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| import numpy as np | |
| from .boxes_utils import assert_and_normalize_shape | |
| def translate_boxes(boxes, x_shift=0, y_shift=0, copy=True): | |
| """translate boxes coordinates in x and y dimensions. | |
| Args: | |
| boxes: (N, 4+K) | |
| x_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift in x dimension | |
| y_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift in y dimension | |
| copy: bool | |
| References: | |
| `datasets.pipelines.RandomCrop` in mmdetection | |
| """ | |
| boxes = np.array(boxes, dtype=np.float32, copy=copy) | |
| x_shift = np.asarray(x_shift, np.float32) | |
| y_shift = np.asarray(y_shift, np.float32) | |
| x_shift = assert_and_normalize_shape(x_shift, boxes.shape[0]) | |
| y_shift = assert_and_normalize_shape(y_shift, boxes.shape[0]) | |
| boxes[:, 0] += x_shift | |
| boxes[:, 1] += y_shift | |
| boxes[:, 2] += x_shift | |
| boxes[:, 3] += y_shift | |
| return boxes | |
| def adjust_boxes(boxes, x_min_shift, y_min_shift, x_max_shift, y_max_shift, copy=True): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| x_min_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift (x_min, y_min) in x dimension | |
| y_min_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift (x_min, y_min) in y dimension | |
| x_max_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift (x_max, y_max) in x dimension | |
| y_max_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| shift (x_max, y_max) in y dimension | |
| copy: bool | |
| """ | |
| boxes = np.array(boxes, dtype=np.float32, copy=copy) | |
| x_min_shift = np.asarray(x_min_shift, np.float32) | |
| y_min_shift = np.asarray(y_min_shift, np.float32) | |
| x_max_shift = np.asarray(x_max_shift, np.float32) | |
| y_max_shift = np.asarray(y_max_shift, np.float32) | |
| x_min_shift = assert_and_normalize_shape(x_min_shift, boxes.shape[0]) | |
| y_min_shift = assert_and_normalize_shape(y_min_shift, boxes.shape[0]) | |
| x_max_shift = assert_and_normalize_shape(x_max_shift, boxes.shape[0]) | |
| y_max_shift = assert_and_normalize_shape(y_max_shift, boxes.shape[0]) | |
| boxes[:, 0] += x_min_shift | |
| boxes[:, 1] += y_min_shift | |
| boxes[:, 2] += x_max_shift | |
| boxes[:, 3] += y_max_shift | |
| return boxes | |
| def inflate_or_deflate_boxes(boxes, width_delta=0, height_delta=0, copy=True): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| width_delta: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| height_delta: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1) | |
| copy: bool | |
| """ | |
| boxes = np.array(boxes, dtype=np.float32, copy=copy) | |
| width_delta = np.asarray(width_delta, np.float32) | |
| height_delta = np.asarray(height_delta, np.float32) | |
| width_delta = assert_and_normalize_shape(width_delta, boxes.shape[0]) | |
| height_delta = assert_and_normalize_shape(height_delta, boxes.shape[0]) | |
| half_width_delta = width_delta * 0.5 | |
| half_height_delta = height_delta * 0.5 | |
| boxes[:, 0] -= half_width_delta | |
| boxes[:, 1] -= half_height_delta | |
| boxes[:, 2] += half_width_delta | |
| boxes[:, 3] += half_height_delta | |
| return boxes | |
| def inflate_boxes_to_square(boxes, copy=True): | |
| """Inflate boxes to square | |
| Args: | |
| boxes: (N, 4+K) | |
| copy: bool | |
| """ | |
| boxes = np.array(boxes, dtype=np.float32, copy=copy) | |
| widths = boxes[:, 2] - boxes[:, 0] | |
| heights = boxes[:, 3] - boxes[:, 1] | |
| max_side_lengths = np.maximum(widths, heights) | |
| width_deltas = np.subtract(max_side_lengths, widths, widths) | |
| height_deltas = np.subtract(max_side_lengths, heights, heights) | |
| width_deltas *= 0.5 | |
| height_deltas *= 0.5 | |
| boxes[:, 0] -= width_deltas | |
| boxes[:, 1] -= height_deltas | |
| boxes[:, 2] += width_deltas | |
| boxes[:, 3] += height_deltas | |
| return boxes | |
| def deflate_boxes_to_square(boxes, copy=True): | |
| """Deflate boxes to square | |
| Args: | |
| boxes: (N, 4+K) | |
| copy: bool | |
| """ | |
| boxes = np.array(boxes, dtype=np.float32, copy=copy) | |
| widths = boxes[:, 2] - boxes[:, 0] | |
| heights = boxes[:, 3] - boxes[:, 1] | |
| min_side_lengths = np.minimum(widths, heights) | |
| width_deltas = np.subtract(min_side_lengths, widths, widths) | |
| height_deltas = np.subtract(min_side_lengths, heights, heights) | |
| width_deltas *= 0.5 | |
| height_deltas *= 0.5 | |
| boxes[:, 0] -= width_deltas | |
| boxes[:, 1] -= height_deltas | |
| boxes[:, 2] += width_deltas | |
| boxes[:, 3] += height_deltas | |
| return boxes | |