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roop/processors/roop_processors_frame_core.py

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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 roop
+
+FRAME_PROCESSORS_MODULES: List[ModuleType] = []
+FRAME_PROCESSORS_INTERFACE = [
+    'pre_check',
+    'pre_start',
+    '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(f'roop.processors.frame.{frame_processor}')
+        for method_name in FRAME_PROCESSORS_INTERFACE:
+            if not hasattr(frame_processor_module, method_name):
+                raise NotImplementedError
+    except ModuleNotFoundError:
+        sys.exit(f'Frame processor {frame_processor} not found.')
+    except NotImplementedError:
+        sys.exit(f'Frame processor {frame_processor} not implemented correctly.')
+    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 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=roop.globals.execution_threads) as executor:
+        futures = []
+        queue = create_queue(temp_frame_paths)
+        queue_per_future = max(len(temp_frame_paths) // roop.globals.execution_threads, 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='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': roop.globals.execution_providers,
+        'execution_threads': roop.globals.execution_threads
+    })
+    progress.refresh()
+    progress.update(1)

roop/processors/roop_processors_frame_face_enhancer.py

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+from typing import Any, List, Callable
+import cv2
+import threading
+from gfpgan.utils import GFPGANer
+
+import roop.globals
+import roop.processors.frame.core
+from roop.core import update_status
+from roop.face_analyser import get_many_faces
+from roop.typing import Frame, Face
+from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
+
+FACE_ENHANCER = None
+THREAD_SEMAPHORE = threading.Semaphore()
+THREAD_LOCK = threading.Lock()
+NAME = 'ROOP.FACE-ENHANCER'
+
+
+def get_face_enhancer() -> Any:
+    global FACE_ENHANCER
+
+    with THREAD_LOCK:
+        if FACE_ENHANCER is None:
+            model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
+            # todo: set models path -> https://github.com/TencentARC/GFPGAN/issues/399
+            FACE_ENHANCER = GFPGANer(model_path=model_path, upscale=1, device=get_device())
+    return FACE_ENHANCER
+
+
+def get_device() -> str:
+    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
+        return 'cuda'
+    if 'CoreMLExecutionProvider' in roop.globals.execution_providers:
+        return 'mps'
+    return 'cpu'
+
+
+def clear_face_enhancer() -> None:
+    global FACE_ENHANCER
+
+    FACE_ENHANCER = None
+
+
+def pre_check() -> bool:
+    download_directory_path = resolve_relative_path('../models')
+    conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
+        return False
+    return True
+
+
+def post_process() -> None:
+    clear_face_enhancer()
+
+
+def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
+    start_x, start_y, end_x, end_y = map(int, target_face['bbox'])
+    padding_x = int((end_x - start_x) * 0.5)
+    padding_y = int((end_y - start_y) * 0.5)
+    start_x = max(0, start_x - padding_x)
+    start_y = max(0, start_y - padding_y)
+    end_x = max(0, end_x + padding_x)
+    end_y = max(0, end_y + padding_y)
+    temp_face = temp_frame[start_y:end_y, start_x:end_x]
+    if temp_face.size:
+        with THREAD_SEMAPHORE:
+            _, _, temp_face = get_face_enhancer().enhance(
+                temp_face,
+                paste_back=True
+            )
+        temp_frame[start_y:end_y, start_x:end_x] = temp_face
+    return temp_frame
+
+
+def process_frame(source_face: Face, reference_face: Face, temp_frame: Frame) -> Frame:
+    many_faces = get_many_faces(temp_frame)
+    if many_faces:
+        for target_face in many_faces:
+            temp_frame = enhance_face(target_face, temp_frame)
+    return temp_frame
+
+
+def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        result = process_frame(None, None, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if update:
+            update()
+
+
+def process_image(source_path: str, target_path: str, output_path: str) -> None:
+    target_frame = cv2.imread(target_path)
+    result = process_frame(None, None, target_frame)
+    cv2.imwrite(output_path, result)
+
+
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)

roop/processors/roop_processors_frame_face_swapper.py

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+from typing import Any, List, Callable
+import cv2
+import insightface
+import threading
+
+import roop.globals
+import roop.processors.frame.core
+from roop.core import update_status
+from roop.face_analyser import get_one_face, get_many_faces, find_similar_face
+from roop.face_reference import get_face_reference, set_face_reference, clear_face_reference
+from roop.typing import Face, Frame
+from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
+
+FACE_SWAPPER = None
+THREAD_LOCK = threading.Lock()
+NAME = 'ROOP.FACE-SWAPPER'
+
+
+def get_face_swapper() -> Any:
+    global FACE_SWAPPER
+
+    with THREAD_LOCK:
+        if FACE_SWAPPER is None:
+            model_path = resolve_relative_path('../models/inswapper_128.onnx')
+            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
+    return FACE_SWAPPER
+
+
+def clear_face_swapper() -> None:
+    global FACE_SWAPPER
+
+    FACE_SWAPPER = None
+
+
+def pre_check() -> bool:
+    download_directory_path = resolve_relative_path('../models')
+    conditional_download(download_directory_path, ['https://huggingface.co/CountFloyd/deepfake/resolve/main/inswapper_128.onnx'])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(roop.globals.source_path):
+        update_status('Select an image for source path.', NAME)
+        return False
+    elif not get_one_face(cv2.imread(roop.globals.source_path)):
+        update_status('No face in source path detected.', NAME)
+        return False
+    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
+        return False
+    return True
+
+
+def post_process() -> None:
+    clear_face_swapper()
+    clear_face_reference()
+
+
+def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
+    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
+
+
+def process_frame(source_face: Face, reference_face: Face, temp_frame: Frame) -> Frame:
+    if roop.globals.many_faces:
+        many_faces = get_many_faces(temp_frame)
+        if many_faces:
+            for target_face in many_faces:
+                temp_frame = swap_face(source_face, target_face, temp_frame)
+    else:
+        target_face = find_similar_face(temp_frame, reference_face)
+        if target_face:
+            temp_frame = swap_face(source_face, target_face, temp_frame)
+    return temp_frame
+
+
+def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
+    source_face = get_one_face(cv2.imread(source_path))
+    reference_face = None if roop.globals.many_faces else get_face_reference()
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = cv2.imread(temp_frame_path)
+        result = process_frame(source_face, reference_face, temp_frame)
+        cv2.imwrite(temp_frame_path, result)
+        if update:
+            update()
+
+
+def process_image(source_path: str, target_path: str, output_path: str) -> None:
+    source_face = get_one_face(cv2.imread(source_path))
+    target_frame = cv2.imread(target_path)
+    reference_face = None if roop.globals.many_faces else get_one_face(target_frame, roop.globals.reference_face_position)
+    result = process_frame(source_face, reference_face, target_frame)
+    cv2.imwrite(output_path, result)
+
+
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    if not roop.globals.many_faces and not get_face_reference():
+        reference_frame = cv2.imread(temp_frame_paths[roop.globals.reference_frame_number])
+        reference_face = get_one_face(reference_frame, roop.globals.reference_face_position)
+        set_face_reference(reference_face)
+    roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)