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import numpy as np |
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def nms(dets, thr): |
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"""Greedily select boxes with high confidence and overlap <= thr. |
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Args: |
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dets: [[x1, y1, x2, y2, score]]. |
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thr: Retain overlap < thr. |
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Returns: |
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list: Indexes to keep. |
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""" |
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if len(dets) == 0: |
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return [] |
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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 len(order) > 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 <= thr)[0] |
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order = order[inds + 1] |
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return keep |
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def oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None): |
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"""Calculate oks ious. |
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Args: |
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g: Ground truth keypoints. |
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d: Detected keypoints. |
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a_g: Area of the ground truth object. |
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a_d: Area of the detected object. |
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sigmas: standard deviation of keypoint labelling. |
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vis_thr: threshold of the keypoint visibility. |
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Returns: |
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list: The oks ious. |
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""" |
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if sigmas is None: |
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sigmas = np.array([ |
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.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, |
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.87, .87, .89, .89 |
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]) / 10.0 |
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vars = (sigmas * 2)**2 |
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xg = g[0::3] |
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yg = g[1::3] |
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vg = g[2::3] |
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ious = np.zeros(len(d), dtype=np.float32) |
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for n_d in range(0, len(d)): |
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xd = d[n_d, 0::3] |
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yd = d[n_d, 1::3] |
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vd = d[n_d, 2::3] |
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dx = xd - xg |
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dy = yd - yg |
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e = (dx**2 + dy**2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2 |
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if vis_thr is not None: |
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ind = list(vg > vis_thr) and list(vd > vis_thr) |
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e = e[ind] |
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ious[n_d] = np.sum(np.exp(-e)) / len(e) if len(e) != 0 else 0.0 |
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return ious |
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def oks_nms(kpts_db, thr, sigmas=None, vis_thr=None): |
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"""OKS NMS implementations. |
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Args: |
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kpts_db: keypoints. |
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thr: Retain overlap < thr. |
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sigmas: standard deviation of keypoint labelling. |
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vis_thr: threshold of the keypoint visibility. |
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Returns: |
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np.ndarray: indexes to keep. |
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""" |
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if len(kpts_db) == 0: |
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return [] |
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scores = np.array([k['score'] for k in kpts_db]) |
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kpts = np.array([k['keypoints'].flatten() for k in kpts_db]) |
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areas = np.array([k['area'] for k in kpts_db]) |
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order = scores.argsort()[::-1] |
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keep = [] |
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while len(order) > 0: |
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i = order[0] |
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keep.append(i) |
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oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], |
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sigmas, vis_thr) |
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inds = np.where(oks_ovr <= thr)[0] |
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order = order[inds + 1] |
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keep = np.array(keep) |
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return keep |
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def _rescore(overlap, scores, thr, type='gaussian'): |
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"""Rescoring mechanism gaussian or linear. |
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Args: |
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overlap: calculated ious |
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scores: target scores. |
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thr: retain oks overlap < thr. |
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type: 'gaussian' or 'linear' |
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Returns: |
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np.ndarray: indexes to keep |
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""" |
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assert len(overlap) == len(scores) |
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assert type in ['gaussian', 'linear'] |
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if type == 'linear': |
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inds = np.where(overlap >= thr)[0] |
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scores[inds] = scores[inds] * (1 - overlap[inds]) |
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else: |
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scores = scores * np.exp(-overlap**2 / thr) |
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return scores |
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def soft_oks_nms(kpts_db, thr, max_dets=20, sigmas=None, vis_thr=None): |
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"""Soft OKS NMS implementations. |
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Args: |
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kpts_db |
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thr: retain oks overlap < thr. |
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max_dets: max number of detections to keep. |
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sigmas: Keypoint labelling uncertainty. |
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Returns: |
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np.ndarray: indexes to keep. |
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""" |
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if len(kpts_db) == 0: |
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return [] |
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scores = np.array([k['score'] for k in kpts_db]) |
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kpts = np.array([k['keypoints'].flatten() for k in kpts_db]) |
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areas = np.array([k['area'] for k in kpts_db]) |
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order = scores.argsort()[::-1] |
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scores = scores[order] |
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keep = np.zeros(max_dets, dtype=np.intp) |
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keep_cnt = 0 |
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while len(order) > 0 and keep_cnt < max_dets: |
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i = order[0] |
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oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], |
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sigmas, vis_thr) |
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order = order[1:] |
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scores = _rescore(oks_ovr, scores[1:], thr) |
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tmp = scores.argsort()[::-1] |
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order = order[tmp] |
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scores = scores[tmp] |
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keep[keep_cnt] = i |
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keep_cnt += 1 |
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keep = keep[:keep_cnt] |
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return keep |
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