(self, detections, threshold, normalized=False)
return keep
Function to perform non-maximal supression.

        Arguments:
            detections (Tensor): detection boxes (box_count, 5), each row has
                                 (x1, y1, x2, y2, score). Assume the boxes have already
                                 been sorted based on score in descending order
            output_mask (Tensor): pre-allocated buffer for mask output from the kernel
            threshold (float): box overlap threshold, boxes with smaller overlaps will be kept
            normalized (bool): whether box coordinates are normalized to image dimensions

        Outputs:
            keep_ind (list): list of indices
Function to perform non-maximal supression.
Function to perform non - maximal supression .
def nms(self, detections, threshold, normalized=False):
        """
        Function to perform non-maximal supression.

        Arguments:
            detections (Tensor): detection boxes (box_count, 5), each row has
                                 (x1, y1, x2, y2, score). Assume the boxes have already
                                 been sorted based on score in descending order
            output_mask (Tensor): pre-allocated buffer for mask output from the kernel
            threshold (float): box overlap threshold, boxes with smaller overlaps will be kept
            normalized (bool): whether box coordinates are normalized to image dimensions

        Outputs:
            keep_ind (list): list of indices
        """
        # for boxes in pixel space, we calculate size as x_max - x_min+1. However,
        # when the boxes are normalized to be between 0 and 1, we calculate
        # the size as x_max - x_min. offset controls this behavior.
        if normalized is True:
            offset = 0
        else:
            offset = 1

        dets = detections.get()

        # remove zero score entries
        keep = np.where(dets[:, 4] != 0)[0]

        x1 = dets[keep, 0]
        y1 = dets[keep, 1]
        x2 = dets[keep, 2]
        y2 = dets[keep, 3]
        scores = dets[keep, 4]

        areas = (x2 - x1 + offset) * (y2 - y1 + offset)
        order = scores.argsort()[::-1]

        keep = []
        while order.size > 0:
            i = order[0]
            keep.append(i)
            xx1 = np.maximum(x1[i], x1[order[1:]])
            yy1 = np.maximum(y1[i], y1[order[1:]])
            xx2 = np.minimum(x2[i], x2[order[1:]])
            yy2 = np.minimum(y2[i], y2[order[1:]])

            w = np.maximum(0.0, xx2 - xx1 + offset)
            h = np.maximum(0.0, yy2 - yy1 + offset)
            inter = w * h
            ovr = inter / (areas[i] + areas[order[1:]] - inter)

            inds = np.where(ovr <= threshold)[0]

            order = order[inds + 1]

        return keep
def nms ( self , detections , threshold , normalized = False ) : # for boxes in pixel space, we calculate size as x_max - x_min+1. However, # when the boxes are normalized to be between 0 and 1, we calculate # the size as x_max - x_min. offset controls this behavior. if normalized is True : offset = 0 else : offset = 1 dets = detections . get ( ) # remove zero score entries keep = np . where ( dets [ : , 4 ] != 0 ) [ 0 ] x1 = dets [ keep , 0 ] y1 = dets [ keep , 1 ] x2 = dets [ keep , 2 ] y2 = dets [ keep , 3 ] scores = dets [ keep , 4 ] areas = ( x2 - x1 + offset ) * ( y2 - y1 + offset ) order = scores . argsort ( ) [ : : - 1 ] keep = [ ] while order . size > 0 : i = order [ 0 ] keep . append ( i ) xx1 = np . maximum ( x1 [ i ] , x1 [ order [ 1 : ] ] ) yy1 = np . maximum ( y1 [ i ] , y1 [ order [ 1 : ] ] ) xx2 = np . minimum ( x2 [ i ] , x2 [ order [ 1 : ] ] ) yy2 = np . minimum ( y2 [ i ] , y2 [ order [ 1 : ] ] ) w = np . maximum ( 0.0 , xx2 - xx1 + offset ) h = np . maximum ( 0.0 , yy2 - yy1 + offset ) inter = w * h ovr = inter / ( areas [ i ] + areas [ order [ 1 : ] ] - inter ) inds = np . where ( ovr <= threshold ) [ 0 ] order = order [ inds + 1 ] return keep