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ParticipantLab.py
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ParticipantLab.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Feb 7 00:45:00 2020
@author: sarna
"""
import os
import numpy as np
import librosa
import scipy
import torch
class ParticipantLab:
"""class for handling participant data"""
m_timestep = 0
a_timestep = 0
m_features = 6
n_mfcc = 30
motion_ftrs = 48
audio_split = 10
def __rearrange(self, a, window, overlap):
if(len(a.shape) == 2):
l, f = a.shape
shape = (int( (l-overlap)/(window-overlap) ), window, f)
stride = (a.itemsize*f*(window-overlap), a.itemsize*f, a.itemsize)
elif(len(a.shape) == 1):
l = len(a)
shape = (int( (l-overlap)/(window-overlap) ), window)
stride = (a.itemsize*(window-overlap), a.itemsize)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=stride)
def __mfcc_stack(self, a, sr=22050, window=22050, hop_length=11025, splits=10, n_mfcc=10):
mfcc = np.zeros((0,splits,n_mfcc))
for i in range(0, len(a)-window, hop_length):
chunk = a[i:i+window]
mfcc_temp = librosa.feature.mfcc(y=chunk, sr=sr, n_mfcc=n_mfcc, n_fft=int(window/splits), hop_length=int(np.ceil(window/splits)))
mfcc_temp = np.expand_dims(np.transpose(mfcc_temp[:,:splits]), axis=0)
mfcc = np.vstack((mfcc, mfcc_temp))
return mfcc
def __init__(self, name, folder, win_size, overlap, normalized=False):
self.name = name
self.a_folder = []
self.m_folder = []
if normalized:
motion_folder = '../Norm_data'
else:
motion_folder = folder
for i in range(1,3):
self.a_folder.append(folder + '/' + name + str(i) + '/SegAudio/')
self.m_folder.append(motion_folder + '/' + name + str(i) + '/SegMotion/')
self.a_folder_s1 = folder + '/' + name + '1/SegAudio/'
self.m_folder_s1 = motion_folder + '/' + name + '1/SegMotion/'
self.a_folder_s2 = folder + '/' + name + '2/SegAudio/'
self.m_folder_s2 = motion_folder + '/' + name + '2/SegMotion/'
self.data_s1 = []
self.data_s2 = []
self.rawMdataX_s1 = []
self.rawAdataX_s1 = []
self.rawdataY_s1 = []
self.rawMdataX_s2 = []
self.rawAdataX_s2 = []
self.rawdataY_s2 = []
self.labels = []
self.win_size = win_size # in seconds
self.overlap = overlap
def readData(self):
for ses in range(2):
a_files = os.listdir(self.a_folder[ses])
a_files.sort()
m_files = os.listdir(self.m_folder[ses])
m_files.sort()
# raise Exception("checkpoint stop")
data_temp = np.zeros((0,self.motion_ftrs+self.n_mfcc+1))
for index in range(0,len(a_files)):
# features vectors
mfcc = np.zeros((0,self.n_mfcc))
f_matrix = np.zeros((0,self.motion_ftrs))
# sound processing part
audio, sr_audio = librosa.load(self.a_folder[ses] + a_files[index], sr=None)
win_audio = self.win_size*sr_audio
hop_length_audio = int((1-self.overlap)*win_audio)
mfcc_temp = np.zeros((0,self.n_mfcc))
for i in range(0, len(audio)-win_audio, hop_length_audio):
chunk = audio[i:i+win_audio]
features = librosa.feature.mfcc(y=chunk, sr=sr_audio, n_mfcc=self.n_mfcc, n_fft=22050, hop_length=22051)
features = np.mean(features, axis=1)
# raise Exception("checkpoint stop")
mfcc_temp = np.vstack((mfcc_temp, features))
arow, col = mfcc_temp.shape
# motion processing part
motion = np.loadtxt(self.m_folder[ses] + m_files[index], delimiter=',')
sr_motion = int(np.round(50.7))
win_motion = self.win_size*sr_motion
hop_length_motion = int((1-self.overlap)*win_motion)
f_matrix_temp = np.zeros((0,self.motion_ftrs))
for i in range(0, len(motion)-win_motion, hop_length_motion):
chunk = motion[i:i+win_motion,:]
mean = np.mean(chunk, axis=0)
median = np.median(chunk, axis=0)
var = np.var(chunk, axis=0)
maxi = chunk.max(axis=0)
mini = chunk.min(axis=0)
rms = np.sqrt(np.mean(chunk**2, axis=0))
skew = scipy.stats.skew(chunk, axis=0)
kurt = scipy.stats.kurtosis(chunk, axis=0)
features = np.hstack((mean,median,maxi,mini,rms,var,skew,kurt))
f_matrix_temp = np.vstack((f_matrix_temp, features))
mrow, col = f_matrix_temp.shape
if(arow>mrow):
mfcc_temp = mfcc_temp[:mrow,:]
elif(arow<mrow):
f_matrix_temp = f_matrix_temp[:arow,:]
mfcc = np.vstack((mfcc, mfcc_temp))
f_matrix = np.vstack((f_matrix, f_matrix_temp))
label = ord(a_files[index][-5])-97
Y = label*np.ones((len(mfcc),1), dtype=np.uint8)
frame = np.hstack((mfcc, f_matrix, Y))
data_temp = np.vstack((data_temp, frame))
if(ses==0):
self.data_s1 = data_temp
if(ses==1):
self.data_s2 = data_temp
def readRawData(self):
# ************ UNDER TEST *********************
sr_motion = 50.7
win_motion = int(np.round(self.win_size*sr_motion))
olp_motion = int(self.overlap*win_motion)
self.m_timestep = win_motion
sr_audio = 22050
win_audio = self.win_size*sr_audio
olp_audio = int((self.overlap)*win_audio)
self.a_timestep = win_audio
# session 1
a_files = os.listdir(self.a_folder_s1)
a_files.sort()
m_files = os.listdir(self.m_folder_s1)
m_files.sort()
# raise Exception("checkpoint stop")
self.rawMdataX_s1 = np.zeros((0, win_motion, 6), dtype=np.float32)
self.rawAdataX_s1 = np.zeros((0, self.a_timestep), dtype=np.float32)
self.rawdataY_s1 = np.zeros((0, 1))
for index in range(0,len(a_files)):
# Audio
audio, sr_audio = librosa.load(self.a_folder_s1 + a_files[index], sr=None)
a_matrix = self.__rearrange(audio, win_audio, olp_audio)
# print(a_matrix.shape)
# Motion
motion = np.loadtxt(self.m_folder_s1 + m_files[index], delimiter=',')
f_matrix = self.__rearrange(motion, win_motion, olp_motion)
# print(f_matrix.shape)
# Labelling and concat
a=len(a_matrix)
m=len(f_matrix)
row=0
if(a>=m):
row = m
a_matrix = a_matrix[:row,:]
elif(a<m):
row = a
f_matrix = f_matrix[:row,:,:]
label = ord(a_files[index][-5])-97
Y = label*np.ones((row,1), dtype=np.uint8)
self.rawAdataX_s1 = np.vstack((self.rawAdataX_s1, a_matrix))
self.rawMdataX_s1 = np.vstack((self.rawMdataX_s1, f_matrix))
self.rawdataY_s1 = np.vstack((self.rawdataY_s1, Y))
self.rawdataY_s1 = np.float32(self.rawdataY_s1)
self.rawMdataX_s1 = np.float32(self.rawMdataX_s1)
self.rawAdataX_s1 = np.float32(self.rawAdataX_s1)
# session2
a_files = os.listdir(self.a_folder_s2)
a_files.sort()
m_files = os.listdir(self.m_folder_s2)
m_files.sort()
self.rawMdataX_s2 = np.zeros((0, win_motion, 6), dtype=np.float32)
self.rawAdataX_s2 = np.zeros((0, self.a_timestep), dtype=np.float32)
self.rawdataY_s2 = np.zeros((0, 1))
for index in range(0,len(a_files)):
# Audio
audio, sr_audio = librosa.load(self.a_folder_s2 + a_files[index], sr=None)
a_matrix = self.__rearrange(audio, win_audio, olp_audio)
# Motion
motion = np.loadtxt(self.m_folder_s2 + m_files[index], delimiter=',')
f_matrix = self.__rearrange(motion, win_motion, olp_motion)
# Labelling and concat
a=len(a_matrix)
m=len(f_matrix)
row=0
if(a>=m):
row = m
a_matrix = a_matrix[:row,:]
elif(a<m):
row = a
f_matrix = f_matrix[:row,:,:]
label = ord(a_files[index][-5])-97
self.labels.append(label)
Y = label*np.ones((row,1), dtype=np.uint8)
self.rawAdataX_s2 = np.vstack((self.rawAdataX_s2, a_matrix))
self.rawMdataX_s2 = np.vstack((self.rawMdataX_s2, f_matrix))
self.rawdataY_s2 = np.vstack((self.rawdataY_s2, Y))
self.rawdataY_s2 = np.float32(self.rawdataY_s2)
self.rawMdataX_s2 = np.float32(self.rawMdataX_s2)
self.rawAdataX_s2 = np.float32(self.rawAdataX_s2)
def readRawAudioMotionData(self):
# ************ UNDER TEST *********************
sr_motion = 50.7 #55.0 #50.7
old_sr = 50.7
sr_audio = 22050
win_motion = int(np.round(self.win_size*sr_motion))
olp_motion = int(self.overlap*win_motion)
self.m_timestep = win_motion
# session 1
if os.path.exists(self.a_folder_s1):
a_files = os.listdir(self.a_folder_s1)
fileNames = [a.split('.')[0] for a in a_files]
m_files = [a + '.csv' for a in fileNames]
#m_files = sorted(os.listdir(self.m_folder_s1))
#import pdb; pdb.set_trace()
self.rawMdataX_s1 = np.zeros((0, int(self.win_size*old_sr), 6), dtype=np.float32)
self.rawAdataX_s1 = np.zeros((0, int(self.win_size*sr_audio)), dtype=np.float32)
self.rawdataY_s1 = np.zeros((0, 1))
for index in range(0,len(a_files)):
# Audio
audio, sr_audio = librosa.load(self.a_folder_s1 + a_files[index], sr=None)
win_audio = int(self.win_size*sr_audio)
hop_length_audio = int((1-self.overlap)*win_audio)
a_matrix = librosa.util.frame(audio, win_audio, hop_length_audio)
#print(np.shape(audio),a_matrix.shape)
# Motion
motion = np.loadtxt(self.m_folder_s1 + m_files[index], delimiter=',')
f_matrix = self.__rearrange(motion, win_motion, olp_motion)
f_matrix = f_matrix[:,:int(self.win_size*old_sr)+1,:]
#print('Motion {}, Audio {}'.format(np.shape(f_matrix), a_matrix.shape))
# Labelling and concat
a=np.shape(a_matrix)[-1]
m=len(f_matrix)
row=0
if(a>m):
#import pdb; pdb.set_trace()
row = m
a_matrix = a_matrix[:,:row]
else:
#import pdb; pdb.set_trace()
row = a
f_matrix = f_matrix[:row,:,:]
if self.name == 'Test':
label = ord(a_files[index][4])-97
else:
label = ord(a_files[index][3])-97
Y = label*np.ones((row,1), dtype=np.uint8)
self.rawMdataX_s1 = np.vstack((self.rawMdataX_s1, f_matrix))
self.rawdataY_s1 = np.vstack((self.rawdataY_s1, Y))
self.rawAdataX_s1 = np.vstack((self.rawAdataX_s1, a_matrix.T))
self.rawdataY_s1 = np.float32(self.rawdataY_s1)
self.rawMdataX_s1 = np.float32(self.rawMdataX_s1)
self.rawAdataX_s1 = np.float32(self.rawAdataX_s1)
# session2
if os.path.exists(self.a_folder_s2):
a_files = os.listdir(self.a_folder_s2)
fileNames = [a.split('.')[0] for a in a_files]
m_files = [a + '.csv' for a in fileNames]
#sorted(os.listdir(self.m_folder_s2))
self.rawMdataX_s2 = np.zeros((0, win_motion, 6), dtype=np.float32)
self.rawAdataX_s2 = np.zeros((0, int(self.win_size*sr_audio)), dtype=np.float32)
self.rawdataY_s2 = np.zeros((0, 1))
for index in range(0,len(a_files)):
# Audio
audio, sr_audio = librosa.load(self.a_folder_s2 + a_files[index], sr=None)
win_audio = int(self.win_size*sr_audio)
hop_length_audio = int((1-self.overlap)*win_audio)
a_matrix = librosa.util.frame(audio, win_audio, hop_length_audio)
# Motion
motion = np.loadtxt(self.m_folder_s2 + m_files[index], delimiter=',')
f_matrix = self.__rearrange(motion, win_motion, olp_motion)
# Labelling and concat
a=np.shape(a_matrix)[-1]
m=len(f_matrix)
row=0
if(a>m):
row = m
a_matrix = a_matrix[:,:row]
else:
row = a
f_matrix = f_matrix[:row,:,:]
if self.name == 'Test':
label = ord(a_files[index][4])-97
else:
label = ord(a_files[index][3])-97
self.labels.append(label)
Y = label*np.ones((row,1), dtype=np.uint8)
#self.rawAdataX_s2 = np.vstack((self.rawAdataX_s2, mfcc))
self.rawMdataX_s2 = np.vstack((self.rawMdataX_s2, f_matrix))
self.rawdataY_s2 = np.vstack((self.rawdataY_s2, Y))
self.rawAdataX_s2 = np.vstack((self.rawAdataX_s2, a_matrix.T))
self.rawdataY_s2 = np.float32(self.rawdataY_s2)
self.rawMdataX_s2 = np.float32(self.rawMdataX_s2)
self.rawAdataX_s2 = np.float32(self.rawAdataX_s2)
# self.rawdataY = [self.rawdataY_s1, self.rawdataY_s2]
# self.rawMdataX = [self.rawMdataX_s1, self.rawMdataX_s2]
# self.rawAdataX = [self.rawAdataX_s1, self.rawAdataX_s2]
#self.actualrawAdata = [self.actualrawAdataX_s1, self.actualrawAdataX_s2]
#print(np.shape(self.rawAdataX_s1[0]), np.shape(self.rawAdataX_s2), np.shape(self.rawAdataX))
def size_of_data(self):
print(np.shape(self.data_s1))
print(np.shape(self.data_s2))
def get_labels(self):
print(self.labels)
# def readRawAudioMotionData(self):
# # ************ UNDER TEST *********************
# sr_motion = 50.7
# sr_audio = 22050
# win_motion = int(np.round(self.win_size*sr_motion))
# olp_motion = int(self.overlap*win_motion)
# self.m_timestep = win_motion
# # session 1
# a_files = os.listdir(self.a_folder_s1)
# a_files.sort()
# m_files = os.listdir(self.m_folder_s1)
# m_files.sort()
# self.rawMdataX_s1 = np.zeros((0, win_motion, 6), dtype=np.float32)
# self.rawAdataX_s1 = np.zeros((0, int(self.win_size*sr_audio)), dtype=np.float32)
# self.rawdataY_s1 = np.zeros((0, 1))
# for index in range(0,len(a_files)):
# # Audio
# audio, sr_audio = librosa.load(self.a_folder_s1 + a_files[index], sr=None)
# win_audio = int(self.win_size*sr_audio)
# hop_length_audio = int((1-self.overlap)*win_audio)
# a_matrix = librosa.util.frame(audio, win_audio, hop_length_audio)
# print(np.shape(audio),a_matrix.shape)
# # Motion
# motion = np.loadtxt(self.m_folder_s1 + m_files[index], delimiter=',')
# f_matrix = self.__rearrange(motion, win_motion, olp_motion)
# print(np.shape(f_matrix))
# # Labelling and concat
# a=np.shape(a_matrix)[-1]
# m=len(f_matrix)
# row=0
# if(a>m):
# row = m
# a_matrix = a_matrix[:,:row]
# else:
# row = a
# f_matrix = f_matrix[:row,:,:]
# label = ord(a_files[index][-5])-97
# Y = label*np.ones((row,1), dtype=np.uint8)
# self.rawMdataX_s1 = np.vstack((self.rawMdataX_s1, f_matrix))
# self.rawdataY_s1 = np.vstack((self.rawdataY_s1, Y))
# self.rawAdataX_s1 = np.vstack((self.rawAdataX_s1, a_matrix.T))
# self.rawdataY_s1 = np.float32(self.rawdataY_s1)
# self.rawMdataX_s1 = np.float32(self.rawMdataX_s1)
# self.rawAdataX_s1 = np.float32(self.rawAdataX_s1)
# # session2
# a_files = os.listdir(self.a_folder_s2)
# a_files.sort()
# m_files = os.listdir(self.m_folder_s2)
# m_files.sort()
# self.rawMdataX_s2 = np.zeros((0, win_motion, 6), dtype=np.float32)
# self.rawAdataX_s2 = np.zeros((0, self.win_size*sr_audio), dtype=np.float32)
# self.rawdataY_s2 = np.zeros((0, 1))
# for index in range(0,len(a_files)):
# # Audio
# audio, sr_audio = librosa.load(self.a_folder_s2 + a_files[index], sr=None)
# win_audio = self.win_size*sr_audio
# hop_length_audio = int((1-self.overlap)*win_audio)
# a_matrix = librosa.util.frame(audio, win_audio, hop_length_audio)
# # Motion
# motion = np.loadtxt(self.m_folder_s2 + m_files[index], delimiter=',')
# f_matrix = self.__rearrange(motion, win_motion, olp_motion)
# # Labelling and concat
# a=np.shape(a_matrix)[-1]
# m=len(f_matrix)
# row=0
# if(a>m):
# row = m
# a_matrix = a_matrix[:,:row]
# else:
# row = a
# f_matrix = f_matrix[:row,:,:]
# label = ord(a_files[index][-5])-97
# self.labels.append(label)
# Y = label*np.ones((row,1), dtype=np.uint8)
# #self.rawAdataX_s2 = np.vstack((self.rawAdataX_s2, mfcc))
# self.rawMdataX_s2 = np.vstack((self.rawMdataX_s2, f_matrix))
# self.rawdataY_s2 = np.vstack((self.rawdataY_s2, Y))
# self.rawAdataX_s2 = np.vstack((self.rawAdataX_s2, a_matrix.T))
# self.rawdataY_s2 = np.float32(self.rawdataY_s2)
# self.rawMdataX_s2 = np.float32(self.rawMdataX_s2)
# self.rawAdataX_s2 = np.float32(self.rawAdataX_s2)
# self.rawdataY = [self.rawdataY_s1, self.rawdataY_s2]
# self.rawMdataX = [self.rawMdataX_s1, self.rawMdataX_s2]
# self.rawAdataX = [self.rawAdataX_s1, self.rawAdataX_s2]
# #self.actualrawAdata = [self.actualrawAdataX_s1, self.actualrawAdataX_s2]
# print(np.shape(self.rawAdataX_s1[0]), np.shape(self.rawAdataX_s2), np.shape(self.rawAdataX))
# def size_of_data(self):
# print(np.shape(self.data_s1))
# print(np.shape(self.data_s2))
# def get_labels(self):
# print(self.labels)