| 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) |