def save_phase_data(bin_path,
save_path,
start_bin,
end_bin,
num_frames,
chirp_index=5,
is_diff=True):
if is_diff:
num_frames -= 1
# load Numpy Data
adc_data = np.fromfile(bin_path, 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)
range_data = dsp.range_processing(adc_data, window_type_1d=Window.BLACKMAN)
range_data = arange_tx(range_data, num_tx=numTxAntennas)
range_data = range_data[:, chirp_index, :, start_bin:end_bin]
range_data = range_data.transpose((1, 2, 0))
# angle and unwrap
sig_phase = np.angle(range_data)
sig_phase = np.unwrap(sig_phase)
# save file
np.save(save_path, sig_phase)
print("{} npy file saved!".format(save_path))
def plot_heatmap_capon(adc_data_path, save_path, bin_start=4, bin_end=14, diff=False, is_log=False,
remove_clutter=True,
cumulative=False):
num_bins = bin_end - bin_start
npy_azi = np.zeros((numFrames, ANGLE_BINS, num_bins))
npy_ele = np.zeros((numFrames, ANGLE_BINS, num_bins))
adc_data = np.fromfile(adc_data_path, 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)
# Start DSP processing
range_azimuth = np.zeros((ANGLE_BINS, BINS_PROCESSED))
range_elevation = np.zeros((ANGLE_BINS, BINS_PROCESSED))
num_vec, steering_vec = dsp.gen_steering_vec(ANGLE_RANGE, ANGLE_RES, VIRT_ANT_AZI)
num_vec_ele, steering_vec_ele = dsp.gen_steering_vec(ANGLE_RANGE, ANGLE_RES, VIRT_ANT_ELE)
if cumulative:
cum_azi = np.zeros((ANGLE_BINS, num_bins))
cum_ele = np.zeros((ANGLE_BINS, num_bins))
if diff:
pre_h = np.zeros((ANGLE_BINS, num_bins))
pre_e = np.zeros((ANGLE_BINS, num_bins))
for frame_index in range(numFrames):
frame = adc_data[frame_index]
radar_cube = dsp.range_processing(frame)
# virtual antenna arrangement
radar_cube = arange_tx(radar_cube, num_tx=numTxAntennas, vx_axis=1, axis=0)
# --- static clutter removal
if remove_clutter:
mean = radar_cube.mean(0)
radar_cube = radar_cube - mean
# --- capon beamforming
radar_cube_azi = radar_cube[:, VIRT_ANT_AZI_INDEX, :]
radar_cube_ele = radar_cube[:, VIRT_ANT_ELE_INDEX, :]
# Note that when replacing with generic doppler estimation functions, radarCube is interleaved and
# has doppler at the last dimension.
for i in range(BINS_PROCESSED):
range_azimuth[:, i], _ = dsp.aoa_capon(radar_cube_azi[:, :, i].T, steering_vec,
magnitude=True)
range_elevation[:, i], _ = dsp.aoa_capon(radar_cube_ele[:, :, i].T, steering_vec_ele,
magnitude=True)
""" 3 (Object Detection) """
if is_log:
heatmap_azi = 20 * np.log10(range_azimuth[:, bin_start:bin_end])
heatmap_ele = 20 * np.log10(range_elevation[:, bin_start:bin_end])
else:
heatmap_azi = range_azimuth[:, bin_start:bin_end]
heatmap_ele = range_elevation[:, bin_start:bin_end]
if cumulative:
cum_azi += heatmap_azi
cum_ele += heatmap_ele
if diff:
heatmap_azi = heatmap_azi - pre_h
heatmap_ele = heatmap_ele - pre_e
pre_h = heatmap_azi
pre_e = heatmap_ele
# normalize
# heatmap_azi = heatmap_azi / heatmap_azi.max()
# heatmap_ele = heatmap_ele / heatmap_ele.max()
npy_azi[frame_index] = heatmap_azi
npy_ele[frame_index] = heatmap_ele
save_path_azi = save_path + "_azi"
save_path_ele = save_path + "_ele"
np.save(save_path_azi, npy_azi)
np.save(save_path_ele, npy_ele)
print("{} npy file saved!".format(save_path))
MapRecord(x.strip().split(), root_path) for x in open(annotaton_path)
]
half_attention = True
plot_debug = True
for record in record_list:
if record.index_err == 1:
adc_data = np.fromfile(record.path, 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!".format(record.path))
# processing range data
range_data = dsp.range_processing(adc_data,
window_type_1d=Window.HANNING)
# reshape frame data
range_data = arange_tx(range_data, num_tx=numTxAntennas)
b_index = 8
out = peak_detection(range_data[..., b_index],
half=half_attention,
plot=plot_debug)