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# -------------------------------------------------------- | ||
# Deformable Convolutional Networks | ||
# Copyright (c) 2015 Microsoft | ||
# Licensed under The MIT License [see LICENSE for details] | ||
# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) | ||
# -------------------------------------------------------- | ||
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import numpy as np | ||
cimport numpy as np | ||
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cdef inline np.float32_t max(np.float32_t a, np.float32_t b): | ||
return a if a >= b else b | ||
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cdef inline np.float32_t min(np.float32_t a, np.float32_t b): | ||
return a if a <= b else b | ||
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def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): | ||
cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] | ||
cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] | ||
cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] | ||
cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] | ||
cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] | ||
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cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) | ||
cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1].astype('i') | ||
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cdef int ndets = dets.shape[0] | ||
cdef np.ndarray[np.int_t, ndim=1] suppressed = \ | ||
np.zeros((ndets), dtype=np.int) | ||
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# nominal indices | ||
cdef int _i, _j | ||
# sorted indices | ||
cdef int i, j | ||
# temp variables for box i's (the box currently under consideration) | ||
cdef np.float32_t ix1, iy1, ix2, iy2, iarea | ||
# variables for computing overlap with box j (lower scoring box) | ||
cdef np.float32_t xx1, yy1, xx2, yy2 | ||
cdef np.float32_t w, h | ||
cdef np.float32_t inter, ovr | ||
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keep = [] | ||
for _i in range(ndets): | ||
i = order[_i] | ||
if suppressed[i] == 1: | ||
continue | ||
keep.append(i) | ||
ix1 = x1[i] | ||
iy1 = y1[i] | ||
ix2 = x2[i] | ||
iy2 = y2[i] | ||
iarea = areas[i] | ||
for _j in range(_i + 1, ndets): | ||
j = order[_j] | ||
if suppressed[j] == 1: | ||
continue | ||
xx1 = max(ix1, x1[j]) | ||
yy1 = max(iy1, y1[j]) | ||
xx2 = min(ix2, x2[j]) | ||
yy2 = min(iy2, y2[j]) | ||
w = max(0.0, xx2 - xx1 + 1) | ||
h = max(0.0, yy2 - yy1 + 1) | ||
inter = w * h | ||
ovr = inter / (iarea + areas[j] - inter) | ||
if ovr >= thresh: | ||
suppressed[j] = 1 | ||
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return keep |
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