greedily select boxes with high confidence and overlap with current maximum <= thresh rule out overlap >= thresh :param dets: [[x1, y1, x2, y2 score]] :param thresh: retain overlap < thresh :return: indexes to keep
(dets, thresh)
| 29 | |
| 30 | |
| 31 | def nms(dets, thresh): |
| 32 | """ |
| 33 | greedily select boxes with high confidence and overlap with current maximum <= thresh |
| 34 | rule out overlap >= thresh |
| 35 | :param dets: [[x1, y1, x2, y2 score]] |
| 36 | :param thresh: retain overlap < thresh |
| 37 | :return: indexes to keep |
| 38 | """ |
| 39 | if dets.shape[0] == 0: |
| 40 | return [] |
| 41 | |
| 42 | x1 = dets[:, 0] |
| 43 | y1 = dets[:, 1] |
| 44 | x2 = dets[:, 2] |
| 45 | y2 = dets[:, 3] |
| 46 | scores = dets[:, 4] |
| 47 | |
| 48 | areas = (x2 - x1 + 1) * (y2 - y1 + 1) |
| 49 | order = scores.argsort()[::-1] |
| 50 | |
| 51 | keep = [] |
| 52 | while order.size > 0: |
| 53 | i = order[0] |
| 54 | keep.append(i) |
| 55 | xx1 = np.maximum(x1[i], x1[order[1:]]) |
| 56 | yy1 = np.maximum(y1[i], y1[order[1:]]) |
| 57 | xx2 = np.minimum(x2[i], x2[order[1:]]) |
| 58 | yy2 = np.minimum(y2[i], y2[order[1:]]) |
| 59 | |
| 60 | w = np.maximum(0.0, xx2 - xx1 + 1) |
| 61 | h = np.maximum(0.0, yy2 - yy1 + 1) |
| 62 | inter = w * h |
| 63 | ovr = inter / (areas[i] + areas[order[1:]] - inter) |
| 64 | |
| 65 | inds = np.where(ovr <= thresh)[0] |
| 66 | order = order[inds + 1] |
| 67 | |
| 68 | return keep |
| 69 | |
| 70 | |
| 71 | def rescore(overlap, scores, thresh, type='gaussian'): |