因此，我有一个基于整体的模型，我想对从N个检测器获得的标签进行后处理。为此，我想使用groupby功能按照我构建的IOU函数对每个帧中的拘留进行分组。这将有助于我为每组检测做出决定，并避免在帧的同一区域进行多次检测。
问：：groupby函数的正确返回值是多少？
熊猫groupby文档说：
by : mapping, function, label, or list of labels
Used to determine the groups for the groupby.
If ``by`` is a function, it's called on each value of the object's
index. If a dict or Series is passed, the Series or dict VALUES
will be used to determine the groups (the Series' values are first
aligned; see ``.align()`` method). If an ndarray is passed, the
values are used as-is determine the groups. A label or list of
labels may be passed to group by the columns in ``self``. Notice
that a tuple is interpreted a (single) key.
我的数据示例：
xmin ymin xmax ... confidence class frame_idx
25092 0.740968 0.379832 0.837317 ... 0.931677 2.0 988.0
25093 0.342227 0.272692 0.421934 ... 0.904239 2.0 988.0
25094 0.624577 0.333927 0.739960 ... 0.854978 2.0 988.0
25095 0.229680 0.260676 0.272042 ... 0.708415 2.0 988.0
25096 0.420753 0.291226 0.514133 ... 0.610117 2.0 988.0
25097 0.061343 0.265538 0.110653 ... 0.369827 2.0 988.0
25098 0.486258 0.711575 0.558676 ... 0.173804 2.0 988.0
25099 0.069983 0.596780 0.571130 ... 0.145625 2.0 988.0
25100 0.756423 0.693024 0.818869 ... 0.126882 1.0 988.0
25101 0.448371 0.000000 0.628947 ... 0.124258 1.0 988.0
25102 0.664900 0.808117 0.728619 ... 0.119267 2.0 988.0
25103 0.000000 0.776122 0.407657 ... 0.119260 2.0 988.0
25104 0.796306 0.878808 0.844132 ... 0.115530 1.0 988.0
25105 0.006830 0.000000 0.045303 ... 0.110469 1.0 988.0
我添加了组检测功能，当前它返回检测结果和索引列表中的索引列表
def group_detections(dets, thresh=0.5):
# NOTE!!! - We assume all dets are from the same class!!!
# thresh = IoU threshold for box elimination (lower - eliminate more boxes)
# initialize the list of picked groups
groups = []
# if there are no boxes, return an empty list
if dets.size == 0:
return dets, groups
# sort by descending quality
dets = dets[(-dets[:, 4]).argsort(), :]
# grab the coordinates of the bounding boxes
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = (x2 - x1) * (y2 - y1)
# keep looping while some indexes still remain in the indexes
# list
all_idxs = np.arange(dets.shape[0])[::-1]
while len(all_idxs) > 0:
# grab the last index in the indexes list and add the
# index value to the list of picked indexes
i = all_idxs[-1]
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = np.maximum(x1[i], x1[all_idxs[:-1]])
yy1 = np.maximum(y1[i], y1[all_idxs[:-1]])
xx2 = np.minimum(x2[i], x2[all_idxs[:-1]])
yy2 = np.minimum(y2[i], y2[all_idxs[:-1]])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1)
h = np.maximum(0, yy2 - yy1)
# compute the ratio of overlap
overlap = (w * h) / area[all_idxs[:-1]]
# delete all indexes from the index list that have
group_idxs = [i] + list(all_idxs[np.where(overlap > thresh)[0]])
groups.append(group_idxs)
remove_idxs = np.concatenate(([len(all_idxs) - 1], np.where(overlap > thresh)[0]))
all_idxs = np.delete(all_idxs, remove_idxs)
return dets, groups
输出示例： 目的地：
array([[0.70187318, 0.37271658, 0.72741735, 0.4855186 , 0.37807396,
1. , 0. ],
[0.15145046, 0.28159913, 0.1774891 , 0.33394483, 0.30386543,
1. , 0. ],
[0.51533937, 0.27924138, 0.5292989 , 0.33969468, 0.19332546,
1. , 0. ],
[0.78109378, 0.87562817, 0.82961792, 1. , 0.13274428,
1. , 0. ],
[0.14983323, 0.28213197, 0.16887194, 0.33710539, 0.12764682,
1. , 0. ],
[0.14637044, 0.033903 , 0.26365086, 0.57686102, 0.12654687,
1. , 0. ],
[0.22914155, 0.26150814, 0.27148777, 0.31108513, 0.10923221,
1. , 0. ],
[0.18359725, 0.02795739, 0.23144765, 0.4838326 , 0.10619797,
1. , 0. ],
[0.22894984, 0.28633049, 0.24446395, 0.35711476, 0.10326774,
1. , 0. ],
[0.15507454, 0.2818917 , 0.18600157, 0.34016567, 0.10280589,
1. , 0. ],
[0.0071237 , 0. , 0.04561174, 0.08170507, 0.10274705,
1. , 0. ],
[0.74405503, 0.38016886, 0.8356874 , 0.4577626 , 0.10082066,
1. , 0. ]])
组：
[[0], [1, 9, 4], [2], [3], [5, 8, 7, 6], [10], [11]]