# it contains examples on how to run the main Segmenter class
from lib.Helpers import load_text, load_file
from Segmenter import Segmenter
from Evaluation import Evaluation
# loading data
file_path = 'data/br-phono-train.txt'
#file_path = 'data/small.txt'
(text, word_freq, char_freq) = load_text(file_path)
segmenter = Segmenter(text=text,
char_freq=char_freq,
p=2,
alpha=20,
p_hash=0.5)
print "Start segmenting \n"
segm_text = segmenter.run(200)
filetext = load_file(file_path)
print "Start evaluating \n"
evaluation = Evaluation(filetext, segm_text)
P, R, F, BP, BR, BF, LP, LR, LF = evaluation.run()
print "Boundary evaluation: \n Precision: %.2f, Recall: %.2f, F-measure: %.2f \n" % (
P, R, F)
print "Ambigious boundary evaluation: \n Precision: %.2f, Recall: %.2f, F-measure: %.2f \n" % (
BP, BR, BF)
print "Lexicon evaluation: \n Precision: %.2f, Recall: %.2f, F-measure: %.2f \n" % (
LP, LR, LF)
from Dataset import Dataset
from Segmenter import Segmenter
from BlobAnalyzer import BlobAnalyzer
# reading dataset
dataset = Dataset("./immagini/*.BMP")
segmenter = Segmenter()
analyzer = BlobAnalyzer()
# set true: show the computation of all the process
show = False
for image in dataset.images:
binary, labels, n_labels = segmenter.run(image, show=show)
stats = analyzer.run(binary, labels, n_labels, show=show)
analyzer.show(image, stats)
