view utils/plotSSM.py @ 17:c01fcb752221

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#!/usr/bin/env python
# encoding: utf-8
"""
plotSSM.py

A helper util to plot SSMs from different features.
"""

import matplotlib
# matplotlib.use('Agg')
import sys, os, optparse, csv
from itertools import combinations
from os.path import join, isdir, isfile, abspath, dirname, basename, split, splitext
from copy import copy

import matplotlib.pyplot as plt

import numpy as np
from scipy.signal import correlate2d, convolve2d, filtfilt, resample
from scipy.stats import mode, kurtosis, skew
from scipy.ndimage import zoom
from scipy.ndimage.morphology import binary_fill_holes
from scipy.ndimage.filters import *
from scipy.spatial.distance import squareform, pdist
from sklearn.decomposition import PCA
from sklearn.mixture import GMM
from sklearn.cluster import KMeans
from sklearn.preprocessing import normalize
from sklearn.metrics.pairwise import pairwise_distances
from skimage.transform import hough_line, hough_line_peaks, probabilistic_hough_line
from skimage.filter import canny, sobel
from skimage.filter.rank import otsu
from skimage import data, measure, segmentation, morphology
from skimage.morphology import disk

from PeakPickerUtil import PeakPicker
from SegUtil import getMean, getStd, getDelta, getSSM, enhanceSSM, upSample, normaliseFeature

def parse_args():
	# define parser
	op = optparse.OptionParser()
	# IO options
	op.add_option('-g', '--gammatonegram-features', action="store", dest="GF", default='/Volumes/c4dm-03/people/mit/features/gammatonegram/qupujicheng/2048', type="str", help="Loading features from.." )
	op.add_option('-s', '--spectrogram-features', action="store", dest="SF", default='/Volumes/c4dm-03/people/mit/features/spectrogram/qupujicheng/2048', type="str", help="Loading features from.." )
	op.add_option('-t', '--tempogram-features', action="store", dest="TF", default='/Volumes/c4dm-03/people/mit/features/tempogram/qupujicheng/tempo_features_6s', type="str", help="Loading features from.." )
	op.add_option('-a', '--annotations', action="store", dest="GT", default='/Volumes/c4dm-03/people/mit/annotation/qupujicheng/lowercase', type="str", help="Loading annotation files from.. ")
	op.add_option('-o', '--ouput', action="store", dest="OUTPUT", default='/Volumes/c4dm-03/people/mit/segmentation/gammatone/qupujicheng', type="str", help="Write segmentation results to ")
	op.add_option('-p', '--plot', action="store_true", dest="PLOT", default=False, help="Save plots")
	op.add_option('-e', '--test-mode', action="store_true", dest="TEST", default=False, help="Test mode")
	op.add_option('-v', '--verbose-output', action="store_true", dest="VERBOSE", default=False, help="Exported raw detections.")

	return op.parse_args()
options, args = parse_args()

class FeatureObj() :
	__slots__ = ['key', 'audio', 'timestamps', 'gammatone_features', 'tempo_features', 'timbre_features', 'fp_features', 'lpc_features' 'harmonic_features', 'gammatone_ssm', 'fp_ssm', 'tempo_ssm', 'timbre_ssm', 'lpc_ssm', 'harmonic_ssm', 'ssm_timestamps']

class AudioObj():
	__slots__ = ['name', 'feature_list', 'gt', 'label', 'gammatone_features', 'tempo_features', 'timbre_features', 'fp_features', 'lpc_features', 'harmonic_features', 'combined_features',\
	'gammatone_ssm', 'tempo_ssm', 'timbre_ssm', 'lpc_ssm', 'harmonic_ssm', 'fp_ssm', 'combined_ssm', 'ssm', 'ssm_timestamps', 'tempo_timestamps']

class EvalObj():
	__slots__ = ['TP', 'FP', 'FN', 'P', 'R', 'F', 'AD', 'DA']
	
class SSMseg(object):
	'''The main segmentation object'''
	def __init__(self):
		self.SampleRate = 44100
		self.NqHz = self.SampleRate/2
		self.timestamp = []
		self.previousSample = 0.0
		self.featureWindow = 6.0
		self.featureStep = 3.0
		self.kernel_size = 64 # Adjust this param according to the feature resolution.pq
		self.blockSize = 2048
		self.stepSize = 1024

		'''NOTE: Match the following params with those used for feature extraction!'''
		
		'''NOTE: Unlike spectrogram ones, Gammatone features are extracted without an FFT or any overlap. The windowing is done under the purpose of chunking
		the audio to facilitate the gammatone filtering. Despite of the overlap in the time domain, only the first half after the filtering is returned, 
		resulting in no overlapping effect in the extracted features. To obtain features for overlapped audio input, make the gammatoneLen equal to blockSize
		and return the whole filter output.'''
		self.gammatoneLen = 2048
		self.gammatoneBandGroups = [0, 16, 34, 50, 64]
		self.nGammatoneBands = 30
		self.histRes = 40
		self.lowFreq = 100
		self.highFreq = self.SampleRate / 4
		
		'''Settings for extracting tempogram features.'''
		self.tempoWindow = 6.0
		self.bpmBands = [30, 45, 60, 80, 100, 120, 180, 240, 400, 600]
		
		'''Peak picking settings'''
		self.threshold = 30
		self.confidence_threshold = 0.5
		self.delta_threshold = 0.0
		self.backtracking_threshold = 1.9
		self.polyfitting_on = True
		self.medfilter_on = True
		self.LPfilter_on = True
		self.whitening_on = False
		self.aCoeffs = [1.0000, -0.5949, 0.2348]
		self.bCoeffs = [0.1600,	 0.3200, 0.1600]
		self.cutoff = 0.34
		self.medianWin = 7

	def getGaussianParams(self, length, featureRate, timeWindow):

		win_len = round(timeWindow * featureRate)
		win_len = win_len + (win_len % 2) - 1

		# a 50% overlap between windows
		stepsize = ceil(win_len * 0.5)
		num_win = int(floor( (length) / stepsize))
		gaussian_rate = featureRate / stepsize

		return stepsize, num_win, win_len, gaussian_rate

	def GaussianDistance(self, feature, featureRate, timeWindow):

		stepsize, num_win, win_len, gr = self.getGaussianParams(feature.shape[0], featureRate, timeWindow)
		print 'stepsize, num_win, feature', stepsize, num_win, feature.shape, featureRate, timeWindow
		gaussian_list = []
		gaussian_timestamps = []
		tsi = 0

		# f = open('/Users/mitian/Documents/experiments/features.txt','w')
		# print 'divergence computing..'
		for num in xrange(num_win): 
			# print num, num * stepsize , (num * stepsize) + win_len
			gf=GaussianFeature(feature[int(num * stepsize) : int((num * stepsize) + win_len), :],2)
			# f.write("\n%s" %str(gf))
			gaussian_list.append(gf)
			tsi = int(floor( num * stepsize + 1)) 
			gaussian_timestamps.append(self.timestamp[tsi])

		# f.close()

		# print 'gaussian_list', len(gaussian_list), len(gaussian_timestamps)
		dm = np.zeros((len(gaussian_list), len(gaussian_list)))

		for v1, v2 in combinations(gaussian_list, 2): 
			i, j = gaussian_list.index(v1), gaussian_list.index(v2)
			dm[i, j] = v1.distance(v2)
			dm[j, i] = v2.distance(v1)
			# print 'dm[i,j]',dm[i,j]
		# sio.savemat("/Users/mitian/Documents/experiments/dm-from-segmenter.mat",{"dm":dm})
		return dm, gaussian_timestamps

	def gaussian_kernel(self, size):
		'''Create a gaussian tapered 45 degrees rotated checkerboard kernel. 
		TODO: Unit testing: Should produce this with kernel size 3:
		0.1353	 -0.3679	0.1353
		0.3679	  1.0000	0.3679
		0.1353	 -0.3679	0.1353
		'''
		n = float(np.ceil(size / 2.0))
		kernel = np.zeros((size,size))
		for i in xrange(1,size+1) :
			for j in xrange(1,size+1) :
				gauss = np.exp( -4.0 * (np.square( (i-n)/n ) + np.square( (j-n)/n )) )
				# gauss = 1			
				if np.logical_xor( j - n > np.floor((i-n) / 2.0), j - n > np.floor((n-i) / 2.0) ) :
					kernel[i-1,j-1] = -gauss
				else:
					kernel[i-1,j-1] = gauss
		return kernel

	def getDiagonalSlice(self, ssm, width):
		''' Return a diagonal slice of the ssm given its width, with 45 degrees rotation. 
		Note: requres 45 degrees rotated kernel also.'''
		w = int(np.floor(width/2.0))
		length = len(np.diagonal(ssm))
		slice = np.zeros((2*w+1,length))
		# print 'diagonal', length, w, slice.shape
		for i in xrange(-w, w+1) :		
			slice[w+i,:] = np.hstack(( np.zeros(int(np.floor(abs(i)/2.0))), np.diagonal(ssm,i), np.zeros(int(np.ceil(abs(i)/2.0))) ))
		return slice

	def getNoveltyCurve(self,dm, kernel_size):
		'''Return novelty score from distance matrix.'''

		kernel_size = int(np.floor(kernel_size/2.0)+1)
		slice = self.getDiagonalSlice(dm, kernel_size)
		kernel = self.gaussian_kernel(kernel_size)
		xc = convolve2d(slice,kernel,mode='same')
		xc[abs(xc)>1e+10]=0.00001			
		# print 'xc', xc.shape, xc
		return xc[int(np.floor(xc.shape[0]/2.0)),:]		
	
	def mergeBlocks(self, SSM, thresh=0.9, size=5):
		'''Merge consequtive small blocks along the diagonal.'''
		# found = False
		# start = 0
		# i = 0
		# while i < len(SSM):
		#	j = i + 1
		#	if found: start = i
		#	while(j < len(SSM) and SSM[i, j]):
		#		if (j-i) > size:
		#			found = True
		#			i = j
		#			# print 'start,end', start, i
		#			start = i
		#		else:
		#			found = False
		#		j += 1
		#	if not found: 
		#		print 'start,end', start, i
		#		SSM[start:i, start:i] = 0.9
		#	i = j	
		idx = 1
		while idx < len(SSM):
			i = 0
			# if ((idx-1-i) > 0 and (idx+1+i) < len(SSM)):
			while ((idx-1-i) > 0 and (idx+1+i) < len(SSM) and SSM[idx-1-i, idx] > 0 and SSM[idx+1+i, idx] > 0): 
				i += 1
			if i > size/2:
				SSM[idx-1-i:min(idx+i,len(SSM)), idx-1-i:min(idx+i,len(SSM))] = 1.0
			idx += max(1, i)
		return SSM
	
	def getGMMs(self, feature, segment_boundaries):
		'''Return GMMs for located segments'''
		gmm_list = []
		gmm_list.append(GmmDistance(feature[: segment_boundaries[0], :], components = 1))
		for i in xrange(1, len(segment_boundaries)):
			gmm_list.append(GmmDistance(feature[segment_boundaries[i-1] : segment_boundaries[i], :], components = 1))
		return gmm_list

	def normaliseFeature(self, feature_array):

		feature_array = np.array(feature_array)
		feature_array[np.isnan(feature_array)] = 0.0
		feature_array[np.isinf(feature_array)] = 0.0

		if len(feature_array.shape) == 1:
			feature_array = (feature_array - feature_array.min()) / (feature_array.max() - feature_array.min())
		else:
			mins = feature_array.min(axis=1)
			maxs = feature_array.max(axis=1)
			feature_array = (feature_array - mins[:, np.newaxis]) / (maxs - mins)[:, np.newaxis]
			feature_array[np.isnan(feature_array)] = 0.0
		return feature_array

	def upSample(self, feature_array, step):
		'''Resample downsized tempogram features, tempoWindo should be in accordance with input features'''
		# print feature_array.shape
		sampleRate = 44100
		stepSize = 1024.0
		# step = np.ceil(sampleRate/stepSize/5.0)
		feature_array = zoom(feature_array, (step,1))
		# print 'resampled', feature_array.shape	
		return feature_array		
	
	def stripeDistance(self, feature_array, feature_len, step, metric='cosine'):
		'''Return distance matrix calculated for 2d time invariant features.'''
		size = feature_array.shape[0] / feature_len
		dm = np.zeros((size, size))
		
		for i in xrange(size):
			for j in xrange(i, size):
				dm[i, j] = np.sum(pairwise_distances(feature_array[i*step:(i+1)*step, :], feature_array[j*step:(j+1)*step, :], metric))
				dm[j, i] = dm[i, j]
		# print 'np.nanmax(dm)', np.nanmax(dm)
		dm[np.isnan(dm)] = np.nanmax(dm) 
		ssm = 1 - (dm - dm.min()) / (dm.max() - dm.min())
		np.fill_diagonal(ssm, 1)
		return ssm
		
	
	def getMean(self, feature, winlen, stepsize):
		means = []
		steps = int((feature.shape[0] - winlen + stepsize) / stepsize)
		for i in xrange(steps):
			means.append(np.mean(feature[i*stepsize:(i*stepsize+winlen), :], axis=0))
		return np.array(means)

	def getStd(self, feature, winlen, stepsize):
		std = []
		steps = int((feature.shape[0] - winlen + stepsize) / stepsize)
		for i in xrange(steps):
			std.append(np.std(feature[i*stepsize:(i*stepsize+winlen), :], axis=0))
		return np.array(std)

	def getDelta(self, feature):
		delta_feature = np.vstack((np.zeros((1, feature.shape[1])), np.diff(feature, axis=0)))
		return delta_feature
	
	def getSkew(self, feature, axis=-1):
		return skew(feature, axis=axis)
	
	def getKurtosis(self, feature, axis=-1):
		return kurtosis(feature, axis=axis)
	
	def getRolloff(self, data, thresh=0.9):
		nFrames, nChannels, nBands = data.shape
		rolloff = np.zeros((nFrames, nChannels))
		tpower = np.sum(data, axis=-1)
		freq = np.linspace(0,10,nBands)
		for i in xrange(nFrames):
			rolloffE = thresh * tpower[i]
			for j in xrange(nChannels):
				temp = 0.0
				tempE = 0.0
				for band in xrange(nBands):
					temp += data[i, j, band]
					if temp > rolloffE[j]: break
				rolloff[i, j] = freq[band]
	#			  rolloff[i, j] = band
		return rolloff
	
	def getCentroid(self, X):
		nFrames, nChannels, nBands = X.shape

		centroid = np.zeros((nFrames, nChannels))
		freq = np.linspace(0,10,nBands)
		freq = np.vstack([freq for i in xrange(nChannels)])
		for i in xrange(nFrames):
			centroid[i, :] = np.sum(X[i, :, :] * freq, axis=-1) / (np.sum(X[i, :, :], axis=-1) + 0.0005*np.ones(nChannels))

		return centroid
	
	def trackDF(self, onset1_index, df2):
		'''In the second round of detection, remove the known onsets from the DF by tracking from the peak given by the first round
		to a valley to deminish the recognised peaks on top of which to start new detection.''' 
		for idx in xrange(len(onset1_index)) :
			remove = True
			for i in xrange(onset1_index[idx], 1, -1) :
				if remove :
					if df2[i] >= df2[i-1] : 
						df2[i] == 0.0
					else:
						remove = False
		return df2	
	
	def getSSM(self, feature_array, metric='cosine', norm='simple'):
		'''Compute SSM given input feature array. 
		args: norm: ['simple', 'remove_noise']
		'''
		dm = pairwise_distances(feature_array, metric=metric)
		dm = np.nan_to_num(dm)
		if norm == 'simple':
			ssm = 1 - (dm - np.min(dm)) / (np.max(dm) - np.min(dm))
		return ssm
			
	def reduceSSM(self, ssm, maxfilter_size = 2, remove_size=50):
		reduced_ssm = ssm
		reduced_ssm[reduced_ssm<0.75] = 0
		# # reduced_ssm = maximum_filter(reduced_ssm,size=maxfilter_size)
		# # reduced_ssm = morphology.remove_small_objects(reduced_ssm.astype(bool), min_size=remove_size)
		local_otsu = otsu(reduced_ssm, disk(5))
		local_otsu = (local_otsu.astype(float) - np.min(local_otsu)) / (np.max(local_otsu) - np.min(local_otsu))
		reduced_ssm = reduced_ssm - 0.6*local_otsu
		return reduced_ssm
			
	def pairwiseF(self, annotation, detection, tolerance=3.0, combine=1.0):
		'''Pairwise F measure evaluation of detection rates.'''

		# print 'detection', detection
		detection = np.append(detection, annotation[-1])
		res = EvalObj()
		res.TP = 0	# Total number of matched ground truth and experimental data points
		gt = len(annotation)	# Total number of ground truth data points
		dt = len(detection) # Total number of experimental data points
		foundIdx = []	
		D_AD = np.zeros(gt)
		D_DA = np.zeros(dt)

		for dtIdx in xrange(dt):
			# print detection[dtIdx], abs(detection[dtIdx] - annotation)
			D_DA[dtIdx] = np.min(abs(detection[dtIdx] - annotation))
			# D_DA[dtIdx] = min([abs(annot - detection[dtIdx]) for annot in annotation])
		for gtIdx in xrange(gt):
			D_AD[gtIdx] = np.min(abs(annotation[gtIdx] - detection))
			# D_AD[gtIdx] = min([abs(det - annotation[gtIdx]) for det in detection])
			for dtIdx in xrange(dt):
				if (annotation[gtIdx] >= detection[dtIdx] - tolerance/2.0) and (annotation[gtIdx] <= detection[dtIdx] + tolerance/2.0):
					res.TP = res.TP + 1.0
					foundIdx.append(gtIdx)
		foundIdx = list(set(foundIdx))		
		res.TP = len(foundIdx)
		res.FP = max(0, dt - res.TP)	
		res.FN = max(0, gt - res.TP)

		res.AD = np.mean(D_AD)		
		res.DA = np.mean(D_DA)
		
		res.P, res.R, res.F = 0.0, 0.0, 0.0
		
		if res.TP == 0:
			return res

		res.P = res.TP / float(dt)
		res.R = res.TP / float(gt)
		res.F = 2 * res.P * res.R / (res.P + res.R)
		# return TP3, FP3, FN3, pairwisePrecision3, pairwiseRecall3, pairwiseFValue3, TP05, FP05, FN05, pairwisePrecision05, pairwiseRecall05, pairwiseFValue05
		return res
		
	def plotDetection(self, ssm, novelty, smoothed_novelty, gt, det, filename):
		'''Plot performance curve.
		x axis: distance threshold for feature selection; y axis: f measure'''
		
		plt.figure(figsize=(10,16))
		gt_plot = gt / gt[-1] * len(novelty)
		det_plot = det / gt[-1] * len(novelty)

		gs = gridspec.GridSpec(2, 1, height_ratios=[3,1])
		ax0 = plt.subplot(gs[0])
		ax1 = plt.subplot(gs[1], sharex=ax0)
		
		ax0.imshow(ssm)
		ax0.vlines(gt_plot, 0, len(ssm), colors ='w', linestyles='solid')
		ax0.vlines(det_plot, 0, len(ssm), colors='k', linestyles='dashed')		
		ax1.plot(np.linspace(0, len(novelty)-1, len(novelty)), novelty, 'g', np.linspace(0, len(novelty)-1, len(novelty)), smoothed_novelty,'b')
		y_min, y_max = min([min(novelty), min(smoothed_novelty)]), max([max(novelty), max(smoothed_novelty)])
		ax1.vlines(gt_plot, y_min, y_max, colors ='r', linestyles='solid')
		ax1.vlines(det_plot, y_min, y_max, colors='k', linestyles='dashed')
		
		# f, ax = plt.subplots(2, sharex=True)
		# ax[0].imshow(ssm)
		# ax[1].plot(np.linspace(0, len(novelty)-1, len(novelty)), novelty)
		# ax[1].vlines(gt_plot, 0, len(novelty), colors ='r', linestyles='solid')
		# ax[1].vlines(det_plot, 0, len(novelty), colors='b', linestyles='dashed')
		# 
		# plt.show()
		plt.savefig(filename)

		return None

	def process(self):
		'''For the aggregated input features, discard a propertion each time as the pairwise distances within the feature space descending. 
		In the meanwhile evaluate the segmentation result and track the trend of perfomance changing by measuring the feature selection 
		threshold - segmentation f measure curve. 
		'''
		
		peak_picker = PeakPicker()
		peak_picker.params.alpha = 9.0 # Alpha norm
		peak_picker.params.delta = self.delta_threshold # Adaptive thresholding delta
		peak_picker.params.QuadThresh_a = (100 - self.threshold) / 1000.0
		peak_picker.params.QuadThresh_b = 0.0
		peak_picker.params.QuadThresh_c = (100 - self.threshold) / 1500.0
		peak_picker.params.rawSensitivity = 20
		peak_picker.params.aCoeffs = self.aCoeffs 
		peak_picker.params.bCoeffs = self.bCoeffs
		peak_picker.params.preWin = self.medianWin
		peak_picker.params.postWin = self.medianWin + 1
		peak_picker.params.LP_on = self.LPfilter_on
		peak_picker.params.Medfilt_on = self.medfilter_on
		peak_picker.params.Polyfit_on = self.polyfitting_on		
		peak_picker.params.isMedianPositive = False

		# Settings used for feature extraction
		feature_window_frame = int(self.SampleRate / self.gammatoneLen * self.featureWindow)
		feature_step_frame = int(0.5 * self.SampleRate / self.gammatoneLen * self.featureStep)
		aggregation_window, aggregation_step = 100, 50
		featureRate = float(self.SampleRate) / self.stepSize
		
		audio_files = [x for x in os.listdir(options.GT) if not x.startswith(".") ]
		audio_files.sort()
		audio_files = audio_files
		audio_list = []

		gammatone_feature_list = [i for i in os.listdir(options.GF) if not i.startswith('.')]
		gammatone_feature_list = ['dct', 'pcamean', 'contrast6']
		tempo_feature_list = [i for i in os.listdir(options.TF) if not i.startswith('.')]
		tempo_feature_list = ['ti_percussive_cdsf', 'tir_percussive_cdsf']
		timbre_feature_list = ['mfcc_harmonic']
		lpc_feature_list = ['lpcc']
		harmonic_feature_list = ['chromagram_harmonic']

		gammatone_feature_list = [join(options.GF, f) for f in gammatone_feature_list]
		timbre_feature_list = [join(options.SF, f) for f in timbre_feature_list]
		# lpc_feature_list = [join(options.SF, f) for f in lpc_feature_list]
		tempo_feature_list = [join(options.TF, f) for f in tempo_feature_list]
		harmonic_feature_list = [join(options.SF, f) for f in harmonic_feature_list]
		
		fobj_list = []

		# For each audio file, load specific features
		for audio in audio_files:
			ao = AudioObj()
			ao.name = splitext(audio)[0]
			# annotation_file = join(options.GT, ao.name+'.txt') # iso, salami
			# ao.gt = np.genfromtxt(annotation_file, usecols=0)	
			# ao.label = np.genfromtxt(annotation_file, usecols=1, dtype=str)
	
			# annotation_file = join(options.GT, ao.name+'.csv') # qupujicheng
			# ao.gt = np.genfromtxt(annotation_file, usecols=0, delimiter=',')	
			# ao.label = np.genfromtxt(annotation_file, usecols=1, delimiter=',', dtype=str)

			annotation_file = join(options.GT, ao.name+'.lab') # beatles
			ao.gt = np.genfromtxt(annotation_file, usecols=(0,1))
			ao.gt = np.unique(np.ndarray.flatten(ao.gt))
			ao.label = np.genfromtxt(annotation_file, usecols=2, dtype=str)

			gammatone_featureset, timbre_featureset, lpc_featureset, tempo_featureset, harmonic_featureset = [], [], [], [], []
			for feature in gammatone_feature_list:
				for f in os.listdir(feature):
					if f[:f.find('_vamp')]==ao.name: 
						data = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:]
						if len(data) == 0: continue
						gammatone_featureset.append(data)
						break
			if len(gammatone_feature_list) > 1:
				n_frame = np.min([x.shape[0] for x in gammatone_featureset])
				gammatone_featureset = [x[:n_frame,:] for x in gammatone_featureset]
				ao.gammatone_features = np.hstack((gammatone_featureset))
			else:
				ao.gammatone_features = gammatone_featureset[0]
			
			for feature in timbre_feature_list:
				for f in os.listdir(feature):
					if f[:f.find('_vamp')]==ao.name: 
						data = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:]
						if len(data) == 0: continue
						timbre_featureset.append(data)
						break
			if len(timbre_feature_list) > 1:
				n_frame = np.min([x.shape[0] for x in timbre_featureset])
				timbre_featureset = [x[:n_frame,:] for x in timbre_featureset]
				ao.timbre_features = np.hstack((timbre_featureset))
			else:
				ao.timbre_features = timbre_featureset[0]
			
			# for feature in lpc_feature_list:
			# 	for f in os.listdir(feature):
			# 		if f[:f.find('_vamp')]==ao.name: 
			# 			lpc_featureset.append(np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:])
			# 			break
			# if len(lpc_feature_list) > 1:
			# 	n_frame = np.min([x.shape[0] for x in lpc_featureset])
			# 	lpc_featureset = [x[:n_frame,:] for x in lpc_featureset]
			# 	ao.lpc_features = np.hstack((lpc_featureset))
			# else:
			# 	ao.lpc_features = lpc_featureset[0]
			
			for feature in tempo_feature_list:
				for f in os.listdir(feature):
					if f[:f.find('_vamp')]==ao.name:
						data = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[1:,1:]
						if len(data) == 0: continue 
						tempo_featureset.append(data)
						ao.tempo_timestamps = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[1:,0]
						break
			if len(tempo_feature_list) > 1:
				n_frame = np.min([x.shape[0] for x in tempo_featureset])
				tempo_featureset = [x[:n_frame,:] for x in tempo_featureset]
				ao.tempo_features = np.hstack((tempo_featureset))
			else:
				ao.tempo_features = tempo_featureset[0]
			for feature in harmonic_feature_list:
				for f in os.listdir(feature):
					if f[:f.find('_vamp')]==ao.name: 
						data = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:]
						if len(data) == 0: continue
						harmonic_featureset.append(data)
						break
			if len(harmonic_feature_list) > 1:
				n_frame = np.min([x.shape[0] for x in harmonic_featureset])
				harmonic_featureset = [x[:n_frame,:] for x in harmonic_featureset]
				ao.harmonic_features = np.hstack((harmonic_featureset))
			else:
				ao.harmonic_features = harmonic_featureset[0]
			
			# Reshape features (downsample) to match tempogram ones 
			step = ao.tempo_features.shape[0]
			# aggregation_step = (n_frames / (step+1.0))
			# Get aggregated features for computing ssm 
			aggregation_window, aggregation_step = 1,1
			featureRate = float(self.SampleRate) /self.stepSize
			pca = PCA(n_components=5)
			
			ao.gammatone_features = normaliseFeature(ao.gammatone_features)	
			ao.gammatone_features = resample(ao.gammatone_features, step)
			ao.gammatone_features[np.isnan(ao.gammatone_features)] = 0.0		
			ao.gammatone_features[np.isinf(ao.gammatone_features)] = 0.0		
			ao.timbre_features = normaliseFeature(ao.timbre_features)
			ao.timbre_features = resample(ao.timbre_features, step)
			ao.timbre_features[np.isnan(ao.timbre_features)] = 0.0
			ao.timbre_features[np.isinf(ao.timbre_features)] = 0.0
			# ao.lpc_features = self.normaliseFeature(ao.lpc_features)
			# ao.lpc_features = resample(ao.lpc_features, step)
			# ao.lpc_features[np.isnan(ao.lpc_features)] = 0.0
			# ao.lpc_features[np.isinf(ao.lpc_features)] = 0.0
			ao.harmonic_features = normaliseFeature(ao.harmonic_features)
			ao.harmonic_features = resample(ao.harmonic_features, step)
			ao.tempo_features = normaliseFeature(ao.tempo_features)	
			ao.harmonic_features[np.isinf(ao.harmonic_features)] = 0.0
			ao.tempo_features[np.isnan(ao.tempo_features)] = 0.0
			ao.tempo_features[np.isinf(ao.tempo_features)] = 0.0
			
			ao.gammatone_features = getMean(ao.gammatone_features, winlen=aggregation_window, stepsize=aggregation_step)
			pca.fit(ao.gammatone_features)
			ao.gammatone_features = pca.transform(ao.gammatone_features)
			ao.gammatone_ssm = getSSM(ao.gammatone_features)
			ao.gammatone_ssm = enhanceSSM(ao.gammatone_ssm)
			
			ao.tempo_features = getMean(ao.tempo_features, winlen=aggregation_window, stepsize=aggregation_step)
			pca.fit(ao.tempo_features)
			ao.tempo_features = pca.transform(ao.tempo_features)
			ao.tempo_ssm = getSSM(ao.tempo_features)
			ao.tempo_ssm = enhanceSSM(ao.tempo_ssm)
			
			ao.timbre_features = getMean(ao.timbre_features, winlen=aggregation_window, stepsize=aggregation_step)
			pca.fit(ao.timbre_features)
			ao.timbre_features = pca.transform(ao.timbre_features)
			ao.timbre_ssm = getSSM(ao.timbre_features)
			ao.timbre_ssm = enhanceSSM(ao.timbre_ssm)

			# ao.lpc_features = self.getMean(ao.lpc_features, winlen=aggregation_window, stepsize=aggregation_step)
			# pca.fit(ao.lpc_features)
			# ao.lpc_features = pca.transform(ao.lpc_features)
			# ao.lpc_ssm = self.getSSM(ao.lpc_features)

			ao.harmonic_features = getMean(ao.harmonic_features, winlen=aggregation_window, stepsize=aggregation_step)
			pca.fit(ao.harmonic_features)
			ao.harmonic_features = pca.transform(ao.harmonic_features)
			ao.harmonic_ssm = getSSM(ao.harmonic_features)
			ao.harmonic_ssm = enhanceSSM(ao.harmonic_ssm)
			
			ao.ssm_timestamps = np.array(map(lambda x: ao.tempo_timestamps[aggregation_step*x], np.arange(0, ao.gammatone_ssm.shape[0])))
			
			# Single feature SSMs.
			# gammatone_ssm = self.reduceSSM(ao.gammatone_ssm)
			plt.figure(figsize=(10, 10))
			plt.vlines(ao.gt / ao.gt[-1] * ao.gammatone_ssm.shape[0], 0, ao.gammatone_ssm.shape[0])
			plt.imshow(ao.gammatone_ssm)
			plt.savefig(join(options.OUTPUT, ao.name+'-enhanced-gammatone.pdf'),format='pdf')
			plt.close()
			
			# tempo_ssm = self.reduceSSM(ao.tempo_ssm)
			plt.figure(figsize=(10, 10))
			plt.vlines(ao.gt / ao.gt[-1] * ao.tempo_ssm.shape[0], 0, ao.tempo_ssm.shape[0])
			plt.imshow(ao.tempo_ssm)
			plt.savefig(join(options.OUTPUT, ao.name+'-enhanced-hpss_tempo.pdf'),format='pdf')
			plt.close()
			
			# timbre_ssm = self.reduceSSM(ao.timbre_ssm)
			plt.figure(figsize=(10, 10))
			plt.vlines(ao.gt / ao.gt[-1] * ao.timbre_ssm.shape[0], 0, ao.timbre_ssm.shape[0])
			plt.imshow(ao.timbre_ssm)
			plt.savefig(join(options.OUTPUT, ao.name+'-enhanced-hpss_mfcc.pdf'),format='pdf')
			plt.close()

			# # lpc_ssm = self.reduceSSM(ao.lpc_ssm)
			# plt.figure(figsize=(10, 10))
			# plt.vlines(ao.gt / ao.gt[-1] * ao.lpc_ssm.shape[0], 0, ao.lpc_ssm.shape[0], colors='k')
			# plt.imshow(ao.lpc_ssm)
			# plt.savefig(join(options.OUTPUT, 'ssm', ao.name+'-lpcc.pdf'),format='pdf')
			# plt.close()
			# np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-lpcc.txt'), np.array(ao.lpc_ssm), delimiter=',')
			
			# harmonic_ssm = self.reduceSSM(ao.harmonic_ssm)
			plt.figure(figsize=(10, 10))
			plt.vlines(ao.gt / ao.gt[-1] * ao.harmonic_ssm.shape[0], 0, ao.harmonic_ssm.shape[0])
			plt.imshow(ao.harmonic_ssm)
			plt.savefig(join(options.OUTPUT, ao.name+'-enhanced-hpss_chroma.pdf'),format='pdf')
			plt.close()

			if options.VERBOSE:
				np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-gammatone.txt'), np.array(ao.gammatone_ssm), delimiter=',')
				np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-tempo.txt'), np.array(ao.tempo_ssm), delimiter=',')
				# np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-hpss_tempo.txt'), np.array(ao.tempo_ssm), delimiter=',')
				# np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-hpss_mfcc.txt'), np.array(ao.timbre_ssm), delimiter=',')
				np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-mfcc.txt'), np.array(ao.timbre_ssm), delimiter=',')
				# np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-lpc.txt'), np.array(ao.lpc_ssm), delimiter=',')
				np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-chroma.txt'), np.array(ao.harmonic_ssm), delimiter=',')
				# np.savetxt(join(options.OUTPUT, 'ssm_data', ao.name+'-hpss_chroma.txt'), np.array(ao.harmonic_ssm), delimiter=',')
			
			# audio_list.append(ao)
			
		
def main():
	segmenter = SSMseg()
	segmenter.process()


if __name__ == '__main__':
	main()