def get_per_video_sift_data_old(split, subsample=1, nr_clusters=64, color=10):
""" Loads the SIFT descriptors per video. `color` is a special flag; if it
is set to 10 the descriptors are loaded unnormalized.
"""
filename = ("/home/lear/oneata/tmp/trecvid11_sift/"
"sift_per_video_%s_subsample%d_k%d_color%d.raw" % (
split, subsample, nr_clusters, color))
if os.path.exists(filename):
# Load data from cache file.
print "Load per video data", filename
with open(filename, "r") as ff:
video_data = np.load(ff)
video_labels = np.load(ff)
video_names = cPickle.load(ff)
return video_data, video_labels, video_names
data, labels, limits = descriptors.load_dan_split(
split, subsample, nr_clusters, color)
# Get uniform weights within each video.
weights = _normalize(np.ones_like(labels, dtype=np.float), limits, 'L1')
# Load or compute data.
video_data = aggregate(data, weights, limits)
video_labels = np.array([labels[low] for low in limits[:-1]])
video_names = descriptors.vid_names_dan_split(split)
# Save data to file.
with open(filename, "w") as ff:
np.save(ff, video_data)
np.save(ff, video_labels)
cPickle.dump(video_names, ff)
return video_data, video_labels, video_names
def test_aggregate_sstats(self):
results = aggregate(self.sstats, self.norm_scores, self.limits)
assert_allclose(results, self.aggregated_sstats)
def test_aggregate_sstats(self):
results = aggregate(self.sstats, self.norm_scores, self.limits)
assert_allclose(results, self.aggregated_sstats)