feat: Enhance play notebook with new data structures and visualization utilities

- Added new TypedDict classes for camera parameters, including Resolution, Intrinsic, and Extrinsic.
- Updated dataset reading logic to accommodate new camera parameters structure.
- Introduced functions for reading datasets by port and visualizing whole body keypoints.
- Improved the affinity matrix calculation logic in the camera module.
- Updated dependencies in pyproject.toml to include Plotly and SciPy for enhanced functionality.

.vscode/settings.json

@@ -1,4 +1,7 @@
 {
     "python.analysis.typeCheckingMode": "basic",
-    "python.analysis.autoImportCompletions": true
+    "python.analysis.autoImportCompletions": true,
+    "cSpell.words": [
+        "triu"
+    ]
 }

app/camera/__init__.py

@@ -350,7 +350,7 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     else:
         camera_wise_split = classify_by_camera(detections)
     num_entries = sum(len(entries) for entries in camera_wise_split.values())
-    affinity_matrix = jnp.zeros((num_entries, num_entries), dtype=jnp.float32)
+    affinity_matrix = jnp.ones((num_entries, num_entries), dtype=jnp.float32) * -jnp.inf
     affinity_matrix_mask = jnp.zeros((num_entries, num_entries), dtype=jnp.bool_)
 
     acc = 0
@@ -361,7 +361,7 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     sorted_detections: list[Detection] = []
     for camera_id, entries in camera_wise_split.items():
         for i, _ in enumerate(entries):
-            camera_id_index_map[camera_id].add(acc + i)
+            camera_id_index_map[camera_id].add(acc)
             sorted_detections.append(entries[i])
             acc += 1
         camera_id_index_map_inverse[camera_id] = (
@@ -374,6 +374,8 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     for i, det in enumerate(sorted_detections):
         other_indices = camera_id_index_map_inverse[det.camera.id]
         for j in other_indices:
+            if i == j:
+                continue
             if affinity_matrix_mask[i, j] or affinity_matrix_mask[j, i]:
                 continue
             a = compute_affinity_epipolar_constraint_with_pairs(

app/solver/__init__.py

@@ -151,7 +151,7 @@ class _BIPSolver:
             clusters[int(label)].append(i)
         return list(clusters.values())
 
-    def solve(self, affinity_matrix: NDArray, rtn_matrix=False):
+    def solve(self, affinity_matrix: NDArray):
         n_nodes = affinity_matrix.shape[0]
         if n_nodes <= 1:
             solution_x, sol_matrix = (
@@ -197,8 +197,6 @@ class _BIPSolver:
             sol_matrix += sol_matrix.T
 
         clusters = self.solution_mat_clusters(sol_matrix)
-        if not rtn_matrix:
-            return clusters
         return clusters, sol_matrix
 
 

app/solver/_old.py

@@ -0,0 +1,221 @@
+import itertools
+from collections import defaultdict
+
+import numpy as np
+from cvxopt import glpk, matrix, spmatrix  # type:ignore
+
+from jaxtyping import jaxtyped, Num
+
+from app._typing import NDArray
+
+glpk.options = {"msg_lev": "GLP_MSG_ERR"}
+
+
+FROZEN_POS_EDGE = -1
+FROZEN_NEG_EDGE = -2
+INVALID_EDGE = -100
+
+
+class _BIPSolver:
+    """
+    Binary Integer Programming solver
+    """
+
+    min_affinity: float
+    max_affinity: float
+
+    def __init__(self, min_affinity: float = -np.inf, max_affinity: float = np.inf):
+        self.min_affinity = min_affinity
+        self.max_affinity = max_affinity
+
+    @staticmethod
+    def _create_bip(
+        affinity_matrix: Num[NDArray, "N N"], min_affinity: float, max_affinity: float
+    ):
+        n_nodes = affinity_matrix.shape[0]
+
+        # mask for selecting pairs of nodes
+        triu_mask = np.triu(np.ones_like(affinity_matrix, dtype=bool), 1)
+
+        affinities = affinity_matrix[triu_mask]
+        frozen_pos_mask = affinities >= max_affinity
+        frozen_neg_mask = affinities <= min_affinity
+        unfrozen_mask = np.logical_not(frozen_pos_mask | frozen_neg_mask)
+
+        # generate objective coefficients
+        objective_coefficients = affinities[unfrozen_mask]
+
+        if len(objective_coefficients) == 0:  # nio unfrozen edges
+
+            objective_coefficients = np.asarray([affinity_matrix[0, -1]])
+            unfrozen_mask = np.zeros_like(unfrozen_mask, dtype=np.bool)
+            unfrozen_mask[affinity_matrix.shape[1] - 1] = 1
+
+        # create matrix whose rows are the indices of the three edges in a
+        # constraint x_ij + x_ik - x_jk <= 1
+        constraints_edges_idx = []
+        if n_nodes >= 3:
+            edges_idx = np.empty_like(affinities, dtype=int)
+            edges_idx[frozen_pos_mask] = FROZEN_POS_EDGE
+            edges_idx[frozen_neg_mask] = FROZEN_NEG_EDGE
+            edges_idx[unfrozen_mask] = np.arange(len(objective_coefficients))
+            nodes_to_edge_matrix = np.empty_like(affinity_matrix, dtype=int)
+            nodes_to_edge_matrix.fill(INVALID_EDGE)
+            nodes_to_edge_matrix[triu_mask] = edges_idx
+
+            triplets = np.asarray(
+                tuple(itertools.combinations(range(n_nodes), 3)), dtype=int
+            )
+            constraints_edges_idx = np.zeros_like(triplets)
+            constraints_edges_idx[:, 0] = nodes_to_edge_matrix[
+                (triplets[:, 0], triplets[:, 1])
+            ]
+            constraints_edges_idx[:, 1] = nodes_to_edge_matrix[
+                (triplets[:, 0], triplets[:, 2])
+            ]
+            constraints_edges_idx[:, 2] = nodes_to_edge_matrix[
+                (triplets[:, 1], triplets[:, 2])
+            ]
+            constraints_edges_idx = constraints_edges_idx[
+                np.any(constraints_edges_idx >= 0, axis=1)
+            ]
+
+        if len(constraints_edges_idx) == 0:  # no constraints
+            constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3)
+
+        # add remaining constraints by permutation
+        constraints_edges_idx = np.vstack(
+            (
+                constraints_edges_idx,
+                np.roll(constraints_edges_idx, 1, axis=1),
+                np.roll(constraints_edges_idx, 2, axis=1),
+            )
+        )
+
+        # clean redundant constraints
+        # x1 + x2 <= 2
+        constraints_edges_idx = constraints_edges_idx[
+            constraints_edges_idx[:, 2] != FROZEN_POS_EDGE
+        ]
+        # x1 - x2 <= 1
+        constraints_edges_idx = constraints_edges_idx[
+            np.all(constraints_edges_idx[:, 0:2] != FROZEN_NEG_EDGE, axis=1)
+        ]
+        if len(constraints_edges_idx) == 0:  # no constraints
+            constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3)
+
+        # generate constraint coefficients
+        constraints_coefficients = np.ones_like(constraints_edges_idx)
+        constraints_coefficients[:, 2] = -1
+
+        # generate constraint upper bounds
+        upper_bounds = np.ones(len(constraints_coefficients), dtype=float)
+        upper_bounds -= np.sum(
+            constraints_coefficients * (constraints_edges_idx == FROZEN_POS_EDGE),
+            axis=1,
+        )
+
+        # flatten constraints data into sparse matrix format
+        constraints_idx = np.repeat(np.arange(len(constraints_edges_idx)), 3)
+        constraints_edges_idx = constraints_edges_idx.reshape(-1)
+        constraints_coefficients = constraints_coefficients.reshape(-1)
+
+        unfrozen_edges = constraints_edges_idx >= 0
+        constraints_idx = constraints_idx[unfrozen_edges]
+        constraints_edges_idx = constraints_edges_idx[unfrozen_edges]
+        constraints_coefficients = constraints_coefficients[unfrozen_edges]
+
+        return (
+            objective_coefficients,
+            unfrozen_mask,
+            frozen_pos_mask,
+            frozen_neg_mask,
+            (constraints_coefficients, constraints_idx, constraints_edges_idx),
+            upper_bounds,
+        )
+
+    @staticmethod
+    def _solve_bip(objective_coefficients, sparse_constraints, upper_bounds):
+        raise NotImplementedError
+
+    @staticmethod
+    def solution_mat_clusters(solution_mat: NDArray) -> list[list[int]]:
+        n = solution_mat.shape[0]
+        labels = np.arange(1, n + 1)
+        for i in range(n):
+            for j in range(i + 1, n):
+                if solution_mat[i, j] > 0:
+                    labels[j] = labels[i]
+
+        clusters = defaultdict(list)
+        for i, label in enumerate(labels):
+            clusters[label].append(i)
+        return list(clusters.values())
+
+    def solve(self, affinity_matrix: Num[NDArray, "N N"]):
+        n_nodes = affinity_matrix.shape[0]
+        if n_nodes <= 1:
+            solution_x, sol_matrix = (
+                np.asarray([], dtype=int),
+                np.asarray([0] * n_nodes, dtype=int),
+            )
+            sol_matrix = sol_matrix[:, None]
+        elif n_nodes == 2:
+            solution_matrix = np.zeros_like(affinity_matrix, dtype=int)
+            solution_matrix[0, 1] = affinity_matrix[0, 1] > 0
+            solution_matrix += solution_matrix.T
+            solution_x = (
+                [solution_matrix[0, 1]]
+                if self.min_affinity < affinity_matrix[0, 1] < self.max_affinity
+                else []
+            )
+            solution_x, sol_matrix = np.asarray(solution_x), solution_matrix
+        else:
+            # create BIP problem
+            (
+                objective_coefficients,
+                unfrozen_mask,
+                frozen_pos_mask,
+                frozen_neg_mask,
+                sparse_constraints,
+                upper_bounds,
+            ) = self._create_bip(affinity_matrix, self.min_affinity, self.max_affinity)
+
+            # solve
+            solution_x = self._solve_bip(
+                objective_coefficients, sparse_constraints, upper_bounds
+            )
+
+            # solution to matrix
+            all_sols = np.zeros_like(unfrozen_mask, dtype=int)
+            all_sols[unfrozen_mask] = np.array(solution_x, dtype=int).reshape(-1)
+            all_sols[frozen_neg_mask] = 0
+            all_sols[frozen_pos_mask] = 1
+            sol_matrix = np.zeros_like(affinity_matrix, dtype=int)
+            sol_matrix[np.triu(np.ones([n_nodes, n_nodes], dtype=int), 1) > 0] = (
+                all_sols
+            )
+            sol_matrix += sol_matrix.T
+
+        clusters = self.solution_mat_clusters(sol_matrix)
+        return clusters, sol_matrix
+
+
+class GLPKSolver(_BIPSolver):
+    def __init__(self, min_affinity=-np.inf, max_affinity=np.inf):
+        super().__init__(min_affinity, max_affinity)
+
+    @staticmethod
+    def _solve_bip(objective_coefficients, sparse_constraints, upper_bounds):
+        c = matrix(-objective_coefficients)  # max -> min
+        G = spmatrix(
+            *sparse_constraints, size=(len(upper_bounds), len(objective_coefficients))
+        )  # G * x <= h
+        # G = spmatrix(sparse_constraints[0],sparse_constraints[1],sparse_constraints[2])  # G * x <= h
+        h = matrix(upper_bounds, tc="d")
+
+        status, solution = glpk.ilp(c, G, h, B=set(range(len(c))))
+
+        assert solution is not None, "Solver error: {}".format(status)
+
+        return np.asarray(solution, int).reshape(-1)

app/visualize/whole_body.py

@@ -0,0 +1,680 @@
+from dataclasses import dataclass, field
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    TypedDict,
+    cast,
+)
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+from beartype import beartype
+from cv2.typing import MatLike
+from jaxtyping import Float, Int, Num, jaxtyped
+
+from app._typing import NDArray
+
+# https://www.researchgate.net/figure/Whole-body-keypoints-as-defined-in-the-COCO-WholeBody-Dataset_fig3_358873962
+# https://github.com/jin-s13/COCO-WholeBody/blob/master/imgs/Fig2_anno.png
+# body landmarks 1-17
+# foot landmarks 18-23 (18-20 right, 21-23 left)
+# face landmarks 24-91
+#   24 start, counterclockwise to 40 as chin
+#   41-45 right eyebrow, 46-50 left eyebrow
+#   https://www.neiltanna.com/face/rhinoplasty/nasal-analysis/
+#   51-54 nose (vertical), 55-59 nose (horizontal)
+#   60-65 right eye, 66-71 left eye
+#   72-83 outer lips (contour, counterclockwise)
+#   ...
+# hand landmarks 92-133 (92-112 right, 113-133 left)
+
+
+Color = Tuple[int, int, int]
+COLOR_SPINE = (138, 201, 38)  # green, spine & head
+COLOR_ARMS = (255, 202, 58)  # yellow, arms & shoulders
+COLOR_LEGS = (25, 130, 196)  # blue, legs & hips
+COLOR_FINGERS = (255, 0, 0)  # red, fingers
+COLOR_FACE = (255, 200, 0)  # yellow, face
+COLOR_FOOT = (255, 128, 0)  # orange, foot
+COLOR_HEAD = (255, 0, 255)  # purple, head
+
+
+@dataclass
+class Landmark:
+    index: int
+    """
+    Note the index is 1-based, corresponding to the COCO WholeBody dataset.
+
+    https://github.com/jin-s13/COCO-WholeBody/blob/master/imgs/Fig2_anno.png
+    """
+    name: str
+    color: Color
+
+    def __post_init__(self):
+        if self.index < 1:
+            raise ValueError(f"Index must be positive, got {self.index}")
+
+    @property
+    def index_base_0(self) -> int:
+        """
+        Returns the 0-based index of the landmark.
+        Useful for indexing into lists or arrays.
+        """
+        return self.index - 1
+
+
+body_landmarks: dict[int, Landmark] = {
+    0: Landmark(index=1, name="nose", color=COLOR_SPINE),
+    1: Landmark(index=2, name="left_eye", color=COLOR_SPINE),
+    2: Landmark(index=3, name="right_eye", color=COLOR_SPINE),
+    3: Landmark(index=4, name="left_ear", color=COLOR_SPINE),
+    4: Landmark(index=5, name="right_ear", color=COLOR_SPINE),
+    5: Landmark(index=6, name="left_shoulder", color=COLOR_ARMS),
+    6: Landmark(index=7, name="right_shoulder", color=COLOR_ARMS),
+    7: Landmark(index=8, name="left_elbow", color=COLOR_ARMS),
+    8: Landmark(index=9, name="right_elbow", color=COLOR_ARMS),
+    9: Landmark(index=10, name="left_wrist", color=COLOR_ARMS),
+    10: Landmark(index=11, name="right_wrist", color=COLOR_ARMS),
+    11: Landmark(index=12, name="left_hip", color=COLOR_LEGS),
+    12: Landmark(index=13, name="right_hip", color=COLOR_LEGS),
+    13: Landmark(index=14, name="left_knee", color=COLOR_LEGS),
+    14: Landmark(index=15, name="right_knee", color=COLOR_LEGS),
+    15: Landmark(index=16, name="left_ankle", color=COLOR_LEGS),
+    16: Landmark(index=17, name="right_ankle", color=COLOR_LEGS),
+}
+
+foot_landmarks: dict[int, Landmark] = {
+    17: Landmark(index=18, name="left_big_toe", color=COLOR_FOOT),
+    18: Landmark(index=19, name="left_small_toe", color=COLOR_FOOT),
+    19: Landmark(index=20, name="left_heel", color=COLOR_FOOT),
+    20: Landmark(index=21, name="right_big_toe", color=COLOR_FOOT),
+    21: Landmark(index=22, name="right_small_toe", color=COLOR_FOOT),
+    22: Landmark(index=23, name="right_heel", color=COLOR_FOOT),
+}
+
+face_landmarks: dict[int, Landmark] = {
+    # Chin contour (24-40)
+    23: Landmark(index=24, name="chin_0", color=COLOR_FACE),
+    24: Landmark(index=25, name="chin_1", color=COLOR_FACE),
+    25: Landmark(index=26, name="chin_2", color=COLOR_FACE),
+    26: Landmark(index=27, name="chin_3", color=COLOR_FACE),
+    27: Landmark(index=28, name="chin_4", color=COLOR_FACE),
+    28: Landmark(index=29, name="chin_5", color=COLOR_FACE),
+    29: Landmark(index=30, name="chin_6", color=COLOR_FACE),
+    30: Landmark(index=31, name="chin_7", color=COLOR_FACE),
+    31: Landmark(index=32, name="chin_8", color=COLOR_FACE),
+    32: Landmark(index=33, name="chin_9", color=COLOR_FACE),
+    33: Landmark(index=34, name="chin_10", color=COLOR_FACE),
+    34: Landmark(index=35, name="chin_11", color=COLOR_FACE),
+    35: Landmark(index=36, name="chin_12", color=COLOR_FACE),
+    36: Landmark(index=37, name="chin_13", color=COLOR_FACE),
+    37: Landmark(index=38, name="chin_14", color=COLOR_FACE),
+    38: Landmark(index=39, name="chin_15", color=COLOR_FACE),
+    39: Landmark(index=40, name="chin_16", color=COLOR_FACE),
+    # Right eyebrow (41-45)
+    40: Landmark(index=41, name="right_eyebrow_0", color=COLOR_FACE),
+    41: Landmark(index=42, name="right_eyebrow_1", color=COLOR_FACE),
+    42: Landmark(index=43, name="right_eyebrow_2", color=COLOR_FACE),
+    43: Landmark(index=44, name="right_eyebrow_3", color=COLOR_FACE),
+    44: Landmark(index=45, name="right_eyebrow_4", color=COLOR_FACE),
+    # Left eyebrow (46-50)
+    45: Landmark(index=46, name="left_eyebrow_0", color=COLOR_FACE),
+    46: Landmark(index=47, name="left_eyebrow_1", color=COLOR_FACE),
+    47: Landmark(index=48, name="left_eyebrow_2", color=COLOR_FACE),
+    48: Landmark(index=49, name="left_eyebrow_3", color=COLOR_FACE),
+    49: Landmark(index=50, name="left_eyebrow_4", color=COLOR_FACE),
+    # Nasal Bridge (51-54)
+    50: Landmark(index=51, name="nasal_bridge_0", color=COLOR_FACE),
+    51: Landmark(index=52, name="nasal_bridge_1", color=COLOR_FACE),
+    52: Landmark(index=53, name="nasal_bridge_2", color=COLOR_FACE),
+    53: Landmark(index=54, name="nasal_bridge_3", color=COLOR_FACE),
+    # Nasal Base (55-59)
+    54: Landmark(index=55, name="nasal_base_0", color=COLOR_FACE),
+    55: Landmark(index=56, name="nasal_base_1", color=COLOR_FACE),
+    56: Landmark(index=57, name="nasal_base_2", color=COLOR_FACE),
+    57: Landmark(index=58, name="nasal_base_3", color=COLOR_FACE),
+    58: Landmark(index=59, name="nasal_base_4", color=COLOR_FACE),
+    # Right eye (60-65)
+    59: Landmark(index=60, name="right_eye_0", color=COLOR_FACE),
+    60: Landmark(index=61, name="right_eye_1", color=COLOR_FACE),
+    61: Landmark(index=62, name="right_eye_2", color=COLOR_FACE),
+    62: Landmark(index=63, name="right_eye_3", color=COLOR_FACE),
+    63: Landmark(index=64, name="right_eye_4", color=COLOR_FACE),
+    64: Landmark(index=65, name="right_eye_5", color=COLOR_FACE),
+    # Left eye (66-71)
+    65: Landmark(index=66, name="left_eye_0", color=COLOR_FACE),
+    66: Landmark(index=67, name="left_eye_1", color=COLOR_FACE),
+    67: Landmark(index=68, name="left_eye_2", color=COLOR_FACE),
+    68: Landmark(index=69, name="left_eye_3", color=COLOR_FACE),
+    69: Landmark(index=70, name="left_eye_4", color=COLOR_FACE),
+    70: Landmark(index=71, name="left_eye_5", color=COLOR_FACE),
+    # lips (72-91)
+    71: Landmark(index=72, name="lip_0", color=COLOR_FACE),
+    72: Landmark(index=73, name="lip_1", color=COLOR_FACE),
+    73: Landmark(index=74, name="lip_2", color=COLOR_FACE),
+    74: Landmark(index=75, name="lip_3", color=COLOR_FACE),
+    75: Landmark(index=76, name="lip_4", color=COLOR_FACE),
+    76: Landmark(index=77, name="lip_5", color=COLOR_FACE),
+    77: Landmark(index=78, name="lip_6", color=COLOR_FACE),
+    78: Landmark(index=79, name="lip_7", color=COLOR_FACE),
+    79: Landmark(index=80, name="lip_8", color=COLOR_FACE),
+    80: Landmark(index=81, name="lip_9", color=COLOR_FACE),
+    81: Landmark(index=82, name="lip_0", color=COLOR_FACE),
+    82: Landmark(index=83, name="lip_1", color=COLOR_FACE),
+    83: Landmark(index=84, name="lip_2", color=COLOR_FACE),
+    84: Landmark(index=85, name="lip_3", color=COLOR_FACE),
+    85: Landmark(index=86, name="lip_4", color=COLOR_FACE),
+    86: Landmark(index=87, name="lip_5", color=COLOR_FACE),
+    87: Landmark(index=88, name="lip_6", color=COLOR_FACE),
+    88: Landmark(index=89, name="lip_7", color=COLOR_FACE),
+    89: Landmark(index=90, name="lip_8", color=COLOR_FACE),
+    90: Landmark(index=91, name="lip_9", color=COLOR_FACE),
+}
+
+hand_landmarks: dict[int, Landmark] = {
+    # Right hand (92-112)
+    91: Landmark(index=92, name="right_wrist", color=COLOR_FINGERS),  # wrist/carpus
+    92: Landmark(
+        index=93, name="right_thumb_metacarpal", color=COLOR_FINGERS
+    ),  # thumb metacarpal
+    93: Landmark(
+        index=94, name="right_thumb_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    94: Landmark(
+        index=95, name="right_thumb_ip", color=COLOR_FINGERS
+    ),  # interphalangeal joint
+    95: Landmark(index=96, name="right_thumb_tip", color=COLOR_FINGERS),  # tip of thumb
+    96: Landmark(
+        index=97, name="right_index_metacarpal", color=COLOR_FINGERS
+    ),  # index metacarpal
+    97: Landmark(
+        index=98, name="right_index_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    98: Landmark(
+        index=99, name="right_index_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    99: Landmark(
+        index=100, name="right_index_tip", color=COLOR_FINGERS
+    ),  # tip of index
+    100: Landmark(
+        index=101, name="right_middle_metacarpal", color=COLOR_FINGERS
+    ),  # middle metacarpal
+    101: Landmark(
+        index=102, name="right_middle_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    102: Landmark(
+        index=103, name="right_middle_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    103: Landmark(
+        index=104, name="right_middle_tip", color=COLOR_FINGERS
+    ),  # tip of middle
+    104: Landmark(
+        index=105, name="right_ring_metacarpal", color=COLOR_FINGERS
+    ),  # ring metacarpal
+    105: Landmark(
+        index=106, name="right_ring_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    106: Landmark(
+        index=107, name="right_ring_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    107: Landmark(index=108, name="right_ring_tip", color=COLOR_FINGERS),  # tip of ring
+    108: Landmark(
+        index=109, name="right_pinky_metacarpal", color=COLOR_FINGERS
+    ),  # pinky metacarpal
+    109: Landmark(
+        index=110, name="right_pinky_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    110: Landmark(
+        index=111, name="right_pinky_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    111: Landmark(
+        index=112, name="right_pinky_tip", color=COLOR_FINGERS
+    ),  # tip of pinky
+    # Left hand (113-133)
+    112: Landmark(index=113, name="left_wrist", color=COLOR_FINGERS),  # wrist/carpus
+    113: Landmark(
+        index=114, name="left_thumb_metacarpal", color=COLOR_FINGERS
+    ),  # thumb metacarpal
+    114: Landmark(
+        index=115, name="left_thumb_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    115: Landmark(
+        index=116, name="left_thumb_ip", color=COLOR_FINGERS
+    ),  # interphalangeal joint
+    116: Landmark(
+        index=117, name="left_thumb_tip", color=COLOR_FINGERS
+    ),  # tip of thumb
+    117: Landmark(
+        index=118, name="left_index_metacarpal", color=COLOR_FINGERS
+    ),  # index metacarpal
+    118: Landmark(
+        index=119, name="left_index_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    119: Landmark(
+        index=120, name="left_index_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    120: Landmark(
+        index=121, name="left_index_tip", color=COLOR_FINGERS
+    ),  # tip of index
+    121: Landmark(
+        index=122, name="left_middle_metacarpal", color=COLOR_FINGERS
+    ),  # middle metacarpal
+    122: Landmark(
+        index=123, name="left_middle_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    123: Landmark(
+        index=124, name="left_middle_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    124: Landmark(
+        index=125, name="left_middle_tip", color=COLOR_FINGERS
+    ),  # tip of middle
+    125: Landmark(
+        index=126, name="left_ring_metacarpal", color=COLOR_FINGERS
+    ),  # ring metacarpal
+    126: Landmark(
+        index=127, name="left_ring_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    127: Landmark(
+        index=128, name="left_ring_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    128: Landmark(index=129, name="left_ring_tip", color=COLOR_FINGERS),  # tip of ring
+    129: Landmark(
+        index=130, name="left_pinky_metacarpal", color=COLOR_FINGERS
+    ),  # pinky metacarpal
+    130: Landmark(
+        index=131, name="left_pinky_mcp", color=COLOR_FINGERS
+    ),  # metacarpophalangeal joint
+    131: Landmark(
+        index=132, name="left_pinky_pip", color=COLOR_FINGERS
+    ),  # proximal interphalangeal joint
+    132: Landmark(
+        index=133, name="left_pinky_tip", color=COLOR_FINGERS
+    ),  # tip of pinky
+}
+"""
+Key corrections made:
+    1.	Each finger has a metacarpal bone in the palm
+    2.	Used standard anatomical abbreviations:
+        - MCP: MetaCarpoPhalangeal joint
+        - PIP: Proximal InterPhalangeal joint
+        - IP: InterPhalangeal joint (for thumb)
+    3.	The thumb has a different structure:
+        - Only one interphalangeal joint (IP)
+        - Different metacarpal orientation
+    4.	Used "tip" instead of specific phalanx names for endpoints
+    5.	Removed redundant bone naming since landmarks represent joints/connections
+This better reflects the actual skeletal and joint structure of human hands while maintaining compatibility with the COCO-WholeBody dataset's keypoint system.
+"""
+
+skeleton_joints = {
+    **body_landmarks,
+    **foot_landmarks,
+    **face_landmarks,
+    **hand_landmarks,
+}
+
+
+@dataclass
+class Bone:
+    start: Landmark
+    end: Landmark
+    name: str
+    color: Color
+
+    @staticmethod
+    def from_landmarks(
+        landmarks: Iterable[Landmark],
+        start_idx: int,
+        end_idx: int,
+        name: str,
+        color: Color,
+    ) -> "Bone":
+        """
+        Note that the start and end indices are 1-based, corresponding to the COCO WholeBody dataset.
+        """
+        start = next(filter(lambda x: x.index == start_idx, landmarks))
+        end = next(filter(lambda x: x.index == end_idx, landmarks))
+        return Bone(start=start, end=end, name=name, color=color)
+
+
+# Note it's 0-based
+# (15, 13), (13, 11), (16, 14), (14, 12), (11, 12), # 腿部
+# (5, 11), (6, 12), (5, 6),                         # 臀部和躯干
+# (5, 7), (7, 9), (6, 8), (8, 10),                  # 手臂
+# (1, 2), (0, 1), (0, 2), (1, 3), (2, 4),           # 头部
+# (15, 17), (15, 18), (15, 19),                     # 左脚
+# (16, 20), (16, 21), (16, 22),                     # 右脚
+body_bones: list[Bone] = [
+    # legs
+    Bone.from_landmarks(
+        skeleton_joints.values(), 16, 14, "left_tibia", COLOR_LEGS
+    ),  # tibia & fibula
+    Bone.from_landmarks(skeleton_joints.values(), 14, 12, "left_femur", COLOR_LEGS),
+    Bone.from_landmarks(skeleton_joints.values(), 17, 15, "right_tibia", COLOR_LEGS),
+    Bone.from_landmarks(skeleton_joints.values(), 15, 13, "right_femur", COLOR_LEGS),
+    Bone.from_landmarks(skeleton_joints.values(), 12, 13, "pelvis", COLOR_LEGS),
+    # torso
+    Bone.from_landmarks(
+        skeleton_joints.values(), 6, 12, "left_contour", COLOR_SPINE
+    ),  # contour of rib cage & pelvis (parallel to spine)
+    Bone.from_landmarks(skeleton_joints.values(), 7, 13, "right_contour", COLOR_SPINE),
+    Bone.from_landmarks(skeleton_joints.values(), 6, 7, "clavicle", COLOR_SPINE),
+    # arms
+    Bone.from_landmarks(
+        skeleton_joints.values(), 6, 8, "left_humerus", COLOR_ARMS
+    ),  # humerus
+    Bone.from_landmarks(
+        skeleton_joints.values(), 8, 10, "left_radius", COLOR_ARMS
+    ),  # radius & ulna
+    Bone.from_landmarks(skeleton_joints.values(), 7, 9, "right_humerus", COLOR_ARMS),
+    Bone.from_landmarks(skeleton_joints.values(), 9, 11, "right_radius", COLOR_ARMS),
+    # head
+    Bone.from_landmarks(skeleton_joints.values(), 2, 3, "head", COLOR_HEAD),
+    Bone.from_landmarks(skeleton_joints.values(), 1, 2, "left_eye", COLOR_HEAD),
+    Bone.from_landmarks(skeleton_joints.values(), 1, 3, "right_eye", COLOR_HEAD),
+    Bone.from_landmarks(skeleton_joints.values(), 2, 4, "left_ear", COLOR_HEAD),
+    Bone.from_landmarks(skeleton_joints.values(), 3, 5, "right_ear", COLOR_HEAD),
+    # foot
+    Bone.from_landmarks(skeleton_joints.values(), 16, 18, "left_foot_toe", COLOR_FOOT),
+    Bone.from_landmarks(
+        skeleton_joints.values(), 16, 19, "left_foot_small_toe", COLOR_FOOT
+    ),
+    Bone.from_landmarks(skeleton_joints.values(), 16, 20, "left_foot_heel", COLOR_FOOT),
+    Bone.from_landmarks(skeleton_joints.values(), 17, 21, "right_foot_toe", COLOR_FOOT),
+    Bone.from_landmarks(
+        skeleton_joints.values(), 17, 22, "right_foot_small_toe", COLOR_FOOT
+    ),
+    Bone.from_landmarks(
+        skeleton_joints.values(), 17, 23, "right_foot_heel", COLOR_FOOT
+    ),
+]
+
+# note it's 0-based
+# (91, 92), (92, 93), (93, 94), (94, 95),           # 左拇指
+# (91, 96), (96, 97), (97, 98), (98, 99),           # 左食指
+# (91, 100), (100, 101), (101, 102), (102, 103),    # 左中指
+# (91, 104), (104, 105), (105, 106), (106, 107),    # 左无名指
+# (91, 108), (108, 109), (109, 110), (110, 111),    # 左小指
+# (112, 113), (113, 114), (114, 115), (115, 116),   # 右拇指
+# (112, 117), (117, 118), (118, 119), (119, 120),   # 右食指
+# (112, 121), (121, 122), (122, 123), (123, 124),   # 右中指
+# (112, 125), (125, 126), (126, 127), (127, 128),   # 右无名指
+# (112, 129), (129, 130), (130, 131), (131, 132)    # 右小指
+hand_bones: list[Bone] = [
+    # Right Thumb (Pollex)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 92, 93, "right_thumb_metacarpal", COLOR_FINGERS
+    ),  # First metacarpal
+    Bone.from_landmarks(
+        hand_landmarks.values(), 93, 94, "right_thumb_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 94, 95, "right_thumb_distal_phalanx", COLOR_FINGERS
+    ),
+    # Right Index (Digit II)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 92, 97, "right_index_metacarpal", COLOR_FINGERS
+    ),  # Second metacarpal
+    Bone.from_landmarks(
+        hand_landmarks.values(), 97, 98, "right_index_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 98, 99, "right_index_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 99, 100, "right_index_distal_phalanx", COLOR_FINGERS
+    ),
+    # Right Middle (Digit III)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 92, 101, "right_middle_metacarpal", COLOR_FINGERS
+    ),  # Third metacarpal
+    Bone.from_landmarks(
+        hand_landmarks.values(),
+        101,
+        102,
+        "right_middle_proximal_phalanx",
+        COLOR_FINGERS,
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 102, 103, "right_middle_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 103, 104, "right_middle_distal_phalanx", COLOR_FINGERS
+    ),
+    # Right Ring (Digit IV)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 92, 105, "right_ring_metacarpal", COLOR_FINGERS
+    ),  # Fourth metacarpal
+    Bone.from_landmarks(
+        hand_landmarks.values(), 105, 106, "right_ring_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 106, 107, "right_ring_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 107, 108, "right_ring_distal_phalanx", COLOR_FINGERS
+    ),
+    # Right Pinky (Digit V)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 92, 109, "right_pinky_metacarpal", COLOR_FINGERS
+    ),  # Fifth metacarpal
+    Bone.from_landmarks(
+        hand_landmarks.values(), 109, 110, "right_pinky_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 110, 111, "right_pinky_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 111, 112, "right_pinky_distal_phalanx", COLOR_FINGERS
+    ),
+    # Left Thumb (Pollex)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 113, 114, "left_thumb_metacarpal", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 114, 115, "left_thumb_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 115, 116, "left_thumb_distal_phalanx", COLOR_FINGERS
+    ),
+    # Left Index (Digit II)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 113, 118, "left_index_metacarpal", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 118, 119, "left_index_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 119, 120, "left_index_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 120, 121, "left_index_distal_phalanx", COLOR_FINGERS
+    ),
+    # Left Middle (Digit III)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 113, 122, "left_middle_metacarpal", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 122, 123, "left_middle_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 123, 124, "left_middle_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 124, 125, "left_middle_distal_phalanx", COLOR_FINGERS
+    ),
+    # Left Ring (Digit IV)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 113, 126, "left_ring_metacarpal", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 126, 127, "left_ring_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 127, 128, "left_ring_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 128, 129, "left_ring_distal_phalanx", COLOR_FINGERS
+    ),
+    # Left Pinky (Digit V)
+    Bone.from_landmarks(
+        hand_landmarks.values(), 113, 130, "left_pinky_metacarpal", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 130, 131, "left_pinky_proximal_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 131, 132, "left_pinky_middle_phalanx", COLOR_FINGERS
+    ),
+    Bone.from_landmarks(
+        hand_landmarks.values(), 132, 133, "left_pinky_distal_phalanx", COLOR_FINGERS
+    ),
+]
+"""
+Key points about the hand bone structure:
+1. Each finger (except thumb) has:
+   - Connection to metacarpal
+   - Proximal phalanx
+   - Middle phalanx
+   - Distal phalanx
+2. Thumb is unique with:
+   - Metacarpal
+   - Proximal phalanx
+   - Distal phalanx (no middle phalanx)
+3. All fingers connect back to the wrist (index 92 for right hand, 113 for left hand)
+4. The anatomical names include the proper terms for each digit (Pollex for thumb, Digits II-V for fingers)
+"""
+
+total_bones = body_bones + hand_bones
+
+
+@jaxtyped(typechecker=beartype)
+def visualize_whole_body(
+    keypoints: Num[NDArray, "133 2"],
+    frame: MatLike,
+    # keyword arguements
+    # kwargs
+    landmark_size: int = 1,
+    bone_size: int = 2,
+    output: Optional[MatLike] = None,
+    confidences: Optional[Num[NDArray, "133 1"]] = None,
+    confidence_threshold: float = 0.1,
+):
+    """
+    Visualize the whole body keypoints on the given frame.
+    """
+    if output is None:
+        output = frame.copy()
+    for bone in total_bones:
+        start = keypoints[bone.start.index_base_0]
+        end = keypoints[bone.end.index_base_0]
+        start = tuple(start.astype(int))
+        end = tuple(end.astype(int))
+        if (
+            confidences is not None
+            and confidences[bone.start.index_base_0] < confidence_threshold
+            and confidences[bone.end.index_base_0] < confidence_threshold
+        ):
+            continue
+        cv2.line(output, start, end, bone.color, bone_size)
+    for landmark in skeleton_joints.values():
+        point = keypoints[landmark.index_base_0]
+        point = tuple(point.astype(int))
+        if (
+            confidences is not None
+            and confidences[landmark.index_base_0] < confidence_threshold
+        ):
+            continue
+        cv2.circle(output, point, landmark_size, landmark.color, -1)
+    return output
+
+
+@jaxtyped(typechecker=beartype)
+def visualize_17_keypoints(
+    keypoints: Num[NDArray, "17 2"],
+    frame: MatLike,
+    output: Optional[MatLike] = None,
+    confidences: Optional[Num[NDArray, "17 1"]] = None,
+    confidence_threshold: float = 0.1,
+    landmark_size: int = 1,
+    bone_size: int = 2,
+):
+    """
+    Visualize the first 17 keypoints on the given frame.
+    """
+    if output is None:
+        output = frame.copy()
+    for bone in total_bones[:17]:
+        start = keypoints[bone.start.index_base_0]
+        end = keypoints[bone.end.index_base_0]
+        start = tuple(start.astype(int))
+        end = tuple(end.astype(int))
+        if (
+            confidences is not None
+            and confidences[bone.start.index_base_0] < confidence_threshold
+            and confidences[bone.end.index_base_0] < confidence_threshold
+        ):
+            continue
+        cv2.line(output, start, end, bone.color, bone_size)
+    for landmark in list(body_landmarks.values())[:17]:
+        point = keypoints[landmark.index_base_0]
+        point = tuple(point.astype(int))
+        if (
+            confidences is not None
+            and confidences[landmark.index_base_0] < confidence_threshold
+        ):
+            continue
+        cv2.circle(output, point, landmark_size, landmark.color, -1)
+    return output
+
+
+@jaxtyped(typechecker=beartype)
+def visualize_whole_body_many(
+    keypoints: Num[NDArray, "N 133 2"],
+    frame: MatLike,
+    landmark_size: int = 1,
+    bone_size: int = 2,
+    output: Optional[MatLike] = None,
+    confidences: Optional[Num[NDArray, "N 133 1"]] = None,
+    confidence_threshold: float = 0.1,
+):
+    """
+    Visualize a batch of whole body keypoints on the given frame.
+    """
+    if len(keypoints) == 0:
+        return frame
+    if output is None:
+        output = frame.copy()
+    if confidences is None:
+        for keypoint in keypoints:
+            output = visualize_whole_body(
+                keypoint,
+                frame,
+                landmark_size,
+                bone_size,
+                output=output,
+                confidences=None,
+            )
+        return output
+    if confidences is not None:
+        assert len(keypoints) == len(
+            confidences
+        ), f"Expected same length, got {len(keypoints)} and {len(confidences)}"
+        for keypoint, confidence in zip(keypoints, confidences):
+            output = visualize_whole_body(
+                keypoint,
+                frame,
+                landmark_size,
+                bone_size,
+                output=output,
+                confidences=confidence,
+                confidence_threshold=confidence_threshold,
+            )
+        return output

pyproject.toml

@@ -12,10 +12,12 @@ dependencies = [
     "jax[cuda12]>=0.5.1",
     "jaxtyping>=0.2.38",
     "matplotlib>=3.10.1",
-    "opencv-contrib-python-headless>=4.11.0.86",
+    "opencv-python-headless>=4.11.0.86",
     "orjson>=3.10.15",
     "pandas>=2.2.3",
+    "plotly>=6.0.1",
     "pyarrow>=19.0.1",
+    "scipy>=1.15.2",
     "torch>=2.6.0",
     "torchvision>=0.21.0",
     "typeguard>=4.4.2",

uv.lock

@@ -451,10 +451,12 @@ dependencies = [
     { name = "jax", extra = ["cuda12"] },
     { name = "jaxtyping" },
     { name = "matplotlib" },
-    { name = "opencv-contrib-python-headless" },
+    { name = "opencv-python-headless" },
     { name = "orjson" },
     { name = "pandas" },
+    { name = "plotly" },
     { name = "pyarrow" },
+    { name = "scipy" },
     { name = "torch" },
     { name = "torchvision" },
     { name = "typeguard" },
@@ -474,10 +476,12 @@ requires-dist = [
     { name = "jax", extras = ["cuda12"], specifier = ">=0.5.1" },
     { name = "jaxtyping", specifier = ">=0.2.38" },
     { name = "matplotlib", specifier = ">=3.10.1" },
-    { name = "opencv-contrib-python-headless", specifier = ">=4.11.0.86" },
+    { name = "opencv-python-headless", specifier = ">=4.11.0.86" },
     { name = "orjson", specifier = ">=3.10.15" },
     { name = "pandas", specifier = ">=2.2.3" },
+    { name = "plotly", specifier = ">=6.0.1" },
     { name = "pyarrow", specifier = ">=19.0.1" },
+    { name = "scipy", specifier = ">=1.15.2" },
     { name = "torch", specifier = ">=2.6.0" },
     { name = "torchvision", specifier = ">=0.21.0" },
     { name = "typeguard", specifier = ">=4.4.2" },
@@ -1501,6 +1505,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 },
 ]
 
+[[package]]
+name = "narwhals"
+version = "1.35.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/6a/a98fa5e9d530a428a0cd79d27f059ed65efd3a07aad61a8c93e323c9c20b/narwhals-1.35.0.tar.gz", hash = "sha256:07477d18487fbc940243b69818a177ed7119b737910a8a254fb67688b48a7c96", size = 265784 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/80/b3/5781eb874f04cb1e882a7d93cf30abcb00362a3205c5f3708a7434a1a2ac/narwhals-1.35.0-py3-none-any.whl", hash = "sha256:7562af132fa3f8aaaf34dc96d7ec95bdca29d1c795e8fcf14e01edf1d32122bc", size = 325708 },
+]
+
 [[package]]
 name = "nbclient"
 version = "0.10.2"
@@ -1808,20 +1821,20 @@ wheels = [
 ]
 
 [[package]]
-name = "opencv-contrib-python-headless"
+name = "opencv-python-headless"
 version = "4.11.0.86"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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