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