Add notebooks for inspecting trajectories (#227)

* Add notebooks for inspecting trajectories with movement and for video overlay

* Add script for generating opencv video overlay

* Add notebook to fix annotations file

* Complete annotations fix notebook

* Separate helper scripts

MANIFEST.in

@@ -6,6 +6,7 @@ recursive-include guides *.md
 recursive-include crabs/tracker *.md
 recursive-include bash_scripts *.sh
 recursive-include notebooks *.py
+recursive-include scripts *.py
 recursive-include crabs *.yaml
 recursive-include guides *.png
 

notebooks/notebook_fix_annotations_file.py

@@ -0,0 +1,98 @@
+"""A notebook to remove duplicates in groundtruth file and save the output"""
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%
+import ast
+from pathlib import Path
+
+import pandas as pd
+from movement.io import load_bboxes
+
+# %%%%%%%%%%
+# Enable interactive plots
+# %matplotlib widget
+
+# %%%%%%%%%%%%%%%%%%%%%
+# load predictions
+groundtruth_csv = (
+    "/Users/sofia/arc/project_Zoo_crabs/escape_clips/"
+    "04.09.2023-04-Right_RE_test_corrected_ST_csv_SM.csv"
+)
+
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Try
+try:
+    ds = load_bboxes.from_via_tracks_file(
+        groundtruth_csv, fps=None, use_frame_numbers_from_file=False
+    )
+    print(ds)
+except Exception as e:
+    print("Error loading file")
+    print(e)
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%
+# Fix ground truth file and save
+
+# read as a dataframe
+df = pd.read_csv(groundtruth_csv, sep=",", header=0)
+
+# find duplicates
+list_unique_filenames = list(set(df.filename))
+filenames_to_rep_ID = {}
+for file in list_unique_filenames:
+    df_one_filename = df.loc[df["filename"] == file]
+
+    # track IDs for one file
+    list_track_ids_one_filename = [
+        int(ast.literal_eval(row.region_attributes)["track"])
+        for row in df_one_filename.itertuples()
+    ]
+
+    # if duplicates: compute the repeated track IDs for this file
+    # there could be more than one duplicate!!!
+    if len(set(list_track_ids_one_filename)) != len(
+        list_track_ids_one_filename
+    ):
+        # remove first occurrence of each track ID
+        for k in set(list_track_ids_one_filename):
+            list_track_ids_one_filename.remove(k)
+
+        # the track IDs that remain are the repeated ones
+        filenames_to_rep_ID[file] = list(set(list_track_ids_one_filename))
+
+# delete duplicate rows
+for file, list_rep_ID in filenames_to_rep_ID.items():
+    for rep_ID in list_rep_ID:
+        # find repeated rows for selected file and rep_ID
+        matching_rows = df[
+            (df["filename"] == file)
+            & (df["region_attributes"] == f'{{"track":"{rep_ID}"}}')
+        ]
+
+        # Identify the index of the first matching row
+        if not matching_rows.empty:
+            # Drop all but the first matching row
+            indices_to_drop = matching_rows.index[1:]
+            df = df.drop(indices_to_drop)
+
+# save to csv
+timestamp = pd.Timestamp.now().strftime("%Y%m%d_%H%M%S")
+groundtruth_csv_corrected = Path(groundtruth_csv).parent / Path(
+    Path(groundtruth_csv).stem + f"_corrected_{timestamp}.csv"
+)
+df.to_csv(groundtruth_csv_corrected, index=False)
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%
+# Check new file can be read correctly into movement
+
+ds_gt = load_bboxes.from_via_tracks_file(
+    groundtruth_csv_corrected, fps=None, use_frame_numbers_from_file=False
+)
+print(ds_gt)
+
+# Print summary
+print(f"{ds_gt.source_file}")
+print(f"Number of frames: {ds_gt.sizes['time']}")
+print(f"Number of individuals: {ds_gt.sizes['individuals']}")
+
+# %%