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https://github.com/facefusion/facefusion.git
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Compare commits
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 Henry RuhsandGitHub
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henryruhs
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henryruhs
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Henry RuhsandGitHub
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Henry RuhsandGitHub
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henryruhs
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Henry RuhsandGitHub
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After Width: | Height: | Size: 1.1 MiB |
@@ -33,9 +33,8 @@ jobs:
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.12'
|
||||
- run: python install.py --onnxruntime default --skip-conda
|
||||
- run: python install.py default --skip-conda
|
||||
- run: pip install pytest
|
||||
- run: pip install httpx
|
||||
- run: pytest
|
||||
report:
|
||||
needs: test
|
||||
@@ -49,11 +48,10 @@ jobs:
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.12'
|
||||
- run: python install.py --onnxruntime default --skip-conda
|
||||
- run: python install.py default --skip-conda
|
||||
- run: pip install coveralls
|
||||
- run: pip install pytest
|
||||
- run: pip install pytest-cov
|
||||
- run: pip install httpx
|
||||
- run: pytest tests --cov facefusion
|
||||
- run: coveralls --service github
|
||||
env:
|
||||
|
||||
+1
-1
@@ -1,3 +1,3 @@
|
||||
OpenRAIL-AS license
|
||||
|
||||
Copyright (c) 2025 Henry Ruhs
|
||||
Copyright (c) 2026 Henry Ruhs
|
||||
|
||||
@@ -8,6 +8,12 @@ FaceFusion
|
||||
|
||||
|
||||
|
||||
Preview
|
||||
-------
|
||||
|
||||
|
||||
|
||||
|
||||
Installation
|
||||
------------
|
||||
|
||||
@@ -28,10 +34,10 @@ options:
|
||||
|
||||
commands:
|
||||
run run the program
|
||||
headless-run run the program in headless mode
|
||||
batch-run run the program in batch mode
|
||||
force-download force automate downloads and exit
|
||||
benchmark benchmark the program
|
||||
api start the API server
|
||||
job-list list jobs by status
|
||||
job-create create a drafted job
|
||||
job-submit submit a drafted job to become a queued job
|
||||
|
||||
+14
-8
@@ -1,6 +1,3 @@
|
||||
[workflow]
|
||||
workflow =
|
||||
|
||||
[paths]
|
||||
temp_path =
|
||||
jobs_path =
|
||||
@@ -35,6 +32,9 @@ reference_face_position =
|
||||
reference_face_distance =
|
||||
reference_frame_number =
|
||||
|
||||
[face_tracker]
|
||||
face_tracker_score =
|
||||
|
||||
[face_masker]
|
||||
face_occluder_model =
|
||||
face_parser_model =
|
||||
@@ -51,6 +51,10 @@ voice_extractor_model =
|
||||
trim_frame_start =
|
||||
trim_frame_end =
|
||||
temp_frame_format =
|
||||
keep_temp =
|
||||
|
||||
[frame_distribution]
|
||||
target_frame_amount =
|
||||
|
||||
[output_creation]
|
||||
output_image_quality =
|
||||
@@ -69,7 +73,8 @@ processors =
|
||||
age_modifier_model =
|
||||
age_modifier_direction =
|
||||
background_remover_model =
|
||||
background_remover_color =
|
||||
background_remover_fill_color =
|
||||
background_remover_despill_color =
|
||||
deep_swapper_model =
|
||||
deep_swapper_morph =
|
||||
expression_restorer_model =
|
||||
@@ -105,6 +110,11 @@ frame_enhancer_blend =
|
||||
lip_syncer_model =
|
||||
lip_syncer_weight =
|
||||
|
||||
[uis]
|
||||
open_browser =
|
||||
ui_layouts =
|
||||
ui_workflow =
|
||||
|
||||
[download]
|
||||
download_providers =
|
||||
download_scope =
|
||||
@@ -114,10 +124,6 @@ benchmark_mode =
|
||||
benchmark_resolutions =
|
||||
benchmark_cycle_count =
|
||||
|
||||
[api]
|
||||
api_host =
|
||||
api_port =
|
||||
|
||||
[execution]
|
||||
execution_device_ids =
|
||||
execution_providers =
|
||||
|
||||
+2
-1
@@ -4,7 +4,8 @@ import os
|
||||
|
||||
os.environ['OMP_NUM_THREADS'] = '1'
|
||||
|
||||
from facefusion import core
|
||||
from facefusion import conda, core
|
||||
|
||||
if __name__ == '__main__':
|
||||
conda.setup()
|
||||
core.cli()
|
||||
|
||||
@@ -1,15 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
from starlette.datastructures import Headers
|
||||
from starlette.types import Scope
|
||||
|
||||
|
||||
def get_sec_websocket_protocol(scope : Scope) -> Optional[str]:
|
||||
protocol_header = Headers(scope = scope).get('Sec-WebSocket-Protocol')
|
||||
|
||||
if protocol_header:
|
||||
protocol, _, _ = protocol_header.partition(',')
|
||||
return protocol.strip()
|
||||
|
||||
return None
|
||||
|
||||
@@ -1,50 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.audio import detect_audio_duration
|
||||
from facefusion.filesystem import is_audio, is_image, is_video
|
||||
from facefusion.types import AudioMetadata, ImageMetadata, MediaType, VideoMetadata
|
||||
from facefusion.vision import count_video_frame_total, detect_image_resolution, detect_video_duration, detect_video_fps, detect_video_resolution
|
||||
|
||||
|
||||
def extract_audio_metadata(file_path : str) -> AudioMetadata:
|
||||
metadata : AudioMetadata =\
|
||||
{
|
||||
'duration': detect_audio_duration(file_path),
|
||||
'sample_rate': 0,
|
||||
'frame_total': 0,
|
||||
'channels': 0,
|
||||
'format': ''
|
||||
}
|
||||
return metadata
|
||||
|
||||
|
||||
def extract_image_metadata(file_path : str) -> ImageMetadata:
|
||||
resolution = detect_image_resolution(file_path)
|
||||
metadata : ImageMetadata =\
|
||||
{
|
||||
'resolution': resolution if resolution else (0, 0)
|
||||
}
|
||||
return metadata
|
||||
|
||||
|
||||
def extract_video_metadata(file_path : str) -> VideoMetadata:
|
||||
resolution = detect_video_resolution(file_path)
|
||||
fps = detect_video_fps(file_path)
|
||||
metadata : VideoMetadata =\
|
||||
{
|
||||
'duration': detect_video_duration(file_path),
|
||||
'frame_total': count_video_frame_total(file_path),
|
||||
'fps': fps if fps else 0.0,
|
||||
'resolution': resolution if resolution else (0, 0)
|
||||
}
|
||||
return metadata
|
||||
|
||||
|
||||
def detect_media_type(file_path : str) -> Optional[MediaType]:
|
||||
if is_audio(file_path):
|
||||
return 'audio'
|
||||
if is_image(file_path):
|
||||
return 'image'
|
||||
if is_video(file_path):
|
||||
return 'video'
|
||||
return None
|
||||
@@ -1,92 +0,0 @@
|
||||
import os
|
||||
import uuid
|
||||
from datetime import datetime, timedelta
|
||||
from typing import List, Optional, cast
|
||||
|
||||
from facefusion.apis.asset_helper import detect_media_type, extract_audio_metadata, extract_image_metadata, extract_video_metadata
|
||||
from facefusion.filesystem import get_file_format, get_file_name
|
||||
from facefusion.types import AssetId, AssetSet, AssetStore, AssetType, AudioAsset, AudioFormat, ImageAsset, ImageFormat, SessionId, VideoAsset, VideoFormat
|
||||
|
||||
ASSET_STORE : AssetStore = {}
|
||||
|
||||
|
||||
def create_asset(session_id : SessionId, asset_type : AssetType, asset_path : str) -> Optional[AudioAsset | ImageAsset | VideoAsset]:
|
||||
asset_id = str(uuid.uuid4())
|
||||
asset_name = get_file_name(asset_path)
|
||||
asset_format = get_file_format(asset_path)
|
||||
asset_size = os.path.getsize(asset_path)
|
||||
media_type = detect_media_type(asset_path)
|
||||
created_at = datetime.now()
|
||||
expires_at = created_at + timedelta(hours = 2)
|
||||
|
||||
if session_id not in ASSET_STORE:
|
||||
ASSET_STORE[session_id] = {}
|
||||
|
||||
if media_type == 'audio':
|
||||
ASSET_STORE[session_id][asset_id] = cast(AudioAsset,
|
||||
{
|
||||
'id': asset_id,
|
||||
'created_at': created_at,
|
||||
'expires_at': expires_at,
|
||||
'type': asset_type,
|
||||
'media': media_type,
|
||||
'name': asset_name,
|
||||
'format': cast(AudioFormat, asset_format),
|
||||
'size': asset_size,
|
||||
'path': asset_path,
|
||||
'metadata': extract_audio_metadata(asset_path)
|
||||
})
|
||||
|
||||
if media_type == 'image':
|
||||
ASSET_STORE[session_id][asset_id] = cast(ImageAsset,
|
||||
{
|
||||
'id': asset_id,
|
||||
'created_at': created_at,
|
||||
'expires_at': expires_at,
|
||||
'type': asset_type,
|
||||
'media': media_type,
|
||||
'name': asset_name,
|
||||
'format': cast(ImageFormat, asset_format),
|
||||
'size': asset_size,
|
||||
'path': asset_path,
|
||||
'metadata': extract_image_metadata(asset_path)
|
||||
})
|
||||
|
||||
if media_type == 'video':
|
||||
ASSET_STORE[session_id][asset_id] = cast(VideoAsset,
|
||||
{
|
||||
'id': asset_id,
|
||||
'created_at': created_at,
|
||||
'expires_at': expires_at,
|
||||
'type': asset_type,
|
||||
'media': media_type,
|
||||
'name': asset_name,
|
||||
'format': cast(VideoFormat, asset_format),
|
||||
'size': asset_size,
|
||||
'path': asset_path,
|
||||
'metadata': extract_video_metadata(asset_path)
|
||||
})
|
||||
|
||||
return ASSET_STORE[session_id].get(asset_id)
|
||||
|
||||
|
||||
def get_assets(session_id : SessionId) -> Optional[AssetSet]:
|
||||
return ASSET_STORE.get(session_id)
|
||||
|
||||
|
||||
def get_asset(session_id : SessionId, asset_id : AssetId) -> Optional[AudioAsset | ImageAsset | VideoAsset]:
|
||||
if session_id in ASSET_STORE:
|
||||
return ASSET_STORE.get(session_id).get(asset_id)
|
||||
return None
|
||||
|
||||
|
||||
def delete_assets(session_id : SessionId, asset_ids : List[AssetId]) -> None:
|
||||
if session_id in ASSET_STORE:
|
||||
for asset_id in asset_ids:
|
||||
if asset_id in ASSET_STORE.get(session_id):
|
||||
del ASSET_STORE[session_id][asset_id]
|
||||
return None
|
||||
|
||||
|
||||
def clear() -> None:
|
||||
ASSET_STORE.clear()
|
||||
@@ -1,162 +0,0 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.status import HTTP_200_OK
|
||||
|
||||
import facefusion.choices
|
||||
from facefusion.execution import get_available_execution_providers
|
||||
from facefusion.ffmpeg import get_available_encoder_set
|
||||
from facefusion.processors.modules.face_debugger import choices as face_debugger_choices
|
||||
from facefusion.processors.modules.face_enhancer import choices as face_enhancer_choices
|
||||
from facefusion.processors.modules.face_swapper import choices as face_swapper_choices
|
||||
from facefusion.processors.modules.frame_enhancer import choices as frame_enhancer_choices
|
||||
|
||||
|
||||
async def get_choices(request : Request) -> JSONResponse:
|
||||
available_execution_providers = get_available_execution_providers()
|
||||
available_encoder_set = get_available_encoder_set()
|
||||
|
||||
choices_data : Dict[str, Any] =\
|
||||
{
|
||||
'face_detector_models': facefusion.choices.face_detector_models,
|
||||
'face_detector_set': facefusion.choices.face_detector_set,
|
||||
'face_landmarker_models': facefusion.choices.face_landmarker_models,
|
||||
'face_selector_modes': facefusion.choices.face_selector_modes,
|
||||
'face_selector_orders': facefusion.choices.face_selector_orders,
|
||||
'face_selector_genders': facefusion.choices.face_selector_genders,
|
||||
'face_selector_races': facefusion.choices.face_selector_races,
|
||||
'face_occluder_models': facefusion.choices.face_occluder_models,
|
||||
'face_parser_models': facefusion.choices.face_parser_models,
|
||||
'face_mask_types': facefusion.choices.face_mask_types,
|
||||
'face_mask_areas': facefusion.choices.face_mask_areas,
|
||||
'face_mask_regions': facefusion.choices.face_mask_regions,
|
||||
'voice_extractor_models': facefusion.choices.voice_extractor_models,
|
||||
'workflows': facefusion.choices.workflows,
|
||||
'audio_formats': facefusion.choices.audio_formats,
|
||||
'image_formats': facefusion.choices.image_formats,
|
||||
'video_formats': facefusion.choices.video_formats,
|
||||
'temp_frame_formats': facefusion.choices.temp_frame_formats,
|
||||
'output_audio_encoders': available_encoder_set.get('audio'),
|
||||
'output_video_encoders': available_encoder_set.get('video'),
|
||||
'output_video_presets': facefusion.choices.output_video_presets,
|
||||
'execution_providers': available_execution_providers,
|
||||
'video_memory_strategies': facefusion.choices.video_memory_strategies,
|
||||
'log_levels': facefusion.choices.log_levels,
|
||||
'face_swapper_models': face_swapper_choices.face_swapper_models,
|
||||
'face_swapper_set': face_swapper_choices.face_swapper_set,
|
||||
'face_enhancer_models': face_enhancer_choices.face_enhancer_models,
|
||||
'frame_enhancer_models': frame_enhancer_choices.frame_enhancer_models,
|
||||
'face_debugger_items': face_debugger_choices.face_debugger_items,
|
||||
'face_detector_angles': list(facefusion.choices.face_detector_angles),
|
||||
'face_detector_score_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_detector_score_range),
|
||||
'max': max(facefusion.choices.face_detector_score_range),
|
||||
'step': facefusion.choices.face_detector_score_range[1] - facefusion.choices.face_detector_score_range[0]
|
||||
},
|
||||
'face_landmarker_score_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_landmarker_score_range),
|
||||
'max': max(facefusion.choices.face_landmarker_score_range),
|
||||
'step': facefusion.choices.face_landmarker_score_range[1] - facefusion.choices.face_landmarker_score_range[0]
|
||||
},
|
||||
'face_mask_blur_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_mask_blur_range),
|
||||
'max': max(facefusion.choices.face_mask_blur_range),
|
||||
'step': facefusion.choices.face_mask_blur_range[1] - facefusion.choices.face_mask_blur_range[0]
|
||||
},
|
||||
'face_mask_padding_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_mask_padding_range),
|
||||
'max': max(facefusion.choices.face_mask_padding_range),
|
||||
'step': 1
|
||||
},
|
||||
'face_selector_age_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_selector_age_range),
|
||||
'max': max(facefusion.choices.face_selector_age_range),
|
||||
'step': 1
|
||||
},
|
||||
'reference_face_distance_range':
|
||||
{
|
||||
'min': min(facefusion.choices.reference_face_distance_range),
|
||||
'max': max(facefusion.choices.reference_face_distance_range),
|
||||
'step': facefusion.choices.reference_face_distance_range[1] - facefusion.choices.reference_face_distance_range[0]
|
||||
},
|
||||
'output_image_quality_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_image_quality_range),
|
||||
'max': max(facefusion.choices.output_image_quality_range),
|
||||
'step': 1
|
||||
},
|
||||
'output_image_scale_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_image_scale_range),
|
||||
'max': max(facefusion.choices.output_image_scale_range),
|
||||
'step': facefusion.choices.output_image_scale_range[1] - facefusion.choices.output_image_scale_range[0]
|
||||
},
|
||||
'output_audio_quality_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_audio_quality_range),
|
||||
'max': max(facefusion.choices.output_audio_quality_range),
|
||||
'step': 1
|
||||
},
|
||||
'output_audio_volume_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_audio_volume_range),
|
||||
'max': max(facefusion.choices.output_audio_volume_range),
|
||||
'step': 1
|
||||
},
|
||||
'output_video_quality_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_video_quality_range),
|
||||
'max': max(facefusion.choices.output_video_quality_range),
|
||||
'step': 1
|
||||
},
|
||||
'output_video_scale_range':
|
||||
{
|
||||
'min': min(facefusion.choices.output_video_scale_range),
|
||||
'max': max(facefusion.choices.output_video_scale_range),
|
||||
'step': facefusion.choices.output_video_scale_range[1] - facefusion.choices.output_video_scale_range[0]
|
||||
},
|
||||
'execution_thread_count_range':
|
||||
{
|
||||
'min': min(facefusion.choices.execution_thread_count_range),
|
||||
'max': max(facefusion.choices.execution_thread_count_range),
|
||||
'step': 1
|
||||
},
|
||||
'face_detector_margin_range':
|
||||
{
|
||||
'min': min(facefusion.choices.face_detector_margin_range),
|
||||
'max': max(facefusion.choices.face_detector_margin_range),
|
||||
'step': 1
|
||||
},
|
||||
'face_swapper_weight_range':
|
||||
{
|
||||
'min': min(face_swapper_choices.face_swapper_weight_range),
|
||||
'max': max(face_swapper_choices.face_swapper_weight_range),
|
||||
'step': face_swapper_choices.face_swapper_weight_range[1] - face_swapper_choices.face_swapper_weight_range[0]
|
||||
},
|
||||
'face_enhancer_blend_range':
|
||||
{
|
||||
'min': min(face_enhancer_choices.face_enhancer_blend_range),
|
||||
'max': max(face_enhancer_choices.face_enhancer_blend_range),
|
||||
'step': 1
|
||||
},
|
||||
'face_enhancer_weight_range':
|
||||
{
|
||||
'min': min(face_enhancer_choices.face_enhancer_weight_range),
|
||||
'max': max(face_enhancer_choices.face_enhancer_weight_range),
|
||||
'step': face_enhancer_choices.face_enhancer_weight_range[1] - face_enhancer_choices.face_enhancer_weight_range[0]
|
||||
},
|
||||
'frame_enhancer_blend_range':
|
||||
{
|
||||
'min': min(frame_enhancer_choices.frame_enhancer_blend_range),
|
||||
'max': max(frame_enhancer_choices.frame_enhancer_blend_range),
|
||||
'step': 1
|
||||
}
|
||||
}
|
||||
|
||||
return JSONResponse(choices_data, status_code = HTTP_200_OK)
|
||||
@@ -1,47 +0,0 @@
|
||||
from starlette.applications import Starlette
|
||||
from starlette.middleware import Middleware
|
||||
from starlette.middleware.cors import CORSMiddleware
|
||||
from starlette.routing import Route, WebSocketRoute
|
||||
|
||||
from facefusion.apis.choices import get_choices
|
||||
from facefusion.apis.endpoints.assets import delete_asset, delete_assets, get_asset, get_assets, upload_asset
|
||||
from facefusion.apis.endpoints.ping import websocket_ping
|
||||
from facefusion.apis.endpoints.session import create_session, create_session_guard, destroy_session, get_session, refresh_session
|
||||
from facefusion.apis.endpoints.state import get_state, set_state
|
||||
from facefusion.apis.endpoints.stream import delete_stream, post_stream, websocket_stream
|
||||
from facefusion.apis.metrics import websocket_metrics
|
||||
from facefusion.apis.remote import remote
|
||||
from facefusion.apis.timeline import get_timeline
|
||||
from facefusion.apis.version import create_version_guard
|
||||
|
||||
|
||||
def create_api() -> Starlette:
|
||||
version_guard = Middleware(create_version_guard)
|
||||
session_guard = Middleware(create_session_guard)
|
||||
routes =\
|
||||
[
|
||||
Route('/session', create_session, methods = [ 'POST' ], middleware = [ version_guard ]),
|
||||
Route('/session', get_session, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/session', refresh_session, methods = [ 'PUT' ], middleware = [ version_guard ]),
|
||||
Route('/session', destroy_session, methods = [ 'DELETE' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/state', get_state, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/state', set_state, methods = [ 'PUT' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/assets', get_assets, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/assets', upload_asset, methods = [ 'POST' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/assets/{asset_id}', get_asset, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/assets/{asset_id}', delete_asset, methods = [ 'DELETE' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/assets', delete_assets, methods = [ 'DELETE' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/choices', get_choices, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/remote', remote, methods = [ 'POST' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/timeline/{count:int}', get_timeline, methods = [ 'GET' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/stream', post_stream, methods = [ 'POST' ], middleware = [ version_guard, session_guard ]),
|
||||
Route('/stream', delete_stream, methods = [ 'DELETE' ], middleware = [ version_guard, session_guard ]),
|
||||
WebSocketRoute('/stream', websocket_stream, middleware = [ version_guard, session_guard ]),
|
||||
WebSocketRoute('/metrics', websocket_metrics, middleware = [ version_guard, session_guard ]),
|
||||
WebSocketRoute('/ping', websocket_ping, middleware = [ version_guard, session_guard ])
|
||||
]
|
||||
|
||||
api = Starlette(routes = routes)
|
||||
api.add_middleware(CORSMiddleware, allow_origins = [ '*' ], allow_methods = [ '*' ], allow_headers = [ '*' ])
|
||||
|
||||
return api
|
||||
@@ -1,163 +0,0 @@
|
||||
import tempfile
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from starlette.datastructures import UploadFile
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import FileResponse, JSONResponse, Response
|
||||
from starlette.status import HTTP_200_OK, HTTP_201_CREATED, HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND
|
||||
|
||||
from facefusion import session_manager
|
||||
from facefusion.apis import asset_store
|
||||
from facefusion.apis.asset_helper import detect_media_type
|
||||
from facefusion.apis.endpoints.session import extract_access_token
|
||||
from facefusion.filesystem import get_file_extension, remove_file
|
||||
from facefusion.types import AudioAsset, ImageAsset, VideoAsset
|
||||
|
||||
|
||||
def translate_asset(asset : AudioAsset | ImageAsset | VideoAsset) -> Optional[Dict[str, Any]]:
|
||||
return\
|
||||
{
|
||||
'id': asset.get('id'),
|
||||
'created_at': asset.get('created_at').isoformat(),
|
||||
'expires_at': asset.get('expires_at').isoformat(),
|
||||
'type': asset.get('type'),
|
||||
'media_type': asset.get('media'),
|
||||
'filename': asset.get('name'),
|
||||
'format': asset.get('format'),
|
||||
'size': asset.get('size'),
|
||||
'metadata': asset.get('metadata')
|
||||
}
|
||||
|
||||
|
||||
async def upload_asset(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
asset_type = request.query_params.get('type')
|
||||
|
||||
if session_id and asset_type in [ 'source', 'target' ]:
|
||||
form = await request.form()
|
||||
upload_files = form.getlist('file')
|
||||
asset_paths = await save_asset_files(upload_files) # type: ignore[arg-type]
|
||||
|
||||
if asset_paths:
|
||||
asset_ids : List[str] = []
|
||||
|
||||
for asset_path in asset_paths:
|
||||
asset = asset_store.create_asset(session_id, asset_type, asset_path) # type: ignore[arg-type]
|
||||
asset_id = asset.get('id')
|
||||
|
||||
if asset_id:
|
||||
asset_ids.append(asset_id)
|
||||
|
||||
if asset_ids:
|
||||
if asset_type == 'target':
|
||||
return JSONResponse(
|
||||
{
|
||||
'asset_id': asset_ids[0]
|
||||
}, status_code = HTTP_201_CREATED)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'asset_ids': asset_ids
|
||||
}, status_code = HTTP_201_CREATED)
|
||||
|
||||
return Response(status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
|
||||
async def save_asset_files(upload_files : List[UploadFile]) -> List[str]:
|
||||
asset_paths : List[str] = []
|
||||
|
||||
for upload_file in upload_files:
|
||||
upload_file_extension = get_file_extension(upload_file.filename)
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix = upload_file_extension, delete = False) as temp_file:
|
||||
|
||||
while upload_chunk := await upload_file.read(1024):
|
||||
temp_file.write(upload_chunk)
|
||||
|
||||
temp_file.flush()
|
||||
|
||||
media_type = detect_media_type(temp_file.name)
|
||||
|
||||
if media_type:
|
||||
asset_paths.append(temp_file.name)
|
||||
else:
|
||||
remove_file(temp_file.name)
|
||||
|
||||
return asset_paths
|
||||
|
||||
|
||||
async def get_assets(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
asset_type = request.query_params.get('type')
|
||||
|
||||
if session_id:
|
||||
asset_set = asset_store.get_assets(session_id)
|
||||
assets = []
|
||||
|
||||
if asset_set:
|
||||
for asset in asset_set.values():
|
||||
if not asset_type or asset.get('type') == asset_type:
|
||||
assets.append(translate_asset(asset))
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'assets': assets,
|
||||
'count': len(assets)
|
||||
}, status_code = HTTP_200_OK)
|
||||
|
||||
return Response(status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
|
||||
async def get_asset(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
asset_id = request.path_params.get('asset_id')
|
||||
action = request.query_params.get('action')
|
||||
|
||||
if session_id and asset_id:
|
||||
asset = asset_store.get_asset(session_id, asset_id)
|
||||
|
||||
if asset:
|
||||
if action == 'download':
|
||||
return FileResponse(asset.get('path'), filename = asset.get('name'))
|
||||
|
||||
return JSONResponse(translate_asset(asset), status_code = HTTP_200_OK)
|
||||
|
||||
return Response(status_code = HTTP_404_NOT_FOUND)
|
||||
|
||||
|
||||
async def delete_asset(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
asset_id = request.path_params.get('asset_id')
|
||||
|
||||
if session_id and asset_id:
|
||||
asset_set = asset_store.get_assets(session_id)
|
||||
|
||||
if asset_set and asset_id in asset_set:
|
||||
remove_file(asset_set.get(asset_id).get('path'))
|
||||
asset_store.delete_assets(session_id, [ asset_id ])
|
||||
return Response(status_code = HTTP_200_OK)
|
||||
|
||||
return Response(status_code = HTTP_404_NOT_FOUND)
|
||||
|
||||
|
||||
async def delete_assets(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
body = await request.json()
|
||||
asset_ids = body.get('asset_ids')
|
||||
|
||||
if session_id and asset_ids:
|
||||
asset_set = asset_store.get_assets(session_id)
|
||||
|
||||
if asset_set:
|
||||
for asset_id in asset_ids:
|
||||
if asset_id in asset_set:
|
||||
remove_file(asset_set.get(asset_id).get('path'))
|
||||
asset_store.delete_assets(session_id, asset_ids)
|
||||
return Response(status_code = HTTP_200_OK)
|
||||
|
||||
return Response(status_code = HTTP_404_NOT_FOUND)
|
||||
@@ -1,16 +0,0 @@
|
||||
from starlette.websockets import WebSocket
|
||||
|
||||
from facefusion.apis.api_helper import get_sec_websocket_protocol
|
||||
|
||||
|
||||
async def websocket_ping(websocket : WebSocket) -> None:
|
||||
subprotocol = get_sec_websocket_protocol(websocket.scope)
|
||||
|
||||
await websocket.accept(subprotocol = subprotocol)
|
||||
|
||||
try:
|
||||
while True:
|
||||
await websocket.receive()
|
||||
|
||||
except Exception:
|
||||
pass
|
||||
@@ -1,148 +0,0 @@
|
||||
import os
|
||||
import secrets
|
||||
from typing import Optional
|
||||
|
||||
from starlette.datastructures import Headers
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.status import HTTP_200_OK, HTTP_201_CREATED, HTTP_401_UNAUTHORIZED, HTTP_426_UPGRADE_REQUIRED
|
||||
from starlette.types import ASGIApp, Receive, Scope, Send
|
||||
|
||||
from facefusion import session_context, session_manager, translator
|
||||
from facefusion.apis.api_helper import get_sec_websocket_protocol
|
||||
from facefusion.types import Token
|
||||
|
||||
|
||||
async def create_session(request : Request) -> JSONResponse:
|
||||
body = await request.json()
|
||||
|
||||
if not body.get('api_key') or body.get('api_key') == os.getenv('FACEFUSION_API_KEY'):
|
||||
session_id = secrets.token_urlsafe(16)
|
||||
session = session_manager.create_session()
|
||||
session_context.set_session_id(session_id)
|
||||
session_manager.set_session(session_id, session)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'access_token': session.get('access_token'),
|
||||
'refresh_token': session.get('refresh_token')
|
||||
}, status_code = HTTP_201_CREATED)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('something_went_wrong', 'facefusion.apis')
|
||||
}, status_code = HTTP_401_UNAUTHORIZED)
|
||||
|
||||
|
||||
async def get_session(request : Request) -> JSONResponse:
|
||||
access_token = extract_access_token(request.scope)
|
||||
|
||||
if access_token:
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if session_id:
|
||||
session = session_manager.get_session(session_id)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'access_token': session.get('access_token'),
|
||||
'refresh_token': session.get('refresh_token'),
|
||||
'created_at': session.get('created_at').isoformat(),
|
||||
'expires_at': session.get('expires_at').isoformat()
|
||||
}, status_code = HTTP_200_OK)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('something_went_wrong', 'facefusion.apis')
|
||||
}, status_code = HTTP_401_UNAUTHORIZED)
|
||||
|
||||
|
||||
async def refresh_session(request : Request) -> JSONResponse:
|
||||
body = await request.json()
|
||||
|
||||
for session_id, session in session_manager.SESSIONS.items():
|
||||
if session.get('refresh_token') == body.get('refresh_token'):
|
||||
__session__ = session_manager.create_session()
|
||||
session_manager.set_session(session_id, __session__)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'access_token': __session__.get('access_token'),
|
||||
'refresh_token': __session__.get('refresh_token')
|
||||
}, status_code = HTTP_200_OK)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('something_went_wrong', 'facefusion.apis')
|
||||
}, status_code = HTTP_401_UNAUTHORIZED)
|
||||
|
||||
|
||||
async def destroy_session(request : Request) -> JSONResponse:
|
||||
access_token = extract_access_token(request.scope)
|
||||
|
||||
if access_token:
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if session_id:
|
||||
session_manager.clear_session(session_id)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('ok', 'facefusion.apis')
|
||||
}, status_code = HTTP_200_OK)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('something_went_wrong', 'facefusion.apis')
|
||||
}, status_code = HTTP_401_UNAUTHORIZED)
|
||||
|
||||
|
||||
def create_session_guard(app : ASGIApp) -> ASGIApp:
|
||||
async def middleware(scope : Scope, receive : Receive, send : Send) -> None:
|
||||
access_token = extract_access_token(scope)
|
||||
|
||||
if access_token:
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if session_id:
|
||||
if session_manager.validate_session(session_id):
|
||||
session_context.set_session_id(session_id)
|
||||
return await app(scope, receive, send)
|
||||
|
||||
response = JSONResponse(
|
||||
{
|
||||
'message': translator.get('invalid_access_token', 'facefusion.apis')
|
||||
}, status_code = HTTP_426_UPGRADE_REQUIRED)
|
||||
|
||||
return await response(scope, receive, send)
|
||||
|
||||
response = JSONResponse(
|
||||
{
|
||||
'message': translator.get('invalid_access_token', 'facefusion.apis')
|
||||
}, status_code = HTTP_401_UNAUTHORIZED)
|
||||
|
||||
return await response(scope, receive, send)
|
||||
|
||||
return middleware
|
||||
|
||||
|
||||
def extract_access_token(scope : Scope) -> Optional[Token]:
|
||||
if scope.get('type') == 'http':
|
||||
auth_header = Headers(scope = scope).get('Authorization')
|
||||
|
||||
if auth_header:
|
||||
auth_prefix, _, access_token = auth_header.partition(' ')
|
||||
|
||||
if auth_prefix.lower() == 'bearer' and access_token:
|
||||
return access_token
|
||||
|
||||
if scope.get('type') == 'websocket':
|
||||
subprotocol = get_sec_websocket_protocol(scope)
|
||||
|
||||
if subprotocol:
|
||||
protocol_prefix, _, access_token = subprotocol.partition('.')
|
||||
|
||||
if protocol_prefix == 'access_token' and access_token:
|
||||
return access_token
|
||||
|
||||
return None
|
||||
@@ -1,80 +0,0 @@
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.status import HTTP_200_OK, HTTP_404_NOT_FOUND
|
||||
|
||||
from facefusion import args_store, session_manager, state_manager, translator
|
||||
from facefusion.apis import asset_store
|
||||
from facefusion.apis.endpoints.session import extract_access_token
|
||||
|
||||
|
||||
async def get_state(request : Request) -> JSONResponse:
|
||||
api_args = args_store.filter_api_args(state_manager.get_state())
|
||||
return JSONResponse(state_manager.collect_state(api_args), status_code = HTTP_200_OK)
|
||||
|
||||
|
||||
async def set_state(request : Request) -> JSONResponse:
|
||||
action = request.query_params.get('action')
|
||||
asset_type = request.query_params.get('asset_type')
|
||||
|
||||
if action == 'select' and asset_type == 'source':
|
||||
return await select_source(request)
|
||||
|
||||
if action == 'select' and asset_type == 'target':
|
||||
return await select_target(request)
|
||||
|
||||
body = await request.json()
|
||||
api_args = args_store.get_api_args()
|
||||
|
||||
for key, value in body.items():
|
||||
if key in api_args:
|
||||
state_manager.set_item(key, value)
|
||||
|
||||
__api_args__ = args_store.filter_api_args(state_manager.get_state())
|
||||
return JSONResponse(state_manager.collect_state(__api_args__), status_code = HTTP_200_OK)
|
||||
|
||||
|
||||
async def select_source(request : Request) -> JSONResponse:
|
||||
body = await request.json()
|
||||
asset_ids = body.get('asset_ids')
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if isinstance(asset_ids, list) and session_id:
|
||||
source_paths = []
|
||||
|
||||
for asset_id in asset_ids:
|
||||
asset = asset_store.get_asset(session_id, asset_id)
|
||||
|
||||
if asset:
|
||||
source_paths.append(asset.get('path'))
|
||||
|
||||
state_manager.set_item('source_paths', source_paths)
|
||||
|
||||
__api_args__ = args_store.filter_api_args(state_manager.get_state())
|
||||
return JSONResponse(state_manager.collect_state(__api_args__), status_code = HTTP_200_OK)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('source_asset_not_found', 'facefusion.apis')
|
||||
}, status_code = HTTP_404_NOT_FOUND)
|
||||
|
||||
|
||||
async def select_target(request : Request) -> JSONResponse:
|
||||
body = await request.json()
|
||||
asset_id = body.get('asset_id')
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if isinstance(asset_id, str) and session_id:
|
||||
asset = asset_store.get_asset(session_id, asset_id)
|
||||
|
||||
if asset:
|
||||
state_manager.set_item('target_path', asset.get('path'))
|
||||
|
||||
__api_args__ = args_store.filter_api_args(state_manager.get_state())
|
||||
return JSONResponse(state_manager.collect_state(__api_args__), status_code = HTTP_200_OK)
|
||||
|
||||
return JSONResponse(
|
||||
{
|
||||
'message': translator.get('target_asset_not_found', 'facefusion.apis')
|
||||
}, status_code = HTTP_404_NOT_FOUND)
|
||||
@@ -1,52 +0,0 @@
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import Response
|
||||
from starlette.status import HTTP_200_OK, HTTP_201_CREATED, HTTP_404_NOT_FOUND
|
||||
from starlette.websockets import WebSocket, WebSocketState
|
||||
|
||||
from facefusion import session_context, session_manager
|
||||
from facefusion.apis.api_helper import get_sec_websocket_protocol
|
||||
from facefusion.apis.endpoints.session import extract_access_token
|
||||
from facefusion.apis.stream_manager import destroy_stream, process_image, process_video
|
||||
|
||||
|
||||
async def websocket_stream(websocket : WebSocket) -> None:
|
||||
subprotocol = get_sec_websocket_protocol(websocket.scope)
|
||||
access_token = extract_access_token(websocket.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
session_context.set_session_id(session_id)
|
||||
|
||||
await websocket.accept(subprotocol = subprotocol)
|
||||
await process_image(websocket)
|
||||
|
||||
if websocket.client_state == WebSocketState.CONNECTED:
|
||||
await websocket.close()
|
||||
|
||||
|
||||
async def post_stream(request : Request) -> Response:
|
||||
headers =\
|
||||
{
|
||||
'Location': request.url_for('delete_stream').path
|
||||
}
|
||||
content_type = request.headers.get('content-type')
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
session_context.set_session_id(session_id)
|
||||
|
||||
if session_id and content_type == 'application/sdp':
|
||||
sdp_offer = await request.body()
|
||||
sdp_answer = process_video(session_id, sdp_offer.decode())
|
||||
|
||||
if sdp_answer:
|
||||
return Response(sdp_answer, status_code = HTTP_201_CREATED, media_type = 'application/sdp', headers = headers)
|
||||
|
||||
return Response(status_code = HTTP_404_NOT_FOUND)
|
||||
|
||||
|
||||
async def delete_stream(request : Request) -> Response:
|
||||
access_token = extract_access_token(request.scope)
|
||||
session_id = session_manager.find_session_id(access_token)
|
||||
|
||||
if session_id and destroy_stream(session_id):
|
||||
return Response(status_code = HTTP_200_OK)
|
||||
|
||||
return Response(status_code = HTTP_404_NOT_FOUND)
|
||||
@@ -1,14 +0,0 @@
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
'ok': 'ok',
|
||||
'something_went_wrong': 'something went wrong',
|
||||
'invalid_access_token': 'invalid access token',
|
||||
'invalid_refresh_token': 'invalid refresh token',
|
||||
'source_asset_not_found': 'source asset not found',
|
||||
'target_asset_not_found': 'target asset not found'
|
||||
}
|
||||
}
|
||||
@@ -1,12 +0,0 @@
|
||||
from facefusion.types import Locals
|
||||
|
||||
LOCALS : Locals =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
'ok': 'ok',
|
||||
'something_went_wrong': 'something went wrong',
|
||||
'invalid_access_token': 'invalid access token',
|
||||
'invalid_refresh_token': 'invalid refresh token'
|
||||
}
|
||||
}
|
||||
@@ -1,76 +0,0 @@
|
||||
import asyncio
|
||||
from functools import lru_cache
|
||||
from typing import Any, Dict, Optional, cast
|
||||
|
||||
from starlette.datastructures import Headers
|
||||
from starlette.websockets import WebSocket, WebSocketDisconnect
|
||||
|
||||
from facefusion import state_manager
|
||||
from facefusion.execution import detect_execution_devices
|
||||
from facefusion.system import get_cpu_info, get_disk_info, get_load_average, get_network_info
|
||||
from facefusion.system import get_operating_system_info, get_python_info, get_ram_info, get_temperature_info
|
||||
from facefusion.types import SystemInfo
|
||||
|
||||
|
||||
@lru_cache(maxsize = 1)
|
||||
def get_cached_static_system_info() -> Dict[str, Any]:
|
||||
return\
|
||||
{
|
||||
'operating_system': get_operating_system_info(),
|
||||
'python': get_python_info()
|
||||
}
|
||||
|
||||
|
||||
@lru_cache(maxsize = 1)
|
||||
def get_cached_semi_static_system_info(temp_path : Optional[str]) -> Dict[str, Any]:
|
||||
return\
|
||||
{
|
||||
'disk': get_disk_info(temp_path),
|
||||
'network': get_network_info()
|
||||
}
|
||||
|
||||
|
||||
def get_optimized_system_info(temp_path : Optional[str] = None) -> SystemInfo:
|
||||
static_data = get_cached_static_system_info()
|
||||
semi_static_data = get_cached_semi_static_system_info(temp_path)
|
||||
dynamic_data : Dict[str, Any] =\
|
||||
{
|
||||
'cpu': get_cpu_info(),
|
||||
'ram': get_ram_info(),
|
||||
'temperatures': get_temperature_info(),
|
||||
'load_average': get_load_average()
|
||||
}
|
||||
|
||||
return cast(SystemInfo, {**static_data, **semi_static_data, **dynamic_data})
|
||||
|
||||
|
||||
async def websocket_metrics(websocket : WebSocket) -> None:
|
||||
subprotocol = get_requested_subprotocol(websocket)
|
||||
await websocket.accept(subprotocol = subprotocol)
|
||||
|
||||
try:
|
||||
while True:
|
||||
temp_path = state_manager.get_temp_path()
|
||||
execution_devices = detect_execution_devices()
|
||||
system_info = get_optimized_system_info(temp_path)
|
||||
metrics =\
|
||||
{
|
||||
'devices': execution_devices,
|
||||
'system': system_info
|
||||
}
|
||||
await websocket.send_json(metrics)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
except (WebSocketDisconnect, Exception):
|
||||
pass
|
||||
|
||||
|
||||
def get_requested_subprotocol(websocket : WebSocket) -> Optional[str]:
|
||||
headers = Headers(scope = websocket.scope)
|
||||
protocol_header = headers.get('Sec-WebSocket-Protocol')
|
||||
|
||||
if protocol_header:
|
||||
protocol, _, _ = protocol_header.partition(',')
|
||||
return protocol.strip()
|
||||
|
||||
return None
|
||||
@@ -1,384 +0,0 @@
|
||||
import os
|
||||
import tempfile
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import httpx
|
||||
import yt_dlp # type: ignore
|
||||
from gallery_dl import config as gallery_config, extractor as gallery_extractor, job as gallery_job
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.status import HTTP_200_OK, HTTP_201_CREATED, HTTP_400_BAD_REQUEST, HTTP_500_INTERNAL_SERVER_ERROR
|
||||
|
||||
from facefusion import logger
|
||||
from facefusion.apis import asset_store
|
||||
from facefusion.choices import audio_formats
|
||||
from facefusion.session_context import get_session_id
|
||||
|
||||
|
||||
def resolve_image_urls(url : str) -> List[str]:
|
||||
gallery_config.load()
|
||||
image_urls : List[str] = []
|
||||
|
||||
try:
|
||||
for extractor_instance in gallery_extractor.extractors():
|
||||
if extractor_instance.pattern and extractor_instance.pattern.match(url):
|
||||
logger.info(f'Detected gallery URL using extractor: {extractor_instance.__name__}', __name__)
|
||||
extractor_obj = extractor_instance.from_url(url)
|
||||
|
||||
if extractor_obj:
|
||||
for msg in extractor_obj:
|
||||
if isinstance(msg, tuple) and len(msg) >= 2:
|
||||
msg_type = msg[0]
|
||||
if msg_type == 5:
|
||||
image_data = msg[1]
|
||||
image_url = image_data.get('url')
|
||||
if image_url:
|
||||
image_urls.append(image_url)
|
||||
break
|
||||
|
||||
if not image_urls:
|
||||
logger.info('Not a gallery URL, treating as direct image URL', __name__)
|
||||
image_urls = [url]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to extract image URLs: {e}', __name__)
|
||||
logger.info('Falling back to treating as direct image URL', __name__)
|
||||
image_urls = [url]
|
||||
|
||||
return image_urls
|
||||
|
||||
|
||||
def download_images_from_url(url : str, asset_type : str) -> List[str]:
|
||||
gallery_config.load()
|
||||
temp_dir = tempfile.gettempdir()
|
||||
asset_ids : List[str] = []
|
||||
|
||||
is_gallery = False
|
||||
for extractor_instance in gallery_extractor.extractors():
|
||||
if extractor_instance.pattern and extractor_instance.pattern.match(url):
|
||||
logger.info(f'Detected gallery URL using extractor: {extractor_instance.__name__}', __name__)
|
||||
is_gallery = True
|
||||
|
||||
output_dir = os.path.join(temp_dir, f'facefusion_gallery_{os.urandom(8).hex()}')
|
||||
os.makedirs(output_dir, exist_ok = True)
|
||||
|
||||
gallery_config.set((), 'base-directory', output_dir)
|
||||
gallery_config.set((), 'skip', False)
|
||||
|
||||
gdl_job = gallery_job.DownloadJob(url)
|
||||
gdl_job.run()
|
||||
|
||||
session_id = get_session_id()
|
||||
for root, dirs, files in os.walk(output_dir):
|
||||
for filename in files:
|
||||
file_path = os.path.join(root, filename)
|
||||
asset = asset_store.create_asset(session_id, asset_type, file_path)
|
||||
if asset:
|
||||
asset_ids.append(asset.get('id'))
|
||||
logger.info(f'Registered image as asset {asset.get("id")}', __name__)
|
||||
|
||||
break
|
||||
|
||||
if not is_gallery:
|
||||
logger.info('Not a gallery URL, treating as direct image URL', __name__)
|
||||
with httpx.stream('GET', url, timeout = 30, follow_redirects = True) as response:
|
||||
response.raise_for_status()
|
||||
|
||||
content_type = response.headers.get('content-type', '')
|
||||
if not content_type.startswith('image/'):
|
||||
raise ValueError(f'URL does not point to an image. Content-Type: {content_type}')
|
||||
|
||||
file_extension = None
|
||||
if 'image/jpeg' in content_type or 'image/jpg' in content_type:
|
||||
file_extension = '.jpg'
|
||||
if 'image/png' in content_type:
|
||||
file_extension = '.png'
|
||||
if 'image/gif' in content_type:
|
||||
file_extension = '.gif'
|
||||
if 'image/webp' in content_type:
|
||||
file_extension = '.webp'
|
||||
|
||||
if not file_extension:
|
||||
url_path = url.split('?')[0]
|
||||
if '.' in url_path:
|
||||
file_extension = '.' + url_path.split('.')[-1].lower()
|
||||
else:
|
||||
file_extension = '.jpg'
|
||||
|
||||
filename = f'facefusion_image_{os.urandom(8).hex()}{file_extension}'
|
||||
file_path = os.path.join(temp_dir, filename)
|
||||
|
||||
with open(file_path, 'wb') as f:
|
||||
for chunk in response.iter_bytes(chunk_size = 8192):
|
||||
f.write(chunk)
|
||||
|
||||
session_id = get_session_id()
|
||||
asset = asset_store.create_asset(session_id, asset_type, file_path)
|
||||
if asset:
|
||||
asset_ids.append(asset.get('id'))
|
||||
logger.info(f'Downloaded and registered image as asset {asset.get("id")}', __name__)
|
||||
|
||||
return asset_ids
|
||||
|
||||
|
||||
def download_audio_from_url(url : str, asset_type : str) -> List[str]:
|
||||
temp_dir = tempfile.gettempdir()
|
||||
asset_ids : List[str] = []
|
||||
|
||||
# Extract file extension from URL
|
||||
url_path = url.split('?')[0]
|
||||
url_extension = os.path.splitext(url_path)[1].lstrip('.')
|
||||
|
||||
# Validate extension against supported audio formats
|
||||
if url_extension not in audio_formats:
|
||||
raise ValueError(f'Unsupported audio format: {url_extension}. Supported formats: {", ".join(audio_formats)}')
|
||||
|
||||
logger.info(f'Downloading audio from URL with extension: {url_extension}', __name__)
|
||||
with httpx.stream('GET', url, timeout = 30, follow_redirects = True) as response:
|
||||
response.raise_for_status()
|
||||
|
||||
filename = f'facefusion_audio_{os.urandom(8).hex()}.{url_extension}'
|
||||
file_path = os.path.join(temp_dir, filename)
|
||||
|
||||
with open(file_path, 'wb') as f:
|
||||
for chunk in response.iter_bytes(chunk_size = 8192):
|
||||
f.write(chunk)
|
||||
|
||||
session_id = get_session_id()
|
||||
asset = asset_store.create_asset(session_id, asset_type, file_path)
|
||||
if asset:
|
||||
asset_ids.append(asset.get('id'))
|
||||
logger.info(f'Downloaded and registered audio as asset {asset.get("id")}', __name__)
|
||||
|
||||
return asset_ids
|
||||
|
||||
|
||||
async def remote(request : Request) -> JSONResponse:
|
||||
body = await request.json()
|
||||
url = body.get('url')
|
||||
action = request.query_params.get('action')
|
||||
media_type = request.query_params.get('media_type', 'video')
|
||||
asset_type = request.query_params.get('asset_type', 'target')
|
||||
|
||||
if not action:
|
||||
return JSONResponse({'message': 'No action provided. Must be "resolve" or "download"'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if action not in ['resolve', 'download']:
|
||||
return JSONResponse({'message': 'Invalid action. Must be "resolve" or "download"'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if media_type not in ['image', 'video', 'audio']:
|
||||
return JSONResponse({'message': 'Invalid media_type. Must be "image", "video", or "audio"'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if asset_type not in ['source', 'target']:
|
||||
return JSONResponse({'message': 'Invalid asset_type. Must be "source" or "target"'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if not url:
|
||||
return JSONResponse({'message': 'No URL provided'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if not isinstance(url, str):
|
||||
return JSONResponse({'message': 'URL must be a string'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
url = url.strip()
|
||||
|
||||
if not url.startswith('http://') and not url.startswith('https://'):
|
||||
return JSONResponse({'message': 'URL must start with http:// or https://'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
quality = body.get('quality', '720p')
|
||||
|
||||
if quality not in ['360p', '480p', '720p', '1080p']:
|
||||
return JSONResponse({'message': 'Quality must be 360p, 480p, 720p, or 1080p'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
|
||||
if action == 'resolve':
|
||||
if media_type == 'image':
|
||||
image_urls = resolve_image_urls(url)
|
||||
logger.info(f'Resolved {len(image_urls)} image URL(s)', __name__)
|
||||
|
||||
response_data =\
|
||||
{
|
||||
'message': 'Image URL(s) resolved successfully',
|
||||
'image_urls': image_urls,
|
||||
'count': len(image_urls)
|
||||
}
|
||||
|
||||
return JSONResponse(response_data, status_code = HTTP_200_OK)
|
||||
|
||||
quality_map =\
|
||||
{
|
||||
'360p': 'bestvideo[height<=360][ext=mp4]+bestaudio[ext=m4a]/best[height<=360]',
|
||||
'480p': 'bestvideo[height<=480][ext=mp4]+bestaudio[ext=m4a]/best[height<=480]',
|
||||
'720p': 'bestvideo[height<=720][ext=mp4]+bestaudio[ext=m4a]/best[height<=720]',
|
||||
'1080p': 'bestvideo[height<=1080][ext=mp4]+bestaudio[ext=m4a]/best[height<=1080]'
|
||||
}
|
||||
|
||||
ydl_opts : Dict[str, Any] =\
|
||||
{
|
||||
'format': quality_map[quality],
|
||||
'quiet': True,
|
||||
'no_warnings': True
|
||||
}
|
||||
|
||||
logger.info(f'Extracting stream URL from {url} at {quality}', __name__)
|
||||
|
||||
try:
|
||||
ydl = yt_dlp.YoutubeDL(ydl_opts)
|
||||
info = ydl.extract_info(url, download = False)
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to extract video information: {e}', __name__)
|
||||
return JSONResponse({'message': f'Failed to extract video information: {str(e)}'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
if not info:
|
||||
logger.error('Failed to extract video information', __name__)
|
||||
return JSONResponse({'message': 'Failed to extract video information'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
stream_url = info.get('url')
|
||||
|
||||
if not stream_url:
|
||||
if 'requested_formats' in info and len(info['requested_formats']) > 0:
|
||||
stream_url = info['requested_formats'][0].get('url')
|
||||
logger.info('Using URL from requested_formats (video track)', __name__)
|
||||
elif 'formats' in info and len(info['formats']) > 0:
|
||||
for fmt in reversed(info['formats']):
|
||||
if fmt.get('url') and fmt.get('vcodec') != 'none':
|
||||
stream_url = fmt['url']
|
||||
logger.info(f'Using URL from format: {fmt.get("format_id")}', __name__)
|
||||
break
|
||||
|
||||
if not stream_url:
|
||||
logger.error('No stream URL found in any format', __name__)
|
||||
logger.debug(f'Available keys in info: {list(info.keys())}', __name__)
|
||||
return JSONResponse({'message': 'No stream URL found'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
audio_url = None
|
||||
if 'requested_formats' in info and len(info['requested_formats']) > 1:
|
||||
audio_url = info['requested_formats'][1].get('url')
|
||||
if audio_url:
|
||||
logger.info('Found separate audio track URL', __name__)
|
||||
|
||||
duration = info.get('duration')
|
||||
fps = info.get('fps')
|
||||
width = info.get('width')
|
||||
height = info.get('height')
|
||||
|
||||
total_frames = None
|
||||
if duration and fps:
|
||||
total_frames = int(duration * fps)
|
||||
logger.info(f'Calculated total frames: {total_frames} ({duration}s * {fps} fps)', __name__)
|
||||
|
||||
logger.info('Stream URL extracted successfully', __name__)
|
||||
|
||||
response_data =\
|
||||
{
|
||||
'message': 'Stream URL resolved successfully',
|
||||
'stream_url': stream_url,
|
||||
'audio_url': audio_url,
|
||||
'duration': duration,
|
||||
'fps': fps,
|
||||
'total_frames': total_frames,
|
||||
'width': width,
|
||||
'height': height
|
||||
}
|
||||
|
||||
return JSONResponse(response_data, status_code = HTTP_200_OK)
|
||||
|
||||
if action == 'download':
|
||||
if media_type == 'image':
|
||||
try:
|
||||
asset_ids = download_images_from_url(url, asset_type)
|
||||
except ValueError as e:
|
||||
return JSONResponse({'message': str(e)}, status_code = HTTP_400_BAD_REQUEST)
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to download image(s): {e}', __name__)
|
||||
return JSONResponse({'message': f'Failed to download image(s): {str(e)}'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
response_data =\
|
||||
{
|
||||
'message': f'Downloaded and registered {len(asset_ids)} image(s)',
|
||||
'asset_ids': asset_ids,
|
||||
'count': len(asset_ids)
|
||||
}
|
||||
|
||||
return JSONResponse(response_data, status_code = HTTP_201_CREATED)
|
||||
|
||||
if media_type == 'audio':
|
||||
try:
|
||||
asset_ids = download_audio_from_url(url, asset_type)
|
||||
except ValueError as e:
|
||||
return JSONResponse({'message': str(e)}, status_code = HTTP_400_BAD_REQUEST)
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to download audio: {e}', __name__)
|
||||
return JSONResponse({'message': f'Failed to download audio: {str(e)}'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
response_data =\
|
||||
{
|
||||
'message': f'Downloaded and registered {len(asset_ids)} audio file(s)',
|
||||
'asset_ids': asset_ids,
|
||||
'count': len(asset_ids)
|
||||
}
|
||||
|
||||
return JSONResponse(response_data, status_code = HTTP_201_CREATED)
|
||||
|
||||
quality_map =\
|
||||
{
|
||||
'360p': 'bestvideo[height<=360][ext=mp4]+bestaudio[ext=m4a]/best[height<=360][ext=mp4]/best[height<=360]',
|
||||
'480p': 'bestvideo[height<=480][ext=mp4]+bestaudio[ext=m4a]/best[height<=480][ext=mp4]/best[height<=480]',
|
||||
'720p': 'bestvideo[height<=720][ext=mp4]+bestaudio[ext=m4a]/best[height<=720][ext=mp4]/best[height<=720]',
|
||||
'1080p': 'bestvideo[height<=1080][ext=mp4]+bestaudio[ext=m4a]/best[height<=1080][ext=mp4]/best[height<=1080]'
|
||||
}
|
||||
|
||||
temp_dir = tempfile.gettempdir()
|
||||
output_path = os.path.join(temp_dir, 'facefusion_remote_%(id)s.%(ext)s')
|
||||
|
||||
download_opts : Dict[str, Any] =\
|
||||
{
|
||||
'format': quality_map[quality],
|
||||
'outtmpl': output_path,
|
||||
'quiet': False,
|
||||
'no_warnings': False
|
||||
}
|
||||
|
||||
logger.info(f'Downloading video from {url} at {quality}', __name__)
|
||||
|
||||
ydl = yt_dlp.YoutubeDL(download_opts)
|
||||
info = ydl.extract_info(url, download = True)
|
||||
|
||||
if not info:
|
||||
logger.error('Failed to download video', __name__)
|
||||
return JSONResponse({'message': 'Failed to download video'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
downloaded_file = ydl.prepare_filename(info)
|
||||
if not os.path.exists(downloaded_file):
|
||||
logger.error(f'Downloaded file not found: {downloaded_file}', __name__)
|
||||
return JSONResponse({'message': 'Downloaded file not found'}, status_code = HTTP_500_INTERNAL_SERVER_ERROR)
|
||||
|
||||
duration = info.get('duration')
|
||||
fps = info.get('fps')
|
||||
width = info.get('width')
|
||||
height = info.get('height')
|
||||
|
||||
total_frames = None
|
||||
if duration and fps:
|
||||
total_frames = int(duration * fps)
|
||||
logger.info(f'Calculated total frames: {total_frames} ({duration}s * {fps} fps)', __name__)
|
||||
|
||||
session_id = get_session_id()
|
||||
asset = asset_store.create_asset(session_id, asset_type, downloaded_file)
|
||||
asset_id = asset.get('id') if asset else None
|
||||
logger.info(f'Video downloaded and registered as asset {asset_id}', __name__)
|
||||
|
||||
response_data =\
|
||||
{
|
||||
'message': 'Video downloaded and registered as asset',
|
||||
'asset_id': asset_id,
|
||||
'metadata':
|
||||
{
|
||||
'duration': duration,
|
||||
'fps': fps,
|
||||
'total_frames': total_frames,
|
||||
'width': width,
|
||||
'height': height
|
||||
}
|
||||
}
|
||||
|
||||
return JSONResponse(response_data, status_code = HTTP_201_CREATED)
|
||||
|
||||
return JSONResponse({'message': 'Invalid request'}, status_code = HTTP_400_BAD_REQUEST)
|
||||
@@ -1,71 +0,0 @@
|
||||
import ctypes
|
||||
import time
|
||||
from functools import partial
|
||||
from queue import Queue
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import numpy
|
||||
|
||||
from facefusion import rtc
|
||||
from facefusion.apis.stream_event import create_receive_event
|
||||
from facefusion.codecs import opus_decoder, opus_encoder
|
||||
from facefusion.types import AudioCodec, AudioFrame, OpusDecoder, RtcPeer, RtcPeerAudio
|
||||
|
||||
|
||||
def run_audio_encode_loop(rtc_peer : RtcPeer, audio_queue : Queue[Tuple[float, AudioFrame]]) -> None:
|
||||
temp_audio_time, temp_audio_frame = audio_queue.get()
|
||||
audio_encoder = opus_encoder.create(48000, 2)
|
||||
audio_timestamp = 0
|
||||
|
||||
while numpy.any(temp_audio_frame):
|
||||
audio_frame_size = len(temp_audio_frame) // 2
|
||||
audio_buffer = opus_encoder.encode(audio_encoder, temp_audio_frame.tobytes(), audio_frame_size)
|
||||
|
||||
if audio_buffer:
|
||||
rtc.send_audio(rtc_peer, audio_buffer, audio_timestamp)
|
||||
audio_timestamp += audio_frame_size
|
||||
|
||||
temp_audio_time, temp_audio_frame = audio_queue.get()
|
||||
|
||||
opus_encoder.destroy(audio_encoder)
|
||||
|
||||
|
||||
def receive_audio_frames(rtc_peer_audio : RtcPeerAudio, audio_queue : Queue[Tuple[float, AudioFrame]]) -> None:
|
||||
audio_track = rtc_peer_audio.get('receiver_track')
|
||||
audio_codec = rtc_peer_audio.get('codec')
|
||||
audio_decoder = create_audio_decoder(audio_codec)
|
||||
|
||||
audio_frame_handler = partial(handle_audio_frame, audio_codec, audio_decoder, audio_queue)
|
||||
receive_event = create_receive_event(audio_track, audio_frame_handler)
|
||||
receive_event.wait()
|
||||
|
||||
empty_audio_frame = numpy.empty(0)
|
||||
audio_queue.put((0.0, empty_audio_frame))
|
||||
destroy_audio_decoder(audio_codec, audio_decoder)
|
||||
|
||||
|
||||
def decode_audio_frame(audio_codec : AudioCodec, audio_decoder : OpusDecoder, input_buffer : bytes) -> Optional[bytes]:
|
||||
if audio_codec == 'opus':
|
||||
return opus_decoder.decode(audio_decoder, input_buffer, 960, 2)
|
||||
return None
|
||||
|
||||
|
||||
def create_audio_decoder(audio_codec : AudioCodec) -> Optional[OpusDecoder]:
|
||||
if audio_codec == 'opus':
|
||||
return opus_decoder.create(48000, 2)
|
||||
return None
|
||||
|
||||
|
||||
def destroy_audio_decoder(audio_codec : AudioCodec, audio_decoder : OpusDecoder) -> None:
|
||||
if audio_codec == 'opus':
|
||||
opus_decoder.destroy(audio_decoder)
|
||||
|
||||
|
||||
#todo: Alias Time for float
|
||||
def handle_audio_frame(audio_codec : AudioCodec, audio_decoder : OpusDecoder, audio_queue : Queue[Tuple[float, AudioFrame]], track : int, data : ctypes.c_void_p, size : int, info : ctypes.c_void_p, pointer : ctypes.c_void_p) -> None:
|
||||
audio_buffer = ctypes.string_at(data, size)
|
||||
audio_frame = decode_audio_frame(audio_codec, audio_decoder, audio_buffer)
|
||||
|
||||
if audio_frame:
|
||||
temp_audio_frame = numpy.frombuffer(audio_frame, dtype = numpy.float32)
|
||||
audio_queue.put((time.monotonic(), temp_audio_frame))
|
||||
@@ -1,24 +0,0 @@
|
||||
import ctypes
|
||||
import threading
|
||||
from functools import partial
|
||||
|
||||
from facefusion.libraries import datachannel as datachannel_module
|
||||
from facefusion.types import FrameHandler
|
||||
|
||||
|
||||
def create_receive_event(track : int, frame_handler : FrameHandler) -> threading.Event:
|
||||
datachannel_library = datachannel_module.create_static_library()
|
||||
receive_event = threading.Event()
|
||||
|
||||
frame_callback = ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_void_p, ctypes.c_int, ctypes.c_void_p, ctypes.c_void_p)(frame_handler)
|
||||
close_callback = ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_void_p)(partial(dispatch_event, receive_event))
|
||||
datachannel_library.rtcSetFrameCallback(track, frame_callback)
|
||||
datachannel_library.rtcSetClosedCallback(track, close_callback)
|
||||
receive_event.frame_callback = frame_callback # type: ignore[attr-defined]
|
||||
receive_event.close_callback = close_callback # type: ignore[attr-defined]
|
||||
|
||||
return receive_event
|
||||
|
||||
|
||||
def dispatch_event(event : threading.Event, track : int, pointer : ctypes.c_void_p) -> None:
|
||||
event.set()
|
||||
@@ -1,137 +0,0 @@
|
||||
import ctypes
|
||||
import threading
|
||||
from collections.abc import AsyncIterator
|
||||
from concurrent.futures import Future, ThreadPoolExecutor
|
||||
from queue import Queue
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
from starlette.websockets import WebSocket
|
||||
|
||||
from facefusion import rtc, rtc_store, state_manager, streamer
|
||||
from facefusion.apis.stream_audio import receive_audio_frames, run_audio_encode_loop
|
||||
from facefusion.apis.stream_video import receive_video_frames, run_video_encode_loop
|
||||
from facefusion.libraries import datachannel as datachannel_module
|
||||
from facefusion.types import AudioCodec, AudioFrame, PeerConnection, Resolution, RtcPeer, RtcPeerAudio, SdpAnswer, SdpOffer, SessionId, VideoCodec, VisionFrame
|
||||
|
||||
|
||||
async def process_image(websocket : WebSocket) -> None:
|
||||
capture_vision_frame = await anext(receive_vision_frames(websocket), None)
|
||||
|
||||
if numpy.any(capture_vision_frame):
|
||||
output_vision_frame = streamer.process_stream_frame(capture_vision_frame)
|
||||
is_success, output_frame_buffer = cv2.imencode('.jpg', output_vision_frame)
|
||||
|
||||
if is_success:
|
||||
await websocket.send_bytes(output_frame_buffer.tobytes())
|
||||
|
||||
|
||||
async def receive_vision_frames(websocket : WebSocket) -> AsyncIterator[VisionFrame]:
|
||||
websocket_event = await websocket.receive()
|
||||
|
||||
while websocket_event.get('type') == 'websocket.receive':
|
||||
frame_buffer = websocket_event.get('bytes') or bytes()
|
||||
vision_frame = cv2.imdecode(numpy.frombuffer(frame_buffer, numpy.uint8), cv2.IMREAD_COLOR)
|
||||
|
||||
if numpy.any(vision_frame):
|
||||
yield vision_frame
|
||||
|
||||
websocket_event = await websocket.receive()
|
||||
|
||||
|
||||
def process_video(session_id : SessionId, sdp_offer : SdpOffer) -> Optional[SdpAnswer]:
|
||||
video_codec : VideoCodec = 'vp8'
|
||||
|
||||
if rtc.get_payload_type(sdp_offer, 'vp9'):
|
||||
video_codec = 'vp9'
|
||||
|
||||
if rtc.get_payload_type(sdp_offer, 'av1'):
|
||||
video_codec = 'av1'
|
||||
|
||||
video_payload_type = rtc.get_payload_type(sdp_offer, video_codec)
|
||||
|
||||
if video_payload_type:
|
||||
peer_connection : PeerConnection = rtc.create_peer_connection()
|
||||
video_receiver_track = rtc.add_video_track(peer_connection, 'recvonly', video_codec, video_payload_type)
|
||||
video_sender_track = rtc.add_video_track(peer_connection, 'sendonly', video_codec, video_payload_type)
|
||||
|
||||
sender_bitrate = ctypes.c_uint(0)
|
||||
receiver_bitrate = ctypes.c_uint(8000)
|
||||
rtc.wire_sender_bitrate(video_sender_track, sender_bitrate)
|
||||
|
||||
audio_codec : AudioCodec = 'opus'
|
||||
audio_payload_type = rtc.get_payload_type(sdp_offer, audio_codec)
|
||||
|
||||
if audio_payload_type:
|
||||
audio_receiver_track = rtc.add_audio_track(peer_connection, 'recvonly', audio_codec, audio_payload_type)
|
||||
audio_sender_track = rtc.add_audio_track(peer_connection, 'sendonly', audio_codec, audio_payload_type)
|
||||
|
||||
rtc.set_remote_description(peer_connection, sdp_offer)
|
||||
sdp_answer = rtc.create_sdp_answer(peer_connection)
|
||||
|
||||
if sdp_answer:
|
||||
rtc_peer : RtcPeer =\
|
||||
{
|
||||
'peer_connection': peer_connection,
|
||||
'video':
|
||||
{
|
||||
'sender_track': video_sender_track,
|
||||
'receiver_track': video_receiver_track,
|
||||
'codec': video_codec
|
||||
},
|
||||
'sender_bitrate': sender_bitrate,
|
||||
'receiver_bitrate': receiver_bitrate
|
||||
}
|
||||
|
||||
if audio_payload_type:
|
||||
rtc_peer['audio'] = RtcPeerAudio(
|
||||
sender_track = audio_sender_track,
|
||||
receiver_track = audio_receiver_track,
|
||||
codec = audio_codec
|
||||
)
|
||||
|
||||
rtc_store.init_peers(session_id)
|
||||
rtc_store.get_peers(session_id).append(rtc_peer)
|
||||
|
||||
threading.Thread(target = run_peer_loop, args = (session_id, rtc_peer), daemon = True).start()
|
||||
|
||||
return sdp_answer
|
||||
|
||||
datachannel_module.create_static_library().rtcDeletePeerConnection(peer_connection)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def run_peer_loop(session_id : SessionId, rtc_peer : RtcPeer) -> None:
|
||||
execution_thread_count = state_manager.get_item('execution_thread_count')
|
||||
#todo: is bytes, Resolution not a XXXPointer type
|
||||
video_queue : Queue[Tuple[float, Future[Tuple[bytes, Resolution]]]] = Queue(maxsize = execution_thread_count)
|
||||
audio_queue : Queue[Tuple[float, AudioFrame]] = Queue(maxsize = execution_thread_count * 10)
|
||||
video_executor = ThreadPoolExecutor(max_workers = execution_thread_count)
|
||||
|
||||
video_receiver_thread = threading.Thread(target = receive_video_frames, args = (rtc_peer.get('video'), video_queue, video_executor), daemon = True)
|
||||
video_encoder_thread = threading.Thread(target = run_video_encode_loop, args = (rtc_peer, video_queue), daemon = True)
|
||||
video_receiver_thread.start()
|
||||
video_encoder_thread.start()
|
||||
|
||||
if rtc_peer.get('audio'):
|
||||
audio_receiver_thread = threading.Thread(target = receive_audio_frames, args = (rtc_peer.get('audio'), audio_queue), daemon = True)
|
||||
audio_encoder_thread = threading.Thread(target = run_audio_encode_loop, args = (rtc_peer, audio_queue), daemon = True)
|
||||
audio_receiver_thread.start()
|
||||
audio_encoder_thread.start()
|
||||
audio_receiver_thread.join()
|
||||
audio_encoder_thread.join()
|
||||
|
||||
video_receiver_thread.join()
|
||||
video_encoder_thread.join()
|
||||
video_executor.shutdown(wait = True)
|
||||
rtc_store.delete_peers(session_id)
|
||||
|
||||
|
||||
def destroy_stream(session_id : SessionId) -> bool:
|
||||
if rtc_store.has_peers(session_id):
|
||||
rtc_store.delete_peers(session_id)
|
||||
return not rtc_store.has_peers(session_id)
|
||||
|
||||
return False
|
||||
@@ -1,204 +0,0 @@
|
||||
import ctypes
|
||||
import time
|
||||
from concurrent.futures import Future, ThreadPoolExecutor
|
||||
from functools import partial
|
||||
from queue import Queue
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
from facefusion import rtc, streamer
|
||||
from facefusion.apis.stream_event import create_receive_event
|
||||
from facefusion.codecs import aom_decoder, aom_encoder, vpx_decoder, vpx_encoder
|
||||
from facefusion.types import AomDecoder, AomEncoder, AomPointer, BitRate, Resolution, RtcPeer, RtcPeerVideo, VideoCodec, VisionFrame, VpxDecoder, VpxEncoder, VpxPointer
|
||||
|
||||
|
||||
def run_video_encode_loop(rtc_peer : RtcPeer, video_queue : Queue[Tuple[float, Future[Tuple[bytes, Resolution]]]]) -> None:
|
||||
video_codec = rtc_peer.get('video').get('codec')
|
||||
video_time, video_future = video_queue.get()
|
||||
video_buffer, video_resolution = video_future.result()
|
||||
|
||||
if video_buffer:
|
||||
temp_resolution : Resolution = video_resolution
|
||||
temp_bitrate : BitRate = 8000
|
||||
video_encoder = create_video_encoder(video_codec, temp_resolution, temp_bitrate)
|
||||
temp_video_time = video_time
|
||||
frame_index = 0
|
||||
|
||||
while video_buffer:
|
||||
encode_start = time.monotonic()
|
||||
sender_bitrate = calculate_sender_bitrate(rtc_peer, temp_bitrate)
|
||||
|
||||
if video_resolution[0] - temp_resolution[0] or video_resolution[1] - temp_resolution[1]:
|
||||
temp_resolution = video_resolution
|
||||
update_video_encoder_resolution(video_codec, video_encoder, temp_resolution)
|
||||
|
||||
if sender_bitrate - temp_bitrate:
|
||||
temp_bitrate = sender_bitrate
|
||||
update_video_encoder_bitrate(video_codec, video_encoder, temp_bitrate)
|
||||
|
||||
__video_buffer__ = encode_video_frame(video_codec, video_encoder, video_buffer, temp_resolution, frame_index)
|
||||
|
||||
if __video_buffer__:
|
||||
video_timestamp = int(video_time * 90000)
|
||||
rtc.send_video(rtc_peer, __video_buffer__, video_timestamp)
|
||||
|
||||
encode_time = time.monotonic() - encode_start
|
||||
frame_interval = video_time - temp_video_time
|
||||
temp_video_time = video_time
|
||||
|
||||
receiver_bitrate = calculate_receiver_bitrate(rtc_peer, encode_time, frame_interval)
|
||||
rtc.adapt_receiver_bitrate(rtc_peer, receiver_bitrate)
|
||||
frame_index += 1
|
||||
|
||||
video_time, video_future = video_queue.get()
|
||||
video_buffer, video_resolution = video_future.result()
|
||||
|
||||
destroy_video_encoder(video_codec, video_encoder)
|
||||
rtc.clear_bitrate(rtc_peer)
|
||||
|
||||
|
||||
def receive_video_frames(rtc_peer_video : RtcPeerVideo, video_queue : Queue[Tuple[float, Future[Tuple[bytes, Resolution]]]], video_executor : ThreadPoolExecutor) -> None:
|
||||
video_track = rtc_peer_video.get('receiver_track')
|
||||
video_codec = rtc_peer_video.get('codec')
|
||||
video_decoder = create_video_decoder(video_codec)
|
||||
|
||||
video_frame_handler = partial(handle_video_frame, video_codec, video_decoder, video_queue, video_executor)
|
||||
receive_event = create_receive_event(video_track, video_frame_handler)
|
||||
receive_event.wait()
|
||||
|
||||
empty_future : Future[Tuple[bytes, Resolution]] = Future()
|
||||
empty_future.set_result((bytes(), (0, 0)))
|
||||
video_queue.put((0.0, empty_future))
|
||||
destroy_video_decoder(video_codec, video_decoder)
|
||||
|
||||
|
||||
def process_video_frame(input_vision_frame : VisionFrame) -> Tuple[bytes, Resolution]:
|
||||
output_vision_frame = streamer.process_stream_frame(input_vision_frame)
|
||||
output_resolution : Resolution = (output_vision_frame.shape[1], output_vision_frame.shape[0])
|
||||
output_buffer = cv2.cvtColor(output_vision_frame, cv2.COLOR_BGR2YUV_I420).tobytes()
|
||||
return output_buffer, output_resolution
|
||||
|
||||
|
||||
def calculate_receiver_bitrate(rtc_peer : RtcPeer, encode_time : float, frame_interval : float) -> BitRate:
|
||||
min_bitrate : BitRate = 500
|
||||
max_bitrate : BitRate = 8000
|
||||
bitrate : BitRate = rtc_peer.get('receiver_bitrate').value
|
||||
|
||||
if frame_interval > 0:
|
||||
scale = frame_interval / encode_time
|
||||
bitrate = int(bitrate * scale)
|
||||
bitrate = max(min_bitrate, min(max_bitrate, bitrate))
|
||||
|
||||
return bitrate
|
||||
|
||||
|
||||
#todo: does not feel final as this is an clamp and not calculate
|
||||
def calculate_sender_bitrate(rtc_peer : RtcPeer, bitrate : BitRate) -> BitRate:
|
||||
min_bitrate : BitRate = 500
|
||||
max_bitrate : BitRate = 8000
|
||||
peer_bitrate : BitRate = rtc_peer.get('sender_bitrate').value
|
||||
|
||||
if peer_bitrate > 0:
|
||||
bitrate = max(min_bitrate, min(max_bitrate, peer_bitrate))
|
||||
|
||||
return bitrate
|
||||
|
||||
|
||||
def decode_video_frame(video_codec : VideoCodec, video_decoder : VpxDecoder | AomDecoder, input_buffer : bytes) -> Optional[VisionFrame]:
|
||||
if video_codec == 'av1':
|
||||
aom_pointer = aom_decoder.decode(video_decoder, input_buffer)
|
||||
|
||||
if aom_pointer:
|
||||
return normalize_vision_frame(aom_pointer)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
vpx_pointer = vpx_decoder.decode(video_decoder, input_buffer)
|
||||
|
||||
if vpx_pointer:
|
||||
return normalize_vision_frame(vpx_pointer)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def encode_video_frame(video_codec : VideoCodec, video_encoder : VpxEncoder | AomEncoder, input_buffer : bytes, frame_resolution : Resolution, frame_index : int) -> bytes:
|
||||
if video_codec == 'av1':
|
||||
return aom_encoder.encode(video_encoder, input_buffer, frame_resolution, frame_index)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
return vpx_encoder.encode(video_encoder, input_buffer, frame_resolution, frame_index)
|
||||
|
||||
return bytes()
|
||||
|
||||
|
||||
def normalize_vision_frame(frame_pointer : AomPointer | VpxPointer) -> VisionFrame:
|
||||
frame_width, frame_height = frame_pointer.get('resolution')
|
||||
vision_frame = numpy.frombuffer(frame_pointer.get('buffer'), dtype = numpy.uint8).reshape((frame_height * 3 // 2, frame_width))
|
||||
return cv2.cvtColor(vision_frame, cv2.COLOR_YUV2BGR_I420)
|
||||
|
||||
|
||||
def create_video_decoder(video_codec : VideoCodec) -> Optional[VpxDecoder | AomDecoder]:
|
||||
if video_codec == 'av1':
|
||||
return aom_decoder.create(8)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
return vpx_decoder.create(video_codec, 8)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def create_video_encoder(video_codec : VideoCodec, frame_resolution : Resolution, bitrate : BitRate) -> Optional[VpxEncoder | AomEncoder]:
|
||||
if video_codec == 'av1':
|
||||
return aom_encoder.create(frame_resolution, bitrate, 8, 10)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
return vpx_encoder.create(video_codec, frame_resolution, bitrate, 8, 10)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def destroy_video_decoder(video_codec : VideoCodec, video_decoder : VpxDecoder | AomDecoder) -> None:
|
||||
if video_codec == 'av1':
|
||||
aom_decoder.destroy(video_decoder)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
vpx_decoder.destroy(video_decoder)
|
||||
|
||||
|
||||
def destroy_video_encoder(video_codec : VideoCodec, video_encoder : VpxEncoder | AomEncoder) -> None:
|
||||
if video_codec == 'av1':
|
||||
aom_encoder.destroy(video_encoder)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
vpx_encoder.destroy(video_encoder)
|
||||
|
||||
|
||||
def update_video_encoder_resolution(video_codec : VideoCodec, video_encoder : VpxEncoder | AomEncoder, frame_resolution : Resolution) -> bool:
|
||||
if video_codec == 'av1':
|
||||
return aom_encoder.update_resolution(video_encoder, frame_resolution)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
return vpx_encoder.update_resolution(video_encoder, frame_resolution)
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def update_video_encoder_bitrate(video_codec : VideoCodec, video_encoder : VpxEncoder | AomEncoder, bitrate : BitRate) -> bool:
|
||||
if video_codec == 'av1':
|
||||
return aom_encoder.update_bitrate(video_encoder, bitrate)
|
||||
|
||||
if video_codec in [ 'vp8', 'vp9' ]:
|
||||
return vpx_encoder.update_bitrate(video_encoder, bitrate)
|
||||
|
||||
return False
|
||||
|
||||
|
||||
#todo: we can remove the dead args or pass audio buffer
|
||||
def handle_video_frame(video_codec : VideoCodec, video_decoder : VpxDecoder | AomDecoder, video_queue : Queue[Tuple[float, Future[Tuple[bytes, Resolution]]]], video_executor : ThreadPoolExecutor, track : int, data : ctypes.c_void_p, size : int, info : ctypes.c_void_p, pointer : ctypes.c_void_p) -> None:
|
||||
video_buffer = ctypes.string_at(data, size)
|
||||
vision_frame = decode_video_frame(video_codec, video_decoder, video_buffer)
|
||||
|
||||
if numpy.any(vision_frame) and video_queue.qsize() < video_queue.maxsize:
|
||||
video_future = video_executor.submit(process_video_frame, vision_frame)
|
||||
video_queue.put((time.monotonic(), video_future))
|
||||
@@ -1,207 +0,0 @@
|
||||
import base64
|
||||
import subprocess
|
||||
from typing import List, Optional
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.status import HTTP_200_OK, HTTP_400_BAD_REQUEST
|
||||
|
||||
from facefusion import logger
|
||||
from facefusion.apis import asset_store
|
||||
from facefusion.filesystem import is_video
|
||||
from facefusion.video_manager import get_video_capture
|
||||
from facefusion.vision import fit_contain_frame
|
||||
|
||||
|
||||
def extract_frame_at_timestamp(stream_url : str, timestamp : float, width : int, height : int) -> Optional[numpy.ndarray]:
|
||||
ffmpeg_command =\
|
||||
[
|
||||
'ffmpeg',
|
||||
'-user_agent', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
|
||||
'-ss', str(timestamp),
|
||||
'-i', stream_url,
|
||||
'-vf', f'scale={width}:{height}',
|
||||
'-frames:v', '1',
|
||||
'-f', 'rawvideo',
|
||||
'-pix_fmt', 'bgr24',
|
||||
'-'
|
||||
]
|
||||
|
||||
try:
|
||||
result = subprocess.run(ffmpeg_command, capture_output = True, timeout = 10)
|
||||
if result.returncode == 0 and result.stdout:
|
||||
frame_size = width * height * 3
|
||||
if len(result.stdout) >= frame_size:
|
||||
frame = numpy.frombuffer(result.stdout[:frame_size], dtype = numpy.uint8).reshape((height, width, 3))
|
||||
return frame
|
||||
except Exception as e:
|
||||
logger.debug(f'Failed to extract frame at {timestamp}s: {e}', __name__)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
async def get_timeline(request: Request) -> JSONResponse:
|
||||
"""
|
||||
Return N preview frames (as base64 JPEGs) from the target video,
|
||||
resized to specified resolution for timeline preview.
|
||||
|
||||
Route: /timeline/{count:int}?target_path=...&is_remote_stream=true&duration=120&fps=30&target_width=1920&target_height=1080&width=160&height=120
|
||||
"""
|
||||
|
||||
# Extract and validate requested count
|
||||
try:
|
||||
count = int(request.path_params.get('count', 0))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid count parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
if count <= 0:
|
||||
return JSONResponse({'message': 'Count must be a positive integer'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
# Extract and validate preview resolution parameters
|
||||
try:
|
||||
preview_width = int(request.query_params.get('width', 160))
|
||||
preview_height = int(request.query_params.get('height', 120))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid width or height parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
if preview_width <= 0 or preview_height <= 0 or preview_width > 1920 or preview_height > 1080:
|
||||
return JSONResponse({'message': 'Width and height must be between 1 and 1920x1080'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
# Extract target_path or asset_id (one is required)
|
||||
target_path = request.query_params.get('target_path')
|
||||
asset_id = request.query_params.get('asset_id')
|
||||
|
||||
# Extract is_remote_stream flag
|
||||
is_remote_stream_param = request.query_params.get('is_remote_stream', 'false').lower()
|
||||
is_remote_stream = is_remote_stream_param in ['true', '1', 'yes']
|
||||
|
||||
# Resolve asset_id to path if provided (for local files)
|
||||
if asset_id and not target_path:
|
||||
from facefusion.session_context import get_session_id
|
||||
|
||||
session_id = get_session_id()
|
||||
asset = asset_store.get_asset(session_id, asset_id)
|
||||
if not asset:
|
||||
return JSONResponse({'message': f'Asset not found: {asset_id}'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
target_path = asset.get('path')
|
||||
if not target_path:
|
||||
return JSONResponse({'message': 'Asset has no path'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
is_remote_stream = False # Assets are always local files
|
||||
logger.debug(f'Resolved asset_id {asset_id} to path for timeline preview', __name__)
|
||||
|
||||
# Now check if we have a target_path
|
||||
if not target_path:
|
||||
return JSONResponse({'message': 'Missing required parameter: either target_path or asset_id'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
# Extract video metadata (optional for local files, required for remote streams)
|
||||
duration = None
|
||||
fps = None
|
||||
width = 1280
|
||||
height = 720
|
||||
|
||||
if request.query_params.get('duration'):
|
||||
try:
|
||||
duration = float(request.query_params.get('duration'))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid duration parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
if request.query_params.get('fps'):
|
||||
try:
|
||||
fps = float(request.query_params.get('fps'))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid fps parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
if request.query_params.get('target_width'):
|
||||
try:
|
||||
width = int(request.query_params.get('target_width'))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid target_width parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
if request.query_params.get('target_height'):
|
||||
try:
|
||||
height = int(request.query_params.get('target_height'))
|
||||
except (TypeError, ValueError):
|
||||
return JSONResponse({'message': 'Invalid target_height parameter'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
previews: List[str] = []
|
||||
|
||||
if is_remote_stream:
|
||||
if not duration or duration <= 0:
|
||||
return JSONResponse({'message': 'Duration not available for remote stream'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
frame_total = 0
|
||||
if duration and fps:
|
||||
try:
|
||||
frame_total = int(float(duration) * float(fps))
|
||||
except Exception:
|
||||
frame_total = 0
|
||||
|
||||
sample_count = min(count, frame_total) if frame_total > 0 else count
|
||||
timestamps = list(numpy.linspace(0, float(duration), num=sample_count, endpoint=False))
|
||||
|
||||
logger.info(f'Extracting {sample_count} frames from remote stream using ffmpeg', __name__)
|
||||
|
||||
for timestamp in timestamps:
|
||||
frame = extract_frame_at_timestamp(target_path, timestamp, width, height)
|
||||
if frame is None:
|
||||
logger.warn(f'Failed to extract frame at {timestamp}s', __name__)
|
||||
continue
|
||||
|
||||
thumb_bgr = fit_contain_frame(frame, (preview_width, preview_height))
|
||||
if thumb_bgr.shape[1] != preview_width or thumb_bgr.shape[0] != preview_height:
|
||||
thumb_bgr = cv2.resize(thumb_bgr, (preview_width, preview_height))
|
||||
|
||||
ok_enc, buf = cv2.imencode('.jpg', thumb_bgr, [cv2.IMWRITE_JPEG_QUALITY, 50])
|
||||
if not ok_enc:
|
||||
logger.warn(f'JPEG encode failed for timestamp {timestamp}s', __name__)
|
||||
continue
|
||||
|
||||
b64 = base64.b64encode(buf.tobytes()).decode('ascii')
|
||||
previews.append(b64)
|
||||
else:
|
||||
video_capture = get_video_capture(target_path)
|
||||
if not video_capture or not video_capture.isOpened():
|
||||
logger.error(f'Unable to open video capture for target: {target_path}', __name__)
|
||||
return JSONResponse({'message': 'Unable to open target video'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
frame_total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
|
||||
|
||||
if frame_total <= 0 and is_video(target_path):
|
||||
return JSONResponse({'message': 'Could not determine frame count for target video'}, status_code=HTTP_400_BAD_REQUEST)
|
||||
|
||||
sample_count = min(count, frame_total)
|
||||
indices: List[int] = list(numpy.linspace(1, frame_total, num=sample_count, endpoint=True, dtype=int))
|
||||
|
||||
for frame_number in indices:
|
||||
video_capture.set(cv2.CAP_PROP_POS_FRAMES, max(0, frame_number - 1))
|
||||
ok_read, frame = video_capture.read()
|
||||
if not ok_read or frame is None:
|
||||
logger.warn(f'Failed reading frame {frame_number}', __name__)
|
||||
continue
|
||||
|
||||
thumb_bgr = fit_contain_frame(frame, (preview_width, preview_height))
|
||||
if thumb_bgr.shape[1] != preview_width or thumb_bgr.shape[0] != preview_height:
|
||||
thumb_bgr = cv2.resize(thumb_bgr, (preview_width, preview_height))
|
||||
|
||||
ok_enc, buf = cv2.imencode('.jpg', thumb_bgr, [cv2.IMWRITE_JPEG_QUALITY, 50])
|
||||
if not ok_enc:
|
||||
logger.warn(f'JPEG encode failed for frame {frame_number}', __name__)
|
||||
continue
|
||||
b64 = base64.b64encode(buf.tobytes()).decode('ascii')
|
||||
previews.append(b64)
|
||||
|
||||
logger.info(f'Returned {len(previews)}/{sample_count} timeline frames at {preview_width}x{preview_height}', __name__)
|
||||
|
||||
return JSONResponse({
|
||||
'message': 'ok',
|
||||
'count': len(previews),
|
||||
'requested': count,
|
||||
'width': preview_width,
|
||||
'height': preview_height,
|
||||
'format': 'jpeg',
|
||||
'frames': previews
|
||||
}, status_code=HTTP_200_OK)
|
||||
@@ -1,98 +0,0 @@
|
||||
import subprocess
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
|
||||
from starlette.datastructures import Headers
|
||||
from starlette.requests import Request
|
||||
from starlette.responses import JSONResponse
|
||||
from starlette.types import ASGIApp, Receive, Scope, Send
|
||||
from starlette.websockets import WebSocket
|
||||
|
||||
|
||||
@lru_cache(maxsize = 1)
|
||||
def get_api_version() -> str:
|
||||
try:
|
||||
result = subprocess.run(['git', 'rev-parse', 'HEAD'], capture_output = True, text = True, check = True)
|
||||
return result.stdout.strip()
|
||||
except Exception:
|
||||
return 'unknown'
|
||||
|
||||
|
||||
def check_version_match(request : Request) -> Optional[JSONResponse]:
|
||||
client_version = request.headers.get('X-API-Version')
|
||||
server_version = get_api_version()
|
||||
|
||||
if not client_version:
|
||||
return JSONResponse({'error': 'Missing X-API-Version header', 'server_version': server_version}, status_code = 400)
|
||||
|
||||
if client_version != server_version:
|
||||
return JSONResponse({'error': 'Version mismatch', 'client_version': client_version, 'server_version': server_version}, status_code = 409)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def check_version_match_websocket(websocket : WebSocket) -> Optional[str]:
|
||||
client_version = websocket.headers.get('X-API-Version')
|
||||
server_version = get_api_version()
|
||||
|
||||
if not client_version:
|
||||
return f'Missing X-API-Version header, server version: {server_version}'
|
||||
|
||||
if client_version != server_version:
|
||||
return f'Version mismatch: client={client_version}, server={server_version}'
|
||||
|
||||
return None
|
||||
|
||||
|
||||
async def version_guard_middleware(scope : Scope, receive : Receive, send : Send, app : ASGIApp) -> None:
|
||||
if scope['type'] == 'http':
|
||||
# Skip version check for OPTIONS requests (CORS preflight)
|
||||
if scope.get('method') == 'OPTIONS':
|
||||
await app(scope, receive, send)
|
||||
return
|
||||
|
||||
headers = Headers(scope = scope)
|
||||
client_version = headers.get('X-API-Version')
|
||||
server_version = get_api_version()
|
||||
|
||||
if not client_version:
|
||||
response = JSONResponse({'error': 'Missing X-API-Version header', 'server_version': server_version}, status_code = 400)
|
||||
await response(scope, receive, send)
|
||||
return
|
||||
|
||||
if client_version != server_version:
|
||||
response = JSONResponse({'error': 'Version mismatch', 'client_version': client_version, 'server_version': server_version}, status_code = 409)
|
||||
await response(scope, receive, send)
|
||||
return
|
||||
|
||||
if scope['type'] == 'websocket':
|
||||
headers = Headers(scope = scope)
|
||||
client_version = headers.get('X-API-Version')
|
||||
|
||||
# For WebSocket connections, also check subprotocols since browsers can't set custom headers
|
||||
if not client_version:
|
||||
protocol_header = headers.get('Sec-WebSocket-Protocol')
|
||||
if protocol_header:
|
||||
# Parse subprotocols to find api_version
|
||||
protocols = [p.strip() for p in protocol_header.split(',')]
|
||||
for protocol in protocols:
|
||||
if protocol.startswith('api_version.'):
|
||||
client_version = protocol.split('.', 1)[1]
|
||||
break
|
||||
|
||||
server_version = get_api_version()
|
||||
|
||||
if not client_version or client_version != server_version:
|
||||
websocket = WebSocket(scope, receive = receive, send = send)
|
||||
reason = f'Missing X-API-Version header, server version: {server_version}' if not client_version else f'Version mismatch: client={client_version}, server={server_version}'
|
||||
await websocket.close(code = 1008, reason = reason)
|
||||
return
|
||||
|
||||
await app(scope, receive, send)
|
||||
|
||||
|
||||
def create_version_guard(app : ASGIApp) -> ASGIApp:
|
||||
async def version_guard_app(scope : Scope, receive : Receive, send : Send) -> None:
|
||||
await version_guard_middleware(scope, receive, send, app)
|
||||
|
||||
return version_guard_app
|
||||
@@ -5,12 +5,14 @@ from facefusion.types import AppContext
|
||||
|
||||
|
||||
def detect_app_context() -> AppContext:
|
||||
jobs_path = os.path.join('facefusion', 'jobs')
|
||||
uis_path = os.path.join('facefusion', 'uis')
|
||||
frame = sys._getframe(1)
|
||||
|
||||
while frame:
|
||||
if os.path.join('facefusion', 'jobs') in frame.f_code.co_filename:
|
||||
if jobs_path in frame.f_code.co_filename:
|
||||
return 'cli'
|
||||
if os.path.join('facefusion', 'apis') in frame.f_code.co_filename:
|
||||
return 'api'
|
||||
if uis_path in frame.f_code.co_filename:
|
||||
return 'ui'
|
||||
frame = frame.f_back
|
||||
return 'cli'
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
from facefusion import state_manager
|
||||
from facefusion.filesystem import get_file_name, is_video, resolve_file_paths
|
||||
from facefusion.jobs import job_store
|
||||
from facefusion.normalizer import normalize_fps, normalize_space
|
||||
from facefusion.processors.core import get_processors_modules
|
||||
from facefusion.types import ApplyStateItem, Args
|
||||
@@ -6,30 +8,22 @@ from facefusion.vision import detect_video_fps
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
# general
|
||||
apply_state_item('command', args.get('command'))
|
||||
# workflow
|
||||
apply_state_item('workflow', args.get('workflow'))
|
||||
# paths
|
||||
apply_state_item('temp_path', args.get('temp_path'))
|
||||
apply_state_item('jobs_path', args.get('jobs_path'))
|
||||
apply_state_item('source_paths', args.get('source_paths'))
|
||||
apply_state_item('target_path', args.get('target_path'))
|
||||
apply_state_item('output_path', args.get('output_path'))
|
||||
# patterns
|
||||
apply_state_item('source_pattern', args.get('source_pattern'))
|
||||
apply_state_item('target_pattern', args.get('target_pattern'))
|
||||
apply_state_item('output_pattern', args.get('output_pattern'))
|
||||
# face detector
|
||||
apply_state_item('face_detector_model', args.get('face_detector_model'))
|
||||
apply_state_item('face_detector_size', args.get('face_detector_size'))
|
||||
apply_state_item('face_detector_margin', normalize_space(args.get('face_detector_margin')))
|
||||
apply_state_item('face_detector_angles', args.get('face_detector_angles'))
|
||||
apply_state_item('face_detector_score', args.get('face_detector_score'))
|
||||
# face landmarker
|
||||
apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
|
||||
apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
|
||||
# face selector
|
||||
apply_state_item('face_selector_mode', args.get('face_selector_mode'))
|
||||
apply_state_item('face_selector_order', args.get('face_selector_order'))
|
||||
apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
|
||||
@@ -39,7 +33,7 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('reference_face_position', args.get('reference_face_position'))
|
||||
apply_state_item('reference_face_distance', args.get('reference_face_distance'))
|
||||
apply_state_item('reference_frame_number', args.get('reference_frame_number'))
|
||||
# face masker
|
||||
apply_state_item('face_tracker_score', args.get('face_tracker_score'))
|
||||
apply_state_item('face_occluder_model', args.get('face_occluder_model'))
|
||||
apply_state_item('face_parser_model', args.get('face_parser_model'))
|
||||
apply_state_item('face_mask_types', args.get('face_mask_types'))
|
||||
@@ -47,13 +41,12 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('face_mask_regions', args.get('face_mask_regions'))
|
||||
apply_state_item('face_mask_blur', args.get('face_mask_blur'))
|
||||
apply_state_item('face_mask_padding', normalize_space(args.get('face_mask_padding')))
|
||||
# voice extractor
|
||||
apply_state_item('voice_extractor_model', args.get('voice_extractor_model'))
|
||||
# frame extraction
|
||||
apply_state_item('trim_frame_start', args.get('trim_frame_start'))
|
||||
apply_state_item('trim_frame_end', args.get('trim_frame_end'))
|
||||
apply_state_item('temp_frame_format', args.get('temp_frame_format'))
|
||||
# output creation
|
||||
apply_state_item('keep_temp', args.get('keep_temp'))
|
||||
apply_state_item('target_frame_amount', args.get('target_frame_amount'))
|
||||
apply_state_item('output_image_quality', args.get('output_image_quality'))
|
||||
apply_state_item('output_image_scale', args.get('output_image_scale'))
|
||||
apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
|
||||
@@ -63,34 +56,63 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('output_video_preset', args.get('output_video_preset'))
|
||||
apply_state_item('output_video_quality', args.get('output_video_quality'))
|
||||
apply_state_item('output_video_scale', args.get('output_video_scale'))
|
||||
|
||||
if args.get('output_video_fps') or is_video(args.get('target_path')):
|
||||
output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
|
||||
apply_state_item('output_video_fps', output_video_fps)
|
||||
# processors
|
||||
|
||||
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
|
||||
apply_state_item('processors', args.get('processors'))
|
||||
|
||||
for processor_module in get_processors_modules(available_processors):
|
||||
processor_module.apply_args(args, apply_state_item)
|
||||
# execution
|
||||
|
||||
apply_state_item('open_browser', args.get('open_browser'))
|
||||
apply_state_item('ui_layouts', args.get('ui_layouts'))
|
||||
apply_state_item('ui_workflow', args.get('ui_workflow'))
|
||||
apply_state_item('execution_device_ids', args.get('execution_device_ids'))
|
||||
apply_state_item('execution_providers', args.get('execution_providers'))
|
||||
apply_state_item('execution_thread_count', args.get('execution_thread_count'))
|
||||
# download
|
||||
apply_state_item('download_providers', args.get('download_providers'))
|
||||
apply_state_item('download_scope', args.get('download_scope'))
|
||||
# benchmark
|
||||
apply_state_item('benchmark_mode', args.get('benchmark_mode'))
|
||||
apply_state_item('benchmark_resolutions', args.get('benchmark_resolutions'))
|
||||
apply_state_item('benchmark_cycle_count', args.get('benchmark_cycle_count'))
|
||||
# api
|
||||
apply_state_item('api_host', args.get('api_host'))
|
||||
apply_state_item('api_port', args.get('api_port'))
|
||||
# memory
|
||||
apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
|
||||
# misc
|
||||
apply_state_item('log_level', args.get('log_level'))
|
||||
apply_state_item('halt_on_error', args.get('halt_on_error'))
|
||||
# jobs
|
||||
apply_state_item('job_id', args.get('job_id'))
|
||||
apply_state_item('job_status', args.get('job_status'))
|
||||
apply_state_item('step_index', args.get('step_index'))
|
||||
|
||||
|
||||
def reduce_step_args(args : Args) -> Args:
|
||||
step_args =\
|
||||
{
|
||||
key: args[key] for key in args if key in job_store.get_step_keys()
|
||||
}
|
||||
return step_args
|
||||
|
||||
|
||||
def reduce_job_args(args : Args) -> Args:
|
||||
job_args =\
|
||||
{
|
||||
key: args[key] for key in args if key in job_store.get_job_keys()
|
||||
}
|
||||
return job_args
|
||||
|
||||
|
||||
def collect_step_args() -> Args:
|
||||
step_args =\
|
||||
{
|
||||
key: state_manager.get_item(key) for key in job_store.get_step_keys() #type:ignore[arg-type]
|
||||
}
|
||||
return step_args
|
||||
|
||||
|
||||
def collect_job_args() -> Args:
|
||||
job_args =\
|
||||
{
|
||||
key: state_manager.get_item(key) for key in job_store.get_job_keys() #type:ignore[arg-type]
|
||||
}
|
||||
return job_args
|
||||
@@ -1,66 +0,0 @@
|
||||
from typing import List
|
||||
|
||||
from facefusion.types import Args, ArgsStore, Scope
|
||||
|
||||
|
||||
ARGS_STORE : ArgsStore =\
|
||||
{
|
||||
'api': [],
|
||||
'cli': [],
|
||||
'sys': []
|
||||
}
|
||||
|
||||
|
||||
def get_api_args() -> List[str]:
|
||||
return ARGS_STORE.get('api')
|
||||
|
||||
|
||||
def get_sys_args() -> List[str]:
|
||||
return ARGS_STORE.get('sys')
|
||||
|
||||
|
||||
def get_cli_args() -> List[str]:
|
||||
return ARGS_STORE.get('cli')
|
||||
|
||||
|
||||
def register_args(keys : List[str], scopes : List[Scope]) -> None:
|
||||
for key in keys:
|
||||
for scope in scopes:
|
||||
if scope == 'api':
|
||||
ARGS_STORE['api'].append(key)
|
||||
if scope == 'cli':
|
||||
ARGS_STORE['cli'].append(key)
|
||||
if scope == 'sys':
|
||||
ARGS_STORE['sys'].append(key)
|
||||
|
||||
|
||||
def filter_api_args(args : Args) -> Args:
|
||||
api_args =\
|
||||
{
|
||||
key: args.get(key) for key in args if key in get_api_args() #type:ignore[literal-required]
|
||||
}
|
||||
return api_args
|
||||
|
||||
|
||||
def filter_sys_args(args : Args) -> Args:
|
||||
sys_args =\
|
||||
{
|
||||
key: args.get(key) for key in args if key in get_sys_args() #type:ignore[literal-required]
|
||||
}
|
||||
return sys_args
|
||||
|
||||
|
||||
def filter_cli_args(args : Args) -> Args:
|
||||
cli_args =\
|
||||
{
|
||||
key: args.get(key) for key in args if key in get_cli_args() #type:ignore[literal-required]
|
||||
}
|
||||
return cli_args
|
||||
|
||||
|
||||
def filter_step_args(args : Args) -> Args:
|
||||
step_args =\
|
||||
{
|
||||
key: args.get(key) for key in args if key in get_cli_args() and key not in get_sys_args() #type:ignore[literal-required]
|
||||
}
|
||||
return step_args
|
||||
@@ -1,111 +0,0 @@
|
||||
import os
|
||||
import uuid
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Dict, List, Optional, TypeAlias
|
||||
|
||||
from facefusion import filesystem, state_manager
|
||||
from facefusion.session_context import get_session_id
|
||||
|
||||
AssetRegistry : TypeAlias = Dict[str, Dict[str, Any]]
|
||||
|
||||
|
||||
def get_asset_registry() -> AssetRegistry:
|
||||
registry = state_manager.get_item('asset_registry')
|
||||
if not registry:
|
||||
registry = {}
|
||||
state_manager.set_item('asset_registry', registry)
|
||||
return registry
|
||||
|
||||
|
||||
def register(asset_type : str, file_path : str, filename : str = None, metadata : Optional[Dict[str, Any]] = None) -> str:
|
||||
if asset_type not in ['source', 'target', 'output']:
|
||||
raise ValueError(f"Invalid asset_type: {asset_type}. Must be 'source', 'target', or 'output'")
|
||||
|
||||
asset_id = str(uuid.uuid4())
|
||||
session_id = get_session_id()
|
||||
|
||||
if not session_id:
|
||||
raise ValueError("No active session - cannot register asset without session_id")
|
||||
|
||||
if not filename:
|
||||
filename = os.path.basename(file_path)
|
||||
|
||||
file_size = os.path.getsize(file_path)
|
||||
file_format = filesystem.get_file_format(file_path)
|
||||
media_type = None
|
||||
|
||||
if filesystem.is_image(file_path):
|
||||
media_type = 'image'
|
||||
if filesystem.is_video(file_path):
|
||||
media_type = 'video'
|
||||
if filesystem.is_audio(file_path):
|
||||
media_type = 'audio'
|
||||
|
||||
asset_data =\
|
||||
{
|
||||
'id': asset_id,
|
||||
'session_id': session_id,
|
||||
'type': asset_type,
|
||||
'media_type': media_type,
|
||||
'format': file_format,
|
||||
'path': file_path,
|
||||
'filename': filename,
|
||||
'size': file_size,
|
||||
'created_at': datetime.now(timezone.utc).isoformat()
|
||||
}
|
||||
|
||||
if metadata:
|
||||
asset_data['metadata'] = metadata
|
||||
|
||||
registry = get_asset_registry()
|
||||
registry[asset_id] = asset_data
|
||||
state_manager.set_item('asset_registry', registry)
|
||||
|
||||
return asset_id
|
||||
|
||||
|
||||
def get_asset(asset_id : str) -> Optional[Dict[str, Any]]:
|
||||
registry = get_asset_registry()
|
||||
return registry.get(asset_id)
|
||||
|
||||
|
||||
def list_assets(asset_type : Optional[str] = None) -> List[Dict[str, Any]]:
|
||||
registry = get_asset_registry()
|
||||
session_id = get_session_id()
|
||||
|
||||
assets = list(registry.values())
|
||||
|
||||
if session_id:
|
||||
assets = [a for a in assets if a.get('session_id') == session_id]
|
||||
|
||||
if asset_type:
|
||||
if asset_type not in ['source', 'target', 'output']:
|
||||
raise ValueError(f"Invalid asset_type: {asset_type}")
|
||||
assets = [a for a in assets if a.get('type') == asset_type]
|
||||
|
||||
return assets
|
||||
|
||||
|
||||
def delete_asset(asset_id : str) -> bool:
|
||||
registry = get_asset_registry()
|
||||
asset = registry.get(asset_id)
|
||||
|
||||
if not asset:
|
||||
return False
|
||||
|
||||
file_path = asset.get('path')
|
||||
if file_path and os.path.exists(file_path):
|
||||
os.remove(file_path)
|
||||
|
||||
del registry[asset_id]
|
||||
state_manager.set_item('asset_registry', registry)
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def cleanup_session_assets(session_id : str) -> None:
|
||||
registry = get_asset_registry()
|
||||
assets_to_delete = [aid for aid, asset in registry.items() if asset.get('session_id') == session_id]
|
||||
|
||||
for asset_id in assets_to_delete:
|
||||
delete_asset(asset_id)
|
||||
+2
-30
@@ -1,6 +1,5 @@
|
||||
import math
|
||||
from functools import lru_cache
|
||||
from typing import Any, List, Optional, Tuple
|
||||
from typing import Any, List, Optional
|
||||
|
||||
import numpy
|
||||
import scipy
|
||||
@@ -8,8 +7,7 @@ from numpy.typing import NDArray
|
||||
|
||||
from facefusion.ffmpeg import read_audio_buffer
|
||||
from facefusion.filesystem import is_audio
|
||||
from facefusion.media_helper import restrict_trim_frame
|
||||
from facefusion.types import Audio, AudioFrame, Duration, Fps, Mel, MelFilterBank, Spectrogram
|
||||
from facefusion.types import Audio, AudioFrame, Fps, Mel, MelFilterBank, Spectrogram
|
||||
from facefusion.voice_extractor import batch_extract_voice
|
||||
|
||||
|
||||
@@ -143,29 +141,3 @@ def create_spectrogram(audio : Audio) -> Spectrogram:
|
||||
spectrogram = scipy.signal.stft(audio, nperseg = mel_bin_total, nfft = mel_bin_total, noverlap = mel_bin_overlap)[2]
|
||||
spectrogram = numpy.dot(mel_filter_bank, numpy.abs(spectrogram))
|
||||
return spectrogram
|
||||
|
||||
|
||||
def count_audio_frame_total(audio_path : str, fps : Fps) -> int:
|
||||
audio_duration = detect_audio_duration(audio_path)
|
||||
if audio_duration > 0:
|
||||
return math.ceil(audio_duration * fps)
|
||||
return 0
|
||||
|
||||
|
||||
def detect_audio_duration(audio_path : str) -> Duration:
|
||||
audio_sample_rate = 48000
|
||||
audio_sample_size = 16
|
||||
audio_channel_total = 2
|
||||
|
||||
if is_audio(audio_path):
|
||||
audio_buffer = read_audio_buffer(audio_path, audio_sample_rate, audio_sample_size, audio_channel_total)
|
||||
if audio_buffer:
|
||||
audio = numpy.frombuffer(audio_buffer, dtype = numpy.int16).reshape(-1, audio_channel_total)
|
||||
audio_duration = len(audio) / audio_sample_rate
|
||||
return audio_duration
|
||||
return 0
|
||||
|
||||
|
||||
def restrict_trim_audio_frame(audio_path : str, fps : Fps, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> Tuple[int, int]:
|
||||
audio_frame_total = count_audio_frame_total(audio_path, fps)
|
||||
return restrict_trim_frame(audio_frame_total, trim_frame_start, trim_frame_end)
|
||||
|
||||
@@ -9,7 +9,7 @@ import facefusion.choices
|
||||
from facefusion import content_analyser, core, state_manager
|
||||
from facefusion.cli_helper import render_table
|
||||
from facefusion.download import conditional_download, resolve_download_url
|
||||
from facefusion.face_store import clear_static_faces
|
||||
from facefusion.face_store import clear_faces
|
||||
from facefusion.filesystem import get_file_extension
|
||||
from facefusion.types import BenchmarkCycleSet
|
||||
from facefusion.vision import count_video_frame_total, detect_video_fps
|
||||
@@ -64,7 +64,7 @@ def cycle(cycle_count : int) -> BenchmarkCycleSet:
|
||||
if state_manager.get_item('benchmark_mode') == 'cold':
|
||||
content_analyser.analyse_image.cache_clear()
|
||||
content_analyser.analyse_video.cache_clear()
|
||||
clear_static_faces()
|
||||
clear_faces()
|
||||
|
||||
start_time = perf_counter()
|
||||
core.conditional_process()
|
||||
@@ -89,7 +89,7 @@ def cycle(cycle_count : int) -> BenchmarkCycleSet:
|
||||
|
||||
def suggest_output_path(target_path : str) -> str:
|
||||
target_file_extension = get_file_extension(target_path)
|
||||
return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_file_extension)
|
||||
return os.path.join(tempfile.gettempdir(), hashlib.sha1(target_path.encode()).hexdigest() + target_file_extension)
|
||||
|
||||
|
||||
def render() -> None:
|
||||
|
||||
@@ -43,9 +43,9 @@ def detect_local_camera_ids(id_start : int, id_end : int) -> List[int]:
|
||||
local_camera_ids = []
|
||||
|
||||
for camera_id in range(id_start, id_end):
|
||||
cv2.setLogLevel(0)
|
||||
cv2.utils.logging.setLogLevel(0)
|
||||
camera_capture = get_local_camera_capture(camera_id)
|
||||
cv2.setLogLevel(3)
|
||||
cv2.utils.logging.setLogLevel(3)
|
||||
|
||||
if camera_capture and camera_capture.isOpened():
|
||||
local_camera_ids.append(camera_id)
|
||||
|
||||
+39
-35
@@ -1,8 +1,8 @@
|
||||
import logging
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range, create_int_range
|
||||
from facefusion.types import Angle, AudioEncoder, AudioFormat, AudioTypeSet, BenchmarkMode, BenchmarkResolution, BenchmarkSet, DownloadProvider, DownloadProviderSet, DownloadScope, EncoderSet, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskArea, FaceMaskAreaSet, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, ImageFormat, ImageTypeSet, JobStatus, LogLevel, LogLevelSet, Race, Score, TempFrameFormat, VideoEncoder, VideoFormat, VideoMemoryStrategy, VideoPreset, VideoTypeSet, VoiceExtractorModel, WorkFlow
|
||||
from facefusion.types import Angle, AudioEncoder, AudioFormat, AudioTypeSet, BenchmarkMode, BenchmarkResolution, BenchmarkSet, DownloadProvider, DownloadProviderSet, DownloadScope, EncoderSet, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskArea, FaceMaskAreaSet, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorGender, FaceSelectorMode, FaceSelectorOrder, FaceSelectorRace, Gender, ImageFormat, ImageTypeSet, JobStatus, LogLevel, LogLevelSet, Race, Score, TempFrameFormat, UiWorkflow, VideoEncoder, VideoFormat, VideoMemoryStrategy, VideoPreset, VideoTypeSet, VoiceExtractorModel
|
||||
|
||||
face_detector_set : FaceDetectorSet =\
|
||||
{
|
||||
@@ -12,15 +12,17 @@ face_detector_set : FaceDetectorSet =\
|
||||
'yolo_face': [ '640x640' ],
|
||||
'yunet': [ '640x640' ]
|
||||
}
|
||||
face_detector_models : List[FaceDetectorModel] = list(face_detector_set.keys())
|
||||
face_landmarker_models : List[FaceLandmarkerModel] = [ 'many', '2dfan4', 'peppa_wutz' ]
|
||||
face_selector_modes : List[FaceSelectorMode] = [ 'many', 'one', 'reference' ]
|
||||
face_selector_orders : List[FaceSelectorOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
|
||||
face_selector_genders : List[Gender] = [ 'female', 'male' ]
|
||||
face_selector_races : List[Race] = [ 'white', 'black', 'latino', 'asian', 'indian', 'arabic' ]
|
||||
face_occluder_models : List[FaceOccluderModel] = [ 'many', 'xseg_1', 'xseg_2', 'xseg_3' ]
|
||||
face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
|
||||
face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'area', 'region' ]
|
||||
face_detector_models : List[FaceDetectorModel] = list(get_args(FaceDetectorModel))
|
||||
face_landmarker_models : List[FaceLandmarkerModel] = list(get_args(FaceLandmarkerModel))
|
||||
face_selector_modes : List[FaceSelectorMode] = list(get_args(FaceSelectorMode))
|
||||
face_selector_orders : List[FaceSelectorOrder] = list(get_args(FaceSelectorOrder))
|
||||
genders : List[Gender] = list(get_args(Gender))
|
||||
races : List[Race] = list(get_args(Race))
|
||||
face_selector_genders : List[FaceSelectorGender] = list(get_args(FaceSelectorGender))
|
||||
face_selector_races : List[FaceSelectorRace] = list(get_args(FaceSelectorRace))
|
||||
face_occluder_models : List[FaceOccluderModel] = list(get_args(FaceOccluderModel))
|
||||
face_parser_models : List[FaceParserModel] = list(get_args(FaceParserModel))
|
||||
face_mask_types : List[FaceMaskType] = list(get_args(FaceMaskType))
|
||||
face_mask_area_set : FaceMaskAreaSet =\
|
||||
{
|
||||
'upper-face': [ 0, 1, 2, 31, 32, 33, 34, 35, 14, 15, 16, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17 ],
|
||||
@@ -40,12 +42,10 @@ face_mask_region_set : FaceMaskRegionSet =\
|
||||
'upper-lip': 12,
|
||||
'lower-lip': 13
|
||||
}
|
||||
face_mask_areas : List[FaceMaskArea] = list(face_mask_area_set.keys())
|
||||
face_mask_regions : List[FaceMaskRegion] = list(face_mask_region_set.keys())
|
||||
face_mask_areas : List[FaceMaskArea] = list(get_args(FaceMaskArea))
|
||||
face_mask_regions : List[FaceMaskRegion] = list(get_args(FaceMaskRegion))
|
||||
|
||||
voice_extractor_models : List[VoiceExtractorModel] = [ 'kim_vocal_1', 'kim_vocal_2', 'uvr_mdxnet' ]
|
||||
|
||||
workflows : List[WorkFlow] = [ 'auto', 'audio-to-image', 'image-to-image', 'image-to-video' ]
|
||||
voice_extractor_models : List[VoiceExtractorModel] = list(get_args(VoiceExtractorModel))
|
||||
|
||||
audio_type_set : AudioTypeSet =\
|
||||
{
|
||||
@@ -76,21 +76,21 @@ video_type_set : VideoTypeSet =\
|
||||
'webm': 'video/webm',
|
||||
'wmv': 'video/x-ms-wmv'
|
||||
}
|
||||
audio_formats : List[AudioFormat] = list(audio_type_set.keys())
|
||||
image_formats : List[ImageFormat] = list(image_type_set.keys())
|
||||
video_formats : List[VideoFormat] = list(video_type_set.keys())
|
||||
temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpeg', 'png', 'tiff' ]
|
||||
audio_formats : List[AudioFormat] = list(get_args(AudioFormat))
|
||||
image_formats : List[ImageFormat] = list(get_args(ImageFormat))
|
||||
video_formats : List[VideoFormat] = list(get_args(VideoFormat))
|
||||
temp_frame_formats : List[TempFrameFormat] = list(get_args(TempFrameFormat))
|
||||
|
||||
output_audio_encoders : List[AudioEncoder] = list(get_args(AudioEncoder))
|
||||
output_video_encoders : List[VideoEncoder] = list(get_args(VideoEncoder))
|
||||
output_encoder_set : EncoderSet =\
|
||||
{
|
||||
'audio': [ 'flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le' ],
|
||||
'video': [ 'libx264', 'libx264rgb', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo' ]
|
||||
'audio': output_audio_encoders,
|
||||
'video': output_video_encoders
|
||||
}
|
||||
output_audio_encoders : List[AudioEncoder] = output_encoder_set.get('audio')
|
||||
output_video_encoders : List[VideoEncoder] = output_encoder_set.get('video')
|
||||
output_video_presets : List[VideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
|
||||
output_video_presets : List[VideoPreset] = list(get_args(VideoPreset))
|
||||
|
||||
benchmark_modes : List[BenchmarkMode] = [ 'warm', 'cold' ]
|
||||
benchmark_modes : List[BenchmarkMode] = list(get_args(BenchmarkMode))
|
||||
benchmark_set : BenchmarkSet =\
|
||||
{
|
||||
'240p': '.assets/examples/target-240p.mp4',
|
||||
@@ -101,20 +101,21 @@ benchmark_set : BenchmarkSet =\
|
||||
'1440p': '.assets/examples/target-1440p.mp4',
|
||||
'2160p': '.assets/examples/target-2160p.mp4'
|
||||
}
|
||||
benchmark_resolutions : List[BenchmarkResolution] = list(benchmark_set.keys())
|
||||
benchmark_resolutions : List[BenchmarkResolution] = list(get_args(BenchmarkResolution))
|
||||
|
||||
execution_provider_set : ExecutionProviderSet =\
|
||||
{
|
||||
'cuda': 'CUDAExecutionProvider',
|
||||
'tensorrt': 'TensorrtExecutionProvider',
|
||||
'directml': 'DmlExecutionProvider',
|
||||
'rocm': 'ROCMExecutionProvider',
|
||||
'migraphx': 'MIGraphXExecutionProvider',
|
||||
'openvino': 'OpenVINOExecutionProvider',
|
||||
'coreml': 'CoreMLExecutionProvider',
|
||||
'openvino': 'OpenVINOExecutionProvider',
|
||||
'qnn': 'QNNExecutionProvider',
|
||||
'directml': 'DmlExecutionProvider',
|
||||
'cpu': 'CPUExecutionProvider'
|
||||
}
|
||||
execution_providers : List[ExecutionProvider] = list(execution_provider_set.keys())
|
||||
execution_providers : List[ExecutionProvider] = list(get_args(ExecutionProvider))
|
||||
download_provider_set : DownloadProviderSet =\
|
||||
{
|
||||
'github':
|
||||
@@ -135,10 +136,10 @@ download_provider_set : DownloadProviderSet =\
|
||||
'path': '/facefusion/{base_name}/resolve/main/{file_name}'
|
||||
}
|
||||
}
|
||||
download_providers : List[DownloadProvider] = list(download_provider_set.keys())
|
||||
download_scopes : List[DownloadScope] = [ 'lite', 'full' ]
|
||||
download_providers : List[DownloadProvider] = list(get_args(DownloadProvider))
|
||||
download_scopes : List[DownloadScope] = list(get_args(DownloadScope))
|
||||
|
||||
video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
|
||||
video_memory_strategies : List[VideoMemoryStrategy] = list(get_args(VideoMemoryStrategy))
|
||||
|
||||
log_level_set : LogLevelSet =\
|
||||
{
|
||||
@@ -147,9 +148,10 @@ log_level_set : LogLevelSet =\
|
||||
'info': logging.INFO,
|
||||
'debug': logging.DEBUG
|
||||
}
|
||||
log_levels : List[LogLevel] = list(log_level_set.keys())
|
||||
log_levels : List[LogLevel] = list(get_args(LogLevel))
|
||||
|
||||
job_statuses : List[JobStatus] = [ 'drafted', 'queued', 'completed', 'failed' ]
|
||||
ui_workflows : List[UiWorkflow] = list(get_args(UiWorkflow))
|
||||
job_statuses : List[JobStatus] = list(get_args(JobStatus))
|
||||
|
||||
benchmark_cycle_count_range : Sequence[int] = create_int_range(1, 10, 1)
|
||||
execution_thread_count_range : Sequence[int] = create_int_range(1, 32, 1)
|
||||
@@ -161,6 +163,8 @@ face_mask_blur_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
|
||||
face_mask_padding_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
face_selector_age_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
reference_face_distance_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
|
||||
face_tracker_score_range : Sequence[Score] = create_float_range(0.0, 0.5, 0.05)
|
||||
target_frame_amount_range : Sequence[int] = create_int_range(0, 10, 1)
|
||||
output_image_quality_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
output_image_scale_range : Sequence[float] = create_float_range(0.25, 8.0, 0.25)
|
||||
output_audio_quality_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
@@ -1,70 +0,0 @@
|
||||
import ctypes
|
||||
import struct
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import aom as aom_module
|
||||
from facefusion.types import AomDecoder, AomPointer
|
||||
|
||||
|
||||
def create(thread_count : int) -> Optional[AomDecoder]:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
aom_decoder = ctypes.create_string_buffer(128)
|
||||
aom_codec = ctypes.c_void_p.in_dll(aom_library, 'aom_codec_av1_dx_algo')
|
||||
config_buffer = ctypes.create_string_buffer(128)
|
||||
|
||||
struct.pack_into('I', config_buffer, 0, thread_count)
|
||||
struct.pack_into('I', config_buffer, 12, 1)
|
||||
|
||||
if aom_library.aom_codec_dec_init_ver(aom_decoder, ctypes.byref(aom_codec), config_buffer, 0, 22) == 0:
|
||||
return aom_decoder
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def decode(aom_decoder : AomDecoder, input_buffer : bytes) -> Optional[AomPointer]:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library and input_buffer:
|
||||
input_total = len(input_buffer)
|
||||
temp_buffer = ctypes.create_string_buffer(input_buffer)
|
||||
|
||||
if aom_library.aom_codec_decode(aom_decoder, temp_buffer, input_total, None) == 0:
|
||||
address = aom_library.aom_codec_get_frame(aom_decoder, ctypes.byref(ctypes.c_void_p(0)))
|
||||
|
||||
if address:
|
||||
frame_width = ctypes.c_uint.from_address(address + 28).value & ~1
|
||||
frame_height = ctypes.c_uint.from_address(address + 32).value & ~1
|
||||
|
||||
return AomPointer(
|
||||
buffer = collect(address, frame_width, frame_height),
|
||||
resolution = (frame_width, frame_height)
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def collect(address : int, frame_width : int, frame_height : int) -> bytes:
|
||||
output_parts = []
|
||||
|
||||
for index in range(3):
|
||||
plane_pointer = ctypes.c_void_p.from_address(address + 64 + index * 8).value
|
||||
stride = ctypes.c_int.from_address(address + 88 + index * 4).value
|
||||
plane_width = frame_width >> (index > 0)
|
||||
plane_height = frame_height >> (index > 0)
|
||||
|
||||
if stride == plane_width:
|
||||
output_parts.append(ctypes.string_at(plane_pointer, plane_width * plane_height))
|
||||
else:
|
||||
for row in range(plane_height):
|
||||
output_parts.append(ctypes.string_at(plane_pointer + row * stride, plane_width))
|
||||
|
||||
return bytes().join(output_parts)
|
||||
|
||||
|
||||
def destroy(aom_decoder : AomDecoder) -> None:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
aom_library.aom_codec_destroy(aom_decoder)
|
||||
@@ -1,94 +0,0 @@
|
||||
import ctypes
|
||||
import struct
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import aom as aom_module
|
||||
from facefusion.types import AomEncoder, BitRate, Resolution
|
||||
|
||||
|
||||
def create(frame_resolution : Resolution, bitrate : BitRate, thread_count : int, cpu_count : int) -> Optional[AomEncoder]:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
aom_encoder = ctypes.create_string_buffer(1152)
|
||||
aom_codec = ctypes.c_void_p.in_dll(aom_library, 'aom_codec_av1_cx_algo')
|
||||
|
||||
config_buffer = ctypes.create_string_buffer(1024)
|
||||
|
||||
if aom_library.aom_codec_enc_config_default(ctypes.byref(aom_codec), config_buffer, 1) == 0:
|
||||
struct.pack_into('I', config_buffer, 4, thread_count)
|
||||
struct.pack_into('I', config_buffer, 12, frame_resolution[0])
|
||||
struct.pack_into('I', config_buffer, 16, frame_resolution[1])
|
||||
struct.pack_into('I', config_buffer, 136, bitrate)
|
||||
struct.pack_into('I', config_buffer, 192, 30)
|
||||
|
||||
if aom_library.aom_codec_enc_init_ver(aom_encoder, ctypes.byref(aom_codec), config_buffer, 0, 25) == 0:
|
||||
aom_library.aom_codec_control(aom_encoder, 13, ctypes.c_int(cpu_count))
|
||||
aom_library.aom_codec_control(aom_encoder, 75, ctypes.c_int(1))
|
||||
aom_library.aom_codec_control(aom_encoder, 106, ctypes.c_int(0))
|
||||
aom_library.aom_codec_control(aom_encoder, 122, ctypes.c_int(0))
|
||||
aom_library.aom_codec_control(aom_encoder, 123, ctypes.c_int(0))
|
||||
ctypes.memmove(ctypes.addressof(aom_encoder) + 128, config_buffer, 1024)
|
||||
return aom_encoder
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def encode(aom_encoder : AomEncoder, input_buffer : bytes, frame_resolution : Resolution, frame_index : int) -> bytes:
|
||||
aom_library = aom_module.create_static_library()
|
||||
output_buffer = bytes()
|
||||
|
||||
if aom_library:
|
||||
temp_buffer = ctypes.create_string_buffer(256)
|
||||
encode_buffer = ctypes.create_string_buffer(input_buffer)
|
||||
|
||||
if aom_library.aom_img_wrap(temp_buffer, 0x102, frame_resolution[0], frame_resolution[1], 1, encode_buffer) and aom_library.aom_codec_encode(aom_encoder, temp_buffer, frame_index, 1, 0, 1) == 0:
|
||||
output_buffer = collect(aom_encoder)
|
||||
|
||||
return output_buffer
|
||||
|
||||
|
||||
def collect(aom_encoder : AomEncoder) -> bytes:
|
||||
aom_library = aom_module.create_static_library()
|
||||
output_parts = []
|
||||
|
||||
packet_cursor = ctypes.c_void_p(0)
|
||||
packet = aom_library.aom_codec_get_cx_data(aom_encoder, ctypes.byref(packet_cursor))
|
||||
|
||||
while packet:
|
||||
if ctypes.c_int.from_address(packet).value == 0:
|
||||
buffer_pointer = ctypes.c_void_p.from_address(packet + 8).value
|
||||
buffer_size = ctypes.c_size_t.from_address(packet + 16).value
|
||||
output_parts.append(ctypes.string_at(buffer_pointer, buffer_size))
|
||||
|
||||
packet = aom_library.aom_codec_get_cx_data(aom_encoder, ctypes.byref(packet_cursor))
|
||||
|
||||
return bytes().join(output_parts)
|
||||
|
||||
|
||||
def update_resolution(aom_encoder : AomEncoder, frame_resolution : Resolution) -> bool:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
struct.pack_into('I', aom_encoder, 128 + 12, frame_resolution[0])
|
||||
struct.pack_into('I', aom_encoder, 128 + 16, frame_resolution[1])
|
||||
return aom_library.aom_codec_enc_config_set(aom_encoder, ctypes.cast(ctypes.addressof(aom_encoder) + 128, ctypes.c_void_p)) == 0
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def update_bitrate(aom_encoder : AomEncoder, bitrate : BitRate) -> bool:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
struct.pack_into('I', aom_encoder, 128 + 136, bitrate)
|
||||
return aom_library.aom_codec_enc_config_set(aom_encoder, ctypes.cast(ctypes.addressof(aom_encoder) + 128, ctypes.c_void_p)) == 0
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def destroy(aom_encoder : AomEncoder) -> None:
|
||||
aom_library = aom_module.create_static_library()
|
||||
|
||||
if aom_library:
|
||||
aom_library.aom_codec_destroy(aom_encoder)
|
||||
@@ -1,36 +0,0 @@
|
||||
import ctypes
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import opus as opus_module
|
||||
from facefusion.types import OpusDecoder
|
||||
|
||||
|
||||
def create(sample_rate : int, channel_total : int) -> Optional[OpusDecoder]:
|
||||
opus_library = opus_module.create_static_library()
|
||||
|
||||
if opus_library:
|
||||
return opus_library.opus_decoder_create(sample_rate, channel_total, ctypes.byref(ctypes.c_int(0)))
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def decode(opus_decoder : OpusDecoder, input_buffer : bytes, frame_size : int, channel_total : int) -> bytes:
|
||||
opus_library = opus_module.create_static_library()
|
||||
output_buffer = bytes()
|
||||
|
||||
if opus_library:
|
||||
input_total = len(input_buffer)
|
||||
decode_buffer = (ctypes.c_float * (frame_size * channel_total))()
|
||||
decode_length = opus_library.opus_decode_float(opus_decoder, input_buffer, input_total, decode_buffer, frame_size, 0)
|
||||
|
||||
if decode_length:
|
||||
output_buffer = ctypes.string_at(ctypes.addressof(decode_buffer), decode_length * channel_total * ctypes.sizeof(ctypes.c_float))
|
||||
|
||||
return output_buffer
|
||||
|
||||
|
||||
def destroy(opus_decoder : OpusDecoder) -> None:
|
||||
opus_library = opus_module.create_static_library()
|
||||
|
||||
if opus_library:
|
||||
opus_library.opus_decoder_destroy(opus_decoder)
|
||||
@@ -1,36 +0,0 @@
|
||||
import ctypes
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import opus as opus_module
|
||||
from facefusion.types import OpusEncoder
|
||||
|
||||
|
||||
def create(sample_rate : int, channel_total : int) -> Optional[OpusEncoder]:
|
||||
opus_library = opus_module.create_static_library()
|
||||
|
||||
if opus_library:
|
||||
return opus_library.opus_encoder_create(sample_rate, channel_total, 2049, ctypes.byref(ctypes.c_int(0)))
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def encode(opus_encoder : OpusEncoder, input_buffer : bytes, frame_size : int) -> bytes:
|
||||
opus_library = opus_module.create_static_library()
|
||||
output_buffer = bytes()
|
||||
|
||||
if opus_library:
|
||||
temp_buffer = ctypes.create_string_buffer(2048)
|
||||
encode_buffer = ctypes.cast(ctypes.create_string_buffer(input_buffer), ctypes.POINTER(ctypes.c_float))
|
||||
encode_length = opus_library.opus_encode_float(opus_encoder, encode_buffer, frame_size, temp_buffer, 2048)
|
||||
|
||||
if encode_length:
|
||||
output_buffer = temp_buffer.raw[:encode_length]
|
||||
|
||||
return output_buffer
|
||||
|
||||
|
||||
def destroy(opus_encoder : OpusEncoder) -> None:
|
||||
opus_library = opus_module.create_static_library()
|
||||
|
||||
if opus_library:
|
||||
opus_library.opus_encoder_destroy(opus_encoder)
|
||||
@@ -1,74 +0,0 @@
|
||||
import ctypes
|
||||
import struct
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import vpx as vpx_module
|
||||
from facefusion.types import VpxDecoder, VpxPointer, VxpVideoCodec
|
||||
|
||||
|
||||
def create(video_codec : VxpVideoCodec, thread_count : int) -> Optional[VpxDecoder]:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
vpx_decoder = ctypes.create_string_buffer(64)
|
||||
vpx_algo = 'vpx_codec_vp8_dx_algo'
|
||||
|
||||
if video_codec == 'vp9':
|
||||
vpx_algo = 'vpx_codec_vp9_dx_algo'
|
||||
|
||||
vpx_codec = ctypes.c_void_p.in_dll(vpx_library, vpx_algo)
|
||||
config_buffer = ctypes.create_string_buffer(128)
|
||||
|
||||
struct.pack_into('I', config_buffer, 0, thread_count)
|
||||
|
||||
if vpx_library.vpx_codec_dec_init_ver(vpx_decoder, ctypes.byref(vpx_codec), config_buffer, 0, 12) == 0:
|
||||
return vpx_decoder
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def decode(vpx_decoder : VpxDecoder, input_buffer : bytes) -> Optional[VpxPointer]:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library and input_buffer:
|
||||
input_total = len(input_buffer)
|
||||
temp_buffer = ctypes.create_string_buffer(input_buffer)
|
||||
|
||||
if vpx_library.vpx_codec_decode(vpx_decoder, temp_buffer, input_total, None, 0) == 0:
|
||||
address = vpx_library.vpx_codec_get_frame(vpx_decoder, ctypes.byref(ctypes.c_void_p(0)))
|
||||
|
||||
if address:
|
||||
frame_width = ctypes.c_uint.from_address(address + 24).value & ~1
|
||||
frame_height = ctypes.c_uint.from_address(address + 28).value & ~1
|
||||
|
||||
return VpxPointer(
|
||||
buffer = collect(address, frame_width, frame_height),
|
||||
resolution = (frame_width, frame_height)
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def collect(address : int, frame_width : int, frame_height : int) -> bytes:
|
||||
output_parts = []
|
||||
|
||||
for index in range(3):
|
||||
plane_pointer = ctypes.c_void_p.from_address(address + 48 + index * 8).value
|
||||
stride = ctypes.c_int.from_address(address + 80 + index * 4).value
|
||||
plane_width = frame_width >> (index > 0)
|
||||
plane_height = frame_height >> (index > 0)
|
||||
|
||||
if stride == plane_width:
|
||||
output_parts.append(ctypes.string_at(plane_pointer, plane_width * plane_height))
|
||||
else:
|
||||
for row in range(plane_height):
|
||||
output_parts.append(ctypes.string_at(plane_pointer + row * stride, plane_width))
|
||||
|
||||
return bytes().join(output_parts)
|
||||
|
||||
|
||||
def destroy(vpx_decoder : VpxDecoder) -> None:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
vpx_library.vpx_codec_destroy(vpx_decoder)
|
||||
@@ -1,104 +0,0 @@
|
||||
import ctypes
|
||||
import struct
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.libraries import vpx as vpx_module
|
||||
from facefusion.types import BitRate, Resolution, VpxEncoder, VxpVideoCodec
|
||||
|
||||
|
||||
def create(video_codec : VxpVideoCodec, frame_resolution : Resolution, bitrate : BitRate, thread_count : int, cpu_count : int) -> Optional[VpxEncoder]:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
vpx_encoder = ctypes.create_string_buffer(640)
|
||||
vpx_algo = 'vpx_codec_vp8_cx_algo'
|
||||
|
||||
if video_codec == 'vp9':
|
||||
vpx_algo = 'vpx_codec_vp9_cx_algo'
|
||||
|
||||
vpx_codec = ctypes.c_void_p.in_dll(vpx_library, vpx_algo)
|
||||
|
||||
config_buffer = ctypes.create_string_buffer(512)
|
||||
|
||||
if vpx_library.vpx_codec_enc_config_default(ctypes.byref(vpx_codec), config_buffer, 0) == 0:
|
||||
struct.pack_into('I', config_buffer, 4, thread_count)
|
||||
struct.pack_into('I', config_buffer, 12, frame_resolution[0])
|
||||
struct.pack_into('I', config_buffer, 16, frame_resolution[1])
|
||||
struct.pack_into('I', config_buffer, 28, 1)
|
||||
struct.pack_into('I', config_buffer, 36, 0)
|
||||
struct.pack_into('I', config_buffer, 44, 0)
|
||||
struct.pack_into('I', config_buffer, 72, 0)
|
||||
struct.pack_into('I', config_buffer, 112, bitrate)
|
||||
struct.pack_into('I', config_buffer, 116, 2)
|
||||
struct.pack_into('I', config_buffer, 120, 50)
|
||||
struct.pack_into('I', config_buffer, 124, 50)
|
||||
struct.pack_into('I', config_buffer, 128, 50)
|
||||
|
||||
if vpx_library.vpx_codec_enc_init_ver(vpx_encoder, ctypes.byref(vpx_codec), config_buffer, 0, 39) == 0:
|
||||
vpx_library.vpx_codec_control_(vpx_encoder, 13, ctypes.c_int(cpu_count))
|
||||
vpx_library.vpx_codec_control_(vpx_encoder, 12, ctypes.c_int(0))
|
||||
vpx_library.vpx_codec_control_(vpx_encoder, 27, ctypes.c_int(10))
|
||||
ctypes.memmove(ctypes.addressof(vpx_encoder) + 64, config_buffer, 512)
|
||||
return vpx_encoder
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def encode(vpx_encoder : VpxEncoder, input_buffer : bytes, frame_resolution : Resolution, frame_index : int) -> bytes:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
output_buffer = bytes()
|
||||
|
||||
if vpx_library:
|
||||
temp_buffer = ctypes.create_string_buffer(256)
|
||||
encode_buffer = ctypes.create_string_buffer(input_buffer)
|
||||
|
||||
if vpx_library.vpx_img_wrap(temp_buffer, 0x102, frame_resolution[0], frame_resolution[1], 1, encode_buffer) and vpx_library.vpx_codec_encode(vpx_encoder, temp_buffer, frame_index, 1, 0, 1) == 0:
|
||||
output_buffer = collect(vpx_encoder)
|
||||
|
||||
return output_buffer
|
||||
|
||||
|
||||
def collect(vpx_encoder : VpxEncoder) -> bytes:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
output_parts = []
|
||||
|
||||
packet_cursor = ctypes.c_void_p(0)
|
||||
packet = vpx_library.vpx_codec_get_cx_data(vpx_encoder, ctypes.byref(packet_cursor))
|
||||
|
||||
while packet:
|
||||
if ctypes.c_int.from_address(packet).value == 0:
|
||||
buffer_pointer = ctypes.c_void_p.from_address(packet + 8).value
|
||||
buffer_size = ctypes.c_size_t.from_address(packet + 16).value
|
||||
output_parts.append(ctypes.string_at(buffer_pointer, buffer_size))
|
||||
|
||||
packet = vpx_library.vpx_codec_get_cx_data(vpx_encoder, ctypes.byref(packet_cursor))
|
||||
|
||||
return bytes().join(output_parts)
|
||||
|
||||
|
||||
def update_resolution(vpx_encoder : VpxEncoder, frame_resolution : Resolution) -> bool:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
struct.pack_into('I', vpx_encoder, 64 + 12, frame_resolution[0])
|
||||
struct.pack_into('I', vpx_encoder, 64 + 16, frame_resolution[1])
|
||||
return vpx_library.vpx_codec_enc_config_set(vpx_encoder, ctypes.cast(ctypes.addressof(vpx_encoder) + 64, ctypes.c_void_p)) == 0
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def update_bitrate(vpx_encoder : VpxEncoder, bitrate : BitRate) -> bool:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
struct.pack_into('I', vpx_encoder, 64 + 112, bitrate)
|
||||
return vpx_library.vpx_codec_enc_config_set(vpx_encoder, ctypes.cast(ctypes.addressof(vpx_encoder) + 64, ctypes.c_void_p)) == 0
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def destroy(vpx_encoder : VpxEncoder) -> None:
|
||||
vpx_library = vpx_module.create_static_library()
|
||||
|
||||
if vpx_library:
|
||||
vpx_library.vpx_codec_destroy(vpx_encoder)
|
||||
@@ -78,6 +78,12 @@ def get_first(__list__ : Any) -> Any:
|
||||
return None
|
||||
|
||||
|
||||
def get_middle(__list__ : Any) -> Any:
|
||||
if isinstance(__list__, Sequence) and __list__:
|
||||
return __list__[len(__list__) // 2]
|
||||
return None
|
||||
|
||||
|
||||
def get_last(__list__ : Any) -> Any:
|
||||
if isinstance(__list__, Reversible):
|
||||
return next(reversed(__list__), None)
|
||||
|
||||
@@ -0,0 +1,41 @@
|
||||
import os
|
||||
import sys
|
||||
from typing import List
|
||||
|
||||
from facefusion.common_helper import is_linux, is_windows
|
||||
|
||||
|
||||
def setup() -> None:
|
||||
conda_prefix = os.getenv('CONDA_PREFIX')
|
||||
conda_ready = os.getenv('CONDA_READY')
|
||||
|
||||
if conda_prefix and not conda_ready:
|
||||
if is_linux():
|
||||
python_id = 'python' + str(sys.version_info.major) + '.' + str(sys.version_info.minor)
|
||||
library_paths : List[str] =\
|
||||
[
|
||||
os.path.join(conda_prefix, 'lib'),
|
||||
os.path.join(conda_prefix, 'lib', python_id, 'site-packages', 'tensorrt_libs')
|
||||
]
|
||||
library_paths = list(filter(os.path.exists, library_paths))
|
||||
|
||||
if library_paths:
|
||||
if os.getenv('LD_LIBRARY_PATH'):
|
||||
library_paths.append(os.getenv('LD_LIBRARY_PATH'))
|
||||
os.environ['LD_LIBRARY_PATH'] = os.pathsep.join(library_paths)
|
||||
os.environ['CONDA_READY'] = '1'
|
||||
os.execv(sys.executable, [ sys.executable ] + sys.argv)
|
||||
|
||||
if is_windows():
|
||||
library_paths =\
|
||||
[
|
||||
os.path.join(conda_prefix, 'Lib'),
|
||||
os.path.join(conda_prefix, 'Lib', 'site-packages', 'tensorrt_libs')
|
||||
]
|
||||
library_paths = list(filter(os.path.exists, library_paths))
|
||||
|
||||
if library_paths:
|
||||
if os.getenv('PATH'):
|
||||
library_paths.append(os.getenv('PATH'))
|
||||
os.environ['PATH'] = os.pathsep.join(library_paths)
|
||||
os.environ['CONDA_READY'] = '1'
|
||||
+12
-21
@@ -1,29 +1,20 @@
|
||||
from configparser import ConfigParser
|
||||
from functools import lru_cache
|
||||
from typing import List, Optional
|
||||
|
||||
from facefusion import state_manager
|
||||
from facefusion.common_helper import cast_bool, cast_float, cast_int
|
||||
|
||||
CONFIG_PARSER = None
|
||||
|
||||
|
||||
def get_config_parser() -> ConfigParser:
|
||||
global CONFIG_PARSER
|
||||
|
||||
if CONFIG_PARSER is None:
|
||||
CONFIG_PARSER = ConfigParser()
|
||||
CONFIG_PARSER.read(state_manager.get_item('config_path'), encoding = 'utf-8')
|
||||
return CONFIG_PARSER
|
||||
|
||||
|
||||
def clear_config_parser() -> None:
|
||||
global CONFIG_PARSER
|
||||
|
||||
CONFIG_PARSER = None
|
||||
@lru_cache
|
||||
def get_static_config_parser() -> ConfigParser:
|
||||
config_parser = ConfigParser()
|
||||
config_parser.read(state_manager.get_item('config_path'), encoding = 'utf-8')
|
||||
return config_parser
|
||||
|
||||
|
||||
def get_str_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[str]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return config_parser.get(section, option)
|
||||
@@ -31,7 +22,7 @@ def get_str_value(section : str, option : str, fallback : Optional[str] = None)
|
||||
|
||||
|
||||
def get_int_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[int]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return config_parser.getint(section, option)
|
||||
@@ -39,7 +30,7 @@ def get_int_value(section : str, option : str, fallback : Optional[str] = None)
|
||||
|
||||
|
||||
def get_float_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[float]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return config_parser.getfloat(section, option)
|
||||
@@ -47,7 +38,7 @@ def get_float_value(section : str, option : str, fallback : Optional[str] = None
|
||||
|
||||
|
||||
def get_bool_value(section : str, option : str, fallback : Optional[str] = None) -> Optional[bool]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return config_parser.getboolean(section, option)
|
||||
@@ -55,7 +46,7 @@ def get_bool_value(section : str, option : str, fallback : Optional[str] = None)
|
||||
|
||||
|
||||
def get_str_list(section : str, option : str, fallback : Optional[str] = None) -> Optional[List[str]]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return config_parser.get(section, option).split()
|
||||
@@ -65,7 +56,7 @@ def get_str_list(section : str, option : str, fallback : Optional[str] = None) -
|
||||
|
||||
|
||||
def get_int_list(section : str, option : str, fallback : Optional[str] = None) -> Optional[List[int]]:
|
||||
config_parser = get_config_parser()
|
||||
config_parser = get_static_config_parser()
|
||||
|
||||
if config_parser.has_option(section, option) and config_parser.get(section, option).strip():
|
||||
return list(map(int, config_parser.get(section, option).split()))
|
||||
|
||||
@@ -1,16 +1,14 @@
|
||||
from functools import lru_cache
|
||||
from typing import List, Tuple
|
||||
from typing import Tuple
|
||||
|
||||
import numpy
|
||||
from tqdm import tqdm
|
||||
|
||||
from facefusion import inference_manager, state_manager, translator
|
||||
from facefusion.common_helper import is_macos
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.execution import has_execution_provider
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.thread_helper import conditional_thread_semaphore
|
||||
from facefusion.types import Detection, DownloadScope, DownloadSet, ExecutionProvider, Fps, InferencePool, ModelSet, VisionFrame
|
||||
from facefusion.types import Detection, DownloadScope, DownloadSet, Fps, InferencePool, ModelSet, VisionFrame
|
||||
from facefusion.vision import detect_video_fps, fit_contain_frame, read_image, read_video_frame
|
||||
|
||||
STREAM_COUNTER = 0
|
||||
@@ -119,12 +117,6 @@ def clear_inference_pool() -> None:
|
||||
inference_manager.clear_inference_pool(__name__, model_names)
|
||||
|
||||
|
||||
def resolve_execution_providers() -> List[ExecutionProvider]:
|
||||
if is_macos() and has_execution_provider('coreml'):
|
||||
return [ 'cpu' ]
|
||||
return state_manager.get_item('execution_providers')
|
||||
|
||||
|
||||
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
|
||||
model_set = create_static_model_set('full')
|
||||
model_hash_set = {}
|
||||
@@ -175,10 +167,12 @@ def analyse_video(video_path : str, trim_frame_start : int, trim_frame_end : int
|
||||
for frame_number in frame_range:
|
||||
if frame_number % int(video_fps) == 0:
|
||||
vision_frame = read_video_frame(video_path, frame_number)
|
||||
total += 1
|
||||
|
||||
if analyse_frame(vision_frame):
|
||||
counter += 1
|
||||
if numpy.any(vision_frame):
|
||||
total += 1
|
||||
|
||||
if analyse_frame(vision_frame):
|
||||
counter += 1
|
||||
|
||||
if counter > 0 and total > 0:
|
||||
rate = counter / total * 100
|
||||
|
||||
+43
-75
@@ -5,23 +5,18 @@ import signal
|
||||
import sys
|
||||
from time import time
|
||||
|
||||
import uvicorn
|
||||
|
||||
from facefusion import args_store, benchmarker, cli_helper, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, hash_helper, logger, state_manager, translator, voice_extractor
|
||||
from facefusion.apis.core import create_api
|
||||
from facefusion.args_helper import apply_args
|
||||
from facefusion import benchmarker, cli_helper, content_analyser, hash_helper, logger, state_manager, translator
|
||||
from facefusion.args import apply_args, collect_job_args, reduce_job_args, reduce_step_args
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources
|
||||
from facefusion.exit_helper import hard_exit, signal_exit
|
||||
from facefusion.filesystem import get_file_extension, get_file_name, resolve_file_paths, resolve_file_pattern
|
||||
from facefusion.filesystem import has_audio, has_image, has_video
|
||||
from facefusion.filesystem import get_file_extension, get_file_name, is_image, is_video, resolve_file_paths, resolve_file_pattern
|
||||
from facefusion.jobs import job_helper, job_manager, job_runner
|
||||
from facefusion.jobs.job_list import compose_job_list
|
||||
from facefusion.libraries import aom as aom_module, datachannel as datachannel_module, opus as opus_module, vpx as vpx_module
|
||||
from facefusion.processors.core import get_processors_modules
|
||||
from facefusion.program import create_program
|
||||
from facefusion.program_helper import validate_args
|
||||
from facefusion.types import Args, ErrorCode, WorkFlow
|
||||
from facefusion.workflows import audio_to_image, image_to_image, image_to_video
|
||||
from facefusion.types import Args, ErrorCode
|
||||
from facefusion.workflows import image_to_image, image_to_video
|
||||
|
||||
|
||||
def cli() -> None:
|
||||
@@ -54,31 +49,37 @@ def route(args : Args) -> None:
|
||||
hard_exit(2)
|
||||
benchmarker.render()
|
||||
|
||||
if state_manager.get_item('command') == 'api':
|
||||
logger.info(translator.get('api_started').format(host = state_manager.get_item('api_host'), port = state_manager.get_item('api_port')), __name__)
|
||||
uvicorn.run(create_api(), host = state_manager.get_item('api_host'), port = state_manager.get_item('api_port'))
|
||||
hard_exit(1)
|
||||
|
||||
if state_manager.get_item('command') in [ 'job-list', 'job-create', 'job-submit', 'job-submit-all', 'job-delete', 'job-delete-all', 'job-add-step', 'job-remix-step', 'job-insert-step', 'job-remove-step' ]:
|
||||
if not job_manager.init_jobs(state_manager.get_jobs_path()):
|
||||
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
|
||||
hard_exit(1)
|
||||
error_code = route_job_manager(args)
|
||||
hard_exit(error_code)
|
||||
|
||||
if state_manager.get_item('command') == 'run':
|
||||
if not job_manager.init_jobs(state_manager.get_jobs_path()):
|
||||
import facefusion.uis.core as ui
|
||||
|
||||
if not common_pre_check() or not processors_pre_check():
|
||||
hard_exit(2)
|
||||
for ui_layout in ui.get_ui_layouts_modules(state_manager.get_item('ui_layouts')):
|
||||
if not ui_layout.pre_check():
|
||||
hard_exit(2)
|
||||
ui.init()
|
||||
ui.launch()
|
||||
|
||||
if state_manager.get_item('command') == 'headless-run':
|
||||
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
|
||||
hard_exit(1)
|
||||
error_code = process_headless(args)
|
||||
hard_exit(error_code)
|
||||
|
||||
if state_manager.get_item('command') == 'batch-run':
|
||||
if not job_manager.init_jobs(state_manager.get_jobs_path()):
|
||||
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
|
||||
hard_exit(1)
|
||||
error_code = process_batch(args)
|
||||
hard_exit(error_code)
|
||||
|
||||
if state_manager.get_item('command') in [ 'job-run', 'job-run-all', 'job-retry', 'job-retry-all' ]:
|
||||
if not job_manager.init_jobs(state_manager.get_jobs_path()):
|
||||
if not job_manager.init_jobs(state_manager.get_item('jobs_path')):
|
||||
hard_exit(1)
|
||||
error_code = route_job_runner()
|
||||
hard_exit(error_code)
|
||||
@@ -100,25 +101,9 @@ def pre_check() -> bool:
|
||||
|
||||
|
||||
def common_pre_check() -> bool:
|
||||
common_modules =\
|
||||
[
|
||||
aom_module,
|
||||
datachannel_module,
|
||||
content_analyser,
|
||||
face_classifier,
|
||||
face_detector,
|
||||
face_landmarker,
|
||||
face_masker,
|
||||
face_recognizer,
|
||||
opus_module,
|
||||
voice_extractor,
|
||||
vpx_module
|
||||
]
|
||||
|
||||
content_analyser_content = inspect.getsource(content_analyser).encode()
|
||||
content_analyser_hash = hash_helper.create_hash(content_analyser_content)
|
||||
|
||||
return all(module.pre_check() for module in common_modules) and content_analyser_hash == 'b14e7b92'
|
||||
return hash_helper.create_hash(content_analyser_content) == '975d67d6'
|
||||
|
||||
|
||||
def processors_pre_check() -> bool:
|
||||
@@ -129,22 +114,19 @@ def processors_pre_check() -> bool:
|
||||
|
||||
|
||||
def force_download() -> ErrorCode:
|
||||
common_modules =\
|
||||
[
|
||||
content_analyser,
|
||||
face_classifier,
|
||||
face_detector,
|
||||
face_landmarker,
|
||||
face_masker,
|
||||
face_recognizer,
|
||||
voice_extractor
|
||||
]
|
||||
download_scope = state_manager.get_item('download_scope')
|
||||
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
|
||||
processor_modules = get_processors_modules(available_processors)
|
||||
common_modules = []
|
||||
|
||||
for processor_module in processor_modules:
|
||||
for common_module in processor_module.get_common_modules():
|
||||
if common_module not in common_modules:
|
||||
common_modules.append(common_module)
|
||||
|
||||
for module in common_modules + processor_modules:
|
||||
if hasattr(module, 'create_static_model_set'):
|
||||
for model in module.create_static_model_set(state_manager.get_item('download_scope')).values():
|
||||
for model in module.create_static_model_set(download_scope).values():
|
||||
model_hash_set = model.get('hashes')
|
||||
model_source_set = model.get('sources')
|
||||
|
||||
@@ -200,7 +182,7 @@ def route_job_manager(args : Args) -> ErrorCode:
|
||||
return 1
|
||||
|
||||
if state_manager.get_item('command') == 'job-add-step':
|
||||
step_args = args_store.filter_step_args(args)
|
||||
step_args = reduce_step_args(args)
|
||||
|
||||
if job_manager.add_step(state_manager.get_item('job_id'), step_args):
|
||||
logger.info(translator.get('job_step_added').format(job_id = state_manager.get_item('job_id')), __name__)
|
||||
@@ -209,7 +191,7 @@ def route_job_manager(args : Args) -> ErrorCode:
|
||||
return 1
|
||||
|
||||
if state_manager.get_item('command') == 'job-remix-step':
|
||||
step_args = args_store.filter_step_args(args)
|
||||
step_args = reduce_step_args(args)
|
||||
|
||||
if job_manager.remix_step(state_manager.get_item('job_id'), state_manager.get_item('step_index'), step_args):
|
||||
logger.info(translator.get('job_remix_step_added').format(job_id = state_manager.get_item('job_id'), step_index = state_manager.get_item('step_index')), __name__)
|
||||
@@ -218,7 +200,7 @@ def route_job_manager(args : Args) -> ErrorCode:
|
||||
return 1
|
||||
|
||||
if state_manager.get_item('command') == 'job-insert-step':
|
||||
step_args = args_store.filter_step_args(args)
|
||||
step_args = reduce_step_args(args)
|
||||
|
||||
if job_manager.insert_step(state_manager.get_item('job_id'), state_manager.get_item('step_index'), step_args):
|
||||
logger.info(translator.get('job_step_inserted').format(job_id = state_manager.get_item('job_id'), step_index = state_manager.get_item('step_index')), __name__)
|
||||
@@ -272,7 +254,7 @@ def route_job_runner() -> ErrorCode:
|
||||
|
||||
def process_headless(args : Args) -> ErrorCode:
|
||||
job_id = job_helper.suggest_job_id('headless')
|
||||
step_args = args_store.filter_step_args(args)
|
||||
step_args = reduce_step_args(args)
|
||||
|
||||
if job_manager.create_job(job_id) and job_manager.add_step(job_id, step_args) and job_manager.submit_job(job_id) and job_runner.run_job(job_id, process_step):
|
||||
return 0
|
||||
@@ -281,9 +263,10 @@ def process_headless(args : Args) -> ErrorCode:
|
||||
|
||||
def process_batch(args : Args) -> ErrorCode:
|
||||
job_id = job_helper.suggest_job_id('batch')
|
||||
step_args = args_store.filter_step_args(args)
|
||||
source_paths = resolve_file_pattern(step_args.get('source_pattern'))
|
||||
target_paths = resolve_file_pattern(step_args.get('target_pattern'))
|
||||
step_args = reduce_step_args(args)
|
||||
job_args = reduce_job_args(args)
|
||||
source_paths = resolve_file_pattern(job_args.get('source_pattern'))
|
||||
target_paths = resolve_file_pattern(job_args.get('target_pattern'))
|
||||
|
||||
if job_manager.create_job(job_id):
|
||||
if source_paths and target_paths:
|
||||
@@ -292,7 +275,7 @@ def process_batch(args : Args) -> ErrorCode:
|
||||
step_args['target_path'] = target_path
|
||||
|
||||
try:
|
||||
step_args['output_path'] = step_args.get('output_pattern').format(index = index, source_name = get_file_name(source_path), target_name = get_file_name(target_path), target_extension = get_file_extension(target_path))
|
||||
step_args['output_path'] = job_args.get('output_pattern').format(index = index, source_name = get_file_name(source_path), target_name = get_file_name(target_path), target_extension = get_file_extension(target_path))
|
||||
except KeyError:
|
||||
return 1
|
||||
|
||||
@@ -306,7 +289,7 @@ def process_batch(args : Args) -> ErrorCode:
|
||||
step_args['target_path'] = target_path
|
||||
|
||||
try:
|
||||
step_args['output_path'] = step_args.get('output_pattern').format(index = index, target_name = get_file_name(target_path), target_extension = get_file_extension(target_path))
|
||||
step_args['output_path'] = job_args.get('output_pattern').format(index = index, target_name = get_file_name(target_path), target_extension = get_file_extension(target_path))
|
||||
except KeyError:
|
||||
return 1
|
||||
|
||||
@@ -319,10 +302,8 @@ def process_batch(args : Args) -> ErrorCode:
|
||||
|
||||
def process_step(job_id : str, step_index : int, step_args : Args) -> bool:
|
||||
step_total = job_manager.count_step_total(job_id)
|
||||
cli_args = args_store.filter_cli_args(state_manager.get_state()) #type:ignore[arg-type]
|
||||
args = cli_args.copy()
|
||||
args.update(step_args)
|
||||
apply_args(args, state_manager.set_item)
|
||||
step_args.update(collect_job_args())
|
||||
apply_args(step_args, state_manager.set_item)
|
||||
|
||||
logger.info(translator.get('processing_step').format(step_current = step_index + 1, step_total = step_total), __name__)
|
||||
if common_pre_check() and processors_pre_check():
|
||||
@@ -334,28 +315,15 @@ def process_step(job_id : str, step_index : int, step_args : Args) -> bool:
|
||||
def conditional_process() -> ErrorCode:
|
||||
start_time = time()
|
||||
|
||||
if state_manager.get_item('workflow') == 'auto':
|
||||
state_manager.set_item('workflow', detect_workflow())
|
||||
|
||||
for processor_module in get_processors_modules(state_manager.get_item('processors')):
|
||||
if not processor_module.pre_process('output'):
|
||||
return 2
|
||||
|
||||
if state_manager.get_item('workflow') == 'audio-to-image':
|
||||
return audio_to_image.process(start_time)
|
||||
if state_manager.get_item('workflow') == 'image-to-image':
|
||||
if is_image(state_manager.get_item('target_path')):
|
||||
return image_to_image.process(start_time)
|
||||
if state_manager.get_item('workflow') == 'image-to-video':
|
||||
if is_video(state_manager.get_item('target_path')):
|
||||
return image_to_video.process(start_time)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
def detect_workflow() -> WorkFlow:
|
||||
if has_video([ state_manager.get_item('target_path') ]):
|
||||
return 'image-to-video'
|
||||
|
||||
if has_audio(state_manager.get_item('source_paths')) and has_image([ state_manager.get_item('target_path') ]):
|
||||
return 'audio-to-image'
|
||||
|
||||
return 'image-to-image'
|
||||
|
||||
@@ -9,14 +9,14 @@ from facefusion.types import Command
|
||||
def run(commands : List[Command]) -> List[Command]:
|
||||
user_agent = metadata.get('name') + '/' + metadata.get('version')
|
||||
|
||||
return [ shutil.which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ] + commands
|
||||
return [ shutil.which('curl'), '--user-agent', user_agent, '--location', '--silent', '--ssl-no-revoke' ] + commands
|
||||
|
||||
|
||||
def chain(*commands : List[Command]) -> List[Command]:
|
||||
return list(itertools.chain(*commands))
|
||||
|
||||
|
||||
def head(url : str) -> List[Command]:
|
||||
def ping(url : str) -> List[Command]:
|
||||
return [ '-I', url ]
|
||||
|
||||
|
||||
@@ -26,3 +26,7 @@ def download(url : str, download_file_path : str) -> List[Command]:
|
||||
|
||||
def set_timeout(timeout : int) -> List[Command]:
|
||||
return [ '--connect-timeout', str(timeout) ]
|
||||
|
||||
|
||||
def set_retry(retry : int) -> List[Command]:
|
||||
return [ '--retry', str(retry) ]
|
||||
|
||||
@@ -29,7 +29,8 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
|
||||
with tqdm(total = download_size, initial = initial_size, desc = translator.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024, ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
|
||||
commands = curl_builder.chain(
|
||||
curl_builder.download(url, download_file_path),
|
||||
curl_builder.set_timeout(5)
|
||||
curl_builder.set_timeout(5),
|
||||
curl_builder.set_retry(5)
|
||||
)
|
||||
open_curl(commands)
|
||||
current_size = initial_size
|
||||
@@ -44,7 +45,7 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
|
||||
@lru_cache(maxsize = 64)
|
||||
def get_static_download_size(url : str) -> int:
|
||||
commands = curl_builder.chain(
|
||||
curl_builder.head(url),
|
||||
curl_builder.ping(url),
|
||||
curl_builder.set_timeout(5)
|
||||
)
|
||||
process = open_curl(commands)
|
||||
@@ -62,7 +63,7 @@ def get_static_download_size(url : str) -> int:
|
||||
@lru_cache(maxsize = 64)
|
||||
def ping_static_url(url : str) -> bool:
|
||||
commands = curl_builder.chain(
|
||||
curl_builder.head(url),
|
||||
curl_builder.ping(url),
|
||||
curl_builder.set_timeout(5)
|
||||
)
|
||||
process = open_curl(commands)
|
||||
|
||||
+61
-27
@@ -1,15 +1,17 @@
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import xml.etree.ElementTree as ElementTree
|
||||
from functools import lru_cache
|
||||
from typing import List, Optional
|
||||
|
||||
from onnxruntime import get_available_providers, set_default_logger_severity
|
||||
import onnxruntime
|
||||
|
||||
import facefusion.choices
|
||||
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceSessionProvider, ValueAndUnit
|
||||
from facefusion.filesystem import create_directory, is_directory
|
||||
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceOptionSet, InferenceProvider, ValueAndUnit
|
||||
|
||||
set_default_logger_severity(3)
|
||||
onnxruntime.set_default_logger_severity(3)
|
||||
|
||||
|
||||
def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
|
||||
@@ -17,7 +19,7 @@ def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
|
||||
|
||||
|
||||
def get_available_execution_providers() -> List[ExecutionProvider]:
|
||||
inference_session_providers = get_available_providers()
|
||||
inference_session_providers = onnxruntime.get_available_providers()
|
||||
available_execution_providers : List[ExecutionProvider] = []
|
||||
|
||||
for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
|
||||
@@ -28,54 +30,86 @@ def get_available_execution_providers() -> List[ExecutionProvider]:
|
||||
return available_execution_providers
|
||||
|
||||
|
||||
def create_inference_session_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceSessionProvider]:
|
||||
inference_session_providers : List[InferenceSessionProvider] = []
|
||||
def create_inference_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceProvider]:
|
||||
inference_providers : List[InferenceProvider] = []
|
||||
cache_path = resolve_cache_path()
|
||||
|
||||
for execution_provider in execution_providers:
|
||||
if execution_provider == 'cuda':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'cudnn_conv_algo_search': resolve_cudnn_conv_algo_search()
|
||||
}))
|
||||
|
||||
if execution_provider == 'tensorrt':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_option_set : InferenceOptionSet =\
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'trt_engine_cache_enable': True,
|
||||
'trt_engine_cache_path': '.caches',
|
||||
'trt_timing_cache_enable': True,
|
||||
'trt_timing_cache_path': '.caches',
|
||||
'trt_builder_optimization_level': 5
|
||||
}))
|
||||
'device_id': execution_device_id
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'trt_engine_cache_enable': True,
|
||||
'trt_engine_cache_path': cache_path,
|
||||
'trt_timing_cache_enable': True,
|
||||
'trt_timing_cache_path': cache_path,
|
||||
'trt_builder_optimization_level': 4
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider in [ 'directml', 'rocm' ]:
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_id': execution_device_id
|
||||
}))
|
||||
|
||||
if execution_provider == 'migraphx':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_option_set =\
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'migraphx_model_cache_dir': '.caches'
|
||||
}))
|
||||
'device_id': execution_device_id
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'migraphx_model_cache_dir': cache_path
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider == 'coreml':
|
||||
inference_option_set =\
|
||||
{
|
||||
'SpecializationStrategy': 'FastPrediction'
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'ModelCacheDirectory': cache_path
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider == 'openvino':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_type': resolve_openvino_device_type(execution_device_id),
|
||||
'precision': 'FP32'
|
||||
}))
|
||||
if execution_provider == 'coreml':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
|
||||
if execution_provider == 'qnn':
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'SpecializationStrategy': 'FastPrediction',
|
||||
'ModelCacheDirectory': '.caches'
|
||||
'device_id': execution_device_id,
|
||||
'backend_type': 'htp'
|
||||
}))
|
||||
|
||||
if 'cpu' in execution_providers:
|
||||
inference_session_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
|
||||
inference_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
|
||||
|
||||
return inference_session_providers
|
||||
return inference_providers
|
||||
|
||||
|
||||
def resolve_cache_path() -> str:
|
||||
return os.path.join('.caches', onnxruntime.get_version_string())
|
||||
|
||||
|
||||
def resolve_cudnn_conv_algo_search() -> str:
|
||||
|
||||
@@ -28,7 +28,7 @@ def graceful_exit(error_code : ErrorCode) -> None:
|
||||
while process_manager.is_processing():
|
||||
sleep(0.5)
|
||||
|
||||
if state_manager.get_item('output_path'):
|
||||
clear_temp_directory(state_manager.get_temp_path(), state_manager.get_item('output_path'))
|
||||
if state_manager.get_item('target_path'):
|
||||
clear_temp_directory(state_manager.get_item('target_path'))
|
||||
|
||||
hard_exit(error_code)
|
||||
|
||||
@@ -20,7 +20,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'dchen236',
|
||||
'license': 'Non-Commercial',
|
||||
'license': 'CC-BY-4.0',
|
||||
'year': 2021
|
||||
},
|
||||
'hashes':
|
||||
|
||||
@@ -2,14 +2,13 @@ from typing import List, Optional
|
||||
|
||||
import numpy
|
||||
|
||||
from facefusion import state_manager
|
||||
from facefusion.common_helper import get_first
|
||||
from facefusion import face_store, state_manager
|
||||
from facefusion.common_helper import get_first, get_middle
|
||||
from facefusion.face_classifier import classify_face
|
||||
from facefusion.face_detector import detect_faces, detect_faces_by_angle
|
||||
from facefusion.face_helper import apply_nms, convert_to_face_landmark_5, estimate_face_angle, get_nms_threshold
|
||||
from facefusion.face_helper import apply_nms, average_points, convert_to_face_landmark_5, estimate_face_angle, get_nms_threshold
|
||||
from facefusion.face_landmarker import detect_face_landmark, estimate_face_landmark_68_5
|
||||
from facefusion.face_recognizer import calculate_face_embedding
|
||||
from facefusion.face_store import get_static_faces, set_static_faces
|
||||
from facefusion.types import BoundingBox, Face, FaceLandmark5, FaceLandmarkSet, FaceScoreSet, Score, VisionFrame
|
||||
|
||||
|
||||
@@ -47,7 +46,9 @@ def create_faces(vision_frame : VisionFrame, bounding_boxes : List[BoundingBox],
|
||||
}
|
||||
face_embedding, face_embedding_norm = calculate_face_embedding(vision_frame, face_landmark_set.get('5/68'))
|
||||
gender, age, race = classify_face(vision_frame, face_landmark_set.get('5/68'))
|
||||
|
||||
faces.append(Face(
|
||||
origin = 'detect',
|
||||
bounding_box = bounding_box,
|
||||
score_set = face_score_set,
|
||||
landmark_set = face_landmark_set,
|
||||
@@ -68,7 +69,102 @@ def get_one_face(faces : List[Face], position : int = 0) -> Optional[Face]:
|
||||
return None
|
||||
|
||||
|
||||
def get_average_face(faces : List[Face]) -> Optional[Face]:
|
||||
def get_many_faces(vision_frames : List[VisionFrame]) -> List[Face]:
|
||||
many_faces : List[Face] = []
|
||||
|
||||
for vision_frame in vision_frames:
|
||||
if numpy.any(vision_frame):
|
||||
all_bounding_boxes = []
|
||||
all_face_scores = []
|
||||
all_face_landmarks_5 = []
|
||||
|
||||
for face_detector_angle in state_manager.get_item('face_detector_angles'):
|
||||
if face_detector_angle == 0:
|
||||
bounding_boxes, face_scores, face_landmarks_5 = detect_faces(vision_frame)
|
||||
else:
|
||||
bounding_boxes, face_scores, face_landmarks_5 = detect_faces_by_angle(vision_frame, face_detector_angle)
|
||||
all_bounding_boxes.extend(bounding_boxes)
|
||||
all_face_scores.extend(face_scores)
|
||||
all_face_landmarks_5.extend(face_landmarks_5)
|
||||
|
||||
if all_bounding_boxes and all_face_scores and all_face_landmarks_5 and state_manager.get_item('face_detector_score') > 0:
|
||||
faces = create_faces(vision_frame, all_bounding_boxes, all_face_scores, all_face_landmarks_5)
|
||||
|
||||
if faces:
|
||||
many_faces.extend(faces)
|
||||
|
||||
return many_faces
|
||||
|
||||
|
||||
def get_static_faces(vision_frames : List[VisionFrame]) -> List[Face]:
|
||||
many_faces : List[Face] = []
|
||||
|
||||
for vision_frame in vision_frames:
|
||||
faces = face_store.get_faces(vision_frame)
|
||||
|
||||
if not faces:
|
||||
with face_store.resolve_lock(vision_frame):
|
||||
faces = face_store.get_faces(vision_frame)
|
||||
|
||||
if not faces:
|
||||
faces = get_many_faces([ vision_frame ])
|
||||
|
||||
if faces:
|
||||
face_store.set_faces(vision_frame, faces)
|
||||
|
||||
many_faces.extend(faces)
|
||||
|
||||
return many_faces
|
||||
|
||||
|
||||
def refill_faces(faces : List[Optional[Face]]) -> List[Face]:
|
||||
fill_faces = []
|
||||
anchor_index_previous = -1
|
||||
|
||||
for index, face in enumerate(faces):
|
||||
if face:
|
||||
for gap_index in range(anchor_index_previous + 1, index):
|
||||
average_factor = (gap_index - anchor_index_previous) / (index - anchor_index_previous)
|
||||
average_face = average_face_geometry([faces[anchor_index_previous], face], average_factor)
|
||||
fill_faces.append(average_face)
|
||||
|
||||
fill_faces.append(face)
|
||||
anchor_index_previous = index
|
||||
|
||||
return fill_faces
|
||||
|
||||
|
||||
def average_face_geometry(faces : List[Face], average_factor : float) -> Face:
|
||||
face_first = get_first(faces)
|
||||
face_middle = get_middle(faces)
|
||||
face_anchor = face_middle
|
||||
|
||||
if average_factor < 0.5:
|
||||
face_anchor = face_first
|
||||
|
||||
landmark_set : FaceLandmarkSet =\
|
||||
{
|
||||
'5': average_points(face_first.landmark_set.get('5'), face_middle.landmark_set.get('5'), average_factor),
|
||||
'5/68': average_points(face_first.landmark_set.get('5/68'), face_middle.landmark_set.get('5/68'), average_factor),
|
||||
'68': average_points(face_first.landmark_set.get('68'), face_middle.landmark_set.get('68'), average_factor),
|
||||
'68/5': average_points(face_first.landmark_set.get('68/5'), face_middle.landmark_set.get('68/5'), average_factor)
|
||||
}
|
||||
|
||||
return Face(
|
||||
origin = 'refill',
|
||||
bounding_box = average_points(face_first.bounding_box, face_middle.bounding_box, average_factor),
|
||||
score_set = face_anchor.score_set,
|
||||
landmark_set = landmark_set,
|
||||
angle = estimate_face_angle(landmark_set.get('68/5')),
|
||||
embedding = face_anchor.embedding,
|
||||
embedding_norm = face_anchor.embedding_norm,
|
||||
gender = face_anchor.gender,
|
||||
age = face_anchor.age,
|
||||
race = face_anchor.race
|
||||
)
|
||||
|
||||
|
||||
def average_face_identity(faces : List[Face]) -> Optional[Face]:
|
||||
face_embeddings = []
|
||||
face_embeddings_norm = []
|
||||
|
||||
@@ -80,6 +176,7 @@ def get_average_face(faces : List[Face]) -> Optional[Face]:
|
||||
face_embeddings_norm.append(face.embedding_norm)
|
||||
|
||||
return Face(
|
||||
origin = first_face.origin,
|
||||
bounding_box = first_face.bounding_box,
|
||||
score_set = first_face.score_set,
|
||||
landmark_set = first_face.landmark_set,
|
||||
@@ -93,37 +190,6 @@ def get_average_face(faces : List[Face]) -> Optional[Face]:
|
||||
return None
|
||||
|
||||
|
||||
def get_many_faces(vision_frames : List[VisionFrame]) -> List[Face]:
|
||||
many_faces : List[Face] = []
|
||||
|
||||
for vision_frame in vision_frames:
|
||||
if numpy.any(vision_frame):
|
||||
static_faces = get_static_faces(vision_frame)
|
||||
if static_faces:
|
||||
many_faces.extend(static_faces)
|
||||
else:
|
||||
all_bounding_boxes = []
|
||||
all_face_scores = []
|
||||
all_face_landmarks_5 = []
|
||||
|
||||
for face_detector_angle in state_manager.get_item('face_detector_angles'):
|
||||
if face_detector_angle == 0:
|
||||
bounding_boxes, face_scores, face_landmarks_5 = detect_faces(vision_frame)
|
||||
else:
|
||||
bounding_boxes, face_scores, face_landmarks_5 = detect_faces_by_angle(vision_frame, face_detector_angle)
|
||||
all_bounding_boxes.extend(bounding_boxes)
|
||||
all_face_scores.extend(face_scores)
|
||||
all_face_landmarks_5.extend(face_landmarks_5)
|
||||
|
||||
if all_bounding_boxes and all_face_scores and all_face_landmarks_5 and state_manager.get_item('face_detector_score') > 0:
|
||||
faces = create_faces(vision_frame, all_bounding_boxes, all_face_scores, all_face_landmarks_5)
|
||||
|
||||
if faces:
|
||||
many_faces.extend(faces)
|
||||
set_static_faces(vision_frame, faces)
|
||||
return many_faces
|
||||
|
||||
|
||||
def scale_face(target_face : Face, target_vision_frame : VisionFrame, temp_vision_frame : VisionFrame) -> Face:
|
||||
scale_x = temp_vision_frame.shape[1] / target_vision_frame.shape[1]
|
||||
scale_y = temp_vision_frame.shape[0] / target_vision_frame.shape[0]
|
||||
@@ -381,11 +381,11 @@ def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> T
|
||||
face_scores.extend(face_scores_raw[keep_indices])
|
||||
face_landmarks_5_raw = numpy.concatenate(
|
||||
[
|
||||
face_landmarks_5_raw[:, [0, 1]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [2, 3]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [4, 5]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [6, 7]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [8, 9]] * feature_stride + anchors
|
||||
face_landmarks_5_raw[:, [ 0, 1 ]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [ 2, 3 ]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [ 4, 5 ]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [ 6, 7 ]] * feature_stride + anchors,
|
||||
face_landmarks_5_raw[:, [ 8, 9 ]] * feature_stride + anchors
|
||||
], axis = -1).reshape(-1, 5, 2)
|
||||
|
||||
for face_landmark_raw_5 in face_landmarks_5_raw[keep_indices]:
|
||||
|
||||
@@ -81,7 +81,7 @@ def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_
|
||||
|
||||
|
||||
def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
|
||||
source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
|
||||
source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [ bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
|
||||
target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ]).astype(numpy.float32)
|
||||
affine_matrix = cv2.getAffineTransform(source_points, target_points)
|
||||
if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
|
||||
@@ -247,10 +247,30 @@ def get_nms_threshold(face_detector_model : FaceDetectorModel, face_detector_ang
|
||||
|
||||
|
||||
def merge_matrix(temp_matrices : List[Matrix]) -> Matrix:
|
||||
matrix = numpy.vstack([temp_matrices[0], [0, 0, 1]])
|
||||
matrix = numpy.vstack([ temp_matrices[0], [ 0, 0, 1 ] ])
|
||||
|
||||
for temp_matrix in temp_matrices[1:]:
|
||||
temp_matrix = numpy.vstack([ temp_matrix, [ 0, 0, 1 ] ])
|
||||
matrix = numpy.dot(temp_matrix, matrix)
|
||||
|
||||
return matrix[:2, :]
|
||||
|
||||
|
||||
def calculate_bounding_box_overlap(bounding_box_a : BoundingBox, bounding_box_b : BoundingBox) -> float:
|
||||
intersection_x1 = max(bounding_box_a[0], bounding_box_b[0])
|
||||
intersection_y1 = max(bounding_box_a[1], bounding_box_b[1])
|
||||
intersection_x2 = min(bounding_box_a[2], bounding_box_b[2])
|
||||
intersection_y2 = min(bounding_box_a[3], bounding_box_b[3])
|
||||
intersection = max(0, intersection_x2 - intersection_x1) * max(0, intersection_y2 - intersection_y1)
|
||||
bounding_box_area = (bounding_box_a[2] - bounding_box_a[0]) * (bounding_box_a[3] - bounding_box_a[1])
|
||||
reference_bounding_box_area = (bounding_box_b[2] - bounding_box_b[0]) * (bounding_box_b[3] - bounding_box_b[1])
|
||||
union = bounding_box_area + reference_bounding_box_area - intersection
|
||||
|
||||
if union > 0:
|
||||
return intersection / union
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
def average_points(points_previous : Points, points_next : Points, average_factor : float) -> Points:
|
||||
return points_previous * (1 - average_factor) + points_next * average_factor
|
||||
|
||||
@@ -233,7 +233,7 @@ def create_area_mask(crop_vision_frame : VisionFrame, face_landmark_68 : FaceLan
|
||||
|
||||
convex_hull = cv2.convexHull(face_landmark_68[landmark_points].astype(numpy.int32))
|
||||
area_mask = numpy.zeros(crop_size).astype(numpy.float32)
|
||||
cv2.fillConvexPoly(area_mask, convex_hull, 1.0) # type: ignore[call-overload]
|
||||
cv2.fillConvexPoly(area_mask, convex_hull, 1.0) #type:ignore[call-overload]
|
||||
area_mask = (cv2.GaussianBlur(area_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2
|
||||
return area_mask
|
||||
|
||||
|
||||
+41
-16
@@ -2,26 +2,35 @@ from typing import List
|
||||
|
||||
import numpy
|
||||
|
||||
import facefusion.choices
|
||||
from facefusion import state_manager
|
||||
from facefusion.face_analyser import get_many_faces, get_one_face
|
||||
from facefusion.common_helper import get_first, get_middle
|
||||
from facefusion.face_creator import get_one_face, get_static_faces
|
||||
from facefusion.face_tracker import track_faces
|
||||
from facefusion.types import Face, FaceSelectorOrder, Gender, Race, Score, VisionFrame
|
||||
|
||||
|
||||
def select_faces(reference_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> List[Face]:
|
||||
target_faces = get_many_faces([ target_vision_frame ])
|
||||
def select_faces(reference_vision_frame : VisionFrame, source_vision_frames : List[VisionFrame], target_vision_frames : List[VisionFrame]) -> List[Face]:
|
||||
source_faces = get_static_faces(source_vision_frames)
|
||||
|
||||
if state_manager.get_item('face_tracker_score') > 0:
|
||||
target_faces = track_faces(target_vision_frames, state_manager.get_item('face_tracker_score'))
|
||||
else:
|
||||
target_faces = get_static_faces([ get_middle(target_vision_frames) ])
|
||||
|
||||
if state_manager.get_item('face_selector_mode') == 'many':
|
||||
return sort_and_filter_faces(target_faces)
|
||||
return sort_and_filter_faces(source_faces, target_faces)
|
||||
|
||||
if state_manager.get_item('face_selector_mode') == 'one':
|
||||
target_face = get_one_face(sort_and_filter_faces(target_faces))
|
||||
target_face = get_one_face(sort_and_filter_faces(source_faces, target_faces))
|
||||
if target_face:
|
||||
return [ target_face ]
|
||||
|
||||
if state_manager.get_item('face_selector_mode') == 'reference':
|
||||
reference_faces = get_many_faces([ reference_vision_frame ])
|
||||
reference_faces = sort_and_filter_faces(reference_faces)
|
||||
reference_faces = get_static_faces([ reference_vision_frame ])
|
||||
reference_faces = sort_and_filter_faces(source_faces, reference_faces)
|
||||
reference_face = get_one_face(reference_faces, state_manager.get_item('reference_face_position'))
|
||||
|
||||
if reference_face:
|
||||
match_faces = find_match_faces([ reference_face ], target_faces, state_manager.get_item('reference_face_distance'))
|
||||
return match_faces
|
||||
@@ -53,17 +62,33 @@ def calculate_face_distance(face : Face, reference_face : Face) -> float:
|
||||
return 0
|
||||
|
||||
|
||||
def sort_and_filter_faces(faces : List[Face]) -> List[Face]:
|
||||
if faces:
|
||||
def sort_and_filter_faces(source_faces : List[Face], target_faces : List[Face]) -> List[Face]:
|
||||
if target_faces:
|
||||
if state_manager.get_item('face_selector_order'):
|
||||
faces = sort_faces_by_order(faces, state_manager.get_item('face_selector_order'))
|
||||
if state_manager.get_item('face_selector_gender'):
|
||||
faces = filter_faces_by_gender(faces, state_manager.get_item('face_selector_gender'))
|
||||
if state_manager.get_item('face_selector_race'):
|
||||
faces = filter_faces_by_race(faces, state_manager.get_item('face_selector_race'))
|
||||
target_faces = sort_faces_by_order(target_faces, state_manager.get_item('face_selector_order'))
|
||||
|
||||
face_selector_gender = state_manager.get_item('face_selector_gender')
|
||||
face_selector_race = state_manager.get_item('face_selector_race')
|
||||
|
||||
if source_faces and face_selector_gender == 'auto' or face_selector_race == 'auto':
|
||||
source_face = get_first(sort_faces_by_order(source_faces, 'large-small'))
|
||||
|
||||
if source_face:
|
||||
if face_selector_gender == 'auto':
|
||||
face_selector_gender = source_face.gender
|
||||
if face_selector_race == 'auto':
|
||||
face_selector_race = source_face.race
|
||||
|
||||
if face_selector_gender in facefusion.choices.genders:
|
||||
target_faces = filter_faces_by_gender(target_faces, face_selector_gender)
|
||||
|
||||
if face_selector_race in facefusion.choices.races:
|
||||
target_faces = filter_faces_by_race(target_faces, face_selector_race)
|
||||
|
||||
if state_manager.get_item('face_selector_age_start') or state_manager.get_item('face_selector_age_end'):
|
||||
faces = filter_faces_by_age(faces, state_manager.get_item('face_selector_age_start'), state_manager.get_item('face_selector_age_end'))
|
||||
return faces
|
||||
target_faces = filter_faces_by_age(target_faces, state_manager.get_item('face_selector_age_start'), state_manager.get_item('face_selector_age_end'))
|
||||
|
||||
return target_faces
|
||||
|
||||
|
||||
def sort_faces_by_order(faces : List[Face], order : FaceSelectorOrder) -> List[Face]:
|
||||
|
||||
+29
-15
@@ -1,28 +1,42 @@
|
||||
import threading
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy
|
||||
|
||||
from facefusion.hash_helper import create_hash
|
||||
from facefusion.types import Face, FaceStore, VisionFrame
|
||||
|
||||
FACE_STORE : FaceStore =\
|
||||
{
|
||||
'static_faces': {}
|
||||
}
|
||||
FACE_STORE : FaceStore = {}
|
||||
|
||||
|
||||
def get_face_store() -> FaceStore:
|
||||
return FACE_STORE
|
||||
def get_faces(vision_frame : VisionFrame) -> Optional[List[Face]]:
|
||||
if numpy.any(vision_frame):
|
||||
vision_hash = create_hash(vision_frame.tobytes())
|
||||
|
||||
if FACE_STORE.get(vision_hash):
|
||||
return FACE_STORE.get(vision_hash).get('faces')
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def get_static_faces(vision_frame : VisionFrame) -> Optional[List[Face]]:
|
||||
vision_hash = create_hash(vision_frame.tobytes())
|
||||
return FACE_STORE.get('static_faces').get(vision_hash)
|
||||
def set_faces(vision_frame : VisionFrame, faces : List[Face]) -> None:
|
||||
if numpy.any(vision_frame):
|
||||
vision_hash = create_hash(vision_frame.tobytes())
|
||||
FACE_STORE.setdefault(vision_hash,
|
||||
{
|
||||
'lock': threading.Lock()
|
||||
})['faces'] = faces
|
||||
|
||||
|
||||
def set_static_faces(vision_frame : VisionFrame, faces : List[Face]) -> None:
|
||||
vision_hash = create_hash(vision_frame.tobytes())
|
||||
if vision_hash:
|
||||
FACE_STORE['static_faces'][vision_hash] = faces
|
||||
def resolve_lock(vision_frame : VisionFrame) -> threading.Lock:
|
||||
if numpy.any(vision_frame):
|
||||
vision_hash = create_hash(vision_frame.tobytes())
|
||||
return FACE_STORE.setdefault(vision_hash,
|
||||
{
|
||||
'lock': threading.Lock()
|
||||
}).get('lock')
|
||||
return threading.Lock()
|
||||
|
||||
|
||||
def clear_static_faces() -> None:
|
||||
FACE_STORE['static_faces'].clear()
|
||||
def clear_faces() -> None:
|
||||
FACE_STORE.clear()
|
||||
|
||||
@@ -0,0 +1,61 @@
|
||||
from typing import List
|
||||
|
||||
from facefusion.common_helper import get_first, get_last
|
||||
from facefusion.face_creator import get_static_faces, refill_faces
|
||||
from facefusion.face_helper import calculate_bounding_box_overlap
|
||||
from facefusion.types import Face, FaceTrack, Score, VisionFrame
|
||||
|
||||
|
||||
def track_faces(vision_frames : List[VisionFrame], score : Score) -> List[Face]:
|
||||
target_index = len(vision_frames) // 2
|
||||
face_tracks = create_face_tracks(vision_frames, score)
|
||||
temp_faces = []
|
||||
|
||||
for face_track in face_tracks:
|
||||
track_indices = sorted(face_track)
|
||||
track_index_first = get_first(track_indices)
|
||||
track_index_last = get_last(track_indices)
|
||||
track_range = range(track_index_first, track_index_last + 1)
|
||||
|
||||
if target_index in track_range:
|
||||
fill_faces = []
|
||||
|
||||
for index in track_range:
|
||||
fill_faces.append(face_track.get(index))
|
||||
|
||||
temp_faces.append(refill_faces(fill_faces)[target_index - track_index_first])
|
||||
|
||||
return temp_faces
|
||||
|
||||
|
||||
def create_face_tracks(vision_frames : List[VisionFrame], score : Score) -> List[FaceTrack]:
|
||||
face_tracks : List[FaceTrack] = []
|
||||
|
||||
for frame_index, vision_frame in enumerate(vision_frames):
|
||||
for face in get_static_faces([ vision_frame ]):
|
||||
face_track = select_face_track(face_tracks, face, score)
|
||||
|
||||
if face_track:
|
||||
face_track[frame_index] = face
|
||||
else:
|
||||
face_tracks.append(
|
||||
{
|
||||
frame_index : face
|
||||
})
|
||||
|
||||
return face_tracks
|
||||
|
||||
|
||||
def select_face_track(face_tracks : List[FaceTrack], face : Face, score : Score) -> FaceTrack:
|
||||
select_track : FaceTrack = {}
|
||||
select_score = score
|
||||
|
||||
for face_track in face_tracks:
|
||||
track_face = face_track.get(get_last(face_track))
|
||||
track_score = calculate_bounding_box_overlap(face.bounding_box, track_face.bounding_box)
|
||||
|
||||
if track_score > select_score:
|
||||
select_score = track_score
|
||||
select_track = face_track
|
||||
|
||||
return select_track
|
||||
+31
-39
@@ -9,7 +9,7 @@ from tqdm import tqdm
|
||||
import facefusion.choices
|
||||
from facefusion import ffmpeg_builder, logger, process_manager, state_manager, translator
|
||||
from facefusion.filesystem import get_file_format, remove_file
|
||||
from facefusion.temp_helper import get_temp_file_path, get_temp_frames_pattern
|
||||
from facefusion.temp_helper import get_temp_file_path, get_temp_frame_pattern
|
||||
from facefusion.types import AudioBuffer, AudioEncoder, Command, EncoderSet, Fps, Resolution, UpdateProgress, VideoEncoder, VideoFormat
|
||||
from facefusion.vision import detect_video_duration, detect_video_fps, pack_resolution, predict_video_frame_total
|
||||
|
||||
@@ -107,16 +107,18 @@ def get_available_encoder_set() -> EncoderSet:
|
||||
return available_encoder_set
|
||||
|
||||
|
||||
def extract_frames(target_path : str, output_path : str, temp_video_resolution : Resolution, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
|
||||
def extract_frames(target_path : str, temp_video_resolution : Resolution, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
|
||||
extract_frame_total = predict_video_frame_total(target_path, temp_video_fps, trim_frame_start, trim_frame_end)
|
||||
temp_frames_pattern = get_temp_frames_pattern(state_manager.get_temp_path(), output_path, state_manager.get_item('temp_frame_format'), '%08d')
|
||||
temp_frame_pattern = get_temp_frame_pattern(target_path, '%08d')
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.set_input(target_path),
|
||||
ffmpeg_builder.set_media_resolution(pack_resolution(temp_video_resolution)),
|
||||
ffmpeg_builder.set_frame_quality(0),
|
||||
ffmpeg_builder.enforce_pixel_format('rgb24'),
|
||||
ffmpeg_builder.select_frame_range(trim_frame_start, trim_frame_end, temp_video_fps),
|
||||
ffmpeg_builder.prevent_frame_drop(),
|
||||
ffmpeg_builder.set_output(temp_frames_pattern)
|
||||
ffmpeg_builder.set_start_number(trim_frame_start),
|
||||
ffmpeg_builder.set_output(temp_frame_pattern)
|
||||
)
|
||||
|
||||
with tqdm(total = extract_frame_total, desc = translator.get('extracting'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
|
||||
@@ -124,26 +126,8 @@ def extract_frames(target_path : str, output_path : str, temp_video_resolution :
|
||||
return process.returncode == 0
|
||||
|
||||
|
||||
def spawn_frames(target_path : str, output_path : str, temp_video_resolution : Resolution, temp_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
|
||||
spawn_frame_total = trim_frame_end - trim_frame_start
|
||||
duration = spawn_frame_total / temp_video_fps
|
||||
temp_frames_pattern = get_temp_frames_pattern(state_manager.get_temp_path(), output_path, state_manager.get_item('temp_frame_format'), '%08d')
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.set_loop(),
|
||||
ffmpeg_builder.set_input(target_path),
|
||||
ffmpeg_builder.set_video_duration(duration),
|
||||
ffmpeg_builder.set_video_fps(temp_video_fps),
|
||||
ffmpeg_builder.set_media_resolution(pack_resolution(temp_video_resolution)),
|
||||
ffmpeg_builder.set_output(temp_frames_pattern)
|
||||
)
|
||||
|
||||
with tqdm(total = spawn_frame_total, desc = translator.get('spawning'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
|
||||
process = run_ffmpeg_with_progress(commands, partial(update_progress, progress))
|
||||
return process.returncode == 0
|
||||
|
||||
|
||||
def copy_image(target_path : str, output_path : str, temp_image_resolution : Resolution) -> bool:
|
||||
temp_image_path = get_temp_file_path(state_manager.get_temp_path(), output_path)
|
||||
def copy_image(target_path : str, temp_image_resolution : Resolution) -> bool:
|
||||
temp_image_path = get_temp_file_path(target_path)
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.set_input(target_path),
|
||||
ffmpeg_builder.set_media_resolution(pack_resolution(temp_image_resolution)),
|
||||
@@ -153,13 +137,13 @@ def copy_image(target_path : str, output_path : str, temp_image_resolution : Res
|
||||
return run_ffmpeg(commands).returncode == 0
|
||||
|
||||
|
||||
def finalize_image(output_path : str, output_image_resolution : Resolution) -> bool:
|
||||
def finalize_image(target_path : str, output_path : str, output_image_resolution : Resolution) -> bool:
|
||||
output_image_quality = state_manager.get_item('output_image_quality')
|
||||
temp_image_path = get_temp_file_path(state_manager.get_temp_path(), output_path)
|
||||
temp_image_path = get_temp_file_path(target_path)
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.set_input(temp_image_path),
|
||||
ffmpeg_builder.set_media_resolution(pack_resolution(output_image_resolution)),
|
||||
ffmpeg_builder.set_image_quality(output_path, output_image_quality),
|
||||
ffmpeg_builder.set_image_quality(target_path, output_image_quality),
|
||||
ffmpeg_builder.force_output(output_path)
|
||||
)
|
||||
return run_ffmpeg(commands).returncode == 0
|
||||
@@ -187,9 +171,10 @@ def restore_audio(target_path : str, output_path : str, trim_frame_start : int,
|
||||
output_audio_quality = state_manager.get_item('output_audio_quality')
|
||||
output_audio_volume = state_manager.get_item('output_audio_volume')
|
||||
target_video_fps = detect_video_fps(target_path)
|
||||
temp_video_path = get_temp_file_path(state_manager.get_temp_path(), output_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
temp_video_path = get_temp_file_path(target_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(temp_video_path))
|
||||
temp_video_duration = detect_video_duration(temp_video_path)
|
||||
output_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
|
||||
output_audio_encoder = fix_audio_encoder(temp_video_format, output_audio_encoder)
|
||||
commands = ffmpeg_builder.chain(
|
||||
@@ -203,18 +188,20 @@ def restore_audio(target_path : str, output_path : str, trim_frame_start : int,
|
||||
ffmpeg_builder.select_media_stream('0:v:0'),
|
||||
ffmpeg_builder.select_media_stream('1:a:0'),
|
||||
ffmpeg_builder.set_video_duration(temp_video_duration),
|
||||
ffmpeg_builder.set_faststart(output_video_format),
|
||||
ffmpeg_builder.force_output(output_path)
|
||||
)
|
||||
return run_ffmpeg(commands).returncode == 0
|
||||
|
||||
|
||||
def replace_audio(audio_path : str, output_path : str) -> bool:
|
||||
def replace_audio(target_path : str, audio_path : str, output_path : str) -> bool:
|
||||
output_audio_encoder = state_manager.get_item('output_audio_encoder')
|
||||
output_audio_quality = state_manager.get_item('output_audio_quality')
|
||||
output_audio_volume = state_manager.get_item('output_audio_volume')
|
||||
temp_video_path = get_temp_file_path(state_manager.get_temp_path(), output_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
temp_video_path = get_temp_file_path(target_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(temp_video_path))
|
||||
temp_video_duration = detect_video_duration(temp_video_path)
|
||||
output_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
|
||||
output_audio_encoder = fix_audio_encoder(temp_video_format, output_audio_encoder)
|
||||
commands = ffmpeg_builder.chain(
|
||||
@@ -225,27 +212,29 @@ def replace_audio(audio_path : str, output_path : str) -> bool:
|
||||
ffmpeg_builder.set_audio_quality(output_audio_encoder, output_audio_quality),
|
||||
ffmpeg_builder.set_audio_volume(output_audio_volume),
|
||||
ffmpeg_builder.set_video_duration(temp_video_duration),
|
||||
ffmpeg_builder.set_faststart(output_video_format),
|
||||
ffmpeg_builder.force_output(output_path)
|
||||
)
|
||||
return run_ffmpeg(commands).returncode == 0
|
||||
|
||||
|
||||
def merge_video(target_path : str, output_path : str, temp_video_fps : Fps, output_video_resolution : Resolution, trim_frame_start : int, trim_frame_end : int) -> bool:
|
||||
output_video_fps = state_manager.get_item('output_video_fps')
|
||||
def merge_video(target_path : str, temp_video_fps : Fps, output_video_resolution : Resolution, output_video_fps : Fps, trim_frame_start : int, trim_frame_end : int) -> bool:
|
||||
output_video_encoder = state_manager.get_item('output_video_encoder')
|
||||
output_video_quality = state_manager.get_item('output_video_quality')
|
||||
output_video_preset = state_manager.get_item('output_video_preset')
|
||||
merge_frame_total = predict_video_frame_total(target_path, output_video_fps, trim_frame_start, trim_frame_end)
|
||||
temp_video_path = get_temp_file_path(state_manager.get_temp_path(), output_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
temp_frames_pattern = get_temp_frames_pattern(state_manager.get_temp_path(), output_path, state_manager.get_item('temp_frame_format'), '%08d')
|
||||
temp_video_path = get_temp_file_path(target_path)
|
||||
temp_video_format = cast(VideoFormat, get_file_format(temp_video_path))
|
||||
temp_frame_pattern = get_temp_frame_pattern(target_path, '%08d')
|
||||
|
||||
output_video_encoder = fix_video_encoder(temp_video_format, output_video_encoder)
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.set_input_fps(temp_video_fps),
|
||||
ffmpeg_builder.set_input(temp_frames_pattern),
|
||||
ffmpeg_builder.set_start_number(trim_frame_start),
|
||||
ffmpeg_builder.set_input(temp_frame_pattern),
|
||||
ffmpeg_builder.set_media_resolution(pack_resolution(output_video_resolution)),
|
||||
ffmpeg_builder.set_video_encoder(output_video_encoder),
|
||||
ffmpeg_builder.set_video_tag(output_video_encoder, temp_video_format),
|
||||
ffmpeg_builder.set_video_quality(output_video_encoder, output_video_quality),
|
||||
ffmpeg_builder.set_video_preset(output_video_encoder, output_video_preset),
|
||||
ffmpeg_builder.concat(
|
||||
@@ -262,7 +251,8 @@ def merge_video(target_path : str, output_path : str, temp_video_fps : Fps, outp
|
||||
|
||||
|
||||
def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
|
||||
concat_video_path = tempfile.mktemp()
|
||||
file_descriptor, concat_video_path = tempfile.mkstemp()
|
||||
os.close(file_descriptor)
|
||||
|
||||
with open(concat_video_path, 'w') as concat_video_file:
|
||||
for temp_output_path in temp_output_paths:
|
||||
@@ -271,11 +261,13 @@ def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
|
||||
concat_video_file.close()
|
||||
|
||||
output_path = os.path.abspath(output_path)
|
||||
output_video_format = cast(VideoFormat, get_file_format(output_path))
|
||||
commands = ffmpeg_builder.chain(
|
||||
ffmpeg_builder.unsafe_concat(),
|
||||
ffmpeg_builder.set_input(concat_video_file.name),
|
||||
ffmpeg_builder.copy_video_encoder(),
|
||||
ffmpeg_builder.copy_audio_encoder(),
|
||||
ffmpeg_builder.set_faststart(output_video_format),
|
||||
ffmpeg_builder.force_output(output_path)
|
||||
)
|
||||
process = run_ffmpeg(commands)
|
||||
|
||||
@@ -5,7 +5,7 @@ from typing import List, Optional
|
||||
import numpy
|
||||
|
||||
from facefusion.filesystem import get_file_format
|
||||
from facefusion.types import AudioEncoder, Command, CommandSet, Duration, Fps, StreamMode, VideoEncoder, VideoPreset
|
||||
from facefusion.types import AudioEncoder, Command, CommandSet, Duration, Fps, StreamMode, VideoEncoder, VideoFormat, VideoPreset
|
||||
|
||||
|
||||
def run(commands : List[Command]) -> List[Command]:
|
||||
@@ -48,7 +48,11 @@ def set_input(input_path : str) -> List[Command]:
|
||||
|
||||
|
||||
def set_input_fps(input_fps : Fps) -> List[Command]:
|
||||
return [ '-r', str(input_fps)]
|
||||
return [ '-r', str(input_fps) ]
|
||||
|
||||
|
||||
def set_start_number(frame_number : int) -> List[Command]:
|
||||
return [ '-start_number', str(frame_number) ]
|
||||
|
||||
|
||||
def set_output(output_path : str) -> List[Command]:
|
||||
@@ -59,10 +63,6 @@ def force_output(output_path : str) -> List[Command]:
|
||||
return [ '-y', output_path ]
|
||||
|
||||
|
||||
def set_loop() -> List[Command]:
|
||||
return [ '-loop', '1' ]
|
||||
|
||||
|
||||
def cast_stream() -> List[Command]:
|
||||
return [ '-' ]
|
||||
|
||||
@@ -83,6 +83,10 @@ def unsafe_concat() -> List[Command]:
|
||||
return [ '-f', 'concat', '-safe', '0' ]
|
||||
|
||||
|
||||
def enforce_pixel_format(pixel_format : str) -> List[Command]:
|
||||
return [ '-pix_fmt', pixel_format ]
|
||||
|
||||
|
||||
def set_pixel_format(video_encoder : VideoEncoder) -> List[Command]:
|
||||
if video_encoder == 'rawvideo':
|
||||
return [ '-pix_fmt', 'rgb24' ]
|
||||
@@ -187,6 +191,18 @@ def copy_video_encoder() -> List[Command]:
|
||||
return set_video_encoder('copy')
|
||||
|
||||
|
||||
def set_faststart(video_format : VideoFormat) -> List[Command]:
|
||||
if video_format in [ 'm4v', 'mov', 'mp4' ]:
|
||||
return [ '-movflags', '+faststart' ]
|
||||
return []
|
||||
|
||||
|
||||
def set_video_tag(video_encoder : VideoEncoder, video_format : VideoFormat) -> List[Command]:
|
||||
if video_format in [ 'm4v', 'mov', 'mp4' ] and video_encoder in [ 'libx265', 'hevc_nvenc', 'hevc_amf', 'hevc_qsv', 'hevc_videotoolbox' ]:
|
||||
return [ '-tag:v', 'hvc1' ]
|
||||
return []
|
||||
|
||||
|
||||
def set_video_quality(video_encoder : VideoEncoder, video_quality : int) -> List[Command]:
|
||||
if video_encoder in [ 'libx264', 'libx264rgb', 'libx265' ]:
|
||||
video_compression = numpy.round(numpy.interp(video_quality, [ 0, 100 ], [ 51, 0 ])).astype(int).item()
|
||||
|
||||
@@ -42,6 +42,15 @@ def get_file_format(file_path : str) -> Optional[str]:
|
||||
return None
|
||||
|
||||
|
||||
def same_file_extension(first_file_path : str, second_file_path : str) -> bool:
|
||||
first_file_extension = get_file_extension(first_file_path)
|
||||
second_file_extension = get_file_extension(second_file_path)
|
||||
|
||||
if first_file_extension and second_file_extension:
|
||||
return get_file_extension(first_file_path) == get_file_extension(second_file_path)
|
||||
return False
|
||||
|
||||
|
||||
def is_file(file_path : str) -> bool:
|
||||
if file_path:
|
||||
return os.path.isfile(file_path)
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import importlib
|
||||
import random
|
||||
from functools import lru_cache
|
||||
from time import sleep, time
|
||||
from typing import List
|
||||
|
||||
@@ -8,16 +9,16 @@ from onnxruntime import InferenceSession
|
||||
from facefusion import logger, process_manager, state_manager, translator
|
||||
from facefusion.app_context import detect_app_context
|
||||
from facefusion.common_helper import is_windows
|
||||
from facefusion.execution import create_inference_session_providers, has_execution_provider
|
||||
from facefusion.execution import create_inference_providers, has_execution_provider
|
||||
from facefusion.exit_helper import fatal_exit
|
||||
from facefusion.filesystem import get_file_name, is_file
|
||||
from facefusion.time_helper import calculate_end_time
|
||||
from facefusion.types import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet
|
||||
from facefusion.types import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet, InferenceProvider
|
||||
|
||||
INFERENCE_POOL_SET : InferencePoolSet =\
|
||||
{
|
||||
'cli': {},
|
||||
'api': {}
|
||||
'ui': {}
|
||||
}
|
||||
|
||||
|
||||
@@ -25,37 +26,38 @@ def get_inference_pool(module_name : str, model_names : List[str], model_source_
|
||||
while process_manager.is_checking():
|
||||
sleep(0.5)
|
||||
execution_device_ids = state_manager.get_item('execution_device_ids')
|
||||
execution_providers = resolve_execution_providers(module_name)
|
||||
execution_providers = state_manager.get_item('execution_providers')
|
||||
app_context = detect_app_context()
|
||||
|
||||
for execution_device_id in execution_device_ids:
|
||||
inference_context = get_inference_context(module_name, model_names, execution_device_id, execution_providers)
|
||||
|
||||
if app_context == 'cli' and INFERENCE_POOL_SET.get('api').get(inference_context):
|
||||
INFERENCE_POOL_SET['cli'][inference_context] = INFERENCE_POOL_SET.get('api').get(inference_context)
|
||||
if app_context == 'api' and INFERENCE_POOL_SET.get('cli').get(inference_context):
|
||||
INFERENCE_POOL_SET['api'][inference_context] = INFERENCE_POOL_SET.get('cli').get(inference_context)
|
||||
if app_context == 'cli' and INFERENCE_POOL_SET.get('ui').get(inference_context):
|
||||
INFERENCE_POOL_SET['cli'][inference_context] = INFERENCE_POOL_SET.get('ui').get(inference_context)
|
||||
if app_context == 'ui' and INFERENCE_POOL_SET.get('cli').get(inference_context):
|
||||
INFERENCE_POOL_SET['ui'][inference_context] = INFERENCE_POOL_SET.get('cli').get(inference_context)
|
||||
if not INFERENCE_POOL_SET.get(app_context).get(inference_context):
|
||||
INFERENCE_POOL_SET[app_context][inference_context] = create_inference_pool(model_source_set, execution_device_id, execution_providers)
|
||||
inference_providers = resolve_static_inference_providers(module_name, execution_device_id)
|
||||
INFERENCE_POOL_SET[app_context][inference_context] = create_inference_pool(model_source_set, inference_providers)
|
||||
|
||||
current_inference_context = get_inference_context(module_name, model_names, random.choice(execution_device_ids), execution_providers)
|
||||
return INFERENCE_POOL_SET.get(app_context).get(current_inference_context)
|
||||
|
||||
|
||||
def create_inference_pool(model_source_set : DownloadSet, execution_device_id : int, execution_providers : List[ExecutionProvider]) -> InferencePool:
|
||||
def create_inference_pool(model_source_set : DownloadSet, inference_providers : List[InferenceProvider]) -> InferencePool:
|
||||
inference_pool : InferencePool = {}
|
||||
|
||||
for model_name in model_source_set.keys():
|
||||
model_path = model_source_set.get(model_name).get('path')
|
||||
if is_file(model_path):
|
||||
inference_pool[model_name] = create_inference_session(model_path, execution_device_id, execution_providers)
|
||||
inference_pool[model_name] = create_inference_session(model_path, inference_providers)
|
||||
|
||||
return inference_pool
|
||||
|
||||
|
||||
def clear_inference_pool(module_name : str, model_names : List[str]) -> None:
|
||||
execution_device_ids = state_manager.get_item('execution_device_ids')
|
||||
execution_providers = resolve_execution_providers(module_name)
|
||||
execution_providers = state_manager.get_item('execution_providers')
|
||||
app_context = detect_app_context()
|
||||
|
||||
if is_windows() and has_execution_provider('directml'):
|
||||
@@ -67,13 +69,12 @@ def clear_inference_pool(module_name : str, model_names : List[str]) -> None:
|
||||
del INFERENCE_POOL_SET[app_context][inference_context]
|
||||
|
||||
|
||||
def create_inference_session(model_path : str, execution_device_id : int, execution_providers : List[ExecutionProvider]) -> InferenceSession:
|
||||
def create_inference_session(model_path : str, inference_providers : List[InferenceProvider]) -> InferenceSession:
|
||||
model_file_name = get_file_name(model_path)
|
||||
start_time = time()
|
||||
|
||||
try:
|
||||
inference_session_providers = create_inference_session_providers(execution_device_id, execution_providers)
|
||||
inference_session = InferenceSession(model_path, providers = inference_session_providers)
|
||||
inference_session = InferenceSession(model_path, providers = inference_providers)
|
||||
logger.debug(translator.get('loading_model_succeeded').format(model_name = model_file_name, seconds = calculate_end_time(start_time)), __name__)
|
||||
return inference_session
|
||||
|
||||
@@ -87,9 +88,15 @@ def get_inference_context(module_name : str, model_names : List[str], execution_
|
||||
return inference_context
|
||||
|
||||
|
||||
def resolve_execution_providers(module_name : str) -> List[ExecutionProvider]:
|
||||
@lru_cache()
|
||||
def resolve_static_inference_providers(module_name : str, execution_device_id : int) -> List[InferenceProvider]:
|
||||
module = importlib.import_module(module_name)
|
||||
execution_providers = state_manager.get_item('execution_providers')
|
||||
|
||||
if hasattr(module, 'resolve_execution_providers'):
|
||||
return getattr(module, 'resolve_execution_providers')()
|
||||
return state_manager.get_item('execution_providers')
|
||||
if hasattr(module, 'resolve_inference_providers'):
|
||||
inference_providers = getattr(module, 'resolve_inference_providers')()
|
||||
|
||||
if inference_providers:
|
||||
return inference_providers
|
||||
|
||||
return create_inference_providers(execution_device_id, execution_providers)
|
||||
|
||||
+23
-49
@@ -10,30 +10,34 @@ from types import FrameType
|
||||
from facefusion import metadata
|
||||
from facefusion.common_helper import is_linux, is_windows
|
||||
|
||||
LOCALS =\
|
||||
LOCALES =\
|
||||
{
|
||||
'install_dependency': 'install the {dependency} package',
|
||||
'force_reinstall': 'force reinstall of packages',
|
||||
'skip_conda': 'skip the conda environment check',
|
||||
'conda_not_activated': 'conda is not activated'
|
||||
}
|
||||
ONNXRUNTIME_SET =\
|
||||
{
|
||||
'default': ('onnxruntime', '1.23.2')
|
||||
'default': ('onnxruntime', '1.26.0')
|
||||
}
|
||||
if is_windows() or is_linux():
|
||||
ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.23.2')
|
||||
ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.23.0')
|
||||
ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.26.0')
|
||||
ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.24.1')
|
||||
if is_windows():
|
||||
ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.23.0')
|
||||
ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.24.4')
|
||||
ONNXRUNTIME_SET['qnn'] = ('onnxruntime-qnn', '1.24.4')
|
||||
if is_linux():
|
||||
ONNXRUNTIME_SET['rocm'] = ('onnxruntime-rocm', '1.21.0')
|
||||
ONNXRUNTIME_SET['migraphx'] = ('onnxruntime-migraphx', '1.25.0')
|
||||
ONNXRUNTIME_SET['rocm'] = ('onnxruntime-rocm', '1.22.2.post1')
|
||||
|
||||
|
||||
def cli() -> None:
|
||||
signal.signal(signal.SIGINT, signal_exit)
|
||||
program = ArgumentParser(formatter_class = partial(HelpFormatter, max_help_position = 50))
|
||||
program.add_argument('--onnxruntime', help = LOCALS.get('install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIME_SET.keys(), required = True)
|
||||
program.add_argument('--skip-conda', help = LOCALS.get('skip_conda'), action = 'store_true')
|
||||
program.add_argument('onnxruntime', help = LOCALES.get('install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIME_SET.keys())
|
||||
program.add_argument('--force-reinstall', help = LOCALES.get('force_reinstall'), action = 'store_true')
|
||||
program.add_argument('--skip-conda', help = LOCALES.get('skip_conda'), action = 'store_true')
|
||||
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
|
||||
run(program)
|
||||
|
||||
@@ -45,56 +49,26 @@ def signal_exit(signum : int, frame : FrameType) -> None:
|
||||
def run(program : ArgumentParser) -> None:
|
||||
args = program.parse_args()
|
||||
has_conda = 'CONDA_PREFIX' in os.environ
|
||||
onnxruntime_name, onnxruntime_version = ONNXRUNTIME_SET.get(args.onnxruntime)
|
||||
|
||||
if not args.skip_conda and not has_conda:
|
||||
sys.stdout.write(LOCALS.get('conda_not_activated') + os.linesep)
|
||||
sys.stdout.write(LOCALES.get('conda_not_activated') + os.linesep)
|
||||
sys.exit(1)
|
||||
|
||||
commands = [ shutil.which('pip'), 'install' ]
|
||||
|
||||
if args.force_reinstall:
|
||||
commands.append('--force-reinstall')
|
||||
|
||||
with open('requirements.txt') as file:
|
||||
|
||||
for line in file.readlines():
|
||||
__line__ = line.strip()
|
||||
if not __line__.startswith('onnxruntime'):
|
||||
subprocess.call([ shutil.which('pip'), 'install', line, '--force-reinstall' ])
|
||||
commands.append(__line__)
|
||||
|
||||
if args.onnxruntime == 'rocm':
|
||||
python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
|
||||
onnxruntime_name, onnxruntime_version = ONNXRUNTIME_SET.get(args.onnxruntime)
|
||||
commands.append(onnxruntime_name + '==' + onnxruntime_version)
|
||||
|
||||
if python_id in [ 'cp310', 'cp312' ]:
|
||||
wheel_name = 'onnxruntime_rocm-' + onnxruntime_version + '-' + python_id + '-' + python_id + '-linux_x86_64.whl'
|
||||
wheel_url = 'https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/' + wheel_name
|
||||
subprocess.call([ shutil.which('pip'), 'install', wheel_url, '--force-reinstall' ])
|
||||
else:
|
||||
subprocess.call([ shutil.which('pip'), 'install', onnxruntime_name + '==' + onnxruntime_version, '--force-reinstall' ])
|
||||
|
||||
if args.onnxruntime == 'cuda' and has_conda:
|
||||
library_paths = []
|
||||
|
||||
if is_linux():
|
||||
if os.getenv('LD_LIBRARY_PATH'):
|
||||
library_paths = os.getenv('LD_LIBRARY_PATH').split(os.pathsep)
|
||||
|
||||
python_id = 'python' + str(sys.version_info.major) + '.' + str(sys.version_info.minor)
|
||||
library_paths.extend(
|
||||
[
|
||||
os.path.join(os.getenv('CONDA_PREFIX'), 'lib'),
|
||||
os.path.join(os.getenv('CONDA_PREFIX'), 'lib', python_id, 'site-packages', 'tensorrt_libs')
|
||||
])
|
||||
library_paths = list(dict.fromkeys([ library_path for library_path in library_paths if os.path.exists(library_path) ]))
|
||||
|
||||
subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'LD_LIBRARY_PATH=' + os.pathsep.join(library_paths) ])
|
||||
|
||||
if is_windows():
|
||||
if os.getenv('PATH'):
|
||||
library_paths = os.getenv('PATH').split(os.pathsep)
|
||||
|
||||
library_paths.extend(
|
||||
[
|
||||
os.path.join(os.getenv('CONDA_PREFIX'), 'Lib'),
|
||||
os.path.join(os.getenv('CONDA_PREFIX'), 'Lib', 'site-packages', 'tensorrt_libs')
|
||||
])
|
||||
library_paths = list(dict.fromkeys([ library_path for library_path in library_paths if os.path.exists(library_path) ]))
|
||||
|
||||
subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'PATH=' + os.pathsep.join(library_paths) ])
|
||||
subprocess.call([ shutil.which('pip'), 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
|
||||
|
||||
subprocess.call(commands)
|
||||
|
||||
@@ -6,6 +6,7 @@ import facefusion.choices
|
||||
from facefusion.filesystem import create_directory, get_file_name, is_directory, is_file, move_file, remove_directory, remove_file, resolve_file_pattern
|
||||
from facefusion.jobs.job_helper import get_step_output_path
|
||||
from facefusion.json import read_json, write_json
|
||||
from facefusion.sanitizer import sanitize_job_id
|
||||
from facefusion.time_helper import get_current_date_time
|
||||
from facefusion.types import Args, Job, JobSet, JobStatus, JobStep, JobStepStatus
|
||||
|
||||
@@ -261,5 +262,6 @@ def find_job_path(job_id : str) -> Optional[str]:
|
||||
|
||||
def get_job_file_name(job_id : str) -> Optional[str]:
|
||||
if job_id:
|
||||
job_id = sanitize_job_id(job_id)
|
||||
return job_id + '.json'
|
||||
return None
|
||||
|
||||
@@ -0,0 +1,27 @@
|
||||
from typing import List
|
||||
|
||||
from facefusion.types import JobStore
|
||||
|
||||
JOB_STORE : JobStore =\
|
||||
{
|
||||
'job_keys': [],
|
||||
'step_keys': []
|
||||
}
|
||||
|
||||
|
||||
def get_job_keys() -> List[str]:
|
||||
return JOB_STORE.get('job_keys')
|
||||
|
||||
|
||||
def get_step_keys() -> List[str]:
|
||||
return JOB_STORE.get('step_keys')
|
||||
|
||||
|
||||
def register_job_keys(job_keys : List[str]) -> None:
|
||||
for job_key in job_keys:
|
||||
JOB_STORE['job_keys'].append(job_key)
|
||||
|
||||
|
||||
def register_step_keys(step_keys : List[str]) -> None:
|
||||
for step_key in step_keys:
|
||||
JOB_STORE['step_keys'].append(step_key)
|
||||
@@ -1,134 +0,0 @@
|
||||
import ctypes
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.common_helper import is_linux, is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.types import LibrarySet
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library_set() -> Optional[LibrarySet]:
|
||||
if is_linux():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libaom.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libaom.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libaom.so'),
|
||||
'path': resolve_relative_path('../.libraries/libaom.so')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_macos():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libaom.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libaom.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libaom.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libaom.dylib')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_windows():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/aom.hash'),
|
||||
'path': resolve_relative_path('../.libraries/aom.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'aom':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/aom.dll'),
|
||||
'path': resolve_relative_path('../.libraries/aom.dll')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
library_hash_set = create_static_library_set().get('hashes')
|
||||
library_source_set = create_static_library_set().get('sources')
|
||||
|
||||
return conditional_download_hashes(library_hash_set) and conditional_download_sources(library_source_set)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library() -> Optional[ctypes.CDLL]:
|
||||
library_path = create_static_library_set().get('sources').get('aom').get('path')
|
||||
|
||||
if library_path:
|
||||
if is_windows():
|
||||
library = ctypes.CDLL(library_path, winmode = 0)
|
||||
else:
|
||||
library = ctypes.CDLL(library_path)
|
||||
|
||||
if library:
|
||||
return init_ctypes(library)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def init_ctypes(library : ctypes.CDLL) -> ctypes.CDLL:
|
||||
library.aom_codec_enc_config_default.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint ]
|
||||
library.aom_codec_enc_config_default.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_enc_init_ver.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_long, ctypes.c_int ]
|
||||
library.aom_codec_enc_init_ver.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_encode.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int64, ctypes.c_ulong, ctypes.c_long, ctypes.c_ulong ]
|
||||
library.aom_codec_encode.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_get_cx_data.argtypes = [ ctypes.c_void_p, ctypes.POINTER(ctypes.c_void_p) ]
|
||||
library.aom_codec_get_cx_data.restype = ctypes.c_void_p
|
||||
|
||||
library.aom_codec_enc_config_set.argtypes = [ ctypes.c_void_p, ctypes.c_void_p ]
|
||||
library.aom_codec_enc_config_set.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_destroy.argtypes = [ ctypes.c_void_p ]
|
||||
library.aom_codec_destroy.restype = ctypes.c_int
|
||||
|
||||
library.aom_img_wrap.argtypes = [ ctypes.c_void_p, ctypes.c_int, ctypes.c_uint, ctypes.c_uint, ctypes.c_uint, ctypes.c_void_p ]
|
||||
library.aom_img_wrap.restype = ctypes.c_void_p
|
||||
|
||||
library.aom_codec_control.argtypes = [ ctypes.c_void_p, ctypes.c_int, ctypes.c_int ]
|
||||
library.aom_codec_control.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_dec_init_ver.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_long, ctypes.c_int ]
|
||||
library.aom_codec_dec_init_ver.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_decode.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint, ctypes.c_void_p ]
|
||||
library.aom_codec_decode.restype = ctypes.c_int
|
||||
|
||||
library.aom_codec_get_frame.argtypes = [ ctypes.c_void_p, ctypes.POINTER(ctypes.c_void_p) ]
|
||||
library.aom_codec_get_frame.restype = ctypes.c_void_p
|
||||
|
||||
return library
|
||||
@@ -1,294 +0,0 @@
|
||||
import ctypes
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.common_helper import is_linux, is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.types import LibrarySet
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library_set() -> Optional[LibrarySet]:
|
||||
if is_linux():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libcrypto.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libcrypto.hash')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libdatachannel_next.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libdatachannel_next.hash')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libssl.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libssl.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libcrypto.so'),
|
||||
'path': resolve_relative_path('../.libraries/libcrypto.so')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libdatachannel_next.so'),
|
||||
'path': resolve_relative_path('../.libraries/libdatachannel_next.so')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libssl.so'),
|
||||
'path': resolve_relative_path('../.libraries/libssl.so')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_macos():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libcrypto.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libcrypto.hash')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libdatachannel_next.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libdatachannel_next.hash')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libssl.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libssl.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libcrypto.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libcrypto.dylib')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libdatachannel_next.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libdatachannel_next.dylib')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libssl.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libssl.dylib')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_windows():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/crypto.hash'),
|
||||
'path': resolve_relative_path('../.libraries/crypto.hash')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/datachannel_next.hash'),
|
||||
'path': resolve_relative_path('../.libraries/datachannel_next.hash')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/ssl.hash'),
|
||||
'path': resolve_relative_path('../.libraries/ssl.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'crypto':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/crypto.dll'),
|
||||
'path': resolve_relative_path('../.libraries/crypto.dll')
|
||||
},
|
||||
'datachannel':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/datachannel_next.dll'),
|
||||
'path': resolve_relative_path('../.libraries/datachannel_next.dll')
|
||||
},
|
||||
'ssl':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/ssl.dll'),
|
||||
'path': resolve_relative_path('../.libraries/ssl.dll')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
library_hash_set = create_static_library_set().get('hashes')
|
||||
library_source_set = create_static_library_set().get('sources')
|
||||
|
||||
return conditional_download_hashes(library_hash_set) and conditional_download_sources(library_source_set)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library() -> Optional[ctypes.CDLL]:
|
||||
crypto_source_path = create_static_library_set().get('sources').get('crypto').get('path')
|
||||
datachannel_source_path = create_static_library_set().get('sources').get('datachannel').get('path')
|
||||
ssl_source_path = create_static_library_set().get('sources').get('ssl').get('path')
|
||||
|
||||
if crypto_source_path and datachannel_source_path and ssl_source_path:
|
||||
if is_windows():
|
||||
ctypes.CDLL(crypto_source_path, winmode = 0)
|
||||
ctypes.CDLL(ssl_source_path, winmode = 0)
|
||||
library = ctypes.CDLL(datachannel_source_path, winmode = 0)
|
||||
else:
|
||||
ctypes.CDLL(crypto_source_path)
|
||||
ctypes.CDLL(ssl_source_path)
|
||||
library = ctypes.CDLL(datachannel_source_path)
|
||||
|
||||
if library:
|
||||
return init_ctypes(library)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def init_ctypes(library : ctypes.CDLL) -> ctypes.CDLL:
|
||||
library.rtcInitLogger.argtypes = [ ctypes.c_int, ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_char_p) ]
|
||||
library.rtcInitLogger.restype = None
|
||||
library.rtcInitLogger(2, ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_char_p)(0))
|
||||
|
||||
library.rtcCreatePeerConnection.restype = ctypes.c_int
|
||||
|
||||
library.rtcDeletePeerConnection.argtypes = [ ctypes.c_int ]
|
||||
library.rtcDeletePeerConnection.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetLocalDescription.argtypes = [ ctypes.c_int, ctypes.c_char_p ]
|
||||
library.rtcSetLocalDescription.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetRemoteDescription.argtypes = [ ctypes.c_int, ctypes.c_char_p, ctypes.c_char_p ]
|
||||
library.rtcSetRemoteDescription.restype = ctypes.c_int
|
||||
|
||||
library.rtcAddTrackEx.restype = ctypes.c_int
|
||||
|
||||
library.rtcSendMessage.argtypes = [ ctypes.c_int, ctypes.c_void_p, ctypes.c_int ]
|
||||
library.rtcSendMessage.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetAV1Packetizer.restype = ctypes.c_int
|
||||
library.rtcSetVP8Packetizer.restype = ctypes.c_int
|
||||
library.rtcSetVP9Packetizer.restype = ctypes.c_int
|
||||
|
||||
library.rtcChainRtcpSrReporter.argtypes = [ ctypes.c_int ]
|
||||
library.rtcChainRtcpSrReporter.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetTrackRtpTimestamp.argtypes = [ ctypes.c_int, ctypes.c_uint32 ]
|
||||
library.rtcSetTrackRtpTimestamp.restype = ctypes.c_int
|
||||
|
||||
library.rtcIsOpen.argtypes = [ ctypes.c_int ]
|
||||
library.rtcIsOpen.restype = ctypes.c_bool
|
||||
|
||||
library.rtcChainRtcpNackResponder.argtypes = [ ctypes.c_int, ctypes.c_uint ]
|
||||
library.rtcChainRtcpNackResponder.restype = ctypes.c_int
|
||||
|
||||
library.rtcGetLocalDescription.argtypes = [ ctypes.c_int, ctypes.c_char_p, ctypes.c_int ]
|
||||
library.rtcGetLocalDescription.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetOpusPacketizer.restype = ctypes.c_int
|
||||
|
||||
library.rtcGetPayloadTypesForCodec.argtypes = [ ctypes.c_char_p, ctypes.c_char_p, ctypes.POINTER(ctypes.c_int), ctypes.c_int ]
|
||||
library.rtcGetPayloadTypesForCodec.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetAV1Depacketizer.argtypes = [ ctypes.c_int, ctypes.c_int ]
|
||||
library.rtcSetAV1Depacketizer.restype = ctypes.c_int
|
||||
library.rtcSetVP8Depacketizer.restype = ctypes.c_int
|
||||
library.rtcSetVP9Depacketizer.restype = ctypes.c_int
|
||||
library.rtcSetOpusDepacketizer.restype = ctypes.c_int
|
||||
|
||||
library.rtcChainRtcpReceivingSession.argtypes = [ ctypes.c_int ]
|
||||
library.rtcChainRtcpReceivingSession.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetFrameCallback.argtypes = [ ctypes.c_int, ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_void_p, ctypes.c_int, ctypes.c_void_p, ctypes.c_void_p) ]
|
||||
library.rtcSetFrameCallback.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetUserPointer.argtypes = [ ctypes.c_int, ctypes.c_void_p ]
|
||||
library.rtcSetUserPointer.restype = None
|
||||
|
||||
library.rtcChainRembHandler.argtypes = [ ctypes.c_int, ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_uint, ctypes.c_void_p) ]
|
||||
library.rtcChainRembHandler.restype = ctypes.c_int
|
||||
|
||||
library.rtcRequestBitrate.argtypes = [ ctypes.c_int, ctypes.c_uint ]
|
||||
library.rtcRequestBitrate.restype = ctypes.c_int
|
||||
|
||||
library.rtcSetClosedCallback.argtypes = [ ctypes.c_int, ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_void_p) ]
|
||||
library.rtcSetClosedCallback.restype = ctypes.c_int
|
||||
|
||||
return library
|
||||
|
||||
|
||||
def define_rtc_configuration() -> ctypes.Structure:
|
||||
return type('RTC_CONFIGURATION', (ctypes.Structure,),
|
||||
{
|
||||
'_fields_':
|
||||
[
|
||||
('iceServers', ctypes.POINTER(ctypes.c_char_p)),
|
||||
('iceServersCount', ctypes.c_int),
|
||||
('proxyServer', ctypes.c_char_p),
|
||||
('bindAddress', ctypes.c_char_p),
|
||||
('certificateType', ctypes.c_int),
|
||||
('iceTransportPolicy', ctypes.c_int),
|
||||
('enableIceTcp', ctypes.c_bool),
|
||||
('enableIceUdpMux', ctypes.c_bool),
|
||||
('disableAutoNegotiation', ctypes.c_bool),
|
||||
('forceMediaTransport', ctypes.c_bool),
|
||||
('portRangeBegin', ctypes.c_ushort),
|
||||
('portRangeEnd', ctypes.c_ushort),
|
||||
('mtu', ctypes.c_int),
|
||||
('maxMessageSize', ctypes.c_int)
|
||||
]
|
||||
})()
|
||||
|
||||
|
||||
def define_rtc_track_init() -> ctypes.Structure:
|
||||
return type('RTC_TRACK_INIT', (ctypes.Structure,),
|
||||
{
|
||||
'_fields_':
|
||||
[
|
||||
('direction', ctypes.c_int),
|
||||
('codec', ctypes.c_int),
|
||||
('payloadType', ctypes.c_int),
|
||||
('ssrc', ctypes.c_uint32),
|
||||
('mid', ctypes.c_char_p),
|
||||
('name', ctypes.c_char_p),
|
||||
('msid', ctypes.c_char_p),
|
||||
('trackId', ctypes.c_char_p),
|
||||
('profile', ctypes.c_char_p)
|
||||
]
|
||||
})()
|
||||
|
||||
|
||||
def define_rtc_packetizer_init() -> ctypes.Structure:
|
||||
return type('RTC_PACKETIZER_INIT', (ctypes.Structure,),
|
||||
{
|
||||
'_fields_':
|
||||
[
|
||||
('ssrc', ctypes.c_uint32),
|
||||
('cname', ctypes.c_char_p),
|
||||
('payloadType', ctypes.c_uint8),
|
||||
('clockRate', ctypes.c_uint32),
|
||||
('sequenceNumber', ctypes.c_uint16),
|
||||
('timestamp', ctypes.c_uint32),
|
||||
('maxFragmentSize', ctypes.c_uint16),
|
||||
('nalSeparator', ctypes.c_int),
|
||||
('obuPacketization', ctypes.c_int)
|
||||
]
|
||||
})()
|
||||
@@ -1,121 +0,0 @@
|
||||
import ctypes
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.common_helper import is_linux, is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.types import LibrarySet
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library_set() -> Optional[LibrarySet]:
|
||||
if is_linux():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libopus.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libopus.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libopus.so'),
|
||||
'path': resolve_relative_path('../.libraries/libopus.so')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if is_macos():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libopus.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libopus.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libopus.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libopus.dylib')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if is_windows():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/opus.hash'),
|
||||
'path': resolve_relative_path('../.libraries/opus.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'opus':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/opus.dll'),
|
||||
'path': resolve_relative_path('../.libraries/opus.dll')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
library_hash_set = create_static_library_set().get('hashes')
|
||||
library_source_set = create_static_library_set().get('sources')
|
||||
|
||||
return conditional_download_hashes(library_hash_set) and conditional_download_sources(library_source_set)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library() -> Optional[ctypes.CDLL]:
|
||||
library_path = create_static_library_set().get('sources').get('opus').get('path')
|
||||
|
||||
if library_path:
|
||||
if is_windows():
|
||||
library = ctypes.CDLL(library_path, winmode = 0)
|
||||
else:
|
||||
library = ctypes.CDLL(library_path)
|
||||
|
||||
if library:
|
||||
return init_ctypes(library)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def init_ctypes(library : ctypes.CDLL) -> ctypes.CDLL:
|
||||
library.opus_encoder_create.argtypes = [ ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_int) ]
|
||||
library.opus_encoder_create.restype = ctypes.c_void_p
|
||||
|
||||
library.opus_encode_float.argtypes = [ ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int, ctypes.c_char_p, ctypes.c_int ]
|
||||
library.opus_encode_float.restype = ctypes.c_int
|
||||
|
||||
library.opus_encoder_destroy.argtypes = [ ctypes.c_void_p ]
|
||||
library.opus_encoder_destroy.restype = None
|
||||
|
||||
library.opus_decoder_create.argtypes = [ ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_int) ]
|
||||
library.opus_decoder_create.restype = ctypes.c_void_p
|
||||
|
||||
library.opus_decode_float.argtypes = [ ctypes.c_void_p, ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_float), ctypes.c_int, ctypes.c_int ]
|
||||
library.opus_decode_float.restype = ctypes.c_int
|
||||
|
||||
library.opus_decoder_destroy.argtypes = [ ctypes.c_void_p ]
|
||||
library.opus_decoder_destroy.restype = None
|
||||
|
||||
return library
|
||||
@@ -1,134 +0,0 @@
|
||||
import ctypes
|
||||
from functools import lru_cache
|
||||
from typing import Optional
|
||||
|
||||
from facefusion.common_helper import is_linux, is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.types import LibrarySet
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library_set() -> Optional[LibrarySet]:
|
||||
if is_linux():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libvpx.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libvpx.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'linux/libvpx.so'),
|
||||
'path': resolve_relative_path('../.libraries/libvpx.so')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_macos():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libvpx.hash'),
|
||||
'path': resolve_relative_path('../.libraries/libvpx.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'macos/libvpx.dylib'),
|
||||
'path': resolve_relative_path('../.libraries/libvpx.dylib')
|
||||
}
|
||||
}
|
||||
}
|
||||
if is_windows():
|
||||
return\
|
||||
{
|
||||
'hashes':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/vpx.hash'),
|
||||
'path': resolve_relative_path('../.libraries/vpx.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'vpx':
|
||||
{
|
||||
'url': resolve_download_url_by_provider('huggingface', 'libraries-4.0.0', 'windows/vpx.dll'),
|
||||
'path': resolve_relative_path('../.libraries/vpx.dll')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
library_hash_set = create_static_library_set().get('hashes')
|
||||
library_source_set = create_static_library_set().get('sources')
|
||||
|
||||
return conditional_download_hashes(library_hash_set) and conditional_download_sources(library_source_set)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def create_static_library() -> Optional[ctypes.CDLL]:
|
||||
library_path = create_static_library_set().get('sources').get('vpx').get('path')
|
||||
|
||||
if library_path:
|
||||
if is_windows():
|
||||
library = ctypes.CDLL(library_path, winmode = 0)
|
||||
else:
|
||||
library = ctypes.CDLL(library_path)
|
||||
|
||||
if library:
|
||||
return init_ctypes(library)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def init_ctypes(library : ctypes.CDLL) -> ctypes.CDLL:
|
||||
library.vpx_codec_enc_config_default.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint ]
|
||||
library.vpx_codec_enc_config_default.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_enc_init_ver.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_long, ctypes.c_int ]
|
||||
library.vpx_codec_enc_init_ver.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_encode.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int64, ctypes.c_ulong, ctypes.c_long, ctypes.c_ulong ]
|
||||
library.vpx_codec_encode.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_get_cx_data.argtypes = [ ctypes.c_void_p, ctypes.POINTER(ctypes.c_void_p) ]
|
||||
library.vpx_codec_get_cx_data.restype = ctypes.c_void_p
|
||||
|
||||
library.vpx_codec_enc_config_set.argtypes = [ ctypes.c_void_p, ctypes.c_void_p ]
|
||||
library.vpx_codec_enc_config_set.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_destroy.argtypes = [ ctypes.c_void_p ]
|
||||
library.vpx_codec_destroy.restype = ctypes.c_int
|
||||
|
||||
library.vpx_img_wrap.argtypes = [ ctypes.c_void_p, ctypes.c_int, ctypes.c_uint, ctypes.c_uint, ctypes.c_uint, ctypes.c_void_p ]
|
||||
library.vpx_img_wrap.restype = ctypes.c_void_p
|
||||
|
||||
library.vpx_codec_control_.argtypes = [ ctypes.c_void_p, ctypes.c_int, ctypes.c_int ]
|
||||
library.vpx_codec_control_.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_dec_init_ver.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_long, ctypes.c_int ]
|
||||
library.vpx_codec_dec_init_ver.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_decode.argtypes = [ ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint, ctypes.c_void_p, ctypes.c_long ]
|
||||
library.vpx_codec_decode.restype = ctypes.c_int
|
||||
|
||||
library.vpx_codec_get_frame.argtypes = [ ctypes.c_void_p, ctypes.POINTER(ctypes.c_void_p) ]
|
||||
library.vpx_codec_get_frame.restype = ctypes.c_void_p
|
||||
|
||||
return library
|
||||
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -12,11 +12,8 @@ LOCALS : Locals =\
|
||||
'extracting_frames': 'extracting frames with a resolution of {resolution} and {fps} frames per second',
|
||||
'extracting_frames_succeeded': 'extracting frames succeeded',
|
||||
'extracting_frames_failed': 'extracting frames failed',
|
||||
'spawning_frames_succeeded': 'spawning frames succeeded',
|
||||
'spawning_frames_failed': 'spawning frames failed',
|
||||
'analysing': 'analysing',
|
||||
'extracting': 'extracting',
|
||||
'spawning': 'spawning',
|
||||
'streaming': 'streaming',
|
||||
'processing': 'processing',
|
||||
'merging': 'merging',
|
||||
@@ -51,9 +48,10 @@ LOCALS : Locals =\
|
||||
'no_source_face_detected': 'no source face detected',
|
||||
'processor_not_loaded': 'processor {processor} could not be loaded',
|
||||
'processor_not_implemented': 'processor {processor} not implemented correctly',
|
||||
'ui_layout_not_loaded': 'ui layout {ui_layout} could not be loaded',
|
||||
'ui_layout_not_implemented': 'ui layout {ui_layout} not implemented correctly',
|
||||
'stream_not_loaded': 'stream {stream_mode} could not be loaded',
|
||||
'stream_not_supported': 'stream not supported',
|
||||
'api_started': 'started API on {host}:{port}',
|
||||
'job_created': 'job {job_id} created',
|
||||
'job_not_created': 'job {job_id} not created',
|
||||
'job_submitted': 'job {job_id} submitted',
|
||||
@@ -101,7 +99,6 @@ LOCALS : Locals =\
|
||||
{
|
||||
'install_dependency': 'choose the variant of {dependency} to install',
|
||||
'skip_conda': 'skip the conda environment check',
|
||||
'workflow': 'choose the workflow',
|
||||
'config_path': 'choose the config file to override defaults',
|
||||
'temp_path': 'specify the directory for the temporary resources',
|
||||
'jobs_path': 'specify the directory to store jobs',
|
||||
@@ -127,6 +124,7 @@ LOCALS : Locals =\
|
||||
'reference_face_position': 'specify the position used to create the reference face',
|
||||
'reference_face_distance': 'specify the similarity between the reference face and target face',
|
||||
'reference_frame_number': 'specify the frame used to create the reference face',
|
||||
'face_tracker_score': 'specify the overlap score used to match the tracked faces',
|
||||
'face_occluder_model': 'choose the model responsible for the occlusion mask',
|
||||
'face_parser_model': 'choose the model responsible for the region mask',
|
||||
'face_mask_types': 'mix and match different face mask types (choices: {choices})',
|
||||
@@ -138,6 +136,8 @@ LOCALS : Locals =\
|
||||
'trim_frame_start': 'specify the starting frame of the target video',
|
||||
'trim_frame_end': 'specify the ending frame of the target video',
|
||||
'temp_frame_format': 'specify the temporary resources format',
|
||||
'keep_temp': 'keep the temporary resources after processing',
|
||||
'target_frame_amount': 'specify the amount of target frames forwarded to the processor',
|
||||
'output_image_quality': 'specify the image quality which translates to the image compression',
|
||||
'output_image_scale': 'specify the image scale based on the target image',
|
||||
'output_audio_encoder': 'specify the encoder used for the audio',
|
||||
@@ -151,13 +151,14 @@ LOCALS : Locals =\
|
||||
'processors': 'load a single or multiple processors (choices: {choices}, ...)',
|
||||
'background-remover-model': 'choose the model responsible for removing the background',
|
||||
'background-remover-color': 'apply red, green blue and alpha values of the background',
|
||||
'open_browser': 'open the browser once the program is ready',
|
||||
'ui_layouts': 'launch a single or multiple UI layouts (choices: {choices}, ...)',
|
||||
'ui_workflow': 'choose the ui workflow',
|
||||
'download_providers': 'download using different providers (choices: {choices}, ...)',
|
||||
'download_scope': 'specify the download scope',
|
||||
'benchmark_mode': 'choose the benchmark mode',
|
||||
'benchmark_resolutions': 'choose the resolutions for the benchmarks (choices: {choices}, ...)',
|
||||
'benchmark_cycle_count': 'specify the amount of cycles per benchmark',
|
||||
'api_host': 'specify the API host',
|
||||
'api_port': 'specify the API port',
|
||||
'execution_device_ids': 'specify the devices used for processing',
|
||||
'execution_providers': 'inference using different providers (choices: {choices}, ...)',
|
||||
'execution_thread_count': 'specify the amount of parallel threads while processing',
|
||||
@@ -165,10 +166,10 @@ LOCALS : Locals =\
|
||||
'log_level': 'adjust the message severity displayed in the terminal',
|
||||
'halt_on_error': 'halt the program once an error occurred',
|
||||
'run': 'run the program',
|
||||
'headless_run': 'run the program in headless mode',
|
||||
'batch_run': 'run the program in batch mode',
|
||||
'force_download': 'force automate downloads and exit',
|
||||
'benchmark': 'benchmark the program',
|
||||
'api': 'start the API server',
|
||||
'job_id': 'specify the job id',
|
||||
'job_status': 'specify the job status',
|
||||
'step_index': 'specify the step index',
|
||||
@@ -189,7 +190,7 @@ LOCALS : Locals =\
|
||||
},
|
||||
'about':
|
||||
{
|
||||
'fund': 'fund training server',
|
||||
'fund': 'fund ai workstation',
|
||||
'subscribe': 'become a member',
|
||||
'join': 'join our community'
|
||||
},
|
||||
@@ -224,6 +225,7 @@ LOCALS : Locals =\
|
||||
'face_selector_mode_dropdown': 'FACE SELECTOR MODE',
|
||||
'face_selector_order_dropdown': 'FACE SELECTOR ORDER',
|
||||
'face_selector_race_dropdown': 'FACE SELECTOR RACE',
|
||||
'face_tracker_score_slider': 'FACE TRACKER SCORE',
|
||||
'face_occluder_model_dropdown': 'FACE OCCLUDER MODEL',
|
||||
'face_parser_model_dropdown': 'FACE PARSER MODEL',
|
||||
'voice_extractor_model_dropdown': 'VOICE EXTRACTOR MODEL',
|
||||
@@ -261,6 +263,7 @@ LOCALS : Locals =\
|
||||
'temp_frame_format_dropdown': 'TEMP FRAME FORMAT',
|
||||
'terminal_textbox': 'TERMINAL',
|
||||
'trim_frame_slider': 'TRIM FRAME',
|
||||
'ui_workflow': 'UI WORKFLOW',
|
||||
'video_memory_strategy_dropdown': 'VIDEO MEMORY STRATEGY',
|
||||
'webcam_fps_slider': 'WEBCAM FPS',
|
||||
'webcam_image': 'WEBCAM',
|
||||
@@ -1,17 +0,0 @@
|
||||
from typing import Optional, Tuple
|
||||
|
||||
|
||||
def restrict_trim_frame(frame_total : int, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> Tuple[int, int]:
|
||||
if isinstance(trim_frame_start, int):
|
||||
trim_frame_start = max(0, min(trim_frame_start, frame_total))
|
||||
if isinstance(trim_frame_end, int):
|
||||
trim_frame_end = max(0, min(trim_frame_end, frame_total))
|
||||
|
||||
if isinstance(trim_frame_start, int) and isinstance(trim_frame_end, int):
|
||||
return trim_frame_start, trim_frame_end
|
||||
if isinstance(trim_frame_start, int):
|
||||
return trim_frame_start, frame_total
|
||||
if isinstance(trim_frame_end, int):
|
||||
return 0, trim_frame_end
|
||||
|
||||
return 0, frame_total
|
||||
@@ -4,7 +4,7 @@ METADATA =\
|
||||
{
|
||||
'name': 'FaceFusion',
|
||||
'description': 'Industry leading face manipulation platform',
|
||||
'version': 'v4',
|
||||
'version': '3.7.1',
|
||||
'license': 'OpenRAIL-AS',
|
||||
'author': 'Henry Ruhs',
|
||||
'url': 'https://facefusion.io'
|
||||
|
||||
@@ -19,9 +19,9 @@ def normalize_space(spaces : Optional[List[int]]) -> Optional[Padding]:
|
||||
if spaces and len(spaces) == 1:
|
||||
return tuple([spaces[0]] * 4) #type:ignore[return-value]
|
||||
if spaces and len(spaces) == 2:
|
||||
return tuple([spaces[0], spaces[1], spaces[0], spaces[1]]) #type:ignore[return-value]
|
||||
return tuple([ spaces[0], spaces[1], spaces[0], spaces[1] ]) #type:ignore[return-value]
|
||||
if spaces and len(spaces) == 3:
|
||||
return tuple([spaces[0], spaces[1], spaces[2], spaces[1]]) #type:ignore[return-value]
|
||||
return tuple([ spaces[0], spaces[1], spaces[2], spaces[1] ]) #type:ignore[return-value]
|
||||
if spaces and len(spaces) == 4:
|
||||
return tuple(spaces) #type:ignore[return-value]
|
||||
return None
|
||||
|
||||
@@ -12,6 +12,7 @@ PROCESSORS_METHODS =\
|
||||
'clear_inference_pool',
|
||||
'register_args',
|
||||
'apply_args',
|
||||
'get_common_modules',
|
||||
'pre_check',
|
||||
'pre_process',
|
||||
'post_process',
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.age_modifier.types import AgeModifierModel
|
||||
|
||||
age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
|
||||
age_modifier_models : List[AgeModifierModel] = list(get_args(AgeModifierModel))
|
||||
|
||||
age_modifier_direction_range : Sequence[int] = create_int_range(-100, 100, 1)
|
||||
|
||||
@@ -1,34 +1,70 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache
|
||||
from types import ModuleType
|
||||
from typing import List
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.choices
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import create_int_metavar, is_macos
|
||||
from facefusion.common_helper import create_int_metavar, get_middle
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.execution import has_execution_provider
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import merge_matrix, paste_back, scale_face_landmark_5, warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_box_mask, create_occlusion_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
from facefusion.processors.modules.age_modifier import choices as age_modifier_choices
|
||||
from facefusion.processors.modules.age_modifier.types import AgeModifierDirection, AgeModifierInputs
|
||||
from facefusion.processors.types import ProcessorOutputs
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.thread_helper import thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import match_frame_color, read_static_image, read_static_video_frame
|
||||
from facefusion.vision import match_frame_color, read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
return\
|
||||
{
|
||||
'fran':
|
||||
{
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'ry-lu',
|
||||
'license': 'mit',
|
||||
'year': 2024
|
||||
},
|
||||
'hashes':
|
||||
{
|
||||
'age_modifier':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'fran.hash'),
|
||||
'path': resolve_relative_path('../.assets/models/fran.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'age_modifier':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'fran.onnx'),
|
||||
'path': resolve_relative_path('../.assets/models/fran.onnx')
|
||||
}
|
||||
},
|
||||
'templates':
|
||||
{
|
||||
'target': 'ffhq_512',
|
||||
},
|
||||
'sizes':
|
||||
{
|
||||
'target': (1024, 1024),
|
||||
},
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
},
|
||||
'styleganex_age':
|
||||
{
|
||||
'__metadata__':
|
||||
@@ -62,7 +98,9 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
{
|
||||
'target': (256, 256),
|
||||
'target_with_background': (384, 384)
|
||||
}
|
||||
},
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 0.5, 0.5, 0.5 ]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -87,9 +125,9 @@ def get_model_options() -> ModelOptions:
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
group_processors = find_argument_group(program, 'processors')
|
||||
if group_processors:
|
||||
group_processors.add_argument('--age-modifier-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'age_modifier_model', 'styleganex_age'), choices = age_modifier_choices.age_modifier_models)
|
||||
group_processors.add_argument('--age-modifier-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'age_modifier_model', 'fran'), choices = age_modifier_choices.age_modifier_models)
|
||||
group_processors.add_argument('--age-modifier-direction', help = translator.get('help.direction', __package__), type = int, default = config.get_int_value('processors', 'age_modifier_direction', '0'), choices = age_modifier_choices.age_modifier_direction_range, metavar = create_int_metavar(age_modifier_choices.age_modifier_direction_range))
|
||||
facefusion.args_store.register_args([ 'age_modifier_model', 'age_modifier_direction' ], scopes = [ 'api', 'cli' ])
|
||||
facefusion.jobs.job_store.register_step_keys([ 'age_modifier_model', 'age_modifier_direction' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
@@ -97,10 +135,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('age_modifier_direction', args.get('age_modifier_direction'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
|
||||
|
||||
@@ -111,22 +157,24 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -134,41 +182,63 @@ def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
|
||||
model_sizes = get_model_options().get('sizes')
|
||||
face_landmark_5 = target_face.landmark_set.get('5/68').copy()
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_templates.get('target'), model_sizes.get('target'))
|
||||
extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
|
||||
extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
|
||||
extend_vision_frame_raw = extend_vision_frame.copy()
|
||||
box_mask = create_box_mask(extend_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
|
||||
crop_masks =\
|
||||
[
|
||||
box_mask
|
||||
]
|
||||
|
||||
if 'occlusion' in state_manager.get_item('face_mask_types'):
|
||||
occlusion_mask = create_occlusion_mask(crop_vision_frame)
|
||||
temp_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
|
||||
occlusion_mask = cv2.warpAffine(occlusion_mask, temp_matrix, model_sizes.get('target_with_background'))
|
||||
crop_masks.append(occlusion_mask)
|
||||
if state_manager.get_item('age_modifier_model') == 'fran':
|
||||
box_mask = create_box_mask(crop_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
|
||||
crop_masks =\
|
||||
[
|
||||
box_mask
|
||||
]
|
||||
|
||||
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
|
||||
extend_vision_frame = prepare_vision_frame(extend_vision_frame)
|
||||
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
|
||||
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
|
||||
extend_vision_frame = normalize_extend_frame(extend_vision_frame)
|
||||
extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
|
||||
extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
|
||||
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
|
||||
crop_mask = cv2.resize(crop_mask, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
|
||||
paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
|
||||
return paste_vision_frame
|
||||
if 'occlusion' in state_manager.get_item('face_mask_types'):
|
||||
occlusion_mask = create_occlusion_mask(crop_vision_frame)
|
||||
crop_masks.append(occlusion_mask)
|
||||
|
||||
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
|
||||
target_age = numpy.mean(target_face.age)
|
||||
age_modifier_direction = numpy.array([ target_age, target_age + state_manager.get_item('age_modifier_direction') ], dtype = numpy.float32) / 100
|
||||
age_modifier_direction = age_modifier_direction.clip(0, 1)
|
||||
crop_vision_frame = forward(crop_vision_frame, crop_vision_frame, age_modifier_direction)
|
||||
crop_vision_frame = normalize_vision_frame(crop_vision_frame)
|
||||
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
|
||||
paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
|
||||
return paste_vision_frame
|
||||
|
||||
if state_manager.get_item('age_modifier_model') == 'styleganex_age':
|
||||
extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
|
||||
extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
|
||||
extend_vision_frame_raw = extend_vision_frame.copy()
|
||||
box_mask = create_box_mask(extend_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
|
||||
crop_masks =\
|
||||
[
|
||||
box_mask
|
||||
]
|
||||
|
||||
if 'occlusion' in state_manager.get_item('face_mask_types'):
|
||||
occlusion_mask = create_occlusion_mask(crop_vision_frame)
|
||||
temp_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
|
||||
occlusion_mask = cv2.warpAffine(occlusion_mask, temp_matrix, model_sizes.get('target_with_background'))
|
||||
crop_masks.append(occlusion_mask)
|
||||
|
||||
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
|
||||
extend_vision_frame = prepare_vision_frame(extend_vision_frame)
|
||||
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
|
||||
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
|
||||
extend_vision_frame = normalize_extend_frame(extend_vision_frame)
|
||||
extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
|
||||
extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
|
||||
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
|
||||
crop_mask = cv2.resize(crop_mask, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
|
||||
paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
|
||||
return paste_vision_frame
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame, age_modifier_direction : AgeModifierDirection) -> VisionFrame:
|
||||
age_modifier = get_inference_pool().get('age_modifier')
|
||||
age_modifier_inputs = {}
|
||||
|
||||
if is_macos() and has_execution_provider('coreml'):
|
||||
age_modifier.set_providers([ facefusion.choices.execution_provider_set.get('cpu') ])
|
||||
|
||||
for age_modifier_input in age_modifier.get_inputs():
|
||||
if age_modifier_input.name == 'target':
|
||||
age_modifier_inputs[age_modifier_input.name] = crop_vision_frame
|
||||
@@ -184,12 +254,24 @@ def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame,
|
||||
|
||||
|
||||
def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_mean = get_model_options().get('mean')
|
||||
model_standard_deviation = get_model_options().get('standard_deviation')
|
||||
vision_frame = vision_frame[:, :, ::-1] / 255.0
|
||||
vision_frame = (vision_frame - 0.5) / 0.5
|
||||
vision_frame = (vision_frame - model_mean) / model_standard_deviation
|
||||
vision_frame = numpy.expand_dims(vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
|
||||
return vision_frame
|
||||
|
||||
|
||||
def normalize_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_mean = get_model_options().get('mean')
|
||||
model_standard_deviation = get_model_options().get('standard_deviation')
|
||||
vision_frame = vision_frame.transpose(1, 2, 0)
|
||||
vision_frame = vision_frame * model_standard_deviation + model_mean
|
||||
vision_frame = vision_frame.clip(0, 1)
|
||||
vision_frame = vision_frame[:, :, ::-1] * 255
|
||||
return vision_frame
|
||||
|
||||
|
||||
def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_sizes = get_model_options().get('sizes')
|
||||
extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1)
|
||||
@@ -203,10 +285,13 @@ def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
|
||||
|
||||
def process_frame(inputs : AgeModifierInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Any, Literal, TypeAlias, TypedDict
|
||||
from typing import Any, List, Literal, TypeAlias, TypedDict
|
||||
|
||||
from numpy.typing import NDArray
|
||||
|
||||
@@ -7,11 +7,12 @@ from facefusion.types import Mask, VisionFrame
|
||||
AgeModifierInputs = TypedDict('AgeModifierInputs',
|
||||
{
|
||||
'reference_vision_frame' : VisionFrame,
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'source_vision_frames' : List[VisionFrame],
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
AgeModifierModel = Literal['styleganex_age']
|
||||
AgeModifierModel = Literal['fran', 'styleganex_age']
|
||||
|
||||
AgeModifierDirection : TypeAlias = NDArray[Any]
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.background_remover.types import BackgroundRemoverModel
|
||||
|
||||
background_remover_models : List[BackgroundRemoverModel] = [ 'ben_2', 'birefnet_general', 'birefnet_portrait', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp' ]
|
||||
background_remover_models : List[BackgroundRemoverModel] = list(get_args(BackgroundRemoverModel))
|
||||
|
||||
background_remover_color_range : Sequence[int] = create_int_range(0, 255, 1)
|
||||
|
||||
@@ -1,17 +1,19 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache, partial
|
||||
from types import ModuleType
|
||||
from typing import List, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.choices
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import is_macos
|
||||
from facefusion.common_helper import is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.execution import has_execution_provider
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
from facefusion.normalizer import normalize_color
|
||||
from facefusion.processors.modules.background_remover import choices as background_remover_choices
|
||||
from facefusion.processors.modules.background_remover.types import BackgroundRemoverInputs
|
||||
@@ -19,8 +21,8 @@ from facefusion.processors.types import ProcessorOutputs
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.sanitizer import sanitize_int_range
|
||||
from facefusion.thread_helper import thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, ExecutionProvider, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import read_static_image, read_static_video_frame
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, InferencePool, InferenceProvider, Mask, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
@@ -51,6 +53,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/ben_2.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'ben',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -79,6 +82,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/birefnet_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'birefnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -107,10 +111,69 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/birefnet_portrait.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'birefnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
},
|
||||
'corridor_key_1024':
|
||||
{
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'nikopueringer',
|
||||
'license': 'Non-Commercial',
|
||||
'year': 2025
|
||||
},
|
||||
'hashes':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_1024.hash'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_1024.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_1024.onnx'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_1024.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'corridor_key',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
},
|
||||
'corridor_key_2048':
|
||||
{
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'nikopueringer',
|
||||
'license': 'Non-Commercial',
|
||||
'year': 2025
|
||||
},
|
||||
'hashes':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_2048.hash'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_2048.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_2048.onnx'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_2048.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'corridor_key',
|
||||
'size': (2048, 2048),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
},
|
||||
'isnet_general':
|
||||
{
|
||||
'__metadata__':
|
||||
@@ -135,6 +198,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/isnet_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'isnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -163,6 +227,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/modnet.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'modnet',
|
||||
'size': (512, 512),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 0.5, 0.5, 0.5 ]
|
||||
@@ -191,6 +256,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/ormbg.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'ormbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -219,6 +285,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/rmbg_1.4.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'rmbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -247,6 +314,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/rmbg_2.0.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'rmbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -275,6 +343,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/silueta.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'silueta',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -303,6 +372,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_cloth.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net_cloth',
|
||||
'size': (768, 768),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -331,6 +401,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -359,6 +430,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_human.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -387,6 +459,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2netp.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2netp',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -406,10 +479,13 @@ def clear_inference_pool() -> None:
|
||||
inference_manager.clear_inference_pool(__name__, model_names)
|
||||
|
||||
|
||||
def resolve_execution_providers() -> List[ExecutionProvider]:
|
||||
if is_macos() and has_execution_provider('coreml'):
|
||||
return [ 'cpu' ]
|
||||
return state_manager.get_item('execution_providers')
|
||||
def resolve_inference_providers() -> List[InferenceProvider]:
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
if is_macos() and has_execution_provider('coreml') or is_windows() and has_execution_provider('directml') and model_type == 'corridor_key':
|
||||
return [ facefusion.choices.execution_provider_set.get('cpu') ]
|
||||
|
||||
return []
|
||||
|
||||
|
||||
def get_model_options() -> ModelOptions:
|
||||
@@ -420,20 +496,30 @@ def get_model_options() -> ModelOptions:
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
group_processors = find_argument_group(program, 'processors')
|
||||
if group_processors:
|
||||
group_processors.add_argument('--background-remover-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'background_remover_model', 'rmbg_2.0'), choices = background_remover_choices.background_remover_models)
|
||||
group_processors.add_argument('--background-remover-color', help = translator.get('help.color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_color', '0 0 0 0'), nargs ='+')
|
||||
facefusion.args_store.register_args([ 'background_remover_model', 'background_remover_color' ], scopes = [ 'api', 'cli' ])
|
||||
group_processors.add_argument('--background-remover-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'background_remover_model', 'modnet'), choices = background_remover_choices.background_remover_models)
|
||||
group_processors.add_argument('--background-remover-fill-color', help = translator.get('help.fill_color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_fill_color', '0 0 0 0'), nargs = '+')
|
||||
group_processors.add_argument('--background-remover-despill-color', help = translator.get('help.despill_color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_despill_color', '0 0 0 0'), nargs = '+')
|
||||
facefusion.jobs.job_store.register_step_keys([ 'background_remover_model', 'background_remover_fill_color', 'background_remover_despill_color' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('background_remover_model', args.get('background_remover_model'))
|
||||
apply_state_item('background_remover_color', normalize_color(args.get('background_remover_color')))
|
||||
apply_state_item('background_remover_fill_color', normalize_color(args.get('background_remover_fill_color')))
|
||||
apply_state_item('background_remover_despill_color', normalize_color(args.get('background_remover_despill_color')))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
|
||||
|
||||
@@ -444,30 +530,47 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def remove_background(temp_vision_frame : VisionFrame) -> Tuple[VisionFrame, Mask]:
|
||||
temp_vision_mask = forward(prepare_temp_frame(temp_vision_frame))
|
||||
temp_vision_mask = normalize_vision_mask(temp_vision_mask)
|
||||
temp_vision_mask = cv2.resize(temp_vision_mask, temp_vision_frame.shape[:2][::-1])
|
||||
temp_vision_frame = apply_background_color(temp_vision_frame, temp_vision_mask)
|
||||
return temp_vision_frame, temp_vision_mask
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
remove_vision_mask, remove_vision_frame = forward_corridor_key(prepare_temp_frame(temp_vision_frame))
|
||||
remove_vision_frame = numpy.squeeze(remove_vision_frame).transpose(1, 2, 0)
|
||||
remove_vision_frame = numpy.clip(remove_vision_frame * 255, 0, 255).astype(numpy.uint8)
|
||||
temp_vision_frame = cv2.resize(remove_vision_frame[:, :, ::-1], temp_vision_frame.shape[:2][::-1])
|
||||
else:
|
||||
remove_vision_mask = forward(prepare_temp_frame(temp_vision_frame))
|
||||
|
||||
remove_vision_mask = normalize_vision_mask(remove_vision_mask)
|
||||
remove_vision_mask = cv2.resize(remove_vision_mask, temp_vision_frame.shape[:2][::-1])
|
||||
temp_vision_frame = apply_despill_color(temp_vision_frame)
|
||||
temp_vision_frame = apply_fill_color(temp_vision_frame, remove_vision_mask)
|
||||
return temp_vision_frame, remove_vision_mask
|
||||
|
||||
|
||||
def forward(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
background_remover = get_inference_pool().get('background_remover')
|
||||
model_name = state_manager.get_item('background_remover_model')
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
with thread_semaphore():
|
||||
remove_vision_frame = background_remover.run(None,
|
||||
@@ -475,20 +578,42 @@ def forward(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
'input': temp_vision_frame
|
||||
})[0]
|
||||
|
||||
if model_name == 'u2net_cloth':
|
||||
if model_type == 'u2net_cloth':
|
||||
remove_vision_frame = numpy.argmax(remove_vision_frame, axis = 1)
|
||||
|
||||
return remove_vision_frame
|
||||
|
||||
|
||||
def forward_corridor_key(temp_vision_frame : VisionFrame) -> Tuple[Mask, VisionFrame]:
|
||||
background_remover = get_inference_pool().get('background_remover')
|
||||
|
||||
with thread_semaphore():
|
||||
remove_vision_mask, remove_vision_frame = background_remover.run(None,
|
||||
{
|
||||
'input': temp_vision_frame
|
||||
})
|
||||
|
||||
return remove_vision_mask, remove_vision_frame
|
||||
|
||||
|
||||
def prepare_temp_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_type = get_model_options().get('type')
|
||||
model_size = get_model_options().get('size')
|
||||
model_mean = get_model_options().get('mean')
|
||||
model_standard_deviation = get_model_options().get('standard_deviation')
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
coarse_color = temp_vision_frame[:, :, ::-1].astype(numpy.float32) / 255.0
|
||||
coarse_bias = coarse_color[:, :, 1] - numpy.maximum(coarse_color[:, :, 0], coarse_color[:, :, 2])
|
||||
coarse_vision_mask = cv2.resize(1.0 - numpy.clip(coarse_bias * 2.0, 0, 1), model_size)[:, :, numpy.newaxis]
|
||||
|
||||
temp_vision_frame = cv2.resize(temp_vision_frame, model_size)
|
||||
temp_vision_frame = temp_vision_frame[:, :, ::-1] / 255.0
|
||||
temp_vision_frame = (temp_vision_frame - model_mean) / model_standard_deviation
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
temp_vision_frame = numpy.concatenate([ temp_vision_frame, coarse_vision_mask ], axis = 2)
|
||||
|
||||
temp_vision_frame = temp_vision_frame.transpose(2, 0, 1)
|
||||
temp_vision_frame = numpy.expand_dims(temp_vision_frame, axis = 0).astype(numpy.float32)
|
||||
return temp_vision_frame
|
||||
@@ -500,16 +625,32 @@ def normalize_vision_mask(temp_vision_mask : Mask) -> Mask:
|
||||
return temp_vision_mask
|
||||
|
||||
|
||||
def apply_background_color(temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
|
||||
background_remover_color = state_manager.get_item('background_remover_color')
|
||||
def apply_fill_color(temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
|
||||
background_remover_fill_color = state_manager.get_item('background_remover_fill_color')
|
||||
temp_vision_mask = temp_vision_mask.astype(numpy.float32) / 255
|
||||
temp_vision_mask = numpy.expand_dims(temp_vision_mask, axis = 2)
|
||||
temp_vision_mask = (1 - temp_vision_mask) * background_remover_color[-1] / 255
|
||||
color_frame = numpy.zeros_like(temp_vision_frame)
|
||||
color_frame[:, :, 0] = background_remover_color[2]
|
||||
color_frame[:, :, 1] = background_remover_color[1]
|
||||
color_frame[:, :, 2] = background_remover_color[0]
|
||||
temp_vision_frame = temp_vision_frame * (1 - temp_vision_mask) + color_frame * temp_vision_mask
|
||||
temp_vision_mask = (1 - temp_vision_mask) * background_remover_fill_color[-1] / 255
|
||||
fill_vision_frame = numpy.zeros_like(temp_vision_frame)
|
||||
fill_vision_frame[:, :, 0] = background_remover_fill_color[2]
|
||||
fill_vision_frame[:, :, 1] = background_remover_fill_color[1]
|
||||
fill_vision_frame[:, :, 2] = background_remover_fill_color[0]
|
||||
temp_vision_frame = temp_vision_frame * (1 - temp_vision_mask) + fill_vision_frame * temp_vision_mask
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.uint8)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
def apply_despill_color(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
background_remover_despill_color = state_manager.get_item('background_remover_despill_color')
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.float32)
|
||||
color_alpha = background_remover_despill_color[3] / 255.0
|
||||
despill_vision_frame = numpy.zeros_like(temp_vision_frame)
|
||||
despill_vision_frame[:, :, 0] = background_remover_despill_color[2]
|
||||
despill_vision_frame[:, :, 1] = background_remover_despill_color[1]
|
||||
despill_vision_frame[:, :, 2] = background_remover_despill_color[0]
|
||||
color_weight = despill_vision_frame / numpy.maximum(numpy.max(background_remover_despill_color[:3]), 1)
|
||||
color_limit = numpy.roll(temp_vision_frame, 1, 2) + numpy.roll(temp_vision_frame, -1, 2)
|
||||
limit_vision_frame = numpy.minimum(temp_vision_frame, color_limit * 0.5)
|
||||
temp_vision_frame = temp_vision_frame + (limit_vision_frame - temp_vision_frame) * color_alpha * color_weight
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.uint8)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
@@ -0,0 +1,26 @@
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
'help':
|
||||
{
|
||||
'model': 'choose the model responsible for removing the background',
|
||||
'fill_color': 'apply red, green, blue and alpha values to the background',
|
||||
'despill_color': 'remove red, green, blue and alpha values from the foreground'
|
||||
},
|
||||
'uis':
|
||||
{
|
||||
'model_dropdown': 'BACKGROUND REMOVER MODEL',
|
||||
'fill_color_red_number': 'FILL COLOR RED',
|
||||
'fill_color_green_number': 'FILL COLOR GREEN',
|
||||
'fill_color_blue_number': 'FILL COLOR BLUE',
|
||||
'fill_color_alpha_number': 'FILL COLOR ALPHA',
|
||||
'despill_color_red_number': 'DESPILL COLOR RED',
|
||||
'despill_color_green_number': 'DESPILL COLOR GREEN',
|
||||
'despill_color_blue_number': 'DESPILL COLOR BLUE',
|
||||
'despill_color_alpha_number': 'DESPILL COLOR ALPHA'
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,21 +0,0 @@
|
||||
from facefusion.types import Locals
|
||||
|
||||
LOCALS : Locals =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
'help':
|
||||
{
|
||||
'model': 'choose the model responsible for removing the background',
|
||||
'color': 'apply red, green blue and alpha values to the background'
|
||||
},
|
||||
'uis':
|
||||
{
|
||||
'model_dropdown': 'BACKGROUND REMOVER MODEL',
|
||||
'color_red_number': 'BACKGROUND COLOR RED',
|
||||
'color_green_number': 'BACKGROUND COLOR GREEN',
|
||||
'color_blue_number': 'BACKGROUND COLOR BLUE',
|
||||
'color_alpha_number': 'BACKGROUND COLOR ALPHA'
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,12 +1,12 @@
|
||||
from typing import Literal, TypedDict
|
||||
from typing import List, Literal, TypedDict
|
||||
|
||||
from facefusion.types import Mask, VisionFrame
|
||||
|
||||
BackgroundRemoverInputs = TypedDict('BackgroundRemoverInputs',
|
||||
{
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
BackgroundRemoverModel = Literal['ben_2', 'birefnet_general', 'birefnet_portrait', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp']
|
||||
BackgroundRemoverModel = Literal['ben_2', 'birefnet_general', 'birefnet_portrait', 'corridor_key_1024', 'corridor_key_2048', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp']
|
||||
|
||||
@@ -1,28 +1,29 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache
|
||||
from typing import Tuple
|
||||
from types import ModuleType
|
||||
from typing import List, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
from cv2.typing import Size
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import create_int_metavar
|
||||
from facefusion.common_helper import create_int_metavar, get_middle
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_area_mask, create_box_mask, create_occlusion_mask, create_region_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import get_file_name, in_directory, is_image, is_video, resolve_file_paths, resolve_relative_path
|
||||
from facefusion.filesystem import get_file_name, in_directory, is_image, is_video, resolve_file_paths, resolve_relative_path, same_file_extension
|
||||
from facefusion.processors.modules.deep_swapper import choices as deep_swapper_choices
|
||||
from facefusion.processors.modules.deep_swapper.types import DeepSwapperInputs, DeepSwapperMorph
|
||||
from facefusion.processors.types import ProcessorOutputs
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.thread_helper import thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import conditional_match_frame_color, read_static_image, read_static_video_frame
|
||||
from facefusion.vision import conditional_match_frame_color, read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
@@ -278,7 +279,7 @@ def register_args(program : ArgumentParser) -> None:
|
||||
if group_processors:
|
||||
group_processors.add_argument('--deep-swapper-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'deep_swapper_model', 'iperov/elon_musk_224'), choices = deep_swapper_choices.deep_swapper_models)
|
||||
group_processors.add_argument('--deep-swapper-morph', help = translator.get('help.morph', __package__), type = int, default = config.get_int_value('processors', 'deep_swapper_morph', '100'), choices = deep_swapper_choices.deep_swapper_morph_range, metavar = create_int_metavar(deep_swapper_choices.deep_swapper_morph_range))
|
||||
facefusion.args_store.register_args([ 'deep_swapper_model', 'deep_swapper_morph' ], scopes = [ 'api', 'cli' ])
|
||||
facefusion.jobs.job_store.register_step_keys([ 'deep_swapper_model', 'deep_swapper_morph' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
@@ -286,10 +287,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('deep_swapper_morph', args.get('deep_swapper_morph'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
if model_hash_set and model_source_set:
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
return True
|
||||
@@ -302,22 +311,24 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -408,10 +419,13 @@ def prepare_crop_mask(crop_source_mask : Mask, crop_target_mask : Mask) -> Mask:
|
||||
|
||||
def process_frame(inputs : DeepSwapperInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Any, TypeAlias, TypedDict
|
||||
from typing import Any, List, TypeAlias, TypedDict
|
||||
|
||||
from numpy.typing import NDArray
|
||||
|
||||
@@ -7,7 +7,8 @@ from facefusion.types import Mask, VisionFrame
|
||||
DeepSwapperInputs = TypedDict('DeepSwapperInputs',
|
||||
{
|
||||
'reference_vision_frame' : VisionFrame,
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'source_vision_frames' : List[VisionFrame],
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.expression_restorer.types import ExpressionRestorerArea, ExpressionRestorerModel
|
||||
|
||||
expression_restorer_models : List[ExpressionRestorerModel] = [ 'live_portrait' ]
|
||||
expression_restorer_models : List[ExpressionRestorerModel] = list(get_args(ExpressionRestorerModel))
|
||||
|
||||
expression_restorer_areas : List[ExpressionRestorerArea] = [ 'upper-face', 'lower-face' ]
|
||||
expression_restorer_areas : List[ExpressionRestorerArea] = list(get_args(ExpressionRestorerArea))
|
||||
|
||||
expression_restorer_factor_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
@@ -1,20 +1,21 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache
|
||||
from typing import Tuple
|
||||
from types import ModuleType
|
||||
from typing import List, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import create_int_metavar
|
||||
from facefusion.common_helper import create_int_metavar, get_middle
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_box_mask, create_occlusion_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
from facefusion.processors.live_portrait import create_rotation, limit_expression
|
||||
from facefusion.processors.modules.expression_restorer import choices as expression_restorer_choices
|
||||
from facefusion.processors.modules.expression_restorer.types import ExpressionRestorerInputs
|
||||
@@ -22,7 +23,7 @@ from facefusion.processors.types import LivePortraitExpression, LivePortraitFeat
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import read_static_image, read_static_video_frame
|
||||
from facefusion.vision import read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
@@ -101,8 +102,8 @@ def register_args(program : ArgumentParser) -> None:
|
||||
if group_processors:
|
||||
group_processors.add_argument('--expression-restorer-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'expression_restorer_model', 'live_portrait'), choices = expression_restorer_choices.expression_restorer_models)
|
||||
group_processors.add_argument('--expression-restorer-factor', help = translator.get('help.factor', __package__), type = int, default = config.get_int_value('processors', 'expression_restorer_factor', '80'), choices = expression_restorer_choices.expression_restorer_factor_range, metavar = create_int_metavar(expression_restorer_choices.expression_restorer_factor_range))
|
||||
group_processors.add_argument('--expression-restorer-areas', help = translator.get('help.areas', __package__).format(choices = ', '.join(expression_restorer_choices.expression_restorer_areas)), default = config.get_str_list('processors', 'expression_restorer_areas', ' '.join(expression_restorer_choices.expression_restorer_areas)), choices = expression_restorer_choices.expression_restorer_areas, nargs ='+', metavar ='EXPRESSION_RESTORER_AREAS')
|
||||
facefusion.args_store.register_args([ 'expression_restorer_model', 'expression_restorer_factor', 'expression_restorer_areas' ], scopes = [ 'api', 'cli' ])
|
||||
group_processors.add_argument('--expression-restorer-areas', help = translator.get('help.areas', __package__).format(choices = ', '.join(expression_restorer_choices.expression_restorer_areas)), default = config.get_str_list('processors', 'expression_restorer_areas', ' '.join(expression_restorer_choices.expression_restorer_areas)), choices = expression_restorer_choices.expression_restorer_areas, nargs = '+', metavar = 'EXPRESSION_RESTORER_AREAS')
|
||||
facefusion.jobs.job_store.register_step_keys([ 'expression_restorer_model', 'expression_restorer_factor', 'expression_restorer_areas' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
@@ -111,10 +112,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('expression_restorer_areas', args.get('expression_restorer_areas'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
|
||||
|
||||
@@ -128,22 +137,24 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def restore_expression(target_face : Face, target_vision_frame : VisionFrame, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -189,12 +200,12 @@ def restrict_expression_areas(temp_expression : LivePortraitExpression, target_e
|
||||
expression_restorer_areas = state_manager.get_item('expression_restorer_areas')
|
||||
|
||||
if 'upper-face' not in expression_restorer_areas:
|
||||
target_expression[:, [1, 2, 6, 10, 11, 12, 13, 15, 16]] = temp_expression[:, [1, 2, 6, 10, 11, 12, 13, 15, 16]]
|
||||
target_expression[:, [ 1, 2, 6, 10, 11, 12, 13, 15, 16 ]] = temp_expression[:, [ 1, 2, 6, 10, 11, 12, 13, 15, 16 ]]
|
||||
|
||||
if 'lower-face' not in expression_restorer_areas:
|
||||
target_expression[:, [3, 7, 14, 17, 18, 19, 20]] = temp_expression[:, [3, 7, 14, 17, 18, 19, 20]]
|
||||
target_expression[:, [ 3, 7, 14, 17, 18, 19, 20 ]] = temp_expression[:, [ 3, 7, 14, 17, 18, 19, 20 ]]
|
||||
|
||||
target_expression[:, [0, 4, 5, 8, 9]] = temp_expression[:, [0, 4, 5, 8, 9]]
|
||||
target_expression[:, [ 0, 4, 5, 8, 9 ]] = temp_expression[:, [ 0, 4, 5, 8, 9 ]]
|
||||
return target_expression
|
||||
|
||||
|
||||
@@ -254,10 +265,13 @@ def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
|
||||
|
||||
def process_frame(inputs : ExpressionRestorerInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -6,7 +6,7 @@ ExpressionRestorerInputs = TypedDict('ExpressionRestorerInputs',
|
||||
{
|
||||
'reference_vision_frame' : VisionFrame,
|
||||
'source_vision_frames' : List[VisionFrame],
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from typing import List
|
||||
from typing import List, get_args
|
||||
|
||||
from facefusion.processors.modules.face_debugger.types import FaceDebuggerItem
|
||||
|
||||
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask' ]
|
||||
face_debugger_items : List[FaceDebuggerItem] = list(get_args(FaceDebuggerItem))
|
||||
|
||||
@@ -1,22 +1,25 @@
|
||||
from argparse import ArgumentParser
|
||||
from types import ModuleType
|
||||
from typing import List
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, state_manager, translator, video_manager
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.common_helper import get_middle
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_area_mask, create_box_mask, create_occlusion_mask, create_region_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import in_directory, is_image, is_video
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, same_file_extension
|
||||
from facefusion.processors.modules.face_debugger import choices as face_debugger_choices
|
||||
from facefusion.processors.modules.face_debugger.types import FaceDebuggerInputs
|
||||
from facefusion.processors.types import ProcessorOutputs
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.types import ApplyStateItem, Args, Face, InferencePool, ProcessMode, VisionFrame
|
||||
from facefusion.vision import read_static_image, read_static_video_frame
|
||||
from facefusion.vision import read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
def get_inference_pool() -> InferencePool:
|
||||
@@ -31,14 +34,21 @@ def register_args(program : ArgumentParser) -> None:
|
||||
group_processors = find_argument_group(program, 'processors')
|
||||
if group_processors:
|
||||
group_processors.add_argument('--face-debugger-items', help = translator.get('help.items', __package__).format(choices = ', '.join(face_debugger_choices.face_debugger_items)), default = config.get_str_list('processors', 'face_debugger_items', 'face-landmark-5/68 face-mask'), choices = face_debugger_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
|
||||
facefusion.args_store.register_args([ 'face_debugger_items' ], scopes = [ 'api', 'cli' ])
|
||||
facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('face_debugger_items', args.get('face_debugger_items'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
@@ -49,20 +59,21 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -91,21 +102,22 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
|
||||
|
||||
def draw_bounding_box(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
|
||||
box_color = 0, 0, 255
|
||||
border_color = 100, 100, 255
|
||||
bounding_box = target_face.bounding_box.astype(numpy.int32)
|
||||
x1, y1, x2, y2 = bounding_box
|
||||
box_color = 0, 0, 255
|
||||
border_scale = calculate_scale(temp_vision_frame)
|
||||
border_color = 100, 100, 255
|
||||
|
||||
cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), box_color, 2)
|
||||
cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), box_color, border_scale)
|
||||
|
||||
if target_face.angle == 0:
|
||||
cv2.line(temp_vision_frame, (x1, y1), (x2, y1), border_color, 3)
|
||||
cv2.line(temp_vision_frame, (x1, y1), (x2, y1), border_color, border_scale + 1)
|
||||
if target_face.angle == 180:
|
||||
cv2.line(temp_vision_frame, (x1, y2), (x2, y2), border_color, 3)
|
||||
cv2.line(temp_vision_frame, (x1, y2), (x2, y2), border_color, border_scale + 1)
|
||||
if target_face.angle == 90:
|
||||
cv2.line(temp_vision_frame, (x2, y1), (x2, y2), border_color, 3)
|
||||
cv2.line(temp_vision_frame, (x2, y1), (x2, y2), border_color, border_scale + 1)
|
||||
if target_face.angle == 270:
|
||||
cv2.line(temp_vision_frame, (x1, y1), (x1, y2), border_color, 3)
|
||||
cv2.line(temp_vision_frame, (x1, y1), (x1, y2), border_color, border_scale + 1)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -119,11 +131,15 @@ def draw_face_mask(target_face : Face, temp_vision_frame : VisionFrame) -> Visio
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5_68, 'arcface_128', (512, 512))
|
||||
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
|
||||
temp_size = temp_vision_frame.shape[:2][::-1]
|
||||
mask_scale = calculate_scale(temp_vision_frame)
|
||||
mask_color = 0, 255, 0
|
||||
|
||||
if numpy.array_equal(face_landmark_5, face_landmark_5_68):
|
||||
mask_color = 255, 255, 0
|
||||
|
||||
if target_face.origin == 'refill':
|
||||
mask_color = 0, 165, 255
|
||||
|
||||
if 'box' in state_manager.get_item('face_mask_types'):
|
||||
box_mask = create_box_mask(crop_vision_frame, 0, state_manager.get_item('face_mask_padding'))
|
||||
crop_masks.append(box_mask)
|
||||
@@ -146,7 +162,7 @@ def draw_face_mask(target_face : Face, temp_vision_frame : VisionFrame) -> Visio
|
||||
inverse_vision_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_size)
|
||||
inverse_vision_frame = cv2.threshold(inverse_vision_frame, 100, 255, cv2.THRESH_BINARY)[1]
|
||||
inverse_contours, _ = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
|
||||
cv2.drawContours(temp_vision_frame, inverse_contours, -1, mask_color, 2)
|
||||
cv2.drawContours(temp_vision_frame, inverse_contours, -1, mask_color, mask_scale)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -154,13 +170,17 @@ def draw_face_mask(target_face : Face, temp_vision_frame : VisionFrame) -> Visio
|
||||
def draw_face_landmark_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
|
||||
face_landmark_5 = target_face.landmark_set.get('5')
|
||||
point_scale = calculate_scale(temp_vision_frame)
|
||||
point_color = 0, 0, 255
|
||||
|
||||
if target_face.origin == 'refill':
|
||||
point_color = 0, 165, 255
|
||||
|
||||
if numpy.any(face_landmark_5):
|
||||
face_landmark_5 = face_landmark_5.astype(numpy.int32)
|
||||
|
||||
for point in face_landmark_5:
|
||||
cv2.circle(temp_vision_frame, tuple(point), 3, point_color, -1)
|
||||
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -169,16 +189,20 @@ def draw_face_landmark_5_68(target_face : Face, temp_vision_frame : VisionFrame)
|
||||
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
|
||||
face_landmark_5 = target_face.landmark_set.get('5')
|
||||
face_landmark_5_68 = target_face.landmark_set.get('5/68')
|
||||
point_scale = calculate_scale(temp_vision_frame)
|
||||
point_color = 0, 255, 0
|
||||
|
||||
if numpy.array_equal(face_landmark_5, face_landmark_5_68):
|
||||
point_color = 255, 255, 0
|
||||
|
||||
if target_face.origin == 'refill':
|
||||
point_color = 0, 165, 255
|
||||
|
||||
if numpy.any(face_landmark_5_68):
|
||||
face_landmark_5_68 = face_landmark_5_68.astype(numpy.int32)
|
||||
|
||||
for point in face_landmark_5_68:
|
||||
cv2.circle(temp_vision_frame, tuple(point), 3, point_color, -1)
|
||||
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -187,16 +211,20 @@ def draw_face_landmark_68(target_face : Face, temp_vision_frame : VisionFrame) -
|
||||
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
|
||||
face_landmark_68 = target_face.landmark_set.get('68')
|
||||
face_landmark_68_5 = target_face.landmark_set.get('68/5')
|
||||
point_scale = calculate_scale(temp_vision_frame)
|
||||
point_color = 0, 255, 0
|
||||
|
||||
if numpy.array_equal(face_landmark_68, face_landmark_68_5):
|
||||
point_color = 255, 255, 0
|
||||
|
||||
if target_face.origin == 'refill':
|
||||
point_color = 0, 165, 255
|
||||
|
||||
if numpy.any(face_landmark_68):
|
||||
face_landmark_68 = face_landmark_68.astype(numpy.int32)
|
||||
|
||||
for point in face_landmark_68:
|
||||
cv2.circle(temp_vision_frame, tuple(point), 3, point_color, -1)
|
||||
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -204,23 +232,36 @@ def draw_face_landmark_68(target_face : Face, temp_vision_frame : VisionFrame) -
|
||||
def draw_face_landmark_68_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
|
||||
face_landmark_68_5 = target_face.landmark_set.get('68/5')
|
||||
point_scale = calculate_scale(temp_vision_frame)
|
||||
point_color = 255, 255, 0
|
||||
|
||||
if target_face.origin == 'refill':
|
||||
point_color = 0, 165, 255
|
||||
|
||||
if numpy.any(face_landmark_68_5):
|
||||
face_landmark_68_5 = face_landmark_68_5.astype(numpy.int32)
|
||||
|
||||
for point in face_landmark_68_5:
|
||||
cv2.circle(temp_vision_frame, tuple(point), 3, point_color, -1)
|
||||
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
|
||||
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
def calculate_scale(temp_vision_frame : VisionFrame) -> int:
|
||||
frame_height, _ = temp_vision_frame.shape[:2]
|
||||
frame_scale = round(frame_height / 270)
|
||||
return max(1, min(10, frame_scale))
|
||||
|
||||
|
||||
def process_frame(inputs : FaceDebuggerInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
@@ -228,5 +269,3 @@ def process_frame(inputs : FaceDebuggerInputs) -> ProcessorOutputs:
|
||||
temp_vision_frame = debug_face(target_face, temp_vision_frame)
|
||||
|
||||
return temp_vision_frame, temp_vision_mask
|
||||
|
||||
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -1,11 +1,12 @@
|
||||
from typing import Literal, TypedDict
|
||||
from typing import List, Literal, TypedDict
|
||||
|
||||
from facefusion.types import Mask, VisionFrame
|
||||
|
||||
FaceDebuggerInputs = TypedDict('FaceDebuggerInputs',
|
||||
{
|
||||
'reference_vision_frame' : VisionFrame,
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'source_vision_frames' : List[VisionFrame],
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range
|
||||
from facefusion.processors.modules.face_editor.types import FaceEditorModel
|
||||
|
||||
face_editor_models : List[FaceEditorModel] = [ 'live_portrait' ]
|
||||
face_editor_models : List[FaceEditorModel] = list(get_args(FaceEditorModel))
|
||||
|
||||
face_editor_eyebrow_direction_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
|
||||
face_editor_eye_gaze_horizontal_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
|
||||
|
||||
@@ -1,20 +1,21 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache
|
||||
from typing import Tuple
|
||||
from types import ModuleType
|
||||
from typing import List, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import create_float_metavar
|
||||
from facefusion.common_helper import create_float_metavar, get_middle
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import paste_back, scale_face_landmark_5, warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_box_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
from facefusion.processors.live_portrait import create_rotation, limit_angle, limit_expression
|
||||
from facefusion.processors.modules.face_editor import choices as face_editor_choices
|
||||
from facefusion.processors.modules.face_editor.types import FaceEditorInputs
|
||||
@@ -22,7 +23,7 @@ from facefusion.processors.types import LivePortraitExpression, LivePortraitFeat
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.thread_helper import conditional_thread_semaphore, thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, FaceLandmark68, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import read_static_image, read_static_video_frame
|
||||
from facefusion.vision import read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
@@ -144,7 +145,7 @@ def register_args(program : ArgumentParser) -> None:
|
||||
group_processors.add_argument('--face-editor-head-pitch', help = translator.get('help.head_pitch', __package__), type = float, default = config.get_float_value('processors', 'face_editor_head_pitch', '0'), choices = face_editor_choices.face_editor_head_pitch_range, metavar = create_float_metavar(face_editor_choices.face_editor_head_pitch_range))
|
||||
group_processors.add_argument('--face-editor-head-yaw', help = translator.get('help.head_yaw', __package__), type = float, default = config.get_float_value('processors', 'face_editor_head_yaw', '0'), choices = face_editor_choices.face_editor_head_yaw_range, metavar = create_float_metavar(face_editor_choices.face_editor_head_yaw_range))
|
||||
group_processors.add_argument('--face-editor-head-roll', help = translator.get('help.head_roll', __package__), type = float, default = config.get_float_value('processors', 'face_editor_head_roll', '0'), choices = face_editor_choices.face_editor_head_roll_range, metavar = create_float_metavar(face_editor_choices.face_editor_head_roll_range))
|
||||
facefusion.args_store.register_args([ 'face_editor_model', 'face_editor_eyebrow_direction', 'face_editor_eye_gaze_horizontal', 'face_editor_eye_gaze_vertical', 'face_editor_eye_open_ratio', 'face_editor_lip_open_ratio', 'face_editor_mouth_grim', 'face_editor_mouth_pout', 'face_editor_mouth_purse', 'face_editor_mouth_smile', 'face_editor_mouth_position_horizontal', 'face_editor_mouth_position_vertical', 'face_editor_head_pitch', 'face_editor_head_yaw', 'face_editor_head_roll' ], scopes = [ 'api', 'cli' ])
|
||||
facefusion.jobs.job_store.register_step_keys([ 'face_editor_model', 'face_editor_eyebrow_direction', 'face_editor_eye_gaze_horizontal', 'face_editor_eye_gaze_vertical', 'face_editor_eye_open_ratio', 'face_editor_lip_open_ratio', 'face_editor_mouth_grim', 'face_editor_mouth_pout', 'face_editor_mouth_purse', 'face_editor_mouth_smile', 'face_editor_mouth_position_horizontal', 'face_editor_mouth_position_vertical', 'face_editor_head_pitch', 'face_editor_head_yaw', 'face_editor_head_roll' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
@@ -165,10 +166,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('face_editor_head_roll', args.get('face_editor_head_roll'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
|
||||
|
||||
@@ -179,22 +188,24 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def edit_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -483,10 +494,13 @@ def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
|
||||
|
||||
def process_frame(inputs : FaceEditorInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
from facefusion.types import Locals
|
||||
from facefusion.types import Locales
|
||||
|
||||
LOCALS : Locals =\
|
||||
LOCALES : Locales =\
|
||||
{
|
||||
'en':
|
||||
{
|
||||
@@ -1,11 +1,12 @@
|
||||
from typing import Literal, TypedDict
|
||||
from typing import List, Literal, TypedDict
|
||||
|
||||
from facefusion.types import Mask, VisionFrame
|
||||
|
||||
FaceEditorInputs = TypedDict('FaceEditorInputs',
|
||||
{
|
||||
'reference_vision_frame' : VisionFrame,
|
||||
'target_vision_frame' : VisionFrame,
|
||||
'source_vision_frames' : List[VisionFrame],
|
||||
'target_vision_frames' : List[VisionFrame],
|
||||
'temp_vision_frame' : VisionFrame,
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range, create_int_range
|
||||
from facefusion.processors.modules.face_enhancer.types import FaceEnhancerModel
|
||||
|
||||
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus' ]
|
||||
face_enhancer_models : List[FaceEnhancerModel] = list(get_args(FaceEnhancerModel))
|
||||
|
||||
face_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
|
||||
@@ -1,25 +1,27 @@
|
||||
from argparse import ArgumentParser
|
||||
from functools import lru_cache
|
||||
from types import ModuleType
|
||||
from typing import List
|
||||
|
||||
import numpy
|
||||
|
||||
import facefusion.args_store
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import create_float_metavar, create_int_metavar
|
||||
from facefusion.common_helper import create_float_metavar, create_int_metavar, get_middle
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.face_analyser import scale_face
|
||||
from facefusion.face_creator import scale_face
|
||||
from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
|
||||
from facefusion.face_masker import create_box_mask, create_occlusion_mask
|
||||
from facefusion.face_selector import select_faces
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
from facefusion.processors.modules.face_enhancer import choices as face_enhancer_choices
|
||||
from facefusion.processors.modules.face_enhancer.types import FaceEnhancerInputs, FaceEnhancerWeight
|
||||
from facefusion.processors.types import ProcessorOutputs
|
||||
from facefusion.program_helper import find_argument_group
|
||||
from facefusion.thread_helper import thread_semaphore
|
||||
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
|
||||
from facefusion.vision import blend_frame, read_static_image, read_static_video_frame
|
||||
from facefusion.vision import blend_frame, read_static_image, read_static_video_chunk, read_static_video_frame
|
||||
|
||||
|
||||
@lru_cache()
|
||||
@@ -295,7 +297,7 @@ def register_args(program : ArgumentParser) -> None:
|
||||
group_processors.add_argument('--face-enhancer-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'face_enhancer_model', 'gfpgan_1.4'), choices = face_enhancer_choices.face_enhancer_models)
|
||||
group_processors.add_argument('--face-enhancer-blend', help = translator.get('help.blend', __package__), type = int, default = config.get_int_value('processors', 'face_enhancer_blend', '80'), choices = face_enhancer_choices.face_enhancer_blend_range, metavar = create_int_metavar(face_enhancer_choices.face_enhancer_blend_range))
|
||||
group_processors.add_argument('--face-enhancer-weight', help = translator.get('help.weight', __package__), type = float, default = config.get_float_value('processors', 'face_enhancer_weight', '0.5'), choices = face_enhancer_choices.face_enhancer_weight_range, metavar = create_float_metavar(face_enhancer_choices.face_enhancer_weight_range))
|
||||
facefusion.args_store.register_args([ 'face_enhancer_model', 'face_enhancer_blend', 'face_enhancer_weight' ], scopes = [ 'api', 'cli' ])
|
||||
facefusion.jobs.job_store.register_step_keys([ 'face_enhancer_model', 'face_enhancer_blend', 'face_enhancer_weight' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
@@ -304,10 +306,18 @@ def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('face_enhancer_weight', args.get('face_enhancer_weight'))
|
||||
|
||||
|
||||
def get_common_modules() -> List[ModuleType]:
|
||||
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_hash_set = get_model_options().get('hashes')
|
||||
model_source_set = get_model_options().get('sources')
|
||||
|
||||
for common_module in get_common_modules():
|
||||
if not common_module.pre_check():
|
||||
return False
|
||||
|
||||
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
|
||||
|
||||
|
||||
@@ -318,22 +328,24 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
|
||||
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
read_static_image.cache_clear()
|
||||
read_static_video_frame.cache_clear()
|
||||
read_static_video_chunk.cache_clear()
|
||||
video_manager.clear_video_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
||||
clear_inference_pool()
|
||||
|
||||
if state_manager.get_item('video_memory_strategy') == 'strict':
|
||||
content_analyser.clear_inference_pool()
|
||||
face_classifier.clear_inference_pool()
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_masker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
for common_module in get_common_modules():
|
||||
common_module.clear_inference_pool()
|
||||
|
||||
|
||||
def enhance_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
@@ -410,10 +422,13 @@ def blend_paste_frame(temp_vision_frame : VisionFrame, paste_vision_frame : Visi
|
||||
|
||||
def process_frame(inputs : FaceEnhancerInputs) -> ProcessorOutputs:
|
||||
reference_vision_frame = inputs.get('reference_vision_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
source_vision_frames = inputs.get('source_vision_frames')
|
||||
target_vision_frames = inputs.get('target_vision_frames')
|
||||
temp_vision_frame = inputs.get('temp_vision_frame')
|
||||
temp_vision_mask = inputs.get('temp_vision_mask')
|
||||
target_faces = select_faces(reference_vision_frame, target_vision_frame)
|
||||
|
||||
target_vision_frame = get_middle(target_vision_frames)
|
||||
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
|
||||
|
||||
if target_faces:
|
||||
for target_face in target_faces:
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user