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add support for numpy > 1.20.0 #158

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4 changes: 2 additions & 2 deletions trackeval/baselines/stp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def track_sequence(seq_file):

# Initialize container for holding previously tracked objects.
prev = {'boxes': np.empty((0, 4)),
'ids': np.array([], np.int),
'ids': np.array([], int),
'timesteps': np.array([])}

# Run tracker for each timestep.
Expand Down Expand Up @@ -80,7 +80,7 @@ def track_sequence(seq_file):
match_cols = match_cols[actually_matched_mask]

# Assign the prev track ID to the current dets if they were matched.
ids = np.nan * np.ones((len(boxes),), np.int)
ids = np.nan * np.ones((len(boxes),), int)
ids[match_cols] = prev['ids'][match_rows]

# Create new track IDs for dets that were not matched to previous tracks.
Expand Down
8 changes: 4 additions & 4 deletions trackeval/datasets/bdd100k.py
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Original file line number Diff line number Diff line change
Expand Up @@ -226,12 +226,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Only extract relevant dets for this class for preproc and eval (cls)
gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
gt_class_mask = gt_class_mask.astype(np.bool)
gt_class_mask = gt_class_mask.astype(np.bool_)
gt_ids = raw_data['gt_ids'][t][gt_class_mask]
gt_dets = raw_data['gt_dets'][t][gt_class_mask]

tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
tracker_class_mask = tracker_class_mask.astype(np.bool)
tracker_class_mask = tracker_class_mask.astype(np.bool_)
tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]
Expand Down Expand Up @@ -276,14 +276,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
10 changes: 5 additions & 5 deletions trackeval/datasets/burst_helpers/burst_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -322,12 +322,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Only extract relevant dets for this class for preproc and eval (cls)
gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
gt_class_mask = gt_class_mask.astype(np.bool)
gt_class_mask = gt_class_mask.astype(np.bool_)
gt_ids = raw_data['gt_ids'][t][gt_class_mask]
gt_dets = raw_data['gt_dets'][t][gt_class_mask]

tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
tracker_class_mask = tracker_class_mask.astype(np.bool)
tracker_class_mask = tracker_class_mask.astype(np.bool_)
tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
Expand All @@ -349,7 +349,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
elif is_not_exhaustively_labeled:
to_remove_tracker = unmatched_indices
else:
to_remove_tracker = np.array([], dtype=np.int)
to_remove_tracker = np.array([], dtype=int)

# remove all unwanted unmatched tracker detections
data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
Expand Down Expand Up @@ -377,14 +377,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
10 changes: 5 additions & 5 deletions trackeval/datasets/burst_helpers/burst_ow_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,12 +332,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Only extract relevant dets for this class for preproc and eval (cls)
gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
gt_class_mask = gt_class_mask.astype(np.bool)
gt_class_mask = gt_class_mask.astype(np.bool_)
gt_ids = raw_data['gt_ids'][t][gt_class_mask]
gt_dets = raw_data['gt_dets'][t][gt_class_mask]

tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
tracker_class_mask = tracker_class_mask.astype(np.bool)
tracker_class_mask = tracker_class_mask.astype(np.bool_)
tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
Expand All @@ -358,7 +358,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
elif is_not_exhaustively_labeled:
to_remove_tracker = unmatched_indices
else:
to_remove_tracker = np.array([], dtype=np.int)
to_remove_tracker = np.array([], dtype=int)

# remove all unwanted unmatched tracker detections
data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
Expand All @@ -382,14 +382,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
6 changes: 3 additions & 3 deletions trackeval/datasets/davis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,7 +240,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
rows, columns = np.where(void_mask_ious > 0)
for r in rows:
det = mask_utils.decode(raw_data['tracker_dets'][t][r])
void = mask_utils.decode(void_mask).astype(np.bool)
void = mask_utils.decode(void_mask).astype(np.bool_)
det[void] = 0
det = mask_utils.encode(np.array(det, order='F').astype(np.uint8))
raw_data['tracker_dets'][t][r] = det
Expand All @@ -253,14 +253,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
8 changes: 4 additions & 4 deletions trackeval/datasets/head_tracking_challenge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,7 +227,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
time_key = str(t+1)
if time_key in read_data.keys():
try:
time_data = np.asarray(read_data[time_key], dtype=np.float)
time_data = np.asarray(read_data[time_key], dtype=float)
except ValueError:
if is_gt:
raise TrackEvalException(
Expand Down Expand Up @@ -365,7 +365,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
# which are labeled as belonging to a distractor class.
to_remove_tracker = np.array([], np.int)
to_remove_tracker = np.array([], int)
if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:

# Check all classes are valid:
Expand Down Expand Up @@ -432,14 +432,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
14 changes: 7 additions & 7 deletions trackeval/datasets/kitti_2d_box.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
for t in range(num_timesteps):
time_key = str(t)
if time_key in read_data.keys():
time_data = np.asarray(read_data[time_key], dtype=np.float)
time_data = np.asarray(read_data[time_key], dtype=float)
raw_data['dets'][t] = np.atleast_2d(time_data[:, 6:10])
raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
raw_data['classes'][t] = np.atleast_1d(time_data[:, 2]).astype(int)
Expand All @@ -215,7 +215,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
raw_data['tracker_confidences'][t] = np.empty(0)
if is_gt:
if time_key in ignore_data.keys():
time_ignore = np.asarray(ignore_data[time_key], dtype=np.float)
time_ignore = np.asarray(ignore_data[time_key], dtype=float)
raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d(time_ignore[:, 6:10])
else:
raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))
Expand Down Expand Up @@ -288,23 +288,23 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Only extract relevant dets for this class for preproc and eval (cls + distractor classes)
gt_class_mask = np.sum([raw_data['gt_classes'][t] == c for c in [cls_id] + distractor_classes], axis=0)
gt_class_mask = gt_class_mask.astype(np.bool)
gt_class_mask = gt_class_mask.astype(np.bool_)
gt_ids = raw_data['gt_ids'][t][gt_class_mask]
gt_dets = raw_data['gt_dets'][t][gt_class_mask]
gt_classes = raw_data['gt_classes'][t][gt_class_mask]
gt_occlusion = raw_data['gt_extras'][t]['occlusion'][gt_class_mask]
gt_truncation = raw_data['gt_extras'][t]['truncation'][gt_class_mask]

tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
tracker_class_mask = tracker_class_mask.astype(np.bool)
tracker_class_mask = tracker_class_mask.astype(np.bool_)
tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]
tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]

# Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
# which are labeled as truncated, occluded, or belonging to a distractor class.
to_remove_matched = np.array([], np.int)
to_remove_matched = np.array([], int)
unmatched_indices = np.arange(tracker_ids.shape[0])
if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
matching_scores = similarity_scores.copy()
Expand Down Expand Up @@ -362,14 +362,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
8 changes: 4 additions & 4 deletions trackeval/datasets/kitti_mots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -311,12 +311,12 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Only extract relevant dets for this class for preproc and eval (cls)
gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
gt_class_mask = gt_class_mask.astype(np.bool)
gt_class_mask = gt_class_mask.astype(np.bool_)
gt_ids = raw_data['gt_ids'][t][gt_class_mask]
gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]

tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
tracker_class_mask = tracker_class_mask.astype(np.bool)
tracker_class_mask = tracker_class_mask.astype(np.bool_)
tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if
tracker_class_mask[ind]]
Expand Down Expand Up @@ -363,14 +363,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
8 changes: 4 additions & 4 deletions trackeval/datasets/mot_challenge_2d_box.py
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Original file line number Diff line number Diff line change
Expand Up @@ -225,7 +225,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
time_key = str(t+1)
if time_key in read_data.keys():
try:
time_data = np.asarray(read_data[time_key], dtype=np.float)
time_data = np.asarray(read_data[time_key], dtype=float)
except ValueError:
if is_gt:
raise TrackEvalException(
Expand Down Expand Up @@ -356,7 +356,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
# which are labeled as belonging to a distractor class.
to_remove_tracker = np.array([], np.int)
to_remove_tracker = np.array([], int)
if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:

# Check all classes are valid:
Expand Down Expand Up @@ -410,14 +410,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
Expand Down
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