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Replace np.<type alias> with base types #327

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Replace np.<type alias> with base types #327

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1b6ad22
Replace np.float with float
zboszor Apr 5, 2023
9958546
Replace np.int with int
zboszor Apr 5, 2023
41060ff
Replace np.bool with bool
zboszor Apr 5, 2023
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2 changes: 1 addition & 1 deletion tutorials/centertrack/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
36 changes: 18 additions & 18 deletions tutorials/centertrack/mot_online/matching.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -69,13 +69,13 @@ def ious(atlbrs, btlbrs):

:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float)
if ious.size == 0:
return ious

ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
np.ascontiguousarray(atlbrs, dtype=float),
np.ascontiguousarray(btlbrs, dtype=float)
)

return ious
Expand Down Expand Up @@ -109,13 +109,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
return cost_matrix

Expand All @@ -127,17 +127,17 @@ def embedding_distance2(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[0] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[0] for track in tracks], dtype=float)
cost_matrix2 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=float)
cost_matrix3 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
for row in range(len(cost_matrix)):
cost_matrix[row] = (cost_matrix[row]+cost_matrix2[row]+cost_matrix3[row])/3
Expand All @@ -149,11 +149,11 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
det_features = []
leg1 = len(tracks)
leg2 = len(detections)
cost_matrix = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
cost_matrix = np.zeros((leg1, leg2), dtype=float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
if leg2 != 0:
cost_matrix_det = np.maximum(0.0, cdist(det_features, det_features, metric))
if leg1 != 0:
Expand All @@ -167,8 +167,8 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
if leg2 > 10:
leg2 = 10
detections = detections[:10]
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
return track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track

def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
Expand Down
2 changes: 1 addition & 1 deletion tutorials/cstrack/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/cstrack/tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score, temp_feat, buffer_size=30):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/ctracker/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
36 changes: 18 additions & 18 deletions tutorials/ctracker/mot_online/matching.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -69,13 +69,13 @@ def ious(atlbrs, btlbrs):

:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float)
if ious.size == 0:
return ious

ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
np.ascontiguousarray(atlbrs, dtype=float),
np.ascontiguousarray(btlbrs, dtype=float)
)

return ious
Expand Down Expand Up @@ -109,13 +109,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
return cost_matrix

Expand All @@ -127,17 +127,17 @@ def embedding_distance2(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[0] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[0] for track in tracks], dtype=float)
cost_matrix2 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=float)
cost_matrix3 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
for row in range(len(cost_matrix)):
cost_matrix[row] = (cost_matrix[row]+cost_matrix2[row]+cost_matrix3[row])/3
Expand All @@ -149,11 +149,11 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
det_features = []
leg1 = len(tracks)
leg2 = len(detections)
cost_matrix = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
cost_matrix = np.zeros((leg1, leg2), dtype=float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
if leg2 != 0:
cost_matrix_det = np.maximum(0.0, cdist(det_features, det_features, metric))
if leg1 != 0:
Expand All @@ -167,8 +167,8 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
if leg2 > 10:
leg2 = 10
detections = detections[:10]
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
return track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track

def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
Expand Down
2 changes: 1 addition & 1 deletion tutorials/fairmot/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/fairmot/tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score, temp_feat, buffer_size=30):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/jde/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/jde/tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score, temp_feat, buffer_size=30):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
2 changes: 1 addition & 1 deletion tutorials/motr/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
36 changes: 18 additions & 18 deletions tutorials/motr/mot_online/matching.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,13 +68,13 @@ def ious(atlbrs, btlbrs):
:type atlbrs: list[tlbr] | np.ndarray
:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float)
if ious.size == 0:
return ious

ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
np.ascontiguousarray(atlbrs, dtype=float),
np.ascontiguousarray(btlbrs, dtype=float)
)

return ious
Expand Down Expand Up @@ -107,13 +107,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
return cost_matrix

Expand All @@ -125,17 +125,17 @@ def embedding_distance2(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
#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.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[0] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[0] for track in tracks], dtype=float)
cost_matrix2 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=np.float)
track_features = np.asarray([track.features[len(track.features)-1] for track in tracks], dtype=float)
cost_matrix3 = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
for row in range(len(cost_matrix)):
cost_matrix[row] = (cost_matrix[row]+cost_matrix2[row]+cost_matrix3[row])/3
Expand All @@ -147,11 +147,11 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
det_features = []
leg1 = len(tracks)
leg2 = len(detections)
cost_matrix = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=np.float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
cost_matrix = np.zeros((leg1, leg2), dtype=float)
cost_matrix_det = np.zeros((leg1, leg2), dtype=float)
cost_matrix_track = np.zeros((leg1, leg2), dtype=float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
if leg2 != 0:
cost_matrix_det = np.maximum(0.0, cdist(det_features, det_features, metric))
if leg1 != 0:
Expand All @@ -165,8 +165,8 @@ def vis_id_feature_A_distance(tracks, detections, metric='cosine'):
if leg2 > 10:
leg2 = 10
detections = detections[:10]
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
return track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track

def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
Expand Down
2 changes: 1 addition & 1 deletion tutorials/qdtrack/byte_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=float)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
Expand Down
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