def non_max_suppression()

in models/01_YoloV5/01_Pytorch/processing.py [0:0]

• 25 lines of code
• 2 McCabe index (conditional complexity)

    def non_max_suppression(self, bboxes, scores, cids):
        x1 = bboxes[:, 0]
        y1 = bboxes[:, 1]
        x2 = bboxes[:, 2]
        y2 = bboxes[:, 3]
        areas = (x2 - x1 + 1) * (y2 - y1 + 1) 
        order = scores.flatten().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 + 1)
            h = np.maximum(0.0, yy2 - yy1 + 1)
            inter = w * h
            iou = inter / (areas[i] + areas[order[1:]] - inter)
            inds = np.where(iou <= self.iou_threshold)[0]
            order = order[inds + 1]
        bboxes = bboxes[keep]
        scores = scores[keep]
        cids = cids[keep]
        return bboxes, scores, cids