def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--num_frames', '-n', type=int, default=None,
help='Number of frames to run for')
parser.add_argument('--preview_alpha', '-pa', type=preview_alpha, default=0,
help='Video preview overlay transparency (0-255)')
parser.add_argument('--image_format', default='jpeg',
choices=('jpeg', 'bmp', 'png'),
help='Format of captured images')
parser.add_argument('--image_folder', default='~/Pictures',
help='Folder to save captured images')
parser.add_argument('--blink_on_error', default=False, action='store_true',
help='Blink red if error occurred')
parser.add_argument('--enable_streaming', default=False, action='store_true',
help='Enable streaming server')
parser.add_argument('--streaming_bitrate', type=int, default=1000000,
help='Streaming server video bitrate (kbps)')
parser.add_argument('--mdns_name', default='',
help='Streaming server mDNS name')
args = parser.parse_args()
try:
joy_detector(args.num_frames, args.preview_alpha, args.image_format, args.image_folder,
args.enable_streaming, args.streaming_bitrate, args.mdns_name)
except KeyboardInterrupt:
pass
except Exception:
logger.exception('Exception while running joy demo.')
if args.blink_on_error:
with Leds() as leds:
leds.pattern = Pattern.blink(100) # 10 Hz
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(1.0)
return 0
def main():
with Leds() as leds:
print('RGB: Solid RED for 1 second')
leds.update(Leds.rgb_on(Color.RED))
time.sleep(1)
print('RGB: Solid GREEN for 1 second')
leds.update(Leds.rgb_on(Color.GREEN))
time.sleep(1)
print('RGB: Solid YELLOW for 1 second')
leds.update(Leds.rgb_on(Color.YELLOW))
time.sleep(1)
print('RGB: Solid BLUE for 1 second')
leds.update(Leds.rgb_on(Color.BLUE))
time.sleep(1)
print('RGB: Solid PURPLE for 1 second')
leds.update(Leds.rgb_on(Color.PURPLE))
time.sleep(1)
print('RGB: Solid CYAN for 1 second')
leds.update(Leds.rgb_on(Color.CYAN))
time.sleep(1)
print('RGB: Solid WHITE for 1 second')
leds.update(Leds.rgb_on(Color.WHITE))
time.sleep(1)
print('RGB: Off for 1 second')
leds.update(Leds.rgb_off())
time.sleep(1)
for _ in range(3):
print('Privacy: On (brightness=default)')
leds.update(Leds.privacy_on())
time.sleep(1)
print('Privacy: Off')
leds.update(Leds.privacy_off())
time.sleep(1)
for _ in range(3):
print('Privacy: On (brightness=5)')
leds.update(Leds.privacy_on(5))
time.sleep(1)
print('Privacy: Off')
leds.update(Leds.privacy_off())
time.sleep(1)
print('Set blink pattern: period=500ms (2Hz)')
leds.pattern = Pattern.blink(500)
print('RGB: Blink RED for 5 seconds')
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(5)
print('RGB: Blink GREEN for 5 seconds')
leds.update(Leds.rgb_pattern(Color.GREEN))
time.sleep(5)
print('RGB: Blink BLUE for 5 seconds')
leds.update(Leds.rgb_pattern(Color.BLUE))
time.sleep(5)
print('Set breathe pattern: period=1000ms (1Hz)')
leds.pattern = Pattern.breathe(1000)
print('RGB: Breathe RED for 5 seconds')
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(5)
print('RGB: Breathe GREEN for 5 seconds')
leds.update(Leds.rgb_pattern(Color.GREEN))
time.sleep(5)
print('RGB: Breathe BLUE for 5 seconds')
leds.update(Leds.rgb_pattern(Color.BLUE))
time.sleep(5)
print('RGB: Increase RED brightness for 3.2 seconds')
for i in range(32):
leds.update(Leds.rgb_on((8 * i, 0, 0)))
time.sleep(0.1)
print('RGB: Decrease RED brightness for 3.2 seconds')
for i in reversed(range(32)):
leds.update(Leds.rgb_on((8 * i, 0, 0)))
time.sleep(0.1)
print('RGB: Blend between GREEN and BLUE for 3.2 seconds')
for i in range(32):
color = Color.blend(Color.BLUE, Color.GREEN, i / 32)
leds.update(Leds.rgb_on(color))
time.sleep(0.1)
print('RGB: Off for 1 second')
leds.update(Leds.rgb_off())
time.sleep(1)
print('Privacy: On for 2 seconds')
with PrivacyLed(leds):
time.sleep(2)
print('RGB: Solid GREEN for 2 seconds')
with RgbLeds(leds, Leds.rgb_on(Color.GREEN)):
time.sleep(2)
print('Custom configuration for 5 seconds')
leds.update({
1: Leds.Channel(Leds.Channel.PATTERN, 128), # Red channel
2: Leds.Channel(Leds.Channel.OFF, 0), # Green channel
3: Leds.Channel(Leds.Channel.ON, 128), # Blue channel
4: Leds.Channel(Leds.Channel.PATTERN, 64), # Privacy channel
})
time.sleep(5)
print('Done')
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():
with Leds() as leds:
print('RGB: Solid RED for 1 second')
leds.update(Leds.rgb_on(Color.RED))
time.sleep(1)
print('RGB: Solid GREEN for 1 second')
leds.update(Leds.rgb_on(Color.GREEN))
time.sleep(1)
print('RGB: Solid YELLOW for 1 second')
leds.update(Leds.rgb_on(Color.YELLOW))
time.sleep(1)
print('RGB: Solid BLUE for 1 second')
leds.update(Leds.rgb_on(Color.BLUE))
time.sleep(1)
print('RGB: Solid PURPLE for 1 second')
leds.update(Leds.rgb_on(Color.PURPLE))
time.sleep(1)
print('RGB: Solid CYAN for 1 second')
leds.update(Leds.rgb_on(Color.CYAN))
time.sleep(1)
print('RGB: Solid WHITE for 1 second')
leds.update(Leds.rgb_on(Color.WHITE))
time.sleep(1)
print('RGB: Off for 1 second')
leds.update(Leds.rgb_off())
time.sleep(1)
for _ in range(3):
print('Privacy: On (brightness=default)')
leds.update(Leds.privacy_on())
time.sleep(1)
print('Privacy: Off')
leds.update(Leds.privacy_off())
time.sleep(1)
for _ in range(3):
print('Privacy: On (brightness=5)')
leds.update(Leds.privacy_on(5))
time.sleep(1)
print('Privacy: Off')
leds.update(Leds.privacy_off())
time.sleep(1)
print('Set blink pattern: period=500ms (2Hz)')
leds.pattern = Pattern.blink(500)
print('RGB: Blink RED for 5 seconds')
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(5)
print('RGB: Blink GREEN for 5 seconds')
leds.update(Leds.rgb_pattern(Color.GREEN))
time.sleep(5)
print('RGB: Blink BLUE for 5 seconds')
leds.update(Leds.rgb_pattern(Color.BLUE))
time.sleep(5)
print('Set breathe pattern: period=1000ms (1Hz)')
leds.pattern = Pattern.breathe(1000)
print('RGB: Breathe RED for 5 seconds')
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(5)
print('RGB: Breathe GREEN for 5 seconds')
leds.update(Leds.rgb_pattern(Color.GREEN))
time.sleep(5)
print('RGB: Breathe BLUE for 5 seconds')
leds.update(Leds.rgb_pattern(Color.BLUE))
time.sleep(5)
print('RGB: Increase RED brightness for 3.2 seconds')
for i in range(32):
leds.update(Leds.rgb_on((8 * i, 0, 0)))
time.sleep(0.1)
print('RGB: Decrease RED brightness for 3.2 seconds')
for i in reversed(range(32)):
leds.update(Leds.rgb_on((8 * i, 0, 0)))
time.sleep(0.1)
print('RGB: Blend between GREEN and BLUE for 3.2 seconds')
for i in range(32):
color = Color.blend(Color.BLUE, Color.GREEN, i / 32)
leds.update(Leds.rgb_on(color))
time.sleep(0.1)
print('RGB: Off for 1 second')
leds.update(Leds.rgb_off())
time.sleep(1)
print('Privacy: On for 2 seconds')
with PrivacyLed(leds):
time.sleep(2)
print('RGB: Solid GREEN for 2 seconds')
with RgbLeds(leds, Leds.rgb_on(Color.GREEN)):
time.sleep(2)
print('Custom configuration for 5 seconds')
leds.update({
1: Leds.Channel(Leds.Channel.PATTERN, 128), # Red channel
2: Leds.Channel(Leds.Channel.OFF, 0), # Green channel
3: Leds.Channel(Leds.Channel.ON, 128), # Blue channel
4: Leds.Channel(Leds.Channel.PATTERN, 64), # Privacy channel
})
time.sleep(5)
print('Done')
def listen_me():
global text, duration
parser = argparse.ArgumentParser()
parser.add_argument('--filename', '-f', default='recording.wav')
args = parser.parse_args()
# 라이브러리 준비
Vokaturi.load("/home/pi/lib/piZero.so")
# 클라우드 스피치랑 텍스트 자연어처리 클라이언트 각각 초기화
client = CloudSpeechClient()
nlp_client = language.LanguageServiceClient()
logging.basicConfig(level=logging.INFO)
with Board() as board:
while True:
print('말해보자.')
text = None
duration = 0.
emotion = None
def wait():
global text, duration
start = time.monotonic()
while text is None:
# 텍스트로 인식
text = client.recognize(language_code='ko-KR')
duration = time.monotonic() - start
# 녹음하면서
record_file(AudioFormat.CD,
filename=args.filename,
wait=wait,
filetype='wav')
print(text)
print('Recorded: %.02f seconds' % duration)
if text in ['들어줘서 고마워', '내 얘기 들어줘서 고마워', '어시스턴트', '잘가', '잘 가']:
return
# 텍스트 감정 분석
document = types.Document(content=text,
type=enums.Document.Type.PLAIN_TEXT)
sentiment = nlp_client.analyze_sentiment(
document=document).document_sentiment
print('텍스트 감정 분석*********************************')
print('Text: {}'.format(text))
print('Sentiment: {}, {}'.format(sentiment.score,
sentiment.magnitude))
##################### 실험후 바꿔도 됨 ####################
pos_standard = 0.6
neg_standard = 0.1
# magnitude_standard = 0.1
# text sentiment analysis is enough
if (sentiment.score < neg_standard
or sentiment.score > pos_standard):
if sentiment.score < neg_standard:
emotion = False
print("@@@negative")
else:
emotion = True
print("@@@positive")
else:
# 녹음 파일 감정 분석
print('오디오 감정 분석*********************************')
(sample_rate, samples) = scipy.io.wavfile.read(args.filename)
# print (" sample rate %.3f Hz" % sample_rate)
# print ("Allocating Vokaturi sample array...")
buffer_length = len(samples)
print(" %d samples, %d channels" %
(buffer_length, samples.ndim))
c_buffer = Vokaturi.SampleArrayC(buffer_length)
if samples.ndim == 1: # mono
c_buffer[:] = samples[:] / 32768.0
else: # stereo
c_buffer[:] = 0.5 * (samples[:, 0] + 0.0 +
samples[:, 1]) / 32768.0
# print ("Creating VokaturiVoice...")
voice = Vokaturi.Voice(sample_rate, buffer_length)
# print ("Filling VokaturiVoice with samples...")
voice.fill(buffer_length, c_buffer)
# print ("Extracting emotions from VokaturiVoice...")
quality = Vokaturi.Quality()
emotionProbabilities = Vokaturi.EmotionProbabilities()
voice.extract(quality, emotionProbabilities)
if quality.valid:
# print ("Neutral: %.3f" % emotionProbabilities.neutrality)
# print ("Happy: %.3f" % emotionProbabilities.happiness)
# print ("Sad: %.3f" % emotionProbabilities.sadness)
# print ("Angry: %.3f" % emotionProbabilities.anger)
# print ("Fear: %.3f" % emotionProbabilities.fear)
# fear 는 무시하도록 하자.
wave_score = emotionProbabilities.happiness - (
emotionProbabilities.sadness +
emotionProbabilities.anger)
if wave_score > 0:
print('@@@긍정')
emotion = True
else:
print('@@@부정')
emotion = False
# text 분석 모호하고 wave 분석 실패했을때 (주로 목소리 짧아서)
if emotion is None:
print('please say again')
# 아님 중립적 반응 넣어도 됨.
continue
# 여기서 부터 반응.
with Leds() as leds:
if emotion is True:
# tts.say('I am glad to hear that.')
# tts.say('진짜? 대박.')
leds.pattern = Pattern.blink(100)
color = (255, 255, 0)
leds.update(Leds.rgb_pattern(color))
time.sleep(1)
# play_wav('laugh.wav')
else:
# tts.say('I am sorry to hear that.')
# tts.say('저런. 힘내.')
leds.pattern = Pattern.breathe(1000)
color = (102, 140, 255)
leds.update(Leds.rgb_on(color))
time.sleep(1)
def main_loop(self):
while True:
with Leds() as leds:
leds.update(Leds.rgb_on(Color.RED))
with Board() as board:
print("Waiting for input")
board.button.wait_for_press()
leds.update(Leds.rgb_on((0, 0, 250)))
#print('ON')
self.start = True
self.counter = 0
self.completed = False
self.stopwatch = time.time()
board.button.wait_for_release()
#print('OFF')
leds.update(Leds.rgb_off())
while self.start:
classes = currentState
#print("current State: ", classes)
if classes == 0 and self.state != 0:
self.standing()
elif classes == 1 and self.state != 1:
self.empty()
elif classes == 2 and self.state != 2 and self.last_detected_state != 2:
self.squat()
# Selecting a State
if (time.time()-self.stopwatch) > 0.15:
print("State:\t ",states_names[self.state] , "\t| [selected]")
if self.state == 2 and self.last_detected_state != 2: # Squat detected
self.counter += 1
leds.update(Leds.rgb_on((0, 0, 250)))
self._newSqaut()
#print("### Current Score: ", self.counter,"###")
if self.state == 2 or self.state == 0:
#self.stopwatch = time.time()
leds.update(Leds.rgb_on(Color.WHITE))
if self.state == 1 and ((time.time()-self.stopwatch) > 1):
leds.update(Leds.rgb_off())
self.last_detected_state = self.state
# Resting the counter if nobody is in the frame
if (time.time()-self.stopwatch) > 10:
if self.state == 1: # if nobody is in the frame reset counter
print("### Reset Score ###")
self.counter = 0
self.start = False
leds.pattern = Pattern.blink(500)
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(2)
leds.update(Leds.rgb_off())
self.stopwatch = time.time()
# Checking of the finish
if self.counter >= TOTAL_SQUATS:
self.completed = True
self.output.on()
self.counter = 0
print("Completed Workout")
self.start = False
leds.pattern = Pattern.blink(500)
leds.update(Leds.rgb_pattern(Color.GREEN))
time.sleep(2)
leds.update(Leds.rgb_on(Color.GREEN))
with Board() as board:
print("Waiting for input")
board.button.wait_for_press()
print('ON')
board.led.state = Led.ON
self.start = False
self.counter = 0
self.completed = False
self.stopwatch = time.time()
board.button.wait_for_release()
print('OFF')
self.output.off()
board.led.state = Led.OFF
leds.pattern = Pattern.blink(500)
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(2)
leds.update(Leds.rgb_off())
def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--num_frames',
'-n',
type=int,
default=None,
help='Number of frames to run for')
parser.add_argument('--preview_alpha',
'-pa',
type=preview_alpha,
default=0,
help='Video preview overlay transparency (0-255)')
parser.add_argument('--image_format',
default='jpeg',
choices=('jpeg', 'bmp', 'png'),
help='Format of captured images')
parser.add_argument('--image_folder',
default='tmpImage',
help='Folder to save captured images')
parser.add_argument('--blink_on_error',
default=False,
action='store_true',
help='Blink red if error occurred')
parser.add_argument('--enable_streaming',
default=True,
action='store_true',
help='Enable streaming server')
parser.add_argument('--streaming_bitrate',
type=int,
default=1000000,
help='Streaming server video bitrate (kbps)')
parser.add_argument('--mdns_name',
default='',
help='Streaming server mDNS name')
parser.add_argument('--cam_width',
type=int,
default=1640,
help='Camera Width')
parser.add_argument('--cam_height',
type=int,
default=1232,
help='Camera Height')
parser.add_argument('--fps',
type=int,
default=30,
help='Camera Frames Per Second')
parser.add_argument(
'--region',
nargs=4,
type=int,
default=[1040, 600, 600, 632],
help='Region for entering/exiting face detection: x, y, width, height')
parser.add_argument(
'--enter_side',
type=int,
default=1,
help=
'Used to determine which side of the region should be considered "entering": 1 = right, 0 = left'
)
parser.add_argument(
'--annotator',
default=False,
help='Shows the annotator overlay, however disables camera snapshots.')
parser.add_argument('--url',
default="http://isrow.net",
help='Url to send the face captures that are taken.')
parser.add_argument(
'--username',
default=None,
help='User name used to authenticate this device initially')
parser.add_argument(
'--password',
default=None,
help='Password used to authenticate this device initially')
parser.add_argument(
'--image_dir',
default="events/",
help='{url + "/" + image_dir} will give us path to send the face data')
args = parser.parse_args()
try:
monitor_run(args.num_frames, args.preview_alpha, args.image_format,
args.image_folder, args.enable_streaming,
args.streaming_bitrate, args.mdns_name, args.cam_width,
args.cam_height, args.fps, args.region, args.enter_side,
args.annotator, args.url, args.username, args.password,
args.image_dir)
except KeyboardInterrupt:
pass
except Exception:
logger.exception('Exception while running joy demo.')
if args.blink_on_error:
with Leds() as leds:
leds.pattern = Pattern.blink(100) # 10 Hz
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(1.0)
return 0
def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--num_frames',
'-n',
type=int,
default=None,
help='Number of frames to run for')
parser.add_argument('--preview_alpha',
'-pa',
type=preview_alpha,
default=0,
help='Video preview overlay transparency (0-255)')
parser.add_argument('--image_format',
default='jpeg',
choices=('jpeg', 'bmp', 'png'),
help='Format of captured images')
parser.add_argument('--image_folder',
default='~/Pictures',
help='Folder to save captured images')
parser.add_argument('--blink_on_error',
default=False,
action='store_true',
help='Blink red if error occurred')
parser.add_argument('--enable_streaming',
default=False,
action='store_true',
help='Enable streaming server')
parser.add_argument('--streaming_bitrate',
type=int,
default=1000000,
help='Streaming server video bitrate (kbps)')
parser.add_argument('--mdns_name',
default='',
help='Streaming server mDNS name')
args = parser.parse_args()
broker_address = "io.adafruit.com"
print("creating new instance")
user = "******"
password = "******"
print("connecting to broker")
client = mqtt.Client("AIY_VISION_KIT") # create new instance
client.username_pw_set(user, password=password)
client.on_log = on_log
client.connect(broker_address, 1883, 60) # connect to broker
client.loop_start()
try:
joy_detector(args.num_frames, args.preview_alpha, args.image_format,
args.image_folder, args.enable_streaming,
args.streaming_bitrate, args.mdns_name, client)
except KeyboardInterrupt:
pass
except Exception:
logger.exception('Exception while running joy demo.')
if args.blink_on_error:
with Leds() as leds:
leds.pattern = Pattern.blink(100) # 10 Hz
leds.update(Leds.rgb_pattern(Color.RED))
time.sleep(1.0)
return 0
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--num_frames',
'-n',
type=int,
dest='num_frames',
default=-1,
help='Number of frames to run for, -1 to not terminate')
parser.add_argument(
'--preview_alpha',
'-pa',
type=int,
dest='preview_alpha',
default=0,
help='Transparency value of the preview overlay (0-255).')
parser.add_argument('--image_format',
type=str,
dest='image_format',
default='jpeg',
choices=('jpeg', 'bmp', 'png'),
help='Format of captured images.')
parser.add_argument('--image_folder',
type=str,
dest='image_folder',
default='~/Pictures',
help='Folder to save captured images.')
parser.add_argument('--blink_on_error',
dest='blink_on_error',
default=False,
action='store_true',
help='Blink red if error occurred.')
parser.add_argument('--enable_streaming',
dest='enable_streaming',
default=False,
action='store_true',
help='Enable streaming server.')
parser.add_argument('--width',
dest='width',
default=640,
action='store_true',
help='Streaming video width.')
args = parser.parse_args()
if args.preview_alpha < 0 or args.preview_alpha > 255:
parser.error('Invalid preview_alpha value: %d' % args.preview_alpha)
if not os.path.exists('/dev/vision_spicomm'):
logger.error(
'AIY Vision Bonnet is not attached or not configured properly.')
return 1
print('Initializing camera')
with picamera.PiCamera() as camera:
# 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.
# Use half of that for video streaming (820x616).
camera.resolution = (WIDTH, HEIGHT)
camera.framerate = FRAMERATE
camera.vflip = VFLIP # flips image rightside up, as needed
camera.hflip = HFLIP # flips image left-right, as needed
camera.sensor_mode = 4
time.sleep(1) # camera warm-up time
print('Initializing websockets server on port %d' % WS_PORT)
WebSocketWSGIHandler.http_version = '1.1'
websocket_server = make_server(
'',
WS_PORT,
server_class=WSGIServer,
handler_class=WebSocketWSGIRequestHandler,
app=WebSocketWSGIApplication(handler_cls=StreamingWebSocket))
websocket_server.initialize_websockets_manager()
websocket_thread = Thread(target=websocket_server.serve_forever)
print('Initializing HTTP server on port %d' % HTTP_PORT)
http_server = StreamingHttpServer()
http_thread = Thread(target=http_server.serve_forever)
print('Initializing broadcast thread')
output = BroadcastOutput(camera)
broadcast_thread = BroadcastThread(output.converter, websocket_server)
print('Starting recording')
camera.start_recording(output, 'yuv')
print('Start Inference')
detector = JoyDetector(camera, args)
try:
print('Starting websockets thread')
websocket_thread.start()
print('Starting HTTP server thread')
http_thread.start()
print('Starting broadcast thread')
broadcast_thread.start()
while True:
camera.wait_recording(1)
except KeyboardInterrupt:
pass
finally:
if args.blink_on_error:
leds = Leds()
leds.pattern = Pattern.blink(500)
leds.update(Leds.rgb_pattern(RED_COLOR))
print('Stopping recording')
camera.stop_recording()
print('Waiting for broadcast thread to finish')
broadcast_thread.join()
print('Shutting down HTTP server')
http_server.shutdown()
print('Shutting down websockets server')
websocket_server.shutdown()
print('Waiting for HTTP server thread to finish')
http_thread.join()
print('Waiting for websockets thread to finish')
websocket_thread.join()