def compute_statistics_worker(dataset, samples, labels, sstats_out,
descs_to_sstats, pca, gmm, **kwargs):
""" Computes the Fisher vectors for each slice that results from the
temporal spliting of get_time_intervals. The resulting Fisher vectors
are outputed to a binary file.
"""
nr_frames_to_skip = kwargs.get('nr_frames_to_skip', 0)
delta = kwargs.get('delta', 120)
spacing = kwargs.get('spacing', 1)
rescale_videos = kwargs.get('rescale_videos', 'none')
per_shot = kwargs.get('per_shot', False)
sample_limits_file = kwargs.get('sample_limits', None)
if sample_limits_file:
with open(sample_limits_file, 'r') as ff:
sample_limits = cPickle.load(ff)
else:
sample_limits = None
_, track_len, _ = parse_ip_type(dataset.FTYPE)
track_len = (nr_frames_to_skip + 1) * int(track_len)
D = gmm.d
K = dataset.VOC_SIZE
for sample, label in izip(samples, labels):
# Still not very nice. Maybe I should create the file on the else
# branch.
if sstats_out.exists(str(sample)):
continue
sstats_out.touch(str(sample))
# The path to the movie.
infile = os.path.join(
dataset.SRC_DIR,
sample.movie + dataset.SRC_EXT)
status = None
if rescale_videos != 'none':
# Rescale movie.
status, infile = rescale(infile, MAX_WIDTH[rescale_videos],
thresh=50)
if status == 'bad_encoding':
print 'Bad encoding ' + sample.movie
continue
if per_shot:
begin_frames, end_frames = dataset.get_shots(sample.movie)
elif sample_limits:
begin_frames = sample_limits[sample]['begin_frames']
end_frames = sample_limits[sample]['end_frames']
else:
begin_frames, end_frames = get_time_intervals(
sample.bf, sample.ef, delta, spacing)
# Count the number of descriptors for each chunk.
nr_slices = len(begin_frames)
N = np.zeros(nr_slices)
sstats = np.zeros((nr_slices, K + 2 * K * D),
dtype=np.float32)
for chunk in read_descriptors_from_video(
infile, nr_descriptors=1, begin_frames=begin_frames,
end_frames=end_frames, nr_skip_frames=nr_frames_to_skip):
xx = pca.transform(chunk[:, 3:])
# Determine slice number based on time.
ii = get_slice_number(
# Get time stamp of the beginning of the track.
chunk[:, 2] - track_len + 1, begin_frames, end_frames)
N[ii] += 1
# Update corresponding sstats cell.
sstats[ii] += descs_to_sstats(xx, gmm)
# Ignore chunks with 0 descriptors
N_not_null = N[N != 0]
sstats = sstats[N != 0, :]
sstats /= N_not_null[:, np.newaxis]
# Write also the label, the number of descriptors and begin and end
# frames.
sstats_out.write(str(sample), sstats, info={
'label': label,
'nr_descs': N,
'begin_frames': begin_frames,
'end_frames': end_frames})
if status == 'rescaled':
os.remove(infile)
def compute_statistics_worker(dataset, samples, labels, sstats_out,
descs_to_sstats, pca, gmm, **kwargs):
""" Computes the Fisher vectors for each slice that results from the
temporal spliting of get_time_intervals. The resulting Fisher vectors
are outputed to a binary file.
"""
nr_frames_to_skip = kwargs.get('nr_frames_to_skip', 0)
delta = kwargs.get('delta', 120)
spacing = kwargs.get('spacing', 1)
rescale_videos = kwargs.get('rescale_videos', 'none')
per_shot = kwargs.get('per_shot', False)
sample_limits_file = kwargs.get('sample_limits', None)
if sample_limits_file:
with open(sample_limits_file, 'r') as ff:
sample_limits = cPickle.load(ff)
else:
sample_limits = None
_, track_len, _ = parse_ip_type(dataset.FTYPE)
track_len = (nr_frames_to_skip + 1) * int(track_len)
D = gmm.d
K = dataset.VOC_SIZE
for sample, label in izip(samples, labels):
# Still not very nice. Maybe I should create the file on the else
# branch.
if sstats_out.exists(str(sample)):
continue
sstats_out.touch(str(sample))
# The path to the movie.
infile = os.path.join(dataset.SRC_DIR, sample.movie + dataset.SRC_EXT)
status = None
if rescale_videos != 'none':
# Rescale movie.
status, infile = rescale(infile,
MAX_WIDTH[rescale_videos],
thresh=50)
if status == 'bad_encoding':
print 'Bad encoding ' + sample.movie
continue
if per_shot:
begin_frames, end_frames = dataset.get_shots(sample.movie)
elif sample_limits:
begin_frames = sample_limits[sample]['begin_frames']
end_frames = sample_limits[sample]['end_frames']
else:
begin_frames, end_frames = get_time_intervals(
sample.bf, sample.ef, delta, spacing)
# Count the number of descriptors for each chunk.
nr_slices = len(begin_frames)
N = np.zeros(nr_slices)
sstats = np.zeros((nr_slices, K + 2 * K * D), dtype=np.float32)
for chunk in read_descriptors_from_video(
infile,
nr_descriptors=1,
begin_frames=begin_frames,
end_frames=end_frames,
nr_skip_frames=nr_frames_to_skip):
xx = pca.transform(chunk[:, 3:])
# Determine slice number based on time.
ii = get_slice_number(
# Get time stamp of the beginning of the track.
chunk[:, 2] - track_len + 1,
begin_frames,
end_frames)
N[ii] += 1
# Update corresponding sstats cell.
sstats[ii] += descs_to_sstats(xx, gmm)
# Ignore chunks with 0 descriptors
N_not_null = N[N != 0]
sstats = sstats[N != 0, :]
sstats /= N_not_null[:, np.newaxis]
# Write also the label, the number of descriptors and begin and end
# frames.
sstats_out.write(str(sample),
sstats,
info={
'label': label,
'nr_descs': N,
'begin_frames': begin_frames,
'end_frames': end_frames
})
if status == 'rescaled':
os.remove(infile)
def compute_statistics_from_video_worker(dataset, samples, labels, sstats_out, descs_to_sstats, pca, gmm, **kwargs):
""" Computes the Fisher vector directly from the video in an online
fashion. The chain of actions is the following: compute descriptors one
by one, get a descriptor and apply PCA to it, then compute the
posterior probabilities and update the Fisher vector.
Inputs
------
dataset: Dataset instance
The dataset on which we are operating.
samples: list of SampID objects
For which samples we compute sufficietn statistics.
sstats_out: SstatsMap instace
Defines the output location and names.
descs_to_sstats: callable
Function that converts the data to sufficient statistics.
pca: PCA instance
Used for dimensionality reduction.
gmm: GMM instance
Note: it doesn't have implemented multiple grids (spatial pyramids)
"""
nr_frames_to_skip = kwargs.get("nr_frames_to_skip", 0)
delta = kwargs.get("delta", 0)
spacing = kwargs.get("spacing", 0)
rescale_videos = kwargs.get("rescale_videos", "none")
sample_limits_file = kwargs.get("sample_limits", None)
if sample_limits_file:
with open(sample_limits, "r") as ff:
sample_limits = cPickle.load(ff)
else:
sample_limits = None
D = gmm.d
K = dataset.VOC_SIZE
for sample, label in izip(samples, labels):
# Still not very nice. Maybe I should create the file on the else
# branch.
if sstats_out.exists(str(sample)):
continue
sstats_out.touch(str(sample))
# The path to the movie.
infile = os.path.join(dataset.SRC_DIR, sample.movie + dataset.SRC_EXT)
status = None
if rescale_videos != "none":
status, infile = rescale(infile, MAX_WIDTH[rescale_videos], thresh=50)
if status == "bad_encoding":
print "Bad encoding " + sample.movie
# sstats_out.remove(str(sample))
continue
if sample_limits:
begin_frames = sample_limits[sample]["begin_frames"]
end_frames = sample_limits[sample]["end_frames"]
else:
begin_frames, end_frames = get_time_intervals(sample.bf, sample.ef, delta, spacing)
N = 0 # Count the number of descriptors for this sample.
sstats = np.zeros(K + 2 * K * D, dtype=np.float32)
for chunk in read_descriptors_from_video(
infile, begin_frames=begin_frames, end_frames=end_frames, nr_skip_frames=nr_frames_to_skip
):
chunk_size = chunk.shape[0]
# Apply PCA to the descriptor.
xx = pca.transform(chunk[:, 3:])
# Update the sufficient statistics for this sample.
sstats += descs_to_sstats(xx, gmm) * chunk_size
N += chunk_size
sstats /= N # Normalize statistics.
sstats_out.write(
str(sample),
sstats,
info={
"label": label,
"nr_descs": np.array([N]),
"begin_frames": np.array([sample.bf]),
"end_frames": np.array([sample.ef]),
},
)
# Delete rescaled video.
if status == "rescaled":
os.remove(infile)
