# Don't mind me - work in progress

import sys

import numpy as np

import scipy as sp

import matplotlib.pyplot as plt

from scipy.io import wavfile

from scipy import fft

from skimage.filters import rank

from skimage.util import img_as_ubyte

from skimage.morphology import disk, remove_small_objects

def pic_to_ubyte (pic):

a = (pic-np.min(pic) ) /(np.max(pic - np.min(pic)))

a = img_as_ubyte(a)

return a

if len(sys.argv) == 2:

input_file = sys.argv[1]

else:

input_file = r'STHELENA-02_20140520_200000_1_0.wav'

fs, sig = wavfile.read(input_file)

#dtype = np.dtype('float64')

#sig = sig.astype(dtype) # / dtype.type(-np.iinfo(sig.dtype).min)

N = len(sig)

K = 512

Step = 4

wind = 0.5*(1 -np.cos(np.array(range(K))*2*np.pi/(K-1) ))

MIN_SEGMENT_SIZE = 99

p = 90

SPEC_SEGMENTS = []

LOG_SPEC_SEGMENTS = []

Spectogram = []

for j in range(int(Step*N/K)-Step):

vec = sig[j * K/Step : (j+Step) * K/Step] * wind

Spectogram.append(abs(fft(vec,K)[:K/2]))

mypic = np.transpose(Spectogram)

mypic_rev = np.zeros_like(mypic)

for i in range(mypic.shape[0]):

mypic_rev[i] = mypic[-i - 1]

fig = plt.figure()

sigma=3

disk_val=3

mypic_rev_small = mypic_rev[120:200,:]

mypic_rev = mypic_rev

mypic_rev_log = np.log10(mypic_rev+ 0.001)

mypic_rev_gauss = sp.ndimage.gaussian_filter(mypic_rev, sigma=sigma)

mypic_rev_log_gauss = sp.ndimage.gaussian_filter(mypic_rev_log, sigma=sigma)

mypic_rev_gauss_bin = mypic_rev_gauss > np.percentile(mypic_rev_gauss, p)

mypic_rev_log_gauss_bin = mypic_rev_log_gauss > np.percentile(mypic_rev_log_gauss,p)

mypic_rev_gauss_bin_close =sp.ndimage.binary_closing( sp.ndimage.binary_opening(mypic_rev_gauss_bin))

mypic_rev_log_gauss_bin_close =sp.ndimage.binary_closing( sp.ndimage.binary_opening(mypic_rev_log_gauss_bin))

mypic_rev_gauss_grad = rank.gradient(pic_to_ubyte(mypic_rev_gauss), disk(disk_val))

mypic_rev_log_gauss_grad = rank.gradient(pic_to_ubyte(mypic_rev_log_gauss), disk(disk_val))

mypic_rev_gauss_grad_bin = mypic_rev_gauss_grad > np.percentile(mypic_rev_gauss_grad,p)

mypic_rev_log_gauss_grad_bin = mypic_rev_log_gauss_grad > np.percentile(mypic_rev_log_gauss_grad,p )

mypic_rev_gauss_grad_bin_close =sp.ndimage.binary_closing( sp.ndimage.binary_opening(mypic_rev_gauss_grad_bin))

mypic_rev_log_gauss_grad_bin_close =sp.ndimage.binary_closing( sp.ndimage.binary_opening(mypic_rev_log_gauss_grad_bin))

bfh = sp.ndimage.binary_fill_holes(mypic_rev_gauss_grad_bin_close)

bfh_rm = remove_small_objects(bfh, MIN_SEGMENT_SIZE)

log_bfh = sp.ndimage.binary_fill_holes(mypic_rev_log_gauss_grad_bin_close)

log_bfh_rm = remove_small_objects(log_bfh, MIN_SEGMENT_SIZE)

labeled_segments, num_seg = sp.ndimage.label(bfh_rm)

#plt.subplot(6,2,1)

#plt.imshow(mypic_rev,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('Spectrogram')

#plt.subplot(6,2,2)

#plt.imshow(mypic_rev_log,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('Spectrogram (log)')

#plt.subplot(6,2,3)

#plt.imshow(mypic_rev_log_gauss,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('+ Gaussian Filtering')

#plt.subplot(6,2,4)

#plt.imshow(mypic_rev_log,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('+ Gaussian Filtering (log)')

#plt.subplot(6,2,5)

#plt.imshow(mypic_rev_gauss_grad,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('+ Gradient')

#plt.subplot(6,2,6)

#plt.imshow(mypic_rev_log_gauss_grad,cmap=plt.cm.afmhot_r)

#plt.axis('off')

#plt.title('+ Gradient (log)')

#plt.subplot(6,2,7)

#plt.imshow(mypic_rev_gauss_grad_bin,cmap=plt.cm.gray)

#plt.axis('off')

#plt.title('+ >90%')

#plt.subplot(6,2,8)

#plt.imshow(mypic_rev_log_gauss_grad_bin,cmap=plt.cm.gray)

#plt.axis('off')

#plt.title('+ >90% (log)')

#plt.subplot(6,2,9)

#plt.imshow(mypic_rev_gauss_grad_bin_close,cmap=plt.cm.gray)

#plt.axis('off')

#plt.title('+ binary_closing + binary_opening')

#plt.subplot(6,2,10)

#plt.imshow(mypic_rev_log_gauss_grad_bin_close,cmap=plt.cm.gray)

#plt.axis('off')

#plt.title('+ binary_closing + binary_opening (log)')

#plt.subplot(6,2,11)

#plt.imshow(labeled_segments)

#plt.axis('off')

#plt.title('+ binary_fill_holes + remove_small_objects')

plt.imshow(labeled_segments)

plt.axis('off')

plt.title('labeled_segments')

for current_segment_id in range(1,num_seg+1):

current_segment = (labeled_segments == current_segment_id)*1

xr = current_segment.max(axis = 0)

yr = current_segment.max(axis = 1)

xr_max = np.max(xr*np.arange(len(xr)))

xr[xr==0] = xr.shape[0]

xr_min = np.argmin(xr)

yr_max = np.max(yr*np.arange(len(yr)))

yr[yr==0] = yr.shape[0]

yr_min = np.argmin(yr)

segment_frame = [yr_min, yr_max, xr_min, xr_max]

subpic = mypic_rev_gauss[yr_min:yr_max+1,xr_min:xr_max+1]

SPEC_SEGMENTS.append([0, current_segment_id, segment_frame, subpic])

fig.savefig('patterns.png',dpi = 300)

fig.clear()

#plt.show()

# LOG SEGMENTS

log_labeled_segments, log_num_seg = sp.ndimage.label(log_bfh_rm)

#plt.subplot(6,2,12)

#plt.imshow(labeled_segments)

#plt.axis('off')

#plt.title('+ binary_fill_holes + remove_small_objects (log)')

for current_segment_id in range(1,log_num_seg+1):

current_segment = (log_labeled_segments == current_segment_id)*1

xr = current_segment.max(axis = 0)

yr = current_segment.max(axis = 1)

xr_max = np.max(xr*np.arange(len(xr)))

xr[xr==0] = xr.shape[0]

xr_min = np.argmin(xr)

yr_max = np.max(yr*np.arange(len(yr)))

yr[yr==0] = yr.shape[0]

yr_min = np.argmin(yr)

segment_frame = [yr_min, yr_max, xr_min, xr_max]

subpic = mypic_rev_log_gauss[yr_min:yr_max+1,xr_min:xr_max+1]

LOG_SPEC_SEGMENTS.append([0, current_segment_id, segment_frame, subpic])