1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
|
op {
graph_op_name: "NonMaxSuppressionV4"
in_arg {
name: "boxes"
description: <<END
A 2-D float tensor of shape `[num_boxes, 4]`.
END
}
in_arg {
name: "scores"
description: <<END
A 1-D float tensor of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
END
}
in_arg {
name: "max_output_size"
description: <<END
A scalar integer tensor representing the maximum number of
boxes to be selected by non max suppression.
END
}
in_arg {
name: "iou_threshold"
description: <<END
A 0-D float tensor representing the threshold for deciding whether
boxes overlap too much with respect to IOU.
END
}
in_arg {
name: "score_threshold"
description: <<END
A 0-D float tensor representing the threshold for deciding when to remove
boxes based on score.
END
}
attr {
name: "pad_to_max_output_size"
description: <<END
If true, the output `selected_indices` is padded to be of length
`max_output_size`. Defaults to false.
END
}
out_arg {
name: "selected_indices"
description: <<END
A 1-D integer tensor of shape `[M]` representing the selected
indices from the boxes tensor, where `M <= max_output_size`.
END
}
out_arg {
name: "valid_outputs"
description: <<END
A 0-D integer tensor representing the number of valid elements in
`selected_indices`, with the valid elements appearing first.
END
}
summary: "Greedily selects a subset of bounding boxes in descending order of score,"
description: <<END
pruning away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes. Bounding boxes with score less than
`score_threshold` are removed. Bounding boxes are supplied as
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
diagonal pair of box corners and the coordinates can be provided as normalized
(i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
is agnostic to where the origin is in the coordinate system and more
generally is invariant to orthogonal transformations and translations
of the coordinate system; thus translating or reflections of the coordinate
system result in the same boxes being selected by the algorithm.
The output of this operation is a set of integers indexing into the input
collection of bounding boxes representing the selected boxes. The bounding
box coordinates corresponding to the selected indices can then be obtained
using the `tf.gather operation`. For example:
selected_indices = tf.image.non_max_suppression_v2(
boxes, scores, max_output_size, iou_threshold, score_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
END
}
|
