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import subprocess
import tempfile, os
import ffmpeg
import torchvision.transforms.functional as TF
import torch.nn.functional as F
import cv2
import tempfile
import imageio
import binascii
import torchvision
import torch
from PIL import Image
import os.path as osp
import json
def rand_name(length=8, suffix=''):
name = binascii.b2a_hex(os.urandom(length)).decode('utf-8')
if suffix:
if not suffix.startswith('.'):
suffix = '.' + suffix
name += suffix
return name
def extract_audio_tracks(source_video, verbose=False, query_only=False):
"""
Extract all audio tracks from a source video into temporary AAC files.
Returns:
Tuple:
- List of temp file paths for extracted audio tracks
- List of corresponding metadata dicts:
{'codec', 'sample_rate', 'channels', 'duration', 'language'}
where 'duration' is set to container duration (for consistency).
"""
probe = ffmpeg.probe(source_video)
audio_streams = [s for s in probe['streams'] if s['codec_type'] == 'audio']
container_duration = float(probe['format'].get('duration', 0.0))
if not audio_streams:
if query_only: return 0
if verbose: print(f"No audio track found in {source_video}")
return [], []
if query_only:
return len(audio_streams)
if verbose:
print(f"Found {len(audio_streams)} audio track(s), container duration = {container_duration:.3f}s")
file_paths = []
metadata = []
for i, stream in enumerate(audio_streams):
fd, temp_path = tempfile.mkstemp(suffix=f'_track{i}.aac', prefix='audio_')
os.close(fd)
file_paths.append(temp_path)
metadata.append({
'codec': stream.get('codec_name'),
'sample_rate': int(stream.get('sample_rate', 0)),
'channels': int(stream.get('channels', 0)),
'duration': container_duration,
'language': stream.get('tags', {}).get('language', None)
})
ffmpeg.input(source_video).output(
temp_path,
**{f'map': f'0:a:{i}', 'acodec': 'aac', 'b:a': '128k'}
).overwrite_output().run(quiet=not verbose)
return file_paths, metadata
def combine_and_concatenate_video_with_audio_tracks(
save_path_tmp, video_path,
source_audio_tracks, new_audio_tracks,
source_audio_duration, audio_sampling_rate,
new_audio_from_start=False,
source_audio_metadata=None,
audio_bitrate='128k',
audio_codec='aac',
verbose = False
):
inputs, filters, maps, idx = ['-i', video_path], [], ['-map', '0:v'], 1
metadata_args = []
sources = source_audio_tracks or []
news = new_audio_tracks or []
duplicate_source = len(sources) == 1 and len(news) > 1
N = len(news) if source_audio_duration == 0 else max(len(sources), len(news)) or 1
for i in range(N):
s = (sources[i] if i < len(sources)
else sources[0] if duplicate_source else None)
n = news[i] if len(news) == N else (news[0] if news else None)
if source_audio_duration == 0:
if n:
inputs += ['-i', n]
filters.append(f'[{idx}:a]apad=pad_dur=100[aout{i}]')
idx += 1
else:
filters.append(f'anullsrc=r={audio_sampling_rate}:cl=mono,apad=pad_dur=100[aout{i}]')
else:
if s:
inputs += ['-i', s]
meta = source_audio_metadata[i] if source_audio_metadata and i < len(source_audio_metadata) else {}
needs_filter = (
meta.get('codec') != audio_codec or
meta.get('sample_rate') != audio_sampling_rate or
meta.get('channels') != 1 or
meta.get('duration', 0) < source_audio_duration
)
if needs_filter:
filters.append(
f'[{idx}:a]aresample={audio_sampling_rate},aformat=channel_layouts=mono,'
f'apad=pad_dur={source_audio_duration},atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')
else:
filters.append(
f'[{idx}:a]apad=pad_dur={source_audio_duration},atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')
if lang := meta.get('language'):
metadata_args += ['-metadata:s:a:' + str(i), f'language={lang}']
idx += 1
else:
filters.append(
f'anullsrc=r={audio_sampling_rate}:cl=mono,atrim=0:{source_audio_duration},asetpts=PTS-STARTPTS[s{i}]')
if n:
inputs += ['-i', n]
start = '0' if new_audio_from_start else source_audio_duration
filters.append(
f'[{idx}:a]aresample={audio_sampling_rate},aformat=channel_layouts=mono,'
f'atrim=start={start},asetpts=PTS-STARTPTS[n{i}]')
filters.append(f'[s{i}][n{i}]concat=n=2:v=0:a=1[aout{i}]')
idx += 1
else:
filters.append(f'[s{i}]apad=pad_dur=100[aout{i}]')
maps += ['-map', f'[aout{i}]']
cmd = ['ffmpeg', '-y', *inputs,
'-filter_complex', ';'.join(filters), # ✅ Only change made
*maps, *metadata_args,
'-c:v', 'copy',
'-c:a', audio_codec,
'-b:a', audio_bitrate,
'-ar', str(audio_sampling_rate),
'-ac', '1',
'-shortest', save_path_tmp]
if verbose:
print(f"ffmpeg command: {cmd}")
try:
subprocess.run(cmd, check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as e:
raise Exception(f"FFmpeg error: {e.stderr}")
def combine_video_with_audio_tracks(target_video, audio_tracks, output_video,
audio_metadata=None, verbose=False):
if not audio_tracks:
if verbose: print("No audio tracks to combine."); return False
dur = float(next(s for s in ffmpeg.probe(target_video)['streams']
if s['codec_type'] == 'video')['duration'])
if verbose: print(f"Video duration: {dur:.3f}s")
cmd = ['ffmpeg', '-y', '-i', target_video]
for path in audio_tracks:
cmd += ['-i', path]
cmd += ['-map', '0:v']
for i in range(len(audio_tracks)):
cmd += ['-map', f'{i+1}:a']
for i, meta in enumerate(audio_metadata or []):
if (lang := meta.get('language')):
cmd += ['-metadata:s:a:' + str(i), f'language={lang}']
cmd += ['-c:v', 'copy', '-c:a', 'copy', '-t', str(dur), output_video]
result = subprocess.run(cmd, capture_output=not verbose, text=True)
if result.returncode != 0:
raise Exception(f"FFmpeg error:\n{result.stderr}")
if verbose:
print(f"Created {output_video} with {len(audio_tracks)} audio track(s)")
return True
def cleanup_temp_audio_files(audio_tracks, verbose=False):
"""
Clean up temporary audio files.
Args:
audio_tracks: List of audio file paths to delete
verbose: Enable verbose output (default: False)
Returns:
Number of files successfully deleted
"""
deleted_count = 0
for audio_path in audio_tracks:
try:
if os.path.exists(audio_path):
os.unlink(audio_path)
deleted_count += 1
if verbose:
print(f"Cleaned up {audio_path}")
except PermissionError:
print(f"Warning: Could not delete {audio_path} (file may be in use)")
except Exception as e:
print(f"Warning: Error deleting {audio_path}: {e}")
if verbose and deleted_count > 0:
print(f"Successfully deleted {deleted_count} temporary audio file(s)")
return deleted_count
def save_video(tensor,
save_file=None,
fps=30,
codec_type='libx264_8',
container='mp4',
nrow=8,
normalize=True,
value_range=(-1, 1),
retry=5):
"""Save tensor as video with configurable codec and container options."""
if torch.is_tensor(tensor) and len(tensor.shape) == 4:
tensor = tensor.unsqueeze(0)
suffix = f'.{container}'
cache_file = osp.join('/tmp', rand_name(suffix=suffix)) if save_file is None else save_file
if not cache_file.endswith(suffix):
cache_file = osp.splitext(cache_file)[0] + suffix
# Configure codec parameters
codec_params = _get_codec_params(codec_type, container)
# Process and save
error = None
for _ in range(retry):
try:
if torch.is_tensor(tensor):
# Preprocess tensor
tensor = tensor.clamp(min(value_range), max(value_range))
tensor = torch.stack([
torchvision.utils.make_grid(u, nrow=nrow, normalize=normalize, value_range=value_range)
for u in tensor.unbind(2)
], dim=1).permute(1, 2, 3, 0)
tensor = (tensor * 255).type(torch.uint8).cpu()
arrays = tensor.numpy()
else:
arrays = tensor
# Write video (silence ffmpeg logs)
writer = imageio.get_writer(cache_file, fps=fps, ffmpeg_log_level='error', **codec_params)
for frame in arrays:
writer.append_data(frame)
writer.close()
return cache_file
except Exception as e:
error = e
print(f"error saving {save_file}: {e}")
def _get_codec_params(codec_type, container):
"""Get codec parameters based on codec type and container."""
if codec_type == 'libx264_8':
return {'codec': 'libx264', 'quality': 8, 'pixelformat': 'yuv420p'}
elif codec_type == 'libx264_10':
return {'codec': 'libx264', 'quality': 10, 'pixelformat': 'yuv420p'}
elif codec_type == 'libx265_28':
return {'codec': 'libx265', 'pixelformat': 'yuv420p', 'output_params': ['-crf', '28', '-x265-params', 'log-level=none','-hide_banner', '-nostats']}
elif codec_type == 'libx265_8':
return {'codec': 'libx265', 'pixelformat': 'yuv420p', 'output_params': ['-crf', '8', '-x265-params', 'log-level=none','-hide_banner', '-nostats']}
elif codec_type == 'libx264_lossless':
if container == 'mkv':
return {'codec': 'ffv1', 'pixelformat': 'rgb24'}
else: # mp4
return {'codec': 'libx264', 'output_params': ['-crf', '0'], 'pixelformat': 'yuv444p'}
else: # libx264
return {'codec': 'libx264', 'pixelformat': 'yuv420p'}
def save_image(tensor,
save_file,
nrow=8,
normalize=True,
value_range=(-1, 1),
quality='jpeg_95', # 'jpeg_95', 'jpeg_85', 'jpeg_70', 'jpeg_50', 'webp_95', 'webp_85', 'webp_70', 'webp_50', 'png', 'webp_lossless'
retry=5):
"""Save tensor as image with configurable format and quality."""
# Get format and quality settings
format_info = _get_format_info(quality)
# Rename file extension to match requested format
save_file = osp.splitext(save_file)[0] + format_info['ext']
# Save image
error = None
for _ in range(retry):
try:
tensor = tensor.clamp(min(value_range), max(value_range))
if format_info['use_pil']:
# Use PIL for WebP and advanced options
grid = torchvision.utils.make_grid(tensor, nrow=nrow, normalize=normalize, value_range=value_range)
# Convert to PIL Image
grid = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
img = Image.fromarray(grid)
img.save(save_file, **format_info['params'])
else:
# Use torchvision for JPEG and PNG
torchvision.utils.save_image(
tensor, save_file, nrow=nrow, normalize=normalize,
value_range=value_range, **format_info['params']
)
break
except Exception as e:
error = e
continue
else:
print(f'cache_image failed, error: {error}', flush=True)
return save_file
def _get_format_info(quality):
"""Get format extension and parameters."""
formats = {
# JPEG with PIL (so 'quality' works)
'jpeg_95': {'ext': '.jpg', 'params': {'quality': 95}, 'use_pil': True},
'jpeg_85': {'ext': '.jpg', 'params': {'quality': 85}, 'use_pil': True},
'jpeg_70': {'ext': '.jpg', 'params': {'quality': 70}, 'use_pil': True},
'jpeg_50': {'ext': '.jpg', 'params': {'quality': 50}, 'use_pil': True},
# PNG with torchvision
'png': {'ext': '.png', 'params': {}, 'use_pil': False},
# WebP with PIL (for quality control)
'webp_95': {'ext': '.webp', 'params': {'quality': 95}, 'use_pil': True},
'webp_85': {'ext': '.webp', 'params': {'quality': 85}, 'use_pil': True},
'webp_70': {'ext': '.webp', 'params': {'quality': 70}, 'use_pil': True},
'webp_50': {'ext': '.webp', 'params': {'quality': 50}, 'use_pil': True},
'webp_lossless': {'ext': '.webp', 'params': {'lossless': True}, 'use_pil': True},
}
return formats.get(quality, formats['jpeg_95'])
from PIL import Image, PngImagePlugin
def _enc_uc(s):
try: return b"ASCII\0\0\0" + s.encode("ascii")
except UnicodeEncodeError: return b"UNICODE\0" + s.encode("utf-16le")
def _dec_uc(b):
if not isinstance(b, (bytes, bytearray)):
try: b = bytes(b)
except Exception: return None
if b.startswith(b"ASCII\0\0\0"): return b[8:].decode("ascii", "ignore")
if b.startswith(b"UNICODE\0"): return b[8:].decode("utf-16le", "ignore")
return b.decode("utf-8", "ignore")
def save_image_metadata(image_path, metadata_dict, **save_kwargs):
try:
j = json.dumps(metadata_dict, ensure_ascii=False)
ext = os.path.splitext(image_path)[1].lower()
with Image.open(image_path) as im:
if ext == ".png":
pi = PngImagePlugin.PngInfo(); pi.add_text("comment", j)
im.save(image_path, pnginfo=pi, **save_kwargs); return True
if ext in (".jpg", ".jpeg"):
im.save(image_path, comment=j.encode("utf-8"), **save_kwargs); return True
if ext == ".webp":
import piexif
exif = {"0th":{}, "Exif":{piexif.ExifIFD.UserComment:_enc_uc(j)}, "GPS":{}, "1st":{}, "thumbnail":None}
im.save(image_path, format="WEBP", exif=piexif.dump(exif), **save_kwargs); return True
raise ValueError("Unsupported format")
except Exception as e:
print(f"Error saving metadata: {e}"); return False
def read_image_metadata(image_path):
try:
ext = os.path.splitext(image_path)[1].lower()
with Image.open(image_path) as im:
if ext == ".png":
val = (getattr(im, "text", {}) or {}).get("comment") or im.info.get("comment")
return json.loads(val) if val else None
if ext in (".jpg", ".jpeg"):
val = im.info.get("comment")
if isinstance(val, (bytes, bytearray)): val = val.decode("utf-8", "ignore")
if val:
try: return json.loads(val)
except Exception: pass
exif = getattr(im, "getexif", lambda: None)()
if exif:
uc = exif.get(37510) # UserComment
s = _dec_uc(uc) if uc else None
if s:
try: return json.loads(s)
except Exception: pass
return None
if ext == ".webp":
exif_bytes = Image.open(image_path).info.get("exif")
if not exif_bytes: return None
import piexif
uc = piexif.load(exif_bytes).get("Exif", {}).get(piexif.ExifIFD.UserComment)
s = _dec_uc(uc) if uc else None
return json.loads(s) if s else None
return None
except Exception as e:
print(f"Error reading metadata: {e}"); return None |