theta_min, theta_max, _ = radar.GetArenaTheta()
logger.info(f'radar theta min: {theta_min}, theta max: {theta_max} (deg)')
# Threshold
radar.SetThreshold(RADAR_THRESHOLD)
# radar filtering
filter_type = radar.FILTER_TYPE_MTI if MTI else radar.FILTER_TYPE_NONE
radar.SetDynamicImageFilter(filter_type)
# Start the system in preparation for scanning.
radar.Start()
# Calibrate scanning to ignore or reduce the signals if not in MTI mode.
if not MTI:
common.calibrate()
frame_rate = radar.GetAdvancedParameter('FrameRate')
logger.info(f'radar frame rate: {frame_rate}')
logger.info(f'desired labels: {args.desired_labels}')
samples, labels = plot_and_capture_data(args.num_samples, args.realtime_plot,
args.save_plot, args.save_plot_path, args.desired_labels)
# Append data file if it already exists, else create a new one.
if samples and labels:
logger.info(f'Captured {len(labels)} new samples with label(s) {set(labels)}.')
try:
with open(os.path.join(common.PRJ_DIR, common.RADAR_DATA), 'rb') as fp:
data = pickle.load(fp)
def main():
radar.Init()
# Configure Walabot database install location.
radar.SetSettingsFolder()
# Establish communication with walabot.
try:
radar.ConnectAny()
except radar.WalabotError as err:
print(f'Failed to connect to Walabot.\nerror code: {str(err.code)}')
exit(1)
# Set radar scan profile.
radar.SetProfile(common.RADAR_PROFILE)
# Set scan arena in polar coords
radar.SetArenaR(common.R_MIN, common.R_MAX, common.R_RES)
radar.SetArenaPhi(common.PHI_MIN, common.PHI_MAX, common.PHI_RES)
radar.SetArenaTheta(common.THETA_MIN, common.THETA_MAX, common.THETA_RES)
# Threshold
radar.SetThreshold(RADAR_THRESHOLD)
# radar filtering
filter_type = radar.FILTER_TYPE_MTI if MTI else radar.FILTER_TYPE_NONE
radar.SetDynamicImageFilter(filter_type)
# Start the system in preparation for scanning.
radar.Start()
# Calibrate scanning to ignore or reduce the signals if not in MTI mode.
if not MTI:
common.calibrate()
try:
while True:
# Scan according to profile and record targets.
radar.Trigger()
# Retrieve any targets from the last recording.
targets = radar.GetSensorTargets()
if not targets:
continue
# Retrieve the last completed triggered recording
raw_image, size_x, size_y, size_z, _ = radar.GetRawImage()
raw_image_np = np.array(raw_image, dtype=np.float32)
for t, target in enumerate(targets):
print(
'Target #{}:\nx: {}\ny: {}\nz: {}\namplitude: {}\n'.format(
t + 1, target.xPosCm, target.yPosCm, target.zPosCm,
target.amplitude))
i, j, k = common.calculate_matrix_indices(
target.xPosCm, target.yPosCm, target.zPosCm, size_x,
size_y, size_z)
# projection_yz is the 2D projection of target in y-z plane.
projection_yz = raw_image_np[i, :, :]
# projection_xz is the 2D projection of target in x-z plane.
projection_xz = raw_image_np[:, j, :]
# projection_xy is 2D projection of target signal in x-y plane.
projection_xy = raw_image_np[:, :, k]
observation = common.process_samples(
[(projection_xy, projection_yz, projection_xz)],
proj_mask=PROJ_MASK)
# Make a prediction.
name, prob = classifier(observation)
if name == 'person':
color_name = colored(name, 'green')
elif name == 'dog':
color_name = colored(name, 'yellow')
elif name == 'cat':
color_name = colored(name, 'blue')
else:
color_name = colored(name, 'red')
print(f'Detected {color_name} with probability {prob}\n')
except KeyboardInterrupt:
pass
finally:
# Stop and Disconnect.
radar.Stop()
radar.Disconnect()
radar.Clean()
print('Successful termination.')