def nms_wr(dets, thresh, force_cpu=False):
"""Dispatch to either CPU or GPU NMS implementations."""
if dets.shape[0] == 0:
return []
if force_cpu:
return cpu_soft_nms(dets, thresh, method=1)
# return cpu_nms(dets, thresh)
# return gpu_nms(dets, thresh)
return cpu_soft_nms(dets, thresh, method=1)
def soft_nms(dets, sigma=0.5, Nt=0.3, threshold=0.001, method=1):
keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
np.float32(sigma), np.float32(Nt),
np.float32(threshold),
np.uint8(method))
return keep
def soft_nms(dets, sigma=0.5, Nt=0.3, threshold=0.001, method=1):
keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
np.float32(sigma), np.float32(Nt),
np.float32(threshold),
np.uint8(method))
return keep
def nms(dets, thresh, force_cpu=False):
"""Dispatch to either CPU or GPU NMS implementations."""
if dets.shape[0] == 0:
return []
if cfg.USE_GPU_NMS and not force_cpu:
return gpu_nms(dets, thresh, device_id=0)
else:
if cfg.USE_SOFT_NMS:
return cpu_soft_nms(dets, thresh)
return cpu_nms(dets, thresh)
def soft_nms(dets, sigma=0.5, Nt=0.3, threshold=0.001, method=1):
"""
Doing Soft-NMS.
Check https://github.com/bharatsingh430/soft-nms for details.
Parameters
----------
dets:
sigma:
Nt:
threshold:
method: 1 for linear, 2 for gaussian and others for original NMS
Returns
-------
"""
keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
np.float32(sigma), np.float32(Nt),
np.float32(threshold), np.uint8(method))
return keep
