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specimage_features.py
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specimage_features.py
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'''
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| ___| | | / _ \ | ___ \_ _| _
| |_ ___ __ _| |_ _ _ _ __ ___ ___ / /_\ \| |_/ / | | (_)
| _/ _ \/ _` | __| | | | '__/ _ \/ __| | _ || __/ | |
| || __/ (_| | |_| |_| | | | __/\__ \ | | | || | _| |_ _
\_| \___|\__,_|\__|\__,_|_| \___||___/ \_| |_/\_| \___/ (_)
___ _ _
/ _ \ | (_)
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| _ | | | |/ _` | |/ _ \
| | | | |_| | (_| | | (_) |
\_| |_/\__,_|\__,_|_|\___/
This will featurize folders of audio files if the default_audio_features = ['specimage_features']
Uses a spectrogram and features extracted from the spectrogram as feature vectors.
'''
import os, sys
import helpers.audio_plot as ap
def prev_dir(directory):
g=directory.split('/')
# print(g)
lastdir=g[len(g)-1]
i1=directory.find(lastdir)
directory=directory[0:i1]
return directory
# import to get image feature script
directory=os.getcwd()
prevdir=prev_dir(directory)
sys.path.append(prevdir+'/image_features')
haar_dir=prevdir+'image_features/helpers/haarcascades'
import image_features as imf
os.chdir(directory)
def specimage_featurize(wavfile, cur_dir, haar_dir):
# create temporary image
imgfile=ap.plot_spectrogram(wavfile)
features, labels=imf.image_featurize(cur_dir, haar_dir, imgfile)
# remove temporary image file
os.remove(wavfile[0:-4]+'.png')
return features, labels