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from typing import Any, List, Callable
import cv2
import threading
from gfpgan.utils import GFPGANer
import DeepFakeAI.globals
import DeepFakeAI.processors.frame.core as frame_processors
from DeepFakeAI import wording
from DeepFakeAI.core import update_status
from DeepFakeAI.face_analyser import get_many_faces
from DeepFakeAI.typing import Frame, Face
from DeepFakeAI.utilities import conditional_download, resolve_relative_path, is_image, is_video
FRAME_PROCESSOR = None
THREAD_SEMAPHORE = threading.Semaphore()
THREAD_LOCK = threading.Lock()
NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'
def get_frame_processor() -> Any:
global FRAME_PROCESSOR
with THREAD_LOCK:
if FRAME_PROCESSOR is None:
model_path = resolve_relative_path('../.assets/models/GFPGANv1.4.pth')
FRAME_PROCESSOR = GFPGANer(
model_path = model_path,
upscale = 1,
device = frame_processors.get_device()
)
return FRAME_PROCESSOR
def clear_frame_processor() -> None:
global FRAME_PROCESSOR
FRAME_PROCESSOR = None
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
conditional_download(download_directory_path, ['https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/GFPGANv1.4.pth'])
return True
def pre_process() -> bool:
if not is_image(DeepFakeAI.globals.target_path) and not is_video(DeepFakeAI.globals.target_path):
update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
return False
return True
def post_process() -> None:
clear_frame_processor()
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)
crop_frame = temp_frame[start_y:end_y, start_x:end_x]
if crop_frame.size:
with THREAD_SEMAPHORE:
_, _, crop_frame = get_frame_processor().enhance(
crop_frame,
paste_back = True
)
temp_frame[start_y:end_y, start_x:end_x] = crop_frame
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_frame = process_frame(None, None, temp_frame)
cv2.imwrite(temp_frame_path, result_frame)
if update:
update()
def process_image(source_path : str, target_path : str, output_path : str) -> None:
target_frame = cv2.imread(target_path)
result_frame = process_frame(None, None, target_frame)
cv2.imwrite(output_path, result_frame)
def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
DeepFakeAI.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
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