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encoding_layer_ori.py
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encoding_layer_ori.py
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from __future__ import division
from keras.engine.topology import Layer, InputSpec
import keras.backend as K
import os, sys, struct, math
from PIL import Image as pil_image
import numpy as np
from scipy.io.wavfile import write as wav_write
import tensorflow as tf
import copy
FL = 80 # Lowest frequency (Hz) in soundscape
FH = 7600 # Highest frequency (Hz)
FS = 22050 # Sample frequency (Hz)
T = 1.05 #1.05 # Image to sound conversion time (s)
D = 1 # Linear|Exponential|tanh=0|1|2 distribution
HIFI = 1 # 8-bit|16-bit=0|1 sound quality
STEREO = 0 # Mono|Stereo=0|1 sound selection
DELAY = 1 # Nodelay|Delay=0|1 model (STEREO=1)
FADE = 1 # Relative fade No|Yes=0|1 (STEREO=1)
DIFFR = 1 # Diffraction No|Yes=0|1 (STEREO=1)
BSPL = 1 # Rectangular|B-spline=0|1 time window
BW = 0 # 16|2-level=0|1 gray format in P[][]
CAM = 1 # Use OpenCV camera input No|Yes=0|1
VIEW = 1 # Screen view for debugging No|Yes=0|1
CONTRAST = 2 # Contrast enhancement, 0=none
PITCH = 0 # the position of high pitch: top|middle|bottom = 0|1|2
# Coefficients used in rnd()
ir = 0
ia = 9301
ic = 49297
im = 233280
TwoPi = 6.283185307179586476925287
HIST = (1 + HIFI) * (1 + STEREO)
WHITE = 1.00
BLACK = 0.00
N = 64
M = 64
# k = 0
b = 0
d = D
ns = 2 * int(0.5 * FS * T)
m = int(ns / N)
sso = 0.0 if HIFI else 128
ssm = 32768.0 if HIFI else 128
scale = 0.5 / math.sqrt(M)
dt = 1.0 / FS
v = 340.0 # v = speed of sound (m/s)
hs = 0.20 # hs = characteristic acoustical size of head (m)
def wi(fp, i):
b0 = int(i % 256)
b1 = int((i - b0) / 256)
fp.write(struct.pack('B', b0 & 0xff))
fp.write(struct.pack('B', b1 & 0xff))
def wl(fp, l):
i0 = l % 65536
i1 = (l - i0) / 65536
wi(fp, i0)
wi(fp, i1)
def rnd():
global ir, ia, ic, im
ir = (ir * ia + ic) % im
return ir / (1.0 * im)
class Encoding_layer(Layer):
"""
# This layer is used for encoding the image into audio segment
# Example
```
model.add(ClusteringLayer(n_clusters=10))
```
# Arguments
n_clusters: number of clusters.
weights: list of Numpy array with shape `(n_clusters, n_features)` witch represents the initial cluster centers.
alpha: parameter in Student's t-distribution. Default to 1.0.
# Input shape
2D tensor with shape: `(n_samples, img_rows, img_cols, img_channels)`.
# Output shape
2D tensor with shape: `(n_samples, audio_length)`.
"""
def __init__(self, **kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(Encoding_layer, self).__init__(**kwargs)
self.input_spec = InputSpec(ndim=4)
def build(self, input_shape):
assert len(input_shape) == 4
self.batch_num = input_shape[0]
self.img_rows = input_shape[1]
self.img_cols = input_shape[2]
self.img_channels = input_shape[3]
self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, self.img_rows, self.img_cols, self.img_channels))
self.built = True
def call(self, inputs, **kwargs):
""" student t-distribution, as same as used in t-SNE algorithm.
q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
Arguments:
inputs: the variable containing data, shape=(n_samples, n_features)
Return:
q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
"""
inputs = inputs*255
#inputs = (inputs+1)*127.5
k = 0
w_pre = [0 for i in range(M)]
phi0_pre = [0 for i in range(M)]
for i in range(0, M): phi0_pre[i] = TwoPi * rnd()
# Set lin|exp (0|1) frequency distribution and random initial phase
if d==1:
for i in range(0, M): w_pre[i] = TwoPi * FL * pow(1.0 * FH / FL, 1.0 * i / (M - 1))
elif d==2:
fre = FH-FL
for i in range(0, M): w_pre[i] = TwoPi * FL + TwoPi * (fre / 2 * np.tanh(0.06*(i-M/2)) + fre/2)
else:
for i in range(0, M): w_pre[i] = TwoPi * FL + TwoPi * (FH - FL) * i / (M - 1)
if PITCH == 0:
w = w_pre
phi0 = phi0_pre
elif PITCH == 1:
w = w_pre[0:None:2]
w_half_tail = w_pre[1:None:2]
w_half_tail.reverse()
w.extend(w_half_tail)
phi0 = phi0_pre[0:None:2]
phi0_half_tail = phi0_pre[1:None:2]
phi0_half_tail.reverse()
phi0.extend(phi0_half_tail)
elif PITCH == 2:
w_pre.reverse()
w = w_pre
phi0_pre.reverse()
phi0 = phi0_pre
# convert to gray scale, image scale 0-1 ? 0-255 ?
if self.img_channels == 3:
inputs = tf.image.rgb_to_grayscale(inputs)
imgs = tf.image.resize_nearest_neighbor(inputs, [M, N])
#imgs = inputs
imgs_reverse = tf.reverse(imgs, [1])
# step 1
avg = tf.reduce_mean(imgs,axis=[1,2,3], keep_dims=True)
#avg = tf.reshape(avg,[-1, self.img_rows, self.img_cols])
px = imgs_reverse + CONTRAST*tf.subtract(imgs_reverse, avg)
px = tf.maximum(px, 0.0)
px = tf.minimum(px, 255.0)
base = tf.constant(10.0,dtype='float32')
zero = tf.constant(0.0,dtype='float32')
A = tf.where(tf.equal(px, zero), px, tf.pow(base, (px / 16 - 15) / 10.0))
# step 2
tau1 = 0.5 / w[M - 1]
tau2 = 0.25 * (tau1 * tau1)
y = yl = yr = z = zl = zr = 0.0
# expanding A
num = int(np.floor(ns/N))
B = tf.reshape(A, [self.batch_num, M, N, 1])
A_expand = tf.reshape(tf.tile(B, [1, 1, 1, num]),[self.batch_num, M, N*num])
A_expand_revese = tf.slice(A_expand, [0,0,self.img_cols*num-(ns-N*num)], [-1,-1,ns-N*num])
A_expand = tf.concat([A_expand, A_expand_revese], axis=2)
A_expand = tf.transpose(A_expand, perm=[0,2,1])
frames = tf.reshape(tf.tile(tf.range(ns, dtype='float32'), [self.batch_num]), [-1, ns])
frames = tf.tile(tf.reshape(frames, [self.batch_num, ns, 1]), [1, 1, M])
frames_dt = frames * dt
w = np.expand_dims(np.expand_dims(w,axis=0), axis=1)
phi0 = np.expand_dims(np.expand_dims(phi0,axis=0), axis=1)
s = tf.reduce_sum(A_expand * tf.sin(w*frames_dt+phi0), axis=-1)
'''
yp = y
y = tau1 / dt + tau2 / (dt * dt)
y = (s + y * yp + tau2 / dt * z) / (1.0 + y)
z = (y - yp) / dt
'''
# Laplacian kernel
#s = tf.expand_dims(s,2)
#filter = tf.constant([1,2,-6,2,1], dtype='float32')
#filter = tf.expand_dims(tf.expand_dims(filter,1),2)
#y = tf.nn.conv1d(value=s, filters=filter, stride=1, padding='SAME',data_format='NHWC')
y = tf.squeeze(s)
l = sso + 0.5 + scale * ssm * y # y = 2nd order filtered s
l = tf.maximum(l, sso - ssm)
audio = tf.minimum(l, sso - 1 + ssm)
return audio
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 4
return (input_shape[0], ns)
def get_config(self):
# config = {'audio_len': self.audio_len}
base_config = super(Encoding_layer, self).get_config()
# return dict(list(base_config.items()) + list(config.items()))
return base_config