Spaces:
Sleeping
Sleeping
File size: 7,573 Bytes
a94bd47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """
MultiSense-DF — Preprocessing Pipeline
Face detection, frame extraction, audio processing, mouth-crop extraction
"""
import cv2
import torch
import numpy as np
import librosa
import soundfile as sf
from pathlib import Path
from torchvision import transforms
# ── Image Transforms ─────────────────────────────────────────────────────────
VISUAL_TRAIN_TRANSFORM = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1),
transforms.RandomRotation(10),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
VISUAL_VAL_TRANSFORM = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
MOUTH_TRANSFORM = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((96, 96)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
# ── Video Processing ──────────────────────────────────────────────────────────
def extract_frames(video_path: str, num_frames: int = 125,
transform=None) -> torch.Tensor:
"""
Uniformly sample `num_frames` frames from a video.
Returns tensor of shape (num_frames, 3, 224, 224).
"""
cap = cv2.VideoCapture(str(video_path))
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
indices = np.linspace(0, total - 1, num_frames, dtype=int)
frames = []
for idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if not ret:
# Pad with last frame if read fails
frame = frames[-1].numpy().transpose(1, 2, 0) if frames else np.zeros((224, 224, 3), dtype=np.uint8)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
tf = transform or VISUAL_VAL_TRANSFORM
frames.append(tf(frame))
cap.release()
return torch.stack(frames) # (T, 3, 224, 224)
def extract_audio_waveform(video_path: str, sr: int = 16000,
duration: float = 5.0) -> torch.Tensor:
"""
Extract audio from video, resample to `sr` Hz, normalise.
Returns tensor of shape (sr * duration,).
"""
import subprocess, tempfile, os
with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
tmp_path = f.name
try:
subprocess.run([
'ffmpeg', '-y', '-i', str(video_path),
'-ar', str(sr), '-ac', '1', '-t', str(duration),
tmp_path
], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True)
wav, _ = librosa.load(tmp_path, sr=sr)
target_len = int(sr * duration)
if len(wav) < target_len:
wav = np.pad(wav, (0, target_len - len(wav)))
else:
wav = wav[:target_len]
wav = (wav - wav.mean()) / (wav.std() + 1e-8)
return torch.from_numpy(wav).float()
finally:
os.remove(tmp_path)
def extract_mel_spectrogram(waveform: torch.Tensor, sr: int = 16000,
n_mels: int = 80, frame_duration: float = 5.0,
num_frames: int = 125) -> torch.Tensor:
"""
Compute mel-spectrogram and split into per-frame windows.
Returns (num_frames, 1, 80, W) where W is the window width in mel bins.
"""
wav_np = waveform.numpy()
mel = librosa.feature.melspectrogram(y=wav_np, sr=sr, n_mels=n_mels,
hop_length=160, n_fft=512)
log_mel = librosa.power_to_db(mel, ref=np.max) # (80, T_mel)
# Normalise
log_mel = (log_mel - log_mel.mean()) / (log_mel.std() + 1e-8)
# Split into num_frames windows
total_mel_cols = log_mel.shape[1]
window_w = max(1, total_mel_cols // num_frames)
mel_frames = []
for i in range(num_frames):
start = i * window_w
end = start + window_w
window = log_mel[:, start:end] if end <= total_mel_cols \
else log_mel[:, -window_w:]
mel_frames.append(torch.from_numpy(window).float().unsqueeze(0)) # (1, 80, W)
return torch.stack(mel_frames) # (T, 1, 80, W)
def extract_mouth_crops(video_path: str, num_frames: int = 125,
transform=None) -> torch.Tensor:
"""
Extract mouth-region crops from uniformly sampled frames.
Uses a simple face detector + landmark estimation.
Falls back to centre-crop if no face found.
Returns (num_frames, 3, 96, 96).
"""
cap = cv2.VideoCapture(str(video_path))
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
indices = np.linspace(0, total - 1, num_frames, dtype=int)
# Attempt to use dlib or mediapipe; fall back to centre-crop
try:
import mediapipe as mp
face_mesh = mp.solutions.face_mesh.FaceMesh(
static_image_mode=True, max_num_faces=1
)
use_landmarks = True
except ImportError:
use_landmarks = False
crops = []
tf = transform or MOUTH_TRANSFORM
for idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if not ret:
crops.append(torch.zeros(3, 96, 96))
continue
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
H, W = rgb.shape[:2]
crop = None
if use_landmarks:
results = face_mesh.process(rgb)
if results.multi_face_landmarks:
lm = results.multi_face_landmarks[0].landmark
# Approximate mouth centre from MediaPipe landmarks
mouth_y = int(np.mean([lm[13].y, lm[14].y]) * H)
mouth_x = int(np.mean([lm[61].x, lm[291].x]) * W)
half = int(min(H, W) * 0.15)
y1, y2 = max(0, mouth_y - half), min(H, mouth_y + half)
x1, x2 = max(0, mouth_x - half), min(W, mouth_x + half)
crop = rgb[y1:y2, x1:x2]
if crop is None or crop.size == 0:
# Fallback: lower centre quarter
y1, y2 = int(H * 0.55), int(H * 0.85)
x1, x2 = int(W * 0.25), int(W * 0.75)
crop = rgb[y1:y2, x1:x2]
crops.append(tf(crop))
cap.release()
return torch.stack(crops) # (T, 3, 96, 96)
def preprocess_video(video_path: str, num_frames: int = 125,
sr: int = 16000, duration: float = 5.0,
split: str = 'train') -> dict:
"""
Full preprocessing pipeline for a single video.
Returns dict with all tensors ready for model input.
"""
transform = VISUAL_TRAIN_TRANSFORM if split == 'train' else VISUAL_VAL_TRANSFORM
frames = extract_frames(video_path, num_frames, transform)
waveform = extract_audio_waveform(video_path, sr, duration)
mels = extract_mel_spectrogram(waveform, sr, num_frames=num_frames)
mouths = extract_mouth_crops(video_path, num_frames)
return {
'frames': frames, # (T, 3, 224, 224)
'waveform': waveform, # (sr*duration,)
'mel_specs': mels, # (T, 1, 80, W)
'mouth_crops': mouths, # (T, 3, 96, 96)
}
|