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recognitionFunctions.py
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recognitionFunctions.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 27 23:57:41 2020
@author: aguasharo
"""
from readDataset import *
from preProcessing import *
from featureExtraction import *
from classificationEMG import *
from sklearn.manifold import TSNE
import seaborn as sns
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
from keras.optimizers import Adam, SGD
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import collections
from collections import Counter
def get_x_train(user,sample):
# This function reads the time series(x) of the user (Training Sample)
train_samples = user['trainingSamples']
x = (train_samples[sample]['emg'])
# Divide to 128 for having a signal between -1 and 1
df = pd.DataFrame.from_dict(x) / 128
# Apply filter
train_filtered_X = df.apply(preProcessEMGSegment)
# Segment the filtered EMG signal
train_segment_X = EMG_segment(train_filtered_X)
return train_segment_X
def get_x_test(user,sample):
# This function reads the time series(x) of the user (Testing Sample)
test_samples = user['testingSamples']
x = (test_samples[sample]['emg'])
df = pd.DataFrame.from_dict(x) / 128
return df
def decode_targets(y_train):
# Encode targets to train the Neural Network
encoder = LabelEncoder()
encoder.fit(y_train)
encoded_Y = encoder.transform(y_train)
target = np_utils.to_categorical(encoded_Y)
return target
def get_xy_val(X_train, targets):
# Get validation data to train Neural Network
data_val = X_train.copy()
data_val['6'] = targets
xy_val = data_val.sample(frac=1).reset_index(drop=True)
X_val = xy_val.iloc[:,0:6]
y_val = decode_targets(xy_val['6'])
return X_val, y_val
def bestCenter_Class(train_segment_X):
# This function returns a set of time series called centers
# for each gesture class
g1 = train_segment_X[0:25]
g2 = train_segment_X[25:50]
g3 = train_segment_X[50:75]
g4 = train_segment_X[75:100]
g5 = train_segment_X[100:125]
g6 = train_segment_X[125:150]
gen = [g1, g2, g3, g4, g5 ,g6]
c = [findCentersClass(g) for g in gen]
return c
def getFeatureExtraction(emg_filtered, centers):
features = featureExtractionf(emg_filtered, centers)
dataX = preProcessFeatureVector(features)
return dataX
def trainFeedForwardNetwork(X_train,y_train, X_test, y_test):
# This function trains an artificial feed-forward neural networks
# Cost lost: categorical cross entropy
# Hidden Layer: Tanh
# Output Layer: softmax
classifier = Sequential()
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = None, input_dim = 6))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'tanh'))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'softmax'))
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 150, epochs = 1500, validation_data = (X_test, y_test), verbose = 0 )
return classifier
def testing_prediction(user,sample,centers,estimator):
test_RawX = get_x_test(user,sample)
[predictedSeq, vec_time, time_seq]= classifyEMG_SegmentationNN(test_RawX, centers, estimator)
predicted_label, t_post = post_ProcessLabels(predictedSeq)
# Computing the time of processing
estimatedTime = [sum(x) for x in zip(time_seq, t_post)]
return predicted_label, predictedSeq, vec_time, estimatedTime
def recognition_results(results):
# This function save the responses of each user into a dictionary
d = collections.defaultdict(dict)
for i in range(0,150):
d['idx_%s' %i]['class'] = code2gesture(results[i][0])
d['idx_%s' %i]['vectorOfLabels'] = code2gesture_labels(results[i][1])
d['idx_%s' %i]['vectorOfTimePoints'] = results[i][2]
d['idx_%s' %i]['vectorOfProcessingTime']= results[i][3]
return d