如何在python中对频谱中的图像进行编码？

Question

我有一个图像编码成一些.wav文件的频谱的问题，这样的结果与此类似：http://www.bastwood.com/projects/aphex_face/aphex.png

我刚开始接触编程，所以我在寻找很容易理解解决方案

有人可以帮忙吗？

Answer 1

为了将图像编码成波谱，您可以使用下面从GitHub下载的程序。 频谱图python代码将图像转换为音频波形文件。

#!/usr/bin/python 
import numpy as np 
import matplotlib.image as mpimg 
import wave 
from array import array 


def make_wav(image_filename): 
    """ Make a WAV file having a spectrogram resembling an image """ 
    # Load image 
    image = mpimg.imread(image_filename) 
    image = np.sum(image, axis = 2).T[:, ::-1] 
    image = image**3 # ??? 
    w, h = image.shape 

    # Fourier transform, normalize, remove DC bias 
    data = np.fft.irfft(image, h*2, axis=1).reshape((w*h*2)) 
    data -= np.average(data) 
    data *= (2**15-1.)/np.amax(data) 
    data = array("h", np.int_(data)).tostring() 

    # Write to disk 
    output_file = wave.open(image_filename+".wav", "w") 
    output_file.setparams((1, 2, 44100, 0, "NONE", "not compressed")) 
    output_file.writeframes(data) 
    output_file.close() 
    print "Wrote %s.wav" % image_filename 


if __name__ == "__main__": 

    my_image = "spectrogram.png" 
    make_wav(my_image) 

为了将波形文件显示为光谱图，您有几个选择。根据您的平台，您可以下载sox和运行

sox <yourImage>.jpg.wav -n spectrogram

SOX，短声音交流会则图像的音频波形文件转换为图像谱图。

或者如果您不想下载SOX，您可以使用以下程序创建图像音频波形文件的频谱图。

#!/usr/bin/env python 
#coding: utf-8 
""" This work is licensed under a Creative Commons Attribution 3.0 Unported License. 
    Frank Zalkow, 2012-2013 """ 

import numpy as np 
from matplotlib import pyplot as plt 
import scipy.io.wavfile as wav 
from numpy.lib import stride_tricks 

""" 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(np.floor(frameSize/2.0)), sig)  
    # cols for windowing 
    cols = 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=(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)) 

    # 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, binsize=2**10, plotpath=None, colormap="jet"): 
    samplerate, samples = wav.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.colorbar() 

    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]) 

    if plotpath: 
     plt.savefig(plotpath, bbox_inches="tight") 
    else: 
     plt.show() 

    plt.clf() 

plotstft("spectrogram.png.wav") 
# 

图片谱图如下：