Compute intersection over union of bounding boxes

[source]

compute_iou function

keras_cv.bounding_box.compute_iou(
    boxes1,
    boxes2,
    bounding_box_format,
    use_masking=False,
    mask_val=-1,
    images=None,
    image_shape=None,
)

Computes a lookup table vector containing the ious for a given set boxes.

The lookup vector is to be indexed by [boxes1_index,boxes2_index] if boxes are unbatched and by [batch, boxes1_index,boxes2_index] if the boxes are batched.

The users can pass boxes1 and boxes2 to be different ranks. For example: 1) boxes1: [batch_size, M, 4], boxes2: [batch_size, N, 4] -> return [batch_size, M, N]. 2) boxes1: [batch_size, M, 4], boxes2: [N, 4] -> return [batch_size, M, N] 3) boxes1: [M, 4], boxes2: [batch_size, N, 4] -> return [batch_size, M, N] 4) boxes1: [M, 4], boxes2: [N, 4] -> return [M, N]

Arguments

• boxes1: a list of bounding boxes in 'corners' format. Can be batched or unbatched.
• boxes2: a list of bounding boxes in 'corners' format. Can be batched or unbatched.
• bounding_box_format: a case-insensitive string which is one of "xyxy", "rel_xyxy", "xyWH", "center_xyWH", "yxyx", "rel_yxyx". For detailed information on the supported format, see the KerasCV bounding box documentation.
• use_masking: whether masking will be applied. This will mask all boxes1 or boxes2 that have values less than 0 in all its 4 dimensions. Default to False.
• mask_val: int to mask those returned IOUs if the masking is True, defaults to -1.

Returns

• iou_lookup_table: a vector containing the pairwise ious of boxes1 and boxes2.