V 3.1.7
1. 移除软件包自带的本地 whisper（需单独安装） 2. 重构版本底层依赖，移除外部依赖 3. 修复 首页 暗黑模式不兼容的问题 4. 修复 SD 合并提示词报错
This commit is contained in:
parent
efa8d3b2a2
commit
f4d042f699
10
.gitignore
vendored
10
.gitignore
vendored
@ -30,3 +30,13 @@ resources/config*
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*.log*
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resources/scripts/db/book.realm.lock
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resources/scripts/db/software.realm.lock
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resources/scripts/localWhisper/__pycache__/*
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resources/scripts/laitool/.venv
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resources/scripts/laitool/build
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resources/scripts/laitool/dist
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resources/scripts/localWhisper/build/*
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resources/scripts/__pycache__/*
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resources/scripts/db/*
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resources/scripts/localWhisper/.venv/*
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resources/scripts/localWhisper/_internal/*
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resources/scripts/localWhisper/local_whisper.exe
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4
package-lock.json
generated
4
package-lock.json
generated
@ -1,12 +1,12 @@
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{
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"name": "laitool",
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"version": "3.1.6",
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"version": "3.1.7",
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"lockfileVersion": 3,
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"requires": true,
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"packages": {
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"": {
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"name": "laitool",
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"version": "3.1.6",
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"version": "3.1.7",
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"hasInstallScript": true,
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"dependencies": {
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"@alicloud/alimt20181012": "^1.2.0",
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@ -1,6 +1,6 @@
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{
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"name": "laitool",
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"version": "3.1.6",
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"version": "3.1.7",
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"description": "An AI tool for image processing, video processing, and other functions.",
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"main": "./out/main/index.js",
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"author": "laitool.cn",
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@ -84,6 +84,7 @@
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"resources/image/style/**",
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"resources/image/zhanwei.png",
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"resources/scripts/model/**",
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"resources/scripts/Lai.exe",
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"resources/scripts/discordScript.js",
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"resources/tmp/**",
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"resources/icon.ico"
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@ -4,7 +4,6 @@ import json
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import os
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import sys
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import clip
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import getgrame
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import Push_back_Prompt
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import public_tools
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import shotSplit
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@ -56,6 +55,8 @@ if sys.argv[1] == "-c":
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clip = clip.Clip(cript_directory, sys.argv[2], sys.argv[3])
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clip.MergeVideosAndClip()
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pass
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# 获取字体
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elif sys.argv[1] == "-f":
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# 获取本地已安装的字幕。然后返回
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@ -77,22 +78,17 @@ elif sys.argv[1] == "-p":
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Push_back_Prompt.init(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# 剪映抽帧
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elif sys.argv[1] == "-k":
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# print("")
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getgrame.init(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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elif sys.argv[1] == "-ka":
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shotSplit.get_fram(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# 智能分镜。字幕识别
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elif sys.argv[1] == "-a":
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print("开始算法分镜：" + sys.argv[2] + " -- 输出文件夹：" + sys.argv[3])
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shotSplit.init(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
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# 本地提取音频
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elif sys.argv[1] == "-t":
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print("开始提取文字：" + sys.argv[2])
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shotSplit.GetTextTask(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# # 智能分镜。字幕识别
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# elif sys.argv[1] == "-a":
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# print("开始算法分镜：" + sys.argv[2] + " -- 输出文件夹：" + sys.argv[3])
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# shotSplit.init(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
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# # 本地提取音频
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# elif sys.argv[1] == "-t":
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# print("开始提取文字：" + sys.argv[2])
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# shotSplit.GetTextTask(sys.argv[2], sys.argv[3], sys.argv[4])
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# pass
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@ -1,36 +1,34 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
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a = Analysis(
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['Lai.py'],
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pathex=[],
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binaries=[],
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datas=datas,
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datas=[],
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hiddenimports=[],
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hookspath=[],
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hooksconfig={},
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runtime_hooks=[],
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excludes=[],
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noarchive=False,
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optimize=0,
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)
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pyz = PYZ(a.pure)
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exe = EXE(
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pyz,
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a.scripts,
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a.binaries,
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a.datas,
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[],
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exclude_binaries=True,
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name='Lai',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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upx_exclude=[],
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runtime_tmpdir=None,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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@ -38,12 +36,3 @@ exe = EXE(
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='Lai',
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)
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@ -1,11 +1,17 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
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a = Analysis(
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['lama_inpaint.py'],
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['Lai.py'],
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pathex=[],
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binaries=[],
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datas=[],
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datas=datas,
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hiddenimports=[],
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hookspath=[],
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hooksconfig={},
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@ -18,16 +24,13 @@ pyz = PYZ(a.pure)
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exe = EXE(
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pyz,
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a.scripts,
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a.binaries,
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a.datas,
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[],
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name='lama_inpaint',
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exclude_binaries=True,
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name='Lai',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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upx_exclude=[],
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runtime_tmpdir=None,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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@ -35,3 +38,12 @@ exe = EXE(
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='Lai',
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)
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2
resources/scripts/localWhisper/install.bat
Normal file
2
resources/scripts/localWhisper/install.bat
Normal file
@ -0,0 +1,2 @@
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@echo off
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pyinstaller --upx-dir="C:\\Users\\27698\\Desktop\\upx-4.2.4-win64\upx.exe" local_whisper.py
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170
resources/scripts/localWhisper/local_whisper.py
Normal file
170
resources/scripts/localWhisper/local_whisper.py
Normal file
@ -0,0 +1,170 @@
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# -*- coding: utf-8 -*-
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import io
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import os
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import sys
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import public_tools
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from pathlib import Path
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from huggingface_hub import hf_hub_download
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from faster_whisper import WhisperModel
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
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# 判断sys.argv 的长度，如果小于2，说明没有传入参数，设置初始参数
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# "C:\\Users\\27698\\Desktop\\LAITool\\resources\\scripts\\Lai.exe" -c "D:/来推项目集/7.4/娱乐：江湖大哥退休，去拍电影/scripts/output_crop_00001.json" "NVIDIA"
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# if len(sys.argv) < 2:
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# sys.argv = [
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# "C:\\Users\\27698\\Desktop\\LAITool\\resources\\scripts\\Lai.exe",
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# "-w",
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# "C:\\Users\\27698\\Desktop\\测试\\test\\mjTestoutput_crop_00001.mp4",
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# "C:\\Users\\27698\\Desktop\\测试\\test\data\\frame",
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# "C:\\Users\\27698\\Desktop\\测试\\test\\tmp\\input_crop",
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# 30,
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# "NVIDIA",
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# ]
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print(sys.argv)
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if len(sys.argv) < 2:
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print("Params: <runtime-config.json>")
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exit(0)
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if getattr(sys, "frozen", False):
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cript_directory = os.path.dirname(sys.executable)
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elif __file__:
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cript_directory = os.path.dirname(__file__)
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def GetText(out_folder, mp3_folder):
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text = []
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# 先获取模型
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print("正在下载或加载模型")
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sys.stdout.flush()
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model_path = Path(
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hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="preprocessor_config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="tokenizer.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="vocabulary.json",
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)
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model = WhisperModel(
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model_size_or_path=os.path.dirname(model_path),
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device="auto",
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local_files_only=True,
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)
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print("模型加载成功，开始识别")
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sys.stdout.flush()
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# 拿到指定文件夹里面的所有的MP3文件
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mp3_list = []
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for root, dirs, files in os.walk(mp3_folder):
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for file in files:
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if file.endswith(".mp3"):
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mp3_list.append(os.path.join(root, file))
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for mp in mp3_list:
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segments, info = model.transcribe(
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mp,
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beam_size=5,
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language="zh",
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=1000),
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)
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tmp_text = ""
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for segment in segments:
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tmp_text += segment.text + "。"
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print(mp + "识别完成")
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sys.stdout.flush()
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text.append(tmp_text)
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# 数据写出
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print("文本全部识别成功，正在写出")
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sys.stdout.flush()
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tools = public_tools.PublicTools()
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tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
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print("写出完成")
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sys.stdout.flush()
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def GetTextTask(out_folder, mp, name):
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text = []
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# 先获取模型
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print("正在下载或加载模型")
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sys.stdout.flush()
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model_path = Path(
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hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="preprocessor_config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="tokenizer.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="vocabulary.json",
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)
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model = WhisperModel(
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model_size_or_path=os.path.dirname(model_path),
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device="auto",
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local_files_only=True,
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)
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print("模型加载成功，开始识别")
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sys.stdout.flush()
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segments, info = model.transcribe(
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mp,
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beam_size=5,
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language="zh",
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=1000),
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)
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tmp_text = ""
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for segment in segments:
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tmp_text += segment.text + "。"
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print(mp + "识别完成")
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sys.stdout.flush()
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text.append(tmp_text)
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# 数据写出
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sys.stdout.flush()
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tools = public_tools.PublicTools()
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tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
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sys.stdout.flush()
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# GetTextTask(
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# "C:\\Users\\27698\\Desktop\\测试\\mjTest",
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# "C:\\Users\\27698\\Desktop\\测试\\mjTest\\data\\frame\\00001.mp4",
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# "00001",
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# )
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if sys.argv[1] == "-ts":
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GetText(
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sys.argv[2],
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sys.argv[3],
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)
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elif sys.argv[1] == "-t":
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GetTextTask(
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sys.argv[2],
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sys.argv[3],
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sys.argv[4],
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)
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else:
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print("Params: <runtime-config.json>")
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exit(0)
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50
resources/scripts/localWhisper/local_whisper.spec
Normal file
50
resources/scripts/localWhisper/local_whisper.spec
Normal file
@ -0,0 +1,50 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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|
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
|
||||
|
||||
|
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a = Analysis(
|
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['local_whisper.py'],
|
||||
pathex=[],
|
||||
binaries=[],
|
||||
datas=[],
|
||||
hiddenimports=[],
|
||||
hookspath=[],
|
||||
hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
noarchive=False,
|
||||
optimize=0,
|
||||
)
|
||||
pyz = PYZ(a.pure)
|
||||
|
||||
exe = EXE(
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||||
pyz,
|
||||
a.scripts,
|
||||
[],
|
||||
exclude_binaries=True,
|
||||
name='local_whisper',
|
||||
debug=False,
|
||||
bootloader_ignore_signals=False,
|
||||
strip=False,
|
||||
upx=True,
|
||||
console=True,
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
|
||||
codesign_identity=None,
|
||||
entitlements_file=None,
|
||||
)
|
||||
coll = COLLECT(
|
||||
exe,
|
||||
a.binaries,
|
||||
a.datas,
|
||||
strip=False,
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
name='local_whisper',
|
||||
)
|
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351
resources/scripts/localWhisper/public_tools.py
Normal file
351
resources/scripts/localWhisper/public_tools.py
Normal file
@ -0,0 +1,351 @@
|
||||
# 读取文件的方法
|
||||
import json
|
||||
import os
|
||||
import win32api
|
||||
import win32con
|
||||
import pywintypes
|
||||
import shutil
|
||||
import re
|
||||
|
||||
|
||||
class PublicTools:
|
||||
"""
|
||||
一些公用的基础方法
|
||||
"""
|
||||
|
||||
def delete_path(self, path):
|
||||
"""
|
||||
删除指定路径的文件或者是文件夹
|
||||
"""
|
||||
# 检查路径是否存在
|
||||
if not os.path.exists(path):
|
||||
return
|
||||
|
||||
# 检查路径是文件还是文件夹
|
||||
if os.path.isfile(path):
|
||||
# 是文件，执行删除
|
||||
try:
|
||||
os.remove(path)
|
||||
except Exception as e:
|
||||
raise e
|
||||
elif os.path.isdir(path):
|
||||
# 是文件夹，执行删除
|
||||
try:
|
||||
shutil.rmtree(path)
|
||||
except Exception as e:
|
||||
raise e
|
||||
else:
|
||||
raise
|
||||
|
||||
def list_files_by_extension(self, folder_path, extension):
|
||||
"""
|
||||
读取指定文件夹下面的所有的指定拓展文件命的文件列表
|
||||
"""
|
||||
file_list = []
|
||||
for root, dirs, files in os.walk(folder_path):
|
||||
for file in files:
|
||||
if file.endswith(extension):
|
||||
file_list.append(os.path.join(root, file))
|
||||
elif file.endswith(extension.upper()):
|
||||
file_list.append(os.path.join(root, file))
|
||||
return file_list
|
||||
|
||||
def get_fonts_from_registry(self, key_path):
|
||||
"""
|
||||
获取注册表中安装的字体文件
|
||||
"""
|
||||
font_names = []
|
||||
try:
|
||||
key = win32api.RegOpenKeyEx(
|
||||
(
|
||||
win32con.HKEY_LOCAL_MACHINE
|
||||
if "HKEY_LOCAL_MACHINE" in key_path
|
||||
else win32con.HKEY_CURRENT_USER
|
||||
),
|
||||
key_path.split("\\", 1)[1],
|
||||
0,
|
||||
win32con.KEY_READ,
|
||||
)
|
||||
i = 0
|
||||
while True:
|
||||
try:
|
||||
value = win32api.RegEnumValue(key, i)
|
||||
font_name = value[0]
|
||||
# 使用正则表达式移除括号及其内容
|
||||
font_name = re.sub(r"\s*\([^)]*\)$", "", font_name)
|
||||
font_names.append(font_name)
|
||||
i += 1
|
||||
except pywintypes.error as e:
|
||||
if e.winerror == 259: # 没有更多的数据
|
||||
break
|
||||
else:
|
||||
raise
|
||||
finally:
|
||||
try:
|
||||
win32api.RegCloseKey(key)
|
||||
except:
|
||||
pass
|
||||
return font_names
|
||||
|
||||
def get_installed_fonts(self):
|
||||
"""
|
||||
获取字体文件名称并返回
|
||||
"""
|
||||
system_fonts = self.get_fonts_from_registry(
|
||||
"HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Fonts"
|
||||
)
|
||||
user_fonts = self.get_fonts_from_registry(
|
||||
"HKEY_CURRENT_USER\\Software\\Microsoft\\Windows NT\\CurrentVersion\\Fonts"
|
||||
)
|
||||
all_fonts = list(set(system_fonts + user_fonts)) # 合并并去重
|
||||
return all_fonts
|
||||
|
||||
# 将RRGGBB转换为BBGGRR
|
||||
def convert_rrggbb_to_bbggrr(self, rrggbb):
|
||||
"""
|
||||
将RRGGBB转换为BBGGRR
|
||||
"""
|
||||
if len(rrggbb) == 7:
|
||||
rr = rrggbb[1:3]
|
||||
gg = rrggbb[3:5]
|
||||
bb = rrggbb[5:7]
|
||||
return bb + gg + rr
|
||||
else:
|
||||
return "Invalid input"
|
||||
|
||||
def write_to_file(self, arr, filename):
|
||||
with open(filename, "w",encoding='utf-8') as f:
|
||||
for item in arr:
|
||||
f.write("%s\n" % item)
|
||||
|
||||
|
||||
# 读取文件
|
||||
def read_file(fileType):
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
txt_path = remove_prefix_and_suffix(txt_path, '"', '"')
|
||||
while txt_path.strip() == "":
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
|
||||
while os.path.exists(txt_path) == False:
|
||||
print("文件路径不存在错误：")
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
txt_path = remove_prefix_and_suffix(txt_path, '"', '"')
|
||||
return txt_path
|
||||
|
||||
|
||||
def format_time_ms(milliseconds):
|
||||
"""
|
||||
时间转换将ms->小时:分钟:秒.毫秒格式
|
||||
"""
|
||||
seconds = milliseconds / 1000
|
||||
# 计算小时、分钟和秒
|
||||
hours = int(seconds // 3600)
|
||||
minutes = int((seconds % 3600) // 60)
|
||||
seconds = seconds % 60
|
||||
# 格式化字符串
|
||||
# 使用`%02d`确保小时和分钟总是显示为两位数，`%.2f`确保秒数显示两位小数
|
||||
formatted_time = f"{hours}:{minutes:02d}:{seconds:05.2f}"
|
||||
return formatted_time
|
||||
|
||||
|
||||
# 删除满足条件的开头和结尾
|
||||
def remove_prefix_and_suffix(input_str, prefix_to_remove, suffix_to_remove):
|
||||
if input_str.startswith(prefix_to_remove):
|
||||
# 删除开头
|
||||
input_str = input_str[len(prefix_to_remove) :]
|
||||
|
||||
if input_str.endswith(suffix_to_remove):
|
||||
# 删除结尾
|
||||
input_str = input_str[: -len(suffix_to_remove)]
|
||||
|
||||
return input_str
|
||||
|
||||
|
||||
# 判断文件夹下面是不是有特定的文件夹
|
||||
def check_if_folder_exists(parent_folder, target_folder_name):
|
||||
# 获取文件夹列表
|
||||
subfolders = [f.name for f in os.scandir(parent_folder) if f.is_dir()]
|
||||
|
||||
# 检查特定文件夹是否存在
|
||||
if target_folder_name in subfolders:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
# 检查指定文件夹中是否存在特定文件。
|
||||
def file_exists_in_folder(folder_path: str, file_name: str) -> bool:
|
||||
# 构建完整的文件路径
|
||||
file_path = os.path.join(folder_path, file_name)
|
||||
|
||||
# 返回文件是否存在
|
||||
return os.path.isfile(file_path)
|
||||
|
||||
|
||||
# 秒数转换，保留一位小数
|
||||
def convert_to_seconds(number, count):
|
||||
seconds = number / 1000000
|
||||
rounded_number = round(seconds, count)
|
||||
return rounded_number
|
||||
|
||||
|
||||
def is_empty(obj):
|
||||
if obj is None:
|
||||
return True
|
||||
elif isinstance(obj, str):
|
||||
return len(obj) == 0
|
||||
elif isinstance(obj, list):
|
||||
return len(obj) == 0
|
||||
elif isinstance(obj, dict):
|
||||
return len(obj) == 0
|
||||
return False
|
||||
|
||||
|
||||
def opt_dict(obj, key, default=None):
|
||||
if obj is None:
|
||||
return default
|
||||
if key in obj:
|
||||
v = obj[key]
|
||||
if not is_empty(v):
|
||||
return v
|
||||
return default
|
||||
|
||||
|
||||
def read_config(path, webui=True):
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
runtime_config = json.load(f)
|
||||
|
||||
if "config" not in runtime_config:
|
||||
print("no filed 'config' in json")
|
||||
return None
|
||||
|
||||
config = runtime_config["config"]
|
||||
if "webui" not in config:
|
||||
print("no filed 'webui' in 'config'")
|
||||
return None
|
||||
|
||||
setting_config_path = config["setting"]
|
||||
if not os.path.exists(setting_config_path):
|
||||
setting_config_path = "config/" + setting_config_path
|
||||
if not os.path.exists(setting_config_path):
|
||||
setting_config_path = "../" + setting_config_path
|
||||
|
||||
# read config
|
||||
with open(setting_config_path, "r", encoding="utf-8") as f:
|
||||
setting_config = json.load(f)
|
||||
|
||||
# set workspace parent:根目录
|
||||
if "workspace" in setting_config:
|
||||
setting_config["workspace"]["parent"] = runtime_config["workspace"]
|
||||
else:
|
||||
setting_config["workspace"] = {"parent": runtime_config["workspace"]}
|
||||
setting_config["video"] = opt_dict(runtime_config, "video")
|
||||
|
||||
# merge setting config
|
||||
if "setting" in config:
|
||||
setting_config.update(runtime_config["setting"])
|
||||
|
||||
# webui config
|
||||
if webui:
|
||||
webui_config_path = config["webui"]
|
||||
if not os.path.exists(webui_config_path):
|
||||
webui_config_path = "config/webui/" + webui_config_path
|
||||
if not os.path.exists(webui_config_path):
|
||||
webui_config_path = "../" + webui_config_path
|
||||
|
||||
with open(webui_config_path, "r", encoding="utf-8") as f:
|
||||
webui_config = json.load(f)
|
||||
|
||||
# merge webui config
|
||||
if "webui" in runtime_config:
|
||||
webui_config.update(runtime_config["webui"])
|
||||
|
||||
return webui_config, setting_config
|
||||
return setting_config
|
||||
|
||||
|
||||
TAG_MODE_NONE = ""
|
||||
|
||||
|
||||
# 工作路径
|
||||
class Workspace:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root: str,
|
||||
input: str,
|
||||
output: str,
|
||||
input_crop: str,
|
||||
output_crop: str,
|
||||
input_tag: str,
|
||||
input_mask: str,
|
||||
input_crop_mask: str,
|
||||
crop_info: str,
|
||||
):
|
||||
self.root = root
|
||||
self.input = input
|
||||
self.output = output
|
||||
self.input_crop = input_crop
|
||||
self.output_crop = output_crop
|
||||
self.input_tag = input_tag
|
||||
self.input_mask = input_mask
|
||||
self.input_crop_mask = input_crop_mask
|
||||
self.crop_info = crop_info
|
||||
|
||||
|
||||
# 定义一个倍数函数
|
||||
def round_up(num, mul):
|
||||
return (num // mul + 1) * mul
|
||||
|
||||
|
||||
class SettingConfig:
|
||||
|
||||
def __init__(self, config: dict, workParent):
|
||||
self.config = config
|
||||
self.webui_work_api = None
|
||||
self.workParent = workParent
|
||||
|
||||
def to_dict(self):
|
||||
return self.__dict__
|
||||
|
||||
def get_tag_mode(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "mode", TAG_MODE_NONE)
|
||||
|
||||
def get_tag_actions(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "actions", [])
|
||||
|
||||
def get_workspace_config(self) -> Workspace:
|
||||
workspace_config = opt_dict(self.config, "workspace")
|
||||
tmp_config = opt_dict(workspace_config, "tmp")
|
||||
|
||||
input = opt_dict(workspace_config, "input", "input")
|
||||
output = opt_dict(workspace_config, "output", "output")
|
||||
workspace_parent = self.workParent
|
||||
|
||||
tmp_parent = opt_dict(tmp_config, "parent", "tmp")
|
||||
input_crop = opt_dict(tmp_config, "input_crop", "input_crop")
|
||||
output_crop = opt_dict(tmp_config, "output_crop", "output_crop")
|
||||
input_tag = opt_dict(tmp_config, "input_tag", "input_crop")
|
||||
input_mask = opt_dict(tmp_config, "input_mask", "input_mask")
|
||||
input_crop_mask = opt_dict(tmp_config, "input_crop_mask", "input_crop_mask")
|
||||
crop_info = opt_dict(tmp_config, "crop_info", "crop_info.txt")
|
||||
|
||||
tmp_path = os.path.join(workspace_parent, tmp_parent)
|
||||
|
||||
return Workspace(
|
||||
workspace_parent,
|
||||
os.path.join(workspace_parent, input),
|
||||
os.path.join(workspace_parent, output),
|
||||
os.path.join(tmp_path, input_crop),
|
||||
os.path.join(tmp_path, output_crop),
|
||||
os.path.join(tmp_path, input_tag),
|
||||
os.path.join(tmp_path, input_mask),
|
||||
os.path.join(tmp_path, input_crop_mask),
|
||||
os.path.join(tmp_path, crop_info),
|
||||
)
|
||||
|
||||
def enable_tag(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "enable", True)
|
||||
307
resources/scripts/localWhisper/shotSplit.py
Normal file
307
resources/scripts/localWhisper/shotSplit.py
Normal file
@ -0,0 +1,307 @@
|
||||
# pip install scenedetect opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
|
||||
from scenedetect.video_manager import VideoManager
|
||||
from scenedetect.scene_manager import SceneManager
|
||||
from scenedetect.stats_manager import StatsManager
|
||||
from scenedetect.detectors.content_detector import ContentDetector
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
from huggingface_hub import hf_hub_download
|
||||
from faster_whisper import WhisperModel
|
||||
from pathlib import Path
|
||||
import public_tools
|
||||
|
||||
# 获取智能画面分割的时间或者秒数
|
||||
def find_scenes(video_path, sensitivity):
|
||||
print(
|
||||
"正在计算分镜数据" + "sensitivity：" + str(sensitivity) + "path : " + video_path
|
||||
)
|
||||
sys.stdout.flush()
|
||||
video_manager = VideoManager([video_path])
|
||||
stats_manager = StatsManager()
|
||||
scene_manager = SceneManager(stats_manager)
|
||||
|
||||
# 使用contect-detector
|
||||
scene_manager.add_detector(ContentDetector(threshold=float(sensitivity)))
|
||||
|
||||
shijian_list = []
|
||||
|
||||
try:
|
||||
video_manager.set_downscale_factor()
|
||||
video_manager.start()
|
||||
scene_manager.detect_scenes(frame_source=video_manager)
|
||||
scene_list = scene_manager.get_scene_list()
|
||||
print("分镜数据列表：")
|
||||
sys.stdout.flush()
|
||||
for i, scene in enumerate(scene_list):
|
||||
shijian_list.append([scene[0].get_timecode(), scene[1].get_timecode()])
|
||||
print(
|
||||
"Scene %2d: Start %s / Frame %d, End %s / Frame %d"
|
||||
% (
|
||||
i + 1,
|
||||
scene[0].get_timecode(),
|
||||
scene[0].get_frames(),
|
||||
scene[1].get_timecode(),
|
||||
scene[1].get_frames(),
|
||||
)
|
||||
)
|
||||
sys.stdout.flush()
|
||||
finally:
|
||||
video_manager.release()
|
||||
|
||||
return shijian_list
|
||||
|
||||
|
||||
# 如果不存在就创建
|
||||
def createDir(file_dir):
|
||||
# 如果不存在文件夹，就创建
|
||||
if not os.path.isdir(file_dir):
|
||||
os.mkdir(file_dir)
|
||||
|
||||
|
||||
# 切分一个视频
|
||||
def ClipVideo(video_path, out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
shijian_list = find_scenes(video_path, sensitivity) # 多组时间列表
|
||||
shijian_list_len = len(shijian_list)
|
||||
|
||||
print("总共有%s个场景" % str(shijian_list_len))
|
||||
sys.stdout.flush()
|
||||
video_list = []
|
||||
for i in range(0, shijian_list_len):
|
||||
start_time_str = shijian_list[i][0]
|
||||
end_time_str = shijian_list[i][1]
|
||||
|
||||
print("开始输出第" + str(i + 1) + "个分镜")
|
||||
video_name = "{:05d}".format(i + 1)
|
||||
out_video_file = os.path.join(out_folder, video_name + ".mp4")
|
||||
sys.stdout.flush()
|
||||
video_list.append(
|
||||
{
|
||||
"start_time_str": start_time_str,
|
||||
"end_time_str": end_time_str,
|
||||
"out_video_file": out_video_file,
|
||||
"video_name": video_name,
|
||||
}
|
||||
)
|
||||
|
||||
# 使用 ffmpeg 裁剪视频
|
||||
command = []
|
||||
command.append("ffmpeg")
|
||||
command.append("-i")
|
||||
command.append(video_path)
|
||||
command.append("-ss")
|
||||
command.append(start_time_str)
|
||||
command.append("-to")
|
||||
command.append(end_time_str)
|
||||
command.append("-c:v")
|
||||
|
||||
if gpu_type == "NVIDIA":
|
||||
command.append("h264_nvenc")
|
||||
elif gpu_type == "AMD":
|
||||
command.append("h264_amf")
|
||||
else:
|
||||
command.append("libx264")
|
||||
|
||||
command.append("-preset")
|
||||
command.append("fast")
|
||||
command.append("-c:a")
|
||||
command.append("copy")
|
||||
command.append(out_video_file)
|
||||
command.append("-loglevel")
|
||||
command.append("error")
|
||||
|
||||
subprocess.run(
|
||||
command,
|
||||
check=True,
|
||||
stderr=subprocess.PIPE,
|
||||
)
|
||||
|
||||
print("分镜输出完成。开始抽帧")
|
||||
sys.stdout.flush()
|
||||
for vi in video_list:
|
||||
h, m, s = vi["start_time_str"].split(":")
|
||||
start_seconds = int(h) * 3600 + int(m) * 60 + float(s)
|
||||
|
||||
h, m, s = vi["end_time_str"].split(":")
|
||||
end_seconds = int(h) * 3600 + int(m) * 60 + float(s)
|
||||
print("正在抽帧：" + vi["video_name"])
|
||||
sys.stdout.flush()
|
||||
subprocess.run(
|
||||
[
|
||||
"ffmpeg",
|
||||
"-ss",
|
||||
str((end_seconds - start_seconds) / 2),
|
||||
"-i",
|
||||
vi["out_video_file"],
|
||||
"-frames:v",
|
||||
"1",
|
||||
os.path.join(image_out_folder, vi["video_name"] + ".png"),
|
||||
"-loglevel",
|
||||
"error",
|
||||
]
|
||||
)
|
||||
|
||||
print("抽帧完成，开始识别文案")
|
||||
sys.stdout.flush()
|
||||
return video_list
|
||||
|
||||
|
||||
def SplitAudio(video_out_folder, video_list):
|
||||
# ffmpeg -i input_file.mp4 -vn -ab 128k output_file.mp3
|
||||
print("正在分离音频!!")
|
||||
mp3_list = []
|
||||
sys.stdout.flush()
|
||||
for v in video_list:
|
||||
mp3_path = os.path.join(video_out_folder, v["video_name"] + ".mp3")
|
||||
mp3_list.append(mp3_path)
|
||||
subprocess.run(
|
||||
[
|
||||
"ffmpeg",
|
||||
"-i",
|
||||
v["out_video_file"],
|
||||
"-vn",
|
||||
"-ab",
|
||||
"128k",
|
||||
mp3_path,
|
||||
"-loglevel",
|
||||
"error",
|
||||
],
|
||||
check=True,
|
||||
)
|
||||
return mp3_list
|
||||
|
||||
|
||||
def GetText(out_folder, mp3_list):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
for mp in mp3_list:
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
print("文本全部识别成功，正在写出")
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
print("写出完成")
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def GetTextTask(out_folder, mp, name):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def get_fram(video_path, out_path, sensitivity):
|
||||
try:
|
||||
shijian_list = find_scenes(video_path, sensitivity) # 多组时间列表
|
||||
print("总共有%s个场景" % str(len(shijian_list)))
|
||||
print("开始输出json")
|
||||
print(shijian_list)
|
||||
# 将数组中的消息写道json文件中
|
||||
with open(out_path, "w") as file:
|
||||
# 将数组写入到指定的json文件
|
||||
json.dump(shijian_list, file)
|
||||
print("输出完成")
|
||||
except Exception as e:
|
||||
print("出现错误" + str(e))
|
||||
exit(0)
|
||||
|
||||
|
||||
def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
v_l = ClipVideo(
|
||||
video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
)
|
||||
|
||||
# 开始分离音频
|
||||
m_l = SplitAudio(video_out_folder, v_l)
|
||||
# 开始识别字幕
|
||||
GetText(os.path.dirname(video_out_folder), m_l)
|
||||
@ -1,298 +0,0 @@
|
||||
accelerate==0.30.1
|
||||
addict==2.4.0
|
||||
aiofiles==23.2.1
|
||||
aiohttp==3.8.6
|
||||
aiosignal==1.3.1
|
||||
alibabacloud-bailian20230601==1.6.1
|
||||
alibabacloud-credentials==0.3.3
|
||||
alibabacloud-endpoint-util==0.0.3
|
||||
alibabacloud-gateway-spi==0.0.1
|
||||
alibabacloud-openapi-util==0.2.2
|
||||
alibabacloud-tea==0.3.6
|
||||
alibabacloud-tea-openapi==0.3.9
|
||||
alibabacloud-tea-util==0.3.12
|
||||
alibabacloud-tea-xml==0.0.2
|
||||
aliyun-python-sdk-core==2.15.0
|
||||
aliyun-python-sdk-kms==2.16.2
|
||||
altair==5.3.0
|
||||
altgraph==0.17.4
|
||||
annotated-types==0.6.0
|
||||
anthropic==0.26.1
|
||||
antlr4-python3-runtime==4.9.3
|
||||
anyio==4.3.0
|
||||
APScheduler==3.10.4
|
||||
arxiv==2.1.0
|
||||
astor==0.8.1
|
||||
asttokens==2.4.1
|
||||
async-timeout==4.0.3
|
||||
attrdict==2.0.1
|
||||
attrs==23.2.0
|
||||
av==11.0.0
|
||||
azure-cognitiveservices-speech==1.37.0
|
||||
Babel==2.15.0
|
||||
backports.tarfile==1.1.1
|
||||
baidu-aip==4.16.13
|
||||
bce-python-sdk==0.9.11
|
||||
beautifulsoup4==4.12.3
|
||||
bidict==0.23.1
|
||||
blinker==1.8.2
|
||||
broadscope-bailian==1.3.1
|
||||
cachetools==5.3.3
|
||||
certifi==2024.2.2
|
||||
cffi==1.16.0
|
||||
cfgv==3.4.0
|
||||
chardet==5.2.0
|
||||
charset-normalizer==3.3.2
|
||||
chatgpt-tool-hub==0.5.0
|
||||
cheroot==10.0.1
|
||||
click==8.1.7
|
||||
colorama==0.4.6
|
||||
coloredlogs==15.0.1
|
||||
comtypes==1.4.2
|
||||
contourpy==1.2.1
|
||||
controlnet-aux==0.0.3
|
||||
crcmod==1.7
|
||||
cryptography==42.0.5
|
||||
cssselect==1.2.0
|
||||
cssutils==2.11.0
|
||||
ctranslate2==4.1.0
|
||||
curl_cffi==0.6.4
|
||||
cx-Logging==3.1.0
|
||||
cx_Freeze==6.15.16
|
||||
cycler==0.12.1
|
||||
Cython==3.0.10
|
||||
dashscope==1.19.2
|
||||
datasets==2.18.0
|
||||
decorator==4.4.2
|
||||
diffusers==0.27.2
|
||||
dill==0.3.8
|
||||
dingtalk-stream==0.18.1
|
||||
distlib==0.3.8
|
||||
distro==1.9.0
|
||||
dnspython==2.6.1
|
||||
dulwich==0.22.1
|
||||
easydict==1.13
|
||||
edge-tts==6.1.12
|
||||
einops==0.7.0
|
||||
elevenlabs==1.0.3
|
||||
email_validator==2.1.1
|
||||
et-xmlfile==1.1.0
|
||||
exceptiongroup==1.2.1
|
||||
executing==2.0.1
|
||||
fastapi==0.108.0
|
||||
fastapi-cli==0.0.2
|
||||
faster-whisper==1.0.1
|
||||
feedparser==6.0.10
|
||||
ffmpy==0.3.2
|
||||
filelock==3.13.1
|
||||
fire==0.6.0
|
||||
Flask==3.0.3
|
||||
flask-babel==4.0.0
|
||||
flatbuffers==24.3.7
|
||||
fonttools==4.53.0
|
||||
frozenlist==1.4.1
|
||||
fsspec==2024.2.0
|
||||
future==1.0.0
|
||||
gast==0.5.4
|
||||
google-ai-generativelanguage==0.6.4
|
||||
google-api-core==2.19.0
|
||||
google-api-python-client==2.130.0
|
||||
google-auth==2.29.0
|
||||
google-auth-httplib2==0.2.0
|
||||
google-generativeai==0.5.4
|
||||
googleapis-common-protos==1.63.0
|
||||
gradio==4.21.0
|
||||
gradio_client==0.12.0
|
||||
grpcio==1.64.0
|
||||
grpcio-status==1.62.2
|
||||
gTTS==2.5.1
|
||||
h11==0.14.0
|
||||
HTMLParser==0.0.2
|
||||
httpcore==1.0.5
|
||||
httplib2==0.22.0
|
||||
httptools==0.6.1
|
||||
httpx==0.27.0
|
||||
huggingface-hub==0.23.2
|
||||
humanfriendly==10.0
|
||||
identify==2.5.36
|
||||
idna==3.6
|
||||
imageio==2.34.0
|
||||
imageio-ffmpeg==0.4.9
|
||||
imgaug==0.4.0
|
||||
importlib_metadata==7.0.2
|
||||
importlib_resources==6.4.0
|
||||
install==1.3.5
|
||||
IOPaint==1.3.3
|
||||
ipython==8.24.0
|
||||
itsdangerous==2.2.0
|
||||
jaraco.context==5.3.0
|
||||
jaraco.functools==4.0.1
|
||||
jedi==0.19.1
|
||||
Jinja2==3.1.3
|
||||
jiter==0.4.0
|
||||
jmespath==0.10.0
|
||||
jsonschema==4.22.0
|
||||
jsonschema-specifications==2023.12.1
|
||||
kiwisolver==1.4.5
|
||||
langid==1.1.6
|
||||
lazy_loader==0.4
|
||||
lief==0.14.1
|
||||
linkai==0.0.6.0
|
||||
lmdb==1.4.1
|
||||
loguru==0.7.2
|
||||
lxml==5.2.2
|
||||
markdown-it-py==3.0.0
|
||||
MarkupSafe==2.1.5
|
||||
matplotlib==3.9.0
|
||||
matplotlib-inline==0.1.7
|
||||
mdurl==0.1.2
|
||||
modelscope==1.13.1
|
||||
more-itertools==10.2.0
|
||||
moviepy==1.0.3
|
||||
mpmath==1.3.0
|
||||
multidict==6.0.5
|
||||
multiprocess==0.70.16
|
||||
networkx==3.2.1
|
||||
nodeenv==1.8.0
|
||||
Nuitka==2.1.2
|
||||
numpy==1.24.2
|
||||
omegaconf==2.3.0
|
||||
onnxruntime==1.17.1
|
||||
openai==0.27.8
|
||||
opencv-contrib-python==4.6.0.66
|
||||
opencv-python==4.6.0.66
|
||||
opencv-python-headless==4.9.0.80
|
||||
openpyxl==3.1.2
|
||||
opt-einsum==3.3.0
|
||||
optionaldict==0.1.2
|
||||
ordered-set==4.1.0
|
||||
orjson==3.10.3
|
||||
oss2==2.18.4
|
||||
packaging==24.0
|
||||
paddleocr==2.7.3
|
||||
paddlepaddle==2.6.1
|
||||
pandas==2.2.1
|
||||
parso==0.8.4
|
||||
pdf2docx==0.5.8
|
||||
pefile==2023.2.7
|
||||
peft==0.7.1
|
||||
piexif==1.1.3
|
||||
pillow==10.3.0
|
||||
platformdirs==4.2.0
|
||||
pre-commit==3.7.1
|
||||
premailer==3.10.0
|
||||
proglog==0.1.10
|
||||
prompt-toolkit==3.0.43
|
||||
proto-plus==1.23.0
|
||||
protobuf==3.20.2
|
||||
psutil==5.9.8
|
||||
pure-eval==0.2.2
|
||||
pyarrow==15.0.1
|
||||
pyarrow-hotfix==0.6
|
||||
pyasn1==0.6.0
|
||||
pyasn1_modules==0.4.0
|
||||
pyclipper==1.3.0.post5
|
||||
pycparser==2.21
|
||||
pycryptodome==3.20.0
|
||||
pydantic==2.5.3
|
||||
pydantic_core==2.14.6
|
||||
pydub==0.25.1
|
||||
Pygments==2.18.0
|
||||
pyinstaller==6.5.0
|
||||
pyinstaller-hooks-contrib==2024.3
|
||||
PyJWT==2.8.0
|
||||
PyMuPDF==1.24.5
|
||||
PyMuPDFb==1.24.3
|
||||
pyOpenSSL==24.1.0
|
||||
pyoxidizer==0.24.0
|
||||
pyparsing==3.1.2
|
||||
pypiwin32==223
|
||||
pypng==0.20220715.0
|
||||
PyQRCode==1.2.1
|
||||
pyreadline3==3.4.1
|
||||
pytesseract==0.3.10
|
||||
python-dateutil==2.9.0.post0
|
||||
python-docx==1.1.2
|
||||
python-dotenv==1.0.1
|
||||
python-engineio==4.9.1
|
||||
python-multipart==0.0.9
|
||||
python-socketio==5.7.2
|
||||
pyttsx3==2.90
|
||||
pytz==2024.1
|
||||
pywin32==306
|
||||
pywin32-ctypes==0.2.2
|
||||
PyYAML==6.0.1
|
||||
qrcode==7.4.2
|
||||
rapidfuzz==3.9.3
|
||||
rarfile==4.2
|
||||
referencing==0.35.1
|
||||
regex==2024.5.15
|
||||
requests==2.31.0
|
||||
rich==13.7.1
|
||||
rpds-py==0.18.1
|
||||
rsa==4.9
|
||||
ruff==0.4.7
|
||||
safetensors==0.4.3
|
||||
scenedetect==0.6.3
|
||||
scikit-image==0.23.2
|
||||
scipy==1.12.0
|
||||
semantic-version==2.10.0
|
||||
sgmllib3k==1.0.0
|
||||
shapely==2.0.4
|
||||
shellingham==1.5.4
|
||||
simple-websocket==1.0.0
|
||||
simplejson==3.19.2
|
||||
six==1.16.0
|
||||
sniffio==1.3.1
|
||||
sortedcontainers==2.4.0
|
||||
soupsieve==2.5
|
||||
SpeechRecognition==3.10.4
|
||||
stack-data==0.6.3
|
||||
starlette==0.32.0.post1
|
||||
sympy==1.12
|
||||
tenacity==8.2.3
|
||||
termcolor==2.4.0
|
||||
tifffile==2024.5.22
|
||||
tiktoken==0.4.0
|
||||
timm==1.0.3
|
||||
tokenizers==0.19.1
|
||||
tomli==2.0.1
|
||||
tomlkit==0.12.0
|
||||
toolz==0.12.1
|
||||
torch==2.1.2+cu118
|
||||
torchvision==0.16.2+cu118
|
||||
tqdm==4.66.2
|
||||
traitlets==5.14.3
|
||||
transformers==4.41.2
|
||||
typer==0.12.3
|
||||
typer-config==1.4.0
|
||||
typing_extensions==4.10.0
|
||||
tzdata==2024.1
|
||||
tzlocal==5.2
|
||||
ujson==5.9.0
|
||||
uritemplate==4.1.1
|
||||
urllib3==2.2.1
|
||||
utility==1.0
|
||||
uvicorn==0.29.0
|
||||
virtualenv==20.26.2
|
||||
visualdl==2.5.3
|
||||
watchfiles==0.21.0
|
||||
wcwidth==0.2.13
|
||||
web.py==0.62
|
||||
websocket-client==1.2.0
|
||||
websockets==11.0.3
|
||||
wechatpy==1.8.18
|
||||
Werkzeug==3.0.3
|
||||
wikipedia==1.4.0
|
||||
win32-setctime==1.1.0
|
||||
wolframalpha==5.0.0
|
||||
wsproto==1.2.0
|
||||
xlrd==2.0.1
|
||||
xmltodict==0.13.0
|
||||
xxhash==3.4.1
|
||||
yacs==0.1.8
|
||||
yapf==0.40.2
|
||||
yarl==1.9.4
|
||||
zhipuai==2.1.0.20240521
|
||||
zipp==3.18.1
|
||||
zstandard==0.22.0
|
||||
@ -8,8 +8,8 @@ import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
from huggingface_hub import hf_hub_download
|
||||
from faster_whisper import WhisperModel
|
||||
# from huggingface_hub import hf_hub_download
|
||||
# from faster_whisper import WhisperModel
|
||||
import public_tools
|
||||
from pathlib import Path
|
||||
|
||||
@ -174,111 +174,111 @@ def SplitAudio(video_out_folder, video_list):
|
||||
return mp3_list
|
||||
|
||||
|
||||
def GetText(out_folder, mp3_list):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
for mp in mp3_list:
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
# def GetText(out_folder, mp3_list):
|
||||
# text = []
|
||||
# # 先获取模型
|
||||
# print("正在下载或加载模型")
|
||||
# sys.stdout.flush()
|
||||
# model_path = Path(
|
||||
# hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="preprocessor_config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="tokenizer.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="vocabulary.json",
|
||||
# )
|
||||
# model = WhisperModel(
|
||||
# model_size_or_path=os.path.dirname(model_path),
|
||||
# device="auto",
|
||||
# local_files_only=True,
|
||||
# )
|
||||
# print("模型加载成功，开始识别")
|
||||
# sys.stdout.flush()
|
||||
# for mp in mp3_list:
|
||||
# segments, info = model.transcribe(
|
||||
# mp,
|
||||
# beam_size=5,
|
||||
# language="zh",
|
||||
# vad_filter=True,
|
||||
# vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
# )
|
||||
# tmp_text = ""
|
||||
# for segment in segments:
|
||||
# tmp_text += segment.text + "。"
|
||||
# print(mp + "识别完成")
|
||||
# sys.stdout.flush()
|
||||
# text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
print("文本全部识别成功，正在写出")
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
print("写出完成")
|
||||
sys.stdout.flush()
|
||||
# # 数据写出
|
||||
# print("文本全部识别成功，正在写出")
|
||||
# sys.stdout.flush()
|
||||
# tools = public_tools.PublicTools()
|
||||
# tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
# print("写出完成")
|
||||
# sys.stdout.flush()
|
||||
|
||||
|
||||
def GetTextTask(out_folder, mp, name):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
# def GetTextTask(out_folder, mp, name):
|
||||
# text = []
|
||||
# # 先获取模型
|
||||
# print("正在下载或加载模型")
|
||||
# sys.stdout.flush()
|
||||
# model_path = Path(
|
||||
# hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="preprocessor_config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="tokenizer.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="vocabulary.json",
|
||||
# )
|
||||
# model = WhisperModel(
|
||||
# model_size_or_path=os.path.dirname(model_path),
|
||||
# device="auto",
|
||||
# local_files_only=True,
|
||||
# )
|
||||
# print("模型加载成功，开始识别")
|
||||
# sys.stdout.flush()
|
||||
# segments, info = model.transcribe(
|
||||
# mp,
|
||||
# beam_size=5,
|
||||
# language="zh",
|
||||
# vad_filter=True,
|
||||
# vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
# )
|
||||
# tmp_text = ""
|
||||
# for segment in segments:
|
||||
# tmp_text += segment.text + "。"
|
||||
# print(mp + "识别完成")
|
||||
# sys.stdout.flush()
|
||||
# text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
sys.stdout.flush()
|
||||
# # 数据写出
|
||||
# sys.stdout.flush()
|
||||
# tools = public_tools.PublicTools()
|
||||
# tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
# sys.stdout.flush()
|
||||
|
||||
|
||||
def get_fram(video_path, out_path, sensitivity):
|
||||
@ -297,12 +297,12 @@ def get_fram(video_path, out_path, sensitivity):
|
||||
exit(0)
|
||||
|
||||
|
||||
def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
v_l = ClipVideo(
|
||||
video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
)
|
||||
# def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
# v_l = ClipVideo(
|
||||
# video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
# )
|
||||
|
||||
# 开始分离音频
|
||||
m_l = SplitAudio(video_out_folder, v_l)
|
||||
# 开始识别字幕
|
||||
GetText(os.path.dirname(video_out_folder), m_l)
|
||||
# # 开始分离音频
|
||||
# m_l = SplitAudio(video_out_folder, v_l)
|
||||
# # 开始识别字幕
|
||||
# GetText(os.path.dirname(video_out_folder), m_l)
|
||||
|
||||
@ -8,7 +8,7 @@ import { DEFINE_STRING } from "../../../define/define_string";
|
||||
import path from 'path'
|
||||
import { BasicReverse } from './basicReverse'
|
||||
import { BookTaskDetailService } from '../../../define/db/service/Book/bookTaskDetailService'
|
||||
import { TaskScheduler } from "../task/taskScheduler"
|
||||
import { LogScheduler } from "../task/logScheduler"
|
||||
import { Book } from '../../../model/book'
|
||||
import { LoggerStatus, OtherData, ResponseMessageType } from '../../../define/enum/softwareEnum'
|
||||
import { GeneralResponse } from '../../../model/generalResponse'
|
||||
@ -28,7 +28,7 @@ import { isEmpty } from 'lodash'
|
||||
*/
|
||||
export class ReverseBook {
|
||||
basicReverse: BasicReverse
|
||||
taskScheduler: TaskScheduler
|
||||
logScheduler: LogScheduler
|
||||
mjOpt: MJOpt = new MJOpt()
|
||||
sdOpt: SDOpt = new SDOpt()
|
||||
tagDefine: TagDefine
|
||||
@ -42,7 +42,7 @@ export class ReverseBook {
|
||||
this.tagDefine = new TagDefine()
|
||||
this.subtitle = new Subtitle()
|
||||
this.watermark = new Watermark()
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.bookServiceBasic = new BookServiceBasic()
|
||||
this.bookBasic = new BookBasic()
|
||||
}
|
||||
@ -301,7 +301,7 @@ export class ReverseBook {
|
||||
await this.bookServiceBasic.AddBookBackTask(book.id, task_type, TaskExecuteType.AUTO, bookTaskDetail.bookTaskId, bookTaskDetail.id, DEFINE_STRING.BOOK.REVERSE_PROMPT_RETURN
|
||||
);
|
||||
// 添加返回日志
|
||||
await this.taskScheduler.AddLogToDB(book.id, book.type, `添加 ${task_type} 反推任务成功`, bookTaskDetail.bookTaskId, LoggerStatus.SUCCESS)
|
||||
await this.logScheduler.AddLogToDB(book.id, book.type, `添加 ${task_type} 反推任务成功`, bookTaskDetail.bookTaskId, LoggerStatus.SUCCESS)
|
||||
}
|
||||
} catch (error) {
|
||||
throw error
|
||||
|
||||
@ -5,7 +5,7 @@ const { exec } = require('child_process')
|
||||
const execAsync = util.promisify(exec)
|
||||
import { define } from '../../../define/define'
|
||||
import { BookService } from '../../../define/db/service/Book/bookService'
|
||||
import { TaskScheduler } from '../task/taskScheduler'
|
||||
import { LogScheduler } from '../task/logScheduler'
|
||||
import { LoggerStatus, LoggerType, OtherData } from '../../../define/enum/softwareEnum'
|
||||
import { errorMessage, successMessage } from '../../Public/generalTools'
|
||||
import { CheckFileOrDirExist, CheckFolderExistsOrCreate } from '../../../define/Tools/file'
|
||||
@ -35,11 +35,11 @@ export class BasicReverse {
|
||||
bookTaskDetailService: BookTaskDetailService
|
||||
bookBackTaskListService: BookBackTaskListService
|
||||
|
||||
taskScheduler: TaskScheduler
|
||||
logScheduler: LogScheduler
|
||||
ffmpegOptions: FfmpegOptions
|
||||
|
||||
constructor() {
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.ffmpegOptions = new FfmpegOptions()
|
||||
}
|
||||
|
||||
@ -109,7 +109,7 @@ export class BasicReverse {
|
||||
if (taskRes.code == 0) {
|
||||
throw new Error(taskRes.message)
|
||||
}
|
||||
this.taskScheduler.AddLogToDB(
|
||||
this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`添加分镜任务成功`,
|
||||
@ -149,7 +149,7 @@ export class BasicReverse {
|
||||
let sensitivity = 30
|
||||
// 开始之前，推送日志
|
||||
let log_content = `开始进行分镜操作，视频地址：${oldVideoPath}，敏感度：${sensitivity}，正在调用程序进行处理`
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
log_content,
|
||||
@ -170,7 +170,7 @@ export class BasicReverse {
|
||||
// 有错误输出
|
||||
if (output.stderr != '') {
|
||||
let error_msg = `分镜成功，但有警告提示：${output.stderr}`
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
error_msg,
|
||||
@ -187,7 +187,7 @@ export class BasicReverse {
|
||||
BookTaskStatus.STORYBOARD_FAIL,
|
||||
error_message
|
||||
)
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
error_message,
|
||||
@ -205,7 +205,7 @@ export class BasicReverse {
|
||||
BookTaskStatus.STORYBOARD_FAIL,
|
||||
error_msg
|
||||
)
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
error_msg,
|
||||
@ -237,7 +237,7 @@ export class BasicReverse {
|
||||
|
||||
this.bookTaskService.UpdateBookTaskStatus(bookTaskId, BookTaskStatus.STORYBOARD_DONE)
|
||||
// 分镜成功，推送日志
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`分镜成功，分镜数据如下：${frameJsonData}`,
|
||||
@ -310,7 +310,7 @@ export class BasicReverse {
|
||||
|
||||
if (bookTaskDetail.data.length <= 0) {
|
||||
// 传入的分镜数据为空，需要重新获取
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`没有传入分镜数据，开始调用分镜方法`,
|
||||
@ -339,7 +339,7 @@ export class BasicReverse {
|
||||
|
||||
this.bookTaskService.UpdateBookTaskStatus(bookTask.id, BookTaskStatus.SPLIT)
|
||||
// 有分镜数据，开始处理
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`成功获取分镜数据，开始添加裁剪视频任务`,
|
||||
@ -363,7 +363,7 @@ export class BasicReverse {
|
||||
}
|
||||
}
|
||||
// 添加日志
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`添加视频裁剪任务成功`,
|
||||
@ -424,7 +424,7 @@ export class BasicReverse {
|
||||
// 小改小说批次的状态
|
||||
this.bookTaskService.UpdateBookTaskStatus(bookTaskDetail.bookTaskId, BookTaskStatus.SPLIT_DONE)
|
||||
// 结束，分镜完毕，推送日志，返回成功
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookTaskDetail.bookId,
|
||||
book.type,
|
||||
`${bookTaskDetail.name}_视频裁剪完成`,
|
||||
@ -490,7 +490,7 @@ export class BasicReverse {
|
||||
})
|
||||
}
|
||||
if (bookTaskRes.data.bookTasks.length <= 0 || bookTaskRes.data.total <= 0) {
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`没有找到对应的小说批次任务数据，请检查bookId是否正确`,
|
||||
@ -508,7 +508,7 @@ export class BasicReverse {
|
||||
bookTaskId: bookTask.id
|
||||
})
|
||||
if (bookTaskDetails.data.length <= 0) {
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`没有找到对应的小说批次任务数据，请检查bookId是否正确，或者手动执行`,
|
||||
@ -531,7 +531,7 @@ export class BasicReverse {
|
||||
throw new Error(taskRes.message)
|
||||
}
|
||||
// 添加日志
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`添加音频 ${taskRes.data.name} 分离任务成功`,
|
||||
@ -588,7 +588,7 @@ export class BasicReverse {
|
||||
})
|
||||
|
||||
// 推送成功消息
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
task.bookId,
|
||||
book.type,
|
||||
`${bookTaskDetail.name}分离音频成功，输出地址：${audioPath}`,
|
||||
@ -656,7 +656,7 @@ export class BasicReverse {
|
||||
throw new Error(taskRes.message)
|
||||
}
|
||||
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`添加 ${taskRes.data.name} 抽帧任务成功`,
|
||||
@ -701,7 +701,7 @@ export class BasicReverse {
|
||||
})
|
||||
|
||||
// 推送成功消息
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
book.id,
|
||||
book.type,
|
||||
`${bookTaskDetail.name}抽帧成功，输出地址：${outputFramePath}`,
|
||||
@ -797,7 +797,7 @@ export class BasicReverse {
|
||||
}
|
||||
}
|
||||
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`添加提取字幕任务成功`,
|
||||
@ -821,9 +821,9 @@ export class BasicReverse {
|
||||
// 判断是不是用本地的wisper服务
|
||||
if (isWisper) {
|
||||
// 开始调用wisper
|
||||
// 使用异步的方法调用一个python程序，然后写入到指定的json文件中k
|
||||
// 使用异步的方法调用一个python程序，然后写入到指定的json文件中
|
||||
let out_dir = path.dirname(bookTaskDetail.videoPath)
|
||||
|
||||
// #TODO -t 被移除
|
||||
let command = `"${path.join(define.scripts_path, 'Lai.exe')}" "-t" "${out_dir}" "${bookTaskDetail.audioPath
|
||||
}" "${bookTaskDetail.name}"`
|
||||
const output = await execAsync(command, {
|
||||
@ -833,7 +833,7 @@ export class BasicReverse {
|
||||
// 有错误输出
|
||||
if (output.stderr != '') {
|
||||
let error_msg = `提取字幕成功，但有警告提示：${output.stderr}`
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
book.id,
|
||||
book.type,
|
||||
error_msg,
|
||||
@ -856,7 +856,7 @@ export class BasicReverse {
|
||||
})
|
||||
|
||||
// 提取字幕成功，推送日志
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
book.id,
|
||||
book.type,
|
||||
`${bookTaskDetail.name} 提取字幕成功`,
|
||||
|
||||
@ -7,7 +7,7 @@ import { FfmpegOptions } from "../ffmpegOptions";
|
||||
import { CheckFileOrDirExist, CopyFileOrFolder, DeleteFolderAllFile } from "../../../define/Tools/file";
|
||||
import fs from 'fs';
|
||||
import { Book } from "../../../model/book";
|
||||
import { TaskScheduler } from '../task/taskScheduler';
|
||||
import { LogScheduler } from '../task/logScheduler';
|
||||
import { BookBasic } from "./BooKBasic";
|
||||
import { LoggerStatus, OtherData } from "../../../define/enum/softwareEnum";
|
||||
import { BasicReverse } from "./basicReverse";
|
||||
@ -15,18 +15,17 @@ import { BasicReverse } from "./basicReverse";
|
||||
export class BookFrame {
|
||||
bookServiceBasic: BookServiceBasic
|
||||
ffmpegOptions: FfmpegOptions
|
||||
taskScheduler: TaskScheduler
|
||||
logScheduler: LogScheduler
|
||||
basicReverse: BasicReverse
|
||||
bookBasic: BookBasic
|
||||
constructor() {
|
||||
this.bookServiceBasic = new BookServiceBasic();
|
||||
this.ffmpegOptions = new FfmpegOptions();
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.bookBasic = new BookBasic()
|
||||
this.basicReverse = new BasicReverse()
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 替换指定分镜的视频当前帧
|
||||
* @param bookTaskDetailId 指定的小说分镜ID
|
||||
@ -138,7 +137,7 @@ export class BookFrame {
|
||||
})
|
||||
} catch (error) {
|
||||
// 传入的分镜数据为空，需要重新获取
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
`没有传入分镜数据，请先进行分镜计算`,
|
||||
@ -164,7 +163,7 @@ export class BookFrame {
|
||||
}
|
||||
let res = await this.basicReverse.FrameDataToCutVideoData(item, shortClipData[i]);
|
||||
}
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
bookId,
|
||||
book.type,
|
||||
"所有的视频裁剪完成，开始抽帧",
|
||||
|
||||
@ -173,6 +173,12 @@ export class BookVideo {
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 添加剪映草稿
|
||||
* @param id
|
||||
* @param operateBookType
|
||||
* @returns
|
||||
*/
|
||||
async AddJianyingDraft(id: string, operateBookType: OperateBookType): Promise<GeneralResponse.ErrorItem | GeneralResponse.SuccessItem> {
|
||||
try {
|
||||
await this.InitService();
|
||||
|
||||
@ -13,7 +13,7 @@ import { MJSetting } from "../../../model/Setting/mjSetting";
|
||||
import { GeneralResponse } from "../../../model/generalResponse"
|
||||
import { LoggerStatus, ResponseMessageType } from "../../../define/enum/softwareEnum";
|
||||
import { ImageStyle } from "../Book/imageStyle";
|
||||
import { TaskScheduler } from "../task/taskScheduler";
|
||||
import { LogScheduler } from "../task/logScheduler";
|
||||
import { Tools } from "../../../main/tools"
|
||||
import { BookServiceBasic } from "../ServiceBasic/bookServiceBasic";
|
||||
import { PresetService } from "../presetService";
|
||||
@ -28,14 +28,14 @@ export class MJOpt {
|
||||
mjApi: MJApi;
|
||||
mjSetting: MJSetting.MjSetting
|
||||
imageStyle: ImageStyle;
|
||||
taskScheduler: TaskScheduler;
|
||||
logScheduler: LogScheduler;
|
||||
tools: Tools;
|
||||
bookServiceBasic: BookServiceBasic
|
||||
presetService: PresetService
|
||||
softWareServiceBasic: SoftWareServiceBasic
|
||||
constructor() {
|
||||
this.imageStyle = new ImageStyle()
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.tools = new Tools()
|
||||
this.bookServiceBasic = new BookServiceBasic();
|
||||
this.presetService = new PresetService()
|
||||
@ -528,7 +528,7 @@ export class MJOpt {
|
||||
let taskRes = await this.bookServiceBasic.AddBookBackTask(element.bookId, BookBackTaskType.MJ_IMAGE, TaskExecuteType.AUTO, element.bookTaskId, element.id, responseMessageName
|
||||
);
|
||||
// 添加返回日志
|
||||
await this.taskScheduler.AddLogToDB(element.bookId, BookBackTaskType.MJ_IMAGE, `添加 ${element.name} MJ生成任务成功`, element.bookTaskId, LoggerStatus.SUCCESS)
|
||||
await this.logScheduler.AddLogToDB(element.bookId, BookBackTaskType.MJ_IMAGE, `添加 ${element.name} MJ生成任务成功`, element.bookTaskId, LoggerStatus.SUCCESS)
|
||||
}
|
||||
// 全部完毕
|
||||
return successMessage(null, "MJ添加生成图片任务成功", "MJOpt_AddGenerateImageTask")
|
||||
|
||||
@ -48,8 +48,10 @@ export class SDOpt {
|
||||
const id = ids[i];
|
||||
let scene = await this.presetService.GetScenePresetDetailById(id)
|
||||
if (scene.code == 1) {
|
||||
if (scene.data) {
|
||||
// 这边开始拼接
|
||||
result += scene.data.prompt + ', '
|
||||
}
|
||||
} else {
|
||||
throw new Error(scene.message)
|
||||
}
|
||||
@ -68,10 +70,12 @@ export class SDOpt {
|
||||
const id = ids[i];
|
||||
let character = await this.presetService.GetCharacterPresetDetailById(id)
|
||||
if (character.code == 1) {
|
||||
if (character.data) {
|
||||
result += character.data.prompt + ', '
|
||||
if (character.data.lora && character.data.lora != '无' && character.data.loraWeight) {
|
||||
result += `, <lora:${character.data.lora}:${character.data.lora_weight}>`
|
||||
}
|
||||
}
|
||||
} else {
|
||||
throw new Error(character.message)
|
||||
}
|
||||
|
||||
@ -69,7 +69,7 @@ class BookServiceBasic {
|
||||
GetBookTaskDetailDataById = async (bookTaskDetailId: string) => await this.bookTaskDetailServiceBasic.GetBookTaskDetailDataById(bookTaskDetailId);
|
||||
GetBookTaskDetailData = async (condition: Book.QueryBookTaskDetailCondition, returnEmpty: boolean = false) => await this.bookTaskDetailServiceBasic.GetBookTaskDetailData(condition, returnEmpty);
|
||||
UpdateBookTaskDetail = async (bookTaskDetailId: string, data: Book.SelectBookTaskDetail) => await this.bookTaskDetailServiceBasic.UpdateBookTaskDetail(bookTaskDetailId, data);
|
||||
UpdateBookTaskStatus = async (bookTaskDetailId: string, status: BookTaskStatus) => await this.bookTaskDetailServiceBasic.UpdateBookTaskStatus(bookTaskDetailId, status);
|
||||
UpdateBookTaskStatus = async (bookTaskDetailId: string, status: BookTaskStatus,errorMsg? : string) => await this.bookTaskDetailServiceBasic.UpdateBookTaskStatus(bookTaskDetailId, status,errorMsg);
|
||||
DeleteBookTaskDetailReversePromptById = async (bookTaskDetailId: string, reversePromptId: string) => await this.bookTaskDetailServiceBasic.DeleteBookTaskDetailReversePromptById(bookTaskDetailId);
|
||||
DeleteBoookTaskDetailGenerateImage = async (bookTaskDetailId: string) => await this.bookTaskDetailServiceBasic.DeleteBoookTaskDetailGenerateImage(bookTaskDetailId);
|
||||
UpdateBookTaskDetailReversePrompt = async (bookTaskDetailId: string, reversePromptId: string, data: Book.ReversePrompt) => await this.bookTaskDetailServiceBasic.UpdateBookTaskDetailReversePrompt(bookTaskDetailId, reversePromptId, data);
|
||||
|
||||
@ -16,7 +16,7 @@ import fs from 'fs'
|
||||
import { GeneralResponse } from '../../../model/generalResponse'
|
||||
import { BookServiceBasic } from '../ServiceBasic/bookServiceBasic'
|
||||
import { LoggerStatus, OtherData, ResponseMessageType } from '../../../define/enum/softwareEnum'
|
||||
import { TaskScheduler } from '../task/taskScheduler'
|
||||
import { LogScheduler } from '../task/logScheduler'
|
||||
import { SubtitleModel } from '../../../model/subtitle'
|
||||
import { BookTaskStatus, OperateBookType } from '../../../define/enum/bookEnum'
|
||||
import axios from 'axios'
|
||||
@ -24,8 +24,9 @@ import { GptService } from '../GPT/gpt'
|
||||
import FormData from 'form-data'
|
||||
import { RetryWithBackoff } from '../../../define/Tools/common'
|
||||
import { DEFINE_STRING } from '../../../define/define_string'
|
||||
import { DraftTimeLineJson } from '../jianying/jianyingService'
|
||||
const util = require('util')
|
||||
const { exec } = require('child_process')
|
||||
const { spawn, exec } = require('child_process')
|
||||
const execAsync = util.promisify(exec)
|
||||
const fspromises = fs.promises
|
||||
|
||||
@ -36,12 +37,12 @@ const fspromises = fs.promises
|
||||
export class Subtitle {
|
||||
ffmpegOptions: FfmpegOptions
|
||||
bookServiceBasic: BookServiceBasic
|
||||
taskScheduler: TaskScheduler
|
||||
logScheduler: LogScheduler
|
||||
gptService: GptService
|
||||
|
||||
constructor() {
|
||||
this.bookServiceBasic = new BookServiceBasic()
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.ffmpegOptions = new FfmpegOptions()
|
||||
this.gptService = new GptService()
|
||||
}
|
||||
@ -74,28 +75,6 @@ export class Subtitle {
|
||||
return frameTimes
|
||||
}
|
||||
|
||||
/**
|
||||
* 加载指定的的小说相关的所有的数据
|
||||
* @param bookId 小说ID
|
||||
* @param bookTaskId 小说任务ID
|
||||
* @returns
|
||||
*/
|
||||
async GetBookAllData(bookId: string, bookTaskId: string = null): Promise<{ book: Book.SelectBook, bookTask: Book.SelectBookTask, bookTaskDetails: Book.SelectBookTaskDetail[] }> {
|
||||
let { book, bookTask } = await this.bookServiceBasic.GetBookAndTask(bookId, bookTaskId ? bookTaskId : 'output_00001')
|
||||
if (isEmpty(book.subtitlePosition)) {
|
||||
throw new Error("请先设置小说的字幕位置")
|
||||
}
|
||||
// 获取所有的分镜数据
|
||||
let bookTaskDetails = await this.bookServiceBasic.GetBookTaskDetailData({
|
||||
bookId: bookId,
|
||||
bookTaskId: bookTask.id
|
||||
})
|
||||
if (bookTaskDetails.length <= 0) {
|
||||
throw new Error("没有找到对应的分镜数据，请先执行对应的操作")
|
||||
}
|
||||
return { book, bookTask, bookTaskDetails }
|
||||
}
|
||||
|
||||
/**
|
||||
* 通用的小说获取分案的返回方法
|
||||
* @param content 获取的文案内容
|
||||
@ -123,7 +102,7 @@ export class Subtitle {
|
||||
}, DEFINE_STRING.BOOK.GET_COPYWRITING_RETURN)
|
||||
|
||||
// 添加日志
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
book.id,
|
||||
book.type,
|
||||
`${bookTaskDetail.name} 识别文案成功`,
|
||||
@ -562,7 +541,7 @@ export class Subtitle {
|
||||
})
|
||||
|
||||
// 推送成功消息
|
||||
await this.taskScheduler.AddLogToDB(
|
||||
await this.logScheduler.AddLogToDB(
|
||||
book.id,
|
||||
book.type,
|
||||
`${bookTaskDetail.name}分离音频成功，输出地址：${audioPath}`,
|
||||
@ -667,4 +646,73 @@ export class Subtitle {
|
||||
}
|
||||
}
|
||||
//#endregion
|
||||
|
||||
|
||||
//#region 本地Whisper识别字幕相关操作
|
||||
|
||||
async GetTextByLocalWhisper(frameTimeList: DraftTimeLineJson[], outDir: string, mp3Dir: string, localWhisperPath?: string): Promise<void> {
|
||||
try {
|
||||
let localWhisperPathExePath = localWhisperPath
|
||||
if (isEmpty(localWhisperPathExePath)) {
|
||||
localWhisperPathExePath = path.join(define.scripts_path, 'localWhisper/local_whisper.exe')
|
||||
}
|
||||
return new Promise((resolve, reject) => {
|
||||
let child = spawn(
|
||||
localWhisperPathExePath,
|
||||
['-ts', outDir, mp3Dir],
|
||||
{ encoding: 'utf-8' }
|
||||
);
|
||||
child.on('error', (error) => {
|
||||
console.log('error=', error)
|
||||
this.logScheduler.ReturnLogger(errorMessage("使用localWhisper识别字幕失败输出，失败信息如下：" + error.message))
|
||||
reject(new Error(error.message))
|
||||
})
|
||||
child.stdout.on('data', (data) => {
|
||||
console.log(data.toString())
|
||||
this.logScheduler.ReturnLogger(successMessage(data.toString(), "使用localWhisper识别字幕输出"))
|
||||
})
|
||||
child.stderr.on('data', (data) => {
|
||||
console.log('stderr=', data.toString())
|
||||
this.logScheduler.ReturnLogger(errorMessage("使用localWhisper识别字幕失败输出，失败信息如下：stderr = " + data.toString()))
|
||||
reject(new Error(data.toString()))
|
||||
})
|
||||
|
||||
child.on('close', async (data) => {
|
||||
console.log('data=', data.toString())
|
||||
this.logScheduler.ReturnLogger(successMessage(data.toString(), "使用localWhisper识别字幕完成"))
|
||||
let textPath = path.join(outDir, '文案.txt')
|
||||
if (!await CheckFileOrDirExist(textPath)) {
|
||||
throw new Error('没有找到识别输出的文案文件')
|
||||
}
|
||||
let text = await fspromises.readFile(textPath, 'utf-8')
|
||||
let textLines = text.split(/\r?\n/)
|
||||
let lastLine = textLines[textLines.length - 1]
|
||||
// 丢掉最后一行
|
||||
textLines = textLines.slice(0, -1)
|
||||
|
||||
if (textLines.length != frameTimeList.length) {
|
||||
throw new Error('分镜和识别文案数量不一致')
|
||||
}
|
||||
// 保存文案
|
||||
for (let i = 0; i < textLines.length; i++) {
|
||||
const element = textLines[i];
|
||||
frameTimeList[i].text = element
|
||||
}
|
||||
// 写出
|
||||
await fspromises.writeFile(path.join(global.config.project_path, '文案.txt'), textLines.join('\n'), 'utf-8')
|
||||
if (data == 0) {
|
||||
this.logScheduler.ReturnLogger(successMessage(null, "使用localWhisper识别字幕完成"))
|
||||
} else {
|
||||
this.logScheduler.ReturnLogger(errorMessage("使用localWhisper识别字幕失败，失败信息请查看日志"))
|
||||
}
|
||||
resolve();
|
||||
})
|
||||
})
|
||||
|
||||
} catch (error) {
|
||||
this.logScheduler.ReturnLogger(errorMessage("使用localWhisper识别字幕失败，失败信息如下：" + error.message))
|
||||
throw error
|
||||
}
|
||||
}
|
||||
//#endregion
|
||||
}
|
||||
|
||||
@ -11,7 +11,7 @@ import fs from 'fs'
|
||||
import { CheckFileOrDirExist } from "../../../define/Tools/file";
|
||||
import { BookServiceBasic } from "../ServiceBasic/bookServiceBasic";
|
||||
import { Subtitle } from "./subtitle";
|
||||
import { TaskScheduler } from "../task/taskScheduler";
|
||||
import { LogScheduler } from "../task/logScheduler";
|
||||
import { BookTaskStatus, BookType, OperateBookType } from "../../../define/enum/bookEnum";
|
||||
import { Book } from "../../../model/book";
|
||||
import { TimeStringToMilliseconds } from "../../../define/Tools/time";
|
||||
@ -20,7 +20,7 @@ export class SubtitleService {
|
||||
softWareServiceBasic: SoftWareServiceBasic
|
||||
bookServiceBasic: BookServiceBasic
|
||||
subtitle: Subtitle
|
||||
taskScheduler: TaskScheduler
|
||||
logScheduler: LogScheduler
|
||||
constructor() {
|
||||
this.softWareServiceBasic = new SoftWareServiceBasic();
|
||||
this.bookServiceBasic = new BookServiceBasic();
|
||||
|
||||
@ -98,8 +98,6 @@ export class FfmpegOptions {
|
||||
|
||||
/**
|
||||
* FFmpeg裁剪视频，将一个视频将裁剪指定的时间内的片段
|
||||
* @param {*} book 小说对象类
|
||||
* @param {*} bookTask 小说批次任务对象类
|
||||
* @param {*} startTime 开始时间
|
||||
* @param {*} endTime 结束时间
|
||||
* @param {*} videoPath 视频地址
|
||||
@ -225,7 +223,7 @@ export class FfmpegOptions {
|
||||
// 判断分镜是不是和数据库中的数据匹配的上
|
||||
let res = await new Promise((resolve, reject) => {
|
||||
Ffmpeg(videoPath)
|
||||
.inputOptions([`-ss ${MillisecondsToTimeString(frameTime)}`])
|
||||
.inputOptions([`-ss ${MillisecondsToTimeString(Math.ceil(frameTime))}`])
|
||||
.output(outFramePath)
|
||||
.frames(1)
|
||||
.on('end', async function () {
|
||||
|
||||
216
src/main/Service/jianying/jianyingService.ts
Normal file
216
src/main/Service/jianying/jianyingService.ts
Normal file
@ -0,0 +1,216 @@
|
||||
import path from 'path';
|
||||
import { CheckFileOrDirExist, DeleteFolderAllFile } from '../../../define/Tools/file';
|
||||
import fs from "fs";
|
||||
import { ValidateJson } from '../../../define/Tools/validate';
|
||||
import { FfmpegOptions } from '../ffmpegOptions';
|
||||
|
||||
/**
|
||||
* 存放剪映草稿的时间轴数据
|
||||
*/
|
||||
export type DraftTimeLineJson = {
|
||||
name: string;
|
||||
startTime: number;
|
||||
endTime: number;
|
||||
durationTime: number;
|
||||
middleTime: number;
|
||||
videoPath: string;
|
||||
text: string;
|
||||
framePath: string;
|
||||
subVideoPath?: string;
|
||||
audioPath?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* 剪映的一些服务
|
||||
*/
|
||||
class JianyingService {
|
||||
draftTimeLine: DraftTimeLineJson[];
|
||||
draftJson: any;
|
||||
ffmpegOptions: FfmpegOptions
|
||||
constructor() {
|
||||
this.draftTimeLine = [];
|
||||
this.ffmpegOptions = new FfmpegOptions();
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取剪映草稿的关键帧和文本
|
||||
* @param draftDir 草稿目录
|
||||
* @param projectDir 项目目录
|
||||
* @param packagePath 包路径
|
||||
*/
|
||||
async GetDraftFrameAndText(draftDir: string, projectDir: string, packagePath: string) {
|
||||
try {
|
||||
// 获取草稿文件路径
|
||||
let draftJsonPath = path.resolve(draftDir, "draft_content.json");
|
||||
if (!await CheckFileOrDirExist(draftJsonPath)) {
|
||||
throw new Error("剪映草稿文件不存在，请先检查");
|
||||
}
|
||||
// 读取草稿文件内容
|
||||
let draftJsonString = await fs.promises.readFile(draftJsonPath, "utf-8");
|
||||
if (!ValidateJson(draftJsonString)) {
|
||||
throw new Error("剪映草稿文件格式错误，请检查");
|
||||
}
|
||||
this.draftJson = JSON.parse(draftJsonString);
|
||||
|
||||
// 检查输出文件夹是否存在
|
||||
let projectTmp = path.resolve(projectDir, "tmp");
|
||||
if (await CheckFileOrDirExist(projectTmp)) {
|
||||
// 删除文件夹
|
||||
await DeleteFolderAllFile(projectTmp);
|
||||
}
|
||||
// 创建输出文件夹
|
||||
let projectInput = path.resolve(projectTmp, "input_crop");
|
||||
console.log("projectInput", projectInput);
|
||||
|
||||
// 获取剪映的轨道，并且判断是否包含一个video轨道和一个text轨道
|
||||
let draftTracks = this.draftJson.tracks;
|
||||
if (!draftTracks) {
|
||||
throw new Error("剪映草稿文件格式错误，没有轨道，请检查");
|
||||
}
|
||||
let hasVideo = draftTracks.some((track: any) => track.type === "video");
|
||||
let hasText = draftTracks.some((track: any) => track.type === "text");
|
||||
|
||||
if (!(this.draftJson.tracks && hasVideo && hasText)) {
|
||||
throw new Error("没有检测到剪映草稿文件中的video和text轨道，请检查");
|
||||
}
|
||||
|
||||
// 获取视频节点
|
||||
let videoNodes = draftTracks.filter((track: any) => track.type === "video")[0].segments;
|
||||
this.GetVideoTime(videoNodes);
|
||||
|
||||
// 获取文本节点
|
||||
let textNodes = draftTracks.filter((track: any) => track.type === "text")[0].segments;
|
||||
this.GetTextTime(textNodes);
|
||||
|
||||
console.log("场景数：", this.draftTimeLine.length);
|
||||
|
||||
// 将数据写入到文件中
|
||||
let txtData = this.draftTimeLine.map((item) => item.text);
|
||||
let txtPath = path.resolve(projectDir, "文案.txt");
|
||||
await fs.promises.writeFile(txtPath, txtData.join("\n"), "utf-8");
|
||||
|
||||
// 开始抽取关键帧
|
||||
await this.GetDraftFrame(projectInput);
|
||||
|
||||
// 将数据写入到json文件中
|
||||
let jsonPath = path.resolve(projectDir, "draftFrameData.json");
|
||||
await fs.promises.writeFile(jsonPath, JSON.stringify(this.draftTimeLine), "utf-8");
|
||||
console.log("GetDraftFrameAndText", jsonPath);
|
||||
} catch (error) {
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 在节点数组中查找指定类型和值的节点
|
||||
* @param nodes 节点数组
|
||||
* @param type 节点类型
|
||||
* @param value 节点值
|
||||
* @returns 找到的节点
|
||||
* @throws 如果没有找到对应的节点则抛出错误
|
||||
*/
|
||||
private FindNode(nodes: any[], type: string, value: any) {
|
||||
let res = nodes.filter((node: any) => node[type] === value);
|
||||
if (res.length === 0) {
|
||||
throw new Error("没有找到对应的节点");
|
||||
}
|
||||
return res[0];
|
||||
}
|
||||
|
||||
/**
|
||||
* 判断文本是否在时间轴内
|
||||
* @param draftTimeObject 时间轴对象
|
||||
* @param textStartTime 文本开始时间
|
||||
* @param textEndTile 文本结束时间
|
||||
* @returns 如果文本在时间轴内则返回 true，否则返回 false
|
||||
*/
|
||||
private TextIsInTimeLine(draftTimeObject: DraftTimeLineJson, textStartTime: number, textEndTile: number) {
|
||||
return textStartTime >= draftTimeObject.startTime && textEndTile <= draftTimeObject.endTime;
|
||||
}
|
||||
|
||||
/**
|
||||
* 抽取剪映草稿的关键帧
|
||||
* @param projectInput 项目输入目录
|
||||
*/
|
||||
private async GetDraftFrame(projectInput: string): Promise<void> {
|
||||
for (let i = 0; i < this.draftTimeLine.length; i++) {
|
||||
const element = this.draftTimeLine[i];
|
||||
let outImagePath = path.resolve(projectInput, (i + 1).toString().padStart(5, "0") + ".png");
|
||||
|
||||
// 使用 ffmpeg 抽取关键帧
|
||||
let frameRes = await this.ffmpegOptions.FfmpegGetFrame(element.middleTime / 1000, element.videoPath, outImagePath);
|
||||
if (frameRes.code == 0) {
|
||||
throw new Error(frameRes.message);
|
||||
}
|
||||
this.draftTimeLine[i].framePath = outImagePath;
|
||||
console.log("已经抽取第", i + 1, "帧");
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文本时间
|
||||
* @param textNodes 文本节点数组
|
||||
*/
|
||||
private GetTextTime(textNodes: any[]): void {
|
||||
let tempText = "";
|
||||
let count = 0;
|
||||
for (let i = 0; i < textNodes.length; i++) {
|
||||
const element = textNodes[i];
|
||||
let textStartTime = element.target_timerange.start;
|
||||
let textEndTime = textStartTime + element.target_timerange.duration;
|
||||
let textMaterialId = element.material_id;
|
||||
let textMaterialNode = this.FindNode(this.draftJson.materials.texts, "id", textMaterialId);
|
||||
let textContent = textMaterialNode.content;
|
||||
let textContentJson = JSON.parse(textContent);
|
||||
|
||||
let text = textContentJson.text + "。";
|
||||
|
||||
// 不在视频的时间轴内，丢弃
|
||||
if (count > this.draftTimeLine.length - 1) {
|
||||
break;
|
||||
}
|
||||
if (this.TextIsInTimeLine(this.draftTimeLine[count], textStartTime, textEndTime)) {
|
||||
tempText += text;
|
||||
if (i == textNodes.length - 1) {
|
||||
this.draftTimeLine[count].text = tempText;
|
||||
}
|
||||
} else {
|
||||
this.draftTimeLine[count].text = tempText;
|
||||
tempText = text;
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取视频时间
|
||||
* @param videoNodes 视频节点数组
|
||||
*/
|
||||
private GetVideoTime(videoNodes: any[]): void {
|
||||
for (let i = 0; i < videoNodes.length; i++) {
|
||||
const element = videoNodes[i];
|
||||
let startTime = element.target_timerange.start;
|
||||
let endTime = startTime + element.target_timerange.duration;
|
||||
let durationTime = element.target_timerange.duration;
|
||||
let middleTime = startTime + ((endTime - startTime) / 2);
|
||||
|
||||
let videoId = element.material_id;
|
||||
let materialNode = this.FindNode(this.draftJson.materials.videos, "id", videoId);
|
||||
let videoPath = materialNode.path;
|
||||
this.draftTimeLine.push({
|
||||
name: (i + 1).toString().padStart(5, "0"),
|
||||
startTime,
|
||||
endTime,
|
||||
durationTime,
|
||||
middleTime,
|
||||
videoPath,
|
||||
text: "",
|
||||
framePath: undefined
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
export default JianyingService;
|
||||
@ -4,7 +4,7 @@ import { LoggerStatus, OtherData } from '../../../define/enum/softwareEnum'
|
||||
import { successMessage, errorMessage } from '../../Public/generalTools'
|
||||
import { GeneralResponse } from '../../../model/generalResponse'
|
||||
|
||||
export class TaskScheduler {
|
||||
export class LogScheduler {
|
||||
constructor() { }
|
||||
/**
|
||||
* 添加日志到数据库，然后返回日志信息到前端，日志记录失败不会报错
|
||||
@ -20,7 +20,7 @@ export class TaskScheduler {
|
||||
type: string,
|
||||
content: string,
|
||||
bookTaskId: string,
|
||||
status = LoggerStatus.DOING
|
||||
status: LoggerStatus = LoggerStatus.DOING
|
||||
): Promise<GeneralResponse.ErrorItem | GeneralResponse.SuccessItem> {
|
||||
try {
|
||||
let log = {
|
||||
@ -38,7 +38,16 @@ export class TaskScheduler {
|
||||
|
||||
return res
|
||||
} catch (error) {
|
||||
return errorMessage(error.message, 'TaskScheduler_AddLogToDB')
|
||||
return errorMessage(error.message, 'LogScheduler_AddLogToDB')
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 将日志返回到前端
|
||||
* @param {*} log 日志信息
|
||||
* @returns
|
||||
*/
|
||||
ReturnLogger(log: GeneralResponse.ErrorItem | GeneralResponse.SuccessItem) {
|
||||
global.newWindow[0].win.webContents.send(DEFINE_STRING.SYSTEM.RETURN_LOGGER, log)
|
||||
}
|
||||
}
|
||||
333
src/main/Service/videoService/videoHandle.ts
Normal file
333
src/main/Service/videoService/videoHandle.ts
Normal file
@ -0,0 +1,333 @@
|
||||
|
||||
import path from 'path';
|
||||
import { CheckFileOrDirExist, DeleteFolderAllFile } from '../../../define/Tools/file';
|
||||
import fs from 'fs';
|
||||
import { LogScheduler } from '../task/logScheduler';
|
||||
import { successMessage } from '../../Public/generalTools';
|
||||
import { define } from '../../../define/define';
|
||||
import util from 'util';
|
||||
import { exec } from 'child_process';
|
||||
import { ValidateJson } from '../../../define/Tools/validate';
|
||||
import { DraftTimeLineJson } from '../jianying/jianyingService';
|
||||
const execAsync = util.promisify(exec)
|
||||
import { FfmpegOptions } from '../ffmpegOptions';
|
||||
import { TimeStringToMilliseconds } from '../../../define/Tools/time';
|
||||
import { isEmpty } from 'lodash';
|
||||
import { Subtitle } from '../Subtitle/subtitle';
|
||||
|
||||
|
||||
type VideoHandleShortVideoTimeLine = {
|
||||
name: string,
|
||||
startTime: number;
|
||||
endTime: number;
|
||||
videoPath: string,
|
||||
duration: number
|
||||
}
|
||||
|
||||
class VideoHandle {
|
||||
logScheduler: LogScheduler;
|
||||
ffmpegOptions: FfmpegOptions;
|
||||
subtitle: Subtitle;
|
||||
constructor() {
|
||||
this.logScheduler = new LogScheduler()
|
||||
this.ffmpegOptions = new FfmpegOptions();
|
||||
this.subtitle = new Subtitle();
|
||||
}
|
||||
|
||||
|
||||
public async StartStoryboarding(videoPath: string, sensitivity: number) {
|
||||
// 检查抽帧文件是不是存在
|
||||
let framePath = path.resolve(global.config.project_path, "data/frame");
|
||||
if (await CheckFileOrDirExist(framePath)) {
|
||||
await DeleteFolderAllFile(framePath);
|
||||
} else {
|
||||
await fs.promises.mkdir(framePath, { recursive: true })
|
||||
}
|
||||
// 检查输入文件是不是存在
|
||||
let inputPath = path.resolve(global.config.project_path, "tmp/input_crop");
|
||||
if (await CheckFileOrDirExist(inputPath)) {
|
||||
await DeleteFolderAllFile(inputPath);
|
||||
} else {
|
||||
await fs.promises.mkdir(inputPath, { recursive: true })
|
||||
}
|
||||
|
||||
// 检查本事localwhisper是不是存在
|
||||
let localwhisperPath = path.resolve(define.scripts_path, "localWhisper/local_whisper.exe");
|
||||
if (!await CheckFileOrDirExist(localwhisperPath)) {
|
||||
throw new Error('localwhisper 不存在，请查看文档安装localwhisper插件环境');
|
||||
}
|
||||
|
||||
// 判断输出文件是不是存在，存在删除
|
||||
let frameJson = path.resolve(global.config.project_path, "data/frameTimeLine.json");
|
||||
if (await CheckFileOrDirExist(frameJson)) {
|
||||
await fs.promises.unlink(frameJson);
|
||||
}
|
||||
|
||||
// 开始计算分镜
|
||||
let frameTimeList = await this.ComputedFrameTime(videoPath, frameJson, sensitivity);
|
||||
|
||||
// 开始对视频进行切割
|
||||
// 先计算时间点
|
||||
let shortVideo = [] as VideoHandleShortVideoTimeLine[];
|
||||
shortVideo = await this.VideoShortClip(frameTimeList, videoPath, 0.5 * 60 * 1000);
|
||||
|
||||
// 检查长度
|
||||
if (shortVideo.length != frameTimeList.length) {
|
||||
throw new Error('分镜数据和切割视频数据不一致，请检查');
|
||||
}
|
||||
|
||||
// 开始切割视频
|
||||
console.log(shortVideo);
|
||||
let subVideoPath = await this.CutViodeToShortClip(shortVideo, frameTimeList) as string[];
|
||||
|
||||
// 开始抽帧
|
||||
await this.GetFrameFromCutVideo(shortVideo, inputPath, subVideoPath, frameTimeList);
|
||||
|
||||
// 开始分离音频
|
||||
await this.SplitAudio(frameTimeList, framePath);
|
||||
|
||||
// 开始提取字幕
|
||||
await this.subtitle.GetTextByLocalWhisper(frameTimeList, framePath, framePath, localwhisperPath);
|
||||
|
||||
console.log(frameTimeList);
|
||||
await fs.promises.writeFile(frameJson, JSON.stringify(frameTimeList), 'utf-8');
|
||||
}
|
||||
|
||||
/**
|
||||
* 分离音频
|
||||
* @param frameTimeList
|
||||
* @param framePath
|
||||
*/
|
||||
async SplitAudio(frameTimeList: DraftTimeLineJson[], framePath: string) {
|
||||
for (let i = 0; i < frameTimeList.length; i++) {
|
||||
const element = frameTimeList[i];
|
||||
if (isEmpty(element.subVideoPath)) {
|
||||
throw new Error('没有找到待分离的视频数据，请检查');
|
||||
}
|
||||
if (!await CheckFileOrDirExist(element.subVideoPath)) {
|
||||
throw new Error(`视频片段 ${element.subVideoPath} 不存在，请检查`);
|
||||
}
|
||||
let audioPath = path.resolve(framePath, `${element.name}.mp3`);
|
||||
if (await CheckFileOrDirExist(audioPath)) {
|
||||
await fs.promises.unlink(audioPath);
|
||||
}
|
||||
let res = await this.ffmpegOptions.FfmpegExtractAudio(element.subVideoPath, audioPath)
|
||||
if (res.code == 0) {
|
||||
throw new Error(res.message);
|
||||
}
|
||||
if (!await CheckFileOrDirExist(audioPath)) {
|
||||
throw new Error(`分离音频 ${audioPath} 失败，没有找到分离后的音频文件，请检查`);
|
||||
}
|
||||
element.audioPath = audioPath;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
async GetFrameFromCutVideo(shortVideo: VideoHandleShortVideoTimeLine[], inputPath: string, subVideoPath: string[], frameTimeList: DraftTimeLineJson[]) {
|
||||
if (shortVideo.length != subVideoPath.length) {
|
||||
throw new Error('视频片段和分镜数据不一致');
|
||||
}
|
||||
if (shortVideo.length != frameTimeList.length) {
|
||||
throw new Error('视频片段和分镜数据不一致');
|
||||
}
|
||||
|
||||
let imagePath = [] as string[];
|
||||
for (let i = 0; i < shortVideo.length; i++) {
|
||||
const element = shortVideo[i];
|
||||
if (!frameTimeList[i]) {
|
||||
throw new Error('分镜数据和切割视频数据不一致，请检查');
|
||||
}
|
||||
|
||||
let middleTime = element.startTime + ((element.endTime - element.startTime) / 2);
|
||||
let outImagePath = path.resolve(inputPath, `${element.name}.png`);
|
||||
let res = await this.ffmpegOptions.FfmpegGetFrame(middleTime, element.videoPath, outImagePath);
|
||||
if (res.code == 0) {
|
||||
throw new Error(res.message);
|
||||
}
|
||||
imagePath.push(outImagePath);
|
||||
// 检查图片是否存在
|
||||
if (!await CheckFileOrDirExist(outImagePath)) {
|
||||
throw new Error(`抽取的图片 ${outImagePath} 不存在，请检查`);
|
||||
}
|
||||
frameTimeList[i].framePath = outImagePath;
|
||||
}
|
||||
return imagePath;
|
||||
}
|
||||
|
||||
/**
|
||||
* 将长视频按照指定的视频长度，给分割为一个个的小视频片段
|
||||
* @param shortVideo
|
||||
*/
|
||||
async CutViodeToShortClip(shortVideo: VideoHandleShortVideoTimeLine[], frameTimeList: DraftTimeLineJson[]): Promise<string[]> {
|
||||
let subVideoPaths = [] as string[];
|
||||
for (let i = 0; i < shortVideo.length; i++) {
|
||||
const element = shortVideo[i];
|
||||
if (!frameTimeList[i]) {
|
||||
throw new Error('分镜数据和切割视频数据不一致，请检查');
|
||||
}
|
||||
let subVideoPath = path.resolve(global.config.project_path, `data/frame/${element.name}.mp4`);
|
||||
// 开始截取视频
|
||||
let res = await this.ffmpegOptions.FfmpegCutVideo(
|
||||
element.startTime,
|
||||
element.endTime,
|
||||
element.videoPath,
|
||||
subVideoPath
|
||||
)
|
||||
subVideoPaths.push(subVideoPath);
|
||||
|
||||
if (res.code == 0) {
|
||||
throw new Error(res.message);
|
||||
}
|
||||
if (!await CheckFileOrDirExist(subVideoPath)) {
|
||||
throw new Error(`截取视频片段 ${subVideoPath} 不存在，请检查`);
|
||||
}
|
||||
frameTimeList[i].subVideoPath = subVideoPath;
|
||||
}
|
||||
return subVideoPaths;
|
||||
}
|
||||
|
||||
/**
|
||||
* 预处理视频，将视频切割成小段，减少计算时间
|
||||
* @param frameTimeList 要处理的时间线，是个json数组
|
||||
* @param videoPath 要处理的视频地址
|
||||
* @param duration 视频的持续时间
|
||||
* @returns
|
||||
*/
|
||||
public async VideoShortClip(frameTimeList: any[], videoPath: string, duration: number): Promise<VideoHandleShortVideoTimeLine[]> {
|
||||
let shortVideo = [] as VideoHandleShortVideoTimeLine[];
|
||||
let durationTime = 0; // 小视频片段的持续时间
|
||||
// let duration = 5 * 60 * 1000; // 5分钟
|
||||
let tempCount = 0;
|
||||
let shotVideoPath = path.resolve(global.config.project_path, `data/temp_frame_${tempCount}.mp4`); // 新的视频路径
|
||||
let startTime = 0; // 开始时间
|
||||
let endTime = 0; // 结束时间
|
||||
let lastEndTime = 0; // 上一个结束时间
|
||||
for (let i = 0; i < frameTimeList.length; i++) {
|
||||
const item = frameTimeList[i];
|
||||
let temRes = {
|
||||
name: (i + 1).toString().padStart(5, "0"),
|
||||
startTime: item.startTime - lastEndTime,
|
||||
endTime: item.endTime - lastEndTime,
|
||||
videoPath: shotVideoPath,
|
||||
duration: item.endTime - item.startTime
|
||||
}
|
||||
endTime = item.endTime;
|
||||
durationTime += item.endTime - item.startTime;
|
||||
if (durationTime > duration) { // 判断条件切割视频
|
||||
// 开始切割视频
|
||||
let res = await this.ffmpegOptions.FfmpegCutVideo(
|
||||
startTime,
|
||||
endTime,
|
||||
videoPath,
|
||||
shotVideoPath
|
||||
)
|
||||
if (res.code == 0) {
|
||||
throw new Error(res.message);
|
||||
}
|
||||
lastEndTime = item.endTime;
|
||||
tempCount++;
|
||||
durationTime = 0;
|
||||
startTime = endTime;
|
||||
endTime = 0;
|
||||
shotVideoPath = path.resolve(global.config.project_path, `data/temp_frame_${tempCount}.mp4`);
|
||||
}
|
||||
shortVideo.push(temRes)
|
||||
}
|
||||
// 最后一个也要切割
|
||||
if (durationTime > 0) {
|
||||
let res = await this.ffmpegOptions.FfmpegCutVideo(
|
||||
startTime,
|
||||
endTime,
|
||||
videoPath,
|
||||
shotVideoPath
|
||||
)
|
||||
if (res.code == 0) {
|
||||
throw new Error(res.message);
|
||||
}
|
||||
}
|
||||
// 将数据写出
|
||||
let shortVideoJson = path.resolve(global.config.project_path, "data/shortVideo.json");
|
||||
await fs.promises.writeFile(shortVideoJson, JSON.stringify(shortVideo), 'utf-8');
|
||||
return shortVideo;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 计算视频的帧时间。
|
||||
*
|
||||
* @param {string} videoPath - 视频文件的路径。
|
||||
* @param {number} sensitivity - 分镜的敏感度。
|
||||
* @throws {Error} 如果视频文件不存在或分镜失败，将抛出错误。
|
||||
*
|
||||
* @remarks
|
||||
* 该方法首先检查视频文件是否存在，如果不存在则抛出错误。
|
||||
* 然后检查输出文件是否存在，如果存在则删除。
|
||||
* 接着调用外部程序进行分镜处理，并记录日志。
|
||||
* 如果分镜成功但有警告信息，将记录警告日志。
|
||||
* 最后检查分镜输出文件是否存在并读取数据，如果没有找到输出文件或数据为空，将抛出错误。
|
||||
*/
|
||||
public async ComputedFrameTime(videoPath: string, frameJson: string, sensitivity: number): Promise<DraftTimeLineJson[]> {
|
||||
|
||||
if (!await CheckFileOrDirExist(videoPath)) {
|
||||
throw new Error('视频文件不存在，请检查');
|
||||
}
|
||||
|
||||
this.logScheduler.ReturnLogger(successMessage(null, "前置检查结束，开始进行分镜", "VideoHandle_StartStoryboarding"));
|
||||
|
||||
// 开始调用分镜
|
||||
let command = `"${path.join(
|
||||
define.scripts_path,
|
||||
'Lai.exe'
|
||||
)}" "-ka" "${videoPath}" "${frameJson}" "${sensitivity}"`
|
||||
const output = await execAsync(command, {
|
||||
maxBuffer: 1024 * 1024 * 10,
|
||||
encoding: 'utf-8'
|
||||
})
|
||||
// 有错误输出
|
||||
if (output.stderr != '') {
|
||||
let error_msg = `分镜成功，但有警告提示：${output.stderr}`
|
||||
this.logScheduler.ReturnLogger(successMessage(null, error_msg, "VideoHandle_StartStoryboarding"));
|
||||
}
|
||||
|
||||
// 分镜成功，处理输出
|
||||
let josnIsExist = await CheckFileOrDirExist(frameJson);
|
||||
if (!josnIsExist) {
|
||||
let error_message = `分镜失败，没有找到对应的分镜输出文件：${frameJson}`
|
||||
this.logScheduler.ReturnLogger(successMessage(null, error_message, "VideoHandle_StartStoryboarding"));
|
||||
throw new Error(error_message);
|
||||
}
|
||||
let frameJsonDataString = await fs.promises.readFile(frameJson, 'utf-8');
|
||||
let res = ValidateJson(frameJsonDataString);
|
||||
if (!res) {
|
||||
throw new Error('分镜数据不是有效的JSON格式，请检查');
|
||||
}
|
||||
let frameJsonData = JSON.parse(frameJsonDataString);
|
||||
if (frameJsonData.length <= 0) {
|
||||
let error_msg = `分镜失败，没有找到对应的分镜数据`
|
||||
this.logScheduler.ReturnLogger(successMessage(null, error_msg, "VideoHandle_StartStoryboarding"));
|
||||
throw new Error(error_msg);
|
||||
}
|
||||
let result = [] as DraftTimeLineJson[];
|
||||
// 这边将分镜的数据进行一个处理
|
||||
for (let i = 0; i < frameJsonData.length; i++) {
|
||||
const element = frameJsonData[i];
|
||||
let st = TimeStringToMilliseconds(element[0]);
|
||||
let et = TimeStringToMilliseconds(element[1]);
|
||||
let tempObject = {
|
||||
name: (i + 1).toString().padStart(5, "0"),
|
||||
startTime: st,
|
||||
endTime: et,
|
||||
middleTime: st + ((et - st) / 2),
|
||||
videoPath: videoPath,
|
||||
framePath: '',
|
||||
text: "",
|
||||
durationTime: et - st
|
||||
} as DraftTimeLineJson;
|
||||
result.push(tempObject);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
export default VideoHandle;
|
||||
@ -13,7 +13,7 @@ import { define } from '../../define/define'
|
||||
import { LOGGER_DEFINE } from '../../define/logger_define'
|
||||
import axios from 'axios'
|
||||
import { Base64ToFile, GetImageBase64 } from '../../define/Tools/image'
|
||||
import { TaskScheduler } from './task/taskScheduler';
|
||||
import { LogScheduler } from './task/logScheduler';
|
||||
import { LoggerStatus, OtherData, ResponseMessageType } from '../../define/enum/softwareEnum';
|
||||
import { basicApi } from '../../api/apiBasic';
|
||||
import { FfmpegOptions } from './ffmpegOptions';
|
||||
@ -28,7 +28,7 @@ import { BookTaskService } from '../../define/db/service/Book/bookTaskService';
|
||||
|
||||
export class Watermark {
|
||||
softwareService: SoftwareService
|
||||
taskScheduler: TaskScheduler;
|
||||
logScheduler: LogScheduler;
|
||||
bookService: BookService
|
||||
bookTaskDetailService: BookTaskDetailService
|
||||
bookTaskService: BookTaskService
|
||||
@ -39,8 +39,8 @@ export class Watermark {
|
||||
if (!this.softwareService) {
|
||||
this.softwareService = await SoftwareService.getInstance()
|
||||
}
|
||||
if (!this.taskScheduler) {
|
||||
this.taskScheduler = new TaskScheduler()
|
||||
if (!this.logScheduler) {
|
||||
this.logScheduler = new LogScheduler()
|
||||
}
|
||||
if (!this.bookService) {
|
||||
this.bookService = await BookService.getInstance()
|
||||
@ -449,7 +449,7 @@ export class Watermark {
|
||||
}, DEFINE_STRING.BOOK.REMOVE_WATERMARK_RETURN)
|
||||
|
||||
|
||||
this.taskScheduler.AddLogToDB(book.id, book.type, `${element.name} 去除水印完成`, element.bookTaskId, LoggerStatus.SUCCESS)
|
||||
this.logScheduler.AddLogToDB(book.id, book.type, `${element.name} 去除水印完成`, element.bookTaskId, LoggerStatus.SUCCESS)
|
||||
}
|
||||
// 全部完毕
|
||||
if (operateBookType == OperateBookType.BOOKTASKDETAIL) {
|
||||
|
||||
788
src/main/func.js
788
src/main/func.js
File diff suppressed because it is too large
Load Diff
@ -73,9 +73,8 @@ const api = {
|
||||
},
|
||||
|
||||
// 分镜语音识别消息
|
||||
StartStoryboarding: async (value) => {
|
||||
let res = await ipcRenderer.invoke(DEFINE_STRING.START_STORY_BOARDING, value)
|
||||
},
|
||||
StartStoryboarding: async (value) =>
|
||||
await ipcRenderer.invoke(DEFINE_STRING.START_STORY_BOARDING, value),
|
||||
|
||||
// 获取设置的初始数据
|
||||
getSettingDafultData: async (callback) =>
|
||||
|
||||
@ -37,7 +37,6 @@
|
||||
label-placement="left"
|
||||
inline
|
||||
:model="frameValue"
|
||||
:rules="rules"
|
||||
size="medium"
|
||||
>
|
||||
<n-form-item label="分割敏感度" path="sensitivity" style="width: 300px">
|
||||
@ -68,7 +67,7 @@
|
||||
<n-code style="padding: 0; font-size: small" :code="code" language="js" word-wrap />
|
||||
</template>
|
||||
|
||||
<script>
|
||||
<script setup>
|
||||
import { defineComponent, ref, onMounted, toRaw } from 'vue'
|
||||
import {
|
||||
NSelect,
|
||||
@ -83,39 +82,26 @@ import {
|
||||
NForm,
|
||||
NFormItem,
|
||||
NSlider,
|
||||
NInput
|
||||
NInput,
|
||||
useDialog
|
||||
} from 'naive-ui'
|
||||
import { DEFINE_STRING } from '../../../../define/define_string'
|
||||
export default defineComponent({
|
||||
components: {
|
||||
NSelect,
|
||||
NButton,
|
||||
NSpin,
|
||||
NDivider,
|
||||
NCode,
|
||||
NTabs,
|
||||
NTabPane,
|
||||
NSpace,
|
||||
NForm,
|
||||
NFormItem,
|
||||
NSlider,
|
||||
NInput
|
||||
},
|
||||
setup() {
|
||||
let options = []
|
||||
let out_dir = ref(null)
|
||||
let selectedValue = ref(null)
|
||||
let show = ref(false)
|
||||
let code = ref('')
|
||||
const message = useMessage()
|
||||
let storyLoading = ref(false)
|
||||
|
||||
let frameValue = ref({
|
||||
let options = []
|
||||
let out_dir = ref(null)
|
||||
let selectedValue = ref(null)
|
||||
let show = ref(false)
|
||||
let code = ref('')
|
||||
const message = useMessage()
|
||||
let storyLoading = ref(false)
|
||||
let dialog = useDialog()
|
||||
|
||||
let frameValue = ref({
|
||||
sensitivity: 30,
|
||||
video_path: null
|
||||
})
|
||||
})
|
||||
|
||||
onMounted(() => {
|
||||
onMounted(() => {
|
||||
window.api.getDraftFileList((value) => {
|
||||
value.forEach((element) => {
|
||||
let obj = {
|
||||
@ -138,9 +124,9 @@ export default defineComponent({
|
||||
}
|
||||
code.value = code.value + '\n' + value.data
|
||||
})
|
||||
})
|
||||
})
|
||||
|
||||
async function getFrameFunc(e) {
|
||||
async function getFrameFunc(e) {
|
||||
out_dir = window.config.project_path
|
||||
if (selectedValue.value == null || selectedValue.value == undefined) {
|
||||
message.error('请选择剪映草稿和输出草稿')
|
||||
@ -158,9 +144,9 @@ export default defineComponent({
|
||||
code.value = value.data
|
||||
show.value = false
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
function selectExportFolder(e) {
|
||||
function selectExportFolder(e) {
|
||||
window.api.selectFolder(null, (value) => {
|
||||
if (value.length <= 0) {
|
||||
message.error('必须选择输出文件夹位置')
|
||||
@ -168,12 +154,12 @@ export default defineComponent({
|
||||
}
|
||||
out_dir.value = value[0]
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
/**
|
||||
* 选择指定的视频文件
|
||||
*/
|
||||
async function GetVideoFile() {
|
||||
async function GetVideoFile() {
|
||||
await window.api.SelectFile(['mp4'], (value) => {
|
||||
if (value.code == 0) {
|
||||
message.error(value.message)
|
||||
@ -181,12 +167,12 @@ export default defineComponent({
|
||||
}
|
||||
frameValue.value.video_path = value.value
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
/**
|
||||
* 开始分镜执行分镜任务
|
||||
*/
|
||||
async function StartStoryboarding() {
|
||||
async function StartStoryboarding() {
|
||||
storyLoading.value = true
|
||||
if (frameValue.value.video_path == null) {
|
||||
message.error('选择分镜的视频地址')
|
||||
@ -202,42 +188,36 @@ export default defineComponent({
|
||||
return
|
||||
}
|
||||
|
||||
await window.api.StartStoryboarding(toRaw(frameValue.value))
|
||||
let res = await window.api.StartStoryboarding(toRaw(frameValue.value))
|
||||
if (res.code == 0) {
|
||||
dialog.error({
|
||||
title: '抽帧错误输出',
|
||||
content: res.message
|
||||
})
|
||||
return
|
||||
} else {
|
||||
dialog.success({
|
||||
title: '抽帧成功',
|
||||
content: '视频分镜，抽帧，问题提取都已完成'
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
/**
|
||||
* 打开环境安装网站
|
||||
*/
|
||||
function OpenTeachDoc() {
|
||||
function OpenTeachDoc() {
|
||||
window.api.OpenUrl(
|
||||
'https://pvwu1oahp5m.feishu.cn/docx/VrBVd2KUDosmNfxat3OceWuInjd?from=from_copylink'
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
function openExportFolder() {
|
||||
function openExportFolder() {
|
||||
window.system.OpenFolder({
|
||||
folderPath: 'tmp/input_crop',
|
||||
baseProject: true
|
||||
})
|
||||
}
|
||||
|
||||
return {
|
||||
selectedValue,
|
||||
options,
|
||||
out_dir,
|
||||
getFrameFunc,
|
||||
selectExportFolder,
|
||||
show,
|
||||
code,
|
||||
frameValue,
|
||||
GetVideoFile,
|
||||
StartStoryboarding,
|
||||
OpenTeachDoc,
|
||||
storyLoading,
|
||||
openExportFolder
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
</script>
|
||||
|
||||
<style>
|
||||
|
||||
@ -47,7 +47,6 @@ export default defineComponent({
|
||||
onMounted(async () => {
|
||||
// 监听返回的日志数据
|
||||
window.api.setEventListen([DEFINE_STRING.SYSTEM.RETURN_LOGGER], (value) => {
|
||||
debuger
|
||||
if (value.code == 0) {
|
||||
message.error('添加日志输出失败，但是不影响使用')
|
||||
logger.value += value.message + '\n'
|
||||
|
||||
@ -1,9 +1,8 @@
|
||||
<template>
|
||||
<div style="width: 100%; height: 100%">
|
||||
<div
|
||||
id="showmessage"
|
||||
style="font-size: 15px; display: flex; justify-content: center; width: 100%; height: 100%"
|
||||
></div>
|
||||
<div style="font-size: 15px; display: flex; justify-content: center; width: 100%; height: 100%">
|
||||
<div id="showmessage"></div>
|
||||
</div>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
@ -34,6 +33,7 @@ async function GetRemoteSystemInformation() {
|
||||
iframe.style.padding = '0'
|
||||
iframe.style.height = '98vh' // Adjust the height as needed
|
||||
showMessageDiv.innerHTML = ''
|
||||
showMessageDiv.style.width = '100%'
|
||||
showMessageDiv.appendChild(iframe)
|
||||
} else {
|
||||
showMessageDiv.innerHTML = remoteHomePage
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user