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_chunk_normalization(self):
result = np.zeros_like(self.scores)
for limits in chunker(self.limits, 2):
low = limits[0]
high = limits[-1]
result[low:high] = _normalize(self.scores[low:high], limits - low)
assert_allclose(result, self.norm_scores)
def test_chunk_normalization(self):
result = np.zeros_like(self.scores)
for limits in chunker(self.limits, 2):
low = limits[0]
high = limits[-1]
result[low:high] = _normalize(self.scores[low:high], limits - low)
assert_allclose(result, self.norm_scores)
def test_normalize_scores_by_number(self):
rr = _normalize(self.scores, self.limits, "L0")
assert_allclose(rr, self.norm_scores_by_nr)
def test_normalize_scores(self):
results = _normalize(self.scores, self.limits)
assert_allclose(results, self.norm_scores)
def test_normalize_scores_by_number(self):
rr = _normalize(self.scores, self.limits, 'L0')
assert_allclose(rr, self.norm_scores_by_nr)
def test_normalize_scores(self):
results = _normalize(self.scores, self.limits)
assert_allclose(results, self.norm_scores)
