Lena on TX1 by OpenCV C++
글
Spectral Saliency with QuaternionFT by Python
https://github.com/wandering007/spectral-based-saliency.git
Spectral Residual Saliency by Python-OpenCV, cpp
https://github.com/uoip/SpectralResidualSaliency.git
트랜스퍼런닝과 차별 러닝레이트
https://towardsdatascience.com/transfer-learning-using-differential-learning-rates-638455797f00
six windows
#!/bin/bash for file in *.wav do outfile= "${file%.*}1.png" sox "$file" -n spectrogram -x 512 -w hann -a -r -o "$outfile" outfile= "${file%.*}2.png" sox "$file" -n spectrogram -x 512 -w hamming -a -r -o "$outfile" outfile= "${file%.*}3.png" sox "$file" -n spectrogram -x 512 -w bartlett -a -r -o "$outfile" outfile= "${file%.*}4.png" sox "$file" -n spectrogram -x 512 -w rectangular -a -r -o "$outfile" outfile= "${file%.*}5.png" sox "$file" -n spectrogram -x 512 -w kaiser -a -r -o "$outfile" outfile= "${file%.*}6.png" sox "$file" -n spectrogram -x 512 -w dolph -a -r -o "$outfile" done
bin2Gray.py
#!/usr/bin/python import os import re import numpy import scipy.misc import os, array from scipy import ndimage, misc # aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa images = [] for root, dirnames, filenames in os.walk( "." ): for filename in filenames: if re.search( "\.(vir)$" , filename): filepath = os.path.join(root, filename) f = open(filename, 'rb' ); ln = os.path.getsize(filename); # length of file in bytes width = numpy.int(numpy.ceil(numpy.sqrt(ln))); rem = ln % width; a = array.array( "B" ); # uint8 array a.fromfile(f, ln-rem); ...
wav to scalogram
#!/usr/bin/python import os import re import numpy import scipy.misc import os, array from scipy import ndimage, misc from wavelets import WaveletAnalysis import wave import sys import matplotlib.pyplot as plt images = [] for root, dirnames, filenames in os.walk("."): for filename in filenames: if re.search("\.(wav)$", filename): filepath = os.path.join(root, filename) f = open(filename,'rb'); ln = os.path.getsize(filename); # length of file in bytes spf = wave.open(filename,'r') #Extract Raw Audio from Wav File signal = spf.readframes(-1) signal = numpy.fromstring(signal, 'Int8') # and a sample spacing dt = 10 wa = WaveletAnalysis(signal, dt=dt) # wavelet power spectrum power = wa.wavelet_power # scales scales = wa.scales # associated time vector t = wa.time # reconstruction of the original dat...
PyTorch DataParallel
https://github.com/hunkim/PyTorchZeroToAll/blob/master/tutorials/PyTorchDataParallel.ipynb
rename from CSV
path = 'Malware500' tmp_path = 'data' with open('Malware500.csv','rb') as csvfile: reader = csv.reader(csvfile, delimiter = ',') for row in reader: oldname = os.path.join(path, row[0]) if os.path.exists(oldname): newname = os.path.join(tmp_path, row[1]) os.rename(oldname, newname) print >> sys.stderr, "renamed '%s' to '%s'" % (oldname, newname) else: print >> sys.stderr, "file '%s' not found" % oldname
train data scaling in PyTorch
# Data augmentation and normalization for training # Just normalization for validation data_transforms = { 'train' : transforms.Compose([ transforms.Scale( 256 ), transforms.RandomSizedCrop( 224 ), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([ 0.485 , 0.456 , 0.406 ], [ 0.229 , 0.224 , 0.225 ]) ]), 'val' : transforms.Compose([ transforms.Scale( 256 ), transforms.CenterCrop( 224 ), transforms.ToTensor(), transforms.Normalize([ 0.485 , 0.456 , 0.406 ], [ 0.229 , 0.224 , 0.225 ]) ]), }
files split by csv column
cat Malware_random_list_1st_500_noHeader.csv | awk -F "," '{print "cp Malware_samples_1st_500/"$1".vir Malware_samples_1st_500_class/"$3}' | sh 헤더 제거,
MFCC by librosa
import librosa import librosa.display # Generate mfccs from a time series y, sr = librosa.load( '0acdb43dae5a7a66945bc44ee87ee67cd64f4e9dabf2817ab59ffe8bf9093778.wav' ) librosa.feature.mfcc(y=y, sr=sr) # array([[ -5.229e+02, -4.944e+02, ..., -5.229e+02, -5.229e+02], # [ 7.105e-15, 3.787e+01, ..., -7.105e-15, -7.105e-15], # ..., # [ 1.066e-14, -7.500e+00, ..., 1.421e-14, 1.421e-14], # [ 3.109e-14, -5.058e+00, ..., 2.931e-14, 2.931e-14]]) # Use a pre-computed log-power Mel spectrogram S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels= 128 , fmax= 8000 ) librosa.feature.mfcc(S=librosa.power_to_db(S)) # array([[ -5.207e+02, -4.898e+02, ..., -5.207e+02, -5.207e+02], # [ -2.576e-14, 4.054e...
rename file by a csv column
import os import csv with open ( 'names.csv' ) as f : lines = csv . reader ( f ) for line in lines : os . rename ( line [ 0 ], line [ 1 ])