diff --git a/app/tracking/__init__.py b/app/tracking/__init__.py index ab7d52a..860ab0b 100644 --- a/app/tracking/__init__.py +++ b/app/tracking/__init__.py @@ -1,14 +1,20 @@ +import warnings +import weakref +from collections import deque from dataclasses import dataclass -from datetime import datetime +from datetime import datetime, timedelta +from itertools import chain from typing import ( Any, Callable, Generator, Optional, + Protocol, Sequence, TypeAlias, TypedDict, TypeVar, + Union, cast, overload, ) @@ -18,18 +24,428 @@ from beartype import beartype from beartype.typing import Mapping, Sequence from jax import Array from jaxtyping import Array, Float, Int, jaxtyped -from pyrsistent import PVector +from pyrsistent import PVector, v, PRecord, PMap -from app.camera import Detection +from app.camera import Detection, CameraID + +TrackingID: TypeAlias = int + + +class TrackingPrediction(TypedDict): + velocity: Optional[Float[Array, "J 3"]] + keypoints: Float[Array, "J 3"] + + +class GenericVelocityFilter(Protocol): + """ + a filter interface for tracking velocity estimation + """ + + def predict(self, timestamp: datetime) -> TrackingPrediction: + """ + predict the velocity and the keypoints location + + Args: + timestamp: timestamp of the prediction + + Returns: + velocity: velocity of the tracking + keypoints: keypoints of the tracking + """ + ... # pylint: disable=unnecessary-ellipsis + + def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: + """ + update the filter state with new measurements + + Args: + keypoints: new measurements + timestamp: timestamp of the update + """ + ... # pylint: disable=unnecessary-ellipsis + + def get(self) -> TrackingPrediction: + """ + get the current state of the filter state + + Returns: + velocity: velocity of the tracking + keypoints: keypoints of the tracking + """ + ... # pylint: disable=unnecessary-ellipsis + + +class DummyVelocityFilter(GenericVelocityFilter): + """ + a dummy velocity filter that does nothing + """ + + _keypoints_shape: tuple[int, ...] + + def __init__(self, keypoints: Float[Array, "J 3"]): + self._keypoints_shape = keypoints.shape + + def predict(self, timestamp: datetime) -> TrackingPrediction: + return TrackingPrediction( + velocity=None, + keypoints=jnp.zeros(self._keypoints_shape), + ) + + def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: ... + + def get(self) -> TrackingPrediction: + return TrackingPrediction( + velocity=None, + keypoints=jnp.zeros(self._keypoints_shape), + ) + + +class LastDifferenceVelocityFilter(GenericVelocityFilter): + """ + a naive velocity filter that uses the last difference of keypoints + """ + + _last_timestamp: datetime + _last_keypoints: Float[Array, "J 3"] + _last_velocity: Optional[Float[Array, "J 3"]] = None + + def __init__(self, keypoints: Float[Array, "J 3"], timestamp: datetime): + self._last_keypoints = keypoints + self._last_timestamp = timestamp + + def predict(self, timestamp: datetime) -> TrackingPrediction: + delta_t_s = (timestamp - self._last_timestamp).total_seconds() + if delta_t_s <= 0: + warnings.warn( + "delta_t={}; last={}; current={}".format( + delta_t_s, self._last_timestamp, timestamp + ) + ) + if self._last_velocity is None: + return TrackingPrediction( + velocity=None, + keypoints=self._last_keypoints, + ) + else: + if delta_t_s <= 0: + return TrackingPrediction( + velocity=self._last_velocity, + keypoints=self._last_keypoints, + ) + return TrackingPrediction( + velocity=self._last_velocity, + keypoints=self._last_keypoints + self._last_velocity * delta_t_s, + ) + + def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: + delta_t_s = (timestamp - self._last_timestamp).total_seconds() + if delta_t_s <= 0: + pass + else: + self._last_timestamp = timestamp + self._last_velocity = (keypoints - self._last_keypoints) / delta_t_s + self._last_keypoints = keypoints + + def get(self) -> TrackingPrediction: + if self._last_velocity is None: + return TrackingPrediction( + velocity=None, + keypoints=self._last_keypoints, + ) + else: + return TrackingPrediction( + velocity=self._last_velocity, + keypoints=self._last_keypoints, + ) + + +class LeastMeanSquareVelocityFilter(GenericVelocityFilter): + """ + a velocity filter that uses the least mean square method to estimate the velocity + """ + + _historical_3d_poses: deque[Float[Array, "J 3"]] + _historical_timestamps: deque[datetime] + _velocity: Optional[Float[Array, "J 3"]] = None + _max_samples: int + + def __init__( + self, + historical_3d_poses: Sequence[Float[Array, "J 3"]], + historical_timestamps: Sequence[datetime], + max_samples: int = 10, + ): + assert len(historical_3d_poses) == len(historical_timestamps) + temp = zip(historical_3d_poses, historical_timestamps) + temp_sorted = sorted(temp, key=lambda x: x[1]) + self._historical_3d_poses = deque( + map(lambda x: x[0], temp_sorted), maxlen=max_samples + ) + self._historical_timestamps = deque( + map(lambda x: x[1], temp_sorted), maxlen=max_samples + ) + self._max_samples = max_samples + if len(self._historical_3d_poses) < 2: + self._velocity = None + else: + self._update( + jnp.array(self._historical_3d_poses), + jnp.array(self._historical_timestamps), + ) + + def predict(self, timestamp: datetime) -> TrackingPrediction: + if not self._historical_3d_poses: + raise ValueError("No historical 3D poses available for prediction") + + # use the latest historical detection + latest_3d_pose = self._historical_3d_poses[-1] + latest_timestamp = self._historical_timestamps[-1] + + delta_t_s = (timestamp - latest_timestamp).total_seconds() + + if self._velocity is None: + return TrackingPrediction( + velocity=None, + keypoints=latest_3d_pose, + ) + else: + # Linear motion model: ẋt = xt' + Vt' · (t - t') + predicted_3d_pose = latest_3d_pose + self._velocity * delta_t_s + return TrackingPrediction( + velocity=self._velocity, keypoints=predicted_3d_pose + ) + + @jaxtyped(typechecker=beartype) + def _update( + self, + keypoints: Float[Array, "N J 3"], + timestamps: Float[Array, "N"], + ) -> None: + """ + update measurements with least mean square method + """ + if keypoints.shape[0] < 2: + raise ValueError("Not enough measurements to estimate velocity") + + # Using least squares to fit a linear model for each joint and dimension + # X = timestamps, y = keypoints + # For each joint and each dimension, we solve for velocity + + n_samples = timestamps.shape[0] + n_joints = keypoints.shape[1] + + # Create design matrix for linear regression + # [t, 1] for each timestamp + X = jnp.column_stack([timestamps, jnp.ones(n_samples)]) + + # Reshape keypoints to solve for all joints and dimensions at once + # From [N, J, 3] to [N, J*3] + keypoints_reshaped = keypoints.reshape(n_samples, -1) + + # Use JAX's lstsq to solve the least squares problem + # This is more numerically stable than manually computing pseudoinverse + coefficients, _, _, _ = jnp.linalg.lstsq(X, keypoints_reshaped, rcond=None) + + # Coefficients shape is [2, J*3] + # First row: velocities, Second row: intercepts + velocities = coefficients[0].reshape(n_joints, 3) + + # Update velocity + self._velocity = velocities + + def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: + last_timestamp = self._historical_timestamps[-1] + assert last_timestamp <= timestamp + + # deque would manage the maxlen automatically + self._historical_3d_poses.append(keypoints) + self._historical_timestamps.append(timestamp) + + t_0 = self._historical_timestamps[0] + all_keypoints = jnp.array(self._historical_3d_poses) + + def timestamp_to_seconds(timestamp: datetime) -> float: + assert t_0 <= timestamp + return (timestamp - t_0).total_seconds() + + # timestamps relative to t_0 (the oldest detection timestamp) + all_timestamps = jnp.array( + map(timestamp_to_seconds, self._historical_timestamps) + ) + + self._update(all_keypoints, all_timestamps) + + def get(self) -> TrackingPrediction: + if not self._historical_3d_poses: + raise ValueError("No historical 3D poses available") + + latest_3d_pose = self._historical_3d_poses[-1] + + if self._velocity is None: + return TrackingPrediction(velocity=None, keypoints=latest_3d_pose) + else: + return TrackingPrediction(velocity=self._velocity, keypoints=latest_3d_pose) + + +class OneEuroFilter(GenericVelocityFilter): + """ + Implementation of the 1€ filter (One Euro Filter) for smoothing keypoint data. + + The 1€ filter is an adaptive low-pass filter that adjusts its cutoff frequency + based on movement speed to reduce jitter during slow movements while maintaining + responsiveness during fast movements. + + Reference: https://cristal.univ-lille.fr/~casiez/1euro/ + """ + + _x_filtered: Float[Array, "J 3"] + _dx_filtered: Optional[Float[Array, "J 3"]] = None + _last_timestamp: datetime + _min_cutoff: float + _beta: float + _d_cutoff: float + + def __init__( + self, + keypoints: Float[Array, "J 3"], + timestamp: datetime, + min_cutoff: float = 1.0, + beta: float = 0.0, + d_cutoff: float = 1.0, + ): + """ + Initialize the One Euro Filter. + + Args: + keypoints: Initial keypoints positions + timestamp: Initial timestamp + min_cutoff: Minimum cutoff frequency (lower = more smoothing) + beta: Speed coefficient (higher = less lag during fast movements) + d_cutoff: Cutoff frequency for the derivative filter + """ + self._last_timestamp = timestamp + + # Filter parameters + self._min_cutoff = min_cutoff + self._beta = beta + self._d_cutoff = d_cutoff + + # Filter state + self._x_filtered = keypoints # Position filter state + self._dx_filtered = None # Initially no velocity estimate + + @overload + def _smoothing_factor(self, cutoff: float, dt: float) -> float: ... + + @overload + def _smoothing_factor( + self, cutoff: Float[Array, "J"], dt: float + ) -> Float[Array, "J"]: ... + + @jaxtyped(typechecker=beartype) + def _smoothing_factor( + self, cutoff: Union[float, Float[Array, "J"]], dt: float + ) -> Union[float, Float[Array, "J"]]: + """Calculate the smoothing factor for the low-pass filter.""" + r = 2 * jnp.pi * cutoff * dt + return r / (r + 1) + + @jaxtyped(typechecker=beartype) + def _exponential_smoothing( + self, + a: Union[float, Float[Array, "J"]], + x: Float[Array, "J 3"], + x_prev: Float[Array, "J 3"], + ) -> Float[Array, "J 3"]: + """Apply exponential smoothing to the input.""" + return a * x + (1 - a) * x_prev + + def predict(self, timestamp: datetime) -> TrackingPrediction: + """ + Predict keypoints position at a given timestamp. + + Args: + timestamp: Timestamp for prediction + + Returns: + TrackingPrediction with velocity and keypoints + """ + dt = (timestamp - self._last_timestamp).total_seconds() + + if self._dx_filtered is None: + return TrackingPrediction( + velocity=None, + keypoints=self._x_filtered, + ) + else: + predicted_keypoints = self._x_filtered + self._dx_filtered * dt + return TrackingPrediction( + velocity=self._dx_filtered, + keypoints=predicted_keypoints, + ) + + def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: + """ + Update the filter with new measurements. + + Args: + keypoints: New keypoint measurements + timestamp: Timestamp of the measurements + """ + dt = (timestamp - self._last_timestamp).total_seconds() + if dt <= 0: + raise ValueError( + f"new timestamp is not greater than the last timestamp; expecting: {timestamp} > {self._last_timestamp}" + ) + + dx = (keypoints - self._x_filtered) / dt + + # Determine cutoff frequency based on movement speed + cutoff = self._min_cutoff + self._beta * jnp.linalg.norm( + dx, axis=-1, keepdims=True + ) + + # Apply low-pass filter to velocity + a_d = self._smoothing_factor(self._d_cutoff, dt) + self._dx_filtered = self._exponential_smoothing( + a_d, + dx, + ( + jnp.zeros_like(keypoints) + if self._dx_filtered is None + else self._dx_filtered + ), + ) + + # Apply low-pass filter to position with adaptive cutoff + a_cutoff = self._smoothing_factor(jnp.asarray(cutoff), dt) + self._x_filtered = self._exponential_smoothing( + a_cutoff, keypoints, self._x_filtered + ) + + # Update timestamp + self._last_timestamp = timestamp + + def get(self) -> TrackingPrediction: + """ + Get the current state of the filter. + + Returns: + TrackingPrediction with velocity and keypoints + """ + return TrackingPrediction( + velocity=self._dx_filtered, + keypoints=self._x_filtered, + ) @jaxtyped(typechecker=beartype) @dataclass(frozen=True) -class Tracking: - id: int +class TrackingState: """ - The tracking id + immutable state of a tracking """ + keypoints: Float[Array, "J 3"] """ The 3D keypoints of the tracking @@ -41,50 +457,97 @@ class Tracking: The last active timestamp of the tracking """ - historical_detections: PVector[Detection] + historical_detections_by_camera: PMap[CameraID, Detection] """ Historical detections of the tracking. Used for 3D re-triangulation """ - velocity: Optional[Float[Array, "3"]] = None - """ - Could be `None`. Like when the 3D pose is initialized. - `velocity` should be updated when target association yields a new - 3D pose. - """ +class Tracking: + id: TrackingID + state: TrackingState + velocity_filter: GenericVelocityFilter + + def __init__( + self, + id: TrackingID, + state: TrackingState, + velocity_filter: Optional[GenericVelocityFilter] = None, + ): + self.id = id + self.state = state + self.velocity_filter = velocity_filter or DummyVelocityFilter(state.keypoints) def __repr__(self) -> str: - return f"Tracking({self.id}, {self.last_active_timestamp})" + return f"Tracking({self.id}, {self.state.last_active_timestamp})" + + @overload + def predict(self, time: float) -> Float[Array, "J 3"]: + """ + predict the keypoints at a given time + + Args: + time: the time in seconds to predict the keypoints + + Returns: + the predicted keypoints + """ + ... # pylint: disable=unnecessary-ellipsis + + @overload + def predict(self, time: timedelta) -> Float[Array, "J 3"]: + """ + predict the keypoints at a given time + + Args: + time: the time delta to predict the keypoints + """ + ... # pylint: disable=unnecessary-ellipsis + + @overload + def predict(self, time: datetime) -> Float[Array, "J 3"]: + """ + predict the keypoints at a given time + + Args: + time: the timestamp to predict the keypoints + """ + ... # pylint: disable=unnecessary-ellipsis def predict( self, - delta_t_s: float, + time: float | timedelta | datetime, ) -> Float[Array, "J 3"]: - """ - Predict the 3D pose of a tracking based on its velocity. - JAX-friendly implementation that avoids Python control flow. - - Args: - delta_t_s: Time delta in seconds - - Returns: - Predicted 3D pose keypoints - """ - # ------------------------------------------------------------------ - # Step 1 – decide velocity on the Python side - # ------------------------------------------------------------------ - if self.velocity is None: - velocity = jnp.zeros_like(self.keypoints) # (J, 3) + if isinstance(time, timedelta): + timestamp = self.state.last_active_timestamp + time + elif isinstance(time, datetime): + timestamp = time else: - velocity = self.velocity # (J, 3) + timestamp = self.state.last_active_timestamp + timedelta(seconds=time) + # pylint: disable-next=unsubscriptable-object + return self.velocity_filter.predict(timestamp)["keypoints"] - # ------------------------------------------------------------------ - # Step 2 – pure JAX math - # ------------------------------------------------------------------ - return self.keypoints + velocity * delta_t_s + def update(self, new_3d_pose: Float[Array, "J 3"], timestamp: datetime) -> None: + """ + update the tracking with a new 3D pose + + Note: + equivalent to call `velocity_filter.update(new_3d_pose, timestamp)` + """ + self.velocity_filter.update(new_3d_pose, timestamp) + + @property + def velocity(self) -> Float[Array, "J 3"]: + """ + The velocity of the tracking for each keypoint + """ + # pylint: disable-next=unsubscriptable-object + if (vel := self.velocity_filter.get()["velocity"]) is None: + return jnp.zeros_like(self.state.keypoints) + else: + return vel @jaxtyped(typechecker=beartype) @@ -98,9 +561,9 @@ class AffinityResult: trackings: Sequence[Tracking] detections: Sequence[Detection] indices_T: Int[Array, "T"] # pylint: disable=invalid-name - indices_D: Int[Array, "D"] # pylint: disable=invalid-name + indices_D: Int[Array, "T"] # pylint: disable=invalid-name - def tracking_detections( + def tracking_association( self, ) -> Generator[tuple[float, Tracking, Detection], None, None]: """ diff --git a/filter_object_by_box.py b/filter_object_by_box.py index f211370..ec831fb 100644 --- a/filter_object_by_box.py +++ b/filter_object_by_box.py @@ -1,23 +1,11 @@ -import awkward as ak +from narwhals import Boolean import numpy as np -from pathlib import Path -from matplotlib import pyplot as plt import cv2 -from typing import Optional, cast, Final, TypedDict from typing import ( - Any, - Generator, - Optional, - Sequence, TypeAlias, TypedDict, - cast, - overload, ) -from jaxtyping import Array, Float, Num, jaxtyped -from shapely import box -from app.visualize.whole_body import visualize_whole_body -import pyproj +from jaxtyping import Array, Num from shapely.geometry import Polygon from sympy import false, true @@ -94,9 +82,9 @@ def calculaterCubeVersices(position, dimensions): # 获得盒子三维坐标系 def calculater_box_3d_points(): # 盒子原点位置，相对于六面体中心偏移 - box_ori_potision = [0.205 + 0.2, 0.205 + 0.50, -0.205 - 0.2] + box_ori_potision = [0.205 + 0.2, 0.205 + 0.50, -0.205 - 0.45] # 盒子边长，宽：1.5米，高：1.5米，深度：1.8米 - box_geometry = [0.65, 1.8, 1.5] + box_geometry = [0.65, 1.8, 1] filter_box_points_3d = calculaterCubeVersices(box_ori_potision, box_geometry) filter_box_points_3d = { str(index): element for index, element in enumerate(filter_box_points_3d) @@ -202,22 +190,19 @@ def get_contours(union_polygon): # 筛选落在盒子二维重投影区域内的关键点信息 -def filter_kps_box(kps, contours): - # 存放筛选后的目标框 - # new_boxes_data = [] - # 存放筛选后的2d姿态点数据 - # new_kps_data = [] - # 存放筛选后的2d姿态置信度 - # 遍历未筛选的目标框 +def filter_kps_in_contours(kps, contours) -> Boolean: - x1, y1 = kps[0] - x2, y2 = kps[16] - # 保留目标框中心在范围内的坐标点 - x_center = (x1 + x2) / 2 - y_centet = (y1 + y2) / 2 - if point_in_polygon([x1, y1], contours) and point_in_polygon([x2, y2], contours): - # if point_in_polygon([x_center, y_centet], contours) : + # 4 5 16 17 + keypoint_index: list[list[int]] = [[4, 5], [16, 17]] + centers = [] + for element_keypoint in keypoint_index: + x1, y1 = kps[element_keypoint[0]] + x2, y2 = kps[element_keypoint[1]] + centers.append([(x1 + x2) / 2, (y1 + y2) / 2]) + + if point_in_polygon(centers[0], contours) and point_in_polygon( + centers[1], contours + ): return true else: return false - # return new_kps_data diff --git a/play.ipynb b/play.ipynb index b58074a..6509df2 100644 --- a/play.ipynb +++ b/play.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 290, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,13 +34,12 @@ "\n", "from app.camera import Camera, CameraParams, Detection\n", "from app.visualize.whole_body import visualize_whole_body\n", - "from filter_object_by_box import *\n", - "NDArray: TypeAlias = np.ndarray" + "from filter_object_by_box import *" ] }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -93,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -134,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -179,7 +178,7 @@ "\n", "\n", "def read_dataset_by_port(port: int) -> ak.Array:\n", - " P = DATASET_PATH / f\"{port}.parquet\"\n", + " # P = DATASET_PATH / f\"{port}.parquet\"\n", " P = DATASET_PATH / f\"filter_{port}.parquet\"\n", " return ak.from_parquet(P)\n", "\n", @@ -191,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -203,7 +202,7 @@ " 5609: }" ] }, - "execution_count": 295, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -453,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -485,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -573,12 +572,145 @@ " )\n", "\n", " # returns (P, 3)\n", - " return vmap_triangulate(proj_matrices, points, conf)" + " return vmap_triangulate(proj_matrices, points, conf)\n", + "\n", + "@jaxtyped(typechecker=beartype)\n", + "def triangulate_one_point_from_multiple_views_linear_time_weighted(\n", + " proj_matrices: Float[Array, \"N 3 4\"],\n", + " points: Num[Array, \"N 2\"],\n", + " delta_t: Num[Array, \"N\"],\n", + " lambda_t: float = 10.0,\n", + " confidences: Optional[Float[Array, \"N\"]] = None,\n", + ") -> Float[Array, \"3\"]:\n", + " \"\"\"\n", + " Triangulate one point from multiple views with time-weighted linear least squares.\n", + "\n", + " Implements the incremental reconstruction method from \"Cross-View Tracking for Multi-Human 3D Pose\"\n", + " with weighting formula: w_i = exp(-λ_t(t-t_i)) / ||c^i^T||_2\n", + "\n", + " Args:\n", + " proj_matrices: Shape (N, 3, 4) projection matrices sequence\n", + " points: Shape (N, 2) point coordinates sequence\n", + " delta_t: Time differences between current time and each observation (in seconds)\n", + " lambda_t: Time penalty rate (higher values decrease influence of older observations)\n", + " confidences: Shape (N,) confidence values in range [0.0, 1.0]\n", + "\n", + " Returns:\n", + " point_3d: Shape (3,) triangulated 3D point\n", + " \"\"\"\n", + " assert len(proj_matrices) == len(points)\n", + " assert len(delta_t) == len(points)\n", + "\n", + " N = len(proj_matrices)\n", + "\n", + " # Prepare confidence weights\n", + " confi: Float[Array, \"N\"]\n", + " if confidences is None:\n", + " confi = jnp.ones(N, dtype=np.float32)\n", + " else:\n", + " confi = jnp.sqrt(jnp.clip(confidences, 0, 1))\n", + "\n", + " A = jnp.zeros((N * 2, 4), dtype=np.float32)\n", + "\n", + " # First build the coefficient matrix without weights\n", + " for i in range(N):\n", + " x, y = points[i]\n", + " A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])\n", + " A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])\n", + "\n", + " # Then apply the time-based and confidence weights\n", + " for i in range(N):\n", + " # Calculate time-decay weight: e^(-λ_t * Δt)\n", + " time_weight = jnp.exp(-lambda_t * delta_t[i])\n", + "\n", + " # Calculate normalization factor: ||c^i^T||_2\n", + " row_norm_1 = jnp.linalg.norm(A[2 * i])\n", + " row_norm_2 = jnp.linalg.norm(A[2 * i + 1])\n", + "\n", + " # Apply combined weight: time_weight / row_norm * confidence\n", + " w1 = (time_weight / row_norm_1) * confi[i]\n", + " w2 = (time_weight / row_norm_2) * confi[i]\n", + "\n", + " A = A.at[2 * i].mul(w1)\n", + " A = A.at[2 * i + 1].mul(w2)\n", + "\n", + " # Solve using SVD\n", + " _, _, vh = jnp.linalg.svd(A, full_matrices=False)\n", + " point_3d_homo = vh[-1] # shape (4,)\n", + "\n", + " # Ensure homogeneous coordinate is positive\n", + " point_3d_homo = jnp.where(\n", + " point_3d_homo[3] < 0,\n", + " -point_3d_homo,\n", + " point_3d_homo,\n", + " )\n", + "\n", + " # Convert from homogeneous to Euclidean coordinates\n", + " point_3d = point_3d_homo[:3] / point_3d_homo[3]\n", + " return point_3d\n", + "\n", + "@jaxtyped(typechecker=beartype)\n", + "def triangulate_points_from_multiple_views_linear_time_weighted(\n", + " proj_matrices: Float[Array, \"N 3 4\"],\n", + " points: Num[Array, \"N P 2\"],\n", + " delta_t: Num[Array, \"N\"],\n", + " lambda_t: float = 10.0,\n", + " confidences: Optional[Float[Array, \"N P\"]] = None,\n", + ") -> Float[Array, \"P 3\"]:\n", + " \"\"\"\n", + " Vectorized version that triangulates P points from N camera views with time-weighting.\n", + "\n", + " This function uses JAX's vmap to efficiently triangulate multiple points in parallel.\n", + "\n", + " Args:\n", + " proj_matrices: Shape (N, 3, 4) projection matrices for N cameras\n", + " points: Shape (N, P, 2) 2D points for P keypoints across N cameras\n", + " delta_t: Shape (N,) time differences between current time and each camera's timestamp (seconds)\n", + " lambda_t: Time penalty rate (higher values decrease influence of older observations)\n", + " confidences: Shape (N, P) confidence values for each point in each camera\n", + "\n", + " Returns:\n", + " points_3d: Shape (P, 3) triangulated 3D points\n", + " \"\"\"\n", + " N, P, _ = points.shape\n", + " assert (\n", + " proj_matrices.shape[0] == N\n", + " ), \"Number of projection matrices must match number of cameras\"\n", + " assert delta_t.shape[0] == N, \"Number of time deltas must match number of cameras\"\n", + "\n", + " if confidences is None:\n", + " # Create uniform confidences if none provided\n", + " conf = jnp.ones((N, P), dtype=jnp.float32)\n", + " else:\n", + " conf = confidences\n", + "\n", + " # Define the vmapped version of the single-point function\n", + " # We map over the second dimension (P points) of the input arrays\n", + " vmap_triangulate = jax.vmap(\n", + " triangulate_one_point_from_multiple_views_linear_time_weighted,\n", + " in_axes=(\n", + " None,\n", + " 1,\n", + " None,\n", + " None,\n", + " 1,\n", + " ), # proj_matrices and delta_t static, map over points\n", + " out_axes=0, # Output has first dimension corresponding to points\n", + " )\n", + "\n", + " # For each point p, extract the 2D coordinates from all cameras and triangulate\n", + " return vmap_triangulate(\n", + " proj_matrices, # (N, 3, 4) - static across points\n", + " points, # (N, P, 2) - map over dim 1 (P)\n", + " delta_t, # (N,) - static across points\n", + " lambda_t, # scalar - static\n", + " conf, # (N, P) - map over dim 1 (P)\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -597,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -630,20 +762,35 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Detection(, 2024-04-02 12:01:08.750000),\n", + " Detection(, 2024-04-02 12:01:08.750000),\n", + " Detection(, 2024-04-02 12:01:08.750000),\n", + " Detection(, 2024-04-02 12:01:08.750000),\n", + " Detection(, 2024-04-02 12:01:08.750000)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "detections = next(sync_gen)" + "detections = next(sync_gen)\n", + "display(detections)" ] }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "from app.tracking import AffinityResult, Tracking\n", + "from app.tracking import AffinityResult, Tracking, TrackingState\n", "from copy import copy as shallow_copy\n", "from pyrsistent import v, pvector\n", "from beartype.typing import Mapping, Sequence\n", @@ -655,12 +802,33 @@ " calculate_affinity_matrix_by_epipolar_constraint,\n", " classify_by_camera,\n", ")\n", - "\n", + "from app.tracking import (\n", + " TrackingID,\n", + " AffinityResult,\n", + " LastDifferenceVelocityFilter,\n", + " Tracking,\n", + " TrackingState,\n", + ")\n", + "from pyrsistent import pvector, v, m, pmap, PMap, freeze, thaw\n", "from optax.assignment import hungarian_algorithm as linear_sum_assignment\n", "from itertools import chain\n", "\n", "DELTA_T_MIN = timedelta(milliseconds=10)\n", "\n", + "def group_by_cluster_by_camera(\n", + " cluster: Sequence[Detection],\n", + ") -> PMap[CameraID, Detection]:\n", + " \"\"\"\n", + " group the detections by camera, and preserve the latest detection for each camera\n", + " \"\"\"\n", + " r: dict[CameraID, Detection] = {}\n", + " for el in cluster:\n", + " if el.camera.id in r:\n", + " eld = r[el.camera.id]\n", + " preserved = max([eld, el], key=lambda x: x.timestamp)\n", + " r[el.camera.id] = preserved\n", + " return pmap(r)\n", + "\n", "class GlobalTrackingState:\n", " _last_id: int\n", " _trackings: dict[int, Tracking]\n", @@ -678,19 +846,31 @@ " def trackings(self) -> dict[int, Tracking]:\n", " return shallow_copy(self._trackings)\n", "\n", + " def remove(self, id:int):\n", + " del self._trackings[id]\n", + "\n", " def add_tracking(self, cluster: Sequence[Detection]) -> Tracking:\n", + " if len(cluster) < 2:\n", + " raise ValueError(\n", + " \"cluster must contain at least 2 detections to form a tracking\"\n", + " )\n", " kps_3d, latest_timestamp = triangle_from_cluster(cluster)\n", " next_id = self._last_id + 1\n", - " tracking = Tracking(\n", - " id=next_id,\n", + " tracking_state = TrackingState(\n", " keypoints=kps_3d,\n", " last_active_timestamp=latest_timestamp,\n", - " historical_detections=v(*cluster),\n", + " historical_detections_by_camera=group_by_cluster_by_camera(cluster),\n", + " )\n", + " tracking = Tracking(\n", + " id=next_id,\n", + " state=tracking_state,\n", + " velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp),\n", " )\n", " self._trackings[next_id] = tracking\n", " self._last_id = next_id\n", " return tracking\n", - " \n", + "\n", + "\n", "@jaxtyped(typechecker=beartype)\n", "def triangle_from_cluster(\n", " cluster: Sequence[Detection],\n", @@ -753,7 +933,7 @@ "\n", " # === Tracking-side tensors ===\n", " kps3d_trk: Float[Array, \"T J 3\"] = jnp.stack(\n", - " [trk.keypoints for trk in trackings]\n", + " [trk.state.keypoints for trk in trackings]\n", " ) # (T, J, 3)\n", " J = kps3d_trk.shape[1]\n", " # === Detection-side tensors ===\n", @@ -770,12 +950,12 @@ " # --- timestamps ----------\n", " t0 = min(\n", " chain(\n", - " (trk.last_active_timestamp for trk in trackings),\n", + " (trk.state.last_active_timestamp for trk in trackings),\n", " (det.timestamp for det in camera_detections),\n", " )\n", " ).timestamp() # common origin (float)\n", " ts_trk = jnp.array(\n", - " [trk.last_active_timestamp.timestamp() - t0 for trk in trackings],\n", + " [trk.state.last_active_timestamp.timestamp() - t0 for trk in trackings],\n", " dtype=jnp.float32, # now small, ms-scale fits in fp32\n", " )\n", " ts_det = jnp.array(\n", @@ -868,6 +1048,7 @@ " return total_affinity # type: ignore[return-value]\n", "\n", "\n", + "\n", "@beartype\n", "def calculate_affinity_matrix(\n", " trackings: Sequence[Tracking],\n", @@ -918,18 +1099,76 @@ " indices_D=indices_D,\n", " )\n", " res[camera_id] = affinity_result\n", - " return res\n" + " return res" ] }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def update_tracking(\n", + " tracking: Tracking,\n", + " detections: Sequence[Detection],\n", + " max_delta_t: timedelta = timedelta(milliseconds=100),\n", + " lambda_t: float = 10.0,\n", + ") -> None:\n", + " \"\"\"\n", + " update the tracking with a new set of detections\n", + "\n", + " Args:\n", + " tracking: the tracking to update\n", + " detections: the detections to update the tracking with\n", + " max_delta_t: the maximum time difference between the last active timestamp and the latest detection\n", + " lambda_t: the lambda value for the time difference\n", + "\n", + " Note:\n", + " the function would mutate the tracking object\n", + " \"\"\"\n", + " last_active_timestamp = tracking.state.last_active_timestamp\n", + " latest_timestamp = max(d.timestamp for d in detections)\n", + " d = thaw(tracking.state.historical_detections_by_camera)\n", + " for detection in detections:\n", + " d[detection.camera.id] = detection\n", + " for camera_id, detection in d.items():\n", + " if detection.timestamp - latest_timestamp > max_delta_t:\n", + " del d[camera_id]\n", + " new_detections = freeze(d)\n", + " new_detections_list = list(new_detections.values())\n", + " project_matrices = jnp.stack(\n", + " [detection.camera.params.projection_matrix for detection in new_detections_list]\n", + " )\n", + " delta_t = jnp.array(\n", + " [\n", + " detection.timestamp.timestamp() - last_active_timestamp.timestamp()\n", + " for detection in new_detections_list\n", + " ]\n", + " )\n", + " kps = jnp.stack([detection.keypoints for detection in new_detections_list])\n", + " conf = jnp.stack([detection.confidences for detection in new_detections_list])\n", + " kps_3d = triangulate_points_from_multiple_views_linear_time_weighted(\n", + " project_matrices, kps, delta_t, lambda_t, conf\n", + " )\n", + " new_state = TrackingState(\n", + " keypoints=kps_3d,\n", + " last_active_timestamp=latest_timestamp,\n", + " historical_detections_by_camera=new_detections,\n", + " )\n", + " tracking.update(kps_3d, latest_timestamp)\n", + " tracking.state = new_state" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_values([Tracking(1, 2024-04-02 12:01:08.791667)])" + "dict_values([Tracking(1, 2024-04-02 12:01:08.750000)])" ] }, "metadata": {}, @@ -937,21 +1176,22 @@ } ], "source": [ + "# 初始化追踪\n", "global_tracking_state = GlobalTrackingState()\n", - "detections = next(sync_gen)\n", + "# detections = next(sync_gen)\n", "global_tracking_state.add_tracking(detections)\n", "display(global_tracking_state.trackings.values())" ] }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Tracking(1, 2024-04-02 12:01:08.791667)]" + "[Tracking(1, 2024-04-02 12:01:08.750000)]" ] }, "metadata": {}, @@ -959,49 +1199,130 @@ } ], "source": [ - "W_2D = 1.0\n", - "ALPHA_2D = 1.0\n", - "LAMBDA_A = 0.1\n", - "W_3D = 1.0\n", - "ALPHA_3D = 1.0\n", + "from app.tracking import Tracking\n", "\n", - "detections = next(sync_gen)\n", - "trackings = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)\n", - "# display(detections)\n", + "\n", + "W_2D = 0.8\n", + "ALPHA_2D = 60.0\n", + "LAMBDA_A = 3.0\n", + "W_3D = 0.2\n", + "ALPHA_3D = 0.1\n", + "\n", + "# 获得当前追踪目标\n", + "trackings: list[Tracking] = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)\n", "display(trackings)\n", - "# 跟踪状态\n", - "affinities = calculate_affinity_matrix(\n", - " trackings=trackings,\n", - " detections=detections,\n", - " w_2d=W_2D,\n", - " alpha_2d=ALPHA_2D,\n", - " w_3d=W_3D,\n", - " alpha_3d=ALPHA_3D,\n", - " lambda_a=LAMBDA_A,\n", - ")\n", - "\n", - "# 跟踪上的2d检测数据\n", - "track_detetions = []\n", - "for key in affinities.keys():\n", - " # 每一个机位的跟踪数据\n", - " detection = affinities[key].detections\n", - " tracking = affinities[key].trackings\n", - " if tracking == None:\n", - " display(detection)" + "# 3d数据，键为追踪目标id，值为该目标的所有3d数据\n", + "all_3d_kps: dict[str, list] = {}\n", + "# 初始化每个追踪目标的3d数据\n", + "for element_tracking in trackings:\n", + " all_3d_kps[str(element_tracking.id)] = [element_tracking.state.keypoints.tolist()]" ] }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'AF_03': AffinityResult(matrix=Array([[243.36333]], dtype=float32), trackings=[Tracking(1, 2024-04-02 12:01:08.791667)], detections=[Detection(, 2024-04-02 12:01:08.833333)], indices_T=Array([0], dtype=int32), indices_D=Array([0], dtype=int32, weak_type=True)),\n", - " 'AF_05': AffinityResult(matrix=Array([[207.50095]], dtype=float32), trackings=[Tracking(1, 2024-04-02 12:01:08.791667)], detections=[Detection(, 2024-04-02 12:01:08.833333)], indices_T=Array([0], dtype=int32), indices_D=Array([0], dtype=int32, weak_type=True)),\n", - " 'AE_1A': AffinityResult(matrix=Array([[221.3469]], dtype=float32), trackings=[Tracking(1, 2024-04-02 12:01:08.791667)], detections=[Detection(, 2024-04-02 12:01:08.833333)], indices_T=Array([0], dtype=int32), indices_D=Array([0], dtype=int32, weak_type=True)),\n", - " 'AE_08': AffinityResult(matrix=Array([[235.55804]], dtype=float32), trackings=[Tracking(1, 2024-04-02 12:01:08.791667)], detections=[Detection(, 2024-04-02 12:01:08.833333)], indices_T=Array([0], dtype=int32), indices_D=Array([0], dtype=int32, weak_type=True))}" + "{'AF_03': [], 'AF_05': [], 'AE_1A': [], 'AE_08': []}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "affinity_matrix: dict[str, list] = {}\n", + "for element_camera in detections:\n", + " affinity_matrix[element_camera.camera.id] = []\n", + "display(affinity_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "'''使用已筛选过的数据，采用跟踪获得获得每一帧的3d数据'''\n", + "count = 0\n", + "# 追踪相似度矩阵匹配阈值\n", + "affinities_threshold = 70\n", + "# 遍历59帧的2d数据，测试追踪状态\n", + "while count < 62:\n", + " # 获得最新一帧的数据2d数据\n", + " detections = next(sync_gen)\n", + " # 计算相似度矩阵匹配结果\n", + " affinities: dict[str, AffinityResult] = calculate_affinity_matrix(\n", + " trackings,\n", + " detections,\n", + " w_2d=W_2D,\n", + " alpha_2d=ALPHA_2D,\n", + " w_3d=W_3D,\n", + " alpha_3d=ALPHA_3D,\n", + " lambda_a=LAMBDA_A,\n", + " )\n", + " # display(affinities)\n", + "\n", + " # 遍历追踪目标，获得该目标的匹配2d数据\n", + " for element_tracking in trackings:\n", + "\n", + " tracking_detection = []\n", + " # 匹配检测目标的索引值\n", + " detection_index = None\n", + " # 遍历相机的追踪相似度匹配结果\n", + " for camera_name in affinities.keys():\n", + " # 获得与每个跟踪目标匹配的相似度矩阵\n", + " camera_matrix = np.array(affinities[camera_name].matrix).flatten()\n", + " camera_tracking = affinities[camera_name].trackings\n", + " detection_index = np.argmax(camera_matrix).item()\n", + "\n", + " # 判断相似度矩阵极大值是否大于阈值\n", + " # 目前只有一个跟踪目标，还未实现多跟踪目标的匹配-------------------------\n", + " affinity_matrix[camera_name].append(camera_matrix.tolist())\n", + " if camera_matrix[detection_index].item() > affinities_threshold:\n", + " # 保留应用的2d检测数据\n", + " tracking_detection.append(\n", + " affinities[camera_name].detections[detection_index])\n", + "\n", + " # 跟新追踪目标\n", + " if tracking_detection:\n", + " update_tracking(element_tracking, tracking_detection)\n", + " all_3d_kps[str(element_tracking.id)].append(\n", + " element_tracking.state.keypoints.tolist())\n", + " count += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([Tracking(1, 2024-04-02 12:01:11.333333)])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(global_tracking_state.trackings.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'camera_name:AF_05 max:91.75846099853516 min:-692.5421142578125 avg:-300.3918266296387'" ] }, "metadata": {}, @@ -1010,7 +1331,1627 @@ { "data": { "text/plain": [ - "[Tracking(1, 2024-04-02 12:01:08.791667)]" + "'camera_name:AF_05 max:97.37450408935547 min:-701.7442626953125 avg:-302.1848793029785'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'camera_name:AF_05 max:84.19193267822266 min:-710.1654052734375 avg:-312.9867362976074'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + 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max:80.35160064697266 min:-502.1229248046875 avg:-353.15750579833986'" ] }, "metadata": {}, @@ -1018,19 +2959,36 @@ } ], "source": [ - "display(affinities)\n", - "display(trackings)" + "for camera_name in affinity_matrix.keys():\n", + " # 所有帧的相似度矩阵\n", + " all_matrix = affinity_matrix[camera_name]\n", + " # 遍历每一帧的相似度矩阵（几个检测目标对应几个值）\n", + " for element_matrix in all_matrix:\n", + " if len(element_matrix) == 1:\n", + " continue\n", + " display(\n", + " f\"camera_name:{camera_name} max:{max(element_matrix)} min:{min(element_matrix)} avg:{sum(element_matrix)/len(element_matrix)}\"\n", + " )" ] }, { "cell_type": "code", - "execution_count": 308, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "10" + "dict_keys(['1'])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(63, 133, 3)" ] }, "metadata": {}, @@ -1038,32 +2996,15 @@ } ], "source": [ - "# 使用已经筛选过的数据\n", - "def triangle_from_cluster(cluster: list[Detection]) -> Float[Array, \"3\"]:\n", - " proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster])\n", - " points = jnp.array([el.keypoints for el in cluster])\n", - " confidences = jnp.array([el.confidences for el in cluster])\n", - " return triangulate_points_from_multiple_views_linear(\n", - " proj_matrices, points, confidences=confidences\n", - " )\n", - "\n", - "# 使用已经筛选过的数据\n", - "count = 0\n", - "all_frame_detections = []\n", - "# 遍历筛选后的数据共59帧\n", - "while count < 10:\n", - " detections = next(sync_gen)\n", - " all_frame_detections.append(triangle_from_cluster(detections).tolist())\n", - " count += 1\n", - "\n", - "display(len(all_frame_detections))\n", - "# with open(\"samples/QuanCheng_res_new.json\", \"wb\") as f:\n", - "# f.write(orjson.dumps(all_frame_detections))" + "display(all_3d_kps.keys())\n", + "display(np.array(all_3d_kps['1']).shape)\n", + "# with open(\"samples/QuanCheng_res.json\", \"wb\") as f:\n", + "# f.write(orjson.dumps(all_3d_kps))\n" ] }, { "cell_type": "code", - "execution_count": 309, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1072,7 +3013,7 @@ "'\\nall_frame_detections = []\\nfor i in range(800):\\n detections = next(sync_gen)\\n # 筛选关键点\\n\\n filter_detections = []\\n for element in detections:\\n filter_box_points_3d = calculater_box_3d_points()\\n box_points_2d = calculater_box_2d_points(filter_box_points_3d, element.camera)\\n box_triangles_all_points = calculater_box_common_scope(box_points_2d)\\n union_area, union_polygon = calculate_triangle_union(box_triangles_all_points)\\n contours = get_contours(union_polygon)\\n \\n # 筛选目标框里的数据\\n if filter_kps_box(element.keypoints, contours):\\n filter_detections.append(element)\\n # 判断筛选后的数据是否为空\\n if len(filter_detections)>2:\\n all_frame_detections.append(triangle_from_cluster(filter_detections).tolist())\\n\\n # 筛选只剩一个人的数据，直接进行DLT\\n# res = {\"a\": all_frame_detections}\\n# display(res)\\nwith open(\"samples/QuanCheng_res.json\", \"wb\") as f:\\n f.write(orjson.dumps(all_frame_detections))\\n'" ] }, - "execution_count": 309, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +3051,7 @@ }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1119,7 +3060,7 @@ "'display(len(detections))'" ] }, - "execution_count": 310, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1130,7 +3071,7 @@ }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1144,7 +3085,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1166,7 +3107,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1199,7 +3140,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1217,7 +3158,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1231,7 +3172,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ diff --git a/playground.py b/playground.py index 668711b..e6ef055 100644 --- a/playground.py +++ b/playground.py @@ -31,13 +31,13 @@ from typing import ( TypeVar, cast, overload, + Iterable, ) import awkward as ak import jax import jax.numpy as jnp import numpy as np -import orjson from beartype import beartype from beartype.typing import Mapping, Sequence from cv2 import undistortPoints @@ -46,9 +46,10 @@ from jaxtyping import Array, Float, Num, jaxtyped from matplotlib import pyplot as plt from numpy.typing import ArrayLike from optax.assignment import hungarian_algorithm as linear_sum_assignment -from pyrsistent import v, pvector +from pyrsistent import pvector, v, m, pmap, PMap, freeze, thaw from scipy.spatial.transform import Rotation as R from typing_extensions import deprecated +from collections import defaultdict from app.camera import ( Camera, @@ -59,17 +60,21 @@ from app.camera import ( classify_by_camera, ) from app.solver._old import GLPKSolver -from app.tracking import AffinityResult, Tracking +from app.tracking import ( + TrackingID, + AffinityResult, + LastDifferenceVelocityFilter, + Tracking, + TrackingState, +) from app.visualize.whole_body import visualize_whole_body NDArray: TypeAlias = np.ndarray # %% -CAMERA_PATH = Path( - "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params" -) -AK_CAMERA_DATASET: ak.Array = ak.from_parquet(CAMERA_PATH / "camera_params.parquet") -DELTA_T_MIN = timedelta(milliseconds=10) +DATASET_PATH = Path("samples") / "04_02" +AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / "camera_params.parquet") # type: ignore +DELTA_T_MIN = timedelta(milliseconds=1) display(AK_CAMERA_DATASET) @@ -104,13 +109,6 @@ class ExternalCameraParams(TypedDict): # %% - -# %% -DATASET_PATH = Path( - "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/detect_result/segement_1" -) - - def read_dataset_by_port(port: int) -> ak.Array: P = DATASET_PATH / f"{port}.parquet" return ak.from_parquet(P) @@ -119,7 +117,6 @@ def read_dataset_by_port(port: int) -> ak.Array: KEYPOINT_DATASET = { int(p): read_dataset_by_port(p) for p in ak.to_numpy(AK_CAMERA_DATASET["port"]) } -display(KEYPOINT_DATASET) # %% @@ -194,8 +191,6 @@ def preprocess_keypoint_dataset( ) -# %% - # %% DetectionGenerator: TypeAlias = Generator[Detection, None, None] @@ -338,31 +333,13 @@ def homogeneous_to_euclidean( # %% FPS = 24 +image_gen_5600 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5600], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5600][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore image_gen_5601 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5601], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5601][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore image_gen_5602 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5602], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5602][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5603 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5603], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5603][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5604 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5604], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5604][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5605 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5605], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5605][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5606 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5606], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5606][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5607 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5607], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5607][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5608 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5608], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5608][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore -image_gen_5609 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5609], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET["port"] == 5609][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore - display(1 / FPS) sync_gen = sync_batch_gen( - [ - image_gen_5601, - # image_gen_5602, - # image_gen_5603, - image_gen_5604, - image_gen_5605, - image_gen_5606, - # image_gen_5607, - image_gen_5608, - image_gen_5609, - ], - timedelta(seconds=1 / FPS), + [image_gen_5600, image_gen_5601, image_gen_5602], timedelta(seconds=1 / FPS) ) # %% @@ -375,7 +352,7 @@ display(sorted_detections) display( list( map( - lambda x: {"timestamp": str(x.timestamp), "camera": x.camera.id, "keypoint":x.keypoints.shape}, + lambda x: {"timestamp": str(x.timestamp), "camera": x.camera.id}, sorted_detections, ) ) @@ -443,7 +420,6 @@ for el in clusters_detections[0]: p = plt.imshow(im) display(p) - # %% im_prime = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8) for el in clusters_detections[1]: @@ -535,6 +511,142 @@ def triangulate_points_from_multiple_views_linear( return vmap_triangulate(proj_matrices, points, conf) +# %% +@jaxtyped(typechecker=beartype) +def triangulate_one_point_from_multiple_views_linear_time_weighted( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N 2"], + delta_t: Num[Array, "N"], + lambda_t: float = 10.0, + confidences: Optional[Float[Array, "N"]] = None, +) -> Float[Array, "3"]: + """ + Triangulate one point from multiple views with time-weighted linear least squares. + + Implements the incremental reconstruction method from "Cross-View Tracking for Multi-Human 3D Pose" + with weighting formula: w_i = exp(-λ_t(t-t_i)) / ||c^i^T||_2 + + Args: + proj_matrices: Shape (N, 3, 4) projection matrices sequence + points: Shape (N, 2) point coordinates sequence + delta_t: Time differences between current time and each observation (in seconds) + lambda_t: Time penalty rate (higher values decrease influence of older observations) + confidences: Shape (N,) confidence values in range [0.0, 1.0] + + Returns: + point_3d: Shape (3,) triangulated 3D point + """ + assert len(proj_matrices) == len(points) + assert len(delta_t) == len(points) + + N = len(proj_matrices) + + # Prepare confidence weights + confi: Float[Array, "N"] + if confidences is None: + confi = jnp.ones(N, dtype=np.float32) + else: + confi = jnp.sqrt(jnp.clip(confidences, 0, 1)) + + A = jnp.zeros((N * 2, 4), dtype=np.float32) + + # First build the coefficient matrix without weights + for i in range(N): + x, y = points[i] + A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0]) + A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1]) + + # Then apply the time-based and confidence weights + for i in range(N): + # Calculate time-decay weight: e^(-λ_t * Δt) + time_weight = jnp.exp(-lambda_t * delta_t[i]) + + # Calculate normalization factor: ||c^i^T||_2 + row_norm_1 = jnp.linalg.norm(A[2 * i]) + row_norm_2 = jnp.linalg.norm(A[2 * i + 1]) + + # Apply combined weight: time_weight / row_norm * confidence + w1 = (time_weight / row_norm_1) * confi[i] + w2 = (time_weight / row_norm_2) * confi[i] + + A = A.at[2 * i].mul(w1) + A = A.at[2 * i + 1].mul(w2) + + # Solve using SVD + _, _, vh = jnp.linalg.svd(A, full_matrices=False) + point_3d_homo = vh[-1] # shape (4,) + + # Ensure homogeneous coordinate is positive + point_3d_homo = jnp.where( + point_3d_homo[3] < 0, + -point_3d_homo, + point_3d_homo, + ) + + # Convert from homogeneous to Euclidean coordinates + point_3d = point_3d_homo[:3] / point_3d_homo[3] + return point_3d + + +@jaxtyped(typechecker=beartype) +def triangulate_points_from_multiple_views_linear_time_weighted( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N P 2"], + delta_t: Num[Array, "N"], + lambda_t: float = 10.0, + confidences: Optional[Float[Array, "N P"]] = None, +) -> Float[Array, "P 3"]: + """ + Vectorized version that triangulates P points from N camera views with time-weighting. + + This function uses JAX's vmap to efficiently triangulate multiple points in parallel. + + Args: + proj_matrices: Shape (N, 3, 4) projection matrices for N cameras + points: Shape (N, P, 2) 2D points for P keypoints across N cameras + delta_t: Shape (N,) time differences between current time and each camera's timestamp (seconds) + lambda_t: Time penalty rate (higher values decrease influence of older observations) + confidences: Shape (N, P) confidence values for each point in each camera + + Returns: + points_3d: Shape (P, 3) triangulated 3D points + """ + N, P, _ = points.shape + assert ( + proj_matrices.shape[0] == N + ), "Number of projection matrices must match number of cameras" + assert delta_t.shape[0] == N, "Number of time deltas must match number of cameras" + + if confidences is None: + # Create uniform confidences if none provided + conf = jnp.ones((N, P), dtype=jnp.float32) + else: + conf = confidences + + # Define the vmapped version of the single-point function + # We map over the second dimension (P points) of the input arrays + vmap_triangulate = jax.vmap( + triangulate_one_point_from_multiple_views_linear_time_weighted, + in_axes=( + None, + 1, + None, + None, + 1, + ), # proj_matrices and delta_t static, map over points + out_axes=0, # Output has first dimension corresponding to points + ) + + # For each point p, extract the 2D coordinates from all cameras and triangulate + return vmap_triangulate( + proj_matrices, # (N, 3, 4) - static across points + points, # (N, P, 2) - map over dim 1 (P) + delta_t, # (N,) - static across points + lambda_t, # scalar - static + conf, # (N, P) - map over dim 1 (P) + ) + + # %% @@ -555,6 +667,21 @@ def triangle_from_cluster( # %% +def group_by_cluster_by_camera( + cluster: Sequence[Detection], +) -> PMap[CameraID, Detection]: + """ + group the detections by camera, and preserve the latest detection for each camera + """ + r: dict[CameraID, Detection] = {} + for el in cluster: + if el.camera.id in r: + eld = r[el.camera.id] + preserved = max([eld, el], key=lambda x: x.timestamp) + r[el.camera.id] = preserved + return pmap(r) + + class GlobalTrackingState: _last_id: int _trackings: dict[int, Tracking] @@ -573,13 +700,21 @@ class GlobalTrackingState: return shallow_copy(self._trackings) def add_tracking(self, cluster: Sequence[Detection]) -> Tracking: + if len(cluster) < 2: + raise ValueError( + "cluster must contain at least 2 detections to form a tracking" + ) kps_3d, latest_timestamp = triangle_from_cluster(cluster) next_id = self._last_id + 1 - tracking = Tracking( - id=next_id, + tracking_state = TrackingState( keypoints=kps_3d, last_active_timestamp=latest_timestamp, - historical_detections=v(*cluster), + historical_detections_by_camera=group_by_cluster_by_camera(cluster), + ) + tracking = Tracking( + id=next_id, + state=tracking_state, + velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp), ) self._trackings[next_id] = tracking self._last_id = next_id @@ -702,11 +837,7 @@ def perpendicular_distance_camera_2d_points_to_tracking_raycasting( Array of perpendicular distances for each keypoint """ camera = detection.camera - # Use the delta_t supplied by the caller, but clamp to DELTA_T_MIN to - # avoid division-by-zero / exploding affinities. - delta_t = max(delta_t, DELTA_T_MIN) - delta_t_s = delta_t.total_seconds() - predicted_pose = tracking.predict(delta_t_s) + predicted_pose = tracking.predict(delta_t) # Back-project the 2D points to 3D space # intersection with z=0 plane @@ -786,12 +917,12 @@ def calculate_tracking_detection_affinity( Combined affinity score """ camera = detection.camera - delta_t_raw = detection.timestamp - tracking.last_active_timestamp + delta_t_raw = detection.timestamp - tracking.state.last_active_timestamp # Clamp delta_t to avoid division-by-zero / exploding affinity. delta_t = max(delta_t_raw, DELTA_T_MIN) # Calculate 2D affinity - tracking_2d_projection = camera.project(tracking.keypoints) + tracking_2d_projection = camera.project(tracking.state.keypoints) w, h = camera.params.image_size distance_2d = calculate_distance_2d( tracking_2d_projection, @@ -871,7 +1002,7 @@ def calculate_camera_affinity_matrix_jax( # === Tracking-side tensors === kps3d_trk: Float[Array, "T J 3"] = jnp.stack( - [trk.keypoints for trk in trackings] + [trk.state.keypoints for trk in trackings] ) # (T, J, 3) J = kps3d_trk.shape[1] # === Detection-side tensors === @@ -888,12 +1019,12 @@ def calculate_camera_affinity_matrix_jax( # --- timestamps ---------- t0 = min( chain( - (trk.last_active_timestamp for trk in trackings), + (trk.state.last_active_timestamp for trk in trackings), (det.timestamp for det in camera_detections), ) ).timestamp() # common origin (float) ts_trk = jnp.array( - [trk.last_active_timestamp.timestamp() - t0 for trk in trackings], + [trk.state.last_active_timestamp.timestamp() - t0 for trk in trackings], dtype=jnp.float32, # now small, ms-scale fits in fp32 ) ts_det = jnp.array( @@ -1064,8 +1195,82 @@ display(affinities) # %% -def update_tracking(tracking: Tracking, detection: Detection): - delta_t_ = detection.timestamp - tracking.last_active_timestamp - delta_t = max(delta_t_, DELTA_T_MIN) +def affinity_result_by_tracking( + results: Iterable[AffinityResult], + min_affinity: float = 0.0, +) -> dict[TrackingID, list[Detection]]: + """ + Group affinity results by target ID. - return tracking + Args: + results: the affinity results to group + min_affinity: the minimum affinity to consider + Returns: + a dictionary mapping tracking IDs to a list of detections + """ + res: dict[TrackingID, list[Detection]] = defaultdict(list) + for affinity_result in results: + for affinity, t, d in affinity_result.tracking_association(): + if affinity < min_affinity: + continue + res[t.id].append(d) + return res + + +def update_tracking( + tracking: Tracking, + detections: Sequence[Detection], + max_delta_t: timedelta = timedelta(milliseconds=100), + lambda_t: float = 10.0, +) -> None: + """ + update the tracking with a new set of detections + + Args: + tracking: the tracking to update + detections: the detections to update the tracking with + max_delta_t: the maximum time difference between the last active timestamp and the latest detection + lambda_t: the lambda value for the time difference + + Note: + the function would mutate the tracking object + """ + last_active_timestamp = tracking.state.last_active_timestamp + latest_timestamp = max(d.timestamp for d in detections) + d = thaw(tracking.state.historical_detections_by_camera) + for detection in detections: + d[detection.camera.id] = detection + for camera_id, detection in d.items(): + if detection.timestamp - latest_timestamp > max_delta_t: + del d[camera_id] + new_detections = freeze(d) + new_detections_list = list(new_detections.values()) + project_matrices = jnp.stack( + [detection.camera.params.projection_matrix for detection in new_detections_list] + ) + delta_t = jnp.array( + [ + detection.timestamp.timestamp() - last_active_timestamp.timestamp() + for detection in new_detections_list + ] + ) + kps = jnp.stack([detection.keypoints for detection in new_detections_list]) + conf = jnp.stack([detection.confidences for detection in new_detections_list]) + kps_3d = triangulate_points_from_multiple_views_linear_time_weighted( + project_matrices, kps, delta_t, lambda_t, conf + ) + new_state = TrackingState( + keypoints=kps_3d, + last_active_timestamp=latest_timestamp, + historical_detections_by_camera=new_detections, + ) + tracking.update(kps_3d, latest_timestamp) + tracking.state = new_state + + +# %% +affinity_results_by_tracking = affinity_result_by_tracking(affinities.values()) +for tracking_id, detections in affinity_results_by_tracking.items(): + update_tracking(global_tracking_state.trackings[tracking_id], detections) + +# %% diff --git a/pyproject.toml b/pyproject.toml index 72c4976..7b4ca28 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ dependencies = [ "jaxtyping>=0.2.38", "jupytext>=1.17.0", "matplotlib>=3.10.1", + "more-itertools>=10.7.0", "opencv-python-headless>=4.11.0.86", "optax>=0.2.4", "orjson>=3.10.15", @@ -23,6 +24,7 @@ dependencies = [ "pyrsistent>=0.20.0", "pytest>=8.3.5", "scipy>=1.15.2", + "shapely>=2.1.1", "torch>=2.6.0", "torchvision>=0.21.0", "typeguard>=4.4.2", diff --git a/single_people_detect_track.py b/single_people_detect_track.py new file mode 100644 index 0000000..9468c39 --- /dev/null +++ b/single_people_detect_track.py @@ -0,0 +1,1062 @@ +from _collections_abc import dict_values +from math import isnan +from pathlib import Path +from re import L +import awkward as ak +from typing import ( + Any, + Generator, + Optional, + Sequence, + TypeAlias, + TypedDict, + cast, + TypeVar, +) +from datetime import datetime, timedelta +from jaxtyping import Array, Float, Num, jaxtyped +import numpy as np +from cv2 import undistortPoints +from app.camera import Camera, CameraParams, Detection +import jax.numpy as jnp +from beartype import beartype +from scipy.spatial.transform import Rotation as R +from filter_object_by_box import ( + filter_kps_in_contours, + calculater_box_3d_points, + calculater_box_2d_points, + calculater_box_common_scope, + calculate_triangle_union, + get_contours, +) +from app.tracking import ( + TrackingID, + AffinityResult, + LastDifferenceVelocityFilter, + Tracking, + TrackingState, +) +from app.camera import ( + Camera, + CameraID, + CameraParams, + Detection, + calculate_affinity_matrix_by_epipolar_constraint, + classify_by_camera, +) +from copy import copy as shallow_copy +from pyrsistent import pvector, v, m, pmap, PMap, freeze, thaw +import jax +from optax.assignment import hungarian_algorithm as linear_sum_assignment +from beartype.typing import Mapping, Sequence +from itertools import chain +import orjson + +NDArray: TypeAlias = np.ndarray +DetectionGenerator: TypeAlias = Generator[Detection, None, None] + +DELTA_T_MIN = timedelta(milliseconds=1) +"""所有类型""" + +T = TypeVar("T") + + +def unwrap(val: Optional[T]) -> T: + if val is None: + raise ValueError("None") + return val + + +class KeypointDataset(TypedDict): + frame_index: int + boxes: Num[NDArray, "N 4"] + kps: Num[NDArray, "N J 2"] + kps_scores: Num[NDArray, "N J"] + + +class Resolution(TypedDict): + width: int + height: int + + +class Intrinsic(TypedDict): + camera_matrix: Num[Array, "3 3"] + """ + K + """ + distortion_coefficients: Num[Array, "N"] + """ + distortion coefficients; usually 5 + """ + + +class Extrinsic(TypedDict): + rvec: Num[NDArray, "3"] + tvec: Num[NDArray, "3"] + + +class ExternalCameraParams(TypedDict): + name: str + port: int + intrinsic: Intrinsic + extrinsic: Extrinsic + resolution: Resolution + + +"""获得所有机位的相机内外参""" + + +def get_camera_params(camera_path: Path) -> ak.Array: + camera_dataset: ak.Array = ak.from_parquet(camera_path / "camera_params.parquet") + return camera_dataset + + +"""获取所有机位的2d检测数据""" + + +def get_camera_detect( + detect_path: Path, camera_port: list[int], camera_dataset: ak.Array +) -> dict[int, ak.Array]: + keypoint_data: dict[int, ak.Array] = {} + for element_port in ak.to_numpy(camera_dataset["port"]): + if element_port in camera_port: + keypoint_data[int(element_port)] = ak.from_parquet( + detect_path / f"{element_port}.parquet" + ) + return keypoint_data + + +"""获得指定帧的2d检测数据(一段完整的跳跃片段)""" + + +def get_segment( + camera_port: list[int], frame_index: list[int], keypoint_data: dict[int, ak.Array] +) -> dict[int, ak.Array]: + # for port in camera_port: + # keypoint_data[port] = [ + # element_frame + # for element_frame in KEYPOINT_DATASET[port] + # if element_frame["frame_index"] in frame_index + # ] + for port in camera_port: + segement_data = [] + camera_data = KEYPOINT_DATASET[port] + for index, element_frame in enumerate(ak.to_numpy(camera_data["frame_index"])): + if element_frame in frame_index: + segement_data.append(camera_data[index]) + keypoint_data[port] = ak.Array(segement_data) + + return keypoint_data + + +"""将所有2d检测数据打包""" + + +@jaxtyped(typechecker=beartype) +def undistort_points( + points: Num[NDArray, "M 2"], + camera_matrix: Num[NDArray, "3 3"], + dist_coeffs: Num[NDArray, "N"], +) -> Num[NDArray, "M 2"]: + K = camera_matrix + dist = dist_coeffs + res = undistortPoints(points, K, dist, P=K) # type: ignore + return res.reshape(-1, 2) + + +@jaxtyped(typechecker=beartype) +def to_transformation_matrix( + rvec: Num[NDArray, "3"], tvec: Num[NDArray, "3"] +) -> Num[NDArray, "4 4"]: + res = np.eye(4) + res[:3, :3] = R.from_rotvec(rvec).as_matrix() + res[:3, 3] = tvec + return res + + +def from_camera_params(camera: ExternalCameraParams) -> Camera: + rt = jnp.array( + to_transformation_matrix( + ak.to_numpy(camera["extrinsic"]["rvec"]), + ak.to_numpy(camera["extrinsic"]["tvec"]), + ) + ) + K = jnp.array(camera["intrinsic"]["camera_matrix"]).reshape(3, 3) + dist_coeffs = jnp.array(camera["intrinsic"]["distortion_coefficients"]) + image_size = jnp.array( + (camera["resolution"]["width"], camera["resolution"]["height"]) + ) + return Camera( + id=camera["name"], + params=CameraParams( + K=K, + Rt=rt, + dist_coeffs=dist_coeffs, + image_size=image_size, + ), + ) + + +def preprocess_keypoint_dataset( + dataset: Sequence[KeypointDataset], + camera: Camera, + fps: float, + start_timestamp: datetime, +) -> Generator[Detection, None, None]: + frame_interval_s = 1 / fps + for el in dataset: + frame_index = el["frame_index"] + timestamp = start_timestamp + timedelta(seconds=frame_index * frame_interval_s) + for kp, kp_score, boxes in zip(el["kps"], el["kps_scores"], el["boxes"]): + kp = undistort_points( + np.asarray(kp), + np.asarray(camera.params.K), + np.asarray(camera.params.dist_coeffs), + ) + + yield Detection( + keypoints=jnp.array(kp), + confidences=jnp.array(kp_score), + camera=camera, + timestamp=timestamp, + ) + + +def sync_batch_gen( + gens: list[DetectionGenerator], diff: timedelta +) -> Generator[list[Detection], Any, None]: + from more_itertools import partition + + """ + given a list of detection generators, return a generator that yields a batch of detections + + Args: + gens: list of detection generators + diff: maximum timestamp difference between detections to consider them part of the same batch + """ + N = len(gens) + last_batch_timestamp: Optional[datetime] = None + current_batch: list[Detection] = [] + paused: list[bool] = [False] * N + finished: list[bool] = [False] * N + unmached_detections: list[Detection] = [] + + def reset_paused(): + """ + reset paused list based on finished list + """ + for i in range(N): + if not finished[i]: + paused[i] = False + else: + paused[i] = True + + EPS = 1e-6 + # a small epsilon to avoid floating point precision issues + diff_esp = diff - timedelta(seconds=EPS) + while True: + for i, gen in enumerate(gens): + try: + if finished[i] or paused[i]: + continue + val = next(gen) + if last_batch_timestamp is None: + last_batch_timestamp = val.timestamp + current_batch.append(val) + else: + if abs(val.timestamp - last_batch_timestamp) >= diff_esp: + unmached_detections.append(val) + paused[i] = True + if all(paused): + yield current_batch + reset_paused() + last_batch_timestamp = last_batch_timestamp + diff + bad, good = partition( + lambda x: x.timestamp < unwrap(last_batch_timestamp), + unmached_detections, + ) + current_batch = list(good) + unmached_detections = list(bad) + else: + current_batch.append(val) + except StopIteration: + finished[i] = True + paused[i] = True + if all(finished): + if len(current_batch) > 0: + # All generators exhausted, flush remaining batch and exit + yield current_batch + break + + +def get_batch_detect( + keypoint_dataset, + camera_dataset, + camera_port: list[int], + FPS: int = 24, + batch_fps: int = 10, +) -> Generator[list[Detection], Any, None]: + gen_data = [ + preprocess_keypoint_dataset( + keypoint_dataset[port], + from_camera_params(camera_dataset[camera_dataset["port"] == port][0]), + FPS, + datetime(2024, 4, 2, 12, 0, 0), + ) + for port in camera_port + ] + + sync_gen: Generator[list[Detection], Any, None] = sync_batch_gen( + gen_data, + timedelta(seconds=1 / batch_fps), + ) + return sync_gen + + +"""通过盒子进行筛选构建第一帧匹配数据""" + + +def get_filter_detections(detections: list[Detection]) -> list[Detection]: + filter_detections: list[Detection] = [] + for element_detection in detections: + filter_box_points_3d = calculater_box_3d_points() + box_points_2d = calculater_box_2d_points( + filter_box_points_3d, element_detection.camera + ) + box_triangles_all_points = calculater_box_common_scope(box_points_2d) + union_area, union_polygon = calculate_triangle_union(box_triangles_all_points) + contours = get_contours(union_polygon) + + if filter_kps_in_contours(element_detection.keypoints, contours): + filter_detections.append(element_detection) + return filter_detections + + +"""追踪""" + + +@jaxtyped(typechecker=beartype) +def triangulate_points_from_multiple_views_linear_time_weighted( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N P 2"], + delta_t: Num[Array, "N"], + lambda_t: float = 10.0, + confidences: Optional[Float[Array, "N P"]] = None, +) -> Float[Array, "P 3"]: + """ + Vectorized version that triangulates P points from N camera views with time-weighting. + + This function uses JAX's vmap to efficiently triangulate multiple points in parallel. + + Args: + proj_matrices: Shape (N, 3, 4) projection matrices for N cameras + points: Shape (N, P, 2) 2D points for P keypoints across N cameras + delta_t: Shape (N,) time differences between current time and each camera's timestamp (seconds) + lambda_t: Time penalty rate (higher values decrease influence of older observations) + confidences: Shape (N, P) confidence values for each point in each camera + + Returns: + points_3d: Shape (P, 3) triangulated 3D points + """ + N, P, _ = points.shape + assert ( + proj_matrices.shape[0] == N + ), "Number of projection matrices must match number of cameras" + assert delta_t.shape[0] == N, "Number of time deltas must match number of cameras" + + if confidences is None: + # Create uniform confidences if none provided + conf = jnp.ones((N, P), dtype=jnp.float32) + else: + conf = confidences + + # Define the vmapped version of the single-point function + # We map over the second dimension (P points) of the input arrays + vmap_triangulate = jax.vmap( + triangulate_one_point_from_multiple_views_linear_time_weighted, + in_axes=( + None, + 1, + None, + None, + 1, + ), # proj_matrices and delta_t static, map over points + out_axes=0, # Output has first dimension corresponding to points + ) + + # For each point p, extract the 2D coordinates from all cameras and triangulate + return vmap_triangulate( + proj_matrices, # (N, 3, 4) - static across points + points, # (N, P, 2) - map over dim 1 (P) + delta_t, # (N,) - static across points + lambda_t, # scalar - static + conf, # (N, P) - map over dim 1 (P) + ) + + +@jaxtyped(typechecker=beartype) +def triangulate_one_point_from_multiple_views_linear_time_weighted( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N 2"], + delta_t: Num[Array, "N"], + lambda_t: float = 10.0, + confidences: Optional[Float[Array, "N"]] = None, +) -> Float[Array, "3"]: + """ + Triangulate one point from multiple views with time-weighted linear least squares. + + Implements the incremental reconstruction method from "Cross-View Tracking for Multi-Human 3D Pose" + with weighting formula: w_i = exp(-λ_t(t-t_i)) / ||c^i^T||_2 + + Args: + proj_matrices: Shape (N, 3, 4) projection matrices sequence + points: Shape (N, 2) point coordinates sequence + delta_t: Time differences between current time and each observation (in seconds) + lambda_t: Time penalty rate (higher values decrease influence of older observations) + confidences: Shape (N,) confidence values in range [0.0, 1.0] + + Returns: + point_3d: Shape (3,) triangulated 3D point + """ + assert len(proj_matrices) == len(points) + assert len(delta_t) == len(points) + + N = len(proj_matrices) + + # Prepare confidence weights + confi: Float[Array, "N"] + if confidences is None: + confi = jnp.ones(N, dtype=np.float32) + else: + confi = jnp.sqrt(jnp.clip(confidences, 0, 1)) + + A = jnp.zeros((N * 2, 4), dtype=np.float32) + + # First build the coefficient matrix without weights + for i in range(N): + x, y = points[i] + A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0]) + A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1]) + + # Then apply the time-based and confidence weights + for i in range(N): + # Calculate time-decay weight: e^(-λ_t * Δt) + time_weight = jnp.exp(-lambda_t * delta_t[i]) + + # Calculate normalization factor: ||c^i^T||_2 + row_norm_1 = jnp.linalg.norm(A[2 * i]) + row_norm_2 = jnp.linalg.norm(A[2 * i + 1]) + + # Apply combined weight: time_weight / row_norm * confidence + w1 = (time_weight / row_norm_1) * confi[i] + w2 = (time_weight / row_norm_2) * confi[i] + + A = A.at[2 * i].mul(w1) + A = A.at[2 * i + 1].mul(w2) + + # Solve using SVD + _, _, vh = jnp.linalg.svd(A, full_matrices=False) + point_3d_homo = vh[-1] # shape (4,) + + # Ensure homogeneous coordinate is positive + point_3d_homo = jnp.where( + point_3d_homo[3] < 0, + -point_3d_homo, + point_3d_homo, + ) + + # Convert from homogeneous to Euclidean coordinates + point_3d = point_3d_homo[:3] / point_3d_homo[3] + return point_3d + + +@jaxtyped(typechecker=beartype) +def triangulate_one_point_from_multiple_views_linear( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N 2"], + confidences: Optional[Float[Array, "N"]] = None, +) -> Float[Array, "3"]: + """ + Args: + proj_matrices: 形状为(N, 3, 4)的投影矩阵序列 + points: 形状为(N, 2)的点坐标序列 + confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0] + + Returns: + point_3d: 形状为(3,)的三角测量得到的3D点 + """ + assert len(proj_matrices) == len(points) + + N = len(proj_matrices) + confi: Float[Array, "N"] + + if confidences is None: + confi = jnp.ones(N, dtype=np.float32) + else: + # Use square root of confidences for weighting - more balanced approach + confi = jnp.sqrt(jnp.clip(confidences, 0, 1)) + + # 将置信度小于0.1点的置信度均设置为0 + # valid_mask = confidences >= 0.1 + # confi = jnp.sqrt(jnp.clip(confidences * valid_mask, 0.0, 1.0)) + + A = jnp.zeros((N * 2, 4), dtype=np.float32) + for i in range(N): + x, y = points[i] + A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0]) + A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1]) + A = A.at[2 * i].mul(confi[i]) + A = A.at[2 * i + 1].mul(confi[i]) + + # https://docs.jax.dev/en/latest/_autosummary/jax.numpy.linalg.svd.html + _, _, vh = jnp.linalg.svd(A, full_matrices=False) + point_3d_homo = vh[-1] # shape (4,) + + # replace the Python `if` with a jnp.where + point_3d_homo = jnp.where( + point_3d_homo[3] < 0, # predicate (scalar bool tracer) + -point_3d_homo, # if True + point_3d_homo, # if False + ) + + point_3d = point_3d_homo[:3] / point_3d_homo[3] + return point_3d + + +@jaxtyped(typechecker=beartype) +def triangulate_points_from_multiple_views_linear( + proj_matrices: Float[Array, "N 3 4"], + points: Num[Array, "N P 2"], + confidences: Optional[Float[Array, "N P"]] = None, +) -> Float[Array, "P 3"]: + """ + Batch‐triangulate P points observed by N cameras, linearly via SVD. + + Args: + proj_matrices: (N, 3, 4) projection matrices + points: (N, P, 2) image-coordinates per view + confidences: (N, P, 1) optional per-view confidences in [0,1] + + Returns: + (P, 3) 3D point for each of the P tracks + """ + N, P, _ = points.shape + assert proj_matrices.shape[0] == N + + conf = jnp.array(confidences) + + # vectorize your one‐point routine over P + vmap_triangulate = jax.vmap( + triangulate_one_point_from_multiple_views_linear, + in_axes=(None, 1, 1), # proj_matrices static, map over points[:,p,:], conf[:,p] + out_axes=0, + ) + + # returns (P, 3) + return vmap_triangulate(proj_matrices, points, conf) + + +@jaxtyped(typechecker=beartype) +def triangle_from_cluster( + cluster: Sequence[Detection], +) -> tuple[Float[Array, "N 3"], datetime]: + proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster]) + points = jnp.array([el.keypoints_undistorted for el in cluster]) + confidences = jnp.array([el.confidences for el in cluster]) + latest_timestamp = max(el.timestamp for el in cluster) + return ( + triangulate_points_from_multiple_views_linear( + proj_matrices, points, confidences=confidences + ), + latest_timestamp, + ) + + +def group_by_cluster_by_camera( + cluster: Sequence[Detection], +) -> PMap[CameraID, Detection]: + """ + group the detections by camera, and preserve the latest detection for each camera + """ + r: dict[CameraID, Detection] = {} + for el in cluster: + if el.camera.id in r: + eld = r[el.camera.id] + preserved = max([eld, el], key=lambda x: x.timestamp) + r[el.camera.id] = preserved + return pmap(r) + + +class GlobalTrackingState: + _last_id: int + _trackings: dict[int, Tracking] + + def __init__(self): + self._last_id = 0 + self._trackings = {} + + def __repr__(self) -> str: + return ( + f"GlobalTrackingState(last_id={self._last_id}, trackings={self._trackings})" + ) + + @property + def trackings(self) -> dict[int, Tracking]: + return shallow_copy(self._trackings) + + def add_tracking(self, cluster: Sequence[Detection]) -> Tracking: + if len(cluster) < 2: + raise ValueError( + "cluster must contain at least 2 detections to form a tracking" + ) + kps_3d, latest_timestamp = triangle_from_cluster(cluster) + next_id = self._last_id + 1 + tracking_state = TrackingState( + keypoints=kps_3d, + last_active_timestamp=latest_timestamp, + historical_detections_by_camera=group_by_cluster_by_camera(cluster), + ) + tracking = Tracking( + id=next_id, + state=tracking_state, + velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp), + ) + self._trackings[next_id] = tracking + self._last_id = next_id + return tracking + + +@beartype +def calculate_camera_affinity_matrix_jax( + trackings: Sequence[Tracking], + camera_detections: Sequence[Detection], + w_2d: float, + alpha_2d: float, + w_3d: float, + alpha_3d: float, + lambda_a: float, +) -> Float[Array, "T D"]: + """ + Vectorized implementation to compute an affinity matrix between *trackings* + and *detections* coming from **one** camera. + + Compared with the simple double-for-loop version, this leverages `jax`'s + broadcasting + `vmap` facilities and avoids Python loops over every + (tracking, detection) pair. The mathematical definition of the affinity + is **unchanged**, so the result remains bit-identical to the reference + implementation used in the tests. + """ + + # ------------------------------------------------------------------ + # Quick validations / early-exit guards + # ------------------------------------------------------------------ + if len(trackings) == 0 or len(camera_detections) == 0: + # Return an empty affinity matrix with appropriate shape. + return jnp.zeros((len(trackings), len(camera_detections))) # type: ignore[return-value] + + cam = next(iter(camera_detections)).camera + # Ensure every detection truly belongs to the same camera (guard clause) + cam_id = cam.id + if any(det.camera.id != cam_id for det in camera_detections): + raise ValueError( + "All detections passed to `calculate_camera_affinity_matrix` must come from one camera." + ) + + # We will rely on a single `Camera` instance (all detections share it) + w_img_, h_img_ = cam.params.image_size + w_img, h_img = float(w_img_), float(h_img_) + + # ------------------------------------------------------------------ + # Gather data into ndarray / DeviceArray batches so that we can compute + # everything in a single (or a few) fused kernels. + # ------------------------------------------------------------------ + + # === Tracking-side tensors === + kps3d_trk: Float[Array, "T J 3"] = jnp.stack( + [trk.state.keypoints for trk in trackings] + ) # (T, J, 3) + J = kps3d_trk.shape[1] + # === Detection-side tensors === + kps2d_det: Float[Array, "D J 2"] = jnp.stack( + [det.keypoints for det in camera_detections] + ) # (D, J, 2) + + # ------------------------------------------------------------------ + # Compute Δt matrix – shape (T, D) + # ------------------------------------------------------------------ + # Epoch timestamps are ~1.7 × 10⁹; storing them in float32 wipes out + # sub‑second detail (resolution ≈ 200 ms). Keep them in float64 until + # after subtraction so we preserve Δt‑on‑the‑order‑of‑milliseconds. + # --- timestamps ---------- + t0 = min( + chain( + (trk.state.last_active_timestamp for trk in trackings), + (det.timestamp for det in camera_detections), + ) + ).timestamp() # common origin (float) + ts_trk = jnp.array( + [trk.state.last_active_timestamp.timestamp() - t0 for trk in trackings], + dtype=jnp.float32, # now small, ms-scale fits in fp32 + ) + ts_det = jnp.array( + [det.timestamp.timestamp() - t0 for det in camera_detections], + dtype=jnp.float32, + ) + # Δt in seconds, fp32 throughout + delta_t = ts_det[None, :] - ts_trk[:, None] # (T,D) + min_dt_s = float(DELTA_T_MIN.total_seconds()) + delta_t = jnp.clip(delta_t, a_min=min_dt_s, a_max=None) + + # ------------------------------------------------------------------ + # ---------- 2D affinity ------------------------------------------- + # ------------------------------------------------------------------ + # Project each tracking's 3D keypoints onto the image once. + # `Camera.project` works per-sample, so we vmap over the first axis. + + proj_fn = jax.vmap(cam.project, in_axes=0) # maps over the keypoint sets + kps2d_trk_proj: Float[Array, "T J 2"] = proj_fn(kps3d_trk) # (T, J, 2) + + # Normalise keypoints by image size so absolute units do not bias distance + norm_trk = kps2d_trk_proj / jnp.array([w_img, h_img]) + norm_det = kps2d_det / jnp.array([w_img, h_img]) + + # L2 distance for every (T, D, J) + # reshape for broadcasting: (T,1,J,2) vs (1,D,J,2) + diff2d = norm_trk[:, None, :, :] - norm_det[None, :, :, :] + dist2d: Float[Array, "T D J"] = jnp.linalg.norm(diff2d, axis=-1) + + # Compute per-keypoint 2D affinity + delta_t_broadcast = delta_t[:, :, None] # (T, D, 1) + affinity_2d = ( + w_2d + * (1 - dist2d / (alpha_2d * delta_t_broadcast)) + * jnp.exp(-lambda_a * delta_t_broadcast) + ) + + # ------------------------------------------------------------------ + # ---------- 3D affinity ------------------------------------------- + # ------------------------------------------------------------------ + # For each detection pre-compute back-projected 3D points lying on z=0 plane. + + backproj_points_list = [ + det.camera.unproject_points_to_z_plane(det.keypoints, z=0.0) + for det in camera_detections + ] # each (J,3) + backproj: Float[Array, "D J 3"] = jnp.stack(backproj_points_list) # (D, J, 3) + + zero_velocity = jnp.zeros((J, 3)) + trk_velocities = jnp.stack( + [ + trk.velocity if trk.velocity is not None else zero_velocity + for trk in trackings + ] + ) + + predicted_pose: Float[Array, "T D J 3"] = ( + kps3d_trk[:, None, :, :] # (T,1,J,3) + + trk_velocities[:, None, :, :] * delta_t[:, :, None, None] # (T,D,1,1) + ) + + # Camera center – shape (3,) -> will broadcast + cam_center = cam.params.location + + # Compute perpendicular distance using vectorized formula + # p1 = cam_center (3,) + # p2 = backproj (D, J, 3) + # P = predicted_pose (T, D, J, 3) + # Broadcast plan: v1 = P - p1 → (T, D, J, 3) + # v2 = p2[None, ...]-p1 → (1, D, J, 3) + # Shapes now line up; no stray singleton axis. + p1 = cam_center + p2 = backproj + P = predicted_pose + v1 = P - p1 + v2 = p2[None, :, :, :] - p1 # (1, D, J, 3) + cross = jnp.cross(v1, v2) # (T, D, J, 3) + num = jnp.linalg.norm(cross, axis=-1) # (T, D, J) + den = jnp.linalg.norm(v2, axis=-1) # (1, D, J) + dist3d: Float[Array, "T D J"] = num / den + + affinity_3d = ( + w_3d * (1 - dist3d / alpha_3d) * jnp.exp(-lambda_a * delta_t_broadcast) + ) + + # ------------------------------------------------------------------ + # Combine and reduce across keypoints → (T, D) + # ------------------------------------------------------------------ + total_affinity: Float[Array, "T D"] = jnp.sum(affinity_2d + affinity_3d, axis=-1) + return total_affinity # type: ignore[return-value] + + +@beartype +def calculate_affinity_matrix( + trackings: Sequence[Tracking], + detections: Sequence[Detection] | Mapping[CameraID, list[Detection]], + w_2d: float, + alpha_2d: float, + w_3d: float, + alpha_3d: float, + lambda_a: float, +) -> dict[CameraID, AffinityResult]: + """ + Calculate the affinity matrix between a set of trackings and detections. + + Args: + trackings: Sequence of tracking objects + detections: Sequence of detection objects or a group detections by ID + w_2d: Weight for 2D affinity + alpha_2d: Normalization factor for 2D distance + w_3d: Weight for 3D affinity + alpha_3d: Normalization factor for 3D distance + lambda_a: Decay rate for time difference + Returns: + A dictionary mapping camera IDs to affinity results. + """ + if isinstance(detections, Mapping): + detection_by_camera = detections + else: + detection_by_camera = classify_by_camera(detections) + + res: dict[CameraID, AffinityResult] = {} + for camera_id, camera_detections in detection_by_camera.items(): + affinity_matrix = calculate_camera_affinity_matrix_jax( + trackings, + camera_detections, + w_2d, + alpha_2d, + w_3d, + alpha_3d, + lambda_a, + ) + # row, col + indices_T, indices_D = linear_sum_assignment(affinity_matrix) + affinity_result = AffinityResult( + matrix=affinity_matrix, + trackings=trackings, + detections=camera_detections, + indices_T=indices_T, + indices_D=indices_D, + ) + res[camera_id] = affinity_result + return res + + +DetectionMap: TypeAlias = PMap[CameraID, Detection] + + +def update_tracking( + tracking: Tracking, + detections: Sequence[Detection], + max_delta_t: timedelta = timedelta(milliseconds=100), + lambda_t: float = 10.0, +) -> None: + """ + update the tracking with a new set of detections + + Args: + tracking: the tracking to update + detections: the detections to update the tracking with + max_delta_t: the maximum time difference between the last active timestamp and the latest detection + lambda_t: the lambda value for the time difference + + Note: + the function would mutate the tracking object + """ + last_active_timestamp = tracking.state.last_active_timestamp + latest_timestamp = max(d.timestamp for d in detections) + + d = tracking.state.historical_detections_by_camera + for detection in detections: + d = cast(DetectionMap, d.update({detection.camera.id: detection})) + for camera_id, detection in d.items(): + if detection.timestamp - latest_timestamp > max_delta_t: + d = d.remove(camera_id) + new_detections = d + new_detections_list = list(new_detections.values()) + project_matrices = jnp.stack( + [detection.camera.params.projection_matrix for detection in new_detections_list] + ) + delta_t = jnp.array( + [ + detection.timestamp.timestamp() - last_active_timestamp.timestamp() + for detection in new_detections_list + ] + ) + kps = jnp.stack([detection.keypoints for detection in new_detections_list]) + conf = jnp.stack([detection.confidences for detection in new_detections_list]) + kps_3d = triangulate_points_from_multiple_views_linear_time_weighted( + project_matrices, kps, delta_t, lambda_t, conf + ) + new_state = TrackingState( + keypoints=kps_3d, + last_active_timestamp=latest_timestamp, + historical_detections_by_camera=new_detections, + ) + tracking.update(kps_3d, latest_timestamp) + tracking.state = new_state + + +# 相机内外参路径 +CAMERA_PATH = Path( + "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params" +) +# 所有机位的相机内外参 +AK_CAMERA_DATASET: ak.Array = get_camera_params(CAMERA_PATH) + +# 2d检测数据路径 +DATASET_PATH = Path( + "/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/detect_result/segement_1" +) +# 指定机位的2d检测数据 +camera_port = [5603, 5605, 5608, 5609] +KEYPOINT_DATASET = get_camera_detect(DATASET_PATH, camera_port, AK_CAMERA_DATASET) + +# 获取一段完整的跳跃片段 +FRAME_INDEX = [i for i in range(0, 600)] +KEYPOINT_DATASET = get_segment(camera_port, FRAME_INDEX, KEYPOINT_DATASET) + + +# 将所有的2d检测数据打包 +sync_gen: Generator[list[Detection], Any, None] = get_batch_detect( + KEYPOINT_DATASET, + AK_CAMERA_DATASET, + camera_port, + batch_fps=20, +) + +# 建立追踪目标 +global_tracking_state = GlobalTrackingState() +# 跟踪超参数 +W_2D = 0.2 +ALPHA_2D = 60.0 +LAMBDA_A = 5.0 +W_3D = 0.8 +ALPHA_3D = 0.15 +# 帧数计数器 +count = 0 +# 追踪相似度矩阵匹配阈值 +affinities_threshold = 70 +# 跟踪目标集合 +trackings: list[Tracking] = [] +# 3d数据，键为追踪目标id，值为该目标的所有3d数据 +all_3d_kps: dict[str, list] = {} + +# 遍历2d数据，测试追踪状态 +while count < (max(FRAME_INDEX) - min(FRAME_INDEX)): + count += 1 + # 获得当前追踪目标 + trackings: list[Tracking] = sorted( + global_tracking_state.trackings.values(), key=lambda x: x.id + ) + + try: + detections = next(sync_gen) + except StopIteration: + break + if len(detections) == 0: + continue + # print("Detection len:", len(detections), "count:", count) + # 获得最新一帧的数据2d数据 + # 判断追踪状态是否建立成功，若不成功则跳过这一帧数据，直到追踪建立 + if not trackings: + # 使用盒子筛选后的2d检测数据 + filter_detections = get_filter_detections(detections) + # 当3个机位均有目标时才建立追踪状态 + # if len(filter_detections) == 0: + # continue + if len(filter_detections) < len(camera_port): + print( + "init traincking error, filter detections len:", + len(filter_detections), + "time:", + detections[0].timestamp, + ) + continue + # 添加第一帧数据构建追踪目标 + global_tracking_state.add_tracking(filter_detections) + # 获得当前追踪目标 + trackings: list[Tracking] = sorted( + global_tracking_state.trackings.values(), key=lambda x: x.id + ) + # 保留第一帧的3d姿态数据 + for element_tracking in trackings: + if str(element_tracking.id) not in all_3d_kps.keys(): + all_3d_kps[str(element_tracking.id)] = [ + element_tracking.state.keypoints.tolist() + ] + print("initer tracking:", trackings) + else: + # 计算相似度矩阵匹配结果 + affinities: dict[str, AffinityResult] = calculate_affinity_matrix( + trackings, + detections, + w_2d=W_2D, + alpha_2d=ALPHA_2D, + w_3d=W_3D, + alpha_3d=ALPHA_3D, + lambda_a=LAMBDA_A, + ) + + # 遍历追踪目标，获得该目标的匹配2d数据 + for element_tracking in trackings: + tracking_detection = [] + # 匹配检测目标的索引值 + detection_index = None + + temp_matrix = [] + + # 遍历相机的追踪相似度匹配结果 + for camera_name in affinities.keys(): + # 获得与每个跟踪目标匹配的相似度矩阵 + camera_matrix = jnp.array(affinities[camera_name].matrix).flatten() + detection_index = jnp.argmax(camera_matrix).item() + + if isnan(camera_matrix[detection_index].item()): + breakpoint() + temp_matrix.append( + f"{camera_name} : {camera_matrix[detection_index].item()}" + ) + + # 判断相似度矩阵极大值是否大于阈值 + # 目前只有一个跟踪目标，还未实现多跟踪目标的匹配------------------------- + if camera_matrix[detection_index].item() > affinities_threshold: + # 保留对应的2d检测数据 + tracking_detection.append( + affinities[camera_name].detections[detection_index] + ) + print("affinities matrix:", temp_matrix) + # 当2个及以上数量的机位同时检测到追踪目标时，更新追踪状态 + if len(tracking_detection) >= 2: + update_tracking(element_tracking, tracking_detection) + # 保留对应的3d姿态数据 + all_3d_kps[str(element_tracking.id)].append( + element_tracking.state.keypoints.tolist() + ) + print( + "update tracking:", + global_tracking_state.trackings.values(), + ) + else: + # if len(detections) == 0: + # continue + # 追踪目标丢失的时间间隔 + time_gap = ( + detections[0].timestamp + - element_tracking.state.last_active_timestamp + ) + # 当时间间隔超过1s，删除保留的追踪状态 + if time_gap.seconds > 3: + # trackings.remove(element_tracking) + global_tracking_state._trackings.pop(element_tracking.id) + print( + "remove trackings:", + global_tracking_state.trackings.values(), + "time:", + detections[0].timestamp, + ) + + +with open("samples/QuanCheng_res.json", "wb") as f: + f.write(orjson.dumps(all_3d_kps)) +for element_3d_kps_id in all_3d_kps.keys(): + print(f"{element_3d_kps_id} : {np.array(all_3d_kps[element_3d_kps_id]).shape}") diff --git a/test_filter_object_by_box.ipynb b/test_filter_object_by_box.ipynb index 4155182..5638c67 100644 --- a/test_filter_object_by_box.ipynb +++ b/test_filter_object_by_box.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1473, + "execution_count": 311, "id": "3265b2cc", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 1474, + "execution_count": 312, "id": "e6589e07", "metadata": {}, "outputs": [], @@ -44,12 +44,12 @@ }, { "cell_type": "code", - "execution_count": 1475, + "execution_count": 313, "id": "b8898483", "metadata": {}, "outputs": [], "source": [ - "index = 3\n", + "index = 5\n", "DETECT_INDEX = {\n", " 1:\"5601\",\n", " 2:\"5602\",\n", @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 1476, + "execution_count": 314, "id": "990361fb", "metadata": {}, "outputs": [], @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 1477, + "execution_count": 315, "id": "0b07ef52", "metadata": {}, "outputs": [ @@ -128,17 +128,17 @@ }, { "cell_type": "code", - "execution_count": 1478, + "execution_count": 316, "id": "c39a4e73", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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+       "
{name: 'AF_05',\n",
+       " port: 5605,\n",
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+       " extrinsic: {rvec: [1.25, 0.0803, -2.87], tvec: [-0.767, ...]},\n",
        " resolution: {width: 2560, height: 1440}}\n",
        "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
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@@ -161,7 +161,7 @@
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       ],
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+       ""
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@@ -175,7 +175,7 @@
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+   "execution_count": 317,
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@@ -198,19 +198,19 @@
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   {
    "cell_type": "code",
-   "execution_count": 1480,
+   "execution_count": 318,
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        "        [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]),\n",
-       " 'dist': array([-0.47908944,  0.45129257, -0.00447612,  0.00236029, -0.41700587]),\n",
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        " 'width': np.int64(2560),\n",
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@@ -233,20 +233,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1481,
+   "execution_count": 319,
    "id": "0723986a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
{frame_index: 1680,\n",
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-       " kps_scores: [[0.721, 0.719, 0.741, 0.73, ..., 0.637, 0.646, 0.644, 0.604]]}\n",
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{frame_index: 1650,\n",
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+       " kps: [[[930, 514], [930, 500], ..., [1.03e+03, ...], [1.03e+03, 604]], ...],\n",
+       " kps_scores: [[0.589, 0.625, 0.478, 0.636, ..., 0.242, 0.242, 0.221], ...]}\n",
        "-----------------------------------------------------------------------------------------------------------------------------------------\n",
        "backend: cpu\n",
-       "nbytes: 9.0 MB\n",
+       "nbytes: 16.1 MB\n",
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@@ -255,7 +255,7 @@
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-       ""
+       ""
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      "metadata": {},
@@ -263,21 +263,21 @@
     }
    ],
    "source": [
-    "frame_index = 1680\n",
+    "frame_index = 1650\n",
     "frame_data = detect_data[frame_index]\n",
     "display(frame_data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1482,
+   "execution_count": 320,
    "id": "809ede80",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 133, 2)"
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@@ -297,7 +297,7 @@
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    "cell_type": "code",
-   "execution_count": 1483,
+   "execution_count": 321,
    "id": "7b358faf",
    "metadata": {},
    "outputs": [],
@@ -324,14 +324,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1484,
+   "execution_count": 322,
    "id": "1cc53101",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "metadata": {},
@@ -339,7 +339,7 @@
     },
     {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -354,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1485,
+   "execution_count": 323,
    "id": "f2096d54",
    "metadata": {},
    "outputs": [],
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1486,
+   "execution_count": 324,
    "id": "f6e9abe6",
    "metadata": {},
    "outputs": [],
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1487,
+   "execution_count": 325,
    "id": "375fbdb0",
    "metadata": {},
    "outputs": [
@@ -413,10 +413,10 @@
        " '1': [0.73, -0.19500000000000006, -1.155],\n",
        " '2': [0.73, 1.605, -1.155],\n",
        " '3': [0.08000000000000002, 1.605, -1.155],\n",
-       " '4': [0.08000000000000002, -0.19500000000000006, 0.345],\n",
-       " '5': [0.73, -0.19500000000000006, 0.345],\n",
-       " '6': [0.73, 1.605, 0.345],\n",
-       " '7': [0.08000000000000002, 1.605, 0.345]}"
+       " '4': [0.08000000000000002, -0.19500000000000006, -0.15500000000000003],\n",
+       " '5': [0.73, -0.19500000000000006, -0.15500000000000003],\n",
+       " '6': [0.73, 1.605, -0.15500000000000003],\n",
+       " '7': [0.08000000000000002, 1.605, -0.15500000000000003]}"
       ]
      },
      "metadata": {},
@@ -427,9 +427,9 @@
     "# 获得盒子三维坐标系\n",
     "\n",
     "# 盒子原点位置，相对于六面体中心偏移\n",
-    "box_ori_potision = [0.205+0.2 ,0.205+0.50,-0.205-0.2]\n",
+    "box_ori_potision = [0.205+0.2 ,0.205+0.50,-0.205-0.45]\n",
     "# 盒子边长，宽：1.5米，高：1.5米，深度：1.8米\n",
-    "box_geometry = [0.65,1.8,1.5]\n",
+    "box_geometry = [0.65,1.8,1]\n",
     "filter_box_points_3d = calculaterCubeVersices(box_ori_potision, box_geometry)\n",
     "filter_box_points_3d = {str(index):element for index, element in enumerate(filter_box_points_3d)}\n",
     "display(filter_box_points_3d)"
@@ -437,21 +437,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1488,
+   "execution_count": 326,
    "id": "6943c11b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'0': [1247, 893],\n",
-       " '1': [1102, 869],\n",
-       " '2': [1088, 141],\n",
-       " '3': [1232, 102],\n",
-       " '4': [1730, 843],\n",
-       " '5': [1570, 827],\n",
-       " '6': [1557, 179],\n",
-       " '7': [1716, 152]}"
+       "{'0': [1295, 961],\n",
+       " '1': [1060, 995],\n",
+       " '2': [1092, 43],\n",
+       " '3': [1321, 86],\n",
+       " '4': [977, 899],\n",
+       " '5': [753, 922],\n",
+       " '6': [786, 102],\n",
+       " '7': [1004, 129]}"
       ]
      },
      "metadata": {},
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1489,
+   "execution_count": 327,
    "id": "fe63d521",
    "metadata": {},
    "outputs": [],
@@ -492,25 +492,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1490,
+   "execution_count": 328,
    "id": "18e09dae",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[[[1730, 843], [1557, 179], [1716, 152]],\n",
-       " [[1730, 843], [1570, 827], [1557, 179]],\n",
-       " [[1088, 141], [1570, 827], [1557, 179]],\n",
-       " [[1102, 869], [1088, 141], [1570, 827]],\n",
-       " [[1102, 869], [1088, 141], [1232, 102]],\n",
-       " [[1247, 893], [1102, 869], [1232, 102]],\n",
-       " [[1247, 893], [1232, 102], [1716, 152]],\n",
-       " [[1247, 893], [1730, 843], [1716, 152]],\n",
-       " [[1088, 141], [1557, 179], [1716, 152]],\n",
-       " [[1088, 141], [1232, 102], [1716, 152]],\n",
-       " [[1247, 893], [1730, 843], [1570, 827]],\n",
-       " [[1247, 893], [1102, 869], [1570, 827]]]"
+       "[[[977, 899], [786, 102], [1004, 129]],\n",
+       " [[977, 899], [753, 922], [786, 102]],\n",
+       " [[1092, 43], [753, 922], [786, 102]],\n",
+       " [[1060, 995], [1092, 43], [753, 922]],\n",
+       " [[1060, 995], [1092, 43], [1321, 86]],\n",
+       " [[1295, 961], [1060, 995], [1321, 86]],\n",
+       " [[1295, 961], [1321, 86], [1004, 129]],\n",
+       " [[1295, 961], [977, 899], [1004, 129]],\n",
+       " [[1092, 43], [786, 102], [1004, 129]],\n",
+       " [[1092, 43], [1321, 86], [1004, 129]],\n",
+       " [[1295, 961], [977, 899], [753, 922]],\n",
+       " [[1295, 961], [1060, 995], [753, 922]]]"
       ]
      },
      "metadata": {},
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1491,
+   "execution_count": 329,
    "id": "e456f46e",
    "metadata": {},
    "outputs": [],
@@ -564,14 +564,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1492,
+   "execution_count": 330,
    "id": "003f8fcb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "468130.0"
+       "483849.0"
       ]
      },
      "metadata": {},
@@ -580,10 +580,10 @@
     {
      "data": {
       "image/svg+xml": [
-       ""
+       ""
       ],
       "text/plain": [
-       ""
+       ""
       ]
      },
      "metadata": {},
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1493,
+   "execution_count": 331,
    "id": "104a250d",
    "metadata": {},
    "outputs": [],
@@ -643,13 +643,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1494,
+   "execution_count": 332,
    "id": "0d1af87c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1495,
+   "execution_count": 333,
    "id": "d51c5b86",
    "metadata": {},
    "outputs": [],
@@ -707,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1496,
+   "execution_count": 334,
    "id": "a55035af",
    "metadata": {},
    "outputs": [],
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1497,
+   "execution_count": 335,
    "id": "a5590507",
    "metadata": {},
    "outputs": [],
@@ -736,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1498,
+   "execution_count": 336,
    "id": "6c8f700b",
    "metadata": {},
    "outputs": [],
@@ -752,13 +752,11 @@
     "    # 遍历未筛选的目标框\n",
     "    for index in range(len(unfilter_kps_box[\"boxes\"])):\n",
     "        x1, y1, x2, y2 = unfilter_kps_box[\"boxes\"][index].tolist()\n",
-    "        # 保留目标框中心在范围内的坐标点\n",
-    "        x_center = (x1 + x2)/2\n",
-    "        y_centet = (y1 + y2)/2\n",
-    "        # 臀部离跳台高度<540，针对机位9 (这个条件后续需要优化掉)\n",
-    "        # hip_y = (unfilter_kps_box[\"kps\"][index][6][1] + unfilter_kps_box['kps'][index][9][1])/2\n",
     "\n",
-    "        if point_in_polygon([x_center, y_centet], contours):\n",
+    "        x_center = (x1 + x2) / 2\n",
+    "        y_centet = (y1 + y2) / 2\n",
+    "        # 保留目标框中心左上角在范围内的坐标点\n",
+    "        if point_in_polygon([x_center, y_centet], contours) and point_in_polygon([x1, y1], contours):\n",
     "            new_boxes_data.append([x1, y1, x2, y2])\n",
     "            new_kps_data.append(unfilter_kps_box[\"kps\"][index].tolist())\n",
     "            new_kps_scores.append(unfilter_kps_box[\"kps_scores\"][index].tolist())\n",
@@ -772,13 +770,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1499,
+   "execution_count": 337,
    "id": "b4bf2fc4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ixYetq6ysJCcnh8LCwjbLS0pKqKysTI05NBgdWH9g3ZE0NDTQ0NCQ+j4ed3paSZIkSe3X4UeOtm7dyle+8hWefPJJcnNzO/rlj2r27NkUFBSkasSIEV323pIkSZJ6vg4PR0uWLGHnzp2cd955ZGVlkZWVxcsvv8yPf/xjsrKyKCkpobGxkZqamjbPq6qqorS0FIDS0tLDZq878P2BMR82a9YsYrFYqrZu3drRmyZJkiSpF+vwcHTFFVfw9ttvs2zZslRNmjSJGTNmpB5nZ2czf/781HPWrVvHli1bKC8vB6C8vJy3336bnTt3psbMmzeP/Px8xo8ff8T3jUaj5OfntylJkiRJaq8Ov+Zo4MCBnHXWWW2W9e/fn8GDB6eW33rrrdx9990UFRWRn5/Pl7/8ZcrLy/nEJz4BwNSpUxk/fjw33XQTc+bMobKykn/6p39i5syZRKPRjm5ZkiRJkjpnQobj+cEPfkBGRgbXXXcdDQ0NTJs2jYcffji1PjMzk7lz53LHHXdQXl5O//79ufnmm/nnf/7ndLQrSZIkqQ+IhBBCupvoDPF4nIKCgnS3IUnd2DDgbWBwuhuR0qAJuByoSHcjkrpQLBY75uU3nXafI0mSpO4rE5iQ7iYkdTOGI0nqs/YDH6S7CSlNMoCT0t2EpG7GcCRJfVYceC/dTUiS1G0YjiRJkiQJw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkSYDhSJIkSZIAw5EkSZ0qEkkwoN9+Jo5dR0HRLiCkuyVJ0lFkpbsBSZJ6j0BGRqAoP86E097htJPe59JzlzFm1BZizRm8lvsBf3zuL1n82+toasgFIuluWJJ0CMORJEkfUSSSoF9uPSVFezjn9PVcPPFtTjn5PcacupHqxiwqVo5n7luTeO1nf88H1UWMmriEy2b8O2dPeYHfP3w3m1eeQ0hkpnszJEmtIiGEXnl8Px6PU1BQkO42JKkbiwC/A6anu5EeIzOzhYL+ezl5WCXnn7GWT5z9NmeOW0PRsO1UNkVY+X4ZryyazJuLytm26VT2xQsgtD2DPXdAnHOnzeX8Tz/LtvXjWfCLLxLbWYpHkdLhv4CrgOZ0NyKpi8RiMfLz84+63nAkSX2W4eh4MiIJSgZXM3HMBj55/lJGDd/G+DNWkz20kq0tCd6vGciipeex4q1JbHzrQvbsGE5LcxbHDzqB/CE7uWzG/2LMBa+x/I+f5tVf30TD/v7teK46zhvAJUBTuhuR1EUMR5KkY/gO8I10N9FNBT5z2Z+4/46fsS93LxvrstjTL862ylLWr5rA6j/9GVWbTmF/rJBkoPkooSbBqLOXctnnf8GAQdUsePz/YcMbF5FoaU/A0sdnOJL6GsORJOkYZgI/TXcT3UggI6uZQaXbGT52NQ/e9CtWUs+bW4az9rXL2L5+PLu2jqKlKadD3zUjq4mzLv8jF1//v6ndPYQXH/8i29afcdgpeepohiOprzleOHJCBklS3xZJEO23j9JTNjJm8iuUjVlLdrSe3evHM7hfPf9254+o3DXksIkTIgT++s/nc/64tYe95Lvby1izaXS7W3hvRynvvX4pO1efzSU3PMEt37mblS9OZf5/3EptrACPIklS1zAcSZL6mEBGZguFpdsZccbbjJn8KkXDthH7oIR3lkzmrReuIf5BCeed9g6bR+1mV3XREWeUyx+wlxnT/8D/XfApWhJtj/CcMnw7nzzvN+3uqHhwNSXDtkMkQQToV1hH4rr/j0ebBjLnsdvatU1JhihJ+jgMR5Kk3i+SIG9gnMHDtzDuoj8xbMxaov32sWXlRJb+4Wp2rD+D/fEDp2InA8aVF1cwb9EFNDVnH/Elzx+3jpragfy/L0wjcVh4OvoZ6xkZLWTn1ZE/5AOGj1vF4OFbOaPoPQrrG9i9bSQfbB7NO29dyJ7tJ7FjyyjaE3hKiys5aeR7vPlmeTv+Y0iSjsZwJEnqlTKzGxg4eBennr+Ik8atovTU9ezaOor3157F0p/+N2JVpTQ35XCk8JGd1cTZp23kgVdvOsqrByafuZo314w7QjAi9ZoZmc1k5TQw+KQtFJW9z0lnrGTYaeuI9ttHY10/tqw6m53vncrqhX/G7m0jaG6Mtk7GcGLOrh7M5/YN4M0TfqYk6VCGI0lSL5A8UhPtv5fhp69h5IRlnHLeGzTV5bHr/VEs/+OV/PF/fYl9NYPaNclBUX6cQQNr2bj1pCOuz8hI8IkJq/jnf/9Cm/fPy48xsGgXQ0dtYvTEJRSU7KCwZAf7YoOI7Szl/dUTeHvBNGp3D2HvnsGERAYdcSrc6uYcvtGcQ39g38d+NUnquwxHkqQeKjmzXMHQKkaeuZxxF79MYckO4ruG8v7qCcz94dfZ/f5IWpqzOdEActHZK1n93snsqR14xPVDC2sYMKCWXU0ZjL3oZUaeuZxhY9bSv2APjXX92LNjOJuWn8/bL01l15aTqavNJ9GSecJ9tFc9kAM4t50kfTyGI0lSjxGJJMjJ28+w09Zx6vmLGHnWcnLy9rN9/Rm89cI1VG06jdrdh88sd2ICn5iwktffPguIQCRBVk4jBcWVFAyt4uSzl3LRuPU0FO5i6ldm8/7aM6neNpK1r13O7q0jaajrR0tTtKM2uV32A3GgBKjt0neWpN7FcCRJ6hEi0WbOvmEul1zxJPFdxbzz+if4/SNf5YMto2luPPK1QycmeWpc/wG1XHDWKhZsKeXPvvAIRcO3UjzqXRrr89gfK2Tz2+cyIgse//fb+T8LPtmpR4Taaz/JUFRKYKMz1knSR2Y4kiT1DJHAnsJBPL/wbuojeTRn5NI4qAD2NRHZk0moP3C0qL3hIJCR0cKAwbsYfvoaBp+0hZETlnL6SdsZWNDEkHMWsWn1BN5eMJ0dG8ayv7aA5oYoedEG7rtkKUvXnv6RJk/oLHujdYy98DVefeVTBG8eK0kfSff5qS5J0jGE+ix2vToWMgJ1y4aSNaSO7JP2MvCTW4lEW0jsy6Zh/SAatw2gpSYXmiMcDEqBzOwm+hfsYeCQDzj57LcYMmIzg4ZtIye3jl3vj6R6+0m8/n8/T+n5y/l9VuDJR2/jSEGrbOguIgS2fTC0Kzf/uF5tzOFvV5/N/w4RmtLdjCT1UIYjSVIPEaHhnULyP/0udUuLado+kKbtA9j/ZgkZ/ZqTYWlELfkT3iOS3ULzzn4ktuZxximvMuyUdQw7bR0hRGis68fmt89lxfzpxD4oIbazlJbmrNZZ7AITP/MH/uP30zjaEagJp77Lu9vL2F+f26VbfzwtIZP+u7tXYJOknsZwJEnqMVr2RGmpiZI9fC+N7xUAEUhESOzNoXFvDo3v5bPvtTIyBzSSPXwvgy/fQD+qeW/5+bzy9M3sjxdQv28AhEOPKh1U0H8fw4t3snrTyUfpIFA+YSVz/3RxJ27liQhEcxo4f+wSojVDaR6xHio+3a7pygVwElAMbEt3I5K6CcORJKkHiVC/cgi5Z+2i8b18Dg84EWiJ0BLLpSUWZU9+I4tbTmb/4mHtevVzx66ncvdgKncNPuL6nOwmzjz1XX7y6788wnt3nUgkwekj1rO/oR+XnvMn/vjGFGL1/bi+vl9r8FP7FAAD0t2EpG7EPy1JUp/WwoFZ2nqKpu0DyCxsIHNQw3FGRkjszyKjfxPt3cbJZ63izTXjaDnKVOAnD9tBU3MWO3YXnVjTHSaQndXIjX/+FAUDYuyr68/T/3UDO/eUsL9uIBkfnESGs9VJ0kdmOJKkPm0RyVuI9hyhKYOGDYOInraH44Wepu0DyC7b266DPFmZzZw7dj2LV40/2jvzyfOXsnz9abS0fJz7KH10edE6Pj/1l/z2lat5Y/WFVMcHkwgHezkFGJmWziSpdzAcSVKfVkdPO3IEEerXFREdV00kt/nYQ1siRLITkJk47qsOL/6AwQVxlm887SjvCuUTVvLaigmk55S6wIjircx95S+o3T/wiD1kA9kEet7/U0nqHgxHkqQeJxHLoXlHf3JG7OVYQaAlHoUIZA5sPO5rnnXKJjZXlrB3f94R1w/sv4+Tij9gzXsnf8SuP7pIJMGnzn+Rc05fRiJkcKRglBlJkF3wAZcVv88d1z5CZsZxgqMk6TCGI0lSDxShfvVgcs/adeyDOIkIifosMvofLygELj1nGX9aes5RR4wqreL9nUPZ/sGQj9Lwx3Lh+Ddoas7mNy9dy57aw693igC35++m4J9ncGH5f3H9n/+KvGhdl/cpST2d4UiS1CM1VfUnI9pM1tBjhIAAzTvzyCrZd8zXysxI0JzIZP7iSRwtbdU15PCr336KlkTXf3SufPcsquNFNLdkESHB+NGryM5qBAIZBP6GwBfz9rG1KYcPRq5l7it/QV3DkY+ASZKOzqm8JUk9U0sGdasHEz29muadeRw51ERoqc5tDVDhKGOgJZHJr352EaMTa3mfoUcc1/x+M3/7/k9ZwCQaiXbghrTPkIJdfH7aL6lvyOWHX72LZ1/6HL9/fTp/ddrbXLVrGI0jNnDHr77C7xdPOWL/kqTj88iRJKnHany3kJyRcSLRlqOOad6dR9bg459idnJiE/+Lv+PT/I5M2p6GFyHBbfycKA200PUz1e2rG8DCZZfxu1c/zb76/vyfBX/JxvdPY9iQHVxy2X/yLztGMzs2mAXLLsNgJEkfnUeOJEk9VmJvNs3VeeScEqNhTRFHCgYtsSiR3BYi0RZCw9E/9ubyFxRSw+N8gYf5Et/hH2kiB4BRbOYLPM4z/BUtafvojFCzdxB/eH0af3h9Wmrpn5ZdStnQ7bzz4nU0NnX9ES1J6k0MR5KkHixC3fKhDPzkVhrWDYLE4eEo1GURyUoQyUoQjnHf2EAGv+J6LuQN/jtziBD4Lt+giWxu5zGGsYNaBnbitrRX223csPV0Nmw9PU29SFLvYjiSJPVozR/0IyQiZJfuo2n74eElNGfQXJ1LVsl+Gt/NOeZrNRLlXr5HlAbu5XtM4G1e4RLu5KcEIrzCJZ21GZKkbsBrjiRJPVtzhLplxUTHVXPEex6FCKE+i4y89t33Zz/9mctfkEkLn+U5HuQeBrCXVZzJGs7o2N4lSd2K4UiS1MNFaNwykOyyfWT0O3IAatyUT87JsXa+WoKLeI1qimgmixYy+R2f5q94hi2M7MjG1S04gYWkgzytTpLU44W6LJreH0DOaXuoX3H4VNyJ+mwyclsgEiAc+5fhQISHmMkj3MGfsYBtDOdVLmYfAzpxC5QeucAkYG26G5HUTRiOJEm9QIS6t4cw8FNbqV855LCJGZp35pE5qJ5IdoLQeLypuCNsbT1C9Av+rpP6VfeQCeSnuwlJ3Yin1UmSeoWWPbkAZJ+097B1oSmD0JRBpJ3XHUmS+ibDkST1aQ3A8W+Q2iMkItSvGkLehA/48MQMoTGTREMWmfnHmMtbktTnGY4kqU/bBmxKdxMdJELDuwVkDmogs+DwENS8M4+s4v1p6EuS1FN0Sjjatm0bf/M3f8PgwYPJy8tjwoQJvPnmm6n1IQTuv/9+hg0bRl5eHlOmTGHDhg1tXqO6upoZM2aQn59PYWEht956K3v3Hn6qhCTp4wgccfrrHio0ZNKwsZCc02pou10RWmqiZBY20Ju2V5LUsTo8HO3Zs4eLL76Y7OxsXnjhBVavXs3/+B//g0GDBqXGzJkzhx//+Mc8+uijLFq0iP79+zNt2jTq6+tTY2bMmMGqVauYN28ec+fOZeHChdx+++0d3a4kqVeJ0LC2iOhpNZDZNgQ1Vw4gu3i/50xIko4udLB77703XHLJJUddn0gkQmlpaXjwwQdTy2pqakI0Gg1PPfVUCCGE1atXByAsXrw4NeaFF14IkUgkbNu2rV19xGKxA38OtSzLso5a2QHeCBB6T0USoeCz60PO6D1tlmcW1oWiW1YEMlvS36PVjepLIf3/Di3L6qqKxWLHzBAd/vez//zP/2TSpEn81V/9FcXFxZx77rn8/Oc/T63ftGkTlZWVTJkyJbWsoKCAyZMnU1FRAUBFRQWFhYVMmjQpNWbKlClkZGSwaNGiI75vQ0MD8Xi8TUmS+qAQoW7FUHLHVZP8LExqqc2BROSI1yNJkgSdcHLBu+++yyOPPMKYMWP4wx/+wB133ME//MM/8MQTTwBQWVkJQElJSZvnlZSUpNZVVlZSXFzcZn1WVhZFRUWpMR82e/ZsCgoKUjVixIiO3jRJUg/RtHUgmQUNZAxsOrgwESE0ZSZvBitJ0hF0eDhKJBKcd955fPe73+Xcc8/l9ttv57bbbuPRRx/t6LdqY9asWcRisVRt3bq1U99PktR9haZMGrcOJPfMXaSOHgVo2jaA7JNq09qbJKn76vBwNGzYMMaPH99m2RlnnMGWLVsAKC0tBaCqqqrNmKqqqtS60tJSdu7c2WZ9c3Mz1dXVqTEfFo1Gyc/Pb1OSpL6rfuUQcsdWE8lJtC6J0LI3m4wBjRx6up0kSQd0eDi6+OKLWbduXZtl69evZ9SoUQCMHj2a0tJS5s+fn1ofj8dZtGgR5eXlAJSXl1NTU8OSJUtSYxYsWEAikWDy5Mkd3bIkqRdqiUdp3p1Hzuia1LLmHQPIHrYvfU1Jkrq3dk39dgLeeOONkJWVFb7zne+EDRs2hCeffDL069cv/Md//EdqzAMPPBAKCwvD888/H1asWBGuueaaMHr06FBXV5caM3369HDuueeGRYsWhVdeeSWMGTMm3Hjjje3uw9nqLMuy2lO9cLa6Qyp7eDzkX70hEEkECCGzoC4U3fx2iOQ0pb03qz2VCIxbHRj9TvJxp7yHs9VZVl+q481Wx4nHn+P77W9/G84666wQjUbDuHHjwmOPPdZmfSKRCPfdd18oKSkJ0Wg0XHHFFWHdunVtxuzevTvceOONYcCAASE/Pz/ccsstoba2tt09GI4sy7LaU707HJHVHAr/ek3ILNqf+n7wrctDxsCG9PdmHb8ymwL/NSXw1PWh88LRXSH9/w4ty+qqOl44ioQQAr1QPB6noKAg3W1IUjeXDbwKXJDuRjpJoN+kKiL9mti38CSIQMHnNlC3tJjGTYXpbk7Hk9UEf7oUdg+Gv5gLRDrhTZ4DPtcJryupO4rFYsecm8D7hEuSerEI9WuKyCnbm5yYIUQIdVlk9G86/lPVR0TpnNAlqScyHEmSerXE/mya9+QSPb0agMbN+eSM8kbhkqTDGY4kSb1biFC/ejC5Z1RDRoJEXRYZuc0kTz+XJOkgw5Ek9WkJYHO6m+h0TdsHAJBVvJ/mqv5kFjYQibakuStJUndjOJKkPq0FWJvuJjpfS/Lao9zxuwktEUIiQiTHcCRJastwJEnqAyI0bBxEdsl+iEBibw5Zg+vT3ZQkqZsxHEmS+oRQn0nTzn5Ex+yhJRYls7Ah3S1JkroZw5EkqY+IUL9yCLljq2nelUdW8b50NyRJ6mYMR5KkPqP5gzxCInlPm8xBDZCRSHNHkqTuxHAkSeo7Ehk0rB9EdMweMgc0+ikoSWrDjwVJUp/SsLGQzPzkVN6Z+V53JEk6yHAkSepTQn0WDeuLyBpc772OJEltGI4kSX1MhLoVQwlNGeSMjKe7GUlSN2I4kiT1OS01UerXDyKjXzMQ0t2O0iqS7gYkdSOGI0lS3xMi1K8eTM5Jtf5u3OedCQxOdxOSugnDkSSpT2qu6k+kXzMZ/ZvS3YrSqh+Qle4mJHUThiNJUp/UEs8hkpEg75ydeGqdJAkMR5KkvioRoWVPLtlle9PdiSSpmzAcSVKfF+iTR05ChObq3HR3IUnqRgxHktTnvQUk0t1EWjRtHwDBGRkkSUmGI0nq86rok0eOgNDsx6Ak6SA/FSRJfVMkkDu2GiJ9MxhKkg5nOJIk9UmZhQ1Ex+5Jdxs6lkQG7BgGQ3ZBbn26u5HUBxiOJEl9UCBndIzmyn5ec9SdJTJh+UQ49R0oiKW7G0l9gOFIktT3ZCWIjtlD/eohffVyK0nSERiOJEl9TtbQOiIZgeY90XS3ouPxyJ6kLmQ4kiT1MYHcMXtoeKcQWvwYlCQd5KeCJKlPiWQnyC7bS/3qwYSmDCJZCT8NJUkAZKW7AUmSulL2yDiJhkwS+7IJzRlkDGwiEm0m1GWnuzWlRQTw/72kJP9WJknqQwLRMTXULS9OXsviZAyiEDg33U1I6iYMR5KkPiMjv5Hs4v00bRuQ7lbUXp1+k16PHEk6yHAkSeozoifHaNren9CQme5WJEndkOFIktQ3ZCaSp9StGEryaIEkSW0ZjiRJfULW4Doiuc00785Ldys6Ed7nSFIXMhxJUp+XaK3eLDkRQ+OmAmjxl21J0pEZjiSpz1sPbE93E50rM5B9Ui11q4bgKXWSpKMxHElSn9cENKe7iU6VM6IWWiIk4jnpbkUnqtNnq5OkgwxHkqReLhAdsyd51MjrVyRJx2A4kiT1ahkDmsgq2Ze83kg9j4FWUhcyHEmSerWcUXFaducR6r23kSTp2AxHkqTeKxKInraH/cuKcSKGHsprjiR1IcORJKnXyhxcR0Z+I80feG+jHsvT6iR1IcORJKmXCkRPidGwtgia/bjTxxPBX5qkviAr3Q1IktQpMgI5o+LUzhuFp9TpSCIkyKERSB5ZLAaKWtcNAi7g4J5zFjAS+DnwZNe2KakLGY4kSb1SdtleAFq8t1HP9hGuOYpSTy71AORRxzjWEiH5OuNYy2lsBKCAGBewmAx2ATAUKGx9jQTQcMhrbgE+oLffEUyS4UiS1AsFoqfvoWH9IEh4MlSPFpL5KJMmhrI9FXKGs41TeBeAAezlcl4mkxYARrKFk3kPgCyaGcKu1PNqGcg++qceL2cigfeAHawC3ml92xiwIvn2qe/3HfK9pN7JcCRJ6nUiec1kl+5jX0VZulvRxzRmfQYP3BJnyJ6/YjwbUwEom6bWU+KgkRy2MoLQehLcJkbzf7kOSAagP3EpidYrhrYxnB0MA6CFTGoZCPxv4I2u3TBJ3ZLhSJLU6+SMitNSm0Oo82Oup8v89XXkJSqop4Uf8RWaW391eY+TWcs4IBmONjE6FY6ayaKZ7LT1LKnn8lNDktS7RALR02qoW1qc7k7UAdYmxvMXzAVoPfrj5BqSOk+Hn4jd0tLCfffdx+jRo8nLy+PUU0/lX/7lXwjh4Fm6IQTuv/9+hg0bRl5eHlOmTGHDhg1tXqe6upoZM2aQn59PYWEht956K3v37u3odiVJvUxmYT1ZRfU0V/XneL9Ih6YMWuI5ZA2u75rm9BFESJBJgkwMRpI6W4eHo+9973s88sgj/PSnP2XNmjV873vfY86cOfzkJz9JjZkzZw4//vGPefTRR1m0aBH9+/dn2rRp1Ncf/HCaMWMGq1atYt68ecydO5eFCxdy++23d3S7kiRagNp0N9FBAjmnxGh4p4DQ1I6PuEQGoSmDSNQ5yCRJQOhgV111Vfi7v/u7NsuuvfbaMGPGjBBCCIlEIpSWloYHH3wwtb6mpiZEo9Hw1FNPhRBCWL16dQDC4sWLU2NeeOGFEIlEwrZt29rVRywWCyQnlbEsy7KOW/8zQOj5ldUcBt2wOmQO3t/u5xRcuy7knFqd/t6tNNbjIf3/Bi3L6oqKxWLHzBAdfuTooosuYv78+axfvx6A5cuX88orr3DllVcCsGnTJiorK5kyZUrqOQUFBUyePJmKigoAKioqKCwsZNKkSakxU6ZMISMjg0WLFnV0y5IkQrob6BDZxXUAtOyJprkTSVJP1OETMnz9618nHo8zbtw4MjMzaWlp4Tvf+Q4zZswAoLKyEoCSkpI2zyspKUmtq6yspLi47YW0WVlZFBUVpcZ8WENDAw0NB2/XFo/HO2ybJEk9QSA6Zg8N7xR6byNJ0kfS4Z8ev/71r3nyySf55S9/yVtvvcUTTzzBv/3bv/HEE0909Fu1MXv2bAoKClI1YsSITn0/SVL3EslJkD1sL/WrB6e7FUlSD9Xh4eiee+7h61//OjfccAMTJkzgpptu4qtf/SqzZ88GoLS0FICqqqo2z6uqqkqtKy0tZefOnW3WNzc3U11dnRrzYbNmzSIWi6Vq69atHb1pkqRuLGdknER9Fol93t9GkvTRdHg42r9/PxkZbV82MzOTRCIBwOjRoyktLWX+/Pmp9fF4nEWLFlFeXg5AeXk5NTU1LFmyJDVmwYIFJBIJJk+efMT3jUaj5OfntylJUl+RPKWubvlQnO5ZkvRRdfg1R1dffTXf+c53GDlyJGeeeSZLly7l+9//Pn/3d38HQCQS4a677uJf//VfGTNmDKNHj+a+++6jrKyMz372swCcccYZTJ8+ndtuu41HH32UpqYm7rzzTm644QbKyso6umVJUg+XWdBA1tA6muYPSHcrkqSerF3zYp+AeDwevvKVr4SRI0eG3NzccMopp4R//Md/DA0NDakxiUQi3HfffaGkpCREo9FwxRVXhHXr1rV5nd27d4cbb7wxDBgwIOTn54dbbrkl1NbWtrsPp/K2LMs6kfp5IO3TKX/USoS8iVVhwJ+9FyBxws93Km8LngwQCen/d2hZVmfX8abyjoQQAr1QPB6noKAg3W1IUg/xc+Dv093ER5OZoPBzG9j78giaP+h3wk8vufZtSjftZ+PKM9nf1I/Q8Wecq9vbAJxP77kZsqSjicVix7z8psNPq5MkqStlDakjEm2huTr3hJ+bl7Wf2zJ/xWlj32PUxPd5p/pUnl97DRXvl7N7/2CDUp+RjdeqSQLDkSSpR0tOxND4bgG0nOgvt4E/P3Ue+7YWceuibzGiYCt/Of7/8PVLH6Awt4a3dpzHy+9dzvx3r2BLbCQtIRN/gZak3s3T6iRJ9NjT6rISFF63nvgLo0nEoyf01H7Z+/jpp+/kG/O/S+XeYa1LA7lZ9UwofpsvnPs4f37KPPKjcd7cPonfbfg0r7//CVZ/MJ765lwMSr3Je8BEwBvIS72dp9VJknqtnBFxaM4gUZtzws89f9gSllWeQ+XeQ++fF6G+OY/F2y9k8fYLKMyt4YKyxdx2/s+57/J/oX/2PtbsOoO56/6C/3pnKmt3jSPWUIBBSZJ6B48cSZLomUeOAgOnbqZx80Aa1g0+4WdHM+sBaGg5/rVKGZEWygZu55Mnv8SMs5/k4hGvEs1q4LWtF/HZp58jVl94wu+v7uQ9PHIk9Q3HO3LklaaSpB4oMGLoZk4fvoas7dkkZ2g9MQ0tue0KRgCJkMn78RH8x4q/4Zqnnueif3+Nf1rwr7QkMvnGJd8lO7PxhN9fktT9eORIkgR8HfguPeH0sGhmPTef8wS3TP533mycyNkt63jq7Rt5bu1nW0+R67ptyMvaz8mF77F21zhntuuxAvAyMB1oSHMvkjrb8Y4cGY4kScBngOfo3uEocMaQNXznin+kIDfGN+Z/l/W7TueOCx7hrk/8kNrGgfyf1X/JkytmsHbXOBpbTmyCBvVm9RwMPjuAytbHG4BVwFzgnTT0JamrGY4kSe3QvcNRXtZ+/vac/82dF/6Up1fewE8WfZl4Qz4QIUKCC4e/wff+/F4uHvkqm2tGsaLqbP7nW3/PK1suSY1Tb3Lory71QE3r42pgdevjncDrrY/fBba2Po5z8NqiXvkrkKRjMBxJktqhe4ejSWWL+cal3+XBV+9h0bbJJELmh0YEBubU8g+Tf8znz/4lG6tPY+e+Yk7Kf5+XNn2SZ9d+jg27x3jqW48QgJbWr03A+0ACqAMWAc2tj19uXb8H2Nj63CZgbxf3K6knMRxJktqhe4cjCGRlNNOcyD7mqAgJxg5Zx7c/9U2K++/kkcV3MDx/G1eMns87e07luTWf5dWtF3vKXVrVkww4CZKntTW0Pn4VqG19/DrJozsNJI/6hNblXhMk6eMxHEmS2qG7h6MTk5e1nxlnP8mXJ/+E36y+lv9Y8TcMG7iD287/OTmZjfzXxqn8dv3V7No/hN6yzenz4VPcalsfbwB2tT5eSvIIEMBaYHvr83aQDEqQPFokSZ3LcCRJaofeFY6SAuOGrOVf/+yfOGXQu3zzxW/zX+9MZUTBVj4/4ZdcMHwxS3ecy5MrZvDOnlOPe1SqbzpwihtAI8mJDALJ63c2tC5/D3i79fFODk5sUItHeiR1N4YjSVI79MZwlBTNrGfaaX/gvsv/hTe2Xch3Fv4j22vLKMyt4TNj/5Ppp/2ehpYo31n4j2ysHpPudrtQIHmk58ApaxtJHsXZQ/LankByooPXOXjNzzscvBbIeztJ6nkMR5KkdjgLmA8MpTcGJAgMH7iNf7zsO0wqe5N/efk+Xth4Jc2JbHIyG7hoxGts2jOazbGT093ox3ToR3otB09ZW8fBa3heIhlsmkgGnwPX/GxtHX/o0SJJ6l0MR5KkdsgAzgQuBC4BhgHjgEJgQOv6nh+asjMa+fSY3/GlCx7myy/8hPW7x6a7pROQ4GD4OXQ66s0kT20LwGvA7tblK4FY6+M9eIqbJBmO0t2GJPVQ2UAuMBwoBiYDpcBFQAEwCshqHdfTQlMgmtlAQ0uU7tN7C8kjOZCcinpb6+NdwJLWx6uBTa2PtwNVrY8b8BQ3SWqf44WjrC7sRZLUYzS11trWWti6PBuIAiNJhqbzgFOBM4CTgSKSR5o+fB+i7iRCQ0tuF75fCwfvvdMArCF5FGg/yf+uzSQnOljROmYvyfBD67hmJEldw3AkSToBB0LT6tZ6qXV5hGQw6g+cAwwBPgkMBs5uXV7QOq67HK35OMIhtYuD9+1ZRjL0JIA/kTytbT/wFgeDzk4Onh7XK0/ekKQey3AkSeoAgeS1LruBLa3L/hfJI0gDSYalk0lO/DCa5BGnISSPQGWT/DjqLqGphYMTElRy8KjP263fAywnOYFBgmRIPDDr24GbmEqSeiLDkSSpE7WQnA66huRU0X9sXZ5D8iPoFGAQUA6UAecD/YDTWsd09OlvLSSP5AB8wMHT1zYDq1ofbz3k8fscvKlpE87iJkm9m+FIkpQGja21svX7P7V+zSB5TVMJyQkgziA5i96pwHiSs+cVceRrmgJQTTLAxEkGnEByprZXOXhk58ApbrWt6w4811PcJKmvMxxJkrqRAzcbfa+1Xj9kXT7JI0lnkwxIl5MMQi+RnOigmeSkBo2tj2NIknQinMpbkiRJUp9wvKm8M7qwF0mSJEnqtgxHkiRJkoThSJIkSZIAw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkSYDhSJIkSZIAw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkSYDhSJIkSZIAw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkScBHCEcLFy7k6quvpqysjEgkwnPPPddmfQiB+++/n2HDhpGXl8eUKVPYsGFDmzHV1dXMmDGD/Px8CgsLufXWW9m7d2+bMStWrODSSy8lNzeXESNGMGfOnBPfOkmSJElqpxMOR/v27WPixIk89NBDR1w/Z84cfvzjH/Poo4+yaNEi+vfvz7Rp06ivr0+NmTFjBqtWrWLevHnMnTuXhQsXcvvtt6fWx+Nxpk6dyqhRo1iyZAkPPvgg3/rWt3jsscc+wiZKkiRJUjuEjwEIzz77bOr7RCIRSktLw4MPPphaVlNTE6LRaHjqqadCCCGsXr06AGHx4sWpMS+88EKIRCJh27ZtIYQQHn744TBo0KDQ0NCQGnPvvfeGsWPHtru3WCwWAMuyLMuyLMuyrACEWCx2zAzRodccbdq0icrKSqZMmZJaVlBQwOTJk6moqACgoqKCwsJCJk2alBozZcoUMjIyWLRoUWrMZZddRk5OTmrMtGnTWLduHXv27Dniezc0NBCPx9uUJEmSJLVXh4ajyspKAEpKStosLykpSa2rrKykuLi4zfqsrCyKiorajDnSaxz6Hh82e/ZsCgoKUjVixIiPv0GSJEmS+oxeM1vdrFmziMViqdq6dWu6W5IkSZLUg3RoOCotLQWgqqqqzfKqqqrUutLSUnbu3NlmfXNzM9XV1W3GHOk1Dn2PD4tGo+Tn57cpSZIkSWqvDg1Ho0ePprS0lPnz56eWxeNxFi1aRHl5OQDl5eXU1NSwZMmS1JgFCxaQSCSYPHlyaszChQtpampKjZk3bx5jx45l0KBBHdmyJEmSJCW1e/q3VrW1tWHp0qVh6dKlAQjf//73w9KlS8PmzZtDCCE88MADobCwMDz//PNhxYoV4ZprrgmjR48OdXV1qdeYPn16OPfcc8OiRYvCK6+8EsaMGRNuvPHG1PqamppQUlISbrrpprBy5crw9NNPh379+oWf/exn7e7T2eosy7Isy7Isyzq0jjdb3QmHoxdffPGIb3TzzTeHEJLTed93332hpKQkRKPRcMUVV4R169a1eY3du3eHG2+8MQwYMCDk5+eHW265JdTW1rYZs3z58nDJJZeEaDQahg8fHh544IET6tNwZFmWZVmWZVnWoXW8cBQJIQR6oXg8TkFBQbrbkCRJktRNxGKxY85N0Gtmq5MkSZKkj8NwJEmSJEkYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAgxHkiRJkgQYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAgxHkiRJkgQYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAgxHkiRJkgQYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAgxHkiRJkgQYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAgxHkiRJkgQYjiRJkiQJMBxJkiRJEmA4kiRJkiTAcCRJkiRJgOFIkiRJkgDDkSRJkiQBhiNJkiRJAj5COFq4cCFXX301ZWVlRCIRnnvuudS6pqYm7r33XiZMmED//v0pKyvjb//2b9m+fXub16iurmbGjBnk5+dTWFjIrbfeyt69e9uMWbFiBZdeeim5ubmMGDGCOXPmfLQtlCRJkqR2OOFwtG/fPiZOnMhDDz102Lr9+/fz1ltvcd999/HWW2/xm9/8hnXr1vGZz3ymzbgZM2awatUq5s2bx9y5c1m4cCG33357an08Hmfq1KmMGjWKJUuW8OCDD/Ktb32Lxx577CNsoiRJkiS1Q/gYgPDss88ec8wbb7wRgLB58+YQQgirV68OQFi8eHFqzAsvvBAikUjYtm1bCCGEhx9+OAwaNCg0NDSkxtx7771h7Nix7e4tFosFwLIsy7Isy7IsKwAhFosdM0N0+jVHsViMSCRCYWEhABUVFRQWFjJp0qTUmClTppCRkcGiRYtSYy677DJycnJSY6ZNm8a6devYs2dPZ7csSZIkqQ/K6swXr6+v59577+XGG28kPz8fgMrKSoqLi9s2kZVFUVERlZWVqTGjR49uM6akpCS1btCgQYe9V0NDAw0NDanv4/F4h26LJEmSpN6t044cNTU18dd//deEEHjkkUc6621SZs+eTUFBQapGjBjR6e8pSZIkqffolHB0IBht3ryZefPmpY4aAZSWlrJz584245ubm6murqa0tDQ1pqqqqs2YA98fGPNhs2bNIhaLpWrr1q0duUmSJEmSerkOD0cHgtGGDRv44x//yODBg9usLy8vp6amhiVLlqSWLViwgEQiweTJk1NjFi5cSFNTU2rMvHnzGDt27BFPqQOIRqPk5+e3KUmSJElqrxMOR3v37mXZsmUsW7YMgE2bNrFs2TK2bNlCU1MTf/mXf8mbb77Jk08+SUtLC5WVlVRWVtLY2AjAGWecwfTp07ntttt44403ePXVV7nzzju54YYbKCsrA+Dzn/88OTk53HrrraxatYpf/epX/OhHP+Luu+/uuC2XJEmSpEO1e27sVi+++OIRp8W7+eabw6ZNm446bd6LL76Yeo3du3eHG2+8MQwYMCDk5+eHW265JdTW1rZ5n+XLl4dLLrkkRKPRMHz48PDAAw+cUJ9O5W1ZlmVZlmVZ1qF1vKm8IyGEQC8Uj8cpKChIdxuSJEmSuolYLHbMy286/T5HkiRJktQTGI4kSZIkCcORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSAMORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSAMORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSAMORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSAMORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSAMORJEmSJAGGI0mSJEkCDEeSJEmSBBiOJEmSJAkwHEmSJEkSYDiSJEmSJMBwJEmSJEmA4UiSJEmSgI8QjhYuXMjVV19NWVkZkUiE55577qhjv/jFLxKJRPjhD3/YZnl1dTUzZswgPz+fwsJCbr31Vvbu3dtmzIoVK7j00kvJzc1lxIgRzJkz50RblSRJkqR2O+FwtG/fPiZOnMhDDz10zHHPPvssr7/+OmVlZYetmzFjBqtWrWLevHnMnTuXhQsXcvvtt6fWx+Nxpk6dyqhRo1iyZAkPPvgg3/rWt3jsscdOtF1JkiRJap/wMQDh2WefPWz5+++/H4YPHx5WrlwZRo0aFX7wgx+k1q1evToAYfHixallL7zwQohEImHbtm0hhBAefvjhMGjQoNDQ0JAac++994axY8e2u7dYLBYAy7Isy7Isy7KsAIRYLHbMDNHh1xwlEgluuukm7rnnHs4888zD1ldUVFBYWMikSZNSy6ZMmUJGRgaLFi1KjbnsssvIyclJjZk2bRrr1q1jz549R3zfhoYG4vF4m5IkSZKk9urwcPS9732PrKws/uEf/uGI6ysrKykuLm6zLCsri6KiIiorK1NjSkpK2ow58P2BMR82e/ZsCgoKUjVixIiPuymSJEmS+pAODUdLlizhRz/6EY8//jiRSKQjX/q4Zs2aRSwWS9XWrVu79P0lSZIk9WwdGo7+9Kc/sXPnTkaOHElWVhZZWVls3ryZr33ta5x88skAlJaWsnPnzjbPa25uprq6mtLS0tSYqqqqNmMOfH9gzIdFo1Hy8/PblCRJkiS1V4eGo5tuuokVK1awbNmyVJWVlXHPPffwhz/8AYDy8nJqampYsmRJ6nkLFiwgkUgwefLk1JiFCxfS1NSUGjNv3jzGjh3LoEGDOrJlSZIkSQIg60SfsHfvXjZu3Jj6ftOmTSxbtoyioiJGjhzJ4MGD24zPzs6mtLSUsWPHAnDGGWcwffp0brvtNh599FGampq48847ueGGG1LTfn/+85/n29/+Nrfeeiv33nsvK1eu5Ec/+hE/+MEPPs62SpIkSdLRtXtu7FYvvvjiEafFu/nmm484/sNTeYcQwu7du8ONN94YBgwYEPLz88Mtt9wSamtr24xZvnx5uOSSS0I0Gg3Dhw8PDzzwwAn16VTelmVZlmVZlmUdWsebyjsSQgj0QvF4nIKCgnS3IUmSJKmbiMVix5yboMOn8pYkSZKknshwJEmSJEkYjiRJkiQJMBxJkiRJEtCLw1EvnWdCkiRJ0kd0vIzQa8PR7t27092CJEmSpG6ktrb2mOtP+CawPUVRUREAW7ZscUpvdah4PM6IESPYunXrMaeClD4K9y91FvctdRb3LXWmjtq/QgjU1tZSVlZ2zHG9NhxlZCQPihUUFPgPVZ0iPz/ffUudxv1LncV9S53FfUudqSP2r/YcMOm1p9VJkiRJ0okwHEmSJEkSvTgcRaNRvvnNbxKNRtPdinoZ9y11JvcvdRb3LXUW9y11pq7evyLBOa8lSZIkqfceOZIkSZKkE2E4kiRJkiQMR5IkSZIEGI4kSZIkCeil4eihhx7i5JNPJjc3l8mTJ/PGG2+kuyV1c9/61reIRCJtaty4can19fX1zJw5k8GDBzNgwACuu+46qqqq2rzGli1buOqqq+jXrx/FxcXcc889NDc3d/WmqBtYuHAhV199NWVlZUQiEZ577rk260MI3H///QwbNoy8vDymTJnChg0b2oyprq5mxowZ5OfnU1hYyK233srevXvbjFmxYgWXXnopubm5jBgxgjlz5nT2pinNjrdvfeELXzjsZ9n06dPbjHHf0pHMnj2bCy64gIEDB1JcXMxnP/tZ1q1b12ZMR30WvvTSS5x33nlEo1FOO+00Hn/88c7ePKVRe/atT37yk4f97PriF7/YZkxX7Vu9Lhz96le/4u677+ab3/wmb731FhMnTmTatGns3Lkz3a2pmzvzzDPZsWNHql555ZXUuq9+9av89re/5ZlnnuHll19m+/btXHvttan1LS0tXHXVVTQ2NvLaa6/xxBNP8Pjjj3P//fenY1OUZvv27WPixIk89NBDR1w/Z84cfvzjH/Poo4+yaNEi+vfvz7Rp06ivr0+NmTFjBqtWrWLevHnMnTuXhQsXcvvtt6fWx+Nxpk6dyqhRo1iyZAkPPvgg3/rWt3jsscc6ffuUPsfbtwCmT5/e5mfZU0891Wa9+5aO5OWXX2bmzJm8/vrrzJs3j6amJqZOncq+fftSYzris3DTpk1cddVVfOpTn2LZsmXcdddd/P3f/z1/+MMfunR71XXas28B3HbbbW1+dh36R5ku3bdCL3PhhReGmTNnpr5vaWkJZWVlYfbs2WnsSt3dN7/5zTBx4sQjrqupqQnZ2dnhmWeeSS1bs2ZNAEJFRUUIIYTf/e53ISMjI1RWVqbGPPLIIyE/Pz80NDR0au/q3oDw7LPPpr5PJBKhtLQ0PPjgg6llNTU1IRqNhqeeeiqEEMLq1asDEBYvXpwa88ILL4RIJBK2bdsWQgjh4YcfDoMGDWqzf917771h7NixnbxF6i4+vG+FEMLNN98crrnmmqM+x31L7bVz584AhJdffjmE0HGfhf/9v//3cOaZZ7Z5r+uvvz5MmzatszdJ3cSH960QQrj88svDV77ylaM+pyv3rV515KixsZElS5YwZcqU1LKMjAymTJlCRUVFGjtTT7BhwwbKyso45ZRTmDFjBlu2bAFgyZIlNDU1tdmvxo0bx8iRI1P7VUVFBRMmTKCkpCQ1Ztq0acTjcVatWtW1G6JubdOmTVRWVrbZnwoKCpg8eXKb/amwsJBJkyalxkyZMoWMjAwWLVqUGnPZZZeRk5OTGjNt2jTWrVvHnj17umhr1B299NJLFBcXM3bsWO644w52796dWue+pfaKxWIAFBUVAR33WVhRUdHmNQ6M8fe0vuPD+9YBTz75JEOGDOGss85i1qxZ7N+/P7WuK/etrBMa3c3t2rWLlpaWNv/hAEpKSli7dm2aulJPMHnyZB5//HHGjh3Ljh07+Pa3v82ll17KypUrqaysJCcnh8LCwjbPKSkpobKyEoDKysoj7ncH1kkHHNgfjrS/HLo/FRcXt1mflZVFUVFRmzGjR48+7DUOrBs0aFCn9K/ubfr06Vx77bWMHj2ad955h2984xtceeWVVFRUkJmZ6b6ldkkkEtx1111cfPHFnHXWWQAd9ll4tDHxeJy6ujry8vI6Y5PUTRxp3wL4/Oc/z6hRoygrK2PFihXce++9rFu3jt/85jdA1+5bvSocSR/VlVdemXp89tlnM3nyZEaNGsWvf/1rf1BL6jFuuOGG1OMJEyZw9tlnc+qpp/LSSy9xxRVXpLEz9SQzZ85k5cqVba69lTrC0fatQ697nDBhAsOGDeOKK67gnXfe4dRTT+3SHnvVaXVDhgwhMzPzsJlTqqqqKC0tTVNX6okKCws5/fTT2bhxI6WlpTQ2NlJTU9NmzKH7VWlp6RH3uwPrpAMO7A/H+jlVWlp62CQyzc3NVFdXu8/phJxyyikMGTKEjRs3Au5bOr4777yTuXPn8uKLL3LSSSellnfUZ+HRxuTn5/vHyF7uaPvWkUyePBmgzc+urtq3elU4ysnJ4fzzz2f+/PmpZYlEgvnz51NeXp7GztTT7N27l3feeYdhw4Zx/vnnk52d3Wa/WrduHVu2bEntV+Xl5bz99tttfumYN28e+fn5jB8/vsv7V/c1evRoSktL2+xP8XicRYsWtdmfampqWLJkSWrMggULSCQSqQ+M8vJyFi5cSFNTU2rMvHnzGDt2rKc9KeX9999n9+7dDBs2DHDf0tGFELjzzjt59tlnWbBgwWGnVnbUZ2F5eXmb1zgwxt/Teq/j7VtHsmzZMoA2P7u6bN86oekbeoCnn346RKPR8Pjjj4fVq1eH22+/PRQWFraZ3UL6sK997WvhpZdeCps2bQqvvvpqmDJlShgyZEjYuXNnCCGEL37xi2HkyJFhwYIF4c033wzl5eWhvLw89fzm5uZw1llnhalTp4Zly5aF3//+92Ho0KFh1qxZ6dokpVFtbW1YunRpWLp0aQDC97///bB06dKwefPmEEIIDzzwQCgsLAzPP/98WLFiRbjmmmvC6NGjQ11dXeo1pk+fHs4999ywaNGi8Morr4QxY8aEG2+8MbW+pqYmlJSUhJtuuimsXLkyPP3006Ffv37hZz/7WZdvr7rOsfat2tra8N/+238LFRUVYdOmTeGPf/xjOO+888KYMWNCfX196jXct3Qkd9xxRygoKAgvvfRS2LFjR6r279+fGtMRn4Xvvvtu6NevX7jnnnvCmjVrwkMPPRQyMzPD73//+y7dXnWd4+1bGzduDP/8z/8c3nzzzbBp06bw/PPPh1NOOSVcdtllqdfoyn2r14WjEEL4yU9+EkaOHBlycnLChRdeGF5//fV0t6Ru7vrrrw/Dhg0LOTk5Yfjw4eH6668PGzduTK2vq6sLX/rSl8KgQYNCv379wuc+97mwY8eONq/x3nvvhSuvvDLk5eWFIUOGhK997WuhqampqzdF3cCLL74YgMPq5ptvDiEkp/O+7777QklJSYhGo+GKK64I69ata/Mau3fvDjfeeGMYMGBAyM/PD7fcckuora1tM2b58uXhkksuCdFoNAwfPjw88MADXbWJSpNj7Vv79+8PU6dODUOHDg3Z2dlh1KhR4bbbbjvsj4PuWzqSI+1XQPjFL36RGtNRn4UvvvhiOOecc0JOTk445ZRT2ryHep/j7VtbtmwJl112WSgqKgrRaDScdtpp4Z577gmxWKzN63TVvhVpbVqSJEmS+rRedc2RJEmSJH1UhiNJkiRJwnAkSZIkSYDhSJIkSZIAw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkSYDhSJIkSZIA+P8BqwU+I78vgLsAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
"
       ]
@@ -795,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1500,
+   "execution_count": 338,
    "id": "faab365e",
    "metadata": {},
    "outputs": [],
@@ -806,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1501,
+   "execution_count": 339,
    "id": "3f52fdf7",
    "metadata": {},
    "outputs": [
@@ -818,6 +816,537 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "output_type": "display_data"
+    },
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+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "output_type": "display_data"
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+      "text/plain": [
+       "'超过1人'"
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+      "text/plain": [
+       "'超过1人'"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "output_type": "display_data"
+    },
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+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "output_type": "display_data"
+    },
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+      "text/plain": [
+       "'超过1人'"
+      ]
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+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'超过1人'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -827,26 +1356,38 @@
     "many_frame_data = []\n",
     "for element_index in frame_index:\n",
     "    frame_data = detect_data[element_index]\n",
-    "    # 未筛选的关键点与目标框数据\n",
-    "    unfilter_kps_box = KeypointDataset(\n",
-    "    frame_index=element_index,\n",
-    "    boxes=ak.to_numpy(frame_data[\"boxes\"]),\n",
-    "    kps=ak.to_numpy(frame_data[\"kps\"]),\n",
-    "    kps_scores=ak.to_numpy(frame_data[\"kps_scores\"])\n",
-    ")\n",
+    "    # 只对第一帧进行筛选，剩余帧都保留原始数据\n",
+    "    if element_index > 1651:\n",
+    "        unfilter_kps_box = KeypointDataset(\n",
+    "            frame_index=element_index,\n",
+    "            boxes=ak.to_numpy(frame_data[\"boxes\"]),\n",
+    "            kps=ak.to_numpy(frame_data[\"kps\"]),\n",
+    "            kps_scores=ak.to_numpy(frame_data[\"kps_scores\"]),\n",
+    "        )\n",
+    "        if unfilter_kps_box[\"kps_scores\"].shape[0] > 1:\n",
+    "            display(\"超过1人\")\n",
+    "        many_frame_data.append(unfilter_kps_box)\n",
+    "    else:\n",
+    "        # 未筛选的关键点与目标框数据\n",
+    "        unfilter_kps_box = KeypointDataset(\n",
+    "            frame_index=element_index,\n",
+    "            boxes=ak.to_numpy(frame_data[\"boxes\"]),\n",
+    "            kps=ak.to_numpy(frame_data[\"kps\"]),\n",
+    "            kps_scores=ak.to_numpy(frame_data[\"kps_scores\"]),\n",
+    "        )\n",
     "\n",
-    "    new_kps_box = filter_kps_box(unfilter_kps_box, contours)\n",
-    "    # visualize_triangles_and_union(camera_param,union_polygon, new_kps_box)\n",
-    "    if new_kps_box['kps_scores'].shape[0]>1:\n",
-    "        display('超过1人')\n",
-    "    # display(new_kps_box[\"kps_scores\"].shape)\n",
+    "        new_kps_box = filter_kps_box(unfilter_kps_box, contours)\n",
+    "        # visualize_triangles_and_union(camera_param,union_polygon, new_kps_box)\n",
+    "        if new_kps_box[\"kps_scores\"].shape[0] > 1:\n",
+    "            display(\"超过1人\")\n",
+    "        # display(new_kps_box[\"kps_scores\"].shape)\n",
     "\n",
-    "    many_frame_data.append(new_kps_box)"
+    "        many_frame_data.append(new_kps_box)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1502,
+   "execution_count": 340,
    "id": "cedc1d7f",
    "metadata": {},
    "outputs": [],
@@ -857,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1503,
+   "execution_count": 341,
    "id": "a9d1e5da",
    "metadata": {},
    "outputs": [],
diff --git a/uv.lock b/uv.lock
index 5286035..5897b10 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,12 +1,12 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
@@ -18,16 +18,16 @@ resolution-markers = [
 
 [[package]]
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 [[package]]
 name = "anyio"
-version = "4.8.0"
+version = "4.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -35,30 +35,30 @@ dependencies = [
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 [[package]]
 name = "argon2-cffi"
-version = "23.1.0"
+version = "25.1.0"
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+    { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" },
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@@ -476,10 +496,12 @@ dependencies = [
     { name = "beartype" },
     { name = "cvxopt" },
     { name = "hypothesis" },
-    { name = "jax", extra = ["cuda12"] },
+    { name = "jax", version = "0.5.3", source = { registry = "https://pypi.org/simple" }, extra = ["cuda12"], marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "jax", version = "0.6.1", source = { registry = "https://pypi.org/simple" }, extra = ["cuda12"], marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
     { name = "jaxtyping" },
     { name = "jupytext" },
     { name = "matplotlib" },
+    { name = "more-itertools" },
     { name = "opencv-python-headless" },
     { name = "optax" },
     { name = "orjson" },
@@ -489,6 +511,7 @@ dependencies = [
     { name = "pyrsistent" },
     { name = "pytest" },
     { name = "scipy" },
+    { name = "shapely" },
     { name = "torch" },
     { name = "torchvision" },
     { name = "typeguard" },
@@ -510,6 +533,7 @@ requires-dist = [
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     { name = "jupytext", specifier = ">=1.17.0" },
     { name = "matplotlib", specifier = ">=3.10.1" },
+    { name = "more-itertools", specifier = ">=10.7.0" },
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     { name = "optax", specifier = ">=0.2.4" },
     { name = "orjson", specifier = ">=3.10.15" },
@@ -519,6 +543,7 @@ requires-dist = [
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     { name = "pytest", specifier = ">=8.3.5" },
     { name = "scipy", specifier = ">=1.15.2" },
+    { name = "shapely", specifier = ">=2.1.1" },
     { name = "torch", specifier = ">=2.6.0" },
     { name = "torchvision", specifier = ">=0.21.0" },
     { name = "typeguard", specifier = ">=4.4.2" },
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