def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
import argparse
from picamera import PiCamera
from time import time, strftime
from aiy.vision.leds import Leds
from aiy.vision.leds import PrivacyLed
from aiy.toneplayer import TonePlayer
from aiy.vision.inference import CameraInference
import aiy_cat_detection
# Sound setup
MODEL_LOAD_SOUND = ('C6w', 'c6w', 'C6w')
BEEP_SOUND = ('E6q', 'C6q')
player = TonePlayer(gpio=22, bpm=30)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--num_frames',
'-f',
type=int,
dest='num_frames',
default=-1,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument(
'--num_pics',
'-p',
def main():
"""Face detection camera inference example."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--num_frames',
'-n',
type=int,
dest='num_frames',
default=None,
help='Sets the number of frames to run for, otherwise runs forever.')
args = parser.parse_args()
# Forced sensor mode, 1640x1232, full FoV. See:
# https://picamera.readthedocs.io/en/release-1.13/fov.html#sensor-modes
# This is the resolution inference run on.
with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera,\
Leds() as leds:
leds.update(Leds.privacy_on())
leds.update(Leds.rgb_on(Color.BLUE))
camera.start_preview()
tone_player = TonePlayer(BUZZER_GPIO, bpm=70)
tone_player.play(*LOAD_SOUND)
# Annotator renders in software so use a smaller size and scale results
# for increased performace.
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / 1640
scale_y = 240 / 1232
# Incoming boxes are of the form (x, y, width, height). Scale and
# transform to the form (x1, y1, x2, y2).
def transform(bounding_box):
x, y, width, height = bounding_box
return (scale_x * x, scale_y * y, scale_x * (x + width),
scale_y * (y + height))
with CameraInference(face_detection.model()) as inference:
for result in inference.run(args.num_frames):
faces = face_detection.get_faces(result)
annotator.clear()
for face in faces:
annotator.bounding_box(transform(face.bounding_box),
fill=0)
x, y, width, height = face.bounding_box
annotator.update()
if len(faces) >= 1:
print(
'#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f, x=%.2f, y=%.2f, width=%.2f, height=%.2f'
% (inference.count, inference.rate, len(faces),
avg_joy_score(faces), x, y, width, height))
if x > 0:
alpha = x / float(1200)
else:
alpha = .5
try:
leds.update(
Leds.rgb_on(
Color.blend(LEFT_COLOR, Color.GREEN, alpha)))
except:
pass
distance = focal_length * real_face_width_inches / width
camera.annotate_text = '%d inches' % distance
else:
pass
camera.stop_preview()