def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
self.JOY_SOUND = ('C5q', 'E5q', 'C6q')
self.SAD_SOUND = ('C6q', 'E5q', 'C5q')
self.MODEL_LOAD_SOUND = ('C6w', 'c6w', 'C6w')
self.BEEP_SOUND = ('E6q', 'C6q')
class Player(Service):
"""Controls buzzer."""
def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
def process(self, sound):
self._toneplayer.play(*sound)
def play(self, sound):
self.submit(sound)
class Player(Service):
"""Controls buzzer."""
def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
def process(self, sound):
self._toneplayer.play(*sound)
def play(self, sound):
self.submit(sound)
class Player(Service):
"""Controls buzzer."""
def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
self.JOY_SOUND = ('C5q', 'E5q', 'C6q')
self.SAD_SOUND = ('C6q', 'E5q', 'C5q')
self.MODEL_LOAD_SOUND = ('C6w', 'c6w', 'C6w')
self.BEEP_SOUND = ('E6q', 'C6q')
def process(self, sound):
self._toneplayer.play(*sound)
def play(self, sound):
self.submit(sound)
def startup(self):
with Board() as board, Leds() as leds:
colors = [Color.RED, Color.YELLOW, Color.GREEN, Color.CYAN,
Color.BLUE, Color.PURPLE, Color.BLACK, Color.WHITE]
board.led.state = Led.ON
for color in colors:
leds.update(Leds.rgb_on(color))
time.sleep(0.25)
TonePlayer(22).play(*jingleBells(6))
board.led.state = Led.OFF
def __init__(self, num_frames, preview_alpha):
self._rgbled = RGBLED(debug=False)
self._num_frames = num_frames
self._preview_alpha = preview_alpha
self._toneplayer = TonePlayer(22, bpm=10)
self._sound_played = False
self._detector = threading.Thread(target=self._run_detector)
self._animator = threading.Thread(target=self._run_animator)
self._joy_score_lock = threading.Lock()
self._joy_score = 0.0
self._joy_score_window = collections.deque(maxlen=WINDOW_SIZE)
self._run_event = threading.Event()
signal.signal(signal.SIGINT, lambda signal, frame: self.stop())
signal.signal(signal.SIGTERM, lambda signal, frame: self.stop())
def startup():
with Board() as board, Leds() as leds:
colors = [Color.RED, Color.YELLOW, Color.GREEN, Color.CYAN,
Color.BLUE, Color.PURPLE, Color.BLACK, Color.WHITE]
board.led.state = Led.ON
for color in colors:
leds.update(Leds.rgb_on(color))
time.sleep(0.5)
TonePlayer(22).play(*[
'Be',
'rs',
'C5e',
'rs',
'D5e',
])
board.led.state = Led.OFF
def menu(self):
print('Press Arcade Button to begin photo shoot.' + '\n')
with Board() as board, Leds() as leds:
while True:
# pulse LED to indicate ready state
leds.pattern = Pattern.blink(1000)
leds.update(Leds.rgb_pattern(Color.WHITE))
board.button.wait_for_press()
startTime = datetime.datetime.now()
board.led.state = Led.ON
print('LED is on...')
# update LED to green indicating shoot is live
leds.update(Leds.rgb_on((107, 255, 0)))
self.shoot()
leds.pattern = Pattern.blink(1000)
leds.update(Leds.rgb_pattern(Color.WHITE))
print('Press Arcade Button to start again' + '\n' + 'OR....' +
'\n' + 'Press and HOLD the Arcade Button for 5 seconds to quit')
board.button.wait_for_press()
pressTime = datetime.datetime.now()
board.button.wait_for_release()
releaseTime = datetime.datetime.now()
board.led.state = Led.OFF
print('OFF')
pressDuration = releaseTime - pressTime
sessionDuration = releaseTime - startTime
if pressDuration.seconds >= 5:
leds.update(Leds.rgb_on(Color.PURPLE))
print('Photo booth session ran for ' +
str(sessionDuration.seconds) + ' seconds')
time.sleep(3)
TonePlayer(22).play(*[
'D5e',
'rq',
'C5e',
'rq',
'Be',
'rq',
'Be',
'C5e',
'D5e'
])
break
print('Done')
def main():
startup()
print('Press Button start. Press Button to stop camera.'
+ 'Press Button again (or press Ctrl-C) to quit.')
pressDuration = 0
with Board() as board, Leds() as leds:
while True:
board.button.wait_for_press()
pressTime = datetime.datetime.now()
board.led.state = Led.ON
print('ON')
print('Running facedetect')
facedetect()
leds.update(Leds.rgb_on((107, 255, 0)))
board.button.wait_for_release()
releaseTime = datetime.datetime.now()
board.led.state = Led.OFF
print('OFF')
pressDuration = releaseTime - pressTime
print('Program ran for ' + str(pressDuration.seconds) + ' seconds')
if pressDuration.seconds >= 5:
leds.update(Leds.rgb_on(Color.PURPLE))
time.sleep(3)
TonePlayer(22).play(*[
'D5e',
'rq',
'C5e',
'rq',
'Be',
'rq',
'Be',
'C5e',
'D5e'
])
break
print('Done')
def main():
print("Play tune")
player = TonePlayer(gpio=BUZZER_GPIO_PIN, bpm=10)
player.play(*START_SOUND)
print("Initialize robot")
robot = Robot()
robot.resetPosition()
print("Switch on leds")
with Leds() as leds:
leds.update(Leds.rgb_on(Color.GREEN))
print("Switch on camera")
with PiCamera(sensor_mode=4,
resolution=(CAMERA_WIDTH, CAMERA_HEIGHT),
framerate=30) as camera:
if ENABLE_DISPLAY:
camera.start_preview()
annotator = Annotator(camera, dimensions=(320, 240))
else:
annotator = None
print("Load model")
with CameraInference(face_detection.model()) as inference:
loop(inference=inference,
robot=robot,
annotator=annotator,
leds=leds)
if ENABLE_DISPLAY:
camera.stop_preview()
player.play(*STOP_SOUND)
# Give time for the user to remote its finger.
sleep(3)
robot.resetPosition()
def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)
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
from aiy.vision.annotator import Annotator
import pikachu_object_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',
from aiy.vision.models import dish_classification
from aiy.toneplayer import TonePlayer
from aiy.leds import Leds
from gpiozero import Button
from time import sleep
import picamera
from slacker import Slacker
import time
import os
BUZZER_GPIO = 22
BUTTON_GPIO = 23
slack = Slacker('xxx')
toneplayer = TonePlayer(BUZZER_GPIO)
leds = Leds()
button = Button(BUTTON_GPIO)
photo_filename = 'dish.jpg'
BEEP_SOUND = ('E6q', 'C6q')
RED = (0xFF, 0x00, 0x00)
GREEN = (0x00, 0xFF, 0x00)
BLUE = (0x00, 0x00, 0xFF)
WHITE = (0xFF, 0xFF, 0xFF)
def KeepWatchForSeconds(seconds):
GoFlag = True
while seconds > 0:
time.sleep(0.1)
def main():
def face_data(face):
x, y, width, height = face.bounding_box
x_mean = int(x + width/2)
angle = atan2(x_mean - x_center,focal_length)
distance = 0
if width > 0:
distance = focal_length * real_face_width_inch / width
return angle, distance
parser = argparse.ArgumentParser()
parser.add_argument(
'--num_frames',
'-n',
type=int,
dest='num_frames',
default=-1,
help='Sets the number of frames to run for, otherwise runs forever.')
args = parser.parse_args()
focal_length = 1320 # focal length in pixels for 1640 x 1232 resolution - found by calibration
camera_resolution = (1640, 1232)
x_center = int(camera_resolution[0] / 2)
real_face_width_inch = 11 # width/height of bounding box of human face in inches
min_angle = atan2(-x_center,focal_length) # min angle where face can be detected (leftmost area) in radians
max_angle = atan2(x_center,focal_length)
face_detected_on_prev_frame = False
previous_angle = 0
LOAD_SOUND = ('G5e', 'f5e', 'd5e', 'A5e', 'g5e', 'E5e', 'g5e', 'C6e')
BUZZER_GPIO = 22
with PiCamera(sensor_mode=4, resolution=(1640, 1232), framerate=30) as camera,\
Leds() as leds:
leds.update(Leds.privacy_on())
myCorrectionMin=0.2
myCorrectionMax=0.2
maxPW=(2.0+myCorrectionMax)/1000
minPW=(1.0-myCorrectionMin)/1000
camera.start_preview()
tone_player = TonePlayer(BUZZER_GPIO, bpm=70)
tone_player.play(*LOAD_SOUND)
#servo = AngularServo(PIN_A, min_pulse_width=minPW, max_pulse_width=maxPW)
servo = AngularServo(PIN_A, max_pulse_width = maxPW)
#servo = AngularServo(PIN_A)
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / 1640
scale_y = 240 / 1232
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 i, result in enumerate(inference.run()):
if i == args.num_frames:
break
faces = face_detection.get_faces(result)
annotator.clear()
for face in faces:
annotator.bounding_box(transform(face.bounding_box), fill=0)
annotator.update()
print('Iteration #%d: num_faces=%d' % (i, len(faces)))
if faces:
previous_angle = 0
leds.update(Leds.rgb_on(Color.BLUE))
if face_detected_on_prev_frame:
angle, distance = face_data(face)
#if angle < min_angle:
# angle = min_angle
#if angle > max_angle:
# angle = max_angle
servo.angle = angle*(-100)
previous_angle = angle*(-100)
print('Angle:' + str(angle))
sleep(.05)
face_detected_on_prev_frame = True
else:
leds.update(Leds.rgb_on(Color.RED))
if not face_detected_on_prev_frame:
servo.angle = previous_angle
sleep(.05)
pass
face_detected_on_prev_frame = False
camera.stop_preview()
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))
distance = focal_length * real_face_width_inches / width
if x > 0:
alpha = x / float(1200)
brightness = 254 - (distance * 2)
else:
alpha = .5
brightness = 254
try:
leds.update(
Leds.rgb_on(
Color.blend(Color.BLUE, Color.RED, alpha)))
b.set_light(2, 'bri', brightness)
except:
pass
camera.annotate_text = '%d inches' % distance
else:
pass
camera.stop_preview()
def main():
model_path = '/opt/aiy/models/retrained_graph.binaryproto'
#model_path = '/opt/aiy/models/mobilenet_v1_160res_0.5_imagenet.binaryproto'
label_path = '/opt/aiy/models/retrained_labels_new.txt'
#label_path = '/opt/aiy/models/mobilenet_v1_160res_0.5_imagenet_labels.txt'
model_path = '/opt/aiy/models/rg_v3_new.binaryproto'
label_path = '/opt/aiy/models/retrained_labels_new.txt'
input_height = 160
input_width = 160
input_layer = 'input'
output_layer = 'final_result'
threshold = 0.8
# Model & labels
model = ModelDescriptor(
name='mobilenet_based_classifier',
input_shape=(1, input_height, input_width, 3),
input_normalizer=(128.0, 128.0),
compute_graph=utils.load_compute_graph(model_path))
labels = read_labels(label_path)
new_labels = []
for eachLabel in labels:
if len(eachLabel)>1:
new_labels.append(eachLabel)
labels = new_labels
#print(labels)
s = xmlrpc.client.ServerProxy("http://aiy.mdzz.info:8000/")
player = TonePlayer(BUZZER_GPIO, 10)
player.play(*MODEL_LOAD_SOUND)
while True:
while True:
if s.camera() == 1:
print('vision kit is woken up')
with Leds() as leds:
leds.pattern = Pattern.blink(100)
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(2.0)
start_time = round(time.time())
break
time.sleep(0.2)
print('no signal, sleeping...')
with PiCamera() as camera:
# Configure camera
camera.sensor_mode = 4
camera.resolution = (1664, 1232) # Full Frame, 16:9 (Camera v2)
camera.framerate = 30
camera.start_preview()
while True:
# Do inference on VisionBonnet
#print('Start capturing')
with CameraInference(face_detection.model()) as inference:
for result in inference.run():
#print(type(result))
faces = face_detection.get_faces(result)
if len(faces) >= 1:
#print('camera captures...')
extension = '.jpg'
filename = time.strftime('%Y-%m-%d %H:%M:%S') + extension
camera.capture(filename)
image_npp = np.empty((1664 * 1232 * 3,), dtype=np.uint8)
camera.capture(image_npp, 'rgb')
image_npp = image_npp.reshape((1232, 1664, 3))
image_npp = image_npp[:1232, :1640, :]
# image = Image.open('jj.jpg')
# draw = ImageDraw.Draw(image)
faces_data = []
faces_cropped = []
for i, face in enumerate(faces):
# print('Face #%d: %s' % (i, face))
x, y, w, h = face.bounding_box
#print(x,y,w,h)
w_rm = int(0.3 * w / 2)
face_cropped = crop_np((x, y, w, h), w_rm, image_npp)
if face_cropped is None: continue #print('face_cropped None'); continue
# faces_data.append(image[y: y + h, x + w_rm: x + w - w_rm])
# image[y: y + h, x + w_rm: x + w - w_rm].save('1.jpg')
face_cropped.save('face_cropped_'+str(i)+'.jpg')
faces_cropped.append(face_cropped)
#break
break
# else:
# tt = round(time.time()) - start_time
# if tt > 10:
# break
#print('face cutting finishes')
#print(type(faces_cropped), len(faces_cropped))
player.play(*BEEP_SOUND)
flag = 0
for eachFace in faces_cropped:
#print(type(eachFace))
if eachFace is None: flag = 1
if (len(faces_cropped)) <= 0: flag = 1
if flag == 1: continue
with ImageInference(model) as img_inference:
#with CameraInference(model) as img_inference:
print('Entering classify_hand_gestures()')
output = classify_hand_gestures(img_inference, faces_cropped, model=model, labels=labels,
output_layer=output_layer, threshold=threshold)
#print(output)
if (output == 3):
player.play(*JOY_SOUND)
print('Yani face detected')
print(s.result("Owner", filename))
else:
player.play(*SAD_SOUND)
print('Suspicious face detected')
print(s.result("Unknown Face", filename))
upload(filename)
# Stop preview #
#break
while (s.camera()==0):
print('sleeping')
time.sleep(.2)
print('Waken up')
def __init__(self, gpio, bpm):
super().__init__()
self._toneplayer = TonePlayer(gpio, bpm)