def print_results(number_of_gesture, filename, results, correct_gesture):
print("###########################################################")
print("File:", filename)
for gesture in range(number_of_gesture):
print('{:15s}\t{:10f}'.format(Gesture.gesture_to_string(gesture),
results[gesture]))
print()
print("Correct:", Gesture.gesture_to_string(correct_gesture))
def compare_prerecorded_files():
print("Compare pre-recorded tests")
for file in DataUtility.TEST_FILE_LIST:
print("#####################################################################################")
print("File: " + file.filename)
data_handler = DataHandlers.FileDataHandler(file)
recognized_gesture = CompareMethods.cross_correlation_comparison(data_handler)
print("Gesture: " + Gesture.gesture_to_string(file.gesture))
print("Regcognized Gesture: " + Gesture.gesture_to_string(recognized_gesture))
print("\n\n\n")
def cross_correlation_comparison(gesture_input_data_handler):
print("Sensor Ignored: ", end="")
for sensor in range(Sensor.NUMBER_OF_SENSORS):
if not is_sensor_on(sensor):
print(Sensor.sensor_to_string(sensor) + ", ", end="")
print("\b\b", " ")
print("Compare method: Cross Correlation\n")
corr_rs = [0] * Constant.NUMBER_OF_GESTURES
emg_corrs = [0] * Constant.NUMBER_OF_GESTURES
for i in range(len(DataUtility.TRAINING_FILE_LIST)):
training_data_file = DataUtility.TRAINING_FILE_LIST[i]
print(training_data_file.filename, end="\r")
sys.stdout.write("\033[K") # clean line
current_training_gesture = training_data_file.gesture
gesture_training_data_handler = DataHandlers.FileDataHandler(
training_data_file)
for sensor in range(Sensor.NUMBER_OF_SENSORS):
if not is_sensor_on(sensor):
continue
elif sensor == Sensor.EMG:
emg_corrs[
current_training_gesture] += cross_correlation_compare_sensor_data(
gesture_input_data_handler.get_sensor_data(sensor),
gesture_training_data_handler.get_sensor_data(sensor),
sensor)
else:
corr_rs[
current_training_gesture] += cross_correlation_compare_sensor_data(
gesture_input_data_handler.get_sensor_data(sensor),
gesture_training_data_handler.get_sensor_data(sensor),
sensor)
recognized_gesture = Gesture.NONE_GESTURE
max_similarity = 0.0
for gesture in range(Gesture.NUMBER_OF_GESTURES):
similarity = corr_rs[gesture] + emg_corrs[gesture]
print("{:10s}".format(Gesture.gesture_to_string(gesture)) + ": r = ",
end="")
print("%.8f" % similarity, end="")
print(", IMU = ", end="")
print("%.8f" % corr_rs[gesture], end="")
print(", EMG = ", end="")
print("%.8f" % emg_corrs[gesture])
if (similarity > max_similarity):
max_similarity = similarity
recognized_gesture = gesture
print("")
return recognized_gesture
def print_success_rate(success_list):
print("###########################################################")
print("Success Rate")
success_sum = [0, 0]
for i in range(len(success_list)):
if success_list[i][0] != 0:
print('{:15s}\t{:4d} of {:4d} -> {:.2f}'.format(
Gesture.gesture_to_string(i), success_list[i][1],
success_list[i][0],
100 * success_list[i][1] / success_list[i][0]))
success_sum[0] += success_list[i][1]
success_sum[1] += success_list[i][0]
print()
print("Success rate: {:.2f}".format(100 * success_sum[0] / success_sum[1]))
return (100 * success_sum[0] / success_sum[1])
def raw_success_list(number_of_gesture, json_data):
success_list = []
for i in range(number_of_gesture):
success_list.append([0, 0])
for result_object in json_data:
(filename, correct_gesture,
results) = get_data_from_json_object(result_object)
recognized_gesture = numpy.argmax(results)
success_list[correct_gesture][0] += 1
if correct_gesture == recognized_gesture:
success_list[correct_gesture][1] += 1
else:
print_results(number_of_gesture, filename, results,
correct_gesture)
print("Recognized:", Gesture.gesture_to_string(recognized_gesture))
print()
print_success_rate(success_list)
def live_gesture_recognition():
print("Try Gesture")
libmyo.init('../myo-sdk-win-0.9.0/bin')
listener = DeviceListener.LiveGestureListener()
hub = libmyo.Hub()
hub.run(2000, listener)
try:
while True:
print("#################################################################################\n", end="")
print()
try:
input("press enter to continue...")
except EOFError:
time.sleep(1)
listener.recording_on()
while listener.is_recording:
pass
print()
network_session = NeuralNetworkUtility.NeuralNetwork(NeuralNetwork.SESSION_FOLDERS, NeuralNetwork.DATA_HANDLER_TYPE, False)
network_session.set_sess_path(default_session)
network_session.get_network_meta_data()
results = network_session.input_test_emg_network(listener.data_handler)
network_session.print_results(results)
recognized_gesture = numpy.argmax(results)
print()
print("Recognized:", Gesture.gesture_to_string(recognized_gesture))
except KeyboardInterrupt:
print('\nQuit')
hub.shutdown() # !! crucial
def print_results(self, results):
for gesture in range(self.get_number_of_gesture()):
print('{}) {:15s}\t{:10f}'.format(gesture, Gesture.gesture_to_string(gesture), results[gesture]))
def test_emg_network(self):
self.get_network_meta_data()
print("Session path:", self.sess_path)
is_storeing_result = input("Write result to file (y/n)? ")
if is_storeing_result == 'y':
is_storeing_result = True
else:
is_storeing_result = False
summary_list = []
run_date = time.strftime("%Y-%m-%d-%H%M")
number_of_gestures = self.get_number_of_gesture()
if not self.is_hackathon:
file_list = DataUtility.TEST_FILE_LIST
else:
file_list = HackathonDataNeuralNetwork.get_test_file_list(number_of_gestures)
for test_file in file_list:
data_handler = self.data_handler_type(test_file)
data_handler.set_emg_wavelet_level(self.wavelet_level)
data_handler.set_feature_functions_list(self.feature_function_check_list)
start_time = time.time()
results = self.input_test_emg_network(data_handler)
end_time = time.time()
recognized_gesture = numpy.argmax(results)
print()
print("###########################################################")
self.print_results(results)
print()
print("Recognized:", Gesture.gesture_to_string(np.argmax(results)))
print("Correct gesture:", Gesture.gesture_to_string(test_file.gesture))
print("Analyse time: ", "%.2f" % float(end_time - start_time))
summary_list.append((test_file.gesture, recognized_gesture))
print()
print("File:", test_file.filename)
if is_storeing_result:
self.write_result_to_file(results, test_file.filename, test_file.gesture, run_date)
print("#############################################################")
print("Session path:", self.sess_path)
print("Summary List")
number_of_gestures = self.get_number_of_gesture()
success_list = []
for i in range(number_of_gestures):
success_list.append([0, 0])
for correct_gesture, recognized_gesture in summary_list:
success_list[correct_gesture][0] += 1
if correct_gesture == recognized_gesture:
success_list[correct_gesture][1] += 1
print(Gesture.gesture_to_string(correct_gesture), " -> ", Gesture.gesture_to_string(recognized_gesture))
print()
print("#############################################################")
print("Success Rate")
for i in range(number_of_gestures):
if success_list[i][0] != 0:
print('{:15s}\t{:4d} of {:4d} -> {:.2f}'.format(Gesture.gesture_to_string(i), success_list[i][1], success_list[i][0], 100 * success_list[i][1] / success_list[i][0]))
input("Press Enter to continue...")
def filtered_analyse(number_of_gesture, json_data):
success_list = []
for i in range(number_of_gesture):
success_list.append([0, 0])
gesture_margin = input_parameter("Gesture margin (default = 1): ", 1, 1,
number_of_gesture)
diff_margin = input_parameter("Difference margin (default = 0.0): ",
0.0,
0.0,
1.0,
is_int=False)
value_treshold = input_parameter("Value treshold (default = 0.0): ",
0.0,
0.0,
1.0,
is_int=False)
for result_object in json_data:
(filename, correct_gesture,
results) = get_data_from_json_object(result_object)
is_success = False
copy_results = copy.deepcopy(results)
possible_gesture_results = []
possible_gestures = []
for i in range(gesture_margin):
possible_gesture = numpy.argmax(copy_results)
possible_gestures.append(possible_gesture)
possible_gesture_result = copy_results[possible_gestures[-1]]
copy_results[possible_gestures[-1]] = -1
possible_gesture_results.append(possible_gesture_result)
while numpy.amin(possible_gesture_results) - numpy.amax(
copy_results) < diff_margin:
i = numpy.argmin(possible_gesture_results)
possible_gesture_results.pop(i)
possible_gestures.pop(i)
if not len(possible_gesture_results):
break
i = 0
number_of_possible_gestures = len(possible_gestures)
while i < number_of_possible_gestures:
if possible_gesture_results[i] >= value_treshold:
i += 1
else:
possible_gesture_results.pop(i)
possible_gestures.pop(i)
number_of_possible_gestures -= 1
for i in range(number_of_possible_gestures):
if possible_gesture_results[
i] > value_treshold and possible_gestures[
i] == correct_gesture:
is_success = True
success_list[correct_gesture][0] += 1
if is_success:
success_list[correct_gesture][1] += 1
else:
print_results(number_of_gesture, filename, results,
correct_gesture)
print("Recognized:",
[Gesture.gesture_to_string(x) for x in possible_gestures])
success_rate = print_success_rate(success_list)
print("${}$ & ${:.2f}$ & ${:.2f}$ & ${:.2f}$".format(
gesture_margin, diff_margin, value_treshold, success_rate))
def create_gesture_files_for_notme():
libmyo.init('../myo-sdk-win-0.9.0/bin')
listener = DeviceListener.LiveGestureListener()
listener.expand_data_length(time_margin=1.5)
hub = libmyo.Hub()
hub.run(2000, listener)
print("Create record data set")
last_file = None
try:
person_id = input("Person ID-number: ")
while True:
print()
print("#################################################################################\n", end="")
try:
a = input("Enter 'r' to remove last file, or press enter to continue... ")
except EOFError:
time.sleep(1)
if a == "r":
gesture_recorded = Gesture.NUMBER_OF_GESTURES
else:
listener.recording_on()
sleep(2.0)
while listener.is_recording:
pass
folder_path = "../data/raw_files/notme_set/"
network_session = NeuralNetworkUtility.NeuralNetwork(NeuralNetwork.SESSION_FOLDERS, NeuralNetwork.DATA_HANDLER_TYPE, False)
network_session.set_sess_path(default_session)
network_session.get_network_meta_data()
results = network_session.input_test_emg_network(listener.data_handler)
recognized_gesture = numpy.argmax(results)
print()
print("###########################################################")
print()
network_session.print_results(results)
# Print number to gesture table
print()
print("{})".format(Gesture.NUMBER_OF_GESTURES), "remove last file...")
print("{})".format(Gesture.NUMBER_OF_GESTURES + 1), "continue...")
print()
print("Recognized:", Gesture.gesture_to_string(recognized_gesture))
Freq = 1500 # Set Frequency To 2500 Hertz
Dur = 500 # Set Duration To 1000 ms == 1 second
winsound.Beep(Freq, Dur)
gesture_recorded = -1
while gesture_recorded < 0 or gesture_recorded >= Gesture.NUMBER_OF_GESTURES + 2:
gesture_recorded = input("Correction: ")
if gesture_recorded == "":
gesture_recorded = recognized_gesture
elif not Utility.is_int_input(gesture_recorded):
gesture_recorded = -1
else:
gesture_recorded = int(gesture_recorded)
if(gesture_recorded == Gesture.NUMBER_OF_GESTURES):
if last_file is not None:
os.remove(last_file.get_file_path())
print("Removed file:", last_file.filename)
last_file = None
else:
print("No last file, remove it manually")
continue
if(gesture_recorded == Gesture.NUMBER_OF_GESTURES + 1):
continue
gesture_file_count_list = DataUtility.get_gesture_file_count_in_folder(folder_path)
file_number = gesture_file_count_list[gesture_recorded]
filename = person_id + "-NotMe-" + Gesture.gesture_to_string(gesture_recorded) + "-" + str(file_number) + ".json"
last_file = listener.data_handler.create_json_file2(filename, folder_path)
except KeyboardInterrupt:
print('\nQuit')
hub.shutdown() # !! crucial