def detect(num_frames):
"""Face detection camera inference example"""
# 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:
camera.start_preview()
# Annotator renders in software so use a smaller size and scale results
# for increased performance.
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / 1640
scale_y = 240 / 1232
with CameraInference(face_detection.model()) as inference:
for result in inference.run(num_frames):
faces = face_detection.get_faces(result)
annotator.clear()
for face in faces:
x, y, width, height = face.bounding_box
annotator.bounding_box(
(scale_x * x, scale_y * y, scale_x *
(x + width), scale_y * (y + height)),
fill=0)
annotator.update()
print('#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f' %
(inference.count, inference.rate, len(faces),
avg_joy_score(faces)))
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=-1,
help='Sets the number of frames to run for, otherwise runs forever.')
args = parser.parse_args()
with 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.
camera.sensor_mode = 4
# Scaled and cropped resolution. If different from sensor mode implied
# resolution, inference results must be adjusted accordingly. This is
# true in particular when camera.start_recording is used to record an
# encoded h264 video stream as the Pi encoder can't encode all native
# sensor resolutions, or a standard one like 1080p may be desired.
camera.resolution = (1640, 1232)
# Start the camera stream.
camera.framerate = 30
camera.start_preview()
# 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 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)))
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:
camera.start_preview()
# 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)
print(faces)
annotator.clear()
for index, face in enumerate(faces):
sio.emit('movement', {
'index': index,
'score': face.face_score
})
annotator.bounding_box(transform(face.bounding_box),
fill=0)
annotator.update()
print('#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f' %
(inference.count, inference.rate, len(faces),
avg_joy_score(faces)))
camera.stop_preview()
def main():
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()
with PiCamera(sensor_mode=4, resolution=(1640, 1232),
framerate=30) as camera:
camera.start_preview()
# 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(object_detection.model()) as inference:
for result in inference.run(args.num_frames):
objects = object_detection.get_objects(result)
annotator.clear()
for obj in objects:
rect = transform(obj.bounding_box)
annotator.bounding_box(rect, fill=0)
loc = (rect[0] + 4, rect[1])
annotator.text(loc, objectLabel(obj.kind))
annotator.update()
#print('#%05d (%5.2f fps): num_objects=%d, objects=%s' %
# (inference.count, inference.rate, len(objects), objects))
if len(objects) > 0:
print(
f"num_objects={len(objects)}, objects={[objectLabel(obj.kind) for obj in objects]}"
)
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:
camera.start_preview()
# 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)
annotator.update()
print('#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f' %
(inference.count, inference.rate, len(faces), avg_joy_score(faces)))
camera.stop_preview()
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',
type=int,
dest='num_pics',
default=-1,
help='Sets the max number of pictures to take, otherwise runs forever.'
)
args = parser.parse_args()
with PiCamera() as camera, PrivacyLed(Leds()):
# See the Raspicam documentation for mode and framerate limits:
# https://picamera.readthedocs.io/en/release-1.13/fov.html#sensor-modes
# Set to the highest resolution possible at 16:9 aspect ratio
camera.sensor_mode = 4
camera.resolution = (1640, 1232)
camera.start_preview(fullscreen=True)
with CameraInference(pikachu_object_detection.model()) as inference:
print("Camera inference started")
player.play(*MODEL_LOAD_SOUND)
last_time = time()
pics = 0
save_pic = False
enable_label = True
# 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))
def leftCorner(bounding_box):
x, y, width, height = bounding_box
return (scale_x * x, scale_y * y)
def truncateFloat(value):
return '%.3f' % (value)
for f, result in enumerate(inference.run()):
print("sono dentro al ciclo..")
print(os.getcwd() + '/pikachu_detector.binaryproto')
annotator.clear()
detections = enumerate(
pikachu_object_detection.get_objects(result, 0.3))
for i, obj in detections:
print("sono dentro al secondo ciclo..")
print('%s', obj.label)
annotator.bounding_box(transform(obj.bounding_box), fill=0)
if enable_label:
annotator.text(
leftCorner(obj.bounding_box),
obj.label + " - " + str(truncateFloat(obj.score)))
print('%s Object #%d: %s' %
(strftime("%Y-%m-%d-%H:%M:%S"), i, str(obj)))
x, y, width, height = obj.bounding_box
if obj.label == 'PIKACHU':
save_pic = True
#player.play(*BEEP_SOUND)
# save the image if there was 1 or more cats detected
if save_pic:
# save the clean image
#camera.capture("images/image_%s.jpg" % strftime("%Y%m%d-%H%M%S"))
pics += 1
save_pic = False
#if f == args.num_frames or pics == args.num_pics:
# break
now = time()
duration = (now - last_time)
annotator.update()
# The Movidius chip runs at 35 ms per image.
# Then there is some additional overhead for the object detector to
# interpret the result and to save the image. If total process time is
# running slower than 50 ms it could be a sign the CPU is geting overrun
#if duration > 0.50:
# print("Total process time: %s seconds. Bonnet inference time: %s ms " %
# (duration, result.duration_ms))
last_time = now
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.')
parser.add_argument('--sparse',
'-s',
action='store_true',
default=False,
help='Use sparse tensors.')
parser.add_argument('--threshold',
'-t',
type=float,
default=0.3,
help='Detection probability threshold.')
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')
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=(args.cam_width, args.cam_height),
framerate=args.fps) as camera:
camera.start_preview()
width = args.cam_width
height = args.cam_height
# Annotator renders in software so use a smaller size and scale results
# for increased performace.
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / width
scale_y = 240 / height
size = min(width, height)
offset = (((width - size) / 2), ((height - size) / 2))
# 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))
while True:
with CameraInference(face_detection.model()) as inference, \
CameraInference(object_detection.model()) as inference2:
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)
#annotator.update()
print(
'#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f' %
(inference.count, inference.rate, len(faces),
avg_joy_score(faces)))
for result in inference2.run(args.num_frames):
objects = object_detection.get_objects(
result, args.threshold, offset)
#annotator.clear()
for i, obj in enumerate(objects):
annotator.bounding_box(transform(obj.bounding_box),
fill=0)
print('Object #%d: %s' % (i, obj))
annotator.update()
camera.stop_preview()
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 objdet():
with CameraInference(ObjectDetection.model()) as inference:
print("Camera inference started")
player.play(*MODEL_LOAD_SOUND)
last_time = time()
pics = 0
save_pic = False
enable_label = True
# 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))
def leftCorner(bounding_box):
x, y, width, height = bounding_box
return (scale_x * x, scale_y * y)
def truncateFloat(value):
return '%.3f' % (value)
for f, result in enumerate(inference.run()):
annotator.clear()
detections = enumerate(
ObjectDetection.get_objects(result, 0.3))
for i, obj in detections:
print('%s', obj.label)
annotator.bounding_box(transform(obj.bounding_box),
fill=0)
if enable_label:
annotator.text(
leftCorner(obj.bounding_box), obj.label +
" - " + str(truncateFloat(obj.score)))
print('%s Object #%d: %s' %
(strftime("%Y-%m-%d-%H:%M:%S"), i, str(obj)))
x, y, width, height = obj.bounding_box
if obj.label == 'chair':
#dt = datetime.datetime.now()
#os.system("ffplay -nodisp -autoexit /home/pi/AIY-projects-python/src/LorecObjectSoundFiles/insan.mp3")
#query = ("INSERT INTO Log (Time, Location, GlassNameDbid, ModulDbid, Screenshot, Tag, Distance) VALUES ('"+ dt+"', 'Ankara', '1', '2', 'No Screenshot', 'Insan', '150')")
#save_pic = True
player.play(*BEEP_SOUND)
#elif obj.label == 'tvmonitor':
#os.system("ffplay -nodisp -autoexit /home/pi/AIY-projects-python/src/LorecObjectSoundFiles/Ekran.mp3")
# save the image
if save_pic:
# save the clean image
camera.capture("images/image_%s.jpg" %
strftime("%Y%m%d-%H%M%S"))
pics += 1
save_pic = False
if f == args.num_frames or pics == args.num_pics:
break
annotator.update()
now = time()
duration = (now - last_time)
# The Movidius chip runs at 35 ms per image.
# Then there is some additional overhead for the object detector to
# interpret the result and to save the image. If total process time is
# running slower than 50 ms it could be a sign the CPU is geting overrun
if duration > 0.50:
print(
"Total process time: %s seconds. Bonnet inference time: %s ms "
% (duration, result.duration_ms))
last_time = now
def main():
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()
with PiCamera() as camera:
camera.sensor_mode = 4
camera.resolution = (1640, 1232)
camera.framerate = 30
camera.start_preview()
servo = Servo(PIN_A, min_pulse_width=.0005, max_pulse_width=.0019)
servo.mid()
position = 0
zero_counter = 0
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:
face = faces[0]
x, y, width, height = face.bounding_box
print(' : Face is at %d' % x)
if x < 300:
print(' : Face left of center')
position = position - 0.1
if position < -1:
position = -0.99
elif x > 500:
print(' : Face right of center')
position = position + 0.1
if position > 1:
position = 0.99
else:
print(' : Face in CENTER of image')
positon = position
servo.value = position
else:
zero_counter = zero_counter + 1
if zero_counter == 100:
servo.mid()
position = 0
print(' :Ignoring you')
zero_counter = 0
camera.stop_preview()
def monitor_run(num_frames, preview_alpha, image_format, image_folder,
enable_streaming, streaming_bitrate, mdns_name, width, height,
fps, region, enter_side, use_annotator, url, uname, pw,
image_dir, dev):
# Sign the device in and get an access and a refresh token, if a password and username provided.
access_token = None
refresh_token = None
tokens = None
start_token_timer = timer()
if uname is not None and pw is not None:
try:
tokens = connect_to_server(url, uname, pw)
access_token = tokens['access']
refresh_token = tokens['refresh']
print(access_token)
print(refresh_token)
except:
print("Could not get tokens from the server.")
pass
# location where we want to send the faces + status for classification on web server.
classification_path = url + "/" + image_dir
done = threading.Event()
def stop():
logger.info('Stopping...')
done.set()
# Get the region center point and two corners
r_center = (region[0] + region[2] / 2, region[1] + region[3] / 2)
r_corners = (region[0], region[0] + region[2], region[1],
region[1] + region[3])
signal.signal(signal.SIGINT, lambda signum, frame: stop())
signal.signal(signal.SIGTERM, lambda signum, frame: stop())
logger.info('Starting...')
with contextlib.ExitStack() as stack:
leds = stack.enter_context(Leds())
board = stack.enter_context(Board())
player = stack.enter_context(Player(gpio=BUZZER_GPIO, bpm=10))
photographer = stack.enter_context(
Photographer(image_format, image_folder))
animator = stack.enter_context(Animator(leds))
# 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 = stack.enter_context(
PiCamera(sensor_mode=4, framerate=fps, resolution=(width, height)))
stack.enter_context(PrivacyLed(leds))
# Annotator renders in software so use a smaller size and scale results
# for increased performace.
annotator = None
if use_annotator:
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / width
scale_y = 240 / height
server = None
if enable_streaming:
server = stack.enter_context(
StreamingServer(camera,
bitrate=streaming_bitrate,
mdns_name=mdns_name))
def model_loaded():
logger.info('Model loaded.')
player.play(MODEL_LOAD_SOUND)
def take_photo():
logger.info('Button pressed.')
player.play(BEEP_SOUND)
photographer.shoot(camera)
if preview_alpha > 0:
camera.start_preview(alpha=preview_alpha)
board.button.when_pressed = take_photo
joy_moving_average = moving_average(10)
joy_moving_average.send(None) # Initialize.
joy_threshold_detector = threshold_detector(JOY_SCORE_LOW,
JOY_SCORE_HIGH)
joy_threshold_detector.send(None) # Initialize.
previous_faces3 = []
previous_faces2 = []
previous_faces = []
num_faces = 0
for faces, frame_size in run_inference(num_frames, model_loaded):
# If 4 mins have passed since access token obtained, refresh the token.
end_token_timer = timer() # time in seconds
if refresh_token is not None and end_token_timer - start_token_timer >= 240:
tokens = refresh_access_token(url, refresh_token)
access_token = tokens["access"]
photographer.update_faces((faces, frame_size))
joy_score = joy_moving_average.send(average_joy_score(faces))
animator.update_joy_score(joy_score)
event = joy_threshold_detector.send(joy_score)
if event == 'high':
logger.info('High joy detected.')
player.play(JOY_SOUND)
elif event == 'low':
logger.info('Low joy detected.')
player.play(SAD_SOUND)
num_previous_faces = num_faces
if use_annotator:
annotator.clear()
annotator.bounding_box(transform(region, scale_x, scale_y),
fill=0)
num_faces = 0
tmp_arr = []
faces_in_region = []
photo_taken = False
image = None
for face in faces:
face_center = (face.bounding_box[0] + face.bounding_box[2] / 2,
face.bounding_box[1] + face.bounding_box[3] / 2)
# check if the center of the face is in our region of interest:
if r_corners[0] <= face_center[0] <= r_corners[1] and \
r_corners[2] <= face_center[1] <= r_corners[3]:
if not photo_taken:
stream = io.BytesIO()
with stopwatch('Taking photo'):
camera.capture(stream,
format=image_format,
use_video_port=True)
stream.seek(0)
image = Image.open(stream)
photo_taken = True
num_faces = num_faces + 1
faces_in_region.append(face)
# creates a tuple ( image of the face, entering/exiting status)
tmp_arr.append([
crop_face(image, image_format, image_folder,
face.bounding_box),
get_status(face.bounding_box, r_center, enter_side)
])
if use_annotator:
annotator.bounding_box(
transform(face.bounding_box, scale_x, scale_y),
fill=0) # draw a box around the face
if server:
server.send_overlay(
svg_overlay(faces_in_region, frame_size, region,
joy_score))
if use_annotator:
annotator.update()
if num_faces < num_previous_faces:
# loop through previous faces: send face data, image and status
print(" A face left the region: send previous face data")
#if not use_annotator:
#take_photo()
faces_to_use = previous_faces
if previous_faces2:
faces_to_use = previous_faces2
if previous_faces3:
faces_to_use = previous_faces3
for face in faces_to_use:
print(classification_path, face, access_token)
if access_token is not None:
print("sent face with access token")
send_face(classification_path, face, access_token, dev)
previous_faces3 = previous_faces2
previous_faces2 = previous_faces
previous_faces = tmp_arr
if done.is_set():
break
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.')
parser.add_argument('--sparse',
'-s',
action='store_true',
default=False,
help='Use sparse tensors.')
parser.add_argument('--threshold',
'-t',
type=float,
default=0.3,
help='Detection probability threshold.')
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'
)
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=(args.cam_width, args.cam_height),
framerate=args.fps) as camera:
camera.start_preview()
# Get the camera width and height
width = args.cam_width
height = args.cam_height
# Get the region center point and two corners
r_center = (args.region[0] + args.region[2] / 2,
args.region[1] + args.region[3] / 2)
r_corners = (args.region[0], args.region[0] + args.region[2],
args.region[1], args.region[1] + args.region[3])
# Annotator renders in software so use a smaller size and scale results
# for increased performace.
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / width
scale_y = 240 / height
# 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))
# Both incoming boxes and the user defined region are of the form (x, y, width, height).
# Determines whether the face is entering or exiting the region.
def get_status(bounding_box):
face_center = (bounding_box[0] + bounding_box[2] / 2,
bounding_box[1] + bounding_box[3] / 2)
if face_center[0] > r_center[0]:
if args.enter_side == 0:
return True
else:
return False
else:
if args.enter_side == 0:
return False
else:
return True
previous_faces = []
num_previous_faces = 0
num_faces = 0
faces = []
stream = io.BytesIO()
with CameraInference(face_detection.model()) as inference:
for result in inference.run(args.num_frames):
# get the frame as a picture
num_previous_faces = num_faces
faces = face_detection.get_faces(result)
annotator.clear()
annotator.bounding_box(transform(args.region), fill=0)
num_faces = 0
tmp_arr = []
for face in faces:
face_center = (face.bounding_box[0] +
face.bounding_box[2] / 2,
face.bounding_box[1] +
face.bounding_box[3] / 2)
# check if the center of the face is in our region of interest:
annotator.bounding_box(
transform(face.bounding_box),
fill=0) # draw a box around the face
annotator.update()
camera.stop_preview()
def main():
env = Env()
env.read_env()
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:
camera.start_preview()
# 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))
joy_counter = 0
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)
annotator.update()
if len(faces) > 0:
if avg_joy_score(faces) > 0.8:
if joy_counter < 0:
joy_counter = 0
else:
joy_counter += 1
if avg_joy_score(faces) < 0.1:
if joy_counter > 0:
joy_counter = 0
else:
joy_counter -= 1
if joy_counter > 20:
print("Happy")
joy_counter = 0
if joy_counter < -20:
print("Sad")
request_url = urllib.request.urlopen("https://www.reddit.com/r/dogpictures/random.json")
result = json.loads(request_url.read().decode())[0]["data"]["children"][0]["data"]["url"]
message = Mail(
from_email='*****@*****.**',
to_emails='*****@*****.**',
subject='Sending with Twilio SendGrid is Fun',
html_content='<img src='+result+'>')
try:
sg = SendGridAPIClient(env.str('SENDGRID_API_KEY'))
response = sg.send(message)
print(response.status_code)
# print(response.body)
# print(response.headers)
except Exception as e:
print(e.message)
joy_counter = 0
else:
joy_counter = 0
# print('#%05d (%5.2f fps): num_faces=%d, avg_joy_score=%.2f' %
# (inference.count, inference.rate, len(faces), avg_joy_score(faces)))
camera.stop_preview()
