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新增API

Browse Source 
- /ffmpeg/subtitle-apply
- /ffmpeg/bgm-nosie-reduce
- /ffmpeg/zoom-loop
- /ffmpeg/overlay-gif
This commit is contained in:
shuohigh@gmail.com
2025-05-15 15:20:25 +08:00
parent 0f2b9ddb16
commit 0fca52a271
16 changed files with 904 additions and 271 deletions
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3
.gitignore vendored
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@@ -1 +1,2 @@
*/**/.pypirc
.pypirc
.env

2
.idea/runConfigurations/modal_deploy.xml generated
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@@ -14,7 +14,7 @@
<option name="ADD_SOURCE_ROOTS" value="true" />
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
<option name="SCRIPT_NAME" value="modal" />
<option name="PARAMETERS" value="deploy ./src/cluster/app.py" />
<option name="PARAMETERS" value="deploy -m cluster.app" />
<option name="SHOW_COMMAND_LINE" value="false" />
<option name="EMULATE_TERMINAL" value="false" />
<option name="MODULE_MODE" value="true" />

2
README.md
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@@ -28,5 +28,5 @@ py -m twine upload --config-file=.pypirc --repository=coding-pypi dist/*
## 安装
```bash
uv add bowong-modal-functions --index-url=https://<用户名>:<密钥>@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple
uv pip install bowong-modal-functions --index-url=https://<用户名>:<密钥>@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple
```

9
pyproject.toml
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@@ -1,6 +1,6 @@
[project]
name = "BowongModalFunctions"
version = "0.1.1-pre"
dynamic = ["version"]
authors = [
{ name = "Yudi Xiao", email = "xyd@bowong.ai" },
]
@@ -27,7 +27,6 @@ dependencies = [
"cos-python-sdk-v5>=1.9.36",
"boto3>=1.37.37",
"psutil>=7.0.0",
"twine>=6.1.0",
"scalar-fastapi>=1.0.3",
"modal>=0.76.3",
]
@@ -39,10 +38,16 @@ classifiers = [
license = "MIT"
license-files = ["LICEN[CS]E*"]
[project.optional-dependencies]
cli = ["twine>=6.1.0"]
[build-system]
requires = ["hatchling >= 1.26"]
build-backend = "hatchling.build"
[tool.hatch.version]
path = "src/BowongModalFunctions/__init__.py"
[tool.hatch.build.targets.wheel]
packages = ["src/BowongModalFunctions"]
sources = ["src"]

2
src/BowongModalFunctions/__init__.py
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@@ -1 +1 @@
__version__ = "0.1.1-pre"
__version__ = "0.1.1-pre3"

134
src/BowongModalFunctions/api.py
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@@ -30,10 +30,19 @@ from .models.web_model import (TaskStatus, ErrorCode,
FFMPEGConcatRequest,
FFMPEGExtractAudioRequest,
FFMPEGCornerMirrorRequest,
FFMPEGOverlayGifRequest,
FFMPEGZoomLoopRequest,
FFMPEGSubtitleOverlayRequest,
FFMPEGMixBgmWithNoiseReduceRequest,
FFMPEGSliceTaskStatusResponse,
FFMPEGConcatTaskStatusResponse,
FFMPEGExtractAudioTaskStatusResponse,
FFMPEGCornerMirrorTaskStatusResponse)
FFMPEGCornerMirrorTaskStatusResponse,
FFMPEGOverlayGifTaskStatusResponse,
FFMPEGSubtitleTaskStatusResponse,
FFMPEGZoomLoopTaskStatusResponse,
FFMPEGMixBgmWithNoiseReduceStatusResponse
)
from .config import WorkerConfig
bearer_scheme = HTTPBearer()
@@ -499,7 +508,7 @@ async def corner_mirror(body: FFMPEGCornerMirrorRequest,
@web_app.get("/ffmpeg/corner-mirror/{task_id}",
tags=["查询任务"],
summary="查询镜像小窗去重任务状态",
description="<UNK>",
description="查询镜像小窗去重任务状态",
responses={
status.HTTP_200_OK: {
"description": "",
@@ -514,3 +523,124 @@ async def corner_mirror_status(task_id: str, response: Response) -> FFMPEGCorner
response.headers["x-baggage"] = transaction.x_baggage
return FFMPEGCornerMirrorTaskStatusResponse(taskId=task_id, status=task_status, code=code,
error=reason, result=result)
@web_app.post("/ffmpeg/overlay-gif", tags=["发起任务"], summary="发起叠加gif特效任务",
description="发起叠加gif特效任务", dependencies=[Depends(verify_token)])
async def overlay_gif(body: FFMPEGOverlayGifRequest,
headers: Annotated[SentryTransactionHeader, Header()]) -> ModalTaskResponse:
fn = modal.Function.from_name(config.app_name, "ffmpeg_overlay_gif", environment_name=config.environment)
sentry_trace = None
if headers.x_trace_id and headers.x_baggage:
sentry_trace = SentryTransactionInfo(x_trace_id=headers.x_trace_id, x_baggage=headers.x_baggage)
fn_call = fn.spawn(media=body.media, gif=body.gif, sentry_trace=sentry_trace)
return ModalTaskResponse(success=True, taskId=fn_call.object_id)
@web_app.get("/ffmpeg/overlay-gif/{task_id}", tags=["查询任务"],
summary="查询叠加gif特效任务状态", description="查询叠加gif特效任务状态",
responses={
status.HTTP_200_OK: {
"description": "",
"headers": sentry_header_schema
},
},
dependencies=[Depends(verify_token)])
async def overlay_gif_status(task_id: str, response: Response) -> FFMPEGOverlayGifTaskStatusResponse:
task_status, code, reason, result, transaction = await get_modal_task_status(task_id)
if transaction:
response.headers["x-trace-id"] = transaction.x_trace_id
response.headers["x-baggage"] = transaction.x_baggage
return FFMPEGOverlayGifTaskStatusResponse(taskId=task_id, status=task_status, code=code,
error=reason, result=result)
@web_app.post("/ffmpeg/zoom-loop", tags=["发起任务"], summary="发起放大缩小循环去重任务",
description="发起放大缩小循环去重任务", dependencies=[Depends(verify_token)])
async def zoom_loop(body: FFMPEGZoomLoopRequest,
headers: Annotated[SentryTransactionHeader, Header()]) -> ModalTaskResponse:
fn = modal.Function.from_name(config.app_name, "ffmpeg_zoom_loop", environment_name=config.environment)
sentry_trace = None
if headers.x_trace_id and headers.x_baggage:
sentry_trace = SentryTransactionInfo(x_trace_id=headers.x_trace_id, x_baggage=headers.x_baggage)
fn_call = fn.spawn(media=body.media, duration=body.duration, zoom=body.zoom, sentry_trace=sentry_trace)
return ModalTaskResponse(success=True, taskId=fn_call.object_id)
@web_app.get("/ffmpeg/zoom-loop/{task_id}", tags=["查询任务"],
summary="查询放大缩小循环去重任务状态", description="查询放大缩小循环去重任务状态",
responses={
status.HTTP_200_OK: {
"description": "",
"headers": sentry_header_schema
},
},
dependencies=[Depends(verify_token)])
async def zoom_loop_status(task_id: str, response: Response) -> FFMPEGZoomLoopTaskStatusResponse:
task_status, code, reason, result, transaction = await get_modal_task_status(task_id)
if transaction:
response.headers["x-trace-id"] = transaction.x_trace_id
response.headers["x-baggage"] = transaction.x_baggage
return FFMPEGZoomLoopTaskStatusResponse(taskId=task_id, status=task_status, code=code,
error=reason, result=result)
@web_app.post("/ffmpeg/subtitle-apply", tags=["发起任务"], summary="发起渲染字幕任务",
description="发起渲染字幕任务", dependencies=[Depends(verify_token)])
async def subtitle_apply(body: FFMPEGSubtitleOverlayRequest,
headers: Annotated[SentryTransactionHeader, Header()]) -> ModalTaskResponse:
fn = modal.Function.from_name(config.app_name, "ffmpeg_subtitle_apply", environment_name=config.environment)
sentry_trace = None
if headers.x_trace_id and headers.x_baggage:
sentry_trace = SentryTransactionInfo(x_trace_id=headers.x_trace_id, x_baggage=headers.x_baggage)
fn_call = fn.spawn(media=body.media, subtitle=body.subtitle, sentry_trace=sentry_trace)
return ModalTaskResponse(success=True, taskId=fn_call.object_id)
@web_app.get("/ffmpeg/subtitle-apply/{task_id}", tags=["查询任务"],
summary="查询渲染字幕任务状态", description="查询渲染字幕任务状态",
responses={
status.HTTP_200_OK: {
"description": "",
"headers": sentry_header_schema
},
},
dependencies=[Depends(verify_token)])
async def subtitle_apply_status(task_id: str, response: Response) -> FFMPEGSubtitleTaskStatusResponse:
task_status, code, reason, result, transaction = await get_modal_task_status(task_id)
if transaction:
response.headers["x-trace-id"] = transaction.x_trace_id
response.headers["x-baggage"] = transaction.x_baggage
return FFMPEGSubtitleTaskStatusResponse(taskId=task_id, status=task_status, code=code,
error=reason, result=result)
@web_app.post("/ffmpeg/bgm-nosie-reduce", tags=["发起任务"], summary="发起混合BGM并降噪任务",
description="发起混合BGM并降噪任务", dependencies=[Depends(verify_token)])
async def bgm_nosie_reduce(body: FFMPEGMixBgmWithNoiseReduceRequest,
headers: Annotated[SentryTransactionHeader, Header()]) -> ModalTaskResponse:
fn = modal.Function.from_name(config.app_name, "ffmpeg_bgm_nosie_reduce", environment_name=config.environment)
sentry_trace = None
if headers.x_trace_id and headers.x_baggage:
sentry_trace = SentryTransactionInfo(x_trace_id=headers.x_trace_id, x_baggage=headers.x_baggage)
fn_call = fn.spawn(media=body.media, bgm=body.bgm, video_volume=body.video_volume, music_volume=body.music_volume,
noise_sample=body.noise_sample, sentry_trace=sentry_trace)
return ModalTaskResponse(success=True, taskId=fn_call.object_id)
@web_app.get("/ffmpeg/subtitle-apply/{task_id}", tags=["查询任务"],
summary="查询混合BGM并降噪任务状态", description="查询混合BGM并降噪任务状态",
responses={
status.HTTP_200_OK: {
"description": "",
"headers": sentry_header_schema
},
},
dependencies=[Depends(verify_token)])
async def bgm_nosie_reduce_status(task_id: str, response: Response) -> FFMPEGMixBgmWithNoiseReduceStatusResponse:
task_status, code, reason, result, transaction = await get_modal_task_status(task_id)
if transaction:
response.headers["x-trace-id"] = transaction.x_trace_id
response.headers["x-baggage"] = transaction.x_baggage
return FFMPEGMixBgmWithNoiseReduceStatusResponse(taskId=task_id, status=task_status, code=code,
error=reason, result=result)

6
src/BowongModalFunctions/models/media_model.py
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@@ -1,6 +1,7 @@
import os
from datetime import datetime
from enum import Enum
from functools import cached_property
from typing import List, Union, Optional, Any, Dict
from urllib.parse import urlparse
from pydantic import (BaseModel, Field, field_validator, ValidationError,
@@ -116,6 +117,11 @@ class MediaSource(BaseModel):
case _:
return f"{self.protocol.value}/{self.endpoint}/{self.bucket}/{self.path}"
@computed_field(description="文件后缀名")
@cached_property
def file_extension(self) -> Optional[str]:
return os.path.basename(self.urn).split('.')[-1]
@field_serializer('expired_at')
def serialize_datetime(self, value: Optional[datetime], info: SerializationInfo) -> Optional[str]:
if value:

202
src/BowongModalFunctions/models/web_model.py
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@@ -1,29 +1,12 @@
from enum import Enum
from typing import List, Union, Optional
import pydantic
from pydantic import BaseModel, Field, field_validator, ConfigDict, HttpUrl
from .ffmpeg_worker_model import FFMpegSliceSegment
from .media_model import MediaSource, MediaSources
class SentryTransactionHeader(BaseModel):
x_trace_id: Optional[str] = Field(description="Sentry Transaction ID", default=None)
x_baggage: Optional[str] = Field(description="Sentry Transaction baggage", default=None)
class SentryTransactionInfo(BaseModel):
x_trace_id: str = Field(description="Sentry Transaction ID")
x_baggage: str = Field(description="Sentry Transaction baggage")
class FFMPEGSliceTaskStatusRequest(BaseModel):
taskId: str = Field(description="任务Id")
class ModalTaskResponse(BaseModel):
success: bool = Field(description="任务接受成功")
taskId: str = Field(description="任务Id")
class TaskStatus(str, Enum):
running = "running"
failed = "failed"
@@ -46,6 +29,25 @@ class WebhookMethodEnum(str, Enum):
POST = "post"
class SentryTransactionHeader(BaseModel):
x_trace_id: Optional[str] = Field(description="Sentry Transaction ID", default=None)
x_baggage: Optional[str] = Field(description="Sentry Transaction baggage", default=None)
class SentryTransactionInfo(BaseModel):
x_trace_id: str = Field(description="Sentry Transaction ID")
x_baggage: str = Field(description="Sentry Transaction baggage")
class FFMPEGSliceTaskStatusRequest(BaseModel):
taskId: str = Field(description="任务Id")
class ModalTaskResponse(BaseModel):
success: bool = Field(description="任务接受成功")
taskId: str = Field(description="任务Id")
class WebhookNotify(BaseModel):
endpoint: HttpUrl = Field(description="Webhook回调端点", examples=["https://webhook.example.com"])
method: WebhookMethodEnum = Field(description="Webhook回调请求方法", examples=["get", "post"])
@@ -112,7 +114,7 @@ class FFMPEGExtractAudioTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
class FFMPEGCornerMirrorRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="")
media: MediaSource = Field(description="需要处理的媒体源")
@field_validator('media', mode='before')
@classmethod
@@ -127,3 +129,163 @@ class FFMPEGCornerMirrorRequest(BaseFFMPEGTaskRequest):
class FFMPEGCornerMirrorTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="任务运行结果")
class FFMPEGBgmRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="需要处理的媒体源")
bgm_media: MediaSource = Field(description="添加的BGM媒体源", alias="bgmMedia")
@field_validator('media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
@field_validator('bgm_media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
media = MediaSource.from_str(v)
if not media.file_extension in ['wav', 'mp3']:
raise pydantic.ValidationError("必须使用符合规范的音频格式, 如wav或者mp3")
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
class FFMPEGBgmTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="生成结果的URN")
class FFMPEGZoomLoopRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="需要处理的媒体源")
duration: float = Field(description="放大缩小一个循环的持续时间秒数", default=6.0)
zoom: float = Field(description="放大缩小系数", default=0.1)
@field_validator('media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
class FFMPEGZoomLoopTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="生成结果的URN")
class FFMPEGOverlayGifRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="需要处理的媒体源")
gif: MediaSource = Field(description="叠加的特效gif")
@field_validator('media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
@field_validator('gif', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
media = MediaSource.from_str(v)
if not media.file_extension == 'gif':
raise pydantic.ValidationError("必须使用.gif文件")
return media
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
class FFMPEGOverlayGifTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="生成结果的URN")
class FFMPEGSubtitleOverlayRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="需要处理的媒体源")
subtitle: MediaSource = Field(description="需要叠加的字幕文件")
@field_validator('media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
@field_validator('subtitle', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
media = MediaSource.from_str(v)
if not media.file_extension in ['ass']:
raise pydantic.ValidationError("必须使用标准字幕文件, 如.ass")
return media
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
class FFMPEGSubtitleTaskStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="生成结果的URN")
class FFMPEGMixBgmWithNoiseReduceRequest(BaseFFMPEGTaskRequest):
media: MediaSource = Field(description="需要处理的媒体源")
bgm: MediaSource = Field(description="需要添加的BGM媒体源")
video_volume: float = Field(description="最终输出的视频音量系数", default=1.4)
music_volume: float = Field(description="最终输出的BGM音量系数", default=0.1)
noise_sample: Optional[MediaSource] = Field(description="常考噪音样本，如不指定将使用源视频的前2秒作为样本",
default=None)
@field_validator('media', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
@field_validator('bgm', mode='before')
@classmethod
def parse_inputs(cls, v: Union[str, MediaSource]):
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
@field_validator('noise_sample', mode='before')
@classmethod
def parse_inputs(cls, v: Union[None, str, MediaSource]):
if not v:
return None
if isinstance(v, str):
return MediaSource.from_str(v)
elif isinstance(v, MediaSource):
return v
else:
raise TypeError(v)
class FFMPEGMixBgmWithNoiseReduceStatusResponse(BaseFFMPEGTaskStatusResponse):
result: Optional[str] = Field(default=None, description="生成结果的URN")

9
src/BowongModalFunctions/utils/PathUtils.py
View File

@@ -18,3 +18,12 @@ class FileUtils:
raise IndexError("Depth is out of range")
media_dir_prefix = prefix + '/'.join(media_dirs[depth:])
return media_dir_prefix
@staticmethod
def file_path_change_extension(media_path: str, extension: str) -> str:
media_filename = os.path.basename(media_path)
media_dir = os.path.dirname(media_path) + "/"
filenames = media_filename.split(".")
filenames[-1] = extension
filename = ".".join(filenames)
return os.path.join(media_dir, filename)

346
src/BowongModalFunctions/utils/VideoUtils.py
View File

@@ -1,6 +1,9 @@
from typing import Union, List, Tuple, Optional
import numpy as np
import json, os
import math
from BowongModalFunctions.models.media_model import MediaSource
from .TimeUtils import TimeDelta
from pydantic import BaseModel, ConfigDict, computed_field
from ffmpeg import FFmpeg
from ffmpeg.asyncio import FFmpeg as AsyncFFmpeg
@@ -20,8 +23,7 @@ from pedalboard import (
)
from pedalboard.io import AudioFile
from loguru import logger
from src.cluster.ffmpeg_worker.Utils.PathUtils import FileUtils
from .PathUtils import FileUtils
class MediaStream(BaseModel):
@@ -77,6 +79,14 @@ class VideoUtils:
video_metadata = VideoMetadata.model_validate_json(ffprobe.execute())
return video_metadata.streams[0]
@staticmethod
def ffprobe_audio_duration(media_path: str) -> TimeDelta:
ffprobe_cmd = VideoUtils.ffmpeg_init(use_ffprobe=True)
ffprobe_cmd.input(media_path, print_format="json", show_streams=None)
metadata_json = ffprobe_cmd.execute()
metadata = VideoMetadata.model_validate_json(metadata_json)
return TimeDelta(seconds=metadata.streams[-1].duration)
@staticmethod
async def ffprobe_video_format_async(media_path: str) -> VideoStream:
ffprobe = AsyncFFmpeg(executable="ffprobe").input(
@@ -106,7 +116,8 @@ class VideoUtils:
return video_metadata.streams[0].width, video_metadata.streams[0].height
@staticmethod
def noise_reduce(media_path: str, noise_sample_path: Optional[str] = None) -> str:
def noise_reduce(media_path: str, noise_sample_path: Optional[str] = None,
output_path: Optional[str] = None) -> str:
samplerate = 44100
with AudioFile(media_path).resampled_to(float(samplerate)) as f:
audio = f.read(f.frames)
@@ -119,6 +130,9 @@ class VideoUtils:
noise_sample_length = min(int(2 * samplerate), audio.shape[0])
noise_sample = audio[:noise_sample_length]
if not output_path:
output_path = FileUtils.file_path_extend(media_path, "nr")
reduced_noise = nr.reduce_noise(y=audio, y_noise=noise_sample, sr=samplerate,
stationary=True, prop_decrease=0.75, n_std_thresh_stationary=1.5,
n_fft=2048, win_length=1024, hop_length=512, n_jobs=1)
@@ -171,18 +185,18 @@ class VideoUtils:
if max_peak > 0.85:
additional_safety_factor = 0.85 / max_peak
normalized_audio = normalized_audio * additional_safety_factor
processed_audio_path = FileUtils.file_path_extend(media_path, "nr")
sf.write(
processed_audio_path,
output_path,
normalized_audio,
samplerate,
format="WAV",
subtype="PCM_16",
)
return processed_audio_path
return output_path
@staticmethod
def ffmpeg_init_async(use_ffprobe: bool = False) -> AsyncFFmpeg:
def async_ffmpeg_init(use_ffprobe: bool = False) -> AsyncFFmpeg:
if use_ffprobe:
ffmpeg_cmd = AsyncFFmpeg('ffprobe')
else:
@@ -258,10 +272,81 @@ class VideoUtils:
return ffmpeg_cmd
@staticmethod
async def ffmpeg_extract_audio_async(media_path: str, output_path: str):
import json, os
async def ffmpeg_concat_medias(media_paths: List[str],
target_width: int = 1080,
target_height: int = 1920,
output_path: Optional[str] = None) -> str:
"""
将待处理的视频合并为一个视频
:param media_paths: 待合并的多个视频文件路径
:param target_width: 输出的视频分辨率宽
:param target_height: 输出的视频分辨率高
:param output_path: 指定输出视频路径
:return:
"""
ffprobe_cmd = VideoUtils.ffmpeg_init_async(use_ffprobe=True)
total_videos = len(media_paths)
if total_videos == 0:
raise ValueError("没有可以合并的视频源")
if not output_path:
output_path = FileUtils.file_path_extend(media_paths[0], "concat")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
filter_complex = []
# 2. 统一所有视频的格式、分辨率和帧率
for i in range(total_videos):
filter_complex.extend(
[
# 先缩放到统一分辨率，然后设置帧率和格式
f"[{i}:v]scale={target_width}:{target_height}:force_original_aspect_ratio=decrease,"
f"pad={target_width}:{target_height}:(ow-iw)/2:(oh-ih)/2,"
f"setsar=1:1," # 新增强制设置SAR
f"fps=30,format=yuv420p[v{i}]",
f"[{i}:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[a{i}]",
]
)
# 3. 准备处理后的视频流和音频流的连接字符串
video_streams = "".join(f"[v{i}]" for i in range(total_videos))
audio_streams = "".join(f"[a{i}]" for i in range(total_videos))
# 4. 使用concat过滤器合并视频和音频
filter_complex.extend(
[
f"{video_streams}concat=n={total_videos}:v=1:a=0[vconcated]",
f"{audio_streams}concat=n={total_videos}:v=0:a=1[aconcated]",
]
)
ffmpeg_cmd.output(
output_path,
{
"filter_complex": ";".join(filter_complex),
"map": ["[vconcated]", "[aconcated]"],
"vcodec": "libx264",
"crf": 16,
"r": 30,
"acodec": "aac",
"ar": 44100,
"ac": 2,
"ab": "192k",
},
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_extract_audio_async(media_path: str, output_path: Optional[str] = None):
"""
提取源视频的音频
:param media_path: 待处理的源视频
:param output_path: 指定输出的音频文件路径（可选）
:return: 最终输出音频文件路径
"""
if not output_path:
output_path = FileUtils.file_path_change_extension(output_path, 'wav')
os.makedirs(os.path.dirname(output_path), exist_ok=True)
ffprobe_cmd = VideoUtils.async_ffmpeg_init(use_ffprobe=True)
ffprobe_cmd.input(media_path,
v="quiet",
print_format="json",
@@ -274,10 +359,239 @@ class VideoUtils:
raise RuntimeError(f"Media has no audio streams.")
# output_path = f"{output_path_prefix}/extract_audio/outputs/{fn_id}/output.wav"
os.makedirs(os.path.dirname(output_path), exist_ok=True)
ffmpeg_cmd = VideoUtils.ffmpeg_init_async()
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(media_path).output(output_path,
map="0:a",
acodec="pcm_s16le",
ar=44100,
ac=1)
map="0:a",
acodec="pcm_s16le",
ar=44100,
ac=1)
await ffmpeg_cmd.execute()
@staticmethod
async def ffmpeg_mix_bgm(origin_audio_path: str, bgm_audio_path: str, video_volume: float = 1.4,
music_volume: float = 0.1, output_path: Optional[str] = None) -> str:
"""
给待处理视频混合BGM
:param origin_audio_path: 待处理的源视频
:param bgm_audio_path: 需要混合的BGM
:param video_volume: 最终输出视频的音量系数
:param music_volume: BGM在源视频音量内占比的音量系数
:param output_path: 指定最终输出的视频路径（可选）
:return: 最终输出视频文件路径
"""
if not output_path:
output_path = FileUtils.file_path_extend(origin_audio_path, "bgm")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
origin_audio_duration = VideoUtils.ffprobe_audio_duration(origin_audio_path)
bgm_duration = VideoUtils.ffprobe_audio_duration(bgm_audio_path)
loops_needed = math.ceil(origin_audio_duration.total_seconds() / bgm_duration.total_seconds())
ffmpeg_cmd = VideoUtils.async_ffmpeg_init(use_ffprobe=True)
ffmpeg_cmd.input(origin_audio_path)
ffmpeg_cmd.input(bgm_audio_path)
filter_complex = [
f"[0:a]volume={video_volume}[a1]",
f"[1:a]aloop=loop={loops_needed}:size={bgm_duration.total_seconds()},volume={music_volume}[a2]"
"[a1][a2]amix=inputs=2:duration=first[audio]"
]
ffmpeg_cmd.output(output_path,
options={"filter_complex": ";".join(filter_complex), },
map="[audio]",
acodec='libmp3lame', # 音频编码器
ar=48000, # 音频采样率
ab='192k', # 音频码率
ac=2, # 音频通道数
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_mix_bgm_with_noise_reduce(media_path: str, bgm_audio_path: str,
video_volume: float = 1.4,
music_volume: float = 0.1,
noise_sample_path: Optional[str] = None,
output_path: Optional[str] = None) -> str:
"""
先对待处理的视频音轨降噪，再将降噪后的结果添加BGM，最终输出降噪过且混合BGM的视频；
由于最终视频画面和音轨是同步混合+合成视频，所以处理速度会比分步降噪, 加BGM快；
:param media_path: 待处理的原始视频路径
:param bgm_audio_path: 待处理的BGM音频路径
:param video_volume: 最终输出的视频音量系数
:param music_volume: 最终输出的BGM音量系数
:param noise_sample_path: 降噪使用的噪音样本，如不指定将使用源视频的前2秒作为样本（可选）
:param output_path: 指定输出视频的路径（可选）
:return: 最终输出视频的路径
"""
if not output_path:
output_path = FileUtils.file_path_extend(media_path, "bgm_nr")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
nr_audio_path = VideoUtils.noise_reduce(media_path=media_path, noise_sample_path=noise_sample_path)
video_metadata = VideoUtils.ffprobe_video_format(media_path)
origin_audio_duration = VideoUtils.ffprobe_audio_duration(nr_audio_path)
bgm_duration = VideoUtils.ffprobe_audio_duration(bgm_audio_path)
loops_needed = math.ceil(origin_audio_duration.total_seconds() / bgm_duration.total_seconds())
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(media_path)
ffmpeg_cmd.input(nr_audio_path)
ffmpeg_cmd.input(bgm_audio_path)
filter_complex = [
f"[1:a]volume={video_volume}[a1]",
f"[2:a]aloop=loop={loops_needed}:size={bgm_duration.total_seconds()},volume={music_volume}[a2]"
"[a1][a2]amix=inputs=2:duration=first[audio]"
]
ffmpeg_cmd.output(output_path,
options={"filter_complex": ";".join(filter_complex), },
map=["0:v", "[audio]"],
crf=16,
vcodec='libx264',
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate, # 帧率
acodec='libmp3lame',
ar=48000, ab='192k', ac=2,
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_overlay_gif(media_path: str, overlay_gif_path: str, output_path: Optional[str] = None) -> str:
"""
将GIF特效叠加到视频上，如果视频较长则循环播放GIF
:param media_path: 输入视频路径
:param overlay_gif_path: GIF特效文件路径
:param output_path: 指定输出路径
:return: 输出视频路径
"""
if not output_path:
output_path = FileUtils.file_path_extend(media_path, "overlay")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
video_metadata = VideoUtils.ffprobe_video_format(media_path)
filter_complex = [
# 确保GIF正确解码并循环
"[1:v]fps=30,format=rgba[gif]", # 强制设置30fps
# 叠加GIF到视频上，保持透明通道
"[0:v][gif]overlay=shortest=1:format=auto,setpts=PTS-STARTPTS[v]",
]
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(media_path)
ffmpeg_cmd.input(overlay_gif_path)
ffmpeg_cmd.output(output_path,
options={"filter_complex": ";".join(filter_complex), },
map=["[v]", "0:a"],
crf=16,
vcodec='libx264',
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate, # 帧率
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_zoom_loop(media_path: str, duration: float = 6.0, zoom: float = 0.1,
output_path: Optional[str] = None) -> str:
"""
视频放大缩小循环特效
:param media_path: 待处理的视频文件路径
:param duration: 视频特效循环时间长度
:param zoom: 视频特效放大缩小系数
:param output_path: 指定输出视频地址（可选）
:return: 最终输出视频地址
"""
if not output_path:
output_path = FileUtils.file_path_extend(media_path, 'zoomed')
os.makedirs(os.path.dirname(output_path), exist_ok=True)
video_metadata = VideoUtils.ffprobe_video_format(media_path)
# abs(sin())表达式会导致实际的往复频率为2倍
duration = duration * 2
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(media_path).output(output_path,
options={
"vf": f"scale={4 * video_metadata.width}x{4 * video_metadata.height},fps=30,"
f"zoompan=z='1+{zoom}*abs(sin(2*PI*time/{duration}))':"
"x='trunc(iw/2*(1-1/zoom))':"
"y='trunc(ih/2*(1-1/zoom))':"
f"d=1:s={video_metadata.width}x{video_metadata.height}:fps=30"
},
vcodec="libx264",
acodec="copy",
crf=16,
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate, # 帧率
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_corner_mirror(media_path: str, mirror_scale_down_size: int = 6,
mirror_from_right: bool = True, mirror_position: tuple[float, float] = (40, 40),
output_path: Optional[str] = None) -> str:
"""
对源视频添加镜像小窗特效
:param media_path: 待处理的源视频
:param mirror_scale_down_size: 源视频画面缩放系数
:param mirror_from_right: 小窗原点是否使用右下角
:param mirror_position: 小窗基于原点坐标轴的偏移量
:param output_path: 指定的输出视频路径（可选）
:return: 返回最终输出视频的路径
"""
if not output_path:
output_path = FileUtils.file_path_extend(media_path, 'mir')
os.makedirs(os.path.dirname(output_path), exist_ok=True)
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
mirror_x = (
f"main_w-overlay_w-{mirror_position[0]}"
if mirror_from_right
else str(mirror_position[0])
)
filter_complex = [
"[0:v]split[original][mirror]",
f"[mirror]hflip,scale=iw/{mirror_scale_down_size}:-1,format=rgba[flipped]",
"[flipped]split[fm1][fm2]",
f"[fm2]format=gray,geq=lum='255*(1-pow(min(1,2*sqrt(pow(X/W-0.5,2)+pow(Y/H-0.5,2))),1.5))':a='if(lt(pow(X/W-0.5,2)+pow(Y/H-0.5,2),0.15),(1-pow(2*sqrt(pow(X/W-0.5,2)+pow(Y/H-0.5,2)),1.5))*255,0)'[fm2Blur]",
"[fm1][fm2Blur]alphamerge[flipped_blured]",
f"[original][flipped_blured]overlay=x={mirror_x}:y=main_h-overlay_h-{mirror_position[1]}[video]",
]
video_metadata = VideoUtils.ffprobe_video_format(media_path)
ffmpeg_cmd.input(media_path).output(output_path,
options={"filter_complex": ";".join(filter_complex)},
map=["[video]", "0:a"],
vcodec="libx264",
crf=16,
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate # 帧率
)
await ffmpeg_cmd.execute()
return output_path
@staticmethod
async def ffmpeg_subtitle_apply(media_path: str, subtitle_path: str,
font_dir: str, output_path: Optional[str] = None) -> str:
"""
给视频画面叠加字幕，需要确保字幕文件为ass字幕，并且subtitle文件内设置的字体存在与font_dir文件夹内
:param media_path: 待处理的源视频
:param subtitle_path: ass字幕文件路径
:param font_dir: 字体文件目录路径
:param output_path: 指定输出文件路径（可选）
:return: 返回最终输出视频路径
"""
if not output_path:
output_path = FileUtils.file_path_extend(media_path, 'sub')
video_metadata = VideoUtils.ffprobe_video_format(media_path)
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(media_path)
ffmpeg_cmd.output(output_path,
vf=f"subtitles=filename={subtitle_path}:fontsdir={font_dir}",
vcodec="libx264",
crf=16,
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate, # 帧率
acodec="copy",
)
await ffmpeg_cmd.execute()
return output_path

10
src/cluster/app.py
View File

@@ -1,8 +1,8 @@
import modal
from config import WorkerConfig
from video import media_app
from web import fastapi_app
from ffmpeg_app import ffmpeg_app
from BowongModalFunctions.config import WorkerConfig
from .video import app as media_app
from .web import app as web_app
from .ffmpeg_app import app as ffmpeg_app
config = WorkerConfig(
app_name="bowong-ai-video-test",
@@ -19,6 +19,6 @@ app = modal.App('bowong-modal-ai-video-preview',
secrets=[modal.Secret.from_name("cf-kv-secret",
environment_name=config.environment)])
app.include(fastapi_app)
app.include(media_app)
app.include(ffmpeg_app)
app.include(web_app)

19
src/cluster/config.py
View File

@@ -1,19 +0,0 @@
from typing import Optional, Any
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class WorkerConfig(BaseSettings):
app_name: str = Field(default='bowong-ai-video', description="Modal App集群名称")
video_downloader_concurrency: Optional[int] = Field(default=10, description="处理缓存任务的并行数")
ffmpeg_worker_concurrency: Optional[int] = Field(default=10, description="处理视频合成任务的并行数")
modal_kv_name: Optional[str] = Field(default='media-cache', description="Modal视频缓存KV库")
s3_region: Optional[str] = Field(default='ap-northeast-2', description="S3挂载桶的地域")
s3_bucket_name: Optional[str] = Field(default='modal-media-cache', description="集群挂载的S3存储桶")
s3_mount_dir: str = Field(default='/mntS3', description="集群S3存储桶挂载在本地的根目录")
cdn_endpoint: Optional[str] = Field(default="https://d2nj71io21vkj2.cloudfront.net",
description="集群挂载S3存储桶的对应AWS Cloudfront CDN")
environment: Optional[str] = Field(default="dev", description="Modal worker运行环境")
modal_config: Any = SettingsConfigDict()

7
src/cluster/deploy.py
View File

@@ -1,7 +0,0 @@
import modal.cli.run
from .config import config
if __name__ == '__main__':
# modal.cli.run.deploy(app_ref='src.cluster.app', env=config.environment, use_module_mode=True)
# todo : 通过python命令部署不成功，换https://modal.com/docs/guide/continuous-deployment推荐的方式通过命令行部署
modal.cli.run.deploy(app_ref='src\\cluster\\app.py', env=config.environment, use_module_mode=False)

345
src/cluster/ffmpeg_app.py
View File

@@ -3,13 +3,15 @@ import modal
ffmpeg_worker_image = (
modal.Image.debian_slim(python_version="3.11")
.apt_install('ffmpeg')
.pip_install(
'BowongModalFunctions==0.1.1-pre',
extra_index_url="https://packages-1747117713372:a8865382010036f1e8ad5ca8d0cdab520b78a8ff@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple",
)
.pip_install_from_pyproject("pyproject.toml")
.add_local_python_source('cluster')
.add_local_python_source('BowongModalFunctions')
)
ffmpeg_app = modal.App(image=ffmpeg_worker_image, include_source=False)
app = modal.App(
name="ffmpeg_app",
image=ffmpeg_worker_image,
include_source=False)
with ffmpeg_worker_image.imports():
import shutil, psutil, os, backoff, json, sentry_sdk
@@ -109,7 +111,7 @@ with ffmpeg_worker_image.imports():
return s3_outputs
@ffmpeg_app.function(
@app.function(
timeout=1800,
cloud="aws",
# region='ap-northeast',
@@ -128,94 +130,25 @@ with ffmpeg_worker_image.imports():
fn_id = current_function_call_id()
@sentry_sdk.trace
async def print_media_streams_info(media_paths: List[str]) -> List[Dict[str, Any]]:
"""
打印多个媒体文件的视频和音频流详细信息
:param media_paths: 媒体文件路径列表
"""
res = []
for path in media_paths:
try:
# 使用ffprobe探测媒体文件信息
probe_info_byte = await FFmpeg(executable="ffprobe").input(path, print_format="json",
show_streams=None).execute()
probe_info = json.loads(probe_info_byte)
res.append(probe_info)
except Exception as e:
logger.exception(e)
continue
return res
@SentryUtils.sentry_tracker(name="视频合并任务", op="ffmpeg.concat", fn_id=fn_id,
sentry_trace_id=sentry_trace.x_trace_id if sentry_trace else None,
sentry_baggage=sentry_trace.x_baggage if sentry_trace else None)
async def ffmpeg_process(media_sources: MediaSources, output_filepath: str) -> str:
ffmpeg_cmd = VideoUtils.ffmpeg_init_async()
input_videos = []
for media_source in media_sources.inputs:
cache_filepath = f"{s3_mount}/{media_source.cache_filepath}"
logger.info(cache_filepath)
ffmpeg_cmd.input(cache_filepath)
input_videos.append(cache_filepath)
target_width = 1080
target_height = 1920
# video_info = await print_media_streams_info(media_paths=input_videos)
filter_complex = []
total_videos = len(input_videos)
# 2. 统一所有视频的格式、分辨率和帧率
for i in range(total_videos):
filter_complex.extend(
[
# 先缩放到统一分辨率，然后设置帧率和格式
f"[{i}:v]scale={target_width}:{target_height}:force_original_aspect_ratio=decrease,"
f"pad={target_width}:{target_height}:(ow-iw)/2:(oh-ih)/2,"
f"setsar=1:1," # 新增强制设置SAR
f"fps=30,format=yuv420p[v{i}]",
f"[{i}:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo[a{i}]",
]
)
# 3. 准备处理后的视频流和音频流的连接字符串
video_streams = "".join(f"[v{i}]" for i in range(total_videos))
audio_streams = "".join(f"[a{i}]" for i in range(total_videos))
# 4. 使用concat过滤器合并视频和音频
filter_complex.extend(
[
f"{video_streams}concat=n={total_videos}:v=1:a=0[vconcated]",
f"{audio_streams}concat=n={total_videos}:v=0:a=1[aconcated]",
]
)
ffmpeg_cmd.output(
output_filepath,
{
"filter_complex": ";".join(filter_complex),
"map": ["[vconcated]", "[aconcated]"],
"vcodec": "libx264",
"crf": 16,
"r": 30,
"acodec": "aac",
"ar": 44100,
"ac": 2,
"ab": "192k",
},
)
await ffmpeg_cmd.execute()
s3_outputs = local_copy_to_s3([output_filepath])
return s3_outputs[0]
input_videos = [f"{s3_mount}/{media_source.cache_filepath}" for media_source in media_sources.inputs]
local_output_path = await VideoUtils.ffmpeg_concat_medias(media_paths=input_videos,
output_path=output_filepath)
return local_output_path
output_path = f"{output_path_prefix}/concat/outputs/{fn_id}/output.mp4"
os.makedirs(os.path.dirname(output_path), exist_ok=True)
s3_output = await ffmpeg_process(medias, output_filepath=output_path)
return s3_output, sentry_trace
local_output = await ffmpeg_process(media_sources=medias, output_filepath=output_path)
s3_outputs = local_copy_to_s3([local_output])
if not sentry_trace:
sentry_trace = SentryTransactionInfo(x_trace_id=sentry_sdk.get_traceparent(),
x_baggage=sentry_sdk.get_baggage())
return s3_outputs[0], sentry_trace
@ffmpeg_app.function(
@app.function(
timeout=900,
cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
@@ -243,7 +176,7 @@ with ffmpeg_worker_image.imports():
cache_filepath = f"{s3_mount}/{media_source.cache_filepath}"
logger.info(cache_filepath)
outputs: List[str] = []
ffmpeg_cmd = VideoUtils.ffmpeg_init_async()
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(cache_filepath)
filter_complex: List[str] = []
for index, marker in enumerate(media_markers):
@@ -287,7 +220,7 @@ with ffmpeg_worker_image.imports():
hls_m3u8_url = media_source.path
logger.info(hls_m3u8_url)
outputs: List[str] = []
ffmpeg_cmd = VideoUtils.ffmpeg_init_async()
ffmpeg_cmd = VideoUtils.async_ffmpeg_init()
ffmpeg_cmd.input(hls_m3u8_url,
protocol_whitelist="file,http,https,tcp,tls",
reconnect="1", # 自动重连
@@ -338,18 +271,16 @@ with ffmpeg_worker_image.imports():
return outputs, sentry_trace
@ffmpeg_app.function(
timeout=600,
cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
},
)
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
},
)
@modal.concurrent(max_inputs=1)
async def ffmpeg_extract_audio(media_source: MediaSource,
webhook: Optional[str], # todo handle webhook callback
@@ -376,21 +307,15 @@ with ffmpeg_worker_image.imports():
return output, sentry_trace
async def ffmpeg_overlay_gif(media: MediaSource, gif: MediaSource):
raise NotImplementedError
@ffmpeg_app.function(
timeout=600,
cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@modal.concurrent(max_inputs=1)
async def ffmpeg_corner_mirror(media: MediaSource,
mirror_scale_down_size: int = 6,
@@ -406,34 +331,14 @@ with ffmpeg_worker_image.imports():
mirror_from_right: bool = True,
mirror_position: tuple[float, float] = (40, 40)) -> str:
media_filepath = f"{s3_mount}/{media.cache_filepath}"
video_metadata = VideoUtils.ffprobe_video_format(media_filepath)
mirror_x = (
f"main_w-overlay_w-{mirror_position[0]}"
if mirror_from_right
else str(mirror_position[0])
)
filter_complex = [
"[0:v]split[original][mirror]",
f"[mirror]hflip,scale=iw/{mirror_scale_down_size}:-1,format=rgba[flipped]",
"[flipped]split[fm1][fm2]",
f"[fm2]format=gray,geq=lum='255*(1-pow(min(1,2*sqrt(pow(X/W-0.5,2)+pow(Y/H-0.5,2))),1.5))':a='if(lt(pow(X/W-0.5,2)+pow(Y/H-0.5,2),0.15),(1-pow(2*sqrt(pow(X/W-0.5,2)+pow(Y/H-0.5,2)),1.5))*255,0)'[fm2Blur]",
"[fm1][fm2Blur]alphamerge[flipped_blured]",
f"[original][flipped_blured]overlay=x={mirror_x}:y=main_h-overlay_h-{mirror_position[1]}[video]",
]
output_path = f"{output_path_prefix}/corner_mirror/outputs/{func_id}/output.mp4"
os.makedirs(os.path.dirname(output_path), exist_ok=True)
ffmpeg_cmd = VideoUtils.ffmpeg_init_async()
ffmpeg_cmd.input(media_filepath).output(output_path,
options={"filter_complex": ";".join(filter_complex)},
map=["[video]", "0:a"],
vcodec="libx264",
crf=16,
b=video_metadata.video_bitrate, # 视频码率
r=video_metadata.video_frame_rate # 帧率
)
await ffmpeg_cmd.execute()
s3_outputs = local_copy_to_s3([output_path])
local_output_filepath = await VideoUtils.ffmpeg_corner_mirror(media_path=media_filepath,
output_path=output_path,
mirror_from_right=mirror_from_right,
mirror_position=mirror_position,
mirror_scale_down_size=mirror_scale_down_size)
s3_outputs = local_copy_to_s3([local_output_filepath])
return s3_outputs[0]
result = await ffmpeg_process(media=media, mirror_scale_down_size=mirror_scale_down_size, func_id=fn_id,
@@ -444,23 +349,149 @@ with ffmpeg_worker_image.imports():
return result, sentry_trace
async def ffmpeg_zoom_loop(media: MediaSource, duration: int = 6, zoom: float = 0.1):
raise NotImplementedError
async def ffmpeg_bgm_nosie_reduce(media: MediaSource, bgm: MediaSource, noise_sample: Optional[MediaSource] = None):
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@modal.concurrent(max_inputs=1)
async def ffmpeg_overlay_gif(media: MediaSource, gif: MediaSource,
sentry_trace: Optional[SentryTransactionInfo] = None):
fn_id = current_function_call_id()
async def ffmpeg_process(video: MediaSource, bgm: MediaSource, noise_sample: Optional[MediaSource] = None):
local_input_filepath = f"{s3_mount}/{media.cache_filepath}"
audio_filepath = FileUtils.file_path_extend(local_input_filepath, "audio").replace('mp4', 'wav')
output_audio_filepath = f"{s3_mount}/{audio_filepath}"
await VideoUtils.ffmpeg_extract_audio_async(local_input_filepath, output_audio_filepath)
# VideoUtils.noise_reduce()
raise NotImplementedError
@SentryUtils.sentry_tracker(name="视频叠加GIF特效", op="ffmpeg.overlay", fn_id=fn_id,
sentry_trace_id=sentry_trace.x_trace_id if sentry_trace else None,
sentry_baggage=sentry_trace.x_baggage if sentry_trace else None)
async def ffmpeg_process(media: MediaSource, func_id: str, gif: MediaSource) -> str:
media_filepath = f"{s3_mount}/{media.cache_filepath}"
gif_filepath = f"{s3_mount}/{gif.cache_filepath}"
output_path = f"{output_path_prefix}/overlay/outputs/{func_id}/output.mp4"
local_output_filepath = await VideoUtils.ffmpeg_overlay_gif(media_path=media_filepath,
output_path=output_path,
overlay_gif_path=gif_filepath)
s3_outputs = local_copy_to_s3([local_output_filepath])
return s3_outputs[0]
raise NotImplementedError
result = await ffmpeg_process(media=media, func_id=fn_id, gif=gif)
if not sentry_trace:
sentry_trace = SentryTransactionInfo(x_trace_id=sentry_sdk.get_traceparent(),
x_baggage=sentry_sdk.get_baggage())
return result, sentry_trace
async def ffmpeg_subtitle_apply(media: MediaSource, subtitle: MediaSource):
raise NotImplementedError
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@modal.concurrent(max_inputs=1)
async def ffmpeg_zoom_loop(media: MediaSource, duration: int = 6, zoom: float = 0.1,
sentry_trace: Optional[SentryTransactionInfo] = None):
fn_id = current_function_call_id()
@SentryUtils.sentry_tracker(name="视频放大缩小去重", op="ffmpeg.zoom.loop", fn_id=fn_id,
sentry_trace_id=sentry_trace.x_trace_id if sentry_trace else None,
sentry_baggage=sentry_trace.x_baggage if sentry_trace else None)
async def ffmpeg_process(media: MediaSource, func_id: str, duration: int = 6, zoom: float = 0.1) -> str:
media_filepath = f"{s3_mount}/{media.cache_filepath}"
output_path = f"{output_path_prefix}/zoom_loop/outputs/{func_id}/output.mp4"
local_output_filepath = await VideoUtils.ffmpeg_zoom_loop(media_path=media_filepath,
output_path=output_path,
duration=duration,
zoom=zoom)
s3_outputs = local_copy_to_s3([local_output_filepath])
return s3_outputs[0]
result = await ffmpeg_process(media=media, duration=duration, zoom=zoom, func_id=fn_id)
if not sentry_trace:
sentry_trace = SentryTransactionInfo(x_trace_id=sentry_sdk.get_traceparent(),
x_baggage=sentry_sdk.get_baggage())
return result, sentry_trace
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@modal.concurrent(max_inputs=1)
async def ffmpeg_bgm_nosie_reduce(media: MediaSource, bgm: MediaSource, noise_sample: Optional[MediaSource] = None,
video_volume: float = 1.4, music_volume: float = 0.1,
sentry_trace: Optional[SentryTransactionInfo] = None):
fn_id = current_function_call_id()
@SentryUtils.sentry_tracker(name="视频混合BGM降噪", op="ffmpeg.bgm.nosie.reduce", fn_id=fn_id,
sentry_trace_id=sentry_trace.x_trace_id if sentry_trace else None,
sentry_baggage=sentry_trace.x_baggage if sentry_trace else None)
async def ffmpeg_process(video: MediaSource, bgm: MediaSource, func_id: str,
noise_sample: Optional[MediaSource] = None) -> str:
local_input_filepath = f"{s3_mount}/{video.cache_filepath}"
bgm_filepath = f"{s3_mount}/{bgm.cache_filepath}"
noise_sample_path = f"{s3_mount}/{noise_sample.cache_filepath}" if noise_sample else None
output_path = f"{output_path_prefix}/bgm_nosie_reduce/outputs/{func_id}/output.mp4"
local_output_filepath = await VideoUtils.ffmpeg_mix_bgm_with_noise_reduce(media_path=local_input_filepath,
bgm_audio_path=bgm_filepath,
video_volume=video_volume,
music_volume=music_volume,
noise_sample_path=noise_sample_path,
output_path=output_path)
s3_outputs = local_copy_to_s3([local_output_filepath])
return s3_outputs
result = await ffmpeg_process(video=media, bgm=bgm, video_volume=video_volume,
music_volume=music_volume, noise_sample=noise_sample)
if not sentry_trace:
sentry_trace = SentryTransactionInfo(x_trace_id=sentry_sdk.get_traceparent(),
x_baggage=sentry_sdk.get_baggage())
return result, sentry_trace
@app.function(timeout=600, cloud="aws",
max_containers=config.ffmpeg_worker_concurrency,
volumes={
f"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret",
environment_name=config.environment),
),
}, )
@modal.concurrent(max_inputs=1)
async def ffmpeg_subtitle_apply(media: MediaSource, subtitle: MediaSource,
sentry_trace: Optional[SentryTransactionInfo] = None):
fn_id = current_function_call_id()
@SentryUtils.sentry_tracker(name="视频叠加字幕", op="ffmpeg.subtitle.apply", fn_id=fn_id,
sentry_trace_id=sentry_trace.x_trace_id if sentry_trace else None,
sentry_baggage=sentry_trace.x_baggage if sentry_trace else None)
async def ffmpeg_process(video: MediaSource, subtitle: MediaSource, func_id: str, font_dir: str) -> str:
media_path = f"{s3_mount}/{video.cache_filepath}"
subtitle_path = f"{s3_mount}/{subtitle.cache_filepath}"
output_path = f"{output_path_prefix}/subtitle_apply/{func_id}/output.mp4"
local_output = await VideoUtils.ffmpeg_subtitle_apply(media_path=media_path, subtitle_path=subtitle_path,
font_dir=font_dir, output_path=output_path)
s3_outputs = local_copy_to_s3([local_output])
return s3_outputs[0]
# todo : check if subtitle use fonts exist in fonts dir
font_dir = f"{s3_mount}/fonts"
result = await ffmpeg_process(video=media, subtitle=subtitle, func_id=fn_id, font_dir=font_dir)
if not sentry_trace:
sentry_trace = SentryTransactionInfo(x_trace_id=sentry_sdk.get_traceparent(),
x_baggage=sentry_sdk.get_baggage())
return result, sentry_trace

57
src/cluster/video.py
View File

@@ -3,17 +3,18 @@ import modal
downloader_image = (
modal.Image
.debian_slim(python_version="3.11")
.pip_install(
'BowongModalFunctions==0.1.1-pre',
extra_index_url="https://packages-1747117713372:a8865382010036f1e8ad5ca8d0cdab520b78a8ff@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple",
)
.pip_install_from_pyproject("pyproject.toml")
.add_local_python_source('cluster')
.add_local_python_source('BowongModalFunctions')
)
media_app = modal.App(image=downloader_image,
include_source=False,
secrets=[
modal.Secret.from_name("cf-kv-secret", environment_name='dev'),
])
app = modal.App(
name="media_app",
image=downloader_image,
include_source=False,
secrets=[
modal.Secret.from_name("cf-kv-secret", environment_name='dev'),
])
with downloader_image.imports():
import os, httpx, crcmod
@@ -110,17 +111,17 @@ with downloader_image.imports():
raise e
@media_app.function(cpu=1, timeout=1800,
cloud="aws",
# region='ap-northeast',
max_containers=config.video_downloader_concurrency,
volumes={
"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret", environment_name=config.environment),
),
},
secrets=[modal.Secret.from_name("tencent-cloud-secret", environment_name=config.environment)])
@app.function(cpu=1, timeout=1800,
cloud="aws",
# region='ap-northeast',
max_containers=config.video_downloader_concurrency,
volumes={
"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret", environment_name=config.environment),
),
},
secrets=[modal.Secret.from_name("tencent-cloud-secret", environment_name=config.environment)])
@modal.concurrent(max_inputs=10)
async def cache_submit(media: MediaSource, sentry_trace: SentryTransactionInfo) -> MediaSource:
def vod_init():
@@ -305,14 +306,14 @@ with downloader_image.imports():
return media
@media_app.function(cpu=1, timeout=300,
max_containers=config.video_downloader_concurrency,
volumes={
"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret", environment_name=config.environment),
),
})
@app.function(cpu=1, timeout=300,
max_containers=config.video_downloader_concurrency,
volumes={
"/mntS3": modal.CloudBucketMount(
bucket_name=config.s3_bucket_name,
secret=modal.Secret.from_name("aws-s3-secret", environment_name=config.environment),
),
})
@modal.concurrent(max_inputs=10)
async def cache_delete(cache: MediaSource) -> MediaSource:
if os.path.exists(cache.cache_filepath):

22
src/cluster/web.py
View File

@@ -3,24 +3,24 @@ import modal
fastapi_image = (
modal.Image
.debian_slim(python_version="3.11")
# .pip_install("fastapi[standard]", "sentry-sdk[fastapi]",
# 'loguru', 'pydantic', 'pydantic_settings', 'scalar-fastapi', 'psutil')
.pip_install(
'BowongModalFunctions==0.1.1-pre',
extra_index_url="https://packages-1747117713372:a8865382010036f1e8ad5ca8d0cdab520b78a8ff@g-ldyi2063-pypi.pkg.coding.net/dev/packages/simple",
)
.pip_install_from_pyproject("pyproject.toml")
.add_local_python_source('cluster')
.add_local_python_source('BowongModalFunctions')
)
fastapi_app = modal.App(image=fastapi_image, include_source=False)
app = modal.App(
name="web_app",
image=fastapi_image,
include_source=False)
with fastapi_image.imports():
from BowongModalFunctions.api import web_app
@fastapi_app.function(scaledown_window=60,
secrets=[
modal.Secret.from_name("cf-kv-secret", environment_name='dev'),
])
@app.function(scaledown_window=60,
secrets=[
modal.Secret.from_name("cf-kv-secret", environment_name='dev'),
])
@modal.concurrent(max_inputs=100)
# @modal.asgi_app(custom_domains=["modal-dev.bowong.cc"])
@modal.asgi_app()