import matplotlib.pyplot as plt
import pickle
import random
from scipy.misc import imread
from audioDataAnalysis.Utils import get_full_final_labeled, get_labels, get_files, get_full_final_enhanced
from audioDataAnalysis.representation import get_LDA
from scipy import misc
mypath = '../KaggleData/'
path = '../KaggleData/Spectrograms/scipy/'
labels_path = '../KaggleData/train.csv'
save_path = '../KaggleData/DIYS_scipy/'
matplot = [230, 374, 33, 528]
scipy = 0
mel = 0
if __name__ == '__main__':
label = get_labels(labels_path)
print('Doing it for ' + path)
get_full_final_enhanced(path, save_path, label, noisy_copies=1,
validation_split=0.1, _crop=scipy, _s=[32, 32, 3], gray=True)
from scipy import misc
from audioDataAnalysis.Utils import get_final_image, get_labels, get_files
import matplotlib.pyplot as plt
from numpy.random import choice
from scipy import ndimage
import numpy as np
mypath = '../KaggleData/'
image_path = '../KaggleData/spectograms/'
files = get_files(image_path)
labels = get_labels(mypath + 'train.csv', format='dict')
inv_map = {}
for k, v in labels.items():
inv_map[v] = inv_map.get(v, [])
inv_map[v].append(k)
file1 = choice(inv_map['1'], 1)[0]
file2 = choice(inv_map['0'], 1)[0]
#train10335.aiff
print('File2 printed')
im1 = misc.imread(image_path + file1[:-5] + '.png', mode='I')
im2 = misc.imread(image_path + file2[:-5] + '.png', mode='I')
im1 = get_final_image(im1, size='original', gray=True)
im2 = get_final_image(im2, size='original', gray=True)
print(im1.shape, im1.dtype)
print(im2.shape, im2.dtype)
import numpy as np
from audioDataAnalysis.representation import *
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import time
from audioDataAnalysis.Utils import get_labels
path = '../2015DCLDEWorkshop/SocalLFDevelopmentData/CINMS17B_winter/'
path2 = '../KaggleData/train/'
labels = get_labels('../KaggleData/' + 'train.csv', format='dict')
whale_map = {'1': 'Whale', '0': 'No Whale'}
if __name__ == '__main__': # Main function
# wav_file = path+'CINMS17B_d03_111202_012730.d100.x.wav' # Filename of the wav file
for i in range(1, 11):
aiff_file = path2 + 'train{}.aiff'.format(
i) # Filename of the wav file
save_name = 'wholeWinter111202.png'
y, sr = librosa.load(aiff_file, duration=2)
ps = librosa.feature.melspectrogram(y=y, sr=sr, fmax=1024)
print(ps.shape)
librosa.display.specshow(ps, y_axis='mel', x_axis='time')
plt.title('Spectogram for train{0}.aiff, the label is {1}'.format(
i, whale_map[labels['train{}.aiff'.format(i)]]))
plt.savefig('C:/Users/jorge/Desktop/MAI/example{}.png'.format(i))
plt.show()
# time.sleep(5)
# print(rate)