def soft_nms(dets,
sigma=0.5,
overlap_thresh=0.3,
score_thresh=0.001,
method='linear'):
"""Apply the soft NMS algorithm from https://arxiv.org/abs/1704.04503."""
if dets.shape[0] == 0:
return dets, []
methods = {'hard': 0, 'linear': 1, 'gaussian': 2}
assert method in methods, 'Unknown soft_nms method: {}'.format(method)
dets, keep = cython_nms.soft_nms(
np.ascontiguousarray(dets, dtype=np.float32), np.float32(sigma),
np.float32(overlap_thresh), np.float32(score_thresh),
np.uint8(methods[method]))
return dets, keep
def soft_nms(
dets, sigma=0.5, overlap_thresh=0.3, score_thresh=0.001, method='linear'
):
"""Apply the soft NMS algorithm from https://arxiv.org/abs/1704.04503."""
if dets.shape[0] == 0:
return dets, []
methods = {'hard': 0, 'linear': 1, 'gaussian': 2}
assert method in methods, 'Unknown soft_nms method: {}'.format(method)
dets, keep = cython_nms.soft_nms(
np.ascontiguousarray(dets, dtype=np.float32),
np.float32(sigma),
np.float32(overlap_thresh),
np.float32(score_thresh),
np.uint8(methods[method])
)
return dets, keep