def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model file')
parser.add_argument('--labels', help='.txt label file')
parser.add_argument('--threshold',
help='Class Score Threshold',
type=float,
default=0.7)
parser.add_argument('--top_k', help='Class Top K', type=int, default=2)
parser.add_argument('--manual',
help='Take a picture when key pressed',
action='store_true')
args = parser.parse_args()
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
input_monitor = InputMonitor(gpio_pin=8)
led = LED(gpio_r=6, gpio_g=7, gpio_b=None, invert=True)
led.switch_off_all()
light_duration = 3 if args.manual else 0.1
last_time = time.monotonic()
if args.manual:
input_monitor.daemon = True
input_monitor.start()
def user_callback(image, svg_canvas):
nonlocal last_time
if args.manual:
if not input_monitor.is_key_pressed():
return
start_time = time.monotonic()
results = engine.ClassifyWithImage(image,
threshold=0.1,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
if len(results) == 0:
led.switch_off_all()
else:
results.sort(key=lambda result: result[1], reverse=True)
for index, score in results:
text_lines.append('score=%.2f: %s' % (score, labels[index]))
top_label = labels[results[0][0]]
top_score = results[0][1]
if top_score >= args.threshold:
if top_label == 'roadway_green':
led.switch_green(duration=light_duration)
elif top_label == 'roadway_red':
led.switch_red(duration=light_duration)
elif top_label == 'roadway_yellow':
led.switch_yellow(duration=light_duration)
else:
led.switch_off_all()
last_time = end_time
print(' '.join(text_lines))
generate_svg(svg_canvas, text_lines)
gstreamer.run_pipeline(user_callback)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--project', help='GCP Project')
parser.add_argument('--bucket', help='GCS bucket name')
parser.add_argument('--path', help='GCS path prefix for uploading images')
parser.add_argument('--region', help='GCP Region')
parser.add_argument('--registry_id', help='IoT Core Registry ID')
parser.add_argument('--device_id', help='IoT Core Device ID')
parser.add_argument('--private_key', help='IoT Core Private Key File')
parser.add_argument('--algorithm', help='IoT Core JWT Algorithm',
default='RS256')
parser.add_argument('--ca_certs', help='IoT Core roots.pem',
default='roots.pem')
parser.add_argument('--mqtt_host', help='IoT Core hostname',
default='mqtt.googleapis.com')
parser.add_argument('--mqtt_port', help='IoT Core port',
type=int,
default=443)
args = parser.parse_args()
input_monitor = InputMonitor(gpio_pin=8)
led = LED(gpio_r=6, gpio_g=7, gpio_b=None, invert=True)
led.switch_off_all()
def user_callback(image, svg_canvas):
nonlocal input_monitor
if input_monitor.is_key_pressed():
upload.upload(args.bucket, args.path, image)
input_monitor.daemon = True
input_monitor.start()
print('monitoring keyboard input...')
mqtt.setup_mqtt_client(
args.project,
args.registry_id,
args.private_key,
args.device_id,
args.region,
args.algorithm,
args.ca_certs,
args.mqtt_host,
args.mqtt_port)
def message_callback(payload):
try:
preds = json.loads(payload)
preds.sort(key=lambda pred: pred['class_score'], reverse=True)
top = preds[0]['class_name']
if top == 'roadway_green':
led.switch_green(duration=3)
elif top == 'roadway_red':
led.switch_red(duration=3)
elif top == 'roadway_yellow':
led.switch_yellow(duration=3)
else:
led.switch_off_all()
except Exception as ex:
print(ex)
mqtt.add_message_callback(message_callback)
gstreamer.run_pipeline(user_callback)