Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,708 Bytes
c28dddb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
import numpy as np
from metrics.iou import (
_sample_points_in_box3d,
_apply_backward_transformations,
_apply_forward_transformations,
_count_points_in_box3d,
)
def giou_aabb(bbox1_vertices, bbox2_verices):
"""
Compute the generalized IoU between two axis-aligned bounding boxes\n
- bbox1_vertices: the vertices of the first bounding box in the form: [[x0, y0, z0], [x1, y1, z1], ...]\n
- bbox2_vertices: the vertices of the second bounding box in the form: [[x0, y0, z0], [x1, y1, z1], ...]\n
Return:\n
- giou: the gIoU between the two bounding boxes
"""
volume1 = np.prod(np.max(bbox1_vertices, axis=0) - np.min(bbox1_vertices, axis=0))
volume2 = np.prod(np.max(bbox2_verices, axis=0) - np.min(bbox2_verices, axis=0))
# Compute the intersection and union of the two bounding boxes
min_bbox = np.maximum(np.min(bbox1_vertices, axis=0), np.min(bbox2_verices, axis=0))
max_bbox = np.minimum(np.max(bbox1_vertices, axis=0), np.max(bbox2_verices, axis=0))
intersection = np.prod(np.clip(max_bbox - min_bbox, a_min=0, a_max=None))
union = volume1 + volume2 - intersection
# Compute IoU
iou = intersection / union if union > 0 else 0
# Compute the smallest enclosing box
min_enclosing_bbox = np.minimum(np.min(bbox1_vertices, axis=0), np.min(bbox2_verices, axis=0))
max_enclosing_bbox = np.maximum(np.max(bbox1_vertices, axis=0), np.max(bbox2_verices, axis=0))
volume3 = np.prod(max_enclosing_bbox - min_enclosing_bbox)
# Compute gIoU
giou = iou - (volume3 - union) / volume3 if volume3 > 0 else iou
return giou
def sampling_giou(
bbox1_vertices,
bbox2_vertices,
bbox1_transformations,
bbox2_transformations,
num_samples=10000,
):
"""
Compute the IoU between two bounding boxes\n
- bbox1_vertices: the vertices of the first bounding box\n
- bbox2_vertices: the vertices of the second bounding box\n
- bbox1_transformations: list of transformations applied to the first bounding box\n
- bbox2_transformations: list of transformations applied to the second bounding box\n
- num_samples (optional): the number of samples to use per bounding box\n
Return:\n
- iou: the IoU between the two bounding boxes after applying the transformations
"""
# if no transformations are applied, use the axis-aligned bounding box IoU
if len(bbox1_transformations) == 0 and len(bbox2_transformations) == 0:
return giou_aabb(bbox1_vertices, bbox2_vertices)
# Volume of the two bounding boxes
bbox1_volume = np.prod(
np.max(bbox1_vertices, axis=0) - np.min(bbox1_vertices, axis=0)
)
bbox2_volume = np.prod(
np.max(bbox2_vertices, axis=0) - np.min(bbox2_vertices, axis=0)
)
# Volume of the smallest enclosing box
min_enclosing_bbox = np.minimum(np.min(bbox1_vertices, axis=0), np.min(bbox2_vertices, axis=0))
max_enclosing_bbox = np.maximum(np.max(bbox1_vertices, axis=0), np.max(bbox2_vertices, axis=0))
cbbox_volume = np.prod(max_enclosing_bbox - min_enclosing_bbox)
# Sample points in the two bounding boxes
bbox1_points = _sample_points_in_box3d(bbox1_vertices, num_samples)
bbox2_points = _sample_points_in_box3d(bbox2_vertices, num_samples)
# Transform the points
forward_bbox1_points = _apply_forward_transformations(
bbox1_points, bbox1_transformations
)
forward_bbox2_points = _apply_forward_transformations(
bbox2_points, bbox2_transformations
)
# Transform the forward points to the other box's rest pose frame
forward_bbox1_points_in_rest_bbox2_frame = _apply_backward_transformations(
forward_bbox1_points, bbox2_transformations
)
forward_bbox2_points_in_rest_bbox1_frame = _apply_backward_transformations(
forward_bbox2_points, bbox1_transformations
)
# Count the number of points in the other bounding box
num_bbox1_points_in_bbox2 = _count_points_in_box3d(
forward_bbox1_points_in_rest_bbox2_frame, bbox2_vertices
)
num_bbox2_points_in_bbox1 = _count_points_in_box3d(
forward_bbox2_points_in_rest_bbox1_frame, bbox1_vertices
)
# Compute the IoU
intersect = (
bbox1_volume * num_bbox1_points_in_bbox2
+ bbox2_volume * num_bbox2_points_in_bbox1
) / 2
union = bbox1_volume * num_samples + bbox2_volume * num_samples - intersect
iou = intersect / union
giou = iou - (cbbox_volume * num_samples - union) / (cbbox_volume * num_samples) if cbbox_volume > 0 else iou
return giou
def sampling_cDist(
part1,
part2,
bbox1_transformations,
bbox2_transformations,
):
'''
Compute the centroid distance between two bounding boxes\n
- bbox1_vertices: the vertices of the first bounding box\n
- bbox2_vertices: the vertices of the second bounding box\n
- bbox1_transformations: list of transformations applied to the first bounding box\n
- bbox2_transformations: list of transformations applied to the second bounding box\n
'''
bbox1_centroid = np.array(part1['aabb']['center'], dtype=np.float32).reshape(1, 3)
bbox2_centroid = np.array(part2['aabb']['center'], dtype=np.float32).reshape(1, 3)
# Transform the centroids
bbox1_transformed_centroids = _apply_forward_transformations(bbox1_centroid, bbox1_transformations)
bbox2_transformed_centroids = _apply_forward_transformations(bbox2_centroid, bbox2_transformations)
# Compute the centroid distance
cDist = np.linalg.norm(bbox1_transformed_centroids - bbox2_transformed_centroids)
return cDist |