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import os
import random
import threading
import sys
import numpy as np
import skimage.io as sio
import tensorflow as tf
from definitions import *
from pyutils.iolib.audio import load_wav
class FilenameProvider(object):
def __init__(self, directory,
subset_fn=None,
num_epochs=1,
shuffle=False):
self.directory = directory
self.sample_ids = os.listdir(directory)
assert len(self.sample_ids) > 0, 'Dataset directory is empty.'
if subset_fn is not None:
assert os.path.exists(subset_fn)
subset = open(subset_fn).read().splitlines()
self.sample_ids = [y for y in self.sample_ids if y in subset]
self.num_epochs, self.epoch = num_epochs, 0
self.num_samples = len(self.sample_ids)
self.shuffle = shuffle
self.head = -1
def get_next_sample(self):
self.head = (self.head + 1) % self.num_samples
if self.head == 0:
self.epoch += 1
if self.epoch > self.num_epochs:
return None
if self.shuffle:
random.shuffle(self.sample_ids)
return self.sample_ids[self.head]
def loop_samples(self):
while True:
yid = self.get_next_sample()
if yid is None:
break
yield yid
class AudioReader(object):
def __init__(self, audio_folder, rate=None, ambi_order=1):
from scikits.audiolab import Sndfile
self.audio_folder = audio_folder
fns = os.listdir(audio_folder)
self.num_files = len(fns)
fp = Sndfile(os.path.join(self.audio_folder, fns[0]), 'r')
self.rate = float(fp.samplerate) if rate is None else rate
self.num_channels = min((fp.channels, (ambi_order+1)**2))
self.duration = self.num_files
self.num_frames = int(self.duration * rate)
def get(self, start_time, size, rotation=None):
# Check if padding is necessary
start_frame = int(start_time * self.rate)
pad_before, pad_after = 0, 0
if start_frame < 0:
pad_before = abs(start_frame)
size -= pad_before
start_time, start_frame = 0., 0
if start_frame + size > self.num_frames:
pad_after = start_frame + size - self.num_frames
size -= pad_after
# Load audio
index = range(int(start_time), min(int(np.ceil(start_time + size / float(self.rate))), self.num_files))
fns = ['{}/{:06d}.wav'.format(self.audio_folder, i) for i in index]
chunk = [load_wav(fn, self.rate)[0] for fn in fns]
chunk = np.concatenate(chunk, axis=0) if len(chunk) > 1 else chunk[0]
ss = int((start_time - int(start_time)) * self.rate)
chunk = chunk[ss:ss + size, :self.num_channels]
# Pad
if pad_before > 0:
pad = np.zeros((pad_before, self.num_channels))
chunk = np.concatenate((pad, chunk), axis=0)
if pad_after > 0:
pad = np.zeros((pad_after, self.num_channels))
chunk = np.concatenate((chunk, pad), axis=0)
# Apply rotation
if rotation is not None:
assert -np.pi <= rotation < np.pi
c = np.cos(rotation)
s = np.sin(rotation)
rot_mtx = np.array([[1, 0, 0, 0], # W' = W
[0, c, 0, s], # Y' = X sin + Y cos
[0, 0, 1, 0], # Z' = Z
[0, -s, 0, c]]) # X' = X cos - Y sin
chunk = np.dot(chunk, rot_mtx.T)
return chunk
class VideoReader(object):
def __init__(self, video_folder, rate=None, img_prep=None):
raw_rate = 10.
self.video_folder = video_folder
self.rate = rate if rate is not None else raw_rate
self.img_prep = img_prep if img_prep is not None else lambda x: x
frame_fns = [fn for fn in os.listdir(video_folder) if fn.endswith('.jpg')]
self.num_frames = len(frame_fns)
self.duration = self.num_frames / raw_rate
img = sio.imread(os.path.join(video_folder, frame_fns[0]))
self.frame_shape = self.img_prep(img).shape
def get_by_index(self, start_time, size, rotation=None):
ss = max(int(start_time * self.rate), 0)
chunk = []
for fno in range(ss, ss+size):
fn = os.path.join(self.video_folder, '{:06d}.jpg'.format(fno))
frame = self.img_prep(sio.imread(fn))
chunk.append(frame)
chunk = np.stack(chunk, 0) if len(chunk) > 1 else chunk[0][np.newaxis]
if rotation is not None:
roll = -int(rotation / (2. * np.pi) * self.frame_shape[1])
chunk = np.roll(chunk, roll, axis=2)
return chunk
class FlowReader(object):
def __init__(self, flow_dir, flow_lims_fn, rate=None, flow_prep=None):
self.reader = VideoReader(flow_dir, rate=rate)
self.lims = np.load(flow_lims_fn)
self.rate = self.reader.rate
self.duration = self.reader.duration
self.flow_prep = flow_prep if flow_prep is not None else lambda x: x
dummy_img = self.flow_prep(np.zeros(self.reader.frame_shape[:2], dtype=np.float32))
self.frame_shape = dummy_img.shape + (1,)
self.dtype = dummy_img.dtype
def get_by_index(self, start_time, size, rotation=None):
chunk = self.reader.get_by_index(start_time, size, rotation)
chunk = chunk.astype(np.float32)
ss = max(int(start_time * self.rate), 0)
t = chunk.shape[0]
m_min = self.lims[ss:ss+t, 0].reshape((-1, 1, 1))
m_max = self.lims[ss:ss+t, 1].reshape((-1, 1, 1))
chunk[:, :, :, 2] *= (m_max - m_min) / 255.
chunk[:, :, :, 2] += m_min
chunk[:, :, :, 0] *= (2 * np.pi) / 255.
chunk[:, :, :, 1] = chunk[:, :, :, 2] * np.sin(chunk[:, :, :, 0])
chunk[:, :, :, 0] = chunk[:, :, :, 2] * np.cos(chunk[:, :, :, 0])
return chunk
class SampleReader(object):
""" Sample reader that preprocesses one sample (ambisonics, video)."""
def __init__(self, folder,
ambi_order=1,
audio_rate=48000,
video_rate=10,
context=1.0,
duration=0.1,
return_video=True,
img_prep=None,
return_flow=False,
flow_prep=None,
skip_silence_thr=None,
shuffle=True,
start_time=0.5,
sample_duration=None,
skip_rate=None,
random_rotations=True,
num_threads=1,
thread_id=0):
a2v = float(audio_rate) / video_rate
snd_dur = duration * audio_rate
vid_dur = duration * video_rate
snd_ctx = context * audio_rate
self.video_id = os.path.split(folder)[-1]
# Check input settings
assert a2v==int(a2v)
assert float(snd_dur)==int(snd_dur)
assert float(vid_dur)==int(vid_dur)
assert float(snd_ctx)==int(snd_ctx)
# Readers
self.audio_reader = AudioReader(os.path.join(folder, 'ambix'), audio_rate, ambi_order)
self.video_reader = VideoReader(os.path.join(folder, 'video'), video_rate, img_prep)
if return_flow:
flow_dir = os.path.join(folder, 'flow')
flow_lims = os.path.join(folder, 'flow', 'flow_limits.npy')
self.flow_reader = FlowReader(flow_dir, flow_lims, video_rate, flow_prep)
# Store arguments
self.folder = folder
self.duration = duration
self.context = context
self.audio_rate = audio_rate
self.video_rate = video_rate
self.audio_size = int(snd_dur) + int(snd_ctx) - 1
self.video_size = int(vid_dur)
self.video_shape = self.video_reader.frame_shape
self.return_video = return_video
self.return_flow = return_flow
self.random_rotations = random_rotations
# If is not training, iterate through video, else extract random time frames
audio_pow_fn = os.path.join(folder, 'audio_pow.lst')
chunks_t = [float(l.strip().split()[0]) for l in open(audio_pow_fn)]
chunks_pow = [float(l.strip().split()[1]) for l in open(audio_pow_fn)]
if skip_rate is not None:
num_chunks = len(chunks_t)
chunks_t = [chunks_t[i] for i in range(0, num_chunks, skip_rate)]
chunks_pow = [chunks_pow[i] for i in range(0, num_chunks, skip_rate)]
if skip_silence_thr is not None:
chunks_t = [chunks_t[i] for i in range(len(chunks_t)) if chunks_pow[i]>skip_silence_thr]
if start_time > 0.5:
chunks_t = [chunks_t[i] for i in range(len(chunks_t)) if chunks_t[i]>=start_time]
if sample_duration is not None:
chunks_t = [chunks_t[i] for i in range(len(chunks_t)) if chunks_t[i]<start_time+sample_duration]
if num_threads > 1:
lims = np.linspace(0, len(chunks_t), num_threads+1).astype(int)
chunks_t = chunks_t[lims[thread_id]:lims[thread_id+1]]
if shuffle:
random.shuffle(chunks_t)
self.chunks_t = chunks_t
self.head = -1
def get(self):
self.head += 1
if self.head >= len(self.chunks_t):
return None
self.cur_t = self.chunks_t[self.head]
cur_t = self.cur_t
rotation = random.random() * 2 * np.pi - np.pi if self.random_rotations else None
chunks = {'id': self.video_id + ' ' + str(cur_t)}
# Audio
audio_ss = cur_t - self.context / 2
chunks['ambix'] = self.audio_reader.get(audio_ss, self.audio_size, rotation)
assert chunks['ambix'] is not None, 'Could not get ambix data for file {} (sec: {})'.format(self.folder, audio_ss)
# Video
if self.return_video:
chunks['video'] = self.video_reader.get_by_index(cur_t, self.video_size, rotation)
assert chunks['video'] is not None, 'Could not get video data for file {} (frame: {})'.format(self.folder, cur_t)
# Flow
if self.return_flow:
chunks['flow'] = self.flow_reader.get_by_index(cur_t, self.video_size, rotation)
assert chunks['flow'] is not None, 'Could not get flow data for file {} (frame: {})'.format(self.folder, cur_t)
return chunks
def loop_chunks(self, n=np.inf):
k = 0
while True:
k += 1
if k > n:
break
chunks = self.get()
if chunks is None:
break
else:
yield chunks
class Feeder(object):
""" Background feeder that preprocesses audio and video files
and enqueues them into a TensorFlow queue."""
def __init__(self, sample_dir,
subset_fn=None,
ambi_order=1,
audio_rate=48000,
video_rate=10,
context=1.0,
duration=0.1,
return_video=True,
frame_size=None,
img_prep=None,
return_flow=False,
flow_prep=None,
queue_size=32,
n_threads=1,
for_eval=False):
self.sample_dir, self.subset_fn = sample_dir, subset_fn
self.ambi_order = ambi_order
self.audio_rate, self.video_rate = audio_rate, video_rate
self.context, self.duration = context, duration
self.return_video = return_video
self.img_prep = img_prep
self.return_flow = return_flow
self.flow_prep = flow_prep
self.n_threads, self.threads = n_threads, []
self.for_eval = for_eval
self.skip_silence_thr = None if for_eval else (0.01 if 'REC-Street' in self.subset_fn else 0.2)
audio_layouts = 'meta/audio_layouts.txt'
masks = {'WXYZ': np.array([1., 1., 1., 1.]), 'WXY': np.array([1., 1., 0., 1.])}
self.channel_mask = {l.split()[0]: masks[l.split()[1]] for l in open(audio_layouts).read().splitlines()}
# Placeholders
snd_ctx = int(context * audio_rate)
snd_dur = int(duration * audio_rate)
snd_shape = (snd_dur + snd_ctx - 1, int(ambi_order+1) ** 2)
vid_dur = int(duration * video_rate)
vid_shape = (vid_dur, frame_size[0], frame_size[1], 3)
names = ['id', 'ambix', 'audio_mask']
shapes = [(), snd_shape, ((self.ambi_order+1)**2,)]
dtypes = [tf.string, tf.float32, tf.float32]
if return_video:
names += ['video']
shapes += [vid_shape]
dtypes += [tf.float32]
if return_flow:
names += ['flow']
shapes += [vid_shape]
dtypes += [tf.float32]
self.tba = {m: tf.placeholder(dtype=t, shape=s) for m, s, t in zip(names, shapes, dtypes)}
# Setup tf queue
self.queue = tf.PaddingFIFOQueue(queue_size, names=names, dtypes=dtypes, shapes=shapes)
self.enqueue = self.queue.enqueue(self.tba)
self.queue_state = self.queue.size()
# Print feeder state
fn_provider = FilenameProvider(self.sample_dir, subset_fn=self.subset_fn, num_epochs=1)
n_chunks = 0
for yid in fn_provider.loop_samples():
folder = os.path.join(self.sample_dir, yid)
reader = SampleReader(folder, skip_silence_thr=self.skip_silence_thr,
skip_rate=10 if self.for_eval else None)
n_chunks += len(reader.chunks_t)
print('\n'+'='*20, 'Feeder', '='*20)
print('{:20s} | {}'.format('Input directory', fn_provider.directory))
print('{:20s} | {}'.format('# videos', fn_provider.num_samples))
print('{:20s} | {}'.format('# chunks', n_chunks))
print('{:20s} | {}'.format('# threads', self.n_threads))
print('{:20s} | {}'.format('Mode', 'eval' if self.for_eval else 'train'))
print('{:20s} | {}'.format('Video fps', video_rate))
print('{:20s} | {} frames, {} secs'.format('Video context', 0, 0))
print('{:20s} | {} frames, {} secs'.format('Video duration', vid_dur, duration))
print('{:20s} | {}'.format('Audio rate', audio_rate))
print('{:20s} | {} frames, {} secs'.format('Audio context', snd_ctx, context))
print('{:20s} | {} frames, {} secs'.format('Audio duration', snd_dur, duration))
print('\nFeeder output tensors')
for m, s, t in zip(names, shapes, dtypes):
print(' * {:10s} | {:20s} | {:10s}'.format(m, str(s), str(t)))
sys.stdout.flush()
def dequeue(self, num_elements):
return self.queue.dequeue_many(num_elements)
def thread_main(self, sess, thread_id, num_threads):
thread = threading.currentThread()
fn_provider = FilenameProvider(self.sample_dir, subset_fn=self.subset_fn,
num_epochs=1 if self.for_eval else np.inf,
shuffle=not self.for_eval)
NUM_SAMPLING = np.inf if self.for_eval else 5
SKIP_RATE = 10 if self.for_eval else None
thread_id = thread_id if self.for_eval else 0
num_threads = num_threads if self.for_eval else 1
for yid in fn_provider.loop_samples():
# Start readers
folder = os.path.join(self.sample_dir, yid)
reader = SampleReader(folder,
ambi_order=self.ambi_order,
audio_rate=self.audio_rate,
video_rate=self.video_rate,
context=self.context,
duration=self.duration,
return_video=self.return_video,
img_prep=self.img_prep,
return_flow=self.return_flow,
flow_prep=self.flow_prep,
skip_silence_thr=self.skip_silence_thr,
shuffle=not self.for_eval,
random_rotations=not self.for_eval,
skip_rate=SKIP_RATE,
thread_id=thread_id,
num_threads=num_threads)
# Feed data into tf queue
for chunk in reader.loop_chunks(NUM_SAMPLING):
feed_dict = {self.tba[n]: chunk[n] for n in chunk}
feed_dict[self.tba['audio_mask']] = self.channel_mask[yid]
if not thread.should_stop:
sess.run(self.enqueue, feed_dict=feed_dict)
else:
return
def done(self, sess):
for t in self.threads:
if t.isAlive():
return False
qsize = sess.run(self.queue_state)
if qsize >= 32:
return False
return True
def start_threads(self, sess):
# Launch feeding threads
for i in range(self.n_threads):
thread = threading.Thread(target=self.thread_main, args=(sess, i, self.n_threads))
thread.should_stop = False
thread.daemon = True # Thread will close when parent quits.
thread.start()
self.threads.append(thread)
return self.threads
def join(self):
for t in self.threads:
t.should_stop = True
t.join()