Update scripts to access stored gt tracks

scripts/get_tracking_results.py

@@ -17,34 +17,17 @@ def load_tracking_segmentation(experiment):
     if experiment.startswith("vit"):
         if experiment == "vit_l":
             seg_path = os.path.join(result_dir, "vit_l.tif")
-            seg = imageio.imread(seg_path)
-            # HACK
-            ignore_labels = [8, 44, 57, 102, 50]
-
         elif experiment == "vit_l_lm":
             seg_path = os.path.join(result_dir, "vit_l_lm.tif")
-            seg = imageio.imread(seg_path)
-            # HACK
-            ignore_labels = []
-
         elif experiment == "vit_l_specialist":
             seg_path = os.path.join(result_dir, "vit_l_lm_specialist.tif")
-            seg = imageio.imread(seg_path)
-            # HACK
-            ignore_labels = [88, 45, 30, 46]
-
         # elif experiment == "trackmate_stardist":
         #     seg_path = os.path.join(result_dir, "trackmate_stardist", "every_3rd_fr_result.tif")
-        #     seg = imageio.imread(seg_path)
 
         else:
             raise ValueError(experiment)
 
-        # HACK:
-        # we remove some labels as they have a weird lineage, is creating issues for creating the graph
-        # (e.g. frames where the object exists: 1, 2, 4, 5, 6)
-        seg[np.isin(seg, ignore_labels)] = 0
-
+        seg = imageio.imread(seg_path)
         return seg
 
     else:  # return the result directory for stardist
@@ -117,11 +100,4 @@ def get_tracking_data():
         curr_frames = v["frames"]
         v["frames"] = [frmaps[frval] for frval in curr_frames if frval in chosen_frames]
 
-    # HACK:
-    # we remove label with id 62 as it has a weird lineage, is creating issues for creating the graph
-    ignore_labels = [62, 87, 92, 99, 58]
-    labels[np.isin(labels, ignore_labels)] = 0
-    for _label in ignore_labels:
-        curr_lineages.pop(_label)
-
     return raw, labels, curr_lineages, chosen_frames

scripts/gt_tracks.csv

@@ -0,0 +1,108 @@
+,Cell_ID,Start,End,Parent_ID
+0,1,0,23,0
+1,2,0,23,0
+2,3,0,23,0
+3,4,0,23,0
+4,5,0,20,0
+5,6,0,5,0
+6,7,0,23,0
+7,8,0,23,0
+8,9,0,23,0
+9,10,0,4,0
+10,11,0,22,0
+11,12,0,23,0
+12,13,0,3,0
+13,14,0,23,0
+14,15,0,3,0
+15,16,0,23,0
+16,17,0,23,0
+17,18,0,23,0
+18,19,0,3,0
+19,20,0,23,0
+20,21,0,23,0
+21,22,0,23,0
+22,23,0,22,0
+23,24,0,3,0
+24,25,0,23,0
+25,26,0,23,0
+26,27,0,4,0
+27,28,0,5,0
+28,29,0,23,0
+29,30,0,23,0
+30,31,0,20,0
+31,32,0,11,0
+32,33,0,23,0
+33,34,0,23,0
+34,35,0,7,0
+35,36,0,23,0
+36,37,0,18,0
+37,38,0,23,0
+38,39,0,23,0
+39,40,0,18,0
+40,41,0,1,0
+41,42,0,23,0
+42,43,0,2,0
+43,44,0,23,0
+44,45,0,23,0
+45,46,0,23,0
+46,47,0,23,0
+47,48,0,2,0
+48,49,0,13,0
+49,50,0,13,0
+50,51,0,23,0
+51,52,0,23,0
+52,53,0,3,0
+53,54,0,23,0
+54,55,0,23,0
+55,56,0,1,0
+56,57,0,3,0
+57,58,0,12,0
+58,59,0,23,0
+59,60,0,0,0
+60,61,1,23,60
+61,62,1,23,0
+62,63,2,23,41
+63,64,2,23,41
+64,65,2,23,56
+65,66,2,23,56
+66,67,3,23,48
+67,68,3,23,48
+68,69,3,23,43
+69,70,3,23,43
+70,109,4,23,0
+71,71,4,23,13
+72,72,4,23,13
+73,73,4,23,57
+74,74,4,23,24
+75,75,4,23,24
+76,76,4,23,53
+77,77,4,23,53
+78,78,4,4,0
+79,79,5,23,10
+80,80,5,23,10
+81,81,5,21,27
+82,82,5,9,27
+83,83,6,23,6
+84,84,6,23,6
+85,85,6,23,28
+86,86,6,23,28
+87,87,7,13,0
+88,88,7,21,0
+89,89,8,18,0
+90,90,8,23,35
+91,91,8,23,35
+92,92,12,14,0
+93,93,12,23,32
+94,94,12,23,32
+95,95,13,15,58
+96,96,13,23,58
+97,97,14,14,50
+98,98,14,23,50
+99,99,14,23,49
+100,100,14,23,49
+101,101,18,22,0
+102,102,19,23,0
+103,103,23,23,23
+104,104,23,23,23
+105,105,23,23,11
+106,106,23,23,11

scripts/test_ctc_metric.py

@@ -2,16 +2,12 @@
 import numpy as np
 import pandas as pd
 
-from deepcell_tracking.isbi_utils import trk_to_isbi
-
 from traccuracy import run_metrics
+from traccuracy.matchers import CTCMatcher
 from traccuracy._tracking_graph import TrackingGraph
-from traccuracy.matchers import CTCMatcher, IOUMatcher
 from traccuracy.metrics import CTCMetrics, DivisionMetrics
 from traccuracy.loaders._ctc import _get_node_attributes, ctc_to_graph, _check_ctc, load_ctc_data
 
-from get_tracking_results import get_tracking_data, load_tracking_segmentation
-
 
 def mark_potential_split(frames, last_frame, idx):
     if frames.max() == last_frame:  # object is tracked until the last frame
@@ -65,12 +61,10 @@ def extract_df_from_segmentation(segmentation):
     return pred_tracks_df
 
 
-def evaluate_tracking(labels, curr_lineages, segmentation_method):
-    seg = load_tracking_segmentation(segmentation_method)
-
+def evaluate_tracking(raw, labels, seg, segmentation_method):
     if os.path.isdir(seg):  # for trackmate stardist
         seg_T = load_ctc_data(
-            data_dir=seg, 
+            data_dir=seg,
             track_path=os.path.join(seg, 'res_track.txt'),
             name=f'DynamicNuclearNet-{segmentation_method}'
         )
@@ -84,11 +78,12 @@ def evaluate_tracking(labels, curr_lineages, segmentation_method):
 
     breakpoint()
 
-    # calcuates node attributes for each detection
+    # calcuates node attributes for each detectionc
     gt_nodes = _get_node_attributes(labels)
 
     # converts inputs to isbi-tracking format - the version expected as inputs in traccuracy
-    gt_df = trk_to_isbi(curr_lineages, path=None)
+    # it's preconverted using "from deepcell_tracking.isbi_utils import trk_to_isbi"
+    gt_df = pd.read_csv("./gt_tracks.csv")
 
     # creates graphs from ctc-type info (isbi-type? probably means the same thing)
     gt_G = ctc_to_graph(gt_df, gt_nodes)
@@ -106,22 +101,29 @@ def evaluate_tracking(labels, curr_lineages, segmentation_method):
     )
     print(ctc_results)
 
-    breakpoint()
 
-    iou_results = run_metrics(
-        gt_data=gt_T,
-        pred_data=seg_T,
-        matcher=IOUMatcher(iou_threshold=0.1),
-        metrics=[DivisionMetrics(max_frame_buffer=0)],
-    )
-    print(iou_results)
+def get_tracking_data(segmentation_method):
+    import h5py
+
+    with h5py.File("./tracking_micro_sam.h5", "r") as f:
+        raw = f["raw"][:]
+        labels = f["labels"][:]
+
+        if segmentation_method.startswith("vit"):
+            segmentation = f[f"segmentations/{segmentation_method}"][:]
+        else:
+            ROOT = "/scratch/projects/nim00007/sam/for_tracking"
+            result_dir = os.path.join(ROOT, "results")
+            segmentation = os.path.join(result_dir, "trackmate_stardist", "01_RES")
+
+    return raw, labels, segmentation
 
 
 def main():
-    raw, labels, curr_lineages, chosen_frames = get_tracking_data()
+    segmentation_method = "trackmate_stardist"
 
-    segmentation_method = "vit_l_specialist"
-    evaluate_tracking(labels, curr_lineages, segmentation_method)
+    raw, labels, segmentation = get_tracking_data(segmentation_method)
+    evaluate_tracking(raw, labels, segmentation, segmentation_method)
 
 
 if __name__ == "__main__":