def main(opt):
k1 = Keyword(opt['keyword1'], opt['data_dir'])
text1 = k1.get_all_context_text()
text1_repost = k1.get_all_repost_text()
k2 = Keyword(opt['keyword2'], opt['data_dir'])
text2 = k2.get_all_context_text()
text2_repost = k2.get_all_repost_text()
seg = Segmentation()
document = seg.segmentation([text1, text2, text1_repost, text2_repost])
ex = SubKeyExtractor()
ex.fit(document)
results = ex.extract(document, 20)
print(results)
scorer = SimilarityScore(topn=20)
score = scorer.score(k1.get_top_context_text(), results[0], results[2],
k2.get_top_context_text(), results[1], results[3])
print(score)
print('done')
def pre_processing(image_file, file):
biggest_component = Remove_Calibration.cutout_img(image_file)
#Remove_Calibration.save_image(biggest_component, file.split(".")[0])
bin_image = Binarization.binarize(biggest_component)
#Binarization.save_image(bin_image, file.split(".")[0])
# Returns a list of segmented characters and number of rows
cropped_characters, row, no_ofChar = Segmentation.segmentation(bin_image)
# Segmentation.save_segmented_characters(cropped_characters, row, file)
return cropped_characters, row, no_ofChar
