maad.rois.select_rois

maad.rois.select_rois(im_bin, min_roi=None, max_roi=None, verbose=False, display=False, savefig=None, **kwargs)

Select regions of interest based on its dimensions.

The input is a binary mask, and the output is an image with labelled pixels.

Parameters:

im2d ndarray of scalars

Spectrogram (or image)

min_roi, max_roiscalars, optional, defaultNone

Define the minimum and the maximum area possible for an ROI. If None, the minimum ROI area is 1 pixel and the maximum ROI area is the area of the image

verboseboolean, optional, default is False

print messages

displayboolean, optional, default is False

Display the signal if True

savefigstring, optional, default is None

Root filename (with full path) is required to save the figures. Postfix is added to the root filename.

kwargs, optional. This parameter is used by plt.plot and savefig functions

• savefilenamestr, optional, default :’_spectro_after_noise_subtraction.png’

  Postfix of the figure filename

• figsizetuple of integers, optional, default: (4,10)

  width, height in inches.

• titlestring, optional, default‘Spectrogram’

  title of the figure

• xlabelstring, optional, default‘Time [s]’

  label of the horizontal axis

• ylabelstring, optional, default‘Amplitude [AU]’

  label of the vertical axis

• cmapstring or Colormap object, optional, default is ‘gray’

  See https://matplotlib.org/examples/color/colormaps_reference.html in order to get all the existing colormaps examples: ‘hsv’, ‘hot’, ‘bone’, ‘tab20c’, ‘jet’, ‘seismic’, ‘viridis’…

• vmin, vmaxscalar, optional, default: None

  vmin and vmax are used in conjunction with norm to normalize luminance data. Note if you pass a norm instance, your settings for vmin and vmax will be ignored.

• extentscalars (left, right, bottom, top), optional, default: None

  The location, in data-coordinates, of the lower-left and upper-right corners. If None, the image is positioned such that the pixel centers fall on zero-based (row, column) indices.

• dpiinteger, optional, default is 96

  Dot per inch. For printed version, choose high dpi (i.e. dpi=300) => slow For screen version, choose low dpi (i.e. dpi=96) => fast

• formatstring, optional, default is ‘png’

  Format to save the figure

… and more, see matplotlib

Returns:

im_rois: 2d ndarray

image with labels as values

rois: pandas DataFrame

Regions of interest with future descriptors will be computed. Array have column names: labelID, label, min_y, min_x, max_y, max_x, Use the function maad.util.format_features before using centroid_features to format of the rois DataFrame correctly.

Examples

>>> import maad

Load audio recording compute the spectrogram in dB.

>>> s, fs = maad.sound.load('../data/cold_forest_daylight.wav')
>>> Sxx,tn,fn,ext = maad.sound.spectrogram (s, fs, flims=(0,20000), display=True)           
>>> Sxx_dB = maad.util.power2dB(Sxx) +96

Smooth the spectrogram

>>> Sxx_dB_blurred = maad.sound.smooth(Sxx_dB)

Using image binarization, detect isolated region in the time-frequency domain with high density of energy, i.e. regions of interest (ROIs).

>>> im_bin = maad.rois.create_mask(Sxx_dB_blurred, bin_std=1.5, bin_per=0.5, mode='relative')

Select ROIs from the binary mask.

>>> im_rois, df_rois = maad.rois.select_rois(im_bin, display=True)
>>> print('Without subtracted the background noise, only {} ROIs have been segmented'.format(len(df_rois)))
Without subtracted the background noise, only 13 ROIs have been segmented
>>> df_rois.iloc[-1]
labelID         13
label      unknown
min_y          162
min_x         1105
max_y          193
max_x         1127
Name: 12, dtype: object

We detected the background noise as a ROI, and that multiple ROIs are mixed in a single region. To have better results, it is adviced to preprocess the spectrogram to remove the background noise before creating the mask.

>>> Sxx_noNoise = maad.sound.median_equalizer(Sxx)
>>> Sxx_noNoise_dB = maad.util.power2dB(Sxx_noNoise)     
>>> Sxx_noNoise_dB_blurred = maad.sound.smooth(Sxx_noNoise_dB)        
>>> im_bin2 = maad.rois.create_mask(Sxx_noNoise_dB_blurred, bin_std=6, bin_per=0.5, mode='relative') 
>>> im_rois2, df_rois2 = maad.rois.select_rois(im_bin2, display=True)
>>> print('{} ROIs have been segmented'.format(len(df_rois2)))
274 ROIs have been segmented
>>> df_rois2.iloc[-1]
labelID        274
label      unknown
min_y          214
min_x         4485
max_y          230
max_x         4494
Name: 273, dtype: object