Upload roop_processors_frame_face_enhancer.py

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)