Delete roop/processors/frame/face_enhancer.py

roop/processors/frame/face_enhancer.py

@@ -1,104 +0,0 @@
-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')
-            # 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://huggingface.co/th2w33knd/GFPGANv1.4/resolve/main/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)