record.py

#!/usr/bin/env python
import subprocess
import skimage.io
import traceback
import numpy
import numpy as np
import os
import sys
from os import system
from platform import system as platform
import skimage.io
import wave
import pyaudio
import matplotlib.pyplot as plt


plt.matshow([[1,0],[0,1]], fignum=1)
plt.draw()

if platform() == 'Darwin':  # How Mac OS X is identified by Python
    system('''/usr/bin/osascript -e 'tell app "Finder" to set frontmost of process "Python" to true' ''')

i = 0
width=256
height=256

# CHUNK = 512
# CHUNK = 1024
# CHUNK = 1024
# CHUNK = 2048
CHUNK = 4096
# CHUNK = 9192

# length=512
length = 1024
# length=2048
# length = 4096

# step=32
# step=64
# step = 128
step=256
# step=512

# width=512
image=numpy.array(bytearray(os.urandom(width*width))) # 512,512)
image=image.reshape(width,width)

def get_audio_input_stream():
	INDEX = 0  # 1
	# FORMAT = pyaudio.paInt8
	FORMAT = pyaudio.paInt16
	# FORMAT = pyaudio.paInt32
	# FORMAT = pyaudio.paFloat32
	CHANNELS = 1
	# RATE = 22500
	RATE = 48000 #* 2 = 96000Hz max on mac
	INPUT_BLOCK_TIME = 0.05
	# INPUT_BLOCK_TIME = 0.1
	INPUT_FRAMES_PER_BLOCK = int(RATE * INPUT_BLOCK_TIME)

	stream = pyaudio.PyAudio().open(
		format=FORMAT,
		channels=CHANNELS,
		rate=RATE,
		input=True,
		frames_per_buffer=CHUNK,
		input_device_index=INDEX)
	return stream

def record():
  global i
  global image
  global winName
  FILENAME = 'recording.wav'
  stream = get_audio_input_stream()
  # r = numpy.array()
  hamming_window = np.hamming(length) # minimize fourier frequency drain
  #hamming hanning bartlett 'blackman'
  r = numpy.empty(length)
  offset = 0
  while True:
    try:
	    dataraw = stream.read(CHUNK)
    except IOError as e:
	    print(e) # [Errno -9981] Input overflowed  WHY?
	    stream=get_audio_input_stream()
	    pass
    data0 = numpy.fromstring(dataraw, dtype='int16')
    # data0 = numpy.fromstring(dataraw, dtype='int8')
    if(i<20 and numpy.sum(np.abs(data0))<1000*width):
      continue
    r=numpy.append(r,data0)
    while offset < r.size - length :
      data = r[offset:offset+length]
      data=data*hamming_window  # minimize fourier frequency drain
      offset=offset + step

      data = numpy.fft.fft(data)#.abs()
      data = numpy.absolute(data)
      data = data[0:height]/256.0#.split(data,512)
      data = numpy.log2(data*0.05+1.0)#//*50.0;
      numpy.putmask(data, data > 255, 255)

      image[i] = data
      i = i+1
      if(i==width):
        print("i %d\r"%i)
        i=0
        # image=image.T
        image=numpy.rot90(image)
        plt.matshow(image, fignum=1)
        plt.draw()
        plt.pause(0.01)
        # result=spec2word(image) #todo: reconnect
        # subprocess.call(["say"," %s"%result])
        # cv2.imshow(winName,image)
        # if cv2.waitKey(10) == 27: BREAKS portAudio !!

if __name__ == '__main__':
  record()