mirror of
https://github.com/facefusion/facefusion.git
synced 2026-06-13 07:47:48 +02:00
Next (#107)
* Allow passing the onnxruntime to install.py * Fix CI * Disallow none execution providers in the UI * Use CV2 to detect fps * Respect trim on videos with audio * Respect trim on videos with audio (finally) * Implement caching to speed up preview and webcam * Define webcam UI and webcam performance * Remove layout from components * Add primary buttons * Extract benchmark and webcam settings * Introduce compact UI settings * Caching for IO and **** prediction * Caching for IO and **** prediction * Introduce face analyser caching * Fix some typing * Improve setup for benchmark * Clear image cache via post process * Fix settings in UI, Simplify restore_audio() using shortest * Select resolution and fps via webcam ui * Introduce read_static_image() to stop caching temp images * Use DirectShow under Windows * Multi-threading for webcam * Fix typing * Refactor frame processor * Refactor webcam processing * Avoid warnings due capture.isOpened() * Resume downloads (#110) * Introduce resumable downloads * Fix CURL commands * Break execution_settings into pieces * Cosmetic changes on cv2 webcam * Update Gradio * Move face cache to own file * Uniform namings for threading * Fix sorting of get_temp_frame_paths(), extend get_temp_frames_pattern() * Minor changes from the review * Looks stable to tme * Update the disclaimer * Update the disclaimer * Update the disclaimer
This commit is contained in:
Henry Ruhs
@@ -57,16 +57,19 @@ def clear_frame_processors_modules() -> None:
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FRAME_PROCESSORS_MODULES = []
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def multi_process_frame(source_path : str, temp_frame_paths : List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
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with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
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futures = []
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queue = create_queue(temp_frame_paths)
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queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
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while not queue.empty():
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future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
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futures.append(future)
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for future in as_completed(futures):
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future.result()
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def multi_process_frames(source_path : str, temp_frame_paths : List[str], process_frames : Callable[[str, List[str], Callable[[], None]], None]) -> None:
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progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
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with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
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with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
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futures = []
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queue_temp_frame_paths : Queue[str] = create_queue(temp_frame_paths)
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queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
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while not queue_temp_frame_paths.empty():
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payload_temp_frame_paths = pick_queue(queue_temp_frame_paths, queue_per_future)
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future = executor.submit(process_frames, source_path, payload_temp_frame_paths, lambda: update_progress(progress))
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futures.append(future)
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for future_done in as_completed(futures):
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future_done.result()
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def create_queue(temp_frame_paths : List[str]) -> Queue[str]:
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@@ -84,13 +87,6 @@ def pick_queue(queue : Queue[str], queue_per_future : int) -> List[str]:
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return queues
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def process_video(source_path : str, frame_paths : List[str], process_frames : Callable[[str, List[str], Any], None]) -> None:
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progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
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total = len(frame_paths)
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with tqdm(total = total, desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
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multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
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def update_progress(progress : Any = None) -> None:
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process = psutil.Process(os.getpid())
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memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
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@@ -1,5 +1,4 @@
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from typing import Any, List, Callable
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import cv2
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import threading
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from gfpgan.utils import GFPGANer
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@@ -9,10 +8,11 @@ from facefusion.core import update_status
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from facefusion.face_analyser import get_many_faces
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from facefusion.typing import Frame, Face, ProcessMode
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from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video
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from facefusion.vision import read_image, read_static_image, write_image
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FRAME_PROCESSOR = None
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THREAD_SEMAPHORE = threading.Semaphore()
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THREAD_LOCK = threading.Lock()
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THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
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THREAD_LOCK : threading.Lock = threading.Lock()
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NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'
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@@ -54,6 +54,7 @@ def pre_process(mode : ProcessMode) -> bool:
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def post_process() -> None:
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clear_frame_processor()
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read_static_image.cache_clear()
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def enhance_face(target_face : Face, temp_frame : Frame) -> Frame:
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@@ -83,20 +84,19 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
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return temp_frame
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def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
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def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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temp_frame = read_image(temp_frame_path)
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result_frame = process_frame(None, None, temp_frame)
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cv2.imwrite(temp_frame_path, result_frame)
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if update:
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update()
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write_image(temp_frame_path, result_frame)
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update_progress()
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def process_image(source_path : str, target_path : str, output_path : str) -> None:
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target_frame = cv2.imread(target_path)
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target_frame = read_static_image(target_path)
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result_frame = process_frame(None, None, target_frame)
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cv2.imwrite(output_path, result_frame)
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write_image(output_path, result_frame)
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
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facefusion.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
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facefusion.processors.frame.core.multi_process_frames(None, temp_frame_paths, process_frames)
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@@ -1,5 +1,4 @@
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from typing import Any, List, Callable
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import cv2
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import insightface
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import threading
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@@ -11,9 +10,10 @@ from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_
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from facefusion.face_reference import get_face_reference, set_face_reference
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from facefusion.typing import Face, Frame, ProcessMode
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from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video
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from facefusion.vision import read_image, read_static_image, write_image
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FRAME_PROCESSOR = None
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THREAD_LOCK = threading.Lock()
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THREAD_LOCK : threading.Lock = threading.Lock()
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NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
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@@ -43,7 +43,7 @@ def pre_process(mode : ProcessMode) -> bool:
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if not is_image(facefusion.globals.source_path):
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update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
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return False
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elif not get_one_face(cv2.imread(facefusion.globals.source_path)):
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elif not get_one_face(read_static_image(facefusion.globals.source_path)):
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update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
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return False
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if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
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@@ -56,6 +56,7 @@ def pre_process(mode : ProcessMode) -> bool:
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def post_process() -> None:
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clear_frame_processor()
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read_static_image.cache_clear()
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def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
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@@ -76,32 +77,31 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
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return temp_frame
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def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
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source_face = get_one_face(cv2.imread(source_path))
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def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
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source_face = get_one_face(read_static_image(source_path))
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reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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temp_frame = read_image(temp_frame_path)
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result_frame = process_frame(source_face, reference_face, temp_frame)
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cv2.imwrite(temp_frame_path, result_frame)
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if update:
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update()
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write_image(temp_frame_path, result_frame)
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update_progress()
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def process_image(source_path : str, target_path : str, output_path : str) -> None:
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source_face = get_one_face(cv2.imread(source_path))
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target_frame = cv2.imread(target_path)
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source_face = get_one_face(read_static_image(source_path))
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target_frame = read_static_image(target_path)
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reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_recognition else None
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result_frame = process_frame(source_face, reference_face, target_frame)
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cv2.imwrite(output_path, result_frame)
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write_image(output_path, result_frame)
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
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conditional_set_face_reference(temp_frame_paths)
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frame_processors.process_video(source_path, temp_frame_paths, process_frames)
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frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
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def conditional_set_face_reference(temp_frame_paths : List[str]) -> None:
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if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
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reference_frame = cv2.imread(temp_frame_paths[facefusion.globals.reference_frame_number])
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reference_frame = read_static_image(temp_frame_paths[facefusion.globals.reference_frame_number])
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reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
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set_face_reference(reference_face)
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@@ -1,5 +1,4 @@
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from typing import Any, List, Callable
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import cv2
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import threading
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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@@ -10,10 +9,11 @@ from facefusion import wording, utilities
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from facefusion.core import update_status
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from facefusion.typing import Frame, Face, ProcessMode
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from facefusion.utilities import conditional_download, resolve_relative_path
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from facefusion.vision import read_image, read_static_image, write_image
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FRAME_PROCESSOR = None
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THREAD_SEMAPHORE = threading.Semaphore()
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THREAD_LOCK = threading.Lock()
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THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
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THREAD_LOCK : threading.Lock = threading.Lock()
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NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
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@@ -63,6 +63,7 @@ def pre_process(mode : ProcessMode) -> bool:
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def post_process() -> None:
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clear_frame_processor()
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read_static_image.cache_clear()
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def enhance_frame(temp_frame : Frame) -> Frame:
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@@ -75,20 +76,19 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
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return enhance_frame(temp_frame)
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def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
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def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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temp_frame = read_image(temp_frame_path)
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result_frame = process_frame(None, None, temp_frame)
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cv2.imwrite(temp_frame_path, result_frame)
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if update:
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update()
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write_image(temp_frame_path, result_frame)
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update_progress()
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def process_image(source_path : str, target_path : str, output_path : str) -> None:
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target_frame = cv2.imread(target_path)
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target_frame = read_static_image(target_path)
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result = process_frame(None, None, target_frame)
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cv2.imwrite(output_path, result)
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write_image(output_path, result)
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
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frame_processors.process_video(None, temp_frame_paths, process_frames)
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frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)
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