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# vim: expandtab:ts=4:sw=4
import cv2
import numpy as np
from trackers.strongsort.sort.kalman_filter import KalmanFilter
from collections import deque
class TrackState:
"""
Enumeration type for the single target track state. Newly created tracks are
classified as `tentative` until enough evidence has been collected. Then,
the track state is changed to `confirmed`. Tracks that are no longer alive
are classified as `deleted` to mark them for removal from the set of active
tracks.
"""
Tentative = 1
Confirmed = 2
Deleted = 3
class Track:
"""
A single target track with state space `(x, y, a, h)` and associated
velocities, where `(x, y)` is the center of the bounding box, `a` is the
aspect ratio and `h` is the height.
Parameters
----------
mean : ndarray
Mean vector of the initial state distribution.
covariance : ndarray
Covariance matrix of the initial state distribution.
track_id : int
A unique track identifier.
n_init : int
Number of consecutive detections before the track is confirmed. The
track state is set to `Deleted` if a miss occurs within the first
`n_init` frames.
max_age : int
The maximum number of consecutive misses before the track state is
set to `Deleted`.
feature : Optional[ndarray]
Feature vector of the detection this track originates from. If not None,
this feature is added to the `features` cache.
Attributes
----------
mean : ndarray
Mean vector of the initial state distribution.
covariance : ndarray
Covariance matrix of the initial state distribution.
track_id : int
A unique track identifier.
hits : int
Total number of measurement updates.
age : int
Total number of frames since first occurance.
time_since_update : int
Total number of frames since last measurement update.
state : TrackState
The current track state.
features : List[ndarray]
A cache of features. On each measurement update, the associated feature
vector is added to this list.
"""
def __init__(self, detection, track_id, class_id, conf, n_init, max_age, ema_alpha,
feature=None):
self.track_id = track_id
self.class_id = int(class_id)
self.hits = 1
self.age = 1
self.time_since_update = 0
self.max_num_updates_wo_assignment = 7
self.updates_wo_assignment = 0
self.ema_alpha = ema_alpha
self.state = TrackState.Tentative
self.features = []
if feature is not None:
feature /= np.linalg.norm(feature)
self.features.append(feature)
self.conf = conf
self._n_init = n_init
self._max_age = max_age
self.kf = KalmanFilter()
self.mean, self.covariance = self.kf.initiate(detection)
# Initializing trajectory queue
self.q = deque(maxlen=25)
def to_tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
Returns
-------
ndarray
The bounding box.
"""
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret
def to_tlbr(self):
"""Get kf estimated current position in bounding box format `(min x, miny, max x,
max y)`.
Returns
-------
ndarray
The predicted kf bounding box.
"""
ret = self.to_tlwh()
ret[2:] = ret[:2] + ret[2:]
return ret
def ECC(self, src, dst, warp_mode = cv2.MOTION_EUCLIDEAN, eps = 1e-5,
max_iter = 100, scale = 0.1, align = False):
"""Compute the warp matrix from src to dst.
Parameters
----------
src : ndarray
An NxM matrix of source img(BGR or Gray), it must be the same format as dst.
dst : ndarray
An NxM matrix of target img(BGR or Gray).
warp_mode: flags of opencv
translation: cv2.MOTION_TRANSLATION
rotated and shifted: cv2.MOTION_EUCLIDEAN
affine(shift,rotated,shear): cv2.MOTION_AFFINE
homography(3d): cv2.MOTION_HOMOGRAPHY
eps: float
the threshold of the increment in the correlation coefficient between two iterations
max_iter: int
the number of iterations.
scale: float or [int, int]
scale_ratio: float
scale_size: [W, H]
align: bool
whether to warp affine or perspective transforms to the source image
Returns
-------
warp matrix : ndarray
Returns the warp matrix from src to dst.
if motion models is homography, the warp matrix will be 3x3, otherwise 2x3
src_aligned: ndarray
aligned source image of gray
"""
# BGR2GRAY
if src.ndim == 3:
# Convert images to grayscale
src = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# make the imgs smaller to speed up
if scale is not None:
if isinstance(scale, float) or isinstance(scale, int):
if scale != 1:
src_r = cv2.resize(src, (0, 0), fx = scale, fy = scale,interpolation = cv2.INTER_LINEAR)
dst_r = cv2.resize(dst, (0, 0), fx = scale, fy = scale,interpolation = cv2.INTER_LINEAR)
scale = [scale, scale]
else:
src_r, dst_r = src, dst
scale = None
else:
if scale[0] != src.shape[1] and scale[1] != src.shape[0]:
src_r = cv2.resize(src, (scale[0], scale[1]), interpolation = cv2.INTER_LINEAR)
dst_r = cv2.resize(dst, (scale[0], scale[1]), interpolation=cv2.INTER_LINEAR)
scale = [scale[0] / src.shape[1], scale[1] / src.shape[0]]
else:
src_r, dst_r = src, dst
scale = None
else:
src_r, dst_r = src, dst
# Define 2x3 or 3x3 matrices and initialize the matrix to identity
if warp_mode == cv2.MOTION_HOMOGRAPHY :
warp_matrix = np.eye(3, 3, dtype=np.float32)
else :
warp_matrix = np.eye(2, 3, dtype=np.float32)
# Define termination criteria
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, max_iter, eps)
# Run the ECC algorithm. The results are stored in warp_matrix.
try:
(cc, warp_matrix) = cv2.findTransformECC (src_r, dst_r, warp_matrix, warp_mode, criteria, None, 1)
except cv2.error as e:
print('ecc transform failed')
return None, None
if scale is not None:
warp_matrix[0, 2] = warp_matrix[0, 2] / scale[0]
warp_matrix[1, 2] = warp_matrix[1, 2] / scale[1]
if align:
sz = src.shape
if warp_mode == cv2.MOTION_HOMOGRAPHY:
# Use warpPerspective for Homography
src_aligned = cv2.warpPerspective(src, warp_matrix, (sz[1],sz[0]), flags=cv2.INTER_LINEAR)
else :
# Use warpAffine for Translation, Euclidean and Affine
src_aligned = cv2.warpAffine(src, warp_matrix, (sz[1],sz[0]), flags=cv2.INTER_LINEAR)
return warp_matrix, src_aligned
else:
return warp_matrix, None
def get_matrix(self, matrix):
eye = np.eye(3)
dist = np.linalg.norm(eye - matrix)
if dist < 100:
return matrix
else:
return eye
def camera_update(self, previous_frame, next_frame):
warp_matrix, src_aligned = self.ECC(previous_frame, next_frame)
if warp_matrix is None and src_aligned is None:
return
[a,b] = warp_matrix
warp_matrix=np.array([a,b,[0,0,1]])
warp_matrix = warp_matrix.tolist()
matrix = self.get_matrix(warp_matrix)
x1, y1, x2, y2 = self.to_tlbr()
x1_, y1_, _ = matrix @ np.array([x1, y1, 1]).T
x2_, y2_, _ = matrix @ np.array([x2, y2, 1]).T
w, h = x2_ - x1_, y2_ - y1_
cx, cy = x1_ + w / 2, y1_ + h / 2
self.mean[:4] = [cx, cy, w / h, h]
def increment_age(self):
self.age += 1
self.time_since_update += 1
def predict(self, kf):
"""Propagate the state distribution to the current time step using a
Kalman filter prediction step.
Parameters
----------
kf : kalman_filter.KalmanFilter
The Kalman filter.
"""
self.mean, self.covariance = self.kf.predict(self.mean, self.covariance)
self.age += 1
self.time_since_update += 1
def update_kf(self, bbox, confidence=0.5):
self.updates_wo_assignment = self.updates_wo_assignment + 1
self.mean, self.covariance = self.kf.update(self.mean, self.covariance, bbox, confidence)
tlbr = self.to_tlbr()
x_c = int((tlbr[0] + tlbr[2]) / 2)
y_c = int((tlbr[1] + tlbr[3]) / 2)
self.q.append(('predupdate', (x_c, y_c)))
def update(self, detection, class_id, conf):
"""Perform Kalman filter measurement update step and update the feature
cache.
Parameters
----------
detection : Detection
The associated detection.
"""
self.conf = conf
self.class_id = class_id.int()
self.mean, self.covariance = self.kf.update(self.mean, self.covariance, detection.to_xyah(), detection.confidence)
feature = detection.feature / np.linalg.norm(detection.feature)
smooth_feat = self.ema_alpha * self.features[-1] + (1 - self.ema_alpha) * feature
smooth_feat /= np.linalg.norm(smooth_feat)
self.features = [smooth_feat]
self.hits += 1
self.time_since_update = 0
if self.state == TrackState.Tentative and self.hits >= self._n_init:
self.state = TrackState.Confirmed
tlbr = self.to_tlbr()
x_c = int((tlbr[0] + tlbr[2]) / 2)
y_c = int((tlbr[1] + tlbr[3]) / 2)
self.q.append(('observationupdate', (x_c, y_c)))
def mark_missed(self):
"""Mark this track as missed (no association at the current time step).
"""
if self.state == TrackState.Tentative:
self.state = TrackState.Deleted
elif self.time_since_update > self._max_age:
self.state = TrackState.Deleted
def is_tentative(self):
"""Returns True if this track is tentative (unconfirmed).
"""
return self.state == TrackState.Tentative
def is_confirmed(self):
"""Returns True if this track is confirmed."""
return self.state == TrackState.Confirmed
def is_deleted(self):
"""Returns True if this track is dead and should be deleted."""
return self.state == TrackState.Deleted
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