def Track(self, Folder):
db_name = self.name_from_path(Folder)
db_path = os.path.sep.join(Folder.split(os.path.sep)[:-1] + [""])
if not os.path.exists(db_path + db_name + ".cdb"):
self.CreateDB(Folder, self.MatchedFiles[Folder])
db = DataFileExtended(db_path + db_name + ".cdb")
print([i.sort_index for i in db.getImageIterator()])
TCell_Analysis.Track(db, progress_bar=self.Bar3)
def AnalyzeDB(db_str):
db = DataFileExtended(db_str)
time_step = 110
v_fac = 0.645 / (time_step / 60.)
perc = 30
step = 20
type = 'PT_Track_Marker'
Frame_list = []
for f in db.getImageIterator():
Frame_list.append(f.sort_index)
Frames = np.max(Frame_list)
timer()
Tracks = TCell_Analysis.load_tracks(db_str, type)
timer("Loading Tracks")
Z_posis = TCell_Analysis.getZpos(Tracks, v_fac)
timer("Fetching Z")
Z_posis = np.asarray(Z_posis)
tracks_to_delete = TCell_Analysis.get_Tracks_to_delete(Z_posis, perc)
timer("Getting Tracks to delete")
###
list2 = TCell_Analysis.create_list2(db) # Create List for true dist
timer("Creating List2")
drift, drift_list, missing_frame = TCell_Analysis.Drift(
Frames, list2, 5) # Create List with offsets
timer("Getting Drift")
list2 = TCell_Analysis.list2_with_drift(
db, drift, tracks_to_delete,
del_track=True) # Create list for true dist with drift_cor
timer("List2 with drift")
list = TCell_Analysis.create_list(
Frames, db, drift=drift, Drift=True,
Missing=missing_frame) # Create List for analysis
timer("Create List")
### For Deleting Tracks above and below
list_copy = list[:]
for l, m in enumerate(list):
keys = m.keys()
for k, j in enumerate(keys):
if j in tracks_to_delete:
del list_copy[l][j]
timer("Stuff")
###
print("bla")
directions, velocities, dirt, alternative_vel, vel_mean, dir_mean, alt_vel_mean = TCell_Analysis.measure(
step, time_step, list, Frames) # Calculate directions and velocities
timer("Measure")
motile_percentage, mean_v, mean_dire, number, len_count, mo_p_al, me_v_al, me_d_al = TCell_Analysis.values(
directions,
velocities,
db,
dirt,
alternative_vel,
tracks_to_delete,
del_Tracks=True)
timer("Values")
motile_per_true_dist, real_dirt = TCell_Analysis.motiletruedist(list2)
timer("Motile True Dist")
if not os.path.exists(db_str[:-4] + "_analyzed.txt"):
with open(db_str[:-4] + "_analyzed.txt", "w") as f:
f.write(
'Day \t\t\tData \t\t\tMotile % true dist\t\t\tMotile in %\t\t\tMean velocity\t\t\tMean Directionality\t\t\tMean vel dt1\t\t\t#Tracks\t\t\t#Evaluated Tracks\t\t\t#Dirt\n'
)
Day = getDateFromPath(db_str).strftime("%Y-%M-%d")
if db_str.count('TCell') or db_str.count('T-Cell'):
TCell_Analysis.Colorplot(
directions,
velocities,
db_str,
path=os.path.sep.join(db_str.split(os.path.sep)[:-1]),
Save=True) # Save the velocity vs directionality picture
with open(db_str[:-4] + "_analyzed.txt", "ab") as f:
f.write(
'%s\t\t\t%34s\t\t\t%18f\t\t\t%11f\t\t\t%13f\t\t\t%19f\t\t\t%12f\t\t\t%7d\t\t\t%17d\t\t\t%5d\n'
% (Day, db_str, motile_per_true_dist, motile_percentage,
mean_v, mean_dire, me_v_al, number, len_count, real_dirt))
elif db_str.count('NKCell'):
TCell_Analysis.Colorplot(directions,
velocities,
db_str,
path=os.path.sep.join(
db_str.split(os.path.sep)[:-1]),
Save=True)
with open(db_str[:-4] + "_analyzed.txt", 'ab') as f:
f.write(
'%s\t\t\t%34s\t\t\t%18f\t\t\t%11f\t\t\t%13f\t\t\t%19f\t\t\t%12f\t\t\t%7d\t\t\t%17d\t\t\t%5d\n'
% (Day, db_str, motile_per_true_dist, motile_percentage,
mean_v, mean_dire, me_v_al, number, len_count, real_dirt))
db.db.close()
timer("Write")
import scipy.stats as ss
# Load Database
# file_path = "/home/birdflight/Desktop/PT_Test.cdb"
file_path = "/mnt/mmap/Starter_Full.cdb"
# file_path = "/mnt/mmap/PT_Test3.cdb"
# file_path = "/mnt/mmap/PT_Test4.cdb"
global db
db = DataFileExtended(file_path,"w")
db_start = DataFileExtended("/home/birdflight/Desktop/Starter.cdb")
# images = db_start.getImageIterator(start_frame=2490-30, end_frame=2600)
# images = db_start.getImageIterator(start_frame=1936-210, end_frame=2600)
images = db_start.getImageIterator(start_frame=700)
# images = db_start.getImageIterator(start_frame=1936-20-90, end_frame=2600)
# images = db_start.getImageIterator(start_frame=1500, end_frame=2600)
# images = db_start.getImageIterator()
def getImage():
im = images.next()
fname = im.filename
from datetime import datetime
d = datetime.strptime(fname[0:15], '%Y%m%d-%H%M%S')
time_unix = np.uint32(time.mktime(d.timetuple()))
time_ms = np.uint32(fname[16:17])*100
meta = {'time': time_unix,
'time_ms': time_ms,
'file_name': fname,
'path': im.path.path}
file_path = "/home/user/Desktop/PT_Test_full_n3_r7_A20.cdb"
c = 20
# file_path = "/mnt/mmap/PT_Test3.cdb"
# file_path = "/mnt/mmap/PT_Test4.cdb"
global db
db = DataFileExtended(file_path, "w")
db_start = DataFileExtended(input_file)
# images = db_start.getImageIterator(start_frame=2490-30, end_frame=2600)
# images = db_start.getImageIterator(start_frame=1936-210, end_frame=2600)
# images = db_start.getImageIterator(end_frame=52)
# images = db_start.getImageIterator(start_frame=1936-20-90, end_frame=2600)
# images = db_start.getImageIterator(start_frame=1500, end_frame=2600)
images = db_start.getImageIterator()
def getImage():
try:
im = images.next()
except StopIteration:
print("Done! First!")
return None, None
# im.data = rgb2gray(im.data)
fname = im.filename
from datetime import datetime
# d = datetime.strptime(fname[0:15], '%Y%m%d-%H%M%S')
d = im.timestamp
time_unix = np.uint32(time.mktime(d.timetuple()))
time_ms = 0 #np.uint32(fname[16:17])*100
State_Dist = ss.multivariate_normal(
cov=Q) # Initialize Distributions for Filter
Meas_Dist = ss.multivariate_normal(
cov=R) # Initialize Distributions for Filter
MultiKal = HungarianTracker(
KalmanFilter,
model,
np.diag(Q),
np.diag(R),
meas_dist=Meas_Dist,
state_dist=State_Dist,
measured_variables=["PositionX", "VelocityX", "PositionY", "VelocityY"])
MultiKal.LogProbabilityThreshold = log_prob_threshold
images = db.getImageIterator(start_frame=220)
# images = db.getImageIterator(start_frame=0, end_frame=220)
image = next(images)
# image_data = image.data#[200:400, 0:200]
# image_int = rgb2gray(image_data)
# FD.update(np.ones_like(image_int, dtype=bool), image_int)
i = 0
db_model, db_tracker = db.init_tracker(model, MultiKal)
ED = EmperorDetector(image.data, luminance_threshold=1.8)
for image in images:
MultiKal.predict(i=i)
# image_data = image.data#[200:400,0:200]
# image_int = rgb2gray(image_data)
# flow = FD.segmentate(image_int)
# FD.update(np.ones_like(image_int, dtype=bool), image_int)
measurements = ED.detect(image.data) #[200:300, 750:1000])
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()
for image in images:
print(time()-start)
start = time()
i = image.get_id()
# Prediction step
MultiKal.predict(u=np.zeros((model.Control_dim,)).T, i=i)
# Segmentation step
SegMap = VB.detect(image.data, do_neighbours=False)
print(time()-start)
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
print('Starting Iteration')
images = db.getImageIterator()
for image in images:
i = image.sort_index
# Prediction step, without applied control(vector of zeros)
MultiKal.predict(i=i)
# Detection step
SegMap = TS.detect(image.data)
Positions, Mask = AD.detect(image.data, SegMap)
print("Found %s Objects!" % len(Positions))
# Write Segmentation Mask to Database
db.setMask(image=image, data=(~SegMap).astype(np.uint8))
print("Mask save")