Additional testing

.github/workflows/testing.yml

@@ -43,7 +43,7 @@ jobs:
           df -h
 
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v4 # uses: actions/checkout@v6
 
       - name: Install uv
         uses: astral-sh/setup-uv@v6
@@ -53,9 +53,9 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install the project
-        run: uv sync --no-cache --all-extras --dev
+        run: uv sync --all-extras --dev
         shell: bash
-        
+
       - name: Install ffmpeg
         run: |
           if [ "$RUNNER_OS" == "Linux" ]; then
@@ -67,9 +67,27 @@ jobs:
             choco install ffmpeg
           fi
         shell: bash
-          
-      - name: Run DLC Live Tests
+
+      - name: Run Model Benchmark Test
         run: uv run dlc-live-test --nodisplay
 
-      - name: Run Functional Benchmark Test
+      - name: Run DLC Live Unit Tests
         run: uv run pytest
+        # - name: Run DLC Live Unit Tests
+      #   run: uv run pytest --cov=dlclive --cov-report=xml --cov-report=term-missing
+
+      # - name: Coverage Report
+      #   uses: codecov/codecov-action@v5
+      #   with:
+      #     files: ./coverage.xml
+      #     flags: ${{ matrix.os }}-py${{ matrix.python-version }}
+      #     name: codecov-${{ matrix.os }}-py${{ matrix.python-version }}
+      # - name: Add coverage to job summary
+      #   if: always()
+      #   shell: bash
+      #   run: |
+      #     uv run python -m coverage report -m > coverage.txt
+      #     echo "## Coverage (dlclive)" >> "$GITHUB_STEP_SUMMARY"
+      #     echo '```' >> "$GITHUB_STEP_SUMMARY"
+      #     cat coverage.txt >> "$GITHUB_STEP_SUMMARY"
+      #     echo '```' >> "$GITHUB_STEP_SUMMARY"

.pre-commit-config.yaml

@@ -0,0 +1,19 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      - id: check-docstring-first
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/asottile/setup-cfg-fmt
+    rev: v3.2.0
+    hooks:
+      - id: setup-cfg-fmt
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.10
+    hooks:
+      # Run the formatter.
+      - id: ruff-format
+      # Run the linter.
+      - id: ruff-check
+        args: [--fix,--unsafe-fixes]

dlclive/core/inferenceutils.py

@@ -8,6 +8,10 @@
 #
 # Licensed under GNU Lesser General Public License v3.0
 #
+
+
+# NOTE - DUPLICATED @C-Achard 2026-01-26: Copied from the original DeepLabCut codebase
+# from deeplabcut/core/inferenceutils.py
 from __future__ import annotations
 
 import heapq
@@ -17,9 +21,10 @@
 import pickle
 import warnings
 from collections import defaultdict
+from collections.abc import Iterable
 from dataclasses import dataclass
 from math import erf, sqrt
-from typing import Any, Iterable, Tuple
+from typing import Any
 
 import networkx as nx
 import numpy as np
@@ -41,7 +46,7 @@ def _conv_square_to_condensed_indices(ind_row, ind_col, n):
     return n * ind_col - ind_col * (ind_col + 1) // 2 + ind_row - 1 - ind_col
 
 
-Position = Tuple[float, float]
+Position = tuple[float, float]
 
 
 @dataclass(frozen=True)
@@ -155,7 +160,7 @@ def soft_identity(self):
         unq, idx, cnt = np.unique(data[:, 3], return_inverse=True, return_counts=True)
         avg = np.bincount(idx, weights=data[:, 2]) / cnt
         soft = softmax(avg)
-        return dict(zip(unq.astype(int), soft))
+        return dict(zip(unq.astype(int), soft, strict=False))
 
     @property
     def affinity(self):
@@ -262,7 +267,8 @@ def __init__(
         self._has_identity = "identity" in self[0]
         if identity_only and not self._has_identity:
             warnings.warn(
-                "The network was not trained with identity; setting `identity_only` to False."
+                "The network was not trained with identity; setting `identity_only` to False.",
+                stacklevel=2,
             )
         self.identity_only = identity_only & self._has_identity
         self.nan_policy = nan_policy
@@ -344,15 +350,19 @@ def calibrate(self, train_data_file):
             pass
         n_bpts = len(df.columns.get_level_values("bodyparts").unique())
         if n_bpts == 1:
-            warnings.warn("There is only one keypoint; skipping calibration...")
+            warnings.warn(
+                "There is only one keypoint; skipping calibration...", stacklevel=2
+            )
             return
 
         xy = df.to_numpy().reshape((-1, n_bpts, 2))
         frac_valid = np.mean(~np.isnan(xy), axis=(1, 2))
         # Only keeps skeletons that are more than 90% complete
         xy = xy[frac_valid >= 0.9]
         if not xy.size:
-            warnings.warn("No complete poses were found. Skipping calibration...")
+            warnings.warn(
+                "No complete poses were found. Skipping calibration...", stacklevel=2
+            )
             return
 
         # TODO Normalize dists by longest length?
@@ -369,7 +379,8 @@ def calibrate(self, train_data_file):
         except np.linalg.LinAlgError:
             # Covariance matrix estimation fails due to numerical singularities
             warnings.warn(
-                "The assembler could not be robustly calibrated. Continuing without it..."
+                "The assembler could not be robustly calibrated. Continuing without it...",
+                stacklevel=2,
             )
 
     def calc_assembly_mahalanobis_dist(
@@ -428,10 +439,12 @@ def _flatten_detections(data_dict):
             ids = [np.ones(len(arr), dtype=int) * -1 for arr in confidence]
         else:
             ids = [arr.argmax(axis=1) for arr in ids]
-        for i, (coords, conf, id_) in enumerate(zip(coordinates, confidence, ids)):
+        for i, (coords, conf, id_) in enumerate(
+            zip(coordinates, confidence, ids, strict=False)
+        ):
             if not np.any(coords):
                 continue
-            for xy, p, g in zip(coords, conf, id_):
+            for xy, p, g in zip(coords, conf, id_, strict=False):
                 joint = Joint(tuple(xy), p.item(), i, ind, g)
                 ind += 1
                 yield joint
@@ -474,13 +487,13 @@ def extract_best_links(self, joints_dict, costs, trees=None):
                     (conf >= self.pcutoff * self.pcutoff) & (aff >= self.min_affinity)
                 )
                 candidates = sorted(
-                    zip(rows, cols, aff[rows, cols], lengths[rows, cols]),
+                    zip(rows, cols, aff[rows, cols], lengths[rows, cols], strict=False),
                     key=lambda x: x[2],
                     reverse=True,
                 )
                 i_seen = set()
                 j_seen = set()
-                for i, j, w, l in candidates:
+                for i, j, w, _l in candidates:
                     if i not in i_seen and j not in j_seen:
                         i_seen.add(i)
                         j_seen.add(j)
@@ -502,7 +515,7 @@ def extract_best_links(self, joints_dict, costs, trees=None):
                 ]
                 aff = aff[np.ix_(keep_s, keep_t)]
                 rows, cols = linear_sum_assignment(aff, maximize=True)
-                for row, col in zip(rows, cols):
+                for row, col in zip(rows, cols, strict=False):
                     w = aff[row, col]
                     if w >= self.min_affinity:
                         links.append(Link(dets_s[keep_s[row]], dets_t[keep_t[col]], w))
@@ -548,9 +561,9 @@ def push_to_stack(i):
                 d = self.calc_assembly_mahalanobis_dist(assembly, nan_policy=nan_policy)
                 if d < d_old:
                     push_to_stack(new_ind)
-                    if tabu:  
-                        _, _, link = heapq.heappop(tabu)  
-                        heapq.heappush(stack, (-link.affinity, next(counter), link)) 
+                    if tabu:
+                        _, _, link = heapq.heappop(tabu)
+                        heapq.heappush(stack, (-link.affinity, next(counter), link))
                 else:
                     heapq.heappush(tabu, (d - d_old, next(counter), best))
                     assembly.__dict__.update(assembly._dict)
@@ -665,7 +678,7 @@ def build_assemblies(self, links):
                             for idx in store[j]._idx:
                                 store[idx] = store[i]
                     except KeyError:
-                        # Some links may reference indices that were never added to `store`;  
+                        # Some links may reference indices that were never added to `store`;
                         # in that case we intentionally skip merging for this link
                         pass
 
@@ -791,7 +804,7 @@ def _assemble(self, data_dict, ind_frame):
                 ]
             else:
                 scores = [ass._affinity for ass in assemblies]
-            lst = list(zip(scores, assemblies))
+            lst = list(zip(scores, assemblies, strict=False))
             assemblies = []
             while lst:
                 temp = max(lst, key=lambda x: x[0])
@@ -1074,7 +1087,7 @@ def match_assemblies(
                 if ~np.isnan(oks):
                     mat[i, j] = oks
         rows, cols = linear_sum_assignment(mat, maximize=True)
-        for row, col in zip(rows, cols):
+        for row, col in zip(rows, cols, strict=False):
             matched[row].ground_truth = ground_truth[col]
             matched[row].oks = mat[row, col]
             _ = inds_true.remove(col)
@@ -1087,7 +1100,7 @@ def parse_ground_truth_data_file(h5_file):
     try:
         df.drop("single", axis=1, level="individuals", inplace=True)
     except KeyError:
-        # Ignore if the "single" individual column is absent 
+        # Ignore if the "single" individual column is absent
         pass
     # Cast columns of dtype 'object' to float to avoid TypeError
     # further down in _parse_ground_truth_data.
@@ -1128,7 +1141,7 @@ def find_outlier_assemblies(dict_of_assemblies, criterion="area", qs=(5, 95)):
     for frame_ind, assemblies in dict_of_assemblies.items():
         for assembly in assemblies:
             tuples.append((frame_ind, getattr(assembly, criterion)))
-    frame_inds, vals = zip(*tuples)
+    frame_inds, vals = zip(*tuples, strict=False)
     vals = np.asarray(vals)
     lo, up = np.percentile(vals, qs, interpolation="nearest")
     inds = np.flatnonzero((vals < lo) | (vals > up)).tolist()
@@ -1246,12 +1259,14 @@ def evaluate_assembly(
     ass_pred_dict,
     ass_true_dict,
     oks_sigma=0.072,
-    oks_thresholds=np.linspace(0.5, 0.95, 10),
+    oks_thresholds=None,
     margin=0,
     symmetric_kpts=None,
     greedy_matching=False,
     with_tqdm: bool = True,
 ):
+    if oks_thresholds is None:
+        oks_thresholds = np.linspace(0.5, 0.95, 10)
     if greedy_matching:
         return evaluate_assembly_greedy(
             ass_true_dict,

dlclive/display.py

@@ -7,7 +7,9 @@
 
 try:
     from tkinter import Label, Tk
+
     from PIL import ImageTk
+
     _TKINTER_AVAILABLE = True
 except ImportError:
     _TKINTER_AVAILABLE = False
@@ -59,7 +61,9 @@ def set_display(self, im_size, bodyparts):
         self.lab.pack()
 
         all_colors = getattr(cc, self.cmap)
-        self.colors = all_colors[:: int(len(all_colors) / bodyparts)]
+        # Avoid 0 step
+        step = max(1, int(len(all_colors) / bodyparts))
+        self.colors = all_colors[::step]
 
     def display_frame(self, frame, pose=None):
         """
@@ -75,10 +79,10 @@ def display_frame(self, frame, pose=None):
         """
         if not _TKINTER_AVAILABLE:
             raise ImportError("tkinter is not available. Cannot display frames.")
-        
+
         im_size = (frame.shape[1], frame.shape[0])
+        img = Image.fromarray(frame)  # avoid undefined image if pose is None
         if pose is not None:
-            img = Image.fromarray(frame)
             draw = ImageDraw.Draw(img)
 
             if len(pose.shape) == 2:
@@ -91,33 +95,16 @@ def display_frame(self, frame, pose=None):
                 for j in range(pose.shape[1]):
                     if pose[i, j, 2] > self.pcutoff:
                         try:
-                            x0 = (
-                                pose[i, j, 0] - self.radius
-                                if pose[i, j, 0] - self.radius > 0
-                                else 0
-                            )
-                            x1 = (
-                                pose[i, j, 0] + self.radius
-                                if pose[i, j, 0] + self.radius < im_size[0]
-                                else im_size[1]
-                            )
-                            y0 = (
-                                pose[i, j, 1] - self.radius
-                                if pose[i, j, 1] - self.radius > 0
-                                else 0
-                            )
-                            y1 = (
-                                pose[i, j, 1] + self.radius
-                                if pose[i, j, 1] + self.radius < im_size[1]
-                                else im_size[0]
-                            )
+                            x0 = max(0, pose[i, j, 0] - self.radius)
+                            x1 = min(im_size[0], pose[i, j, 0] + self.radius)
+                            y0 = max(0, pose[i, j, 1] - self.radius)
+                            y1 = min(im_size[1], pose[i, j, 1] + self.radius)
                             coords = [x0, y0, x1, y1]
                             draw.ellipse(
                                 coords, fill=self.colors[j], outline=self.colors[j]
                             )
                         except Exception as e:
                             print(e)
-
         img_tk = ImageTk.PhotoImage(image=img, master=self.window)
         self.lab.configure(image=img_tk)
         self.window.update()

dlclive/dlclive.py

@@ -4,6 +4,7 @@
 
 Licensed under GNU Lesser General Public License v3.0
 """
+
 from __future__ import annotations
 
 from pathlib import Path
@@ -197,12 +198,12 @@ def __init__(
         self.processor = processor
         self.convert2rgb = convert2rgb
 
+        self.pose: np.ndarray | None = None
+
         if isinstance(display, Display):
             self.display = display
         elif display:
-            self.display = Display(
-                pcutoff=pcutoff, radius=display_radius, cmap=display_cmap
-            )
+            self.display = Display(pcutoff=pcutoff, radius=display_radius, cmap=display_cmap)
         else:
             self.display = None
 
@@ -250,9 +251,7 @@ def process_frame(self, frame: np.ndarray) -> np.ndarray:
             processed frame: convert type, crop, convert color
         """
         if self.cropping:
-            frame = frame[
-                self.cropping[2] : self.cropping[3], self.cropping[0] : self.cropping[1]
-            ]
+            frame = frame[self.cropping[2] : self.cropping[3], self.cropping[0] : self.cropping[1]]
 
         if self.dynamic[0]:
             if self.pose is not None:
@@ -263,9 +262,7 @@ def process_frame(self, frame: np.ndarray) -> np.ndarray:
                     elif len(self.pose) == 1:
                         pose = self.pose[0]
                     else:
-                        raise ValueError(
-                            "Cannot use Dynamic Cropping - more than 1 individual found"
-                        )
+                        raise ValueError("Cannot use Dynamic Cropping - more than 1 individual found")
 
                 else:
                     pose = self.pose