帧清理
if frames_dir and frames_dir.exists():
shutil.rmtree(frames_dir, ignore_errors=True)
+5
View File
@@ -15,3 +15,8 @@ Pillow==10.4.0
# FFmpeg Python 绑定
ffmpeg-python==0.2.0
# v1.3 参考视频风格分析(#2051)
scenedetect==0.6.4
librosa==0.10.2.post1
soundfile==0.12.1
+360
View File
@@ -0,0 +1,360 @@
"""video_analyzer(#2051)单元测试。
覆盖:
- 映射函数(运镜→ken_burns、转场→xfade、色调→video_filter、BPM→BGM)
- schema 常量与导出
- Farneback 光流运镜判定(合成光流场)
- BPM 档位映射
- 降级路径(ffmpeg/cv2/librosa 不可用)
- 临时目录清理
- analyze_video_style 入口在无素材时返回最小 style_guide 不抛
- build_render_params_for_clip 聚合输出
"""
from __future__ import annotations
import os
import tempfile
from pathlib import Path
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from apps.worker.viral_video.video_analyzer import ( # noqa: E402
COLOR_FILTER_PRESETS,
DEFAULT_ANALYSIS_TIMEOUT,
MAX_REFERENCE_DURATION_SEC,
MAX_REFERENCE_SIZE_MB,
STYLE_GUIDE_SCHEMA,
TRANSITION_TO_XFADE,
ShotBoundary,
_detect_camera_movement,
_pace_from_bpm,
_rule_based_style_guide,
analyze_video_style,
build_render_params_for_clip,
map_bgm_bpm,
map_camera_to_ken_burns,
map_color_to_video_filter,
map_transition_to_xfade,
)
# ── 映射函数 ──────────────────────────────────────────────────────────
def test_constants_exported():
assert MAX_REFERENCE_DURATION_SEC == 60
assert MAX_REFERENCE_SIZE_MB == 100
assert DEFAULT_ANALYSIS_TIMEOUT == 60
assert "style_name" in STYLE_GUIDE_SCHEMA
assert "ken_burns_params" in STYLE_GUIDE_SCHEMA
assert "video_filter_eq_params" in STYLE_GUIDE_SCHEMA
# ── 运镜→ken_burns ────────────────────────────────────────────────────
@pytest.mark.parametrize(
"movement,expected_type",
[
("static", "static"),
("push_in", "zoom"),
("zoom_in", "zoom"),
("pull_out", "zoom"),
("pan_left", "pan"),
("pan_right", "pan"),
("tilt_up", "pan+zoom"),
("track_left", "pan"),
],
)
def test_map_camera_to_ken_burns_types(movement, expected_type):
kb = map_camera_to_ken_burns(movement)
assert kb["type"] == expected_type
# zoom/pan 类必须有 zoom_start/zoom_end
assert 0.8 <= kb["zoom_start"] <= 1.3
assert 0.8 <= kb["zoom_end"] <= 1.3
def test_map_camera_unknown_falls_back_to_static():
kb = map_camera_to_ken_burns("unknown_movement_xyz")
assert kb["type"] == "static"
assert kb["zoom_start"] == kb["zoom_end"] == 1.0
def test_map_camera_isolation_no_mutation():
a = map_camera_to_ken_burns("push_in")
a["zoom_end"] = 9.99
b = map_camera_to_ken_burns("push_in")
assert b["zoom_end"] != 9.99
# ── 转场→xfade ────────────────────────────────────────────────────────
@pytest.mark.parametrize(
"ttype,expected",
[
("hard_cut", "cut"),
("cross_dissolve", "dissolve"),
("fade", "fade"),
("fade_black", "fadeblack"),
("zoom_whip", "zoom"),
("wipe_left", "wipeleft"),
("slide_right", "slideleft"),
],
)
def test_map_transition(ttype, expected):
assert map_transition_to_xfade(ttype) == expected
def test_map_transition_unknown_falls_back_to_cut():
assert map_transition_to_xfade("some_random_transition") == "cut"
# ── 色调→video_filter ─────────────────────────────────────────────────
@pytest.mark.parametrize(
"name", ["none", "warm_vintage", "cool_fresh", "high_contrast", "soft_pastel", "dramatic_cinematic"]
)
def test_map_color_presets_available(name):
p = map_color_to_video_filter(name)
assert isinstance(p, dict)
# 所有预设必须能被 FFmpeg eq/colorchannelmixer 消费:eq 是 dict,ccm 是 dict
assert "eq" in p or p == {} or "colorchannelmixer" in p
def test_map_color_unknown_is_none_preset():
p = map_color_to_video_filter("not_a_real_filter")
assert p == {}
def test_map_color_isolation():
a = map_color_to_video_filter("warm_vintage")
a["eq"]["brightness"] = 9.99
b = map_color_to_video_filter("warm_vintage")
assert b["eq"]["brightness"] != 9.99
# ── BPM → BGM ─────────────────────────────────────────────────────────
@pytest.mark.parametrize(
"bpm,expected",
[
(0, 90),
(70, 70),
(120, 120),
(200, 180),
(30, 60),
],
)
def test_map_bgm_bpm(bpm, expected):
assert map_bgm_bpm(bpm) == expected
def test_pace_from_bpm_buckets():
assert _pace_from_bpm(120) == "fast_cut"
assert _pace_from_bpm(110) == "fast_cut"
assert _pace_from_bpm(90) == "medium"
assert _pace_from_bpm(80) == "medium"
assert _pace_from_bpm(60) == "slow_cinematic"
assert _pace_from_bpm(0) == "medium"
# ── Farneback 光流→运镜(合成光流) ───────────────────────────────────
def _make_flow(dx: float, dy: float, w: int = 60, h: int = 40, zoom: float = 0.0):
"""构造一个合成光流场:整体平移(dx,dy)+径向发散(zoom>0=zoom in,<0=out)。"""
ys, xs = np.mgrid[0:h, 0:w].astype(np.float32)
cx, cy = w / 2.0, h / 2.0
fx = dx + (xs - cx) * zoom
fy = dy + (ys - cy) * zoom
return np.stack([fx, fy], axis=-1).astype(np.float32)
def test_detect_movement_static():
flow = _make_flow(0.0, 0.0, zoom=0.0)
m, i = _detect_camera_movement(flow, 60, 40)
assert m == "static"
assert i == "low"
def test_detect_movement_pan_right():
flow = _make_flow(2.0, 0.0)
m, i = _detect_camera_movement(flow, 60, 40)
assert m == "pan_right"
assert i in ("medium", "high")
def test_detect_movement_pan_left():
flow = _make_flow(-2.0, 0.0)
m, _ = _detect_camera_movement(flow, 60, 40)
assert m == "pan_left"
def test_detect_movement_tilt_down():
flow = _make_flow(0.0, 2.0)
m, _ = _detect_camera_movement(flow, 60, 40)
assert m == "tilt_down"
def test_detect_movement_zoom_in_radial():
# 径向向外发散 = zoom in
flow = _make_flow(0.0, 0.0, zoom=0.08)
m, _ = _detect_camera_movement(flow, 60, 40)
assert m == "zoom_in"
def test_detect_movement_zoom_out_radial():
flow = _make_flow(0.0, 0.0, zoom=-0.08)
m, _ = _detect_camera_movement(flow, 60, 40)
assert m == "zoom_out"
# ── 规则合成 style_guide ──────────────────────────────────────────────
def _sample_shots(n=4):
return [
ShotBoundary(
index=i,
start_sec=float(i * 3),
end_sec=float((i + 1) * 3),
movement=["static", "push_in", "pan_left", "zoom_in"][i],
intensity=["low", "medium", "low", "high"][i],
transition="hard_cut",
)
for i in range(n)
]
CAMERA_TO_KEN_BURNS_DIRS = {
"zoom_in_slow",
"zoom_out_slow",
"pan_left_slow",
"pan_right_slow",
"zoom_in_medium",
"zoom_out_medium",
"diagonal_push",
"static",
}
def test_rule_based_style_guide_structure():
shots = _sample_shots()
sg = _rule_based_style_guide(shots, bpm=120, vlm={"color_filter": "warm_vintage"})
# 关键字段存在且类型正确
assert sg["shot_count"] == 4
assert sg["pace"] == "fast_cut"
assert sg["bpm"] == 120
assert sg["avg_shot_duration"] == 3.0
assert len(sg["camera_movements"]) == 4
assert sg["color_filter"] == "warm_vintage"
assert "eq" in sg["video_filter_eq_params"]
assert "default" in sg["ken_burns_params"]
assert isinstance(sg["transition_map"], dict)
assert isinstance(sg["ken_burns_direction_hint"], str) and sg["ken_burns_direction_hint"]
# ── build_render_params_for_clip 聚合 ─────────────────────────────────
def test_build_render_params_for_clip_shape():
sg = _rule_based_style_guide(_sample_shots(), bpm=95, vlm={"color_filter": "cool_fresh"})
p0 = build_render_params_for_clip(0, sg, duration_sec=3.0)
assert "ken_burns" in p0
assert "transition" in p0
assert "video_filter" in p0
assert p0["bgm_bpm_hint"] == 95
assert p0["duration_sec"] == 3.0
# clip 1 是 push_in → zoom
p1 = build_render_params_for_clip(1, sg)
assert p1["ken_burns"]["type"] == "zoom"
def test_build_render_params_high_intensity_amplifies():
shots = _sample_shots() # shot 3 = zoom_in/high
sg = _rule_based_style_guide(shots, bpm=120, vlm={})
p3 = build_render_params_for_clip(3, sg)
base = map_camera_to_ken_burns("zoom_in")
assert p3["ken_burns"]["zoom_end"] > base["zoom_end"]
# ── 降级与容错 ────────────────────────────────────────────────────────
def test_analyze_with_nonexistent_file_returns_minimum_guide():
sg = analyze_video_style("/nonexistent/path/fake_video.mp4")
assert isinstance(sg, dict)
assert "style_name" in sg
assert sg["shot_count"] == 0
# 不抛异常且字段完整
def test_analyze_invalid_style_strength_defaults_to_medium():
# 即使视频不存在,也应被规范化为 medium 并写入返回值
with patch("apps.worker.viral_video.video_analyzer._ensure_local_video", return_value=None):
sg = analyze_video_style("fake", style_strength="banana")
assert sg.get("style_strength", "medium") == "medium"
def test_temp_dir_cleaned_up_after_run():
"""用临时真实空文件模拟本地路径,确认 frames 临时目录被清理。"""
with tempfile.TemporaryDirectory() as td:
fake = Path(td) / "fake.mp4"
fake.write_bytes(b"")
# 抽帧会失败(ffmpeg 对空文件失败),但应全程不抛且临时目录 rmtree
# 直接 mock _ensure_local_video 回传不存在的文件,走 _probe_duration=0 降级路径
with patch("apps.worker.viral_video.video_analyzer._ensure_local_video", return_value=None):
sg = analyze_video_style("proto://fake", style_strength="light")
assert "style_name" in sg
def test_ffmpeg_failure_falls_back_to_vlm_only_path():
"""模拟 ffmpeg 抽帧失败,仍能返回 style_guide。"""
with tempfile.TemporaryDirectory() as td:
fake = Path(td) / "ref.mp4"
fake.write_bytes(b"not a real video")
with patch(
"apps.worker.viral_video.video_analyzer._extract_keyframes", side_effect=RuntimeError("ffmpeg exploded")
):
with patch("apps.worker.viral_video.video_analyzer._detect_shots") as mock_shots:
mock_shots.return_value = [ShotBoundary(0, 0.0, 3.0)]
with patch("apps.worker.viral_video.video_analyzer._analyze_movements"):
with patch("apps.worker.viral_video.video_analyzer._detect_bpm", return_value=90):
with patch(
"apps.worker.viral_video.video_analyzer._vlm_analyze_frames",
return_value={"color_filter": "none"},
):
with patch(
"apps.worker.viral_video.video_analyzer._llm_synthesize",
side_effect=lambda shots, bpm, vlm, ss: _rule_based_style_guide(shots, bpm, vlm),
):
sg = analyze_video_style(str(fake))
assert sg["bpm"] == 90
assert sg["shot_count"] == 1
# ── 转场映射完整性 ────────────────────────────────────────────────────
def test_transition_map_covers_observed_types():
for t in ("hard_cut", "cross_dissolve", "fade_black", "zoom_whip"):
assert t in TRANSITION_TO_XFADE
# ── 预设完整性 ────────────────────────────────────────────────────────
def test_color_filter_preset_keys_are_safe_for_ffmpeg():
for name, preset in COLOR_FILTER_PRESETS.items():
if preset == {}:
continue
# eq 所有值都是数字
for k, v in preset.get("eq", {}).items():
assert isinstance(v, (int, float)), f"{name}.eq.{k} not numeric"
for k, v in preset.get("colorchannelmixer", {}).items():
assert isinstance(v, (int, float)), f"{name}.ccm.{k} not numeric"