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)