Python将图片写到音频频谱

# -*- coding: utf-8 -*-

import wave

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from matplotlib.mlab import window_none

def img2wav(img_path, wav_path, fft_size=1024):
"""
把图片写到音频频域
:param img_path: 输入图片路径
:param wav_path: 输出音频路径
:param fft_size: 图片每列代表的音频长度，也是频域长度的两倍
"""

# 读取图片，转为灰度图
img = Image.open(img_path).convert('L')
# 缩放到高度 = fft_size / 2 （令负频率全为0）
img = img.resize((img.width * fft_size // 2 // img.height, fft_size // 2),
Image.BICUBIC)

# 转为numpy数组
img = np.array(img, 'float')
# 变换到-100~0分贝，太大了转到时域时可能会溢出
img = img * (100 / 255) - 100
# 单位从分贝转成1
# amp_dB = 20 * ln(amp / 32767) / ln(10)
# amp = exp(amp_dB / 20 * ln(10)) * 32767
img = np.exp(img * (np.log(10) / 20)) * 32767
# 翻转（索引小的频率小）然后转置（要迭代列）
img = img[::-1].T

with wave.open(wav_path, 'wb') as f:
# (nchannels, sampwidth, framerate, nframes, comptype, compname)
f.setparams((1, 2, 44100, len(img) * fft_size, 'NONE', ''))
for col in img:
# 傅里叶反变换
data = np.fft.ifft(col, fft_size).real

# 限制范围在-32768~32767
for index in np.where(data < -32768):
data[index] = -32768
for index in np.where(data > 32767):
data[index] = 32767
data = data.astype('short')

# 写到wav文件
f.writeframesraw(data)

def draw_spectrum(wav_path, fft_size=1024):
"""
画音频频谱图
:param wav_path: 输入音频路径
:param fft_size: 傅里叶变换用的长度
"""

# 读wav
with wave.open(wav_path, 'rb') as f:
n_samples = f.getnframes()
data = f.readframes(n_samples)
n_channels = f.getnchannels()
sample_rate = f.getframerate()

# 转为numpy数组
data = np.fromstring(data, 'short')
# 取第一个声道
data.shape = (n_samples, n_channels)
data = data.T[0]

# 画频谱，无加窗和重叠
plt.specgram(data / 32767, fft_size, sample_rate, window=window_none,
noverlap=0, scale='dB')
plt.show()

if __name__ == '__main__':
img2wav('test.jpg', 'test.wav')
draw_spectrum('test.wav')