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[classifier] improvements

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This commit is contained in:
Jan Grewe 2025-02-18 18:21:00 +01:00
parent 881194ac66
commit 7a2084e159
1 changed files with 49 additions and 34 deletions
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83
fixtracks/widgets/classifier.py
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@ -10,6 +10,8 @@ import pyqtgraph as pg # needs to be imported after pyside to not import pyqt
from fixtracks.utils.trackingdata import TrackingData
from IPython import embed
class WorkerSignals(QObject):
error = Signal(str)
running = Signal(bool)
@ -64,6 +66,35 @@ class ConsistencyWorker(QRunnable):
@Slot()
def run(self):
def needs_checking(original, new):
res = False
for n, o in zip(new, original):
res = (o == 1 or o == 2) and n != o
if not res:
res = len(new) > 1 and (np.all(new == 1) or np.all(new == 2))
return res
def assign_by_distance(f, p):
t1_step = f - last_frame[0]
t2_step = f - last_frame[1]
if t1_step == 0 or t2_step == 0:
print(f"framecount is zero! current frame {f}, last frame {last_frame[0]} and {last_frame[1]}")
distance_to_trackone = np.linalg.norm(p - last_pos[0])/t1_step
distance_to_tracktwo = np.linalg.norm(p - last_pos[1])/t2_step
most_likely_track = np.argmin([distance_to_trackone, distance_to_tracktwo]) + 1
distances = np.zeros(2)
distances[0] = distance_to_trackone
distances[1] = distance_to_tracktwo
return most_likely_track, distances
def assign_by_orientation(f, o):
t1_step = f - last_frame[0]
t2_step = f - last_frame[1]
orientationchange = np.unwrap((last_angle - o)/np.array([t1_step, t2_step]))
most_likely_track = np.argmin(orientationchange) + 1
return most_likely_track, orientationchange
last_pos = [self.positions[(self.tracks == 1) & (self.frames <= self._startframe)][-1],
self.positions[(self.tracks == 2) & (self.frames <= self._startframe)][-1]]
last_frame = [self.frames[(self.tracks == 1) & (self.frames <= self._startframe)][-1],
@ -79,53 +110,37 @@ class ConsistencyWorker(QRunnable):
startframe = np.max(last_frame)
steps = int((maxframes - startframe) // 200)
for f in range(startframe + 1, maxframes, 1):
for f in np.unique(self.frames[self.frames > startframe]):
if self._stoprequest:
break
indices = np.where(self.frames == f)[0]
pp = self.positions[indices]
originaltracks = self.tracks[indices]
assignments = np.zeros_like(originaltracks)
distances = np.zeros((len(originaltracks), 2))
dist_assignments = np.zeros_like(originaltracks)
angle_assignments = np.zeros_like(originaltracks)
# userlabeld = np.zeros_like(originaltracks)
distances = np.zeros((len(originaltracks), 2))
orientations = np.zeros((len(originaltracks), 2))
for i, (idx, p) in enumerate(zip(indices, pp)):
if self.userlabeled[idx]:
print("userlabeled")
print("user")
processed += 1
last_pos[originaltracks[i]-1] = pp[i]
last_frame[originaltracks[i]-1] = f
last_angle[originaltracks[i]-1] = self.orientations[idx]
continue
if f < last_frame[0]:
print("ping")
self.tracks[idx] = 2
last_frame[1] = f
last_pos[1] = p
# last_angle[1] = self.orientations[idx]
continue
if f < last_frame[1]:
print("pang")
last_frame[0] = f
last_pos[0] = p
# last_angle[0] = self.orientations[idx]
self.tracks[idx] = 1
continue
# else, we have already seen track one and track two entries
if f - last_frame[0] == 0 or f - last_frame[1] == 0:
print(f"framecount is zero! current frame {f}, last frame {last_frame[0]} and {last_frame[1]}")
distance_to_trackone = np.linalg.norm(p - last_pos[0])/(f - last_frame[0])
distance_to_tracktwo = np.linalg.norm(p - last_pos[1])/(f - last_frame[1])
most_likely_track = np.argmin([distance_to_trackone, distance_to_tracktwo]) + 1
distances[i, 0] = distance_to_trackone
distances[i, 1] = distance_to_tracktwo
assignments[i] = most_likely_track
dist_assignments[i], distances[i, :] = assign_by_distance(f, p)
angle_assignments[i], orientations[i,:] = assign_by_orientation(f, self.orientations[idx])
# check (re) assignment update and proceed
if len(assignments) > 1 and (np.all(assignments == 1) or np.all(assignments == 2)):
logging.warning("frame %i: Issues assigning based on distances %s", f, str(distances))
print("dist", distances)
print("angle", orientations)
if needs_checking(originaltracks, dist_assignments):
logging.info("frame %i: Issues assigning based on distances %s", f, str(distances))
assignment_error = True
errors += 1
if self._stoponerror:
from IPython import embed
embed()
break
else:
@ -134,10 +149,10 @@ class ConsistencyWorker(QRunnable):
if assignment_error:
self.tracks[idx] = -1
else:
self.tracks[idx] = assignments[i]
last_pos[assignments[i]-1] = pp[i]
last_frame[assignments[i]-1] = f
last_angle[assignments[i]-1] = self.orientations[idx]
self.tracks[idx] = dist_assignments[i]
last_pos[dist_assignments[i]-1] = pp[i]
last_frame[dist_assignments[i]-1] = f
last_angle[dist_assignments[i]-1] = self.orientations[idx]
assignment_error = False
if steps > 0 and f % steps == 0:
progress += 1