def CreateDB(self, Folder):
Files = self.MatchedFiles[Folder]
db_path = os.path.sep.join(Folder.split(os.path.sep)[:-1] + [""])
db_name = self.name_from_path(Folder)
db = DataFileExtended(db_path + db_name + ".cdb", "w")
path = db.setPath(Folder)
idx_dict = {}
for file in Files:
# self.Bar2.setValue(self.Bar2.value()+1)
self.Bar2.increase()
# QtGui.QApplication.processEvents()
QtGui.QGuiApplication.processEvents()
layer = self.layer_dict[[
k for k in self.layer_dict if file.count(k)
][0]]
time = datetime.datetime.strptime(
file.split("_")[0], "%Y%m%d-%H%M%S")
idx = int([k[3:] for k in file.split("_") if k.count("rep")][0])
if len(idx_dict) < 1:
idx_dict.update({idx: 0})
elif idx not in idx_dict:
idx_dict.update({idx: max(idx_dict.values()) + 1})
image = db.setImage(filename=file,
path=path,
layer=layer,
timestamp=time) # , frame=int(idx))
image.sort_index = idx #idx_dict[idx]
image.save()
def CreateDB(path, files, progress_bar = None):
Folder = os.path.normpath(path)
Files = [os.path.normpath(f) for f in files]
db_path = os.path.sep.join(Folder.split(os.path.sep)[:-1]+[""])
db_name = name_from_path(Folder)
# db_path = "/home/alex/2017-03-10_Tzellen_microwells_bestdata/T-CellMotility/2017-10-17/1_2Gel/24hnachMACS/24himGel/Kontrolle/RB/"
db = DataFileExtended(db_path+db_name+".cdb", "w")
path = db.setPath(Folder)
idx_dict = {}
for file in Files:
if progress_bar is not None:
progress_bar.increase()
layer = LAYER_DICT[[k for k in LAYER_DICT if file.count(k)][0]]
time = datetime.datetime.strptime(file.split("_")[0], "%Y%m%d-%H%M%S")
idx = int([k[3:] for k in file.split("_") if k.count("rep")][0])
if len(idx_dict)<1:
idx_dict.update({idx: 0})
elif idx not in idx_dict:
idx_dict.update({idx: max(idx_dict.values())+1})
image = db.setImage(filename=file, path=path, layer=layer, timestamp=time) # , frame=int(idx))
image.sort_index = idx#idx_dict[idx]
image.save()
return db_path+db_name+".cdb"
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)
MultiKal.predict(i=i)
image = db.setImage("", db_path, timestamp=i)
# Detection step
SegMap = TS.detect(image.data)
X, Y = np.random.rand(0, 1, (2, N))
X *= arena_w
Y *= arena_h
print("Found %s Objects!" % len(Positions))
# Write Segmentation Mask to Database
db.setMask(image=image, data=(~SegMap).astype(np.uint8))
print("Mask save")
if len(Positions) > 0:
# Update Filter with new Detections
MultiKal.update(z=Positions, i=i)