diff --git a/detector/tracker/tracker/matching.py b/detector/tracker/tracker/matching.py index be83ea15..9e3ba8bc 100644 --- a/detector/tracker/tracker/matching.py +++ b/detector/tracker/tracker/matching.py @@ -8,6 +8,9 @@ from tracker.utils import kalman_filter import time +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float + def merge_matches(m1, m2, shape): O,P,Q = shape m1 = np.asarray(m1) @@ -61,13 +64,13 @@ 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=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=DTYPE_FLOAT), + np.ascontiguousarray(btlbrs, dtype=DTYPE_FLOAT) ) return ious @@ -101,10 +104,10 @@ 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=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=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)) return cost_matrix diff --git a/detector/tracker/tracker/multitracker.py b/detector/tracker/tracker/multitracker.py index 64a57998..0ca5da1b 100644 --- a/detector/tracker/tracker/multitracker.py +++ b/detector/tracker/tracker/multitracker.py @@ -13,13 +13,15 @@ from tracker.tracker import matching from tracker.tracker.basetrack import BaseTrack, TrackState +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float 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=DTYPE_FLOAT) self.kalman_filter = None self.mean, self.covariance = None, None self.is_activated = False diff --git a/trackers/ReidModels/net_utils.py b/trackers/ReidModels/net_utils.py index 24078f8c..9ca478de 100755 --- a/trackers/ReidModels/net_utils.py +++ b/trackers/ReidModels/net_utils.py @@ -9,6 +9,8 @@ from utils.log import logger +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float class ConcatAddTable(nn.Module): def __init__(self, *args): @@ -127,7 +129,7 @@ def load_net(fname, net, prefix='', load_state_dict=False): lr = h5f.attrs['learning_rates'] else: lr = h5f.attrs.get('lr', -1) - lr = np.asarray([lr] if lr > 0 else [], dtype=np.float) + lr = np.asarray([lr] if lr > 0 else [], dtype=DTYPE_FLOAT) return epoch, lr diff --git a/trackers/tracker_api.py b/trackers/tracker_api.py index 4967a496..6cce5e02 100644 --- a/trackers/tracker_api.py +++ b/trackers/tracker_api.py @@ -29,13 +29,16 @@ from ReidModels.osnet_ain import osnet_ain_x1_0 from ReidModels.resnet_fc import resnet50_fc512 +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float + class STrack(BaseTrack): shared_kalman = KalmanFilter() def __init__(self, tlwh, score, temp_feat, pose,crop_box,file_name,ps,buffer_size=30): # wait activate - self._tlwh = np.asarray(tlwh, dtype=np.float) + self._tlwh = np.asarray(tlwh, dtype=DTYPE_FLOAT) self.kalman_filter = None self.mean, self.covariance = None, None self.is_activated = False diff --git a/trackers/tracking/matching.py b/trackers/tracking/matching.py index 3da11d37..9cd937ad 100644 --- a/trackers/tracking/matching.py +++ b/trackers/tracking/matching.py @@ -8,6 +8,9 @@ from trackers.utils import kalman_filter import time +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float + def merge_matches(m1, m2, shape): O,P,Q = shape m1 = np.asarray(m1) @@ -61,13 +64,13 @@ 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=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=DTYPE_FLOAT), + np.ascontiguousarray(btlbrs, dtype=DTYPE_FLOAT) ) return ious @@ -101,10 +104,10 @@ 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=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=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)) return cost_matrix diff --git a/trackers/utils/bbox.py b/trackers/utils/bbox.py index 7cd6c6d1..dc30cba8 100755 --- a/trackers/utils/bbox.py +++ b/trackers/utils/bbox.py @@ -1,6 +1,8 @@ import numpy as np import cv2 +# np.float removed in Numpy 1.24 +DTYPE_FLOAT = np.float if hasattr(np, "float") else float def clip_boxes(boxes, im_shape): """ @@ -33,7 +35,7 @@ def clip_box(bbox, im_shape): def int_box(box): - box = np.asarray(box, dtype=np.float) + box = np.asarray(box, dtype=DTYPE_FLOAT) box = np.round(box) return np.asarray(box, dtype=np.int)