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| import numpy as np | |
| from .boxes_utils import assert_and_normalize_shape | |
| def flip_boxes(boxes, x_center=0, y_center=0, direction='h'): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| 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) | |
| direction: str | |
| """ | |
| assert direction in ['x', 'h', 'horizontal', | |
| 'y', 'v', 'vertical', | |
| 'o', 'b', 'both'] | |
| boxes = np.asarray(boxes, np.float32) | |
| ret_boxes = boxes.copy() | |
| 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]) | |
| if direction in ['o', 'b', 'both', 'x', 'h', 'horizontal']: | |
| ret_boxes[:, 0] = 2 * x_center - boxes[:, 2] | |
| ret_boxes[:, 2] = 2 * x_center - boxes[:, 0] | |
| if direction in ['o', 'b', 'both', 'y', 'v', 'vertical']: | |
| ret_boxes[:, 1] = 2 * y_center - boxes[:, 3] | |
| ret_boxes[:, 3] = 2 * y_center - boxes[:, 1] | |
| return ret_boxes | |
| def fliplr_boxes(boxes, x_center=0, y_center=0): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| 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) | |
| """ | |
| boxes = np.asarray(boxes, np.float32) | |
| ret_boxes = boxes.copy() | |
| 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]) | |
| ret_boxes[:, 0] = 2 * x_center - boxes[:, 2] | |
| ret_boxes[:, 2] = 2 * x_center - boxes[:, 0] | |
| return ret_boxes | |
| def flipud_boxes(boxes, x_center=0, y_center=0): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| 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) | |
| """ | |
| boxes = np.asarray(boxes, np.float32) | |
| ret_boxes = boxes.copy() | |
| 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]) | |
| ret_boxes[:, 1] = 2 * y_center - boxes[:, 3] | |
| ret_boxes[:, 3] = 2 * y_center - boxes[:, 1] | |
| return ret_boxes | |
| def transpose_boxes(boxes, x_center=0, y_center=0): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| 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) | |
| """ | |
| boxes = np.asarray(boxes, np.float32) | |
| ret_boxes = boxes.copy() | |
| 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]) | |
| shift = x_center - y_center | |
| ret_boxes[:, 0] = boxes[:, 1] + shift | |
| ret_boxes[:, 1] = boxes[:, 0] - shift | |
| ret_boxes[:, 2] = boxes[:, 3] + shift | |
| ret_boxes[:, 3] = boxes[:, 2] - shift | |
| return ret_boxes | |
| def flip_boxes_in_image(boxes, image_width, image_height, direction='h'): | |
| """ | |
| Args: | |
| boxes: (N, 4+K) | |
| image_width: int | |
| image_width: int | |
| direction: str | |
| References: | |
| `core.bbox.bbox_flip` in mmdetection | |
| `datasets.pipelines.RandomFlip.bbox_flip` in mmdetection | |
| """ | |
| x_center = (image_width - 1) * 0.5 | |
| y_center = (image_height - 1) * 0.5 | |
| ret_boxes = flip_boxes(boxes, x_center, y_center, direction=direction) | |
| return ret_boxes | |
| def rot90_boxes_in_image(boxes, image_width, image_height, n=1): | |
| """Rotate boxes counter-clockwise by 90 degrees. | |
| References: | |
| np.rot90 | |
| cv2.rotate | |
| tf.image.rot90 | |
| """ | |
| n = n % 4 | |
| if n == 0: | |
| ret_boxes = boxes.copy() | |
| elif n == 1: | |
| ret_boxes = transpose_boxes(boxes) | |
| ret_boxes = flip_boxes_in_image(ret_boxes, image_width, image_height, 'v') | |
| elif n == 2: | |
| ret_boxes = flip_boxes_in_image(boxes, image_width, image_height, 'o') | |
| else: | |
| ret_boxes = transpose_boxes(boxes) | |
| ret_boxes = flip_boxes_in_image(ret_boxes, image_width, image_height, 'h'); | |
| return ret_boxes | |