final.py.save

import logging
from time import sleep
/home/andronic/woodbeacon/git/woodbeacon-client/test.py
import numpy as np
import sounddevice
import librosa.display, os, gc
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

from keras.models import model_from_json
from keras.preprocessing.image import img_to_array, load_img

duration = 3
sample_rate=48000

def get_sound():
    # import ipdb; ipdb.set_trace()
    audio = sounddevice.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1)
    audio = np.squeeze(audio)
    sr=sample_rate
    sounddevice.wait()
    S = librosa.feature.melspectrogram(audio, sr=sr, n_mels=128)
    log_S = librosa.power_to_db(S, ref=np.max)
    fig = plt.figure(figsize=[1, 1])
    ax = fig.add_subplot(111)
    fig.subplots_adjust(left=0,right=1,bottom=0,top=1)
    ax.axis("off")
    ax.axis("tight")
    plt.margins(0)
    librosa.display.specshow(log_S, sr=sr)
    fig.savefig('rec.png', dpi=100, pad_inches=0)
    plt.close(fig)
    plt.close('all')
    del audio, S, log_S, ax, fig

def extract_spectrogram(fname, iname):
    audio, sr = librosa.load(fname, res_type='kaiser_fast')
    S = librosa.feature.melspectrogram(audio, sr=sr, n_mels=128)
    log_S = librosa.power_to_db(S, ref=np.max)
    fig = plt.figure(figsize=[1, 1])
    ax = fig.add_subplot(111)
    fig.subplots_adjust(left=0,right=1,bottom=0,top=1)
    ax.axis("off")
    ax.axis("tight")
    plt.margins(0)
    librosa.display.specshow(log_S, sr=sr)
    fig.savefig(iname, dpi=100, pad_inches=0)
    plt.close(fig)
    plt.close('all')
    del audio, S, log_S, ax, fig

class AudioModel(object):

    EMOTIONS_LIST = ['CHAINSAW']

    def __init__(self, model_json_file, model_weights_file):
        # load model from JSON file
        with open(model_json_file, "r") as json_file:
            loaded_model_json = json_file.read()
            self.loaded_model = model_from_json(loaded_model_json)

        # load weights into the new model
        self.loaded_model.load_weights(model_weights_file)
        self.loaded_model._make_predict_function()

    def predict(self, img):
        self.preds = self.loaded_model.predict(img)
        # return FacialExpressionModel.EMOTIONS_LIST[np.argmax(self.preds)]np.argmax(
        return self.preds

model = AudioModel("chainsaw.json", "chainsaw.h5")

def main():
    number_of_detection = 0
    while True:
        get_sound()
        img = img_to_array(load_img('rec.png', target_size=(100, 100)))
        img = np.expand_dims(img, axis=0)
        predict = model.predict(img)[0][0]
        print(predict)
        if predict >= 0.9999 and predict <= 1.0:
            print('Pidaras in padure')
            number_of_detection = 0

if __name__ == "__main__":
    main()