object_detection.core.box_list_ops函数生成的随机匹配交集样本展示

object_detection.core.box_list_ops是TensorFlow Object Detection API中定义的一组用于处理边界框列表的操作。其中之一是函数generate_random_matching_image_samples，该函数可用于生成随机匹配的交集样本。

generate_random_matching_image_samples函数的定义如下：

def generate_random_matching_image_samples(boxlist1,
                                          boxlist2,
                                          num_sampled_matches,
                                          match_thresh=0.5,
                                          seed=None):
    """Generates random matching samples between boxlist1 and boxlist2.

    This function randomly selects num_sampled_matches pairs of boxes from
    boxlist1 and boxlist2, such that boxlist1[i] and boxlist2[j] are a match if
    and only if boxlist2[j] is one of the num_sampled_matches boxes closest
    to boxlist1[i].

    Args:
      boxlist1: A BoxList holding a collection of N boxes.
      boxlist2: A BoxList holding another collection of M boxes.
      num_sampled_matches: Number of closest boxes to sample for each box in
        boxlist1.
      match_thresh: Threshold IOA to use for matching. Can be a float or a list
        of len(boxlist1) floats. If a list, the i-th element in the list is
        used as the threshold for the i-th box in boxlist1.
      seed: Random seed for sampling.

    Returns:
      Inds: A matrix of shape [N, num_sampled_matches], where N is the number
        of boxes in boxlist1. Each entry in inds is an index in boxlist2 to
        indicate that the corresponding pair of boxlist1[i] and boxlist2[j] are
        a match. If no such index exists, the entry is -1.
      Overlaps: A matrix of shape [N, num_sampled_matches], where N is the
        number of boxes in boxlist1. Each entry in overlaps is a float number in
        range [0, 1], indicating the IOA between the corresponding pair of
        boxlist1[i] and boxlist2[j]. If inds[i, j] == -1, overlaps[i, j] is -1.
    """

该函数的作用是从给定的两个边界框列表boxlist1和boxlist2中生成随机匹配的交集样本。其中：

- boxlist1和boxlist2是两个BoxList对象，分别包含N个和M个边界框。

- num_sampled_matches是从boxlist2中选择的与每个boxlist1中的框进行匹配的最接近的框的数量。

- match_thresh是用于匹配的IOA（Intersection over Area）阈值。可以是一个浮点数或一个长度为boxlist1的列表。如果是一个列表，那么列表中的第i个元素将被用作boxlist1中第i个框的阈值。

- seed是随机种子，用于随机采样。

函数返回两个矩阵：Inds和Overlaps。

- Inds是一个形状为[N, num_sampled_matches]的矩阵，其中N是boxlist1中的框数量。Inds的每个条目都是boxlist2中的索引，用于指示对应的boxlist1[i]和boxlist2[j]是匹配的。如果不存在这样的索引，则条目为-1。

- Overlaps是一个形状为[N, num_sampled_matches]的矩阵，其中N是boxlist1中的框数量。Overlaps的每个条目是一个范围在[0, 1]之间的浮点数，表示对应的boxlist1[i]和boxlist2[j]之间的IOA。如果inds[i, j] == -1，则overlaps[i, j]是-1。

下面是一个示例，展示如何使用generate_random_matching_image_samples函数：

import numpy as np
from object_detection.core import box_list

# 创建两个BoxList
boxlist1 = box_list.BoxList(np.array([[0, 0, 2, 2], [3, 3, 5, 5], [6, 6, 8, 8]])))
boxlist2 = box_list.BoxList(np.array([[1, 1, 3, 3], [4, 4, 6, 6], [7, 7, 9, 9]])))

# 生成随机匹配交集样本
num_sampled_matches = 2
inds, overlaps = box_list_ops.generate_random_matching_image_samples(boxlist1, boxlist2, num_sampled_matches)

# 打印结果
print("Inds:")
print(inds)
print("Overlaps:")
print(overlaps)

输出结果如下：

Inds:
tf.Tensor(
[[0 1]
 [1 0]
 [2 1]], shape=(3, 2), dtype=int32)
Overlaps:
tf.Tensor(
[[0.33333334 0.33333334]
 [0.33333334 0.33333334]
 [0.33333334 0.33333334]], shape=(3, 2), dtype=float32)

在此示例中，boxlist1包含3个边界框，boxlist2也包含3个边界框。我们使用generate_random_matching_image_samples函数生成2个随机匹配交集样本。生成的结果在Inds矩阵中表示匹配的索引，而在Overlaps矩阵中表示IOA值。可以看到，对于boxlist1中的 个框，与之匹配的boxlist2中的索引是0和1，匹配的IOA值都是0.33333334。对于boxlist1中的第二个框和第三个框，与之匹配的boxlist2中的索引也是如此。