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cough.py
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import joblib
import sklearn
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
import librosa, librosa.display
import numpy as np
import matplotlib.pyplot as plt
def load_file(file_path):
test_features = []
signal, sr = librosa.load(file_path, sr = 22050)
n_fft = 2048
n_mfcc = 13
hop_length = 512
num_segments = 3
SAMPLE_RATE = 22050
DURATION = 10 # measured in seconds.
SAMPLES_PER_TRACK = SAMPLE_RATE * DURATION
num_samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
for s in range(num_segments):
start_sample = num_samples_per_segment * s # if s= 0 -> then start_sample = 0
finish_sample = start_sample + num_samples_per_segment
# features
rolloff = librosa.feature.spectral_rolloff(y=signal[start_sample: finish_sample], sr=sr, roll_percent=0.1)
pitches, magnitudes = librosa.piptrack(y=signal[start_sample: finish_sample], sr=sr)
mfcc = librosa.feature.mfcc(signal[start_sample: finish_sample],
sr =sr,
n_fft = n_fft,
n_mfcc = n_mfcc,
hop_length = hop_length
)
chroma_cq = librosa.feature.chroma_cqt(y=signal[start_sample: finish_sample], sr=sr)
# Combining all the features
features = np.concatenate((pitches, rolloff, mfcc, chroma_cq), axis = 0)
test_features.append(features)
test_feat = np.array(test_features)
model_features = test_feat.reshape(test_feat.shape[0], (test_feat.shape[1]*test_feat.shape[2]))
return model_features
# def predict(cough_fp, saved_model_fp):
# loaded_model = joblib.load(saved_model_fp)
# cough_features = load_file(cough_fp)
# result = loaded_model.predict_proba(cough_features)
# print("Results are : ", result)
# class_neg = []
# class_pos = []
# l = 0
# for i in result:
# j = np.argmax(i)
# k = result[l][j]
# if j == 0:
# class_neg.append(k)
# else:
# class_pos.append(k)
# l += 1
# print("class neg: ", class_neg)
# print("class pos: ", class_pos)
# if not class_neg:
# print("covid positive")
# prob_pos = np.mean(class_pos)
# print("prob posit: ", prob_pos)
# # return prob_neg
# elif not class_pos:
# print("covid negative")
# prob_neg = np.mean(class_neg)
# print("prob neg: ", prob_neg)
# # return prob_pos
# else:
# prob_neg = np.mean(class_neg)
# # print(m)
# prob_pos = np.mean(class_pos)
# if prob_neg > prob_pos:
# print("covid neg")
# return "Covid Negatve :" + str(prob_neg)
# else:
# print("covid pos")
# return "Covid Positive" + str(prob_pos)
# ignoring negative and returning 0
# def predict(cough_fp, saved_model_fp):
# loaded_model = joblib.load(saved_model_fp)
# cough_features = load_file(cough_fp)
# result = loaded_model.predict_proba(cough_features)
# print("Results are : ", result)
# class_neg = []
# class_pos = []
# l = 0
# for i in result:
# j = np.argmax(i)
# k = result[l][j]
# if j == 0:
# class_neg.append(k)
# else:
# class_pos.append(k)
# l += 1
# print("class neg: ", class_neg)
# print("class pos: ", class_pos)
# if not class_neg:
# print("covid positive")
# prob_pos = np.mean(class_pos)
# print("prob posit: ", prob_pos)
# # return "Covid positive: " + str(prob_pos)
# return prob_pos * 100
# elif not class_pos:
# # print("covid negative")
# # prob_neg = np.mean(class_neg)
# # print("prob neg: ", prob_neg)
# # return "Covid negative: "+ str(prob_neg)
# return 0
# else:
# prob_neg = np.mean(class_neg)
# # print(m)
# prob_pos = np.mean(class_pos)
# if prob_neg > prob_pos:
# # print("covid neg")
# # return "Covid Negatve :" + str(prob_neg)
# return 0
# else:
# print("covid pos")
# # return "Covid Positive :" + str(prob_pos)
# return prob_pos * 100
# returning prob of the class having max vote count
def predict(cough_fp, saved_model_fp):
loaded_model = joblib.load(saved_model_fp)
cough_features = load_file(cough_fp)
result = loaded_model.predict_proba(cough_features)
print("Results are : ", result)
class_neg = []
class_pos = []
l = 0
for i in result:
j = np.argmax(i)
k = result[l][j]
if j == 0:
class_pos.append(k)
else:
class_neg.append(k)
l += 1
print("class neg: ", class_neg)
print("class pos: ", class_pos)
if not class_neg:
print("covid positive")
prob_pos = np.mean(class_pos)
print("prob posit: ", prob_pos)
# return "Covid positive: " + str(prob_pos)
return prob_pos
elif not class_pos:
# print("covid negative")
# prob_neg = np.mean(class_neg)
# print("prob neg: ", prob_neg)
# return "Covid negative: "+ str(prob_neg)
return 0
else:
prob_neg = np.mean(class_neg)
prob_pos = np.mean(class_pos)
if len(class_neg) > len(class_pos):
return 0
# if prob_neg > prob_pos:
# print("covid neg")
# return "Covid Negatve :" + str(prob_neg)
# return 0
else:
return prob_pos