| import tensorflow as tf |
| import tensorflow_io as tfio |
| import IPython.display as ipd |
| import matplotlib.pyplot as plt |
| import scipy as sp |
| import PIL.Image |
| import numpy as np |
|
|
| def wav_to_tf(filename): |
| bits = tf.io.read_file(filename) |
| x = tfio.audio.decode_wav(bits,dtype=tf.int16)[:,0] |
| x = tf.cast(x,tf.float32) |
| x = x - tf.math.reduce_mean(x); |
| x = x / tf.math.reduce_std(x) |
| return tf.Variable(x) |
|
|
| def play(x,rate=24000): |
| ipd.display(ipd.Audio(x,rate=rate,autoplay=False)) |
|
|
| def slog(x): |
| return tf.sign(x) * tf.math.log(1+ tf.math.abs(x) ) |
| |
| def show(X,clim=(-3,3), xlim=(0,300), ylim=(0,100)): |
| plt.figure(figsize=(15,6),dpi=200) |
| plt.imshow(tf.transpose(X),origin='lower',cmap='RdBu') |
| plt.colorbar() |
| plt.clim(clim) |
| plt.xlim(xlim) |
| plt.ylim(ylim) |
| |
| def mdct(x,L=624): |
| X = tf.signal.mdct(x,L); |
| return tf.Variable(X) |
|
|
| def imdct(X): |
| y = tf.signal.inverse_mdct(X) |
| return y |
|
|
| Γ = sp.special.gamma |
|
|
| def F(x,μ,σ,γ): |
| return sp.stats.gennorm.cdf(x, beta=γ, loc=μ, scale=σ) |
|
|
| def Finv(x,μ,σ,γ): |
| return sp.stats.gennorm.ppf(x, beta=γ, loc=μ, scale=σ) |
|
|
| def r(γ): |
| return Γ(1/γ)*Γ(3/γ)/Γ(2/γ) |
|
|
| def estimate_GGD(X): |
| μ = tf.math.reduce_mean(X) |
| σ = tf.math.reduce_std(X) |
| E = tf.math.reduce_mean(tf.abs(X - μ)) |
| ρ = tf.square(σ/E) |
| |
| γ = sp.optimize.bisect(lambda γ:r(γ)-ρ, 0.3, 1.5,maxiter=50) |
| return μ,σ,γ |
|
|
| def tf_to_pil(x): |
| x = np.array(x) |
| return PIL.Image.fromarray(x,mode="L") |
| def pil_to_tf(x): |
| x = np.array(x) |
| return tf.convert_to_tensor(x) |
|
|
| def σ_prior(band): |
| def sc(z,μ,σ,γ): |
| return sp.stats.skewcauchy.pdf(z, γ, loc=μ, scale=σ) |
| return 10000*(2*sc(band,20,100,0.9)+sc(band,22,12,0.5)) |
|
|
| def img_to_mdct(img): |
| X = [] |
| q = 256; |
| Y = pil_to_tf(img) |
| Y = tf.cast(Y,tf.float32)/q |
| for i_band in range(512): |
| band = Y[:,i_band] |
| σ = σ_prior(i_band) |
| X.append(Finv(band,0,σ,0.85)) |
| X = tf.stack(X) |
| X = tf.transpose(X) |
| X = tf.where(tf.math.is_inf(X), tf.ones_like(X), X) |
| return tf.cast(X,tf.float32) |
|
|
| def mdct_to_img(X): |
| Y = [] |
| q = 256; |
| for i_band in range(512): |
| band = X[:,i_band] |
| σ = σ_prior(i_band) |
| Y.append(F(band,0,σ,0.85)) |
| Y = tf.stack(Y) |
| Y = tf.transpose(Y) |
| Y = tf.round(q*Y) |
| Y = tf.cast(Y,tf.uint8) |
| return tf_to_pil(Y) |
