def test_get_descriptors(self):
descrs = [
random_descriptor(), # [0]
random_descriptor(), # [1]
random_descriptor(), # [2]
random_descriptor(), # [3]
random_descriptor(), # [4]
]
index = MemoryDescriptorIndex()
index.add_many_descriptors(descrs)
# single descriptor reference
r = index.get_descriptor(descrs[1].uuid())
ntools.assert_equal(r, descrs[1])
# multiple descriptor reference
r = list(
index.get_many_descriptors([descrs[0].uuid(), descrs[3].uuid()]))
ntools.assert_equal(len(r), 2)
ntools.assert_equal(set(r), {descrs[0], descrs[3]})
def test_get_descriptors(self):
descrs = [
random_descriptor(), # [0]
random_descriptor(), # [1]
random_descriptor(), # [2]
random_descriptor(), # [3]
random_descriptor(), # [4]
]
index = MemoryDescriptorIndex()
index.add_many_descriptors(descrs)
# single descriptor reference
r = index.get_descriptor(descrs[1].uuid())
ntools.assert_equal(r, descrs[1])
# multiple descriptor reference
r = list(index.get_many_descriptors([descrs[0].uuid(),
descrs[3].uuid()]))
ntools.assert_equal(len(r), 2)
ntools.assert_equal(set(r),
{descrs[0], descrs[3]})
log.info("Classifying phone imagery descriptors")
i = 0
descriptor_index_shas = set(descriptor_index.iterkeys())
for p in phone2shas:
log.info('%s (%d / %d)', p, i + 1, len(phone2shas))
# Not all source "images" have descriptors since some URLs returned
# non-image files. Intersect phone sha's with what was actually
# computed. Warn if this reduces descriptors for classification to zero.
indexed_shas = set(phone2shas[p]) & descriptor_index_shas
if not indexed_shas:
raise RuntimeError(
"Phone number '%s' has no valid images associated "
"with it.\nBefore:\n%s\n\nAfter:\n%s" %
(p, phone2shas[p], indexed_shas))
descriptor_elems = descriptor_index.get_many_descriptors(*indexed_shas)
e2c = classifier.classify_async(descriptor_elems,
c_factory,
use_multiprocessing=True,
ri=1.)
pos_scores = [c['positive'] for c in e2c.values()]
# Max of pool
phone2score[p] = max(pos_scores)
i += 1
log.info("Saving score map")
csv.writer(open(PHONE2SCORE_OUTPUT_FILEPATH, 'w')) \
.writerows(sorted(phone2score.iteritems()))
log.info("Classifying phone imagery descriptors")
i = 0
descriptor_index_shas = set(descriptor_index.iterkeys())
for p in phone2shas:
log.info('%s (%d / %d)', p, i + 1, len(phone2shas))
# Not all source "images" have descriptors since some URLs returned
# non-image files. Intersect phone sha's with what was actually
# computed. Warn if this reduces descriptors for classification to zero.
indexed_shas = set(phone2shas[p]) & descriptor_index_shas
if not indexed_shas:
raise RuntimeError(
"Phone number '%s' has no valid images associated "
"with it.\nBefore:\n%s\n\nAfter:\n%s"
% (p, phone2shas[p], indexed_shas))
descriptor_elems = descriptor_index.get_many_descriptors(*indexed_shas)
e2c = classifier.classify_async(descriptor_elems, c_factory,
use_multiprocessing=True, ri=1.)
pos_scores = [c['positive'] for c in e2c.values()]
# Max of pool
phone2score[p] = max(pos_scores)
i += 1
log.info("Saving score map")
csv.writer(open(PHONE2SCORE_OUTPUT_FILEPATH, 'w')) \
.writerows(sorted(phone2score.iteritems()))
log.info("Done")
