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import json
from pathlib import Path
from typing import List, Tuple, Union
import cv2
import numpy as np
import nota_wav2lip.audio as audio
from config import hparams as hp
class VideoSlicer:
def __init__(self, frame_dir: Union[Path, str], bbox_path: Union[Path, str]):
self.fps = hp.face.video_fps
self.frame_dir = frame_dir
self.frame_path_list = sorted(Path(self.frame_dir).glob("*.jpg"))
self.frame_array_list: List[np.ndarray] = [cv2.imread(str(image)) for image in self.frame_path_list]
with open(bbox_path, 'r') as f:
metadata = json.load(f)
self.bbox: List[List[int]] = [metadata['bbox'][key] for key in sorted(metadata['bbox'].keys())]
self.bbox_format = metadata['format']
assert len(self.bbox) == len(self.frame_array_list)
def __len__(self):
return len(self.frame_array_list)
def __getitem__(self, idx) -> Tuple[np.ndarray, List[int]]:
bbox = self.bbox[idx]
frame_original: np.ndarray = self.frame_array_list[idx]
# return frame_original[bbox[0]:bbox[1], bbox[2]:bbox[3], :]
return frame_original, bbox
class AudioSlicer:
def __init__(self, audio_path: Union[Path, str]):
self.fps = hp.face.video_fps
self.mel_chunks = self._audio_chunk_generator(audio_path)
self._audio_path = audio_path
@property
def audio_path(self):
return self._audio_path
def __len__(self):
return len(self.mel_chunks)
def _audio_chunk_generator(self, audio_path):
wav: np.ndarray = audio.load_wav(audio_path, hp.audio.sample_rate)
mel: np.ndarray = audio.melspectrogram(wav)
if np.isnan(mel.reshape(-1)).sum() > 0:
raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
mel_chunks: List[np.ndarray] = []
mel_idx_multiplier = 80. / self.fps
i = 0
while True:
start_idx = int(i * mel_idx_multiplier)
if start_idx + hp.face.mel_step_size > len(mel[0]):
mel_chunks.append(mel[:, len(mel[0]) - hp.face.mel_step_size:])
return mel_chunks
mel_chunks.append(mel[:, start_idx: start_idx + hp.face.mel_step_size])
i += 1
def __getitem__(self, idx: int) -> np.ndarray:
return self.mel_chunks[idx]
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