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| 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 | |