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spec_plot.py
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spec_plot.py
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import glob
import pandas as pd
import os
import sys
#from Tkinter import *
import tkinter
from tkinter import *
from tkinter import filedialog
import matplotlib.mlab
import numpy as np
from matplotlib import pyplot as plt
import scipy.io.wavfile as wav
from numpy.lib import stride_tricks
import soundfile as sf
def select_dir():
root = tkinter.Tk()
print("Select the folder containing .wav files to create spectrograms")
root.directory = filedialog.askdirectory()
print (root.directory)
wav_path = root.directory + "/"
original_dir_name = root.directory.split("/")[-1]
png_path = root.directory.rsplit("/",1)[0] + "/"+ "spectrograms" + "/" + "spectrograms_" + original_dir_name + "/"
print("wav_path: ")
print(wav_path)
print("original_dir_name: ")
print(original_dir_name)
print("png_path: ")
print(png_path)
return wav_path, original_dir_name, png_path
def create_spectrograms():
wav_path, original_dir_name, png_path = select_dir()
wavfiles = glob.glob(os.path.join(wav_path+"*.wav"))
count = 0
for audiofile in wavfiles:
png_name = audiofile.split("/")[-1].rsplit(".",1)[0]
png_final_path = png_path + png_name + ".png"
if os.path.exists(png_path):
plotstft(audiofile,png_final_path)
count +=1
print(count)
else:
os.makedirs(png_path)
plotstft(audiofile,png_final_path)
count +=1
print(count)
return count, png_path, wav_path
def create_spectrograms_internal(wav_path,png_path):
#wav_path, original_dir_name, png_path = select_dir()
wavfiles = glob.glob(os.path.join(wav_path+"*.wav"))
count = 0
for audiofile in wavfiles:
png_name = audiofile.split("/")[-1].rsplit(".",1)[0]
#png_path = png_path + "/" + "spectrograms" + "/"
png_final_path = png_path + png_name + ".png"
print(png_final_path)
if os.path.exists(png_path):
plotstft(audiofile,png_final_path)
count +=1
print(count)
else:
os.makedirs(png_path)
plotstft(audiofile,png_final_path)
count +=1
print(count)
return count
""" short time fourier transform of audio signal """
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
win = window(frameSize)
hopSize = int(frameSize - np.floor(overlapFac * frameSize))
# zeros at beginning (thus center of 1st window should be for sample nr. 0)
samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig)
# cols for windowing
cols = int(np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1)
# zeros at end (thus samples can be fully covered by frames)
samples = np.append(samples, np.zeros(frameSize))
frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
frames *= win
return np.fft.rfft(frames)
""" scale frequency axis logarithmically """
def logscale_spec(spec, sr=44100, factor=20.):
timebins, freqbins = np.shape(spec)
scale = np.linspace(0, 1, freqbins) ** factor
scale *= (freqbins-1)/max(scale)
scale = np.unique(np.round(scale))
scale = scale.astype(int)
# create spectrogram with new freq bins
newspec = np.complex128(np.zeros([timebins, len(scale)]))
for i in range(0, len(scale)):
if i == len(scale)-1:
newspec[:,i] = np.sum(spec[:,scale[i]:], axis=1)
else:
newspec[:,i] = np.sum(spec[:,scale[i]:scale[i+1]], axis=1)
# list center freq of bins
allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
freqs = []
for i in range(0, len(scale)):
if i == len(scale)-1:
freqs += [np.mean(allfreqs[scale[i]:])]
else:
freqs += [np.mean(allfreqs[scale[i]:scale[i+1]])]
return newspec, freqs
""" plot spectrogram"""
def plotstft(audiopath, plotpath=None, colormap="jet", binsize=2**10):
samples, samplerate = sf.read(audiopath)
s = stft(samples, binsize)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
timebins, freqbins = np.shape(ims)
#plt.figure(figsize=(15, 7.5))
plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
#plt.tick_params(direction='out', length=6, width=2, colors='r',grid_color='r', grid_alpha=0.5)
plt.tick_params(axis='both', which='both', bottom='off',top='off',left='off',right='off',labelbottom='off',labelleft='off')
#plt.colorbar()
#plt.axis('off')
fig = plt.gcf()
#plt.xlabel("time (s)")
#plt.ylabel("frequency (hz)")
#plt.xlim([0, timebins-1])
#plt.ylim([0, freqbins])
#xlocs = np.float32(np.linspace(0, timebins-1, 5))
#plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
#ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
#plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
fig.savefig(plotpath, bbox_inches="tight", pad_inches=0)
#else:
#plt.show()
plt.clf()
plt.close()
#plotstft("/home/aratrika/aratrika_work/data sets/UrbanSound_ex/test_wav_png/7061-6-0-0.wav")
#count = create_spectrograms()
def iterate_folds_spec():
wav_path_list=[]
png_path_list=[]
user_input = 'c'
while user_input == 'c':
#count = create_spectrograms()
wav_path, original_dir_name, png_path = select_dir()
wav_path_list.append(wav_path)
png_path_list.append(png_path)
print('Press c to continue selecting paths: \n')
user_input = raw_input()
length = int(len(wav_path_list))
for j in range(length):
print(wav_path_list[j])
print(png_path_list[j])
for i in range(length):
create_spectrograms_internal(wav_path_list[i],png_path_list[i])
def copy_spec(): #Copy spectrograms from one folder to another
user_input = 'c'
while(user_input == 'c'):
root1 = tikinter.Tk()
print("Select the folder containing spectrograms: ")
root1.directory = filedialog.askdirectory()
print (root1.directory)
selected_path = root1.directory + "/"
print("Select the folder to be copied to: ")
root2 = tkinter.Tk()
root2.directory = filedialog.askdirectory()
print (root2.directory)
final_path = root2.directory + "/"
specfiles = glob.glob(os.path.join(selected_path+"*.png"))
for spectrograms in specfiles:
if os.path.exists(final_path):
shutil.copy2(spectrograms,final_path)
else:
os.makedirs(final_path)
shutil.copy2(spectrograms,final_path)
print('Enter c to continue')
user_input = raw_input()
def see_spec(wav_path,png_path):
#wav_path, original_dir_name, png_path = select_dir()
wavfiles = glob.glob(os.path.join(wav_path+"*.wav"))
count = 0
for audiofile in wavfiles:
png_name = audiofile.split("/")[-1].rsplit(".",1)[0]
#png_path = png_path + "/" + "spectrograms" + "/"
png_final_path = png_path + png_name + ".png"
print(png_final_path)
if os.path.exists(png_path):
plotstft(audiofile,png_final_path)
count +=1
print(count)
else:
os.makedirs(png_path)
plotstft(audiofile,png_final_path)
count +=1
print(count)
return count
# create_spectrograms()