def spotter(self, args):
engine = BasicEngine(args.model_file)
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic,
engine,
labels_file="config/labels_gc2.raw.txt",
commands_file="config/commands_v2_snake.txt",
dectection_callback=self._controler.callback,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
def main():
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
engine = BasicEngine(args.model_file)
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic,
engine,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
def spotter(self, args):
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic,
interpreter,
labels_file="config/labels_gc2.raw.txt",
commands_file="config/commands_v2_snake.txt",
dectection_callback=self._controler.callback,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
def main():
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic,
interpreter,
labels_file="config/labels_simple_audio.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
def main():
gpio6.write(False)
gpio73.write(False)
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic, interpreter, 3,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 1:
gpio6.write(True)
elif answer == 2:
gpio73.write(True)
def main():
isListening = True
while True:
try:
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic, interpreter,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
except:
logger.error("crashed and restarting the model: " + str(sys.exc_info()[0]))
sys.stdout.write("Crashed so trying to restart")
sys.stdout.write("print Unexpected error" + str(sys.exc_info()[0]))
def main():
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
engine = BasicEngine(args.model_file)
mic = args.mic if args.mic is None else int(args.mic)
yt_control = YoutubeControl()
sys.stdout.write("--------------------\n")
sys.stdout.write("This script will control Youtube.\n")
sys.stdout.write("Just ensure that focus is on the YouTube player.\n")
sys.stdout.write("--------------------\n")
model.classify_audio(mic,
engine,
labels_file="config/labels_gc2.raw.txt",
commands_file="config/commands_v2.txt",
dectection_callback=yt_control.run_command,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
def main():
def user_callback(input_tensor, src_size, inference_box):
global access
global house
global parcel
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
results = get_output(interpreter, args.top_k, args.threshold)
end_time = time.monotonic()
text_lines = [
' ',
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(result.score, labels.get(result.id, result.id)))
if house:
if labels.get(result.id, result.id) == "tree frog, tree-frog":
access = 1
gpio6.write(True)
Gtk.main_quit()
elif labels.get(result.id, result.id) == "acoustic guitar" or labels.get(result.id, result.id) == "jigsaw puzzle" or labels.get(result.id, result.id) == "jellyfish" or labels.get(result.id, result.id) == "basketball" or labels.get(result.id, result.id) == "soccer ball":
access = 0
gpio73.write(True)
Gtk.main_quit()
elif parcel:
if labels.get(result.id, result.id) == "acoustic guitar":
access = 1
gpio7.write(True)
Gtk.main_quit()
elif labels.get(result.id, result.id) == "tree frog, tree-frog" or labels.get(result.id, result.id) == "jigsaw puzzle" or labels.get(result.id, result.id) == "jellyfish" or labels.get(result.id, result.id) == "basketball" or labels.get(result.id, result.id) == "soccer ball":
access = 0
gpio8.write(True)
Gtk.main_quit()
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
while(1):
global access
global answer
global house
global parcel
gpio6.write(False)
gpio73.write(False)
gpio7.write(False)
gpio8.write(False)
motion.start_ranging(VL53L0X.VL53L0X_BETTER_ACCURACY_MODE)
timing = motion.get_timing()
if (timing < 20000):
timing = 20000
distance = motion.get_distance()
while(distance > 500):
distance = motion.get_distance()
time.sleep(timing/1000000.00)
motion.stop_ranging()
wave_obj = sa.WaveObject.from_wave_file("welcome.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
wave_obj = sa.WaveObject.from_wave_file("entry.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
# Voice Recognition
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic, interpreter, 1,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 1:
wave_obj = sa.WaveObject.from_wave_file("key.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
house = True
parcel = False
elif answer == 2:
wave_obj = sa.WaveObject.from_wave_file("package.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
house = False
# Voice Recognition
model.classify_audio(mic, interpreter, 2,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 1:
wave_obj = sa.WaveObject.from_wave_file("key.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
parcel = True
elif answer == 2:
wave_obj = sa.WaveObject.from_wave_file("goodday.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
parcel = False
if house or parcel:
default_model_dir = '../all_models'
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold', type=float, default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc', help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt', help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
if access:
if house:
wave_obj = sa.WaveObject.from_wave_file("stay.wav")
elif parcel:
wave_obj = sa.WaveObject.from_wave_file("parcel.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
else:
wave_obj = sa.WaveObject.from_wave_file("denied.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
time.sleep(3)
def main():
#///-----------------------------------------------------------\\\
#// Scanning Image \\
def user_callback(input_tensor, src_size, inference_box):
global access
global house
global parcel
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
results = get_output(interpreter, args.top_k, args.threshold)
end_time = time.monotonic()
text_lines = [
' ',
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(
result.score, labels.get(result.id, result.id)))
if gpio6.read() == True:
access = 2
Gtk.main_quit()
elif house:
if labels.get(
result.id, result.id
) == "tree frog, tree-frog" and result.score > 0.3:
access = 1
Gtk.main_quit()
elif (labels.get(result.id, result.id) == "acoustic guitar"
or labels.get(result.id, result.id) == "jigsaw puzzle"
or labels.get(result.id, result.id) == "jellyfish"
or labels.get(result.id, result.id) == "basketball"
or labels.get(result.id, result.id)
== "soccer ball") and result.score > 0.3:
access = 0
Gtk.main_quit()
elif parcel:
if labels.get(
result.id,
result.id) == "acoustic guitar" and result.score > 0.3:
access = 1
Gtk.main_quit()
elif (labels.get(result.id,
result.id) == "tree frog, tree-frog"
or labels.get(result.id, result.id) == "jigsaw puzzle"
or labels.get(result.id, result.id) == "jellyfish"
or labels.get(result.id, result.id) == "basketball"
or labels.get(result.id, result.id)
== "soccer ball") and result.score > 0.3:
access = 0
Gtk.main_quit()
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
#\\ //
#\\\-----------------------------------------------------------///
while (1):
global access
global answer
global house
global parcel
gpio7.write(True)
gpio8.write(True)
while (gpio6.read() == False): # Waiting for signal
time.sleep(0.05)
time.sleep(2)
# Setting up voice recogniton
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(
mic,
interpreter,
1, # Calling Listening Function
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 3: # Timed out
answer = 0
house = False
parcel = False
elif answer == 1: # Yes
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
house = True
parcel = False
elif answer == 2: # No
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
house = False
time.sleep(1)
model.classify_audio(
mic,
interpreter,
2, # Calling Listening Function
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 3: # Timed out
answer = 0
parcel = False
elif answer == 1: # Yes
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
parcel = True
elif answer == 2: # No
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
parcel = False
if house or parcel:
# Setting up image recogniton
default_model_dir = '../all_models'
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir,
default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument(
'--top_k',
type=int,
default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc',
help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt',
help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
result = gstreamer.run_pipeline(
user_callback, # Calling Scanning Image Function
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
# Communication with ESP32 Board
if access == 1:
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
elif access == 0:
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
time.sleep(2)