| import numpy as np | |
| import torch | |
| from mmdet.utils.util_random import ensure_rng | |
| def random_boxes(num=1, scale=1, rng=None): | |
| """Simple version of ``kwimage.Boxes.random`` | |
| Returns: | |
| Tensor: shape (n, 4) in x1, y1, x2, y2 format. | |
| References: | |
| https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 | |
| Example: | |
| >>> num = 3 | |
| >>> scale = 512 | |
| >>> rng = 0 | |
| >>> boxes = random_boxes(num, scale, rng) | |
| >>> print(boxes) | |
| tensor([[280.9925, 278.9802, 308.6148, 366.1769], | |
| [216.9113, 330.6978, 224.0446, 456.5878], | |
| [405.3632, 196.3221, 493.3953, 270.7942]]) | |
| """ | |
| rng = ensure_rng(rng) | |
| tlbr = rng.rand(num, 4).astype(np.float32) | |
| tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) | |
| tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) | |
| br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) | |
| br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) | |
| tlbr[:, 0] = tl_x * scale | |
| tlbr[:, 1] = tl_y * scale | |
| tlbr[:, 2] = br_x * scale | |
| tlbr[:, 3] = br_y * scale | |
| boxes = torch.from_numpy(tlbr) | |
| return boxes | |