def range_doppler(self):
if self.__range_doppler is not None:
return self.__range_doppler
else:
range_doppler = dsp.doppler_processing(self.raw_cube)
self.__range_doppler = range_doppler
return self.__range_doppler
def plot_range_velocity(range_data, is_plot=True):
# rd_map = np.zeros((numFrames, numTxAntennas * numRxAntennas, numADCSamples, numDopplerBins))
for i, radar_cube in enumerate(range_data[10:15:5]):
det_matrix, aoa_input = dsp.doppler_processing(
radar_cube,
num_tx_antennas=numTxAntennas,
clutter_removal_enabled=True,
window_type_2d=Window.HAMMING,
interleaved=True,
accumulate=False)
det_matrix = det_matrix.transpose((1, 0, 2))
if is_plot:
fig, axes = plt.subplots(3, 4, figsize=(120, 90))
det_matrix_vis = np.fft.fftshift(det_matrix, axes=2)
# det_matrix_vis = det_matrix_vis[:, :32, :]
mean = np.mean(det_matrix_vis, axis=0)
std = np.std(det_matrix_vis, axis=0)
for ax, virtual_index in zip(fig.axes,
range(numTxAntennas * numRxAntennas)):
plt_matrix = det_matrix_vis[virtual_index]
# img = ax.imshow(plt_matrix / plt_matrix.max(), cmap='viridis', aspect='auto')
img = ax.imshow((plt_matrix - mean) / std,
cmap='bwr',
aspect='auto')
# cbar = fig.colorbar(img, ax=ax)
# cbar.minorticks_on()
fig.tight_layout()
plt.show() if DebugMode else None
plt.savefig("{}_range_velocity_{}.pdf".format(
os.path.join(figpath, fig_prefix), i))
# det_matrix_vis = np.fft.fftshift(det_matrix, axes=1)
# rd_map[i] = det_matrix
return aoa_input
radar_cube = dsp.range_processing(frame,
window_type_1d=Window.BLACKMAN)
assert radar_cube.shape == (
numChirpsPerFrame, numRxAntennas,
numADCSamples), "[ERROR] Radar cube is not the correct shape!"
# (3) Doppler Processing
# det_matrix, aoa_input = dsp.doppler_processing(radar_cube, num_tx_antennas=numTxAntennas,
# clutter_removal_enabled=True,
# window_type_2d=Window.HAMMING,
# interleaved=True, accumulate=True)
det_matrix, aoa_input = dsp.doppler_processing(
radar_cube,
num_tx_antennas=numTxAntennas,
clutter_removal_enabled=True,
window_type_2d=Window.HAMMING,
interleaved=True,
accumulate=True)
# --- Show output
if plotRangeDopp:
det_matrix_vis = np.fft.fftshift(det_matrix, axes=1)
# doppler_data[i] = det_matrix_vis
if plotMakeMovie:
ims.append((plt.imshow(det_matrix_vis / det_matrix_vis.max(),
interpolation='nearest',
aspect='auto'), ))
else:
plt.imshow(det_matrix_vis / det_matrix_vis.max(),
interpolation='nearest',
for i, frame in enumerate(dataCube):
# print(i,end=',') # Frame tracker
# (2) Range Processing
from mmwave.dsp.utils import Window
cluster = None
radar_cube = dsp.range_processing(frame,
window_type_1d=Window.BLACKMAN)
assert radar_cube.shape == (
numChirpsPerFrame, numRxAntennas,
numADCSamples), "[ERROR] Radar cube is not the correct shape!"
# (3) Doppler Processing
det_matrix, aoa_input = dsp.doppler_processing(
radar_cube,
num_tx_antennas=3,
clutter_removal_enabled=True,
window_type_2d=Window.HAMMING)
# --- Show output
if plotRangeDopp:
det_matrix_vis = np.fft.fftshift(det_matrix, axes=1)
if plotMakeMovie:
ims.append(
(plt.imshow(det_matrix_vis / det_matrix_vis.max()), ))
else:
plt.imshow(det_matrix_vis / det_matrix_vis.max())
plt.title("Range-Doppler plot " + str(i))
plt.pause(0.05)
plt.clf()
while True:
cluster = None
# (1) Reading in adc data
adc_data = dca.read()
frame = dca.organize(adc_data, num_chirps=numChirpsPerFrame, num_rx=numRxAntennas, num_samples=numADCSamples)
# (2) Range Processing
from mmwave.dsp.utils import Window
radar_cube = dsp.range_processing(frame, window_type_1d=Window.BLACKMAN)
assert radar_cube.shape == (
numChirpsPerFrame, numRxAntennas, numADCSamples), "[ERROR] Radar cube is not the correct shape!"
# (3) Doppler Processing
det_matrix, aoa_input = dsp.doppler_processing(radar_cube, num_tx_antennas=numTxAntennas,
clutter_removal_enabled=True)
# --- Show output
if plotRangeDopp:
det_matrix_vis = np.fft.fftshift(det_matrix, axes=1)
plt.imshow(det_matrix_vis / det_matrix_vis.max(), interpolation='nearest', aspect='auto')
plt.pause(0.05)
plt.clf()
# (4) Object Detection
# --- CFAR, SNR is calculated as well.
fft2d_sum = det_matrix.astype(np.int64)
thresholdDoppler, noiseFloorDoppler = np.apply_along_axis(func1d=dsp.ca_,
axis=0,
arr=fft2d_sum.T,
l_bound=1.5,
def generate_spectrum_from_RDC(filename,
numFrames=500,
numADCSamples=128,
numTxAntennas=3,
numRxAntennas=4,
numLoopsPerFrame=128,
numAngleBins=64,
chirpPeriod=0.06,
logGabor=False,
accumulate=True,
save_full=False):
numChirpsPerFrame = numTxAntennas * numLoopsPerFrame
# =============================================================================
# numADCSamples = number of range bins
# numLoopsPerFrame = number of doppler bins
# =============================================================================
range_resolution, bandwidth = dsp.range_resolution(numADCSamples)
doppler_resolution = dsp.doppler_resolution(bandwidth)
if filename[-4:] != '.bin':
filename += '.bin'
adc_data = np.fromfile(filename, dtype=np.int16)
adc_data = adc_data.reshape(numFrames, -1)
adc_data = np.apply_along_axis(DCA1000.organize,
1,
adc_data,
num_chirps=numChirpsPerFrame,
num_rx=numRxAntennas,
num_samples=numADCSamples)
print("Data Loaded!")
dataCube = adc_data
micro_doppler_data = np.zeros((numFrames, numLoopsPerFrame, numADCSamples),
dtype=np.float64)
theta_data = np.zeros((numFrames, numLoopsPerFrame,
numTxAntennas * numRxAntennas, numADCSamples),
dtype=np.complex)
for i, frame in enumerate(dataCube):
# (2) Range Processing
from mmwave.dsp.utils import Window
radar_cube = dsp.range_processing(frame,
window_type_1d=Window.BLACKMAN)
assert radar_cube.shape == (
numChirpsPerFrame, numRxAntennas,
numADCSamples), "[ERROR] Radar cube is not the correct shape!"
# (3) Doppler Processing
det_matrix, theta_data[i] = dsp.doppler_processing(
radar_cube,
num_tx_antennas=3,
clutter_removal_enabled=True,
window_type_2d=Window.HAMMING)
# --- Shifts & Store
det_matrix_vis = np.fft.fftshift(det_matrix, axes=1)
micro_doppler_data[i, :, :] = det_matrix_vis
# Data should now be ready. Needs to be in micro_doppler_data, a 3D-numpy array with shape [numDoppler, numRanges, numFrames]
# LOG GABOR
if logGabor:
if accumulate:
image = micro_doppler_data.sum(axis=1).T
else:
image = micro_doppler_data.T
from LogGabor import LogGabor
import holoviews as hv
lg = LogGabor("default_param.py")
lg.set_size(image)
lg.pe.datapath = 'database/'
image = lg.normalize(image, center=True)
# display input image
# hv.Image(image)
# display log gabor'd image
image = lg.whitening(image) * lg.mask
hv.Image(image)
uDoppler = image
elif accumulate:
uDoppler = micro_doppler_data.sum(axis=1).T
else:
uDoppler = micro_doppler_data.T
if save_full:
return range_resolution, doppler_resolution, uDoppler, theta_data
else:
return range_resolution, doppler_resolution, uDoppler