Fix yolox no detections

boxmot/__init__.py

@@ -1,13 +1,13 @@
-__version__ = '10.0.15'
+__version__ = '10.0.14'
 
 from pathlib import Path
 
-from boxmot.trackers import StrongSORT
-from boxmot.trackers import OCSort as OCSORT
-from boxmot.trackers import BYTETracker
-from boxmot.trackers import BoTSORT
-from boxmot.trackers import DeepOCSort as DeepOCSORT
-from boxmot.appearance.reid_multibackend import ReIDDetectMultiBackend
+from boxmot.strongsort.strong_sort import StrongSORT
+from boxmot.ocsort.ocsort import OCSort as OCSORT
+from boxmot.bytetrack.byte_tracker import BYTETracker
+from boxmot.botsort.bot_sort import BoTSORT
+from boxmot.deepocsort.ocsort import OCSort as DeepOCSORT
+from boxmot.deep.reid_multibackend import ReIDDetectMultiBackend
 
 from boxmot.tracker_zoo import create_tracker, get_tracker_config
 

boxmot/trackers/botsort/bot_sort.py → boxmot/botsort/bot_sort.py

@@ -1,18 +1,20 @@
+import cv2
+import matplotlib.pyplot as plt
 import numpy as np
 from collections import deque
 
-from ...utils.matching import iou_distance, fuse_score, linear_assignment, embedding_distance, fuse_motion
-from ...utils.gmc import GlobalMotionCompensation
+from .matching import iou_distance, fuse_score, linear_assignment, embedding_distance, fuse_motion
+from .gmc import GMC
 from .basetrack import BaseTrack, TrackState
-from ...motion.adapters import BotSortKalmanFilterAdapter
+from .kalman_filter import KalmanFilter
 import torch
 
-from ...appearance.reid_multibackend import ReIDDetectMultiBackend
-from ...utils.ops import xyxy2xywh, xywh2xyxy
+from boxmot.deep.reid_multibackend import ReIDDetectMultiBackend
+from boxmot.utils.ops import xyxy2xywh, xywh2xyxy
 
 
 class STrack(BaseTrack):
-    shared_kalman = BotSortKalmanFilterAdapter()
+    shared_kalman = KalmanFilter()
 
     def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
 
@@ -254,7 +256,7 @@ def __init__(self,
 
         self.buffer_size = int(frame_rate / 30.0 * track_buffer)
         self.max_time_lost = self.buffer_size
-        self.kalman_filter = BotSortKalmanFilterAdapter()
+        self.kalman_filter = KalmanFilter()
 
         # ReID module
         self.proximity_thresh = proximity_thresh
@@ -263,7 +265,7 @@ def __init__(self,
 
         self.model = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16)
 
-        self.gmc = GlobalMotionCompensation(method=cmc_method, verbose=[None,False])
+        self.gmc = GMC(method=cmc_method, verbose=[None,False])
 
     def update(self, dets, img):
 

boxmot/utils/gmc.py → boxmot/botsort/gmc.py

@@ -5,8 +5,10 @@
 import time
 
 
-class GlobalMotionCompensation:
+class GMC:
     def __init__(self, method='sparseOptFlow', downscale=2, verbose=None):
+        super(GMC, self).__init__()
+
         self.method = method
         self.downscale = max(1, int(downscale))
 
@@ -53,7 +55,7 @@ def __init__(self, method='sparseOptFlow', downscale=2, verbose=None):
         elif self.method == 'none' or self.method == 'None':
             self.method = 'none'
         else:
-            raise ValueError("Error: Unknown GMC method:" + method)
+            raise ValueError("Error: Unknown CMC method:" + method)
 
         self.prevFrame = None
         self.prevKeyPoints = None

boxmot/botsort/kalman_filter.py

@@ -0,0 +1,269 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, w, h, vx, vy, vw, vh
+
+    contains the bounding box center position (x, y), width w, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, w, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, w, h) with center position (x, y),
+            width w, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[2],
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[2],
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[2],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[2],
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[2],
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[2],
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(mean, self._motion_mat.T)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def multi_predict(self, mean, covariance):
+        """Run Kalman filter prediction step (Vectorized version).
+        Parameters
+        ----------
+        mean : ndarray
+            The Nx8 dimensional mean matrix of the object states at the previous
+            time step.
+        covariance : ndarray
+            The Nx8x8 dimensional covariance matrics of the object states at the
+            previous time step.
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[:, 2],
+            self._std_weight_position * mean[:, 3],
+            self._std_weight_position * mean[:, 2],
+            self._std_weight_position * mean[:, 3]]
+        std_vel = [
+            self._std_weight_velocity * mean[:, 2],
+            self._std_weight_velocity * mean[:, 3],
+            self._std_weight_velocity * mean[:, 2],
+            self._std_weight_velocity * mean[:, 3]]
+        sqr = np.square(np.r_[std_pos, std_vel]).T
+
+        motion_cov = []
+        for i in range(len(mean)):
+            motion_cov.append(np.diag(sqr[i]))
+        motion_cov = np.asarray(motion_cov)
+
+        mean = np.dot(mean, self._motion_mat.T)
+        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
+        covariance = np.dot(left, self._motion_mat.T) + motion_cov
+
+        return mean, covariance
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, w, h), where (x, y)
+            is the center position, w the width, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False, metric='maha'):
+        """Compute gating distance between state distribution and measurements.
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        d = measurements - mean
+        if metric == 'gaussian':
+            return np.sum(d * d, axis=1)
+        elif metric == 'maha':
+            cholesky_factor = np.linalg.cholesky(covariance)
+            z = scipy.linalg.solve_triangular(
+                cholesky_factor, d.T, lower=True, check_finite=False,
+                overwrite_b=True)
+            squared_maha = np.sum(z * z, axis=0)
+            return squared_maha
+        else:
+            raise ValueError('invalid distance metric')