2023-09-24 21:36:57 +08:00
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from typing import Any, List
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import cv2
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import insightface
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import threading
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2024-10-04 15:57:48 +08:00
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import os
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2023-09-24 21:36:57 +08:00
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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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2024-10-04 15:57:48 +08:00
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from modules.face_analyser import get_one_face, get_many_faces
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2023-09-24 21:36:57 +08:00
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from modules.typing import Face, Frame
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from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
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import numpy as np
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2023-09-24 21:36:57 +08:00
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FACE_SWAPPER = None
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THREAD_LOCK = threading.Lock()
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NAME = 'DLC.FACE-SWAPPER'
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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, [
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'https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128.onnx'
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])
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2023-09-24 21:36:57 +08:00
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return True
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def pre_start() -> bool:
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if not is_image(modules.globals.source_path):
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update_status('Select an image for source path.', NAME)
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return False
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elif not get_one_face(cv2.imread(modules.globals.source_path)):
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update_status('No face detected in the source path.', NAME)
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return False
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if not is_image(modules.globals.target_path) and not is_video(modules.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 get_face_swapper() -> Any:
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global FACE_SWAPPER
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with THREAD_LOCK:
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if FACE_SWAPPER is None:
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model_path = resolve_relative_path('../models/inswapper_128.onnx')
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FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=modules.globals.execution_providers)
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return FACE_SWAPPER
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def upscale_image(image: np.ndarray, scaling_factor: int = modules.globals.source_image_scaling_factor) -> np.ndarray:
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"""
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Upscales the given image by the specified scaling factor.
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Args:
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image (np.ndarray): The input image to upscale.
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scaling_factor (int): The factor by which to upscale the image.
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Returns:
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np.ndarray: The upscaled image.
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"""
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height, width = image.shape[:2]
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new_size = (width * scaling_factor, height * scaling_factor)
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upscaled_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC)
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return upscaled_image
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2024-09-23 09:25:14 +08:00
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def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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if modules.globals.many_faces:
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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 = swap_face(source_face, target_face, temp_frame)
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else:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = swap_face(source_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], progress: Any = None) -> None:
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source_image = cv2.imread(source_path)
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if source_image is None:
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print(f"Failed to load source image from {source_path}")
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return
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# Upscale the source image for better quality
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source_image_upscaled = upscale_image(source_image, scaling_factor=2)
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source_face = get_one_face(source_image_upscaled)
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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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try:
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result = process_frame(source_face, temp_frame)
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cv2.imwrite(temp_frame_path, result)
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except Exception as exception:
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print(f"Error processing frame {temp_frame_path}: {exception}")
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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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source_face = get_one_face(cv2.imread(source_path))
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target_frame = cv2.imread(target_path)
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result = process_frame(source_face, 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(source_path, temp_frame_paths, process_frames)
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