def __get_user_rate(self, type, port):
old_completer = readline.get_completer()
rates = []
for rate in Connection.BAUDRATES:
rates.append(str(rate))
compl_rates = Completer(rates)
readline.set_completer(compl_rates.list_completer)
rate = None
while rate is None:
rate = input(f'{Fore.YELLOW}{type} rate:{Fore.RESET} ').strip()
if rate in ['q', 'exit']:
break
elif rate == '':
rate = Connection.get_baudrate(port)
elif rate not in rates:
error(f'Rate {rate} is not valid.')
warning('Valid baudrates:')
for rate in rates:
warning('\t' + rate)
warning('Type \'exit\' to return.')
rate = None
else:
rate = int(rate)
readline.set_completer(old_completer)
return rate
def get_ack(self, timeout):
length = b''
while length == b'':
length = self.connection.read(2)
time.sleep(0.01)
self.connection.read(1) # Checksum
self.connection.read(1) # 0x00
response = self.connection.read(1)
while response not in [OPCODE.ACK, OPCODE.NACK]:
if self.start_time == 0:
self.start_time = time.perf_counter()
if time.perf_counter() - self.start_time > timeout:
response = None
break
response = self.connection.read(1)
time.sleep(0.01)
self.start_time = 0
if response == OPCODE.ACK:
return True
elif response == OPCODE.NACK:
error('Received NACK')
return False
else:
error('Received unexpected data or timeout!')
return False
def do_listen(self, args=''):
'''
Start listener and parser thread
Starting listener on connected ports. If mmWave is not configured,
default configuration will be send first.
Look \'connect\' and \'autoconnect\' command for connecting to mmWave.
Usage:
>> listen
'''
if args != '':
error('Unknown arguments.')
return
if not self.configured:
warning('mmWave not configured.')
return
with self.listening_lock:
if self.listening:
warning('Listener already started.')
return
with self.listening_lock:
self.listening = True
self.__listen_thread()
self.__parse_thread()
def __get_user_port(self, type):
old_completer = readline.get_completer()
ports = []
for port in serial.tools.list_ports.comports():
ports.append(port[0])
compl_ports = Completer(ports)
readline.set_completer_delims('\t')
readline.parse_and_bind('tab: complete')
readline.set_completer(compl_ports.list_completer)
port = None
while port is None:
port = input(f'{Fore.YELLOW}{type} port:{Fore.RESET} ').strip()
if port in ['q', 'exit']:
return
elif port not in ports:
error(f'Port {port} is not valid.')
warning('Valid ports:')
for port in ports:
warning('\t' + port)
warning('Type \'exit\' to return.')
port = None
readline.set_completer(old_completer)
return port
def do_train(self, args=''):
'''
Train neural network
Command will first load cached X and y data located in
'mmwave/data/.X_data' and 'mmwave/data/.y_data' files. This data will be
used for the training process. If you want to read raw .csv files,
provide \'refresh\' (this will take few minutes).
Usage:
>> train
>> train refresh
'''
if len(args.split()) > 1:
error('Unknown arguments.')
return
if args == '':
refresh_data = False
elif args == 'refresh':
refresh_data = True
else:
warning(f'Unknown argument: {args}')
return
X, y = Logger.get_all_data(refresh_data=refresh_data)
Logger.get_stats(X, y)
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=.3,
stratify=y,
random_state=12)
self.model.train(X_train, y_train, X_val, y_val)
def do_print(self, args=''):
'''
Pretty print
Printing received frames. Listener should be started before using
this command. Use <Ctrl-C> to stop this command.
Usage:
>> print
'''
if args != '':
error('Unknown arguments.')
return
with self.listening_lock:
if not self.listening:
error('Listener not started.')
return
with self.printing_lock:
self.printing = True
self.console_queue.get()
with self.printing_lock:
self.printing = False
def do_predict(self, args=''):
'''
Start prediction
Passing received frames through neural network and printing results.
Listener should be started before using this command.
Use <Ctrl-C> to stop this command.
Usage:
>> predict
'''
if args != '':
error('Unknown arguments.')
return
with self.listening_lock:
if not self.listening:
error('Listener not started.')
return
if not self.__model_loaded():
self.model.load()
print()
with self.predicting_lock:
self.predicting = True
self.__predict_thread()
self.console_queue.get()
with self.predicting_lock:
self.predicting = False
self.model_queue.put(None)
def do_remove(self, args=''):
'''
Remove last sample
Removing last gesture sample. Data will be removed from
\'mmwave/data/gesture_foder\' folder.
Possible options: \'up\', \'down\', \'left\', \'right\', \'cw\',
\'ccw\', \'s\', \'z\', \'x\'
Usage:
>> remove up
>> remove ccw
>> remove z
'''
if args == '':
error('too few arguments.')
return
if len(args.split()) > 1:
error('Unknown arguments.')
return
if not GESTURE.check(args):
warning(f'Unknown argument: {args}')
return
self.logger.set_gesture(args)
self.logger.discard_last_sample()
def default(self, line):
'''
Called on an input line when the command prefix is not recognized.
In that case we execute the line as Python code.
'''
try:
exec(line) in self._locals, self._globals
except Exception:
error('Unknown arguments.')
return
def do_send(self, args=''):
'''
Sending configuration to mmWave
Configuring mmWave with given configuration file. All configuration
files should be placed in \'mmwave/communication/profiles\' folder.
Use <Tab> for autocompletion on available configuration files.
All configuration files should have .cfg extension.
If no configuration is provided, default configuration file will be
used.
Usage:
>> send
>> send profile
'''
if args == '':
args = self.default_config
if len(args.split()) > 1:
error('Too many arguments.')
return
if not self.__is_connected():
error('Not connected.')
return
cfg = os.path.join(self.config_dir, args + '.cfg')
if cfg not in glob.glob(os.path.join(self.config_dir, '*.cfg')):
print('Unknown profile.')
return
mmwave_configured = False
cnt = 0
while not mmwave_configured and cnt < 5:
mmwave_configured = self.mmwave.configure(cfg)
if not mmwave_configured:
signal_cnt = 0
while signal_cnt < 5:
if not self.console_queue.empty():
self.console_queue.get()
return
time.sleep(.2)
signal_cnt += 1
cnt += 1
if not mmwave_configured:
return
if os.path.basename(cfg) == 'stop.cfg':
self.configured = False
else:
self.configured = True
self.config = cfg
def do_exit(self, args):
'''Exits from the console'''
if args != '':
error('Unknown arguments.')
return
self.do_stop()
if self.__is_connected():
self.mmwave.disconnect()
os._exit(0)
def send_cmd(self, command, get_status=False, resp=True):
self.send_packet(command)
if not self.get_ack(command.timeout):
error('Command was not successful!')
return
if get_status:
self.send_packet(CMD(OPCODE.GET_STATUS))
response = ''
if resp:
response = self.get_response()
return response
def do_get_model(self, args=''):
'''
Get current model type.
Usage:
>> get_model
'''
if args != '':
error('Unknown arguments.')
return
print(f'Current model type: {self.model_type}')
def do_eval(self, args=''):
'''
Evaluate neural network
Command will first load cached X and y data located in
\'mmwave/data/.X_data\' and \'mmwave/data/.y_data\' files. This data
will be used for the evaluating process. If you want to read raw .csv
files, provide \'refresh\' (this will take few minutes).
Usage:
>> eval
>> eval refresh
'''
if len(args.split()) > 1:
error('Unknown arguments.')
return
if args == '':
refresh_data = False
elif args == 'refresh':
refresh_data = True
else:
warning(f'Unknown argument: {args}')
return
X, y = Logger.get_all_data(refresh_data=refresh_data)
Logger.get_stats(X, y)
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=.3,
stratify=y,
random_state=12)
if not self.__model_loaded():
self.model.load()
print()
print('Eval validation dataset:')
self.model.evaluate(X_val, y_val)
print()
print('Eval train dataset:')
self.model.evaluate(X_train, y_train)
print()
print('Eval full dataset:')
self.model.evaluate(X, y)
print()
def get_response(self):
header = self.connection.read(3)
packet_size, checksum = struct.unpack('>HB', header)
packet_size -= 2 # Compensate for the header
payload = self.connection.read(packet_size)
self.connection.write(OPCODE.ACK, size=0) # Ack the packet
calculated_checksum = sum(payload) & 0xff
if (calculated_checksum != checksum):
error('Checksum error on received packet.')
return
return payload
def do_connect(self, args=''):
'''
Manually connecting to mmWave
Command will ask you to manualy type ports and baudrates.
Use <Tab> for autocompletion on available ports and baudrates.
Type \'exit\' for exiting manual connection.
If you only press enter on baudrates, console will try to find
baudrate automatically. Note that this will work only if connection
is already opened from both sides. (On mmWave startup only cli port
is open, and data port will open only after \'senstrStart\' command)
Look \'autoconnect\' command for automatic connection.
Usage:
>> connect
cli port: /dev/ttyACM0
cli rate: 115200
data port: /dev/ttyACM1
data rate: 921600
'''
if args != '':
error('Unknown arguments.')
return
if self.__is_connected():
self.mmwave.disconnect()
print()
port = self.__get_user_port('cli')
if port is not None:
self.cli_port = port
self.cli_rate = self.__get_user_rate('cli', port)
else:
return
port = self.__get_user_port('data')
if port is not None:
self.data_port = port
self.data_rate = self.__get_user_rate('data', port)
else:
return
self.__mmwave_init()
def do_plot(self, args=''):
'''
Start plotter
Plotting received frames. Listener should be started before using
this command. Use \'stop plot\' to stop this command.
Usage:
>> plot
'''
if args != '':
error('Unknown arguments.')
return
self.plotter_queues['cli'].put('init')
with self.plotting_lock:
self.plotting = True
def do_redraw(self, args=''):
'''
Redraw sample
Redrawing last captured gesture file.
Possible options: \'up\', \'down\', \'left\', \'right\', \'cw\',
\'ccw\', \'s\', \'z\', \'x\'
Usage:
>> redraw up
>> redraw ccw
>> redraw z
'''
if args == '':
error('too few arguments.')
return
if len(args.split()) > 1:
error('Unknown arguments.')
return
with self.plotting_lock:
if not self.plotting:
error('Plotter not started.')
return
if not GESTURE.check(args):
warning(f'Unknown argument: {args}')
return
self.plotter_queues['cli'].put('redraw')
self.plotter_queues['cli'].put(args)
def do_flash(self, args=''):
'''
Sending .bin files to mmWave
Flashing bin files to connected mmWave. It is possible to specify up to
4 meta files. SOP0 and SOP2 have be closed and power reseted before
running this command.
Look \'connect\' and \'autoconnect\' command for connecting to mmWave.
Usage:
>> flash xwr16xx_mmw_demo.bin
'''
if len(args.split()) > 4:
error('Too many arguments.')
return
if args == '':
error('Too few arguments.')
return
filepaths = []
for arg in args.split():
filepath = os.path.join(self.firmware_dir, arg)
if not os.path.isfile(filepath):
error(f'File \'{filepath}\' doesn\'t exist.')
return
filepaths.append(filepath)
if not self.__is_connected():
error('Not connected.')
return
print('Ping mmWave...', end='')
response = self.flasher.send_cmd(CMD(OPCODE.PING), resp=False)
if response is None:
warning('Check if SOP0 and SOP2 are closed, and reset the power.')
return
print(f'{Fore.GREEN}Done.')
print('Get version...', end='')
response = self.flasher.send_cmd(CMD(OPCODE.GET_VERSION))
if response is None:
return
print(f'{Fore.GREEN}Done.')
print(f'{Fore.BLUE}Version:', binascii.hexlify(response))
print()
self.flasher.flash(filepaths, erase=True)
print(f'{Fore.GREEN}Done.')
def do_set_model(self, args=''):
'''
Set model type used for prediction. Available models are
\'conv\' (convolutional 1D), \'lstm\' (long short-term memory) and
\'trans\' (transformer). Default is lstm.
Usage:
>> set_model conv
>> set_model lstm
>> set_model trans
'''
if len(args.split()) > 1:
error('Too many arguments.')
return
if args not in ['conv', 'lstm', 'trans']:
warning(f'Unknown argument: {args}')
return
self.model_type = args
self.__set_model(args)
def do_start(self, args=''):
'''
Start listener, plotter and prediction.
If mmWave is not configured, default configuration will be send
first. Use <Ctrl-C> to stop this command.
Usage:
>> start
'''
if args != '':
error('Unknown arguments.')
return
if not self.__model_loaded():
self.model.load()
print()
self.do_send(self.default_config)
self.do_listen()
self.do_plot()
self.do_predict()
self.do_stop()
def do_autoconnect(self, args=''):
'''
Auto connecting to mmWave
Automatically looking for \'XDS\' ports and connecting with default
baudrates. Auto-detection is only applicable for eval boards with
XDS110.
Look \'connect\' command for manual connection.
Usage:
>> autoconnect
'''
if args != '':
error('Unknown arguments.')
return
if self.__is_connected():
warning('Already connected.')
print('Reconnecting...')
self.cli_port = None
self.data_port = None
self.__mmwave_init()
def do_log(self, args=''):
'''
Log data
Logging specified gesture. Data will be saved in
\'mmwave/data/gesture_foder/sameple_num.csv\' file.
Possible options: \'up\', \'down\', \'left\', \'right\', \'cw\',
\'ccw\', \'s\', \'z\', \'x\'
Usage:
>> log up
>> log ccw
>> log z
'''
if args == '':
error('too few arguments.')
return
if len(args.split()) > 1:
error('Unknown arguments.')
return
with self.listening_lock:
if not self.listening:
error('Listener not started.')
return
if not GESTURE.check(args):
warning(f'Unknown argument: {args}')
return
self.logger.set_gesture(args)
with self.logging_lock:
self.logging = True
self.__logging_thread().join()
with self.logging_lock:
self.logging = False
def flash(self,
files,
file_id=4,
storage='SFLASH',
mirror_enabled=0,
erase=False):
if not isinstance(files, list):
files = [files]
if erase:
print('Formating flash...', end='')
self.erase()
print(f'{Fore.GREEN}Done.')
for file in files:
file_size = os.path.getsize(file)
print(
f'Writing {list(Flasher.FILES)[file_id]} [{file_size} bytes]')
if file_size < 0 and file_size > Flasher.MAX_FILE_SIZE:
error('Invalid file size')
return False
with open(file, 'rb') as f:
resp = self.send_cmd(CMD(
OPCODE.OPEN_FILE,
data=(struct.pack('>I', file_size) +
Flasher.STORAGES[storage] +
list(Flasher.FILES.values())[file_id] +
struct.pack('>I', mirror_enabled))),
get_status=True)
if resp != OPCODE.RET_SUCCESS:
error('Opening file failed.')
return False
pbar = tqdm(total=file_size // Flasher.BLOCK_SIZE + 1,
desc='Blocks')
offset = 0
while offset < file_size:
block = f.read(Flasher.BLOCK_SIZE)
if (storage == 'SRAM'):
resp = self.send_cmd(CMD(OPCODE.WRITE_FILE_RAM,
data=block),
get_status=True)
else:
resp = self.send_cmd(CMD(OPCODE.WRITE_FILE,
data=block),
get_status=True)
if resp != OPCODE.RET_SUCCESS:
error('Sending file failed.')
return False
offset += len(block)
pbar.update(1)
pbar.close()
self.send_cmd(CMD(OPCODE.CLOSE_FILE,
data=list(Flasher.FILES.values())[file_id]),
resp=False)
file_id += 1
if file_id > 7:
break
return True
def do_history(self, args):
'''Print a list of commands that have been entered'''
if args != '':
error('Unknown arguments.')
return
print(self._hist)
