demo.py

import os
import yaml
import zipfile
import numpy as np
import pickle
import urllib
import matplotlib.pyplot as plt
import sys
sys.path.append('lib')

from prediction.tf_classifier import TFClassifier, HOP_LENGTH

# where to download the pretrained models from
models_dl_path = 'https://www.dropbox.com/s/50ketdmtn6bd1oa/tf_models.zip?dl=1'


#############################################

print("-> Downloading and unzipping pre-trained model...")

if not os.path.exists('tf_models/biotic/network_opts.yaml'):

    if not os.path.isdir("tf_models"):
        os.makedirs("tf_models")

    if not os.path.exists('tf_models/models.zip'):
        urllib.request.urlretrieve(models_dl_path, "tf_models/models.zip")
        # wget.download(models_dl_path, 'models')

    with zipfile.ZipFile('tf_models/models.zip', 'r') as zip_ref:
        zip_ref.extractall('./')

print("-> ...Done")


############################################################

print("->  Making predictions for biotic and anthropogenic separately")

preds = {}

for classifier_type in ['biotic', 'anthrop']:

    with open('tf_models/%s/network_opts.yaml' % classifier_type) as f:
        options = yaml.full_load(f)

    model_path = 'tf_models/%s/weights_99.pkl-1' % classifier_type

    predictor = TFClassifier(options, model_path)
    preds[classifier_type] = predictor.classify("demo/NW23SH-13548_20150811_16300021.wav")

print("-> ...Done")

############################

print("-> Saving predictions to disk")

with open('demo/predictions.pkl', 'wb') as f:
    pickle.dump(preds, f, -1)

print("-> ...Done")


######################

print("-> Plotting predictions")

plt.figure(figsize=(15, 5))
cols = {'anthrop': 'b', 'biotic': 'g'}

for key, val in preds.items():
    len_in_s = val.shape[0] * HOP_LENGTH / predictor.sample_rate
    print(len_in_s)

    x = np.linspace(0, len_in_s, val.shape[0])
    plt.plot(x, val, cols[key], label=key)

    plt.xlabel('Time (s)')
    plt.ylabel('Activity level')

# plt.ylim(0, 1.2)
plt.xlim(0, 60)
plt.legend()
plt.savefig('demo/predictions.pdf')

print("-> ...Done")