def __init__(self, nargs):
self.args = []
self.complete = np.ones(nargs, dtype=np.bool)
self.complete[:] = False
self.source = ports.EventSource()
for i in range(nargs):
self.args.append(None)
def __init__(self,
dist,
min_neighborhood=1,
thresh_intensity=50,
scale_intensity=1,
debug=False):
self.algo = DBSCAN(eps=dist, min_samples=min_neighborhood, n_jobs=-1)
self.sink_image = ports.StateSink()
self.thresh_intensity = thresh_intensity
self.scale_intensity = scale_intensity
self.debug = debug
self.out_debug_image = ports.EventSource()
self.data = None
self.debug_buf = None
def __init__(self,
detection_threshold=0.05,
tracking_threshold=10,
use_opencv=False):
self.detector = dlib.get_frontal_face_detector()
self.tracker = dlib.correlation_tracker()
model_dir = path.join(path.dirname(path.abspath(__file__)), 'models')
self.predictor = dlib.shape_predictor(
path.join(model_dir, 'shape_predictor_68_face_landmarks.dat'))
self.face_cascade = cv2.CascadeClassifier(
path.join(model_dir, 'haarcascade_frontalface_alt2.xml'))
self.good = False
self.dt = detection_threshold
self.tt = tracking_threshold
self.u_cv2 = use_opencv
self.roi_size = (800, 600)
self.roi_buffer = np.zeros((self.roi_size[0], self.roi_size[1], 3),
np.uint8)
self.dlib_roi = dlib.rectangle(0, 0, self.roi_size[1],
self.roi_size[0])
self.source_bounds = ports.EventSource()
self.source_landmarks = ports.EventSource()
self.sink_image = ports.EventSink(self.track_face)
def __init__(self, thresh_intensity=50, debug=False):
self.sink_image = ports.StateSink()
self.thresh_intensity = thresh_intensity
self.debug = debug
self.out_debug_image = ports.EventSource()
def __init__(self):
BaseVideoSource.__init__(self)
self.sink_filename = ports.StateSink()
self.source_eof = ports.EventSource()
self.source_file_change = ports.EventSource()
self.cap = None
from __future__ import print_function
from dataflow import ports
def add(x): # Returns a callable object
return lambda y: x + y
# Event example code
e_source = ports.EventSource()
e_terminal = ports.EventTerminal()
# 1 shall be added to the value fired from the source, then forwarded to the terminal
e_source >> add(1) >> e_terminal
# 4 shall be added to the value from the terminal, then printed
e_terminal >> add(4) >> print
# the value from the terminal shall be printed
e_terminal >> print
e_source.fire(5) # fires 5, thereby printing 10 and 6.
# State example code
def s_source(): # Callable object
return 5
s_sink_1 = ports.StateSink()
s_sink_2 = ports.StateSink()
def __init__(self):
self.img = None
self.source_mask = ports.EventSource()
def __init__(self, model):
self.detector = cv2.CascadeClassifier(model)
self.source_detection = ports.EventSource()
def __init__(self, models):
self.detectors = []
for model in models:
self.detectors.append(cv2.CascadeClassifier(model))
self.source_detection = ports.EventSource()
def __init__(self, n, length):
self.arr = NpCircularArray(n, length)
self.source_buf = ports.EventSource()
def __init__(self):
self.sink_control = ports.StateSink()
self.source = ports.EventSource()
def __init__(self):
self.buf = None
self.source = ports.EventSource()
def __init__(self):
self.c = 0
self.out_event = ports.EventSource()
def __init__(self):
self.sink_control = ports.StateSink()
self.sink = ports.StateSink()
def __call__(self, *args):
if self.sink_control.get():
return self.sink.get(*args)
else:
return None
class EventGate:
def __init__(self):
self.sink_control = ports.StateSink()
self.source = ports.EventSource()
def sink(self, *args):
if self.sink_control.get():
self.source.fire(*args)
if __name__ == '__main__':
es_buf = EventToState()
so = ports.EventSource()
si = ports.StateSink()
so >> es_buf.in_event
si << es_buf.out_state
so.fire(5)
print(si.get())