AcoularBuzzCam/main.py at main · IsamarZhu/AcoularBuzzCam · GitHub

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import acoular
import matplotlib.pylab as plt
from scipy.io import wavfile
import tables
import numpy as np
from os import path


# Read data from stereo WAV file
fs, data = wavfile.read('./24y_10m_21d_20h_28m_13s/audio_0_trim_5000_small.wav')
print("fs", fs)
print("data.shape[0]", data.shape[0])

# Ensure data is in the correct shape (two channels)
if data.ndim == 1:
    # If it's mono, replicate the channel
    data = np.stack([data, data], axis=-1)

# Output
folder = './output_folder/'
name = 'audio_0_trim_5000.h5'

# Save to Acoular HDF5 format
acoularh5 = tables.open_file(folder + name, mode="w", title=name)
acoularh5.create_earray('/', 'time_data', atom=tables.Float32Atom(), title='',
                         filters=None, expectedrows=data.shape[0],
                         chunkshape=None,
                         byteorder=None, createparents=False, obj=data.astype(np.float32))
acoularh5.set_node_attr('/time_data', 'sample_freq', fs)
acoularh5.close()

print(acoularh5)


# # Read data from WAV
# fs, data = wavfile.read('./24y_10m_21d_20h_28m_13s/audio_0.wav')

# # Ensure data is in the correct shape (handle mono/stereo cases)
# if data.ndim == 1:
#     data = data[:, np.newaxis]  # Convert mono to shape (samples, 1)

# # Convert data to float32 for Acoular
# data = data.astype(np.float32)

# # Output
# folder = './output_folder/'
# name = 'audio_0_NEW.h5'

# # Save to Acoular HDF5 format
# acoularh5 = tables.open_file(folder + name, mode="w", title=name)
# acoularh5.create_earray('/', 'time_data', atom=tables.Float32Atom(), title='',
#                          filters=None, expectedrows=data.shape[0],
#                          chunkshape=(256, data.shape[1]), obj=data)
# acoularh5.set_node_attr('/time_data', 'sample_freq', fs)
# acoularh5.close()


# start beamforming analysis
ts = acoular.TimeSamples( name='./output_folder/audio_0_trim_5000.h5' )

# to do beamforming in freq domain
ps = acoular.PowerSpectra( time_data=ts, block_size=128, window="Hanning" )

# rg = acoular.RectGrid( x_min=-2, x_max=2, y_min=-2, y_max=2, z=2, increment=0.1 )

# Define microphone spacing in meters (144 mm = 0.144 m)
mg = acoular.MicGeom( from_file="mic_array.xml" )
# Print microphone positions for verification
print("Microphone positions:\n", mg.mpos)


# Load microphone geometry from XML
mg = acoular.MicGeom(from_file="mic_array.xml")

# Check if microphone positions are loaded correctly
print("Microphone positions:\n", mg.mpos)

# Plot microphone positions
plt.figure()  # Create a new figure for the microphone positions
if mg.mpos.size > 0:
    plt.plot(mg.mpos[0], mg.mpos[1], 'o')  # Plot x and y positions
    plt.axis('equal')
    plt.xlabel('X Position (m)')
    plt.ylabel('Y Position (m)')
    plt.title('Microphone Positions')
    plt.show(block=False)  # Show the plot and keep it open
else:
    print("No microphone positions available.")


# plot sounds
rg = acoular.RectGrid(x_min=-1, x_max=1, y_min=-1, y_max=1, z=0.030, increment=0.001)

st = acoular.SteeringVector( grid=rg, mics=mg )

bb = acoular.BeamformerCapon(freq_data=ps, steer=st)
pm = bb.synthetic(5000, 0)
Lm = acoular.L_p(pm)


print(Lm.shape)
print(Lm)

# Plot beamforming map with bicubic interpolation
plt.figure()  # Create a new figure for the beamforming plot
plt.imshow(Lm.T, origin="lower", vmin=Lm.max() - 3,  # 3 dB threshold
           extent=rg.extend(), interpolation='bicubic')  # Bicubic interpolation for smoother visuals
plt.colorbar()  # Show color scale
plt.title("Beamforming Map")
plt.xlabel("X Position (m)")
plt.ylabel("Y Position (m)")
# plt.show()  # Show the plot

plt.show(block=True)  # Ensure this plot stays open


# import acoular
# import matplotlib.pylab as plt
# from scipy.io import wavfile
# import numpy as np

# # Path to your stereo WAV file
# wav_file = './24y_10m_21d_20h_28m_13s/audio_0_trim.wav'

# # Start beamforming analysis, use WAV file directly
# ts = acoular.TimeSamples(name=wav_file)

# # Perform beamforming in the frequency domain
# ps = acoular.PowerSpectra(time_data=ts, block_size=128, window="Hanning")

# # Define microphone geometry
# mg = acoular.MicGeom(from_file="mic_array.xml")

# # Verify microphone positions
# print("Microphone positions:\n", mg.mpos)

# # Plot microphone positions
# plt.figure()
# if mg.mpos.size > 0:
#     plt.plot(mg.mpos[0], mg.mpos[1], 'o')
#     plt.axis('equal')
#     plt.xlabel('X Position (m)')
#     plt.ylabel('Y Position (m)')
#     plt.title('Microphone Positions')
#     plt.show(block=False)
# else:
#     print("No microphone positions available.")

# # Rectangular grid for sound source localization
# rg = acoular.RectGrid(x_min=-1, x_max=1,
#                       y_min=-1, y_max=1,
#                       z=1, increment=0.005)

# # Create a steering vector and perform beamforming
# st = acoular.SteeringVector(grid=rg, mics=mg)
# bb = acoular.BeamformerBase(freq_data=ps, steer=st)
# pm = bb.synthetic(5000, 3)  # Beamform at 5000 Hz

# # Convert beamforming result to decibel scale
# Lm = acoular.L_p(pm)

# # Print shape and values of the beamforming map
# print(Lm.shape)
# print(Lm)

# # Plot the beamforming map
# plt.figure()
# plt.imshow(Lm.T, origin="lower", vmin=Lm.max() - 15, extent=rg.extend())
# plt.colorbar()
# plt.title("Beamforming Map")
# plt.xlabel("X Position (m)")
# plt.ylabel("Y Position (m)")
# plt.show(block=True)