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:
print("Running face detection...")
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.text((5, 5), "Welcome to the family room!")
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=10) 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))
def textXYTransform(bounding_box):
x, y, width, height = bounding_box
return (scale_x * x, scale_y * y)
with CameraInference(object_detection.model()) as inference:
for result in inference.run():
objs = object_detection.get_objects(result, 0.3);
annotator.clear()
for obj in objs:
# blue for person, green for cat, purple for dog, red for anything else
outlineColor = "blue" if obj.kind == 1 else "green" if obj.kind == 2 else "purple" if obj.kind == 3 else "red"
print(obj.kind)
annotator.bounding_box(transform(obj.bounding_box), fill=0 , outline=outlineColor)
annotator.text(textXYTransform(obj.bounding_box), "person" if obj.kind == 1 else "cat" if obj.kind == 2 else "dog" if obj.kind == 3 else "other", color=outlineColor)
annotator.update()
camera.stop_preview()
def main():
#### Setup stepper motor ####
# create a default object, no changes to I2C address or frequency
mh = Adafruit_MotorHAT(addr=0x60)
# recommended for auto-disabling motors on shutdown!
def turnOffMotors():
mh.getMotor(1).run(Adafruit_MotorHAT.RELEASE)
mh.getMotor(2).run(Adafruit_MotorHAT.RELEASE)
mh.getMotor(3).run(Adafruit_MotorHAT.RELEASE)
mh.getMotor(4).run(Adafruit_MotorHAT.RELEASE)
atexit.register(turnOffMotors)
mh = Adafruit_MotorHAT(addr=0x60)
myStepper = mh.getStepper(200, 1) # 200 steps/rev, motor port #1
myStepper.setSpeed(30) # 30 RPM
#### setup camera ####
"""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
# Tempolary disable camera preview so that I can see the log on Terminal
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)
# Print the (x, y) location of face
print('X = %d, Y = %d' %
(face.bounding_box[0], face.bounding_box[1]))
# Move stepper motor
if face.bounding_box[0] > 1640 / 2 and abs(
face.bounding_box[0] - 1640 / 2) > 200:
print("Double coil step - right")
myStepper.step(10, Adafruit_MotorHAT.FORWARD,
Adafruit_MotorHAT.DOUBLE)
elif face.bounding_box[0] < 1640 / 2 and abs(
face.bounding_box[0] - 1640 / 2) > 300:
print("Double coil step - left")
myStepper.step(10, Adafruit_MotorHAT.BACKWARD,
Adafruit_MotorHAT.DOUBLE)
annotator.update()
# print('Iteration #%d: num_faces=%d' % (i, len(faces)))
camera.stop_preview()
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()
ser = serial.Serial('/dev/ttyACM0', 115200, write_timeout=0.03)
ser.write(b"<0.0,0.0,0.0,0.0>")
with PiCamera() as camera:
camera.sensor_mode = 4
camera.resolution = (1640, 1232)
camera.framerate = 30
camera.start_preview()
global shutdown_bool
shutdown_bool = False
position = 0
NoFaceCount = 0
NoFaceReset = 60
NoFaceShut = 120
Top_Lid_Offset = 25 # Top Lid = L2
Bottom_Lid_Offset = 25 # Bottom Lid = L1
Top_Lid_Limit = 45
Bottom_Lid_Limit = 45
step_size = 2.0
X_Degrees = 0
Y_Degrees = 0
L1_Degrees = 0
L2_Degrees = 0
# Trying to slow down serial writing
Write_Delay = 5 # Milliseconds
Last_Write_Time = time.time() * 1000 # Milliseconds
# Soft shutdown function
def soft_shutdown():
# X_Degrees = 0
# Y_Degrees = 0
# L1_Degrees = -60
# L2_Degrees = -60
# ser.write(b"<%d,%d,%d,%d>" % (X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
# camera.stop_preview()
# quit()
# thread.interrupt_main()
print("Shutdown Initiated")
global shutdown_bool
shutdown_bool = True
# Add shutdown function to button
button = Button(23)
button.when_pressed = soft_shutdown #pretty sure this doesn't work
annotator = Annotator(camera, dimensions=(320, 240))
scale_x = 320 / 1640
scale_y = 240 / 1232
# Transform is a function to transform the face bounding box. Incoming boxes are of the form (x, y, width, height). Scale and
# transform to the form (x1, y1, x2, y2). X and Y are at the top left corner of the face bounding box.
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 x_y_to_angles(x, y):
Span_X = 1640 # This is hard-coded resolution
Span_Y = 1232
MinDeg_X = 23 # These are hard coded servo angles, will need to be adjusted to match camera FOV
MaxDeg_X = -23
MinDeg_Y = -18
MaxDeg_Y = 19
SpanDeg_X = MaxDeg_X - MinDeg_X
X_Degrees = MinDeg_X + (SpanDeg_X * (x / Span_X))
SpanDeg_Y = MaxDeg_Y - MinDeg_Y
Y_Degrees = MinDeg_Y + (SpanDeg_Y * (y / Span_Y))
return X_Degrees, Y_Degrees
with CameraInference(face_detection.model()) as inference:
for i, result in enumerate(inference.run()):
if i == args.num_frames:
break
if shutdown_bool is True:
X_Degrees = 0
Y_Degrees = 0
L1_Degrees = 0
L2_Degrees = 0
ser.write(b"<%d,%d,%d,%d>" %
(X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
break
faces = face_detection.get_faces(result)
annotator.clear()
for face in faces:
annotator.bounding_box(
transform(face.bounding_box),
fill=0) # adding bounding box to preview
annotator.update()
print('Iteration #%d: num_faces=%d' % (i, len(faces)))
if faces: # Give servo commands if a face is detected
face = faces[0]
x_corner, y_corner, width, height = face.bounding_box # bounding box is top left corner
x = x_corner + width / 2
y = y_corner + height / 2
print(' : Face is at X = %d' % x)
print(' : Face is at Y = %d' % y)
Current_Time = time.time() * 1000
if Current_Time >= (Last_Write_Time + Write_Delay):
X_Degrees, Y_Degrees = x_y_to_angles(x, y)
L1_Degrees = min(Y_Degrees + Bottom_Lid_Offset,
Bottom_Lid_Limit)
L2_Degrees = min(-Y_Degrees + Top_Lid_Offset,
Top_Lid_Limit)
ser.cancel_write() # Cancels previous write
ser.reset_output_buffer(
) # Removes any data that hasn't been sent yet
ser.write(
b"<%d,%d,%d,%d>" %
(X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
NoFaceCount = 0
# print(' : X Servo Angle =%d' % X_Degrees)
# print(' : Y Servo Angle =%d' % Y_Degrees)
print(b"<%d,%d,%d,%d>" %
(X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
Last_Write_Time = time.time() * 1000
else:
print("Waiting for Write Delay")
else:
print('NoFaceCount = %d' % NoFaceCount)
if NoFaceReset <= NoFaceCount < NoFaceShut:
X_Degrees = 0
Y_Degrees = 0
L1_Degrees = 12
L2_Degrees = 12
ser.write(
b"<%d,%d,%d,%d>" %
(X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
NoFaceCount = NoFaceCount + 1
if NoFaceCount >= NoFaceShut:
L1_Degrees = 0
L2_Degrees = 0
ser.write(
b"<%d,%d,%d,%d>" %
(X_Degrees, Y_Degrees, L1_Degrees, L2_Degrees))
# NoFaceCount = NoFaceCount + 1
else:
NoFaceCount = NoFaceCount + 1
camera.stop_preview()
def main():
"""object detection camera inference example."""
parser = argparse.ArgumentParser()
countdown = 20
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()
servoX = AngularServo(PIN_B)
servoY = AngularServo(PIN_A)
relay = DigitalOutputDevice(PIN_C, active_high=True, initial_value=True)
#relay.blink(n=1)
relay.blink(on_time=0.05, n=1)
# 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=10) 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))
def textXYTransform(bounding_box):
x, y, width, height = bounding_box
return (scale_x * x, scale_y * y)
with CameraInference(object_detection.model()) as inference:
for result in inference.run():
objs = object_detection.get_objects(result, 0.3)
annotator.clear()
for obj in objs:
# blue for person, green for cat, purple for dog, red for anything else
outlineColor = "blue" if obj.kind == 1 else "green" if obj.kind == 2 else "purple" if obj.kind == 3 else "red"
print(obj.kind)
tBoundingBox = transform(obj.bounding_box)
annotator.bounding_box(tBoundingBox,
fill=0,
outline=outlineColor)
annotator.text(
textXYTransform(obj.bounding_box),
"person" if obj.kind == 1 else "cat" if obj.kind == 2
else "dog" if obj.kind == 3 else "other",
color=outlineColor)
if len(objs) == 1:
x1, y1, x2, y2 = transform(obj.bounding_box)
midX = ((x2 - x1) / 2) + x1
midY = ((y2 - y1) / 2) + y1
servoPosX = remap(midX, 0, 320, 75, -75)
servoPosY = remap(midY, 0, 240, -90,
80) # 90 is low, -90 is high
servoPosX = min(90, servoPosX)
servoPosX = max(-90, servoPosX)
servoPosY = min(90, servoPosY)
servoPosY = max(-90, servoPosY)
print("x", midX, servoPosX)
print("y", midY, servoPosY)
servoX.angle = servoPosX
servoY.angle = servoPosY
countdown -= 1
if countdown == -1:
# squirt
annotator.text((midX, midY),
"Squirt!!",
color=outlineColor)
relay.blink(on_time=0.5, n=1)
countdown = 20
else:
annotator.text((midX, midY),
str(countdown),
color=outlineColor)
if len(objs) == 0:
countdown = 20
annotator.update()
camera.stop_preview()
def main():
"""Image classification 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.')
parser.add_argument(
'--num_objects',
'-c',
type=int,
dest='num_objects',
default=1,
help='Sets the number of object interences to print.')
args = parser.parse_args()
def print_classes(classes, object_count):
s = ''
for index, (obj, prob) in enumerate(classes):
if index > object_count - 1:
break
s += '%s=%1.2f\t|\t' % (obj, prob)
print('%s\r' % s)
def print_to_screen(classes, object_count,annotator):
annotator.clear()
s = ''
for index, (obj, prob) in enumerate(classes):
if index > object_count - 1:
break
#s += '%s=%1.2f\t|\t' % (obj, prob)
elif prob >= 0.9:
s = "I see a "+ str(obj)
annotator.text((5,5), s)
elif prob >= 0.5:
s = "I think I see a "+ str(obj)
annotator.text((5,5), s)
annotator.update()
return
print("Running Image Classification Camera...")
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 = Annotator(camera, dimensions=(320, 240))
with CameraInference(image_classification.model()) as inference:
for i, result in enumerate(inference.run()):
if i == args.num_frames:
break
classes = image_classification.get_classes(result)
#print_classes(classes, args.num_objects)
print_to_screen(classes, args.num_objects,annotator)
camera.stop_preview()
def run(self, num_frames, preview_alpha, image_format, image_folder):
logger.info('Starting...')
leds = Leds()
player = Player(gpio=22, bpm=10)
photographer = Photographer(image_format, image_folder)
animator = Animator(leds, self._done)
try:
# 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)) as camera, PrivacyLed(leds):
def take_photo():
logger.info('Button pressed.')
player.play(BEEP_SOUND)
photographer.shoot(camera)
def sound_alarm():
logger.info('Button pressed.')
player.play(SIREN_SOUND)
animator.danger()
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))
# Blend the preview layer with the alpha value from the flags.
#camera.start_preview(alpha=preview_alpha)
camera.start_preview()
annotator = Annotator(camera, dimensions=(320, 240))
button = Button(23)
button.when_pressed = sound_alarm
joy_score_moving_average = MovingAverage(10)
prev_joy_score = 0.0
with CameraInference(face_detection.model()) as inference:
logger.info('Model loaded.')
#player.play(MODEL_LOAD_SOUND)
for i, result in enumerate(inference.run()):
annotator.clear()
faces = face_detection.get_faces(result)
joy_score = joy_score_moving_average.next(average_joy_score(faces))
animator.update_joy_score(joy_score)
photographer.update_faces(faces)
for face in faces:
annotator.bounding_box(transform(face.bounding_box), fill=0)
if joy_score >= JOY_SCORE_PEAK:
annotator.text((5,5), "I detect a happy human")
elif joy_score > 0 and joy_score <= JOY_SCORE_MIN:
annotator.text((5,5), "I detect a sad human")
elif joy_score > 0:
annotator.text((5,5), "I detect a human")
#if joy_score > JOY_SCORE_PEAK > prev_joy_score:
# player.play(JOY_SOUND)
#elif joy_score < JOY_SCORE_MIN < prev_joy_score:
# player.play(SAD_SOUND)
prev_joy_score = joy_score
annotator.update()
if self._done.is_set() or i == num_frames:
break
finally:
player.stop()
photographer.stop()
player.join()
photographer.join()
animator.join()
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)
# print('size (width by height): %d by %d' % (face.bounding_box[2], face.bounding_box[3]))
closest_face_size = 0
closest_face = None
for face in faces:
face_size = face.bounding_box[2] * face.bounding_box[
3] # width * height
if face_size > closest_face_size:
closest_face_size = face_size
closest_face = face
if closest_face != None:
annotator.bounding_box(transform(
closest_face.bounding_box),
fill=0)
annotator.update()
print('Iteration #%d: num_faces=%d' % (i, len(faces)))
if closest_face != None:
closest_face_dims_transformed = transform(
closest_face.bounding_box)
closest_face_center = (
(closest_face_dims_transformed[0] +
closest_face_dims_transformed[2]) / 2,
(closest_face_dims_transformed[1] +
closest_face_dims_transformed[3]) / 2
) # (x + width) / 2
# this currently will only find the vertical offset after handling the horizontal
# offset. TODO: implement diagonal directions
# find direction from the center with buffer of 1/4 the dimension of the camera view
# print('center is %d x %d' % closest_face_center)
# print('compare: %d vs %d' % (closest_face_center[0], annotator._dims[0] / 2 + annotator._dims[0] * 0.05))
if closest_face_center[0] > annotator._dims[
0] / 2 + annotator._dims[0] * 0.10:
print('Move camera right')
elif closest_face_center[0] < annotator._dims[
0] / 2 - annotator._dims[0] * 0.10:
print('Move camera left')
elif closest_face_center[1] > annotator._dims[
1] / 2 + annotator._dims[1] * 0.10:
print('Move camera down')
elif closest_face_center[1] < annotator._dims[
1] / 2 - annotator._dims[1] * 0.10:
print('Move camera right')
else:
print('Don\'t move camera')
closest_face = None # reset
camera.stop_preview()
def __init__(self, camera):
self._clear_needed = False
self._annotator = Annotator(camera,
default_color=(0xFF, 0xFF, 0xFF, 0xFF),
dimensions=(320, 240))
class OverlayManager(object):
"""Overlay utility for managing state and drawing of overlay."""
LINE_HEIGHT = 12
ROW_HEIGHT = 50
def __init__(self, camera):
self._clear_needed = False
self._annotator = Annotator(camera,
default_color=(0xFF, 0xFF, 0xFF, 0xFF),
dimensions=(320, 240))
def _draw_annotation(self, result, category, index):
self._annotator.text(
(5, index * self.ROW_HEIGHT + 5 + 0 * self.LINE_HEIGHT),
'{:.2%}'.format(result[1]))
self._annotator.text(
(5, index * self.ROW_HEIGHT + 5 + 1 * self.LINE_HEIGHT),
'{:25.25}'.format(result[0]))
self._annotator.text(
(5, index * self.ROW_HEIGHT + 5 + 2 * self.LINE_HEIGHT),
'category: {:20.20}'.format(category))
def clear(self):
if self._clear_needed:
self._annotator.stop()
self._clear_needed = False
def update(self, classes, categories):
self._annotator.clear()
self._clear_needed = True
for i, result in enumerate(classes):
self._draw_annotation(result, categories[i], i)
self._annotator.update()