diff --git a/easymocap/mytools/camera_utils.py b/easymocap/mytools/camera_utils.py index 666b5cf..4b49e55 100644 --- a/easymocap/mytools/camera_utils.py +++ b/easymocap/mytools/camera_utils.py @@ -173,6 +173,7 @@ def write_camera(camera, path): if 'H' in val.keys() and 'W' in val.keys(): intri.write('H_{}'.format(key), val['H'], dt='int') intri.write('W_{}'.format(key), val['W'], dt='int') + assert val['R'].shape == (3, 3), f"{val['R'].shape} must == (3, 3)" if 'Rvec' not in val.keys(): val['Rvec'] = cv2.Rodrigues(val['R'])[0] extri.write('R_{}'.format(key), val['Rvec']) diff --git a/easymocap/mytools/triangulator.py b/easymocap/mytools/triangulator.py index a675e03..b319109 100644 --- a/easymocap/mytools/triangulator.py +++ b/easymocap/mytools/triangulator.py @@ -11,7 +11,7 @@ def batch_triangulate(keypoints_, Pall, min_view=2): Args: keypoints_ (nViews, nJoints, 3): 2D detections - Pall (nViews, 3, 4): projection matrix of each view + Pall (nViews, 3, 4) | (nViews, nJoints, 3, 4): projection matrix of each view min_view (int, optional): min view for visible points. Defaults to 2. Returns: @@ -25,9 +25,14 @@ def batch_triangulate(keypoints_, Pall, min_view=2): keypoints = keypoints_[:, valid_joint] conf3d = keypoints[:, :, -1].sum(axis=0)/v[valid_joint] # P2: P矩阵的最后一行：(1, nViews, 1, 4) - P0 = Pall[None, :, 0, :] - P1 = Pall[None, :, 1, :] - P2 = Pall[None, :, 2, :] + if len(Pall.shape) == 3: + P0 = Pall[None, :, 0, :] + P1 = Pall[None, :, 1, :] + P2 = Pall[None, :, 2, :] + else: + P0 = Pall[:, :, 0, :].swapaxes(0, 1) + P1 = Pall[:, :, 1, :].swapaxes(0, 1) + P2 = Pall[:, :, 2, :].swapaxes(0, 1) # uP2: x坐标乘上P2: (nJoints, nViews, 1, 4) uP2 = keypoints[:, :, 0].T[:, :, None] * P2 vP2 = keypoints[:, :, 1].T[:, :, None] * P2 @@ -44,9 +49,17 @@ def batch_triangulate(keypoints_, Pall, min_view=2): result[valid_joint, 3] = conf3d #* (conf[..., 0].sum(axis=-1)>min_view) return result -def project_points(keypoints, RT, einsum='vab,kb->vka'): +def project_points(keypoints, RT, einsum=None): homo = np.concatenate([keypoints[..., :3], np.ones_like(keypoints[..., :1])], axis=-1) - kpts2d = np.einsum(einsum, RT, homo) + if einsum is None: + if len(homo.shape) == 2 and len(RT.shape) == 3: + kpts2d = np.einsum('vab,kb->vka', RT, homo) + elif len(homo.shape) == 2 and len(RT.shape) == 4: + kpts2d = np.einsum('vkab,kb->vka', RT, homo) + else: + import ipdb; ipdb.set_trace() + else: + kpts2d = np.einsum(einsum, RT, homo) kpts2d[..., :2] /= kpts2d[..., 2:] return kpts2d @@ -240,17 +253,20 @@ class BaseTriangulator: class SimpleTriangulator(BaseTriangulator): def __init__(self, keys, debug, config, - pid=0) -> None: + pid=0, disable_previous=False) -> None: super().__init__(config, debug, keys) self.results = [] self.infos = [] self.dim_name = ['_joints', '_views'] self.pid = pid + self.disable_previous = disable_previous def __call__(self, data, results=None): info = {} if results is None: results = self.results + if self.disable_previous: + results = [] new = {'id': self.pid} for key in self.keys: if key not in data.keys(): continue @@ -385,7 +401,7 @@ def check_cluster(affinity, row, views, dimGroups, indices, p2dAssigned, visited return indices_all def views_from_dimGroups(dimGroups): - views = np.zeros(dimGroups[-1], dtype=int) + views = np.zeros(dimGroups[-1], dtype=np.int) for nv in range(len(dimGroups) - 1): views[dimGroups[nv]:dimGroups[nv+1]] = nv return views @@ -445,6 +461,7 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): 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]) @@ -462,11 +479,11 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): sum1 += affinity[:, start:end].max(axis=-1) n2d = affinity.shape[0] nViews = len(dimGroups) - 1 - idx_zero = np.zeros(nViews, dtype=int) - 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=int) - 1 - visited = np.zeros(n2d, dtype=int) + p2dAssigned = np.zeros(n2d, dtype=np.int) - 1 + visited = np.zeros(n2d, dtype=np.int) sortidx = np.argsort(-sum1) pid = 0 k3dresults = [] @@ -523,7 +540,8 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): k3dresults.sort(key=lambda x: -x['keypoints2d'][..., -1].sum()) return k3dresults - def _calculate_affinity_MxM(self, dims, dimGroups, data, key): + @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) @@ -535,18 +553,18 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): if dims[v0] == 0 or dims[v1] == 0: continue if True: - pts0 = data[key][v0] - pts1 = data[key][v1] - K0, K1 = data['K'][v0], data['K'][v1] + 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 = self.distance_by_epipolar(pts0, pts1, K0, K1, R0, T0, R1, T1) + 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 - DIST_MAX = self.cfg_track.track_dist_max + for nv in range(nViews): distance[dimGroups[nv]:dimGroups[nv+1], dimGroups[nv]:dimGroups[nv+1]] = DIST_MAX distance -= np.eye(M) * DIST_MAX @@ -554,6 +572,10 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): 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) @@ -599,7 +621,6 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): plt.vlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w') plt.ioff() plt.show() - import ipdb;ipdb.set_trace() return aff_svt def _track_add(self, res): @@ -743,4 +764,22 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): results = self._track_and_update(data, results) results.sort(key=lambda x:x['id']) self.people = results - return results \ No newline at end of file + 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 \ No newline at end of file