def get_similar(image):
db = Database()
method = Color()
samples = method.make_samples(db)
# make sure image is ndarray
query = {
'img': str(uuid.uuid1()),
'cls': 'TBD',
'hist': method.histogram(image)
}
_, result = infer(query, samples=samples)
return [r['url'] for r in result]
for d in depths:
APs = evaluate_class(db, f_instance=fusion, d_type=d_type, depth=d)
cls_MAPs = []
for cls, cls_APs in APs.items():
MAP = np.mean(cls_APs)
cls_MAPs.append(MAP)
r = "{},{},{},{}".format(",".join(combination), d, d_type,
np.mean(cls_MAPs))
print(r)
result.write('\n' + r)
print()
result.close()
if __name__ == "__main__":
db = Database()
# evaluate features double-wise
evaluate_feats(db, N=2, d_type='d1')
# evaluate features triple-wise
evaluate_feats(db, N=3, d_type='d1')
# evaluate features quadra-wise
evaluate_feats(db, N=4, d_type='d1')
# evaluate features penta-wise
evaluate_feats(db, N=5, d_type='d1')
# evaluate features hexa-wise
evaluate_feats(db, N=6, d_type='d1')
continue
samples.append({
'img': d_img,
'cls': d_cls,
'url': d_url,
'hist': d_hist
})
cPickle.dump(
samples, open(os.path.join(cache_dir, sample_cache), "wb",
True))
return samples
if __name__ == "__main__":
db = Database()
data = db.get_data()
color = Color()
# test normalize
print(os.path.join(DATASETS_PATH, data['cls'][0], data['img'][0]))
hist = color.histogram(os.path.join(DATASETS_PATH, data['cls'][0],
data['img'][0] + '.jpg'),
type='global')
assert hist.sum() - 1 < 1e-9, "normalize false"
# test histogram bins
def sigmoid(z):
a = 1.0 / (1.0 + np.exp(-1. * z))
return a