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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)
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