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| import logging | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from insightface.app import FaceAnalysis | |
| from moviepy.editor import AudioFileClip, VideoClip | |
| from PIL import Image | |
| from torchvision import transforms | |
| logger = logging.getLogger(__name__) | |
| def tensor_to_video(tensor, output_video_path, input_audio_path, fps=30): | |
| """ | |
| Converts a Tensor with shape [c, f, h, w] into a video and adds an audio track from the specified audio file. | |
| Args: | |
| tensor (Tensor): The Tensor to be converted, shaped [c, f, h, w]. | |
| output_video_path (str): The file path where the output video will be saved. | |
| input_audio_path (str): The path to the audio file (WAV file) that contains the audio track to be added. | |
| fps (int): The frame rate of the output video. Default is 30 fps. | |
| """ | |
| tensor = tensor.permute(1, 2, 3, 0).cpu().numpy() # convert to [f, h, w, c] | |
| tensor = np.clip(tensor * 255, 0, 255).astype(np.uint8) # to [0, 255] | |
| def make_frame(t): | |
| frame_index = min(int(t * fps), tensor.shape[0] - 1) | |
| return tensor[frame_index] | |
| video_duration = tensor.shape[0] / fps | |
| audio_clip = AudioFileClip(input_audio_path) | |
| audio_duration = audio_clip.duration | |
| final_duration = min(video_duration, audio_duration) | |
| audio_clip = audio_clip.subclip(0, final_duration) | |
| new_video_clip = VideoClip(make_frame, duration=final_duration) | |
| new_video_clip = new_video_clip.set_audio(audio_clip) | |
| new_video_clip.write_videofile(output_video_path, fps=fps, codec="libx264", audio_codec="aac") | |
| def preprocess_image(face_analysis_model: str, image_path: str, image_size: int = 512): | |
| """ | |
| Preprocess the image and extract face embedding. | |
| Args: | |
| face_analysis_model (str): Path to the FaceAnalysis model directory. | |
| image_path (str): Path to the image file. | |
| image_size (int, optional): Target size for resizing the image. Default is 512. | |
| Returns: | |
| tuple: A tuple containing: | |
| - pixel_values (torch.Tensor): Tensor of the preprocessed image. | |
| - face_emb (torch.Tensor): Tensor of the face embedding. | |
| """ | |
| # Define the image transformation | |
| transform = transforms.Compose( | |
| [ | |
| transforms.Resize((image_size, image_size)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5], [0.5]), | |
| ] | |
| ) | |
| # Initialize the FaceAnalysis model | |
| face_analysis = FaceAnalysis( | |
| # name='', | |
| root=face_analysis_model, | |
| providers=["CUDAExecutionProvider", "CPUExecutionProvider"], | |
| ) | |
| face_analysis.prepare(ctx_id=0, det_size=(640, 640)) | |
| # Load and preprocess the image | |
| image = Image.open(image_path).convert("RGB") | |
| pixel_values = transform(image) | |
| pixel_values = pixel_values.unsqueeze(0) | |
| # Detect faces and extract the face embedding | |
| image_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) | |
| faces = face_analysis.get(image_bgr) | |
| if not faces: | |
| logger.warning("No faces detected in the image. Using a zero vector as the face embedding.") | |
| face_emb = np.zeros(512) | |
| else: | |
| # Sort faces by size and select the largest one | |
| faces_sorted = sorted( | |
| faces, | |
| key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]), | |
| reverse=True, | |
| ) | |
| face_emb = faces_sorted[0]["embedding"] | |
| # Convert face embedding to a PyTorch tensor | |
| face_emb = face_emb.reshape(1, -1) | |
| face_emb = torch.tensor(face_emb) | |
| del face_analysis | |
| return pixel_values, face_emb | |