# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def unique_boxes(boxes, scale=1.0):
"""Return indices of unique boxes."""
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)
def xywh_to_xyxy(boxes):
"""Convert [x y w h] box format to [x1 y1 x2 y2] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
def xyxy_to_xywh(boxes):
"""Convert [x1 y1 x2 y2] box format to [x y w h] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
def validate_boxes(boxes, width=0, height=0):
"""Check that a set of boxes are valid."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
assert (x1 >= 0).all()
assert (y1 >= 0).all()
assert (x2 >= x1).all()
assert (y2 >= y1).all()
assert (x2 < width).all()
assert (y2 < height).all()
def filter_small_boxes(boxes, min_size):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = np.where((w >= min_size) & (h > min_size))[0]
return keep
def bbox_overlaps(bboxes, ref_bboxes):
"""
ref_bboxes: N x 4;
bboxes: K x 4
return: K x N
"""
refx1, refy1, refx2, refy2 = np.vsplit(np.transpose(ref_bboxes), 4)
x1, y1, x2, y2 = np.hsplit(bboxes, 4)
minx = np.maximum(refx1, x1)
miny = np.maximum(refy1, y1)
maxx = np.minimum(refx2, x2)
maxy = np.minimum(refy2, y2)
inter_area = (maxx - minx + 1) * (maxy - miny + 1)
ref_area = (refx2 - refx1 + 1) * (refy2 - refy1 + 1)
area = (x2 - x1 + 1) * (y2 - y1 + 1)
iou = inter_area / (ref_area + area - inter_area)
return iou
