class Evaluator(object):
def __init__(self):
self.pos_System_Markers = {}
self.neg_System_Markers = {}
self.GT_Markers = {}
self.gt_db = None
self.system_db = None
def load_GT_marker_from_clickpoints(self, path, type):
self.gt_db = DataFileExtended(path)
markers = np.asarray([[m.image.sort_index, m.x, m.y] for m in self.gt_db.getMarkers(type=type)])
self.GT_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def load_pos_System_marker_from_clickpoints(self, path, type):
self.system_db = DataFileExtended(path)
markers = np.asarray([[self.gt_db.getImage(filename=m.image.filename).sort_index, m.x, m.y] for m in self.system_db.getMarkers(type=type)
if self.gt_db.getImage(filename=m.image.filename) is not None])
self.pos_System_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def load_neg_System_marker_from_clickpoints(self, path, type):
self.system_db = DataFileExtended(path)
markers = np.asarray([[self.gt_db.getImage(filename=m.image.filename).sort_index, m.x, m.y] for m in self.system_db.getMarkers(type=type)
if self.gt_db.getImage(filename=m.image.filename) is not None])
self.neg_System_Markers.update(dict([[t, markers[markers.T[0]==t].T[1:]] for t in set(markers.T[0])]))
def save_marker_to_GT_db(self, markers, path, type):
if function is None:
function = lambda x : x
self.gt_db = DataFileExtended(path)
with self.gt_db.db.atomic() as transaction:
self.gt_db.deleteMarkers(type=type)
for t in markers:
self.gt_db.setMarkers(image=self.gt_db.getImage(frame=t),x=markers[t].T[0],y=markers[t].T[1], type=type)
def save_marker_to_System_db(self, markers, path, type):
if function is None:
function = lambda x : x
self.system_db = DataFileExtended(path)
with self.system_db.db.atomic() as transaction:
self.system_db.deleteMarkers(type=type)
for t in markers:
self.system_db.setMarkers(image=self.system_db.getImage(frame=t),x=markers[t].T[0],y=markers[t].T[1], type=type)
model,
np.diag(Q),
np.diag(R),
meas_dist=Meas_Dist,
state_dist=State_Dist)
MultiKal.LogProbabilityThreshold = log_prob_threshold
# Extended Clickpoints Database for usage with pengutack
from PenguTrack.DataFileExtended import DataFileExtended
# Open ClickPoints Database
db = DataFileExtended("./ExampleData/sim_data.cdb", "w")
# Define ClickPoints Marker
detection_marker_type = db.setMarkerType(name="Detection_Marker",
color="#FF0000",
style='{"scale":1.2}')
db.deleteMarkers(type=detection_marker_type)
track_marker_type = db.setMarkerType(name="Track_Marker",
color="#00FF00",
mode=db.TYPE_Track)
db.deleteMarkers(type=track_marker_type)
prediction_marker_type = db.setMarkerType(name="Prediction_Marker",
color="#0000FF")
db.deleteMarkers(type=prediction_marker_type)
# Delete Old Tracks
db.deleteTracks(type=track_marker_type)
# Start Iteration over Images
db_path = db.setPath("")
print('Starting Iteration')
for i in range(T):
# Prediction step, without applied control(vector of zeros)
PT_Detection_Type = db.setMarkerType(name="PT_Detection_Marker", color="#FFFF00", style='{"scale":0.8}')
if db.getMarkerType(name="PT_Track_Marker"):
PT_Track_Type = db.getMarkerType(name="PT_Track_Marker")
else:
PT_Track_Type = db.setMarkerType(name="PT_Track_Marker", color="#00FF00", mode=db.TYPE_Track)
if db.getMarkerType(name="PT_Prediction_Marker"):
PT_Prediction_Type = db.getMarkerType(name= "PT_Prediction_Marker")
else:
PT_Prediction_Type = db.setMarkerType(name="PT_Prediction_Marker", color="#0000FF")
if db.getMarkerType(name="PT_Stitch_Marker"):
PT_Stitch_Type = db.getMarkerType(name="PT_Stitch_Marker")
else:
PT_Stitch_Type = db.setMarkerType(name="PT_Stitch_Marker", color="#FF8800", mode=db.TYPE_Track)
# Delete Old Tracks
db.deleteMarkers(type=PT_Detection_Type)
db.deleteMarkers(type=PT_Track_Type)
db.deleteMarkers(type=PT_Prediction_Type)
db.deleteMarkers(type=PT_Stitch_Type)
db.deleteTracks(type=PT_Track_Type)
db.deleteTracks(type=PT_Stitch_Type)
# Start Iteration over Images
print('Starting Iteration')
from multiprocessing import Process,Queue,Pipe
# segmentation_queue = Queue(10)
Image_write_queue = Queue()
SegMap_write_queue = Queue()
marker_type = db.setMarkerType(name="PT_Detection_Marker", color="#FFFF00", style='{"scale":0.8}')
if db.getMarkerType(name="PT_Track_Marker"):
marker_type2 = db.getMarkerType(name="PT_Track_Marker")
else:
marker_type2 = db.setMarkerType(name="PT_Track_Marker", color="#00FF00", mode=db.TYPE_Track)
if db.getMarkerType(name="PT_Prediction_Marker"):
marker_type3 = db.getMarkerType(name= "PT_Prediction_Marker")
else:
marker_type3 = db.setMarkerType(name="PT_Prediction_Marker", color="#0000FF")
if db.getMarkerType(name="PT_Stitch_Marker"):
marker_type4 = db.getMarkerType(name="PT_Stitch_Marker")
else:
marker_type4 = db.setMarkerType(name="PT_Stitch_Marker", color="#FF8800", mode=db.TYPE_Track)
# Delete Old Tracks
db.deleteMarkers(type=marker_type)
db.deleteMarkers(type=marker_type2)
db.deleteMarkers(type=marker_type3)
db.deleteMarkers(type=marker_type4)
db.deleteTracks(type=marker_type2)
db.deleteTracks(type=marker_type4)
# Start Iteration over Images
print('Starting Iteration')
images = db.getImageIterator(start_frame=20)#start_frame=start_frame, end_frame=3)
# images = db.getImageIterator(start_frame=10272, end_frame=10311)#start_frame=start_frame, end_frame=3)
start = time()