785 lines
34 KiB
Python
785 lines
34 KiB
Python
import numpy as np
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import cv2
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from easymocap.datasets.base import crop_image
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from easymocap.estimator.wrapper_base import bbox_from_keypoints
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from easymocap.mytools.vis_base import merge, plot_keypoints_auto
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from .debug_utils import log, mywarn, myerror
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def batch_triangulate(keypoints_, Pall, min_view=2):
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""" triangulate the keypoints of whole body
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Args:
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keypoints_ (nViews, nJoints, 3): 2D detections
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Pall (nViews, 3, 4) | (nViews, nJoints, 3, 4): projection matrix of each view
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min_view (int, optional): min view for visible points. Defaults to 2.
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Returns:
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keypoints3d: (nJoints, 4)
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"""
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# keypoints: (nViews, nJoints, 3)
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# Pall: (nViews, 3, 4)
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# A: (nJoints, nViewsx2, 4), x: (nJoints, 4, 1); b: (nJoints, nViewsx2, 1)
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v = (keypoints_[:, :, -1]>0).sum(axis=0)
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valid_joint = np.where(v >= min_view)[0]
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keypoints = keypoints_[:, valid_joint]
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conf3d = keypoints[:, :, -1].sum(axis=0)/v[valid_joint]
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# P2: P矩阵的最后一行：(1, nViews, 1, 4)
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if len(Pall.shape) == 3:
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P0 = Pall[None, :, 0, :]
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P1 = Pall[None, :, 1, :]
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P2 = Pall[None, :, 2, :]
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else:
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P0 = Pall[:, :, 0, :].swapaxes(0, 1)
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P1 = Pall[:, :, 1, :].swapaxes(0, 1)
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P2 = Pall[:, :, 2, :].swapaxes(0, 1)
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# uP2: x坐标乘上P2: (nJoints, nViews, 1, 4)
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uP2 = keypoints[:, :, 0].T[:, :, None] * P2
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vP2 = keypoints[:, :, 1].T[:, :, None] * P2
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conf = keypoints[:, :, 2].T[:, :, None]
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Au = conf * (uP2 - P0)
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Av = conf * (vP2 - P1)
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A = np.hstack([Au, Av])
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u, s, v = np.linalg.svd(A)
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X = v[:, -1, :]
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X = X / X[:, 3:]
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# out: (nJoints, 4)
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result = np.zeros((keypoints_.shape[1], 4))
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result[valid_joint, :3] = X[:, :3]
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result[valid_joint, 3] = conf3d #* (conf[..., 0].sum(axis=-1)>min_view)
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return result
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def project_points(keypoints, RT, einsum=None):
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homo = np.concatenate([keypoints[..., :3], np.ones_like(keypoints[..., :1])], axis=-1)
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if einsum is None:
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if len(homo.shape) == 2 and len(RT.shape) == 3:
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kpts2d = np.einsum('vab,kb->vka', RT, homo)
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elif len(homo.shape) == 2 and len(RT.shape) == 4:
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kpts2d = np.einsum('vkab,kb->vka', RT, homo)
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else:
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import ipdb; ipdb.set_trace()
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else:
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kpts2d = np.einsum(einsum, RT, homo)
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kpts2d[..., :2] /= kpts2d[..., 2:]
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return kpts2d
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def make_Cnk(n, k):
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import itertools
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res = {}
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for n_ in range(3, n+1):
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n_0 = [i for i in range(n_)]
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for k_ in range(2, k+1):
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res[(n_, k_)] = list(map(list, itertools.combinations(n_0, k_)))
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return res
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MAX_VIEWS = 30
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Cnk = make_Cnk(MAX_VIEWS, 3)
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def robust_triangulate_point(kpts2d, Pall, dist_max, min_v = 3):
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nV = kpts2d.shape[0]
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if len(kpts2d) < min_v:# 重建失败
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return [], None
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# min_v = max(2, nV//2)
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# 1. choose the combination of min_v
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index_ = Cnk[(len(kpts2d), min(min_v, len(kpts2d)))]
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# 2. proposals: store the reconstruction points of each proposal
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proposals = np.zeros((len(index_), 4))
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weight_self = np.zeros((nV, len(index_)))
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for i, index in enumerate(index_):
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weight_self[index, i] = 100.
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point = batch_triangulate(kpts2d[index, :], Pall[index], min_view=min_v)
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proposals[i] = point
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# 3. project the proposals to each view
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# and calculate the reprojection error
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# (nViews, nProposals, 4)
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kpts_repro = project_points(proposals, Pall)
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conf = (proposals[None, :, -1] > 0) * (kpts2d[..., -1] > 0)
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# err: (nViews, nProposals)
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err = np.linalg.norm(kpts_repro[..., :2] - kpts2d[..., :2], axis=-1) * conf
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valid = 1. - err/dist_max
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valid[valid<0] = 0
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# consider the weight of different view
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# TODO:naive weight:
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conf = kpts2d[..., -1]
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weight = conf
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# (valid > 0)*weight_self 一项用于强制要求使用到的两个视角都需要被用到
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# 增加一项使用的视角数的加成
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weight_sum = (weight * valid).sum(axis=0) + ((valid > 0)*weight_self).sum(axis=0) - min_v * 100
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if weight_sum.max() < 0:# 重建失败
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return [], None
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best = weight_sum.argmax()
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if (err[index_[best], best] > dist_max).any():
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return [], None
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# 对于选出来的proposal，寻找其大于0的其他视角
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point = proposals[best]
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best_add = np.where(valid[:, best])[0].tolist()
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index = list(index_[best])
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best_add.sort(key=lambda x:-weight[x])
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for add in best_add:
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if add in index:
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continue
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index.append(add)
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point = batch_triangulate(kpts2d[index, :], Pall[index], min_view=min_v)
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kpts_repro = project_points(point, Pall[index])
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err = np.linalg.norm(kpts_repro[..., :2] - kpts2d[index, ..., :2], axis=-1)
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if (err > dist_max).any():
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index.remove(add)
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break
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return index, point
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def remove_outview(kpts2d, out_view, debug):
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if len(out_view) == 0:
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return False
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outv = out_view[0]
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if debug:
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mywarn('[triangulate] remove outview: {} from {}'.format(outv, out_view))
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kpts2d[outv] = 0.
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return True
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def remove_outjoint(kpts2d, Pall, out_joint, dist_max, min_view=3, debug=False):
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if len(out_joint) == 0:
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return False
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if debug:
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mywarn('[triangulate] remove outjoint: {}'.format(out_joint))
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for nj in out_joint:
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valid = np.where(kpts2d[:, nj, -1] > 0)[0]
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if len(valid) < min_view:
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# if less than 3 visible view, set these unvisible
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kpts2d[:, nj, -1] = 0
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continue
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if len(valid) > MAX_VIEWS:
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# only select max points
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conf = -kpts2d[:, nj, -1]
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valid = conf.argsort()[:MAX_VIEWS]
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index_j, point = robust_triangulate_point(kpts2d[valid, nj:nj+1], Pall[valid], dist_max=dist_max, min_v=3)
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index_j = valid[index_j]
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# print('select {} for joint {}'.format(index_j, nj))
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set0 = np.zeros(kpts2d.shape[0])
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set0[index_j] = 1.
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kpts2d[:, nj, -1] *= set0
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return True
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def project_and_distance(kpts3d, RT, kpts2d):
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kpts_proj = project_points(kpts3d, RT)
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# 1. distance between input and projection
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conf = (kpts3d[None, :, -1] > 0) * (kpts2d[:, :, -1] > 0)
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dist = np.linalg.norm(kpts_proj[..., :2] - kpts2d[..., :2], axis=-1) * conf
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return dist, conf
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def iterative_triangulate(kpts2d, RT, previous=None,
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min_conf=0.1, min_view=3, min_joints=3, dist_max=0.05, dist_vel=0.05,
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thres_outlier_view=0.4, thres_outlier_joint=0.4, debug=False):
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kpts2d = kpts2d.copy()
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conf = kpts2d[..., -1]
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kpts2d[conf<min_conf] = 0.
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if debug:
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log('[triangulate] kpts2d: {}'.format(kpts2d.shape))
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# TODO: consider large motion
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if previous is not None:
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dist, conf = project_and_distance(previous, RT, kpts2d)
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nottrack = (dist > dist_vel) & conf
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if nottrack.sum() > 0:
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kpts2d[nottrack] = 0.
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if debug:
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log('[triangulate] Remove with track {}'.format(np.where(nottrack)))
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while True:
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# 0. triangulate and project
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kpts3d = batch_triangulate(kpts2d, RT, min_view=min_view)
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dist, conf = project_and_distance(kpts3d, RT, kpts2d)
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# 2. find the outlier
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vv, jj = np.where(dist > dist_max)
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if vv.shape[0] < 1:
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if debug:
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log('[triangulate] Not found outlier, break')
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break
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ratio_outlier_view = (dist>dist_max).sum(axis=1)/(1e-5 + conf.sum(axis=1))
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ratio_outlier_joint = (dist>dist_max).sum(axis=0)/(1e-5 + conf.sum(axis=0))
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# 3. find the totally wrong detections
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out_view = np.where(ratio_outlier_view > thres_outlier_view)[0]
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out_joint = np.where(ratio_outlier_joint > thres_outlier_joint)[0]
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if len(out_view) > 1:
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dist_view = dist.sum(axis=1)/(1e-5 + conf.sum(axis=1))
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out_view = out_view.tolist()
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out_view.sort(key=lambda x:-dist_view[x])
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if debug: mywarn('[triangulate] Remove outlier view: {}'.format(ratio_outlier_view))
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if remove_outview(kpts2d, out_view, debug): continue
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if remove_outjoint(kpts2d, RT, out_joint, dist_max, debug=debug): continue
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if debug:
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log('[triangulate] Directly remove {}, {}'.format(vv, jj))
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kpts2d[vv, jj, -1] = 0.
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if debug:
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log('[triangulate] finally {} valid points'.format((kpts3d[..., -1]>0).sum()))
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if (kpts3d[..., -1]>0).sum() < min_joints:
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kpts3d[..., -1] = 0.
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kpts2d[..., -1] = 0.
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return kpts3d, kpts2d
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return kpts3d, kpts2d
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class BaseTriangulator:
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def __init__(self, config, debug, keys) -> None:
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self.config = config
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self.debug = debug
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self.keys = keys
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def project_and_check(self, kpts3d, kpts2d, RT):
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kpts_proj = project_points(kpts3d, RT)
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conf = (kpts3d[None, :, -1] > 0) * (kpts2d[:, :, -1] > 0)
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dist = np.linalg.norm(kpts_proj[..., :2] - kpts2d[..., :2], axis=-1) * conf
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return conf, dist
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def triangulate_with_results(self, pid, data, results):
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new = {'id': pid}
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for key in self.keys:
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key3d = key.replace('2d', '3d')
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if len(results) == 0:
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kpts3d, kpts2d = iterative_triangulate(data[key + '_unproj'], data['RT'],
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debug=self.debug, **self.config[key])
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else:
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if len(results) == 1:
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previous = results[-1][key3d] # TODO: mean previous frame
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elif len(results) >= 2:
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# TODO: mean previous velocity
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previous0 = results[-2][key3d] # TODO: mean previous frame
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previous1 = results[-1][key3d] # TODO: mean previous frame
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vel = (previous1[:, :3] - previous0[:, :3])*((previous0[:, -1:]>0)&(previous0[:, -1:]>0))
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previous = previous1.copy()
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previous[:, :3] += vel
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kpts3d, kpts2d = iterative_triangulate(data[key + '_unproj'], data['RT'],
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debug=self.debug, previous=previous, **self.config[key])
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vel = np.linalg.norm(kpts3d[:, :3] - previous[:, :3], axis=-1)
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new[key] = np.concatenate([data[key+'_distort'][..., :-1], kpts2d[..., -1:]], axis=-1)
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new[key3d] = kpts3d
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return new
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class SimpleTriangulator(BaseTriangulator):
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def __init__(self, keys, debug, config,
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pid=0, disable_previous=False) -> None:
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super().__init__(config, debug, keys)
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self.results = []
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self.infos = []
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self.dim_name = ['_joints', '_views']
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self.pid = pid
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self.disable_previous = disable_previous
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def __call__(self, data, results=None):
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info = {}
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if results is None:
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results = self.results
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if self.disable_previous:
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results = []
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new = {'id': self.pid}
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for key in self.keys:
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if key not in data.keys(): continue
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key3d = key.replace('2d', '3d')
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if self.debug:
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log('[triangulate] {}'.format(key))
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if len(results) == 0:
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kpts3d, kpts2d = iterative_triangulate(data[key + '_unproj'], data['RT'],
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debug=self.debug, **self.config[key])
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else:
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if len(results) == 1:
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previous = results[-1][key3d] # TODO: mean previous frame
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elif len(results) >= 2:
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# TODO: mean previous velocity
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previous0 = results[-2][key3d] # TODO: mean previous frame
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previous1 = results[-1][key3d] # TODO: mean previous frame
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vel = (previous1[:, :3] - previous0[:, :3])*((previous0[:, -1:]>0)&(previous0[:, -1:]>0))
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previous = previous1.copy()
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previous[:, :3] += vel
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kpts3d, kpts2d = iterative_triangulate(data[key + '_unproj'], data['RT'],
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debug=self.debug, previous=previous, **self.config[key])
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vel = np.linalg.norm(kpts3d[:, :3] - previous[:, :3], axis=-1)
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new[key] = np.concatenate([data[key+'_distort'][..., :-1], kpts2d[..., -1:]], axis=-1)
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new[key.replace('2d', '3d')] = kpts3d
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if self.debug:
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conf, dist = self.project_and_check(kpts3d, kpts2d, data['RT'])
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for dim in [0, 1]:
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info_dim = {
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'valid': conf.sum(axis=dim),
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'dist': 10000*dist.sum(axis=dim)/(1e-5 + conf.sum(axis=dim)),
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}
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info[key+self.dim_name[dim]] = info_dim
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info[key+'_joints']['valid3d'] = kpts3d[:, -1] >0
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results.append(new)
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self.infos.append(info)
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return [new]
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def report(self):
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if not self.debug:
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return 0
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from .debug_utils import print_table
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for key in self.infos[0].keys():
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metrics = list(self.infos[0][key].keys())
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values = [np.mean(np.stack([info[key][metric] for info in self.infos]), axis=0) for metric in metrics]
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metrics = [key] + metrics
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values = [[i for i in range(values[0].shape[0])]] + values
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print_table(metrics, values)
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class SimpleTriangulatorMulti(SimpleTriangulator):
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def __init__(self, pids, **cfg) -> None:
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super().__init__(**cfg)
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self.results = {}
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def __call__(self, data, results=None):
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res_now = []
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for ipid, pid in enumerate(data['pid']):
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if pid not in self.results.keys():
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self.results[pid] = []
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data_ = {'RT': data['RT']}
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for key in self.keys:
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data_[key+'_distort'] = data[key+'_distort'][:, ipid]
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data_[key+'_unproj'] = data[key+'_unproj'][:, ipid]
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data_[key] = data[key][:, ipid]
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res = self.triangulate_with_results(pid, data_, self.results[pid])
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self.results[pid].append(res)
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res_now.append(res)
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return res_now
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def skew_op(x):
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skew_op = lambda x: np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]], [-x[1], x[0], 0]])
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res = np.zeros((3, 3), dtype=x.dtype)
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# 0, -z, y
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res[0, 1] = -x[2, 0]
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res[0, 2] = x[1, 0]
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# z, 0, -x
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res[1, 0] = x[2, 0]
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res[1, 2] = -x[0, 0]
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# -y, x, 0
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res[2, 0] = -x[1, 0]
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res[2, 1] = x[0, 0]
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return res
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def fundamental_op(K0, K1, R_0, T_0, R_1, T_1):
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invK0 = np.linalg.inv(K0)
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return invK0.T @ (R_0 @ R_1.T) @ K1.T @ skew_op(K1 @ R_1 @ R_0.T @ (T_0 - R_0 @ R_1.T @ T_1))
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def drawlines(img1,img2,lines,pts1,pts2):
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''' img1 - image on which we draw the epilines for the points in img2
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lines - corresponding epilines '''
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r,c = img1.shape[:2]
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for r,pt1,pt2 in zip(lines,pts1,pts2):
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pt1 = list(map(lambda x:int(x+0.5), pt1[:2].tolist()))
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pt2 = list(map(lambda x:int(x+0.5), pt2[:2].tolist()))
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if pt1[0] < 0 or pt1[1] < 0:
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continue
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color = tuple(np.random.randint(0,255,3).tolist())
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x0,y0 = map(int, [0, -r[2]/r[1] ])
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x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ])
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img1 = cv2.line(img1, (x0,y0), (x1,y1), color,1)
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img1 = cv2.circle(img1,tuple(pt1),5,color,-1)
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img2 = cv2.circle(img2,tuple(pt2),5,color,-1)
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return img1,img2
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def SimpleConstrain(dimGroups):
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constrain = np.ones((dimGroups[-1], dimGroups[-1]))
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for i in range(len(dimGroups)-1):
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start, end = dimGroups[i], dimGroups[i+1]
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constrain[start:end, start:end] = 0
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N = constrain.shape[0]
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constrain[range(N), range(N)] = 1
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return constrain
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def check_cluster(affinity, row, views, dimGroups, indices, p2dAssigned, visited):
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affinity_row = affinity[row].copy()
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# given affinity and row, select the combine of all possible set
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cluster = np.where((affinity[row]>0)&(p2dAssigned==-1)&(visited==0))[0].tolist()
|
||
cluster.sort(key=lambda x:-affinity[row, x])
|
||
views_ = views[cluster]
|
||
view_count = np.bincount(views[cluster])
|
||
indices_all = [indices]
|
||
for col in cluster:
|
||
v = views[col]
|
||
nOld = len(indices_all)
|
||
if indices[v] != -1: # already assigned, copy and make new
|
||
for i in range(nOld):
|
||
ind = indices_all[i].copy()
|
||
ind[v] = col
|
||
indices_all.append(ind)
|
||
else: # not assigned, assign
|
||
for i in range(nOld):
|
||
indices_all[i][v] = col
|
||
return indices_all
|
||
|
||
def views_from_dimGroups(dimGroups):
|
||
views = np.zeros(dimGroups[-1], dtype=np.int)
|
||
for nv in range(len(dimGroups) - 1):
|
||
views[dimGroups[nv]:dimGroups[nv+1]] = nv
|
||
return views
|
||
|
||
class SimpleMatchAndTriangulator(SimpleTriangulator):
|
||
def __init__(self, num_joints, min_views, min_joints, cfg_svt, cfg_track, **cfg) -> None:
|
||
super().__init__(**cfg)
|
||
self.nJoints = num_joints
|
||
self.cfg_svt = cfg_svt
|
||
self.cfg_track = cfg_track
|
||
self.min_views = min_views
|
||
self.min_joints = min_joints
|
||
self.time = -1
|
||
self.max_id = 0
|
||
self.tracks = {}
|
||
self.loglevel_dict = {
|
||
'info': 0,
|
||
'warn': 1,
|
||
'error': 2,
|
||
}
|
||
self.loglevel = self.loglevel_dict['info'] # ['info', 'warn', 'error']
|
||
self.debug = False
|
||
self.data = None
|
||
self.people = None
|
||
|
||
def log(self, text):
|
||
if self.loglevel > 0:
|
||
return 0
|
||
log(text)
|
||
|
||
def warn(self, text):
|
||
if self.loglevel > 1:
|
||
return 0
|
||
mywarn(text)
|
||
|
||
|
||
@staticmethod
|
||
def distance_by_epipolar(pts0, pts1, K0, K1, R0, T0, R1, T1):
|
||
F = fundamental_op(K0, K1, R0, T0, R1, T1)
|
||
# Find epilines corresponding to points in left image (first image) and
|
||
# drawing its lines on right image
|
||
lines0 = cv2.computeCorrespondEpilines(pts0[..., :2].reshape (-1,1,2), 2, F)
|
||
# Find epilines corresponding to points in right image (second image) and
|
||
# drawing its lines on left image
|
||
lines1 = cv2.computeCorrespondEpilines(pts1[..., :2].reshape(-1,1,2), 1, F)
|
||
if False:
|
||
H, W = 1080, 1920
|
||
img0 = np.zeros((H, W, 3), dtype=np.uint8) +255
|
||
img4, img3 = drawlines(img0.copy(), img0.copy(), lines0.reshape(-1, 3), pts1.reshape(-1, 3), pts0.reshape(-1,3))
|
||
img5,img6 = drawlines(img0.copy(), img0.copy(), lines1.reshape(-1, 3), pts0.reshape(-1,3), pts1.reshape(-1,3))
|
||
import matplotlib.pyplot as plt
|
||
plt.subplot(121)
|
||
plt.imshow(img5)
|
||
plt.subplot(122)
|
||
plt.imshow(img4)
|
||
plt.show()
|
||
lines0 = lines0.reshape(pts0.shape)
|
||
lines1 = lines1.reshape(pts1.shape)
|
||
# dist: (D_v0, D_v1, nJoints)
|
||
# TODO: / sqrt(A^2 + B^2)
|
||
dist01 = np.abs(np.sum(lines0[:, None, :, :2] * pts1[None, :, :, :2], axis=-1) + lines0[:, None, :, 2])
|
||
conf = pts0[:, None, :, 2] * pts1[None, :, :, 2]
|
||
dist10 = np.abs(np.sum(lines1[:, None, :, :2] * pts0[None, :, :, :2], axis=-1) + lines1[:, None, :, 2])
|
||
|
||
dist = np.sum(dist01 * conf + dist10.transpose(1, 0, 2) * conf, axis=-1)/(conf.sum(axis=-1) + 1e-5)/2
|
||
return dist
|
||
|
||
def _simple_associate2d_triangulate(self, data, affinity, dimGroups, prev_id):
|
||
# sum1 = affinity.sum(axis=1)
|
||
# 注意：这里的排序应该是对每个视角，挑选最大的一个
|
||
sum1 = np.zeros((affinity.shape[0]))
|
||
for i in range(len(dimGroups)-1):
|
||
start, end = dimGroups[i], dimGroups[i+1]
|
||
if end == start:continue
|
||
sum1 += affinity[:, start:end].max(axis=-1)
|
||
n2d = affinity.shape[0]
|
||
nViews = len(dimGroups) - 1
|
||
idx_zero = np.zeros(nViews, dtype=np.int) - 1
|
||
views = views_from_dimGroups(dimGroups)
|
||
# the assigned results of each person
|
||
p2dAssigned = np.zeros(n2d, dtype=np.int) - 1
|
||
visited = np.zeros(n2d, dtype=np.int)
|
||
sortidx = np.argsort(-sum1)
|
||
pid = 0
|
||
k3dresults = []
|
||
for idx in sortidx:
|
||
if p2dAssigned[idx] != -1:
|
||
continue
|
||
if prev_id[idx] != -1:
|
||
results = [self.people[prev_id[idx]]]
|
||
else:
|
||
results = []
|
||
proposals = check_cluster(affinity, row=idx, views=views,
|
||
dimGroups=dimGroups, indices=idx_zero.copy(), p2dAssigned=p2dAssigned, visited=visited)
|
||
for indices in proposals:
|
||
if (indices > -1).sum() < self.min_views - (len(results)):
|
||
continue
|
||
# set keypoints2d
|
||
info = {'RT': data['RT']}
|
||
for name in ['keypoints2d', 'keypoints2d_unproj', 'keypoints2d_distort']:
|
||
info[name] = np.zeros((nViews, self.nJoints, 3), dtype=np.float32)
|
||
for nv in range(nViews):
|
||
if indices[nv] == -1: continue
|
||
for name in ['keypoints2d', 'keypoints2d_unproj', 'keypoints2d_distort']:
|
||
info[name][nv] = data[name][nv][indices[nv]-dimGroups[nv]]
|
||
|
||
res = super().__call__(info, results=results)[0]
|
||
|
||
k2d = res['keypoints2d']
|
||
valid_view = (k2d[..., 2] > 0).sum(axis=-1) > self.min_joints
|
||
# if valid_view.sum() < self.min_views - len(results): # 这里如果是有前一帧的话，len(results)会是2；不知道之前为啥有这个条件使用
|
||
if valid_view.sum() < self.min_views:
|
||
self.log('[associate] Skip proposal {}->{} with not enough valid view {}'.format(idx, indices, (k2d[..., 2] > 0).sum(axis=-1)))
|
||
continue
|
||
valid_joint = res['keypoints3d'][:, -1] > 0.1
|
||
if valid_joint.sum() < self.min_joints:
|
||
self.log('[associate] Skip proposal {}->{} as not enough joints'.format(idx, indices))
|
||
continue
|
||
indices[~valid_view] = -1
|
||
if (indices < 0).all():
|
||
import ipdb; ipdb.set_trace()
|
||
self.log('[associate] Add indices {}, valid {}'.format(indices, (k2d[..., 2] > 0).sum(axis=-1)))
|
||
res['id'] = pid
|
||
res['indices'] = indices
|
||
res['valid_view'] = valid_view
|
||
res['valid_joints'] = res['keypoints3d'][:, -1] > 0.1
|
||
k3dresults.append(res)
|
||
for nv in range(nViews):
|
||
if valid_view[nv] and indices[nv] != -1:
|
||
p2dAssigned[indices[nv]] = pid
|
||
visited[indices[nv]] = 1
|
||
pid += 1
|
||
break
|
||
visited[idx] = 1
|
||
self.log('[associate] {} points not visited, {} not assigned'.format(visited.shape[0] - visited.sum(), (p2dAssigned==-1).sum()))
|
||
k3dresults.sort(key=lambda x: -x['keypoints2d'][..., -1].sum())
|
||
return k3dresults
|
||
|
||
@staticmethod
|
||
def calculate_affinity_MxM(dims, dimGroups, data, key, DIST_MAX):
|
||
M = dimGroups[-1]
|
||
distance = np.zeros((M, M), dtype=np.float32)
|
||
nViews = len(dims)
|
||
for v0 in range(nViews-1):
|
||
for v1 in range(1, nViews):
|
||
# calculate distance between (v0, v1)
|
||
if v0 >= v1:
|
||
continue
|
||
if dims[v0] == 0 or dims[v1] == 0:
|
||
continue
|
||
if True:
|
||
pts0 = data[key][v0] # (nPerson0, nKeypoints, 3)
|
||
pts1 = data[key][v1] # (nPerson1, nKeypoints, 3)
|
||
K0, K1 = data['K'][v0], data['K'][v1] # K0, K1: (3, 3)
|
||
R0, T0 = data['Rc'][v0], data['Tc'][v0]
|
||
R1, T1 = data['Rc'][v1], data['Tc'][v1]
|
||
dist = SimpleMatchAndTriangulator.distance_by_epipolar(pts0, pts1, K0, K1, R0, T0, R1, T1)
|
||
dist /= (K0[0, 0] + K1[0, 0])/2
|
||
else:
|
||
dist = self.distance_by_ray(pts0, pts1, R0, T0, R1, T1)
|
||
distance[dimGroups[v0]:dimGroups[v0+1], dimGroups[v1]:dimGroups[v1+1]] = dist
|
||
distance[dimGroups[v1]:dimGroups[v1+1], dimGroups[v0]:dimGroups[v0+1]] = dist.T
|
||
|
||
for nv in range(nViews):
|
||
distance[dimGroups[nv]:dimGroups[nv+1], dimGroups[nv]:dimGroups[nv+1]] = DIST_MAX
|
||
distance -= np.eye(M) * DIST_MAX
|
||
aff = (DIST_MAX - distance)/DIST_MAX
|
||
aff = np.clip(aff, 0, 1)
|
||
return aff
|
||
|
||
def _calculate_affinity_MxM(self, dims, dimGroups, data, key):
|
||
DIST_MAX = self.cfg_track.track_dist_max
|
||
return self.calculate_affinity_MxM(dims, dimGroups, data, key, DIST_MAX=DIST_MAX)
|
||
|
||
def _calculate_affinity_MxN(self, dims, dimGroups, data, key, results):
|
||
M = dimGroups[-1]
|
||
N = len(results)
|
||
distance = np.zeros((M, N), dtype=np.float32)
|
||
nViews = len(dims)
|
||
k3d = np.stack([r['keypoints3d'] for r in results])
|
||
kpts_proj = project_points(k3d, data['KRT'], einsum='vab,pkb->vpka')
|
||
depth = kpts_proj[..., -1]
|
||
kpts_proj[depth<0] = -10000
|
||
for v in range(nViews):
|
||
if dims[v] == 0:
|
||
continue
|
||
focal = data['K'][v][0, 0]
|
||
pts2d = data[key][v][:, None]
|
||
pts_repro = kpts_proj[v][None]
|
||
conf = np.sqrt(pts2d[..., -1]*k3d[None, ..., -1])
|
||
diff = np.linalg.norm(pts2d[..., :2] - pts_repro[..., :2], axis=-1)
|
||
diff = np.sum(diff*conf, axis=-1)/(1e-5 + np.sum(conf, axis=-1))
|
||
dist = diff / focal
|
||
distance[dimGroups[v]:dimGroups[v+1], :] = dist
|
||
DIST_MAX = self.cfg_track.track_repro_max
|
||
aff = (DIST_MAX - distance)/DIST_MAX
|
||
aff = np.clip(aff, 0, 1)
|
||
return aff
|
||
|
||
def _svt_optimize_affinity(self, affinity, dimGroups):
|
||
# match SVT
|
||
import pymatchlr
|
||
observe = np.ones_like(affinity)
|
||
aff_svt = pymatchlr.matchSVT(affinity, dimGroups, SimpleConstrain(dimGroups), observe, self.cfg_svt)
|
||
aff_svt[aff_svt<self.cfg_svt.aff_min] = 0.
|
||
if False:
|
||
import matplotlib.pyplot as plt
|
||
M = affinity.shape[0]
|
||
plt.subplot(121)
|
||
plt.imshow(affinity)
|
||
plt.hlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w')
|
||
plt.vlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w')
|
||
plt.subplot(122)
|
||
sum_row = aff_svt.sum(axis=1, keepdims=True)/(len(dimGroups) - 1)
|
||
plt.imshow(np.hstack([aff_svt, sum_row]))
|
||
plt.hlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w')
|
||
plt.vlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w')
|
||
plt.ioff()
|
||
plt.show()
|
||
return aff_svt
|
||
|
||
def _track_add(self, res):
|
||
pid = res['id']
|
||
if pid == -1:
|
||
pid = self.max_id
|
||
res['id'] = pid
|
||
self.max_id += 1
|
||
self.log('[{:06d}] Create track {} <- {}'.format(self.time, pid, res['indices']))
|
||
if False:
|
||
crops = []
|
||
data = self.data
|
||
kpts = np.vstack(data['keypoints2d'])
|
||
for nv in range(len(data['imgname'])):
|
||
img = cv2.imread(data['imgname'][nv])
|
||
if res['indices'][nv] == -1: continue
|
||
_kpts = kpts[res['indices'][nv]]
|
||
bbox = bbox_from_keypoints(_kpts)
|
||
plot_keypoints_auto(img, _kpts, pid)
|
||
crop = crop_image(img, bbox, crop_square=True)
|
||
crops.append(crop)
|
||
debug = merge(crops)
|
||
cv2.imwrite('debug/{:06d}.jpg'.format(pid), debug)
|
||
else:
|
||
self.max_id = max(self.max_id, pid+1)
|
||
self.log('[{:06d}] Initialize track {}, valid joints={}'.format(self.time, pid, (res['keypoints3d'][:, -1]>0.01).sum()))
|
||
self.tracks[pid] = {
|
||
'start_time': self.time,
|
||
'end_time': self.time+1,
|
||
'missing_frame': [],
|
||
'infos': [res]
|
||
}
|
||
|
||
def _track_update(self, res, pid):
|
||
res['id'] = pid
|
||
info = self.tracks[pid]
|
||
self.log('[{:06d}] Update track {} [{}->{}], valid joints={}'.format(self.time, pid, info['start_time'], info['end_time'], (res['keypoints3d'][:, -1]>0.1).sum()))
|
||
self.tracks[pid]['end_time'] = self.time + 1
|
||
self.tracks[pid]['infos'].append(res)
|
||
|
||
def _track_merge(self, res, pid):
|
||
res['id'] = -1
|
||
# TODO: merge
|
||
|
||
def _track_and_update(self, data, results):
|
||
cfg = self.cfg_track
|
||
self.time += 1
|
||
if self.time == 0:
|
||
# initialize the tracks
|
||
for res in results:
|
||
self._track_add(res)
|
||
return results
|
||
# filter the missing frames
|
||
for pid in list(self.tracks.keys()):
|
||
if self.time - self.tracks[pid]['end_time'] > cfg.max_missing_frame:
|
||
self.warn('[{:06d}] Remove track {}'.format(self.time, pid))
|
||
self.tracks.pop(pid)
|
||
# track the results with greedy matching
|
||
for idx_match, res in enumerate(results):
|
||
res['id'] = -1
|
||
# compute the distance
|
||
k3d = res['keypoints3d'][None]
|
||
pids_free = [pid for pid in self.tracks.keys() if self.tracks[pid]['end_time'] != self.time+1]
|
||
pids_used = [pid for pid in self.tracks.keys() if self.tracks[pid]['end_time'] == self.time+1]
|
||
def check_dist(k3d_check):
|
||
dist = np.linalg.norm(k3d[..., :3] - k3d_check[..., :3], axis=-1)
|
||
conf = np.sqrt(k3d[..., 3] * k3d_check[..., 3])
|
||
dist_mean = ((conf>0.1).sum(axis=-1) < self.min_joints)*cfg.track_dist_max + np.sum(dist * conf, axis=-1)/(1e-5 + np.sum(conf, axis=-1))
|
||
argmin = dist_mean.argmin()
|
||
dist_min = dist_mean[argmin]
|
||
return dist_mean, argmin, dist_min
|
||
# check free
|
||
NOT_VISITED = -2
|
||
NOT_FOUND = -1
|
||
flag_tracked, flag_current = NOT_VISITED, NOT_VISITED
|
||
if len(pids_free) > 0:
|
||
k3d_check = np.stack([self.tracks[pid]['infos'][-1]['keypoints3d'] for pid in pids_free])
|
||
dist_track, best, best_dist_track = check_dist(k3d_check)
|
||
if best_dist_track < cfg.track_dist_max:
|
||
flag_tracked = best
|
||
else:
|
||
flag_tracked = NOT_FOUND
|
||
# check used
|
||
if len(pids_used) > 0:
|
||
k3d_check = np.stack([self.tracks[pid]['infos'][-1]['keypoints3d'] for pid in pids_used])
|
||
dist_cur, best, best_dist_curr = check_dist(k3d_check)
|
||
if best_dist_curr < cfg.track_dist_max:
|
||
flag_current = best
|
||
else:
|
||
flag_current = NOT_FOUND
|
||
if flag_tracked >= 0 and (flag_current == NOT_VISITED or flag_current == NOT_FOUND):
|
||
self._track_update(res, pids_free[flag_tracked])
|
||
elif (flag_tracked == NOT_FOUND or flag_tracked==NOT_VISITED) and flag_current >= 0:
|
||
# 没有跟踪到，但是有当前帧的3D的，合并
|
||
self.log('[{:06d}] Merge track {} to {}'.format(self.time, idx_match, pids_used[flag_current]))
|
||
self._track_merge(res, pids_used[flag_current])
|
||
elif flag_tracked == NOT_FOUND and flag_current == NOT_FOUND:
|
||
# create a new track
|
||
self._track_add(res)
|
||
else:
|
||
# 丢弃
|
||
self.log('[{:06d}] Remove track {}. No close points'.format(self.time, idx_match))
|
||
|
||
for pid in list(self.tracks.keys()):
|
||
if self.tracks[pid]['end_time'] != self.time + 1:
|
||
self.warn('[{:06d}] Tracking {} missing'.format(self.time, pid))
|
||
results = [r for r in results if r['id']!=-1]
|
||
return results
|
||
|
||
def __call__(self, data):
|
||
# match the data
|
||
self.data = data
|
||
key = 'keypoints2d'
|
||
dims = [d.shape[0] for d in data[key]]
|
||
dimGroups = np.cumsum([0] + dims)
|
||
# 1. compute affinity
|
||
affinity = self._calculate_affinity_MxM(dims, dimGroups, data, key)
|
||
N2D = affinity.shape[0]
|
||
if self.people is not None and len(self.people) > 0:
|
||
# add 3d affinity
|
||
_affinity = affinity
|
||
affinity_3d = self._calculate_affinity_MxN(dims, dimGroups, data, key, self.people)
|
||
affinity = np.concatenate([affinity, affinity_3d], axis=1)
|
||
eye3d = np.eye(affinity_3d.shape[1])
|
||
affinity = np.concatenate([affinity, np.hstack((affinity_3d.T, eye3d))], axis=0)
|
||
dimGroups = dimGroups.tolist()
|
||
dimGroups.append(dimGroups[-1]+affinity_3d.shape[1])
|
||
affinity = self._svt_optimize_affinity(affinity, dimGroups)
|
||
# affinity = self._svt_optimize_affinity(_affinity, dimGroups[:-1])
|
||
# recover
|
||
affinity_3d = np.hstack([np.ones((N2D, 1))*0.5, affinity[:N2D, N2D:]])
|
||
prev_id = affinity_3d.argmax(axis=-1) - 1
|
||
affinity = affinity[:N2D, :N2D]
|
||
dimGroups = np.array(dimGroups[:-1])
|
||
else:
|
||
affinity = self._svt_optimize_affinity(affinity, dimGroups)
|
||
prev_id = np.zeros(N2D) - 1
|
||
# 2. associate and triangulate
|
||
results = self._simple_associate2d_triangulate(data, affinity, dimGroups, prev_id)
|
||
# 3. track, filter and return
|
||
results = self._track_and_update(data, results)
|
||
results.sort(key=lambda x:x['id'])
|
||
self.people = results
|
||
return results
|
||
|
||
def simple_match(data):
|
||
key = 'keypoints2d'
|
||
dims = [d.shape[0] for d in data[key]]
|
||
dimGroups = np.cumsum([0] + dims)
|
||
affinity = SimpleMatchAndTriangulator.calculate_affinity_MxM(dims, dimGroups, data, key, DIST_MAX=0.1)
|
||
import pymatchlr
|
||
observe = np.ones_like(affinity)
|
||
cfg_svt = {
|
||
'debug': 1,
|
||
'maxIter': 10,
|
||
'w_sparse': 0.1,
|
||
'w_rank': 50,
|
||
'tol': 0.0001,
|
||
'aff_min': 0.3,
|
||
}
|
||
affinity = pymatchlr.matchSVT(affinity, dimGroups, SimpleConstrain(dimGroups), observe, cfg_svt)
|
||
return affinity, dimGroups |