Dev

.gitignore

@@ -1,7 +1,6 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
-*$py.class
 
 # C extensions
 *.so
@@ -12,9 +11,7 @@ env/
 build/
 develop-eggs/
 dist/
-downloads/
 eggs/
-.eggs/
 lib/
 lib64/
 parts/
@@ -24,12 +21,6 @@ var/
 .installed.cfg
 *.egg
 
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
 # Installer logs
 pip-log.txt
 pip-delete-this-directory.txt
@@ -38,52 +29,21 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .coverage
-.coverage.*
 .cache
 nosetests.xml
 coverage.xml
-*,cover
-.hypothesis/
 
 # Translations
 *.mo
 *.pot
 
 # Django stuff:
 *.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
 
 # Sphinx documentation
 docs/_build/
 
-# PyBuilder
-target/
-
-# IPython Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# dotenv
-.env
-
-# virtualenv
-venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
+model_data
+model_data/
+.ipynb_checkpoints/
 
-# Rope project settings
-.ropeproject

MyReadMe.txt

@@ -0,0 +1,6 @@
+python my_deep_sort_app.py \
+    --sequence_dir=$HOME/e/dataset_tiptical/MOT16/MOT16-06 \
+    --detection_file=$HOME/e/dataset_tiptical/MOT16/MOT16-06/MOT16-06.npy \
+    --min_confidence=0.3 \
+    --nn_budget=100 \
+    --display=True

deep_sort/linear_assignment.py

@@ -1,7 +1,13 @@
 # vim: expandtab:ts=4:sw=4
 from __future__ import absolute_import
 import numpy as np
-from sklearn.utils.linear_assignment_ import linear_assignment
+
+import sklearn
+#FutureWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
+if sklearn.__version__>'0.22':
+    from scipy.optimize import linear_sum_assignment as linear_assignment # tf1.15, sklearn=>0.23
+else:
+    from sklearn.utils.linear_assignment_ import linear_assignment       # tf1.14, sklearn=>0.22
 from . import kalman_filter
 
 
@@ -57,6 +63,10 @@ def min_cost_matching(
     cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
     indices = linear_assignment(cost_matrix)
 
+    # Fix for tf1.15 envirement when use scipy.optimize.linear_sum_assignment instead from sklearn.utils.linear_assignment_
+    if type(indices)==tuple:
+        indices = np.array([[x,y] for x,y in zip(*indices)])
+
     matches, unmatched_tracks, unmatched_detections = [], [], []
     for col, detection_idx in enumerate(detection_indices):
         if col not in indices[:, 1]:

deep_sort/nn_matching.py

@@ -49,8 +49,8 @@ def _cosine_distance(a, b, data_is_normalized=False):
 
     """
     if not data_is_normalized:
-        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
-        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+        a = np.asarray(a) / (np.linalg.norm(a, axis=1, keepdims=True)+1e-5)
+        b = np.asarray(b) / (np.linalg.norm(b, axis=1, keepdims=True)+1e-5)
     return 1. - np.dot(a, b.T)
 
 

deep_sort_ex/__init__.py

@@ -0,0 +1 @@
+# vim: expandtab:ts=4:sw=4

deep_sort_ex/detection.py

@@ -0,0 +1,67 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection(object):
+    """
+    This class represents a bounding box detection in a single image.
+
+    Parameters
+    ----------
+    tlwh : array_like
+        Bounding box in format `(x, y, w, h)`.
+    confidence : float
+        Detector confidence score.
+    feature : array_like
+        A feature vector that describes the object contained in this image.
+
+    Attributes
+    ----------
+    tlwh : ndarray
+        Bounding box in format `(top left x, top left y, width, height)`.
+    confidence : ndarray
+        Detector confidence score.
+    feature : ndarray | NoneType
+        A feature vector that describes the object contained in this image.
+
+    """
+
+    def __init__(self, tlwh, confidence, feature, t=None, exts1=None, exts2=None, binding_obj=None, flag=None):
+        '''
+        @param tlwh         - bbox: top, left, width, height
+        @param confidence   - 目标检测置信度
+        @param ffeature     - 目标图像特征码
+        @param t            - 检测时间(秒)
+        @param exts1        - 扩展属性: 扩展卡尔曼滤波器的向量(mean)， 需要与 tracker,track的n_extend参数配合使用
+        @param exts2        - 扩展属性: 独立于卡尔曼滤波器，对扩展通道单独处理
+        @param binding_obj  - 绑定原始目标检测的序号(或结构体对象)，方便数据源跟踪
+        @param flag         - 跟踪目标标记， 如用于目标类别，由外部解析
+        '''
+        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.t = t 
+        self.exts1 = exts1
+        self.exts2 = exts2
+        self.binding_obj = binding_obj
+        self.flag = flag
+        self.confidence = float(confidence)
+        self.feature = np.asarray(feature, dtype=np.float32)
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        if self.exts1 is None:
+            return ret
+        else:
+            return np.hstack([ret, self.exts1])

deep_sort_ex/iou_matching.py

@@ -0,0 +1,81 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import linear_assignment
+
+
+def iou(bbox, candidates):
+    """Computer intersection over union.
+
+    Parameters
+    ----------
+    bbox : ndarray
+        A bounding box in format `(top left x, top left y, width, height)`.
+    candidates : ndarray
+        A matrix of candidate bounding boxes (one per row) in the same format
+        as `bbox`.
+
+    Returns
+    -------
+    ndarray
+        The intersection over union in [0, 1] between the `bbox` and each
+        candidate. A higher score means a larger fraction of the `bbox` is
+        occluded by the candidate.
+
+    """
+    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
+    candidates_tl = candidates[:, :2]
+    candidates_br = candidates[:, :2] + candidates[:, 2:]
+
+    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
+               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
+    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
+               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
+    wh = np.maximum(0., br - tl)
+
+    area_intersection = wh.prod(axis=1)
+    area_bbox = bbox[2:].prod()
+    area_candidates = candidates[:, 2:].prod(axis=1)
+    return area_intersection / (area_bbox + area_candidates - area_intersection)
+
+
+def iou_cost(tracks, detections, track_indices=None,
+             detection_indices=None):
+    """An intersection over union distance metric.
+
+    Parameters
+    ----------
+    tracks : List[deep_sort.track.Track]
+        A list of tracks.
+    detections : List[deep_sort.detection.Detection]
+        A list of detections.
+    track_indices : Optional[List[int]]
+        A list of indices to tracks that should be matched. Defaults to
+        all `tracks`.
+    detection_indices : Optional[List[int]]
+        A list of indices to detections that should be matched. Defaults
+        to all `detections`.
+
+    Returns
+    -------
+    ndarray
+        Returns a cost matrix of shape
+        len(track_indices), len(detection_indices) where entry (i, j) is
+        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
+    for row, track_idx in enumerate(track_indices):
+        if tracks[track_idx].time_since_update > 1:
+            cost_matrix[row, :] = linear_assignment.INFTY_COST
+            continue
+
+        bbox = tracks[track_idx].to_tlwh()
+        candidates = np.asarray([detections[i].tlwh for i in detection_indices])
+        cost_matrix[row, :] = 1. - iou(bbox, candidates)
+    return cost_matrix