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 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 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():
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 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()