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from typing import Any, List
import cv2
import threading
import gfpgan
import os
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_one_face
from modules.typing import Frame, Face
import platform
import torch
from modules.utilities import (
conditional_download,
is_image,
is_video,
)
FACE_ENHANCER = None
THREAD_SEMAPHORE = threading.Semaphore()
THREAD_LOCK = threading.Lock()
NAME = "DLC.FACE-ENHANCER"
abs_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
)
def pre_check() -> bool:
download_directory_path = models_dir
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(modules.globals.target_path) and not is_video(
modules.globals.target_path
):
update_status("Select an image or video for target path.", NAME)
return False
return True
TENSORRT_AVAILABLE = False
try:
import torch_tensorrt
TENSORRT_AVAILABLE = True
except ImportError as im:
print(f"TensorRT is not available: {im}")
pass
except Exception as e:
print(f"TensorRT is not available: {e}")
pass
def get_face_enhancer() -> Any:
global FACE_ENHANCER
with THREAD_LOCK:
if FACE_ENHANCER is None:
model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
selected_device = None
device_priority = []
if TENSORRT_AVAILABLE and torch.cuda.is_available():
selected_device = torch.device("cuda")
device_priority.append("TensorRT+CUDA")
elif torch.cuda.is_available():
selected_device = torch.device("cuda")
device_priority.append("CUDA")
elif torch.backends.mps.is_available() and platform.system() == "Darwin":
selected_device = torch.device("mps")
device_priority.append("MPS")
elif not torch.cuda.is_available():
selected_device = torch.device("cpu")
device_priority.append("CPU")
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
# for debug:
print(f"Selected device: {selected_device} and device priority: {device_priority}")
return FACE_ENHANCER
def enhance_face(temp_frame: Frame) -> Frame:
with THREAD_SEMAPHORE:
_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
return temp_frame
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
target_face = get_one_face(temp_frame)
if target_face:
temp_frame = enhance_face(temp_frame)
return temp_frame
def process_frames(
source_path: str, temp_frame_paths: List[str], progress: Any = None
) -> None:
for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path)
result = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result)
if progress:
progress.update(1)
def process_image(source_path: str, target_path: str, output_path: str) -> None:
target_frame = cv2.imread(target_path)
result = process_frame(None, target_frame)
cv2.imwrite(output_path, result)
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
def process_frame_v2(temp_frame: Frame) -> Frame:
target_face = get_one_face(temp_frame)
if target_face:
temp_frame = enhance_face(temp_frame)
return temp_frame
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