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[trackingdata] implement better selection handling ...

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allow deletion of entries
This commit is contained in:
Jan Grewe 2025-02-24 16:03:42 +01:00
parent d176925796
commit 6fbbb52370
1 changed files with 37 additions and 26 deletions
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63
fixtracks/utils/trackingdata.py
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@ -37,17 +37,22 @@ class TrackingData(QObject):
def numDetections(self):
return self._data["track"].shape[0]
@property
def selectionRange(self):
return self._start, self._stop
@property
def selectionRangeColumn(self):
return self._selection_column
def _find(self, ids):
ids = np.sort(ids)
indexes = np.ones_like(ids, dtype=int) * -1
j = 0
for idx, i in enumerate(self._indices):
if i == ids[j]:
indexes[j] = idx
j += 1
if j == len(indexes):
break
indexes = indexes[indexes >= 0]
return indexes
@property
def selectionIndices(self):
return self._indices
return self._selection
def setSelectionRange(self, col, start, stop):
logging.debug("Trackingdata: set selection range based on column %s to %.2f - %.2f", col, start, stop)
@ -59,18 +64,19 @@ class TrackingData(QObject):
def selectedData(self, col:str):
if col not in self.columns:
logging.error("TrackingData:selectedData: Invalid column name! %s", col)
return self[col][self._indices]
return self[col][self._selection]
def setUserSelection(self, ids):
def setSelection(self, ids):
"""
Set the user selections. That is, e.g. when the user selected a number of detection ids (aka the index of the original data frame entries).
Set the selection based on the detection IDs.
Parameters
----------
ids : array-like
An array-like object containing the IDs to be set as user selections.
The IDs will be converted to integers.
"""
self._user_selections = ids.astype(int)
self._selection = self._find(ids)
self._selected_ids = ids
def setTrack(self, track_id:int, setUserLabeled:bool=True)-> None:
"""Assign a new track_id to the user-selected detections
@ -111,14 +117,15 @@ class TrackingData(QObject):
logging.debug("TrackingData: Reverting all track assignments!")
self["track"][:] = -1
def deleteDetections(self):
# from IPython import embed
# if self._user_selections is not None:
# ids = self._user_selections
# for c in self.columns:
# pass
# embed()
pass
def deleteDetections(self, ids=None):
if ids is not None:
del_indices = self._find(ids)
else:
del_indices = self._indices
for c in self._columns:
self._data[c] = np.delete(self._data[c], del_indices, axis=0)
self._indices = self["index"]
self._selected_ids = np.setdiff1d(self._selected_ids, del_indices)
def assignTracks(self, tracks:np.ndarray):
"""assigns the given tracks to the user-selected detections. If the sizes of
@ -299,22 +306,26 @@ def main():
plt.plot([positions[si, 0], positions[ei, 0]],
[positions[si, 1], positions[ei, 1]], color="tab:green")
datafile = PACKAGE_ROOT / "data/merged_small.pkl"
datafile = PACKAGE_ROOT / "data/merged_small_tracked.pkl"
with open(datafile, "rb") as f:
df = pickle.load(f)
data = TrackingData()
data.setData(as_dict(df))
data = TrackingData(as_dict(df))
test_indices = [32, 88, 99, 2593]
data.deleteDetections(test_indices)
embed()
data.deleteDetections(test_indices)
data.setSelection(test_indices)
all_cogs = data.centerOfGravity()
orientations = data.orientation()
lengths = data.animalLength()
frames = data["frame"]
tracks = data["track"]
bendedness = data.bendedness()
indices = data._indices
# positions = data.coordinates()[[160388, 160389]]
embed()
tracks = data["track"]
cogs = all_cogs[tracks==1]
all_dists = neighborDistances(cogs, 2, False)