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