def test():
import os
import sys
lib_path = os.path.abspath('../utilization/')
sys.path.append(lib_path)
from dir_processing import DirProcessing
from file_op import FileOp
LSF.build_dictionary()
landmarks_urls = DirProcessing.get_all_landmarks_urls_from_sequence('87','4')
test_one_sequence = True
if test_one_sequence:
video = LSF.lsf_from_sequence(landmarks_urls)
print video.landmarks_urls
test_one_landmark = False
if test_one_landmark:
landmarks = FileOp.read_landmarks_file(landmarks_urls[11])
landmarks_neural = FileOp.read_landmarks_file(landmarks_urls[0])
lsf_document = LSF.lsf(landmarks, landmarks_neural)
landmark = landmarks[35,:]
landmark_neural = landmarks_neural[35, :]
word = LSF.compute_word(35, landmark, landmark_neural)
print landmark, landmark_neural, word
def get_topic_proportions_for_every_image():
from dir_processing import DirProcessing
landmarks_urls_list = []
person_ids = DirProcessing.get_all_person_ids()
for person_id in person_ids:
perform_ids = DirProcessing.get_all_perform_ids_from_person_id(person_id)
for perform_id in perform_ids:
landmarks_urls = DirProcessing.get_all_landmarks_urls_from_sequence(person_id, perform_id)
landmarks_urls_list.extend(landmarks_urls)
doc_num = len(landmarks_urls_list)
dt_file = '../ctm-dist/CTM46/final-lambda.dat'
dt_vector = np.loadtxt(dt_file)
topic_num = dt_vector.size / doc_num
dt_matrix = np.reshape(dt_vector, (doc_num, topic_num))
np.set_printoptions(suppress=True)
final_theta = np.exp(dt_matrix)
final_theta = final_theta / np.sum(final_theta, axis=1)[:, np.newaxis]
return landmarks_urls_list, final_theta
def generate_corpus_and_write_to_file():
""" generate the copus, write it to files and store the LSF corpus features """
import os
import sys
lib_path = os.path.abspath('../utilization/')
sys.path.append(lib_path)
from dir_processing import DirProcessing
LSF.build_dictionary()
lsf_corpus = []
person_ids = DirProcessing.get_all_person_ids()
for person_id in person_ids:
perform_ids = DirProcessing.get_all_perform_ids_from_person_id(person_id)
for perform_id in perform_ids:
landmarks_urls = DirProcessing.get_all_landmarks_urls_from_sequence(person_id, perform_id)
expression_sequence = LSF.lsf_from_sequence(landmarks_urls)
print 'The feature extraction of expression person S{} and perform time {} has ' \
'been done.'.format(person_id, perform_id)
lsf_corpus.append(expression_sequence)
import cPickle
with open('../model/corpus.pk', 'wb') as f:
cPickle.dump(lsf_corpus, f)
with open('../model/corpus.txt', 'w') as f:
for expression_sequence in lsf_corpus:
lsf_sequence = expression_sequence.lsf_sequence
for lsf_document in lsf_sequence:
f.write(str(len(lsf_document)))
for word, count in lsf_document.iteritems():
wid = LSF.word2id[word]
s = " %d:%d" %(wid, count)
f.write(s)
f.write("\n")
