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()
class OverlayManager:
"""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()
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 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