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ultralytics 8.0.49 task, exports and metadata updates (#1197)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
Co-authored-by: Paul Guerrie <97041392+paulguerrie@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-01 21:16:09 -08:00
committed by GitHub
parent 74e4c94806
commit 3861e6c82a
20 changed files with 111 additions and 101 deletions
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7
ultralytics/tracker/track.py
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@ -1,12 +1,11 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
check_requirements('lap') # for linear_assignment
import torch
from ultralytics.yolo.utils import IterableSimpleNamespace, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
check_requirements('lap') # for linear_assignment
from .trackers import BOTSORT, BYTETracker

28
ultralytics/tracker/trackers/bot_sort.py
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@ -65,17 +65,18 @@ class BOTrack(STrack):
@staticmethod
def multi_predict(stracks):
if len(stracks) > 0:
multi_mean = np.asarray([st.mean.copy() for st in stracks])
multi_covariance = np.asarray([st.covariance for st in stracks])
for i, st in enumerate(stracks):
if st.state != TrackState.Tracked:
multi_mean[i][6] = 0
multi_mean[i][7] = 0
multi_mean, multi_covariance = BOTrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
stracks[i].mean = mean
stracks[i].covariance = cov
if len(stracks) <= 0:
return
multi_mean = np.asarray([st.mean.copy() for st in stracks])
multi_covariance = np.asarray([st.covariance for st in stracks])
for i, st in enumerate(stracks):
if st.state != TrackState.Tracked:
multi_mean[i][6] = 0
multi_mean[i][7] = 0
multi_mean, multi_covariance = BOTrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
stracks[i].mean = mean
stracks[i].covariance = cov
def convert_coords(self, tlwh):
return self.tlwh_to_xywh(tlwh)
@ -112,10 +113,9 @@ class BOTSORT(BYTETracker):
return []
if self.args.with_reid and self.encoder is not None:
features_keep = self.encoder.inference(img, dets)
detections = [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)]
return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)] # detections
else:
detections = [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)]
return detections
return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] # detections
def get_dists(self, tracks, detections):
dists = matching.iou_distance(tracks, detections)

1
ultralytics/tracker/trackers/byte_tracker.py
View File

@ -92,7 +92,6 @@ class STrack(BaseTrack):
Update a matched track
:type new_track: STrack
:type frame_id: int
:type update_feature: bool
:return:
"""
self.frame_id = frame_id

12
ultralytics/tracker/utils/gmc.py
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@ -71,7 +71,7 @@ class GMC:
def apply(self, raw_frame, detections=None):
if self.method in ['orb', 'sift']:
return self.applyFeaures(raw_frame, detections)
return self.applyFeatures(raw_frame, detections)
elif self.method == 'ecc':
return self.applyEcc(raw_frame, detections)
elif self.method == 'sparseOptFlow':
@ -116,7 +116,7 @@ class GMC:
return H
def applyFeaures(self, raw_frame, detections=None):
def applyFeatures(self, raw_frame, detections=None):
# Initialize
height, width, _ = raw_frame.shape
@ -190,13 +190,13 @@ class GMC:
meanSpatialDistances = np.mean(spatialDistances, 0)
stdSpatialDistances = np.std(spatialDistances, 0)
inliesrs = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances
inliers = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances
goodMatches = []
prevPoints = []
currPoints = []
for i in range(len(matches)):
if inliesrs[i, 0] and inliesrs[i, 1]:
if inliers[i, 0] and inliers[i, 1]:
goodMatches.append(matches[i])
prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
currPoints.append(keypoints[matches[i].trainIdx].pt)
@ -226,7 +226,7 @@ class GMC:
# Find rigid matrix
if (np.size(prevPoints, 0) > 4) and (np.size(prevPoints, 0) == np.size(prevPoints, 0)):
H, inliesrs = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
# Handle downscale
if self.downscale > 1.0:
@ -285,7 +285,7 @@ class GMC:
# Find rigid matrix
if (np.size(prevPoints, 0) > 4) and (np.size(prevPoints, 0) == np.size(prevPoints, 0)):
H, inliesrs = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
# Handle downscale
if self.downscale > 1.0:

4
ultralytics/tracker/utils/kalman_filter.py
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@ -136,7 +136,7 @@ class KalmanFilterXYAH:
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
The Nx8x8 dimensional covariance matrix of the object states at the
previous time step.
Returns
-------
@ -362,7 +362,7 @@ class KalmanFilterXYWH:
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
The Nx8x8 dimensional covariance matrix of the object states at the
previous time step.
Returns
-------

2
ultralytics/tracker/utils/matching.py
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@ -119,7 +119,7 @@ def embedding_distance(tracks, detections, metric='cosine'):
# for i, track in enumerate(tracks):
# cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Normalized features
return cost_matrix