【三维目标检测】VoteNet：Deep Hough Voting for 3D Object Detection in Point Clouds

 1 def box3d_iou(corners1, corners2):
 2     ‘‘‘ Compute 3D bounding box IoU.
 3     Input:
 4         corners1: numpy array (8,3), assume up direction is negative Y
 5         corners2: numpy array (8,3), assume up direction is negative Y
 6     Output:
 7         iou: 3D bounding box IoU
 8         iou_2d: bird‘s eye view 2D bounding box IoU
 9     todo (rqi): add more description on corner points‘ orders.
10     ‘‘‘
11     # corner points are in counter clockwise order
12     rect1 = [(corners1[i,0], corners1[i,2]) for i in range(3,-1,-1)]
13     rect2 = [(corners2[i,0], corners2[i,2]) for i in range(3,-1,-1)] 
14     area1 = poly_area(np.array(rect1)[:,0], np.array(rect1)[:,1])
15     area2 = poly_area(np.array(rect2)[:,0], np.array(rect2)[:,1])
16     inter, inter_area = convex_hull_intersection(rect1, rect2)
17     iou_2d = inter_area/(area1+area2-inter_area)
18     ymax = min(corners1[0,1], corners2[0,1])
19     ymin = max(corners1[4,1], corners2[4,1])
20     inter_vol = inter_area * max(0.0, ymax-ymin)
21     vol1 = box3d_vol(corners1)
22     vol2 = box3d_vol(corners2)
23     iou = inter_vol / (vol1 + vol2 - inter_vol)
24     return iou, iou_2d

 1 def get_iou(bb1, bb2):
 2     """
 3     Calculate the Intersection over Union (IoU) of two 2D bounding boxes.
 4     Parameters
 5     ----------
 6     bb1 : dict
 7         Keys: {‘x1‘, ‘x2‘, ‘y1‘, ‘y2‘}
 8         The (x1, y1) position is at the top left corner,
 9         the (x2, y2) position is at the bottom right corner
10     bb2 : dict
11         Keys: {‘x1‘, ‘x2‘, ‘y1‘, ‘y2‘}
12         The (x, y) position is at the top left corner,
13         the (x2, y2) position is at the bottom right corner
14     Returns
15     -------
16     float
17         in [0, 1]
18     """
19     assert bb1[‘x1‘] < bb1[‘x2‘]
20     assert bb1[‘y1‘] < bb1[‘y2‘]
21     assert bb2[‘x1‘] < bb2[‘x2‘]
22     assert bb2[‘y1‘] < bb2[‘y2‘]
23 
24     # determine the coordinates of the intersection rectangle
25     x_left = max(bb1[‘x1‘], bb2[‘x1‘])
26     y_top = max(bb1[‘y1‘], bb2[‘y1‘])
27     x_right = min(bb1[‘x2‘], bb2[‘x2‘])
28     y_bottom = min(bb1[‘y2‘], bb2[‘y2‘])
29 
30     if x_right < x_left or y_bottom < y_top:
31         return 0.0
32 
33     # The intersection of two axis-aligned bounding boxes is always an
34     # axis-aligned bounding box
35     intersection_area = (x_right - x_left) * (y_bottom - y_top)
36 
37     # compute the area of both AABBs
38     bb1_area = (bb1[‘x2‘] - bb1[‘x1‘]) * (bb1[‘y2‘] - bb1[‘y1‘])
39     bb2_area = (bb2[‘x2‘] - bb2[‘x1‘]) * (bb2[‘y2‘] - bb2[‘y1‘])
40 
41     # compute the intersection over union by taking the intersection
42     # area and dividing it by the sum of prediction + ground-truth
43     # areas - the interesection area
44     iou = intersection_area / float(bb1_area + bb2_area - intersection_area)
45     assert iou >= 0.0
46     assert iou <= 1.0
47     return iou