Merge pull request #99 from anwai98/u-tracking-metrics

Add scripts for traccuracy metric evaluation

scripts/get_tracking_results.py

@@ -0,0 +1,103 @@
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import imageio.v3 as imageio
+
+from deepcell_tracking.utils import load_trks
+
+
+ROOT = "/scratch/projects/nim00007/sam/for_tracking"
+
+
+def load_tracking_segmentation(experiment):
+    result_dir = os.path.join(ROOT, "results")
+
+    if experiment.startswith("vit"):
+        if experiment == "vit_l":
+            seg_path = os.path.join(result_dir, "vit_l.tif")
+        elif experiment == "vit_l_lm":
+            seg_path = os.path.join(result_dir, "vit_l_lm.tif")
+        elif experiment == "vit_l_specialist":
+            seg_path = os.path.join(result_dir, "vit_l_lm_specialist.tif")
+        # elif experiment == "trackmate_stardist":
+        #     seg_path = os.path.join(result_dir, "trackmate_stardist", "every_3rd_fr_result.tif")
+
+        else:
+            raise ValueError(experiment)
+
+        seg = imageio.imread(seg_path)
+        return seg
+
+    else:  # return the result directory for stardist
+        return os.path.join(result_dir, "trackmate_stardist", "01_RES")
+
+
+def check_tracking_results(raw, labels, curr_lineages, chosen_frames):
+    seg_default = load_tracking_segmentation("vit_l")
+    seg_generalist = load_tracking_segmentation("vit_l_lm")
+    seg_specialist = load_tracking_segmentation("vit_l_specialist")
+
+    # let's get the tracks only for the objects present per frame
+    for idx in np.unique(labels)[1:]:
+        lineage = curr_lineages[idx]
+        lineage["frames"] = [frame for frame in lineage["frames"] if frame in chosen_frames]
+
+    import napari
+    v = napari.Viewer()
+    v.add_image(raw)
+    v.add_labels(labels)
+
+    v.add_labels(seg_default, visible=False)
+    v.add_labels(seg_generalist, visible=False)
+    v.add_labels(seg_specialist, visible=False)
+
+    napari.run()
+
+
+def get_tracking_data():
+    data_dir = os.path.join(ROOT, "data", "DynamicNuclearNet-tracking-v1_0")
+    data_source = np.load(os.path.join(data_dir, "data-source.npz"), allow_pickle=True)
+
+    fname = "test.trks"
+    track_file = os.path.join(data_dir, fname)
+    split_name = Path(track_file).stem
+
+    data = load_trks(track_file)
+
+    X = data["X"]
+    y = data["y"]
+    lineages = data["lineages"]
+
+    meta = pd.DataFrame(
+        data_source[split_name],
+        columns=["filename", "experiment", "pixel_size", "screening_passed", "time_step", "specimen"]
+    )
+    # print(meta)
+
+    # let's convert the data to expected shape
+    X = X.squeeze(-1)
+    y = y.squeeze(-1)
+
+    # NOTE: chosen slice for the tracking user study.
+    _slice = 7
+    raw, labels = X[_slice, ...], y[_slice, ...]
+    curr_lineages = lineages[_slice]
+
+    # NOTE: let's get every third frame of data and see how it looks
+    chosen_frames = list(range(0, raw.shape[0], 3))
+    raw = np.stack([raw[frame] for frame in chosen_frames])
+    labels = np.stack([labels[frame] for frame in chosen_frames])
+
+    # let's create a value map
+    frmaps = {}
+    for i, frval in enumerate(chosen_frames):
+        frmaps[frval] = i
+
+    # let's remove frames which are not a part of our chosen frames
+    for k, v in curr_lineages.items():
+        curr_frames = v["frames"]
+        v["frames"] = [frmaps[frval] for frval in curr_frames if frval in chosen_frames]
+
+    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