def testHotdog(self):
with TestImage('hotdog.jpg') as image:
with ImageInference(dish_classification.model()) as inference:
classes = dish_classification.get_classes(inference.run(image))
label, score = classes[0]
self.assertEqual('Hot dog', label)
self.assertAlmostEqual(score, 0.744, delta=0.001)
label, score = classes[1]
self.assertEqual('Lobster roll', label)
self.assertAlmostEqual(score, 0.119, delta=0.001)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input', '-i', dest='input', required=True)
args = parser.parse_args()
with ImageInference(dish_classification.model()) as inference:
image = Image.open(args.input)
classes = dish_classification.get_classes(
inference.run(image), top_k=5, threshold=0.1)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))
def testHotdog(self):
with TestImage('hotdog.jpg') as image:
with ImageInference(dish_classification.model()) as inference:
classes = dish_classification.get_classes(inference.run(image))
label, score = classes[0]
self.assertEqual('Hot dog', label)
self.assertAlmostEqual(score, 0.744, delta=0.001)
label, score = classes[1]
self.assertEqual('Lobster roll', label)
self.assertAlmostEqual(score, 0.119, delta=0.001)
def run():
if KeepWatchForSeconds(3):
print("Going shutdown by GPIO")
leds.update(Leds.rgb_off())
os.system("/sbin/shutdown -h now 'Poweroff by GPIO'")
else:
print("Beep sound")
toneplayer.play(*BEEP_SOUND)
leds.update(Leds.rgb_on(RED))
print("Taking photo")
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_preview()
sleep(3.000)
camera.capture(photo_filename)
leds.update(Leds.rgb_on(GREEN))
print("Dish classifier")
with ImageInference(dish_classification.model()) as inference:
image = Image.open(photo_filename)
classes = dish_classification.get_classes(
inference.run(image),
max_num_objects=5,
object_prob_threshold=0.1)
dish_name = ''
for i, (label, score) in enumerate(classes):
dish_name += label + '/'
print('Result %d: %s (prob=%f)' % (i, label, score))
leds.update(Leds.rgb_on(BLUE))
print("Post to slack")
slack.files.upload(photo_filename,
channels='#food_diary',
title=dish_name)
leds.update(Leds.rgb_on(WHITE))
def test_dish_classification(self):
avg_end_to_end, avg_bonnet = self.benchmarkModel(
dish_classification.model(), dish_classification.get_classes)
self.assertLatency(avg_bonnet, 304.0)
self.assertLatency(avg_end_to_end, 328.0)
def test_dish_classification(self):
avg_end_to_end, avg_bonnet = self.benchmarkModel(
dish_classification.model(), dish_classification.get_classes)
self.assertLatency(avg_bonnet, 304.0)
self.assertLatency(avg_end_to_end, 328.0)