Spaces:
Running
Running
File size: 3,115 Bytes
67a9b5d |
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 79 80 81 82 83 84 85 86 87 |
import numpy as np
from .boxes_utils import assert_and_normalize_shape
def scale_boxes(boxes, x_scale=1, y_scale=1, x_center=0, y_center=0, copy=True):
"""Scale boxes coordinates in x and y dimensions.
Args:
boxes: (N, 4+K)
x_scale: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
scale factor in x dimension
y_scale: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
scale factor in y dimension
x_center: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
y_center: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
References:
`core.box_list_ops.scale` in TensorFlow object detection API
`utils.box_list_ops.scale` in TensorFlow object detection API
`datasets.pipelines.Resize._resize_bboxes` in mmdetection
`core.anchor.guided_anchor_target.calc_region` in mmdetection where comments may be misleading!
`layers.mask_ops.scale_boxes` in detectron2
`mmcv.bbox_scaling`
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
x_scale = np.asarray(x_scale, np.float32)
y_scale = np.asarray(y_scale, np.float32)
x_scale = assert_and_normalize_shape(x_scale, boxes.shape[0])
y_scale = assert_and_normalize_shape(y_scale, boxes.shape[0])
x_center = np.asarray(x_center, np.float32)
y_center = np.asarray(y_center, np.float32)
x_center = assert_and_normalize_shape(x_center, boxes.shape[0])
y_center = assert_and_normalize_shape(y_center, boxes.shape[0])
x_shift = 1 - x_scale
y_shift = 1 - y_scale
x_shift *= x_center
y_shift *= y_center
boxes[:, 0] *= x_scale
boxes[:, 1] *= y_scale
boxes[:, 2] *= x_scale
boxes[:, 3] *= y_scale
boxes[:, 0] += x_shift
boxes[:, 1] += y_shift
boxes[:, 2] += x_shift
boxes[:, 3] += y_shift
return boxes
def scale_boxes_wrt_centers(boxes, x_scale=1, y_scale=1, copy=True):
"""
Args:
boxes: (N, 4+K)
x_scale: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
scale factor in x dimension
y_scale: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
scale factor in y dimension
References:
`core.anchor.guided_anchor_target.calc_region` in mmdetection where comments may be misleading!
`layers.mask_ops.scale_boxes` in detectron2
`mmcv.bbox_scaling`
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
x_scale = np.asarray(x_scale, np.float32)
y_scale = np.asarray(y_scale, np.float32)
x_scale = assert_and_normalize_shape(x_scale, boxes.shape[0])
y_scale = assert_and_normalize_shape(y_scale, boxes.shape[0])
x_factor = (x_scale - 1) * 0.5
y_factor = (y_scale - 1) * 0.5
x_deltas = boxes[:, 2] - boxes[:, 0]
y_deltas = boxes[:, 3] - boxes[:, 1]
x_deltas *= x_factor
y_deltas *= y_factor
boxes[:, 0] -= x_deltas
boxes[:, 1] -= y_deltas
boxes[:, 2] += x_deltas
boxes[:, 3] += y_deltas
return boxes
|