2020-02-15 11:57:42 +08:00
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import numpy as np
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from ..cython.cpu_nms import cpu_nms
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try:
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from ..cython.gpu_nms import gpu_nms
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except ImportError:
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gpu_nms = None
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def py_nms_wrapper(thresh):
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def _nms(dets):
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return nms(dets, thresh)
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2020-11-06 13:59:21 +08:00
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2020-02-15 11:57:42 +08:00
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return _nms
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def cpu_nms_wrapper(thresh):
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def _nms(dets):
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return cpu_nms(dets, thresh)
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2020-11-06 13:59:21 +08:00
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2020-02-15 11:57:42 +08:00
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return _nms
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def gpu_nms_wrapper(thresh, device_id):
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def _nms(dets):
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return gpu_nms(dets, thresh, device_id)
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2020-11-06 13:59:21 +08:00
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2020-02-15 11:57:42 +08:00
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if gpu_nms is not None:
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return _nms
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else:
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return cpu_nms_wrapper(thresh)
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def nms(dets, thresh):
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"""
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greedily select boxes with high confidence and overlap with current maximum <= thresh
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rule out overlap >= thresh
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:param dets: [[x1, y1, x2, y2 score]]
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:param thresh: retain overlap < thresh
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:return: indexes to keep
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"""
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x1 = dets[:, 0]
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y1 = dets[:, 1]
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x2 = dets[:, 2]
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y2 = dets[:, 3]
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scores = dets[:, 4]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= thresh)[0]
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order = order[inds + 1]
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return keep
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