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- import numpy as np
- def paired_intersection(boxes1, boxes2):
- """
- Args:
- boxes1: a numpy array with shape [N, 4] holding N boxes
- boxes2: a numpy array with shape [N, 4] holding N boxes
-
- Returns:
- a numpy array with shape [N,] representing itemwise intersection area
-
- References:
- `core.box_list_ops.matched_intersection` in Tensorflow object detection API
-
- Notes:
- can called as itemwise_intersection, matched_intersection, aligned_intersection
- """
- x_mins1, y_mins1, x_maxs1, y_maxs1 = np.split(boxes1[:,:4], 4, axis=1)
- x_mins2, y_mins2, x_maxs2, y_maxs2 = np.split(boxes2[:,:4], 4, axis=1)
- max_xmins = np.maximum(x_mins1, x_mins2)
- min_xmaxs = np.minimum(x_maxs1, x_maxs2)
- max_ymins = np.maximum(y_mins1, y_mins2)
- min_ymaxs = np.minimum(y_maxs1, y_maxs2)
- intersect_widths = np.maximum(0., min_xmaxs - max_xmins)
- intersect_heights = np.maximum(0., min_ymaxs - max_ymins)
- return intersect_widths * intersect_heights
-
-
- def pairwise_intersection(boxes1, boxes2):
- """Compute pairwise intersection areas between boxes.
-
- Args:
- boxes1: a numpy array with shape [N, 4] holding N boxes.
- boxes2: a numpy array with shape [M, 4] holding M boxes.
-
- Returns:
- a numpy array with shape [N, M] representing pairwise intersection area.
-
- References:
- `core.box_list_ops.intersection` in Tensorflow object detection API
- `utils.box_list_ops.intersection` in Tensorflow object detection API
- `core.evaluation.bbox_overlaps.bbox_overlaps` in mmdetection
- """
- rows = boxes1.shape[0]
- cols = boxes2.shape[0]
- intersect_areas = np.zeros((rows, cols), dtype=boxes1.dtype)
- if rows * cols == 0:
- return intersect_areas
- swap = False
- if boxes1.shape[0] > boxes2.shape[0]:
- boxes1, boxes2 = boxes2, boxes1
- intersect_areas = np.zeros((cols, rows), dtype=boxes1.dtype)
- swap = True
- for i in range(boxes1.shape[0]):
- x_start = np.maximum(boxes1[i, 0], boxes2[:, 0])
- y_start = np.maximum(boxes1[i, 1], boxes2[:, 1])
- x_end = np.minimum(boxes1[i, 2], boxes2[:, 2])
- y_end = np.minimum(boxes1[i, 3], boxes2[:, 3])
- x_end -= x_start
- y_end -= y_start
- np.maximum(x_end, 0, x_end)
- np.maximum(y_end, 0, y_end)
- x_end *= y_end
- intersect_areas[i, :] = x_end
- if swap:
- intersect_areas = intersect_areas.T
- return intersect_areas
-
-
- def pairwise_overlap_ratio(boxes1, boxes2, ratio_type='iou'):
- """Compute pairwise overlap ratio between boxes.
-
- Args:
- boxes1: a numpy array with shape [N, 4] holding N boxes
- boxes2: a numpy array with shape [M, 4] holding M boxes
- ratio_type:
- iou: Intersection-over-union (iou).
- ioa: Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
- their intersection area over box2's area. Note that ioa is not symmetric,
- that is, IOA(box1, box2) != IOA(box2, box1).
-
- Returns:
- a numpy array with shape [N, M] representing pairwise overlap ratio.
-
- References:
- `utils.np_box_ops.iou` in Tensorflow object detection API
- `utils.np_box_ops.ioa` in Tensorflow object detection API
- `core.evaluation.bbox_overlaps.bbox_overlaps` in mmdetection
- http://ww2.mathworks.cn/help/vision/ref/bboxoverlapratio.html
- """
- intersect_area = pairwise_intersection(boxes1, boxes2)
- area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
- area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
-
- if ratio_type in ['union', 'iou']:
- union_area = np.expand_dims(area1, axis=1) - intersect_area
- union_area += np.expand_dims(area2, axis=0)
- intersect_area /= union_area
- elif ratio_type == 'min':
- min_area = np.minimum(np.expand_dims(area1, axis=1), np.expand_dims(area2, axis=0))
- intersect_area /= min_area
- elif ratio_type == 'ioa':
- intersect_area /= np.expand_dims(area2, axis=0)
- else:
- raise ValueError('Unsupported ratio_type. Got {}'.format(ratio_type))
- return intersect_area
-
-
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