def print_result(result, color):
for score, node in result:
# 1° column: score
score = round(score, 2)
# 2° column: text d
summary_length = 60
node_summary = node_text_summary(node, length=summary_length)
node_summary = '"{}"'.format(node_summary)
# 3° column: feature vector
descriptor = DefaultFeatures.table if node.tag == "table" else DefaultFeatures.list
selected = DefaultFeatures.table_selected if node.tag == "table" else DefaultFeatures.list_selected
ft = FeaturesExtractor()
features = ft.extract(node,
selected=selected,
features_descriptor=descriptor)
features_array = ft.toArray(features)
padding = " " * (summary_length - len(node_summary))
print(with_color(score, color=color), node_summary, padding,
str(list(features_array)))
for candidate in candidates:
feature_vectors.append(
features_extractor.extract_features(tomogram, candidate))
# this sets each candidate's label
labels.append(labeler.label(candidate))
return (candidates, feature_vectors, labels)
#this is tuple of tuples of TiltedTemplates (each group has the same template_id)
templates = TemplateGenerator.generate_tilted_templates()
#save templates to files
candidate_selector = CandidateSelector.CandidateSelector(templates)
features_extractor = FeaturesExtractor.FeaturesExtractor(templates)
#Training
feature_vectors = []
#a label is a template_id, where 0 is junk
labels = []
criteria = (Candidate.fromTuple(1, 0, 10,
10), Candidate.fromTuple(1, 2, 27, 18),
Candidate.fromTuple(0, 0, 10, 28))
for i in range(TRAINING_SET_SIZE):
# configuration for tomogram generation
#with a set composition
tomogram = TomogramGenerator.generate_tomogram_with_given_candidates(
os.makedirs(videoFeatureDir)
for featureName in featureNameListNew:
videoFeatureList = []
for i in range(0, 3):
videoFeatureList.extend(
np.loadtxt(featureName + "_feature_" + str(i)))
np.savetxt(videoFeatureDir + os.sep +
os.path.basename(featureName),
videoFeatureList,
newline=" ")
if __name__ == '__main__':
starttime = datetime.datetime.now()
fe.FeaturesExtractor(
r"/home/sunbite/MFSSEL/keyframe_not_on_spark/",
r"/home/sunbite/MFSSEL/features_not_on_spark/").featuresExtractor()
# fe.FeaturesExtractor(
# r"/home/sunbite/MFSSEL/keyframe_not_on_spark/",
# r"/home/sunbite/MFSSEL/features_not_on_spark/").getAllVideoFeature()
endtime = datetime.datetime.now()
print(
'----------------------------------------------------------------------------'
)
print(
'----------------------------------------------------------------------------'
)
print(
'-------------FeaturesExtractor Running time: %s Seconds--------------'
% (endtime - starttime).seconds)
def __init__(self, relevant_threshold=0.8):
self.fe = FeaturesExtractor()
self.relevant_threshold = relevant_threshold
self.tableClassifier = Classifier('models/table_classifier.h5')
self.listClassifier = Classifier('models/list_classifier.h5')
videoFeatureList.extend(
np.loadtxt(featureName + "_feature_" + str(i)))
np.savetxt(videoFeatureDir + os.sep +
os.path.basename(featureName),
videoFeatureList,
newline=" ")
if __name__ == '__main__':
starttime = datetime.datetime.now()
# fe.FeaturesExtractor(
# r"/home/sunbite/keyframe/",
# r"/home/sunbite/features_new_1").featuresExtractor()
fe.FeaturesExtractor(
r"/home/sunbite/MFSSEL/keyframe/",
r"/home/sunbite/MFSSEL/features_new_1/").getAllVideoFeature()
endtime = datetime.datetime.now()
print(
'----------------------------------------------------------------------------'
)
print(
'----------------------------------------------------------------------------'
)
print(
'-------------FeaturesExtractor Running time: %s Seconds--------------'
% (endtime - starttime).seconds)
print(
'----------------------------------------------------------------------------'
)
print(