def sift_matrix():
featurelist = sift_feature_listnames_generator()
imlist = getFiles(
'/home/aurora/hdd/workspace/PycharmProjects/data/N20040103G/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
# print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
if d1.shape[0] == 0 or d2.shape[0] == 0:
matchscores[i, j] = 0
else:
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save(
'/home/aurora/hdd/workspace/PycharmProjects/data/aurora_img_matches_matrix_20151212',
matchscores)
print matchscores
def get_sift_distance_matrix_without_constraint(img_vectors):
img_vector = img_vectors.astype(dtype=np.int32)
img_matrix = np.zeros((img_vector.shape[0], img_vector.shape[0]))
for i in range(0, img_vector.shape[0]):
for j in range(0, img_vector.shape[0]):
l1, d1 = sift.read_feature_from_file('aurora'+str(i)+'.sift')
l2, d2 = sift.read_feature_from_file('aurora'+str(j)+'.sift')
img_matrix[i, j] = sift_distance(d1, d2)
np.save('/home/aurora/hdd/workspace/PycharmProjects/data/sift_distance_2015_1211_without_constraint',img_matrix)
return img_matrix
def get_sift_distance_matrix_without_constraint(img_vectors):
img_vector = img_vectors.astype(dtype=np.int32)
img_matrix = np.zeros((img_vector.shape[0], img_vector.shape[0]))
for i in range(0, img_vector.shape[0]):
for j in range(0, img_vector.shape[0]):
l1, d1 = sift.read_feature_from_file('aurora' + str(i) + '.sift')
l2, d2 = sift.read_feature_from_file('aurora' + str(j) + '.sift')
img_matrix[i, j] = sift_distance(d1, d2)
np.save(
'/home/aurora/hdd/workspace/PycharmProjects/data/sift_distance_2015_1211_without_constraint',
img_matrix)
return img_matrix
def get_all_dsift_features(filepath):
features_files = [os.path.join(filepath, filename) for index, filename in enumerate(os.listdir(filepath)) if filename.endswith('.dsift')]
prefix = '/home/aurora/workspace/PycharmProjects/aurora_detection/dsift_features/dsiftaurora'
suffix = '.dsift'
feature = []
for i in range(len(features_files)):
file = prefix + str(i) + suffix
l1, d1 = sift.read_feature_from_file(file)
feature.append(d1.flatten())
feature = np.array(feature)
np.save('/home/aurora/hdd/workspace/PycharmProjects/data/dsift_features_2015_1214', feature)
return feature
def sift_matrix():
featurelist = sift_feature_listnames_generator()
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/N20040103G/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
# print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
if d1.shape[0] == 0 or d2.shape[0] == 0:
matchscores[i, j] = 0
else:
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save('/home/aurora/hdd/workspace/PycharmProjects/data/aurora_img_matches_matrix_20151212', matchscores)
print matchscores
def get_all_dsift_features(filepath):
features_files = [
os.path.join(filepath, filename)
for index, filename in enumerate(os.listdir(filepath))
if filename.endswith('.dsift')
]
prefix = '/home/aurora/workspace/PycharmProjects/aurora_detection/dsift_features/dsiftaurora'
suffix = '.dsift'
feature = []
for i in range(len(features_files)):
file = prefix + str(i) + suffix
l1, d1 = sift.read_feature_from_file(file)
feature.append(d1.flatten())
feature = np.array(feature)
np.save(
'/home/aurora/hdd/workspace/PycharmProjects/data/dsift_features_2015_1214',
feature)
return feature
