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import os
import sys
import importlib
import psutil
from concurrent.futures import ThreadPoolExecutor, as_completed
from queue import Queue
from types import ModuleType
from typing import Any, List, Callable
from tqdm import tqdm
import DeepFakeAI.globals
from DeepFakeAI import wording
FRAME_PROCESSORS_MODULES : List[ModuleType] = []
FRAME_PROCESSORS_METHODS =\
[
'get_frame_processor',
'clear_frame_processor',
'pre_check',
'pre_process',
'process_frame',
'process_frames',
'process_image',
'process_video',
'post_process'
]
def load_frame_processor_module(frame_processor : str) -> Any:
try:
frame_processor_module = importlib.import_module('DeepFakeAI.processors.frame.modules.' + frame_processor)
for method_name in FRAME_PROCESSORS_METHODS:
if not hasattr(frame_processor_module, method_name):
raise NotImplementedError
except ModuleNotFoundError:
sys.exit(wording.get('frame_processor_not_loaded').format(frame_processor = frame_processor))
except NotImplementedError:
sys.exit(wording.get('frame_processor_not_implemented').format(frame_processor = frame_processor))
return frame_processor_module
def get_frame_processors_modules(frame_processors : List[str]) -> List[ModuleType]:
global FRAME_PROCESSORS_MODULES
if not FRAME_PROCESSORS_MODULES:
for frame_processor in frame_processors:
frame_processor_module = load_frame_processor_module(frame_processor)
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
return FRAME_PROCESSORS_MODULES
def clear_frame_processors_modules() -> None:
global FRAME_PROCESSORS_MODULES
for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
frame_processor_module.clear_frame_processor()
FRAME_PROCESSORS_MODULES = []
def multi_process_frame(source_path : str, temp_frame_paths : List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
with ThreadPoolExecutor(max_workers = DeepFakeAI.globals.execution_thread_count) as executor:
futures = []
queue = create_queue(temp_frame_paths)
queue_per_future = max(len(temp_frame_paths) // DeepFakeAI.globals.execution_thread_count * DeepFakeAI.globals.execution_queue_count, 1)
while not queue.empty():
future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
futures.append(future)
for future in as_completed(futures):
future.result()
def create_queue(temp_frame_paths : List[str]) -> Queue[str]:
queue: Queue[str] = Queue()
for frame_path in temp_frame_paths:
queue.put(frame_path)
return queue
def pick_queue(queue : Queue[str], queue_per_future : int) -> List[str]:
queues = []
for _ in range(queue_per_future):
if not queue.empty():
queues.append(queue.get())
return queues
def process_video(source_path : str, frame_paths : List[str], process_frames : Callable[[str, List[str], Any], None]) -> None:
progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
total = len(frame_paths)
with tqdm(total = total, desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
def update_progress(progress : Any = None) -> None:
process = psutil.Process(os.getpid())
memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
progress.set_postfix(
{
'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
'execution_providers': DeepFakeAI.globals.execution_providers,
'execution_thread_count': DeepFakeAI.globals.execution_thread_count,
'execution_queue_count': DeepFakeAI.globals.execution_queue_count
})
progress.refresh()
progress.update(1)
def get_device() -> str:
if 'CUDAExecutionProvider' in DeepFakeAI.globals.execution_providers:
return 'cuda'
if 'CoreMLExecutionProvider' in DeepFakeAI.globals.execution_providers:
return 'mps'
return 'cpu'
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