Convert np.float to float

convert_floats.py

@@ -0,0 +1,24 @@
+import os
+import re
+
+def replace_np_float(directory):
+    # Compile a regex pattern for 'float' surrounded by word boundaries
+    pattern = re.compile(r'\bnp.float\b')
+
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith('.py'):
+                file_path = os.path.join(root, file)
+                with open(file_path, 'r') as f:
+                    content = f.read()
+
+                # Use the regex pattern to replace 'float' with 'float'
+                modified_content = pattern.sub('float', content)
+
+                # Write the modified content back to the file
+                with open(file_path, 'w') as f:
+                    f.write(modified_content)
+
+# Specify the directory
+directory = './'
+replace_np_float(directory)

macos_installation.txt

@@ -0,0 +1,28 @@
+conda create --name btrack_exp python=3.8
+conda activate btrack_exp
+
+cd $BASE_STATION_ROOT$
+pip3 install -r requirements.txt
+pip3 uninstall numpy opencv-python
+conda install numpy=1.19.5 opencv=4.6
+conda install pybind11 loguru pandas seaborn scipy
+
+cd ~
+mkdir 3rd_party
+cd 3rd_party
+
+git clone https://github.com/gatagat/lap
+cd lap
+python3 setup.py build
+python3 setup.py install
+
+cd ..
+git clone https://github.com/samson-wang/cython_bbox.git
+cd cython_bbox
+python3 setup.py build
+python3 setup.py install
+
+cd..
+git clone git@github.com:inspiren-inc/ins_btrack.git -b ins-btrack
+cd ins_btrack
+python3 setup.py develop

setup.py

@@ -1,16 +1,9 @@
-#!/usr/bin/env python
-# Copyright (c) Megvii, Inc. and its affiliates. All Rights Reserved
-
-import re
+from setuptools import setup, Extension, find_namespace_packages
 import setuptools
 import glob
 from os import path
-import torch
-from torch.utils.cpp_extension import CppExtension
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-assert torch_ver >= [1, 3], "Requires PyTorch >= 1.3"
-
+import re
+import pybind11  # Import Pybind11
 
 def get_extensions():
     this_dir = path.dirname(path.abspath(__file__))
@@ -20,19 +13,15 @@ def get_extensions():
     sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
 
     sources = [main_source] + sources
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": ["-O3"]}
-    define_macros = []
 
-    include_dirs = [extensions_dir]
+    extra_compile_args = ["-O3", "-std=c++11"]
+    include_dirs = [extensions_dir, pybind11.get_include()]  # Add Pybind11 include directory
 
     ext_modules = [
-        extension(
+        Extension(
             "yolox._C",
             sources,
             include_dirs=include_dirs,
-            define_macros=define_macros,
             extra_compile_args=extra_compile_args,
         )
     ]
@@ -47,18 +36,17 @@ def get_extensions():
     ).group(1)
 
 
-with open("README.md", "r") as f:
+with open("README.md", "r", encoding="utf-8") as f:
     long_description = f.read()
 
 
-setuptools.setup(
+setup(
     name="yolox",
     version=version,
     author="basedet team",
     python_requires=">=3.6",
     long_description=long_description,
     ext_modules=get_extensions(),
     classifiers=["Programming Language :: Python :: 3", "Operating System :: OS Independent"],
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-    packages=setuptools.find_namespace_packages(),
+    packages=find_namespace_packages(),
 )

tutorials/centertrack/byte_tracker.py

@@ -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

tutorials/centertrack/mot_online/matching.py

@@ -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
@@ -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
 
@@ -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
@@ -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:
@@ -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):

tutorials/cstrack/byte_tracker.py

@@ -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

tutorials/cstrack/tracker.py

@@ -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

tutorials/ctracker/byte_tracker.py

@@ -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

tutorials/ctracker/mot_online/matching.py

@@ -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
@@ -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
 
@@ -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
@@ -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:
@@ -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):

tutorials/fairmot/byte_tracker.py

@@ -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

tutorials/fairmot/tracker.py

@@ -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