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"""Adapted from Fast R-CNN
Written by Sergey Karayev
Licensed under The MIT License
Copyright (c) 2015 Microsoft.
"""
import numpy as np
from skimage.measure import regionprops
def _union_slice(a: tuple[slice], b: tuple[slice]):
"""Returns the union of slice tuples a and b."""
starts = tuple(min(_a.start, _b.start) for _a, _b in zip(a, b))
stops = tuple(max(_a.stop, _b.stop) for _a, _b in zip(a, b))
return tuple(slice(start, stop) for start, stop in zip(starts, stops))
def get_labels_with_overlap(gt_frame, res_frame):
"""Get all labels IDs in gt_frame and res_frame whose bounding boxes
overlap.
Args:
gt_frame (np.ndarray): ground truth segmentation for a single frame
res_frame (np.ndarray): result segmentation for a given frame
Returns:
overlapping_gt_labels: List[int], labels of gt boxes that overlap with res boxes
overlapping_res_labels: List[int], labels of res boxes that overlap with gt boxes
intersections_over_gt: List[float], list of (intersection gt vs res) / (gt area)
"""
gt_frame = gt_frame.astype(np.uint16, copy=False)
res_frame = res_frame.astype(np.uint16, copy=False)
gt_props = regionprops(gt_frame)
gt_boxes = [np.array(gt_prop.bbox) for gt_prop in gt_props]
gt_boxes = np.array(gt_boxes).astype(np.float64)
gt_box_labels = np.asarray(
[int(gt_prop.label) for gt_prop in gt_props], dtype=np.uint16
)
res_props = regionprops(res_frame)
res_boxes = [np.array(res_prop.bbox) for res_prop in res_props]
res_boxes = np.array(res_boxes).astype(np.float64)
res_box_labels = np.asarray(
[int(res_prop.label) for res_prop in res_props], dtype=np.uint16
)
if len(gt_props) == 0 or len(res_props) == 0:
return [], [], []
if gt_frame.ndim == 3:
overlaps = compute_overlap_3D(gt_boxes, res_boxes)
else:
overlaps = compute_overlap(
gt_boxes, res_boxes
) # has the form [gt_bbox, res_bbox]
# Find the bboxes that have overlap at all (ind_ corresponds to box number - starting at 0)
ind_gt, ind_res = np.nonzero(overlaps)
ind_gt = np.asarray(ind_gt, dtype=np.uint16)
ind_res = np.asarray(ind_res, dtype=np.uint16)
overlapping_gt_labels = gt_box_labels[ind_gt]
overlapping_res_labels = res_box_labels[ind_res]
intersections_over_gt = []
for i, j in zip(ind_gt, ind_res):
sslice = _union_slice(gt_props[i].slice, res_props[j].slice)
gt_mask = gt_frame[sslice] == gt_box_labels[i]
res_mask = res_frame[sslice] == res_box_labels[j]
area_inter = np.count_nonzero(np.logical_and(gt_mask, res_mask))
area_gt = np.count_nonzero(gt_mask)
intersections_over_gt.append(area_inter / area_gt)
return overlapping_gt_labels, overlapping_res_labels, intersections_over_gt
def compute_overlap(boxes: np.ndarray, query_boxes: np.ndarray) -> np.ndarray:
"""Args:
a: (N, 4) ndarray of float
b: (K, 4) ndarray of float.
Returns:
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
N = boxes.shape[0]
K = query_boxes.shape[0]
overlaps = np.zeros((N, K), dtype=np.float64)
for k in range(K):
box_area = (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (
query_boxes[k, 3] - query_boxes[k, 1] + 1
)
for n in range(N):
iw = (
min(boxes[n, 2], query_boxes[k, 2])
- max(boxes[n, 0], query_boxes[k, 0])
+ 1
)
if iw > 0:
ih = (
min(boxes[n, 3], query_boxes[k, 3])
- max(boxes[n, 1], query_boxes[k, 1])
+ 1
)
if ih > 0:
ua = np.float64(
(boxes[n, 2] - boxes[n, 0] + 1)
* (boxes[n, 3] - boxes[n, 1] + 1)
+ box_area
- iw * ih
)
overlaps[n, k] = iw * ih / ua
return overlaps
def compute_overlap_3D(boxes: np.ndarray, query_boxes: np.ndarray) -> np.ndarray:
"""Args:
a: (N, 6) ndarray of float
b: (K, 6) ndarray of float.
Returns:
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
N = boxes.shape[0]
K = query_boxes.shape[0]
overlaps = np.zeros((N, K), dtype=np.float64)
for k in range(K):
box_volume = (
(query_boxes[k, 3] - query_boxes[k, 0] + 1)
* (query_boxes[k, 4] - query_boxes[k, 1] + 1)
* (query_boxes[k, 5] - query_boxes[k, 2] + 1)
)
for n in range(N):
id_ = (
min(boxes[n, 3], query_boxes[k, 3])
- max(boxes[n, 0], query_boxes[k, 0])
+ 1
)
if id_ > 0:
iw = (
min(boxes[n, 4], query_boxes[k, 4])
- max(boxes[n, 1], query_boxes[k, 1])
+ 1
)
if iw > 0:
ih = (
min(boxes[n, 5], query_boxes[k, 5])
- max(boxes[n, 2], query_boxes[k, 2])
+ 1
)
if ih > 0:
ua = np.float64(
(boxes[n, 3] - boxes[n, 0] + 1)
* (boxes[n, 4] - boxes[n, 1] + 1)
* (boxes[n, 5] - boxes[n, 2] + 1)
+ box_volume
- iw * ih * id_
)
overlaps[n, k] = iw * ih * id_ / ua
return overlaps
try:
import numba
except ImportError:
import os
import warnings
if not os.getenv("NO_JIT_WARNING", False):
warnings.warn(
"Numba not installed, falling back to slower numpy implementation. "
"Install numba for a significant speedup. Set the environment "
"variable NO_JIT_WARNING=1 to disable this warning.",
stacklevel=2,
)
else:
# compute_overlap 2d and 3d have the same signature
signature = [
"f8[:,::1](f8[:,::1], f8[:,::1])",
numba.types.Array(numba.float64, 2, "C", readonly=True)(
numba.types.Array(numba.float64, 2, "C", readonly=True),
numba.types.Array(numba.float64, 2, "C", readonly=True),
),
]
# variables that appear in the body of each function
common_locals = {
"N": numba.uint64,
"K": numba.uint64,
"overlaps": numba.types.Array(numba.float64, 2, "C"),
"iw": numba.float64,
"ih": numba.float64,
"ua": numba.float64,
"n": numba.uint64,
"k": numba.uint64,
}
compute_overlap = numba.njit(
signature,
locals={**common_locals, "box_area": numba.float64},
fastmath=True,
nogil=True,
boundscheck=False,
)(compute_overlap)
compute_overlap_3D = numba.njit(
signature,
locals={**common_locals, "id_": numba.float64, "box_volume": numba.float64},
fastmath=True,
nogil=True,
boundscheck=False,
)(compute_overlap_3D)
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