def main():
parser = argparse.ArgumentParser(
description='This script detects motion using an '
'attached webcam. When it detects '
'motion, it captures photos on an '
'attached gPhoto2-compatible photo '
'camera.')
parser.add_argument('--debug',
type=bool,
default=False,
help='print debugging information')
parser.add_argument('--cam-index',
type=int,
default=-1,
help='device index for detection camera '
'(default=0)')
parser.add_argument('--width',
type=int,
default=320,
help='capture width for detection camera '
'(default=320)')
parser.add_argument('--height',
type=int,
default=240,
help='capture height for detection camera '
'(default=240)')
parser.add_argument('--detection-interval',
type=float,
default=0.25,
help='interval between detection frames, in seconds '
'(default=0.25)')
parser.add_argument('--learning-rate',
type=float,
default=0.008,
help='learning rate for background subtractor, which '
'is used in motion detection (default=0.008)')
parser.add_argument('--min-motion',
type=float,
default=0.15,
help='proportion of frame that must be classified as '
'foreground to trigger motion event '
'(default=0.15, valid_range=[0.0, 1.0])')
parser.add_argument('--photo-count',
type=int,
default=1,
help='number of photo frames per motion event '
'(default=1)')
parser.add_argument('--photo-interval',
type=float,
default=3.0,
help='interval between photo frames, in seconds '
'(default=3.0)')
parser.add_argument('--photo-ev-step',
type=float,
default=None,
help='exposure step between photo frames, in EV. If '
'this is specified, --photo-interval is ignored '
'and --photo-count refers to the length of an '
'exposure bracketing sequence, not a time-lapse '
'sequence.')
args = parser.parse_args()
debug = args.debug
cam_index = args.cam_index
w, h = args.width, args.height
detection_interval = args.detection_interval
learning_rate = args.learning_rate
min_motion = args.min_motion
photo_count = args.photo_count
photo_interval = args.photo_interval
photo_ev_step = args.photo_ev_step
cap = cv2.VideoCapture(cam_index)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
bgr = None
gray = None
fg_mask = None
bg_sub = cv2.createBackgroundSubtractorMOG2()
cc = CameraCommander.CameraCommander()
while True:
time.sleep(detection_interval)
success, bgr = cap.read(bgr)
if success:
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY, gray)
gray = cv2.equalizeHist(gray, gray)
fg_mask = bg_sub.apply(gray, fg_mask, learning_rate)
h, w = fg_mask.shape
motion = numpy.sum(numpy.where(fg_mask == 255, 1, 0))
motion /= float(h * w)
if debug:
print('motion=%f' % motion)
if motion >= min_motion and not cc.capturing:
if photo_ev_step is not None:
cc.capture_exposure_bracket(photo_ev_step, photo_count)
else:
cc.capture_time_lapse(photo_interval, photo_count)
def main():
parser = argparse.ArgumentParser(
description='This script detects motion using an '
'attached webcam. When it detects '
'motion, it captures photos on an '
'attached gPhoto2-compatible photo '
'camera.')
parser.add_argument(
'--debug', type=bool, default=False,
help='print debugging information')
parser.add_argument(
'--cam-index', type=int, default=-1,
help='device index for detection camera '
'(default=0)')
parser.add_argument(
'--width', type=int, default=320,
help='capture width for detection camera '
'(default=320)')
parser.add_argument(
'--height', type=int, default=240,
help='capture height for detection camera '
'(default=240)')
parser.add_argument(
'--detection-interval', type=float, default=0.25,
help='interval between detection frames, in seconds '
'(default=0.25)')
parser.add_argument(
'--learning-rate', type=float, default=0.008,
help='learning rate for background subtractor, which '
'is used in motion detection (default=0.008)')
parser.add_argument(
'--min-motion', type=float, default=0.15,
help='proportion of frame that must be classified as '
'foreground to trigger motion event '
'(default=0.15, valid_range=[0.0, 1.0])')
parser.add_argument(
'--photo-count', type=int, default=1,
help='number of photo frames per motion event '
'(default=1)')
parser.add_argument(
'--photo-interval', type=float, default=3.0,
help='interval between photo frames, in seconds '
'(default=3.0)')
parser.add_argument(
'--photo-ev-step', type=float, default=None,
help='exposure step between photo frames, in EV. If '
'this is specified, --photo-interval is ignored '
'and --photo-count refers to the length of an '
'exposure bracketing sequence, not a time-lapse '
'sequence.')
args = parser.parse_args()
debug = args.debug
cam_index = args.cam_index
w, h = args.width, args.height
detection_interval = args.detection_interval
learning_rate = args.learning_rate
min_motion = args.min_motion
photo_count = args.photo_count
photo_interval = args.photo_interval
photo_ev_step = args.photo_ev_step
cap = cv2.VideoCapture(cam_index)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
bgr = None
gray = None
fg_mask = None
bg_sub = cv2.createBackgroundSubtractorMOG2()
cc = CameraCommander.CameraCommander()
while True:
time.sleep(detection_interval)
success, bgr = cap.read(bgr)
if success:
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY, gray)
gray = cv2.equalizeHist(gray, gray)
fg_mask = bg_sub.apply(gray, fg_mask,
learning_rate)
h, w = fg_mask.shape
motion = numpy.sum(numpy.where(
fg_mask == 255, 1, 0))
motion /= float(h * w)
if debug:
print('motion=%f' % motion)
if motion >= min_motion and not cc.capturing:
if photo_ev_step is not None:
cc.capture_exposure_bracket(
photo_ev_step, photo_count)
else:
cc.capture_time_lapse(
photo_interval, photo_count)
def main():
parser = argparse.ArgumentParser(
description='This script detects objects using an '
'attached webcam. When it detects '
'objects that match a given cascade '
'file, it captures photos on an attached '
'gPhoto2-compatible photo camera.')
parser.add_argument('--debug',
type=bool,
default=False,
help='print debugging information')
parser.add_argument('--cam-index',
type=int,
default=-1,
help='device index for the detection camera '
'(default=0)')
parser.add_argument('--width',
type=int,
default=320,
help='capture width for the detection camera '
'(default=320)')
parser.add_argument('--height',
type=int,
default=240,
help='capture height for the detection camera '
'(default=240)')
parser.add_argument('--detection-interval',
type=float,
default=0.25,
help='interval between detection frames, in seconds '
'(default=0.25)')
parser.add_argument('--cascade-file',
type=str,
required=True,
help='path to cascade file that classifier will use '
'to detect objects in scene')
parser.add_argument('--scale-factor',
type=float,
default=1.05,
help='relative difference in scale between '
'iterations of multi-scale classification '
'(default=1.05)')
parser.add_argument('--min-neighbors',
type=int,
default=8,
help='minimum number of overlapping objects that '
'classifier must detect in order to trigger '
'classification event (default=8)')
parser.add_argument('--min-object-width',
type=int,
default=40,
help='minimum width of each detected object'
'(default=40)')
parser.add_argument('--min-object-height',
type=int,
default=40,
help='minimum height of each detected object'
'(default=40)')
parser.add_argument('--photo-count',
type=int,
default=1,
help='number of photo frames per motion event '
'(default=1)')
parser.add_argument('--photo-interval',
type=float,
default=3.0,
help='interval between photo frames, in seconds '
'(default=3.0)')
parser.add_argument('--photo-ev-step',
type=float,
default=None,
help='exposure step between photo frames, in EV. If '
'this is specified, --photo-interval is ignored '
'and --photo-count refers to the length of an '
'exposure bracketing sequence, not a time-lapse '
'sequence.')
args = parser.parse_args()
debug = args.debug
cam_index = args.cam_index
w, h = args.width, args.height
detection_interval = args.detection_interval
classifier = cv2.CascadeClassifier(args.cascade_file)
if classifier.empty():
print('Failed to read cascade file: %s' % args.cascade_file,
file=sys.stderr)
return
scale_factor = args.scale_factor
min_neighbors = args.min_neighbors
min_size = (args.min_object_width, args.min_object_height)
photo_count = args.photo_count
photo_interval = args.photo_interval
photo_ev_step = args.photo_ev_step
cap = cv2.VideoCapture(cam_index)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
bgr = None
gray = None
cc = CameraCommander.CameraCommander()
while True:
time.sleep(detection_interval)
success, bgr = cap.read(bgr)
if success:
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY, gray)
gray = cv2.equalizeHist(gray, gray)
objects = classifier.detectMultiScale(gray,
scaleFactor=scale_factor,
minNeighbors=min_neighbors,
minSize=min_size)
num_objects = len(objects)
if debug:
print('num_objects=%d' % num_objects)
if num_objects > 0 and not cc.capturing:
if photo_ev_step is not None:
cc.capture_exposure_bracket(photo_ev_step, photo_count)
else:
cc.capture_time_lapse(photo_interval, photo_count)
def main():
parser = argparse.ArgumentParser(
description='This script detects objects using an '
'attached webcam. When it detects '
'objects that match a given cascade '
'file, it captures photos on an attached '
'gPhoto2-compatible photo camera.')
parser.add_argument(
'--debug', type=bool, default=False,
help='print debugging information')
parser.add_argument(
'--cam-index', type=int, default=-1,
help='device index for the detection camera '
'(default=0)')
parser.add_argument(
'--width', type=int, default=320,
help='capture width for the detection camera '
'(default=320)')
parser.add_argument(
'--height', type=int, default=240,
help='capture height for the detection camera '
'(default=240)')
parser.add_argument(
'--detection-interval', type=float, default=0.25,
help='interval between detection frames, in seconds '
'(default=0.25)')
parser.add_argument(
'--cascade-file', type=str, required=True,
help='path to cascade file that classifier will use '
'to detect objects in scene')
parser.add_argument(
'--scale-factor', type=float, default=1.05,
help='relative difference in scale between '
'iterations of multi-scale classification '
'(default=1.05)')
parser.add_argument(
'--min-neighbors', type=int, default=8,
help='minimum number of overlapping objects that '
'classifier must detect in order to trigger '
'classification event (default=8)')
parser.add_argument(
'--min-object-width', type=int, default=40,
help='minimum width of each detected object'
'(default=40)')
parser.add_argument(
'--min-object-height', type=int, default=40,
help='minimum height of each detected object'
'(default=40)')
parser.add_argument(
'--photo-count', type=int, default=1,
help='number of photo frames per motion event '
'(default=1)')
parser.add_argument(
'--photo-interval', type=float, default=3.0,
help='interval between photo frames, in seconds '
'(default=3.0)')
parser.add_argument(
'--photo-ev-step', type=float, default=None,
help='exposure step between photo frames, in EV. If '
'this is specified, --photo-interval is ignored '
'and --photo-count refers to the length of an '
'exposure bracketing sequence, not a time-lapse '
'sequence.')
args = parser.parse_args()
debug = args.debug
cam_index = args.cam_index
w, h = args.width, args.height
detection_interval = args.detection_interval
classifier = cv2.CascadeClassifier(args.cascade_file)
if classifier.empty():
print('Failed to read cascade file: %s' %
args.cascade_file, file=sys.stderr)
return
scale_factor = args.scale_factor
min_neighbors = args.min_neighbors
min_size = (args.min_object_width, args.min_object_height)
photo_count = args.photo_count
photo_interval = args.photo_interval
photo_ev_step = args.photo_ev_step
cap = cv2.VideoCapture(cam_index)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
bgr = None
gray = None
cc = CameraCommander.CameraCommander()
while True:
time.sleep(detection_interval)
success, bgr = cap.read(bgr)
if success:
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY, gray)
gray = cv2.equalizeHist(gray, gray)
objects = classifier.detectMultiScale(
gray, scaleFactor=scale_factor,
minNeighbors=min_neighbors,
minSize=min_size)
num_objects = len(objects)
if debug:
print('num_objects=%d' % num_objects)
if num_objects > 0 and not cc.capturing:
if photo_ev_step is not None:
cc.capture_exposure_bracket(
photo_ev_step, photo_count)
else:
cc.capture_time_lapse(
photo_interval, photo_count)