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from typing import Any, List |
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import cv2 |
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import threading |
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import gfpgan |
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import os |
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import modules.globals |
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import modules.processors.frame.core |
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from modules.core import update_status |
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from modules.face_analyser import get_one_face |
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from modules.typing import Frame, Face |
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import platform |
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import torch |
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from modules.utilities import ( |
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conditional_download, |
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is_image, |
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is_video, |
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) |
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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 = "DLC.FACE-ENHANCER" |
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abs_dir = os.path.dirname(os.path.abspath(__file__)) |
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models_dir = os.path.join( |
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os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models" |
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) |
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def pre_check() -> bool: |
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download_directory_path = models_dir |
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conditional_download( |
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download_directory_path, |
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[ |
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"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" |
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], |
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) |
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return True |
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def pre_start() -> bool: |
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if not is_image(modules.globals.target_path) and not is_video( |
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modules.globals.target_path |
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): |
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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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TENSORRT_AVAILABLE = False |
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try: |
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import torch_tensorrt |
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TENSORRT_AVAILABLE = True |
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except ImportError as im: |
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print(f"TensorRT is not available: {im}") |
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pass |
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except Exception as e: |
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print(f"TensorRT is not available: {e}") |
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pass |
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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 = os.path.join(models_dir, "GFPGANv1.4.pth") |
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selected_device = None |
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device_priority = [] |
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if TENSORRT_AVAILABLE and torch.cuda.is_available(): |
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selected_device = torch.device("cuda") |
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device_priority.append("TensorRT+CUDA") |
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elif torch.cuda.is_available(): |
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selected_device = torch.device("cuda") |
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device_priority.append("CUDA") |
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elif torch.backends.mps.is_available() and platform.system() == "Darwin": |
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selected_device = torch.device("mps") |
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device_priority.append("MPS") |
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elif not torch.cuda.is_available(): |
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selected_device = torch.device("cpu") |
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device_priority.append("CPU") |
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device) |
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print(f"Selected device: {selected_device} and device priority: {device_priority}") |
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return FACE_ENHANCER |
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def enhance_face(temp_frame: Frame) -> Frame: |
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with THREAD_SEMAPHORE: |
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_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True) |
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return temp_frame |
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame: |
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target_face = get_one_face(temp_frame) |
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if target_face: |
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temp_frame = enhance_face(temp_frame) |
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return temp_frame |
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def process_frames( |
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source_path: str, temp_frame_paths: List[str], progress: Any = None |
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) -> 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, temp_frame) |
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cv2.imwrite(temp_frame_path, result) |
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if progress: |
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progress.update(1) |
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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, 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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modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames) |
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def process_frame_v2(temp_frame: Frame) -> Frame: |
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target_face = get_one_face(temp_frame) |
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if target_face: |
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temp_frame = enhance_face(temp_frame) |
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return temp_frame |
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