Modernize packaging with uv support and upgrade to NumPy 2.0

.gitignore

@@ -1,5 +1,7 @@
 .idea
 .vscode
+.venv/
+*.egg-info/
 **.pyc
 **__pycache__
 gt_data/*

Readme.md → README.md

@@ -118,19 +118,38 @@ To convert formats, check out the format specifications defined [here](docs).
 
 By default, we would recommend the MOTChallenge format, although any implemented format should work. Note that for many cases you will want to use the argument ```--DO_PREPROC False``` unless you want to run preprocessing to remove distractor objects.
 
-## Requirements
- Code tested on Python 3.7.
-
- - Minimum requirements: numpy, scipy
- - For plotting: matplotlib
- - For segmentation datasets (KITTI MOTS, MOTS-Challenge, DAVIS, YouTube-VIS): pycocotools
- - For DAVIS dataset: Pillow
- - For J & F metric: opencv_python, scikit_image
- - For simples test-cases for metrics: pytest
-
-use ```pip3 -r install requirements.txt``` to install all possible requirements.
-
-use ```pip3 -r install minimum_requirments.txt``` to only install the minimum if you don't need the extra functionality as listed above.
+## Installation
+
+Code tested on Python 3.7.
+
+### Using uv (recommended)
+
+```bash
+# Install with minimum dependencies (numpy, scipy)
+uv pip install trackeval
+
+# Install with all dependencies
+uv pip install "trackeval[all]"
+
+# Install specific extras
+uv pip install "trackeval[plot,segmentation]"
+```
+
+### Using pip
+
+```bash
+pip install trackeval
+pip install "trackeval[all]"
+```
+
+### Available extras
+
+- `plot` - matplotlib for plotting results
+- `segmentation` - pycocotools, Pillow for segmentation datasets (KITTI MOTS, MOTS-Challenge, DAVIS, YouTube-VIS)
+- `jf` - opencv-python, scikit-image for J & F metric
+- `burst` - tabulate for BURST benchmark
+- `dev` - pytest, tqdm for development and testing
+- `all` - all optional dependencies
 
 ## Timing analysis
 

pyproject.toml

@@ -1,6 +1,55 @@
 [build-system]
-requires = [
-    "setuptools>=42",
-    "wheel"
-]
+requires = ["setuptools>=42", "wheel"]
 build-backend = "setuptools.build_meta"
+
+[project]
+name = "trackeval"
+version = "1.0.dev1"
+description = "Code for evaluating object tracking"
+readme = "README.md"
+license = {text = "MIT"}
+authors = [
+    {name = "Jonathon Luiten, Arne Hoffhues", email = "jonoluiten@gmail.com"}
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering",
+]
+requires-python = ">=3.10"
+dependencies = [
+    "numpy>=2.0.0",
+    "scipy>=1.14.0",
+]
+
+[project.optional-dependencies]
+plot = [
+    "matplotlib==3.2.1",
+]
+segmentation = [
+    "pycocotools==2.0.2",
+    "Pillow==8.1.2",
+]
+jf = [
+    "opencv-python==4.4.0.46",
+    "scikit-image==0.16.2",
+]
+burst = [
+    "tabulate",
+]
+dev = [
+    "pytest>=7.0.0",
+    "tqdm>=4.64.0",
+]
+all = [
+    "trackeval[plot,segmentation,jf,burst,dev]",
+]
+
+[project.urls]
+Homepage = "https://github.com/JonathonLuiten/TrackEval"
+"Bug Tracker" = "https://github.com/JonathonLuiten/TrackEval/issues"
+
+[tool.setuptools.packages.find]
+include = ["trackeval*"]

trackeval/baselines/stp.py

@@ -49,7 +49,7 @@ def track_sequence(seq_file):
 
         # Initialize container for holding previously tracked objects.
         prev = {'boxes': np.empty((0, 4)),
-                'ids': np.array([], np.int),
+                'ids': np.array([], np.intp),
                 'timesteps': np.array([])}
 
         # Run tracker for each timestep.
@@ -80,7 +80,7 @@ def track_sequence(seq_file):
             match_cols = match_cols[actually_matched_mask]
 
             # Assign the prev track ID to the current dets if they were matched.
-            ids = np.nan * np.ones((len(boxes),), np.int)
+            ids = np.nan * np.ones((len(boxes),), np.intp)
             ids[match_cols] = prev['ids'][match_rows]
 
             # Create new track IDs for dets that were not matched to previous tracks.

trackeval/datasets/bdd100k.py

@@ -226,12 +226,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):
 
             # Only extract relevant dets for this class for preproc and eval (cls)
             gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
-            gt_class_mask = gt_class_mask.astype(np.bool)
+            gt_class_mask = gt_class_mask.astype(np.bool_)
             gt_ids = raw_data['gt_ids'][t][gt_class_mask]
             gt_dets = raw_data['gt_dets'][t][gt_class_mask]
 
             tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
-            tracker_class_mask = tracker_class_mask.astype(np.bool)
+            tracker_class_mask = tracker_class_mask.astype(np.bool_)
             tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
             tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
             similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]
@@ -276,14 +276,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.intp)
         if len(unique_tracker_ids) > 0:
             unique_tracker_ids = np.unique(unique_tracker_ids)
             tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
             tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.intp)
 
         # Record overview statistics.
         data['num_tracker_dets'] = num_tracker_dets

trackeval/datasets/burst_helpers/burst_base.py

@@ -322,12 +322,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):
 
             # Only extract relevant dets for this class for preproc and eval (cls)
             gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
-            gt_class_mask = gt_class_mask.astype(np.bool)
+            gt_class_mask = gt_class_mask.astype(np.bool_)
             gt_ids = raw_data['gt_ids'][t][gt_class_mask]
             gt_dets = raw_data['gt_dets'][t][gt_class_mask]
 
             tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
-            tracker_class_mask = tracker_class_mask.astype(np.bool)
+            tracker_class_mask = tracker_class_mask.astype(np.bool_)
             tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
             tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
             tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
@@ -349,7 +349,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
                 elif is_not_exhaustively_labeled:
                     to_remove_tracker = unmatched_indices
                 else:
-                    to_remove_tracker = np.array([], dtype=np.int)
+                    to_remove_tracker = np.array([], dtype=np.intp)
 
                 # remove all unwanted unmatched tracker detections
                 data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
@@ -377,14 +377,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.intp)
         if len(unique_tracker_ids) > 0:
             unique_tracker_ids = np.unique(unique_tracker_ids)
             tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
             tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.intp)
 
         # Record overview statistics.
         data['num_tracker_dets'] = num_tracker_dets

trackeval/datasets/burst_helpers/burst_ow_base.py

@@ -332,12 +332,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):
 
             # Only extract relevant dets for this class for preproc and eval (cls)
             gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
-            gt_class_mask = gt_class_mask.astype(np.bool)
+            gt_class_mask = gt_class_mask.astype(np.bool_)
             gt_ids = raw_data['gt_ids'][t][gt_class_mask]
             gt_dets = raw_data['gt_dets'][t][gt_class_mask]
 
             tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
-            tracker_class_mask = tracker_class_mask.astype(np.bool)
+            tracker_class_mask = tracker_class_mask.astype(np.bool_)
             tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
             tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
             tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
@@ -358,7 +358,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             elif is_not_exhaustively_labeled:
                 to_remove_tracker = unmatched_indices
             else:
-                to_remove_tracker = np.array([], dtype=np.int)
+                to_remove_tracker = np.array([], dtype=np.intp)
 
             # remove all unwanted unmatched tracker detections
             data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
@@ -382,14 +382,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.intp)
         if len(unique_tracker_ids) > 0:
             unique_tracker_ids = np.unique(unique_tracker_ids)
             tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
             tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.intp)
 
         # Record overview statistics.
         data['num_tracker_dets'] = num_tracker_dets

trackeval/datasets/davis.py

@@ -240,7 +240,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
                     rows, columns = np.where(void_mask_ious > 0)
                     for r in rows:
                         det = mask_utils.decode(raw_data['tracker_dets'][t][r])
-                        void = mask_utils.decode(void_mask).astype(np.bool)
+                        void = mask_utils.decode(void_mask).astype(np.bool_)
                         det[void] = 0
                         det = mask_utils.encode(np.array(det, order='F').astype(np.uint8))
                         raw_data['tracker_dets'][t][r] = det
@@ -253,14 +253,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.intp)
         if len(unique_tracker_ids) > 0:
             unique_tracker_ids = np.unique(unique_tracker_ids)
             tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
             tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.intp)
 
         # Record overview statistics.
         data['num_tracker_dets'] = num_tracker_dets

trackeval/datasets/head_tracking_challenge.py

@@ -227,7 +227,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
             time_key = str(t+1)
             if time_key in read_data.keys():
                 try:
-                    time_data = np.asarray(read_data[time_key], dtype=np.float)
+                    time_data = np.asarray(read_data[time_key], dtype=np.float64)
                 except ValueError:
                     if is_gt:
                         raise TrackEvalException(
@@ -365,7 +365,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
 
             # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
             # which are labeled as belonging to a distractor class.
-            to_remove_tracker = np.array([], np.int)
+            to_remove_tracker = np.array([], np.intp)
             if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
 
                 # Check all classes are valid:
@@ -432,14 +432,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
             gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.intp)
         if len(unique_tracker_ids) > 0:
             unique_tracker_ids = np.unique(unique_tracker_ids)
             tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
             tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
             for t in range(raw_data['num_timesteps']):
                 if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.intp)
 
         # Record overview statistics.
         data['num_tracker_dets'] = num_tracker_dets