Delete old py code.
This commit is contained in:
Daniel
@ -188,111 +188,6 @@ def load_labels(dataset: dict):
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# ==================================================================================================
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def add_extra_joints(poses3D, poses2D, joint_names_3d):
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# Update "head" joint as average of "ear" joints
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idx_h = joint_names_3d.index("head")
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idx_el = joint_names_3d.index("ear_left")
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idx_er = joint_names_3d.index("ear_right")
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for i in range(len(poses3D)):
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if poses3D[i, idx_h, 3] == 0:
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ear_left = poses3D[i, idx_el]
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ear_right = poses3D[i, idx_er]
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if ear_left[3] > 0.1 and ear_right[3] > 0.1:
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head = (ear_left + ear_right) / 2
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head[3] = min(ear_left[3], ear_right[3])
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poses3D[i, idx_h] = head
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for j in range(len(poses2D)):
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ear_left = poses2D[j][i, idx_el]
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ear_right = poses2D[j][i, idx_er]
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if ear_left[2] > 0.1 and ear_right[2] > 0.1:
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head = (ear_left + ear_right) / 2
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head[2] = min(ear_left[2], ear_right[2])
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poses2D[j][i, idx_h] = head
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return poses3D, poses2D
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# ==================================================================================================
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def add_missing_joints(poses3D, joint_names_3d):
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"""Replace missing joints with their nearest adjacent joints"""
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adjacents = {
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"hip_right": ["hip_middle", "hip_left"],
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"hip_left": ["hip_middle", "hip_right"],
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"knee_right": ["hip_right", "knee_left"],
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"knee_left": ["hip_left", "knee_right"],
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"ankle_right": ["knee_right", "ankle_left"],
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"ankle_left": ["knee_left", "ankle_right"],
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"shoulder_right": ["shoulder_middle", "shoulder_left"],
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"shoulder_left": ["shoulder_middle", "shoulder_right"],
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"elbow_right": ["shoulder_right", "hip_right"],
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"elbow_left": ["shoulder_left", "hip_left"],
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"wrist_right": ["elbow_right"],
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"wrist_left": ["elbow_left"],
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"nose": ["shoulder_middle", "shoulder_right", "shoulder_left"],
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"head": ["shoulder_middle", "shoulder_right", "shoulder_left"],
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"foot_*_left_*": ["ankle_left"],
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"foot_*_right_*": ["ankle_right"],
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"face_*": ["nose"],
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"hand_*_left_*": ["wrist_left"],
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"hand_*_right_*": ["wrist_right"],
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}
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for i in range(len(poses3D)):
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valid_joints = np.where(poses3D[i, :, 3] > 0.1)[0]
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if len(valid_joints) == 0:
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continue
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body_center = np.mean(poses3D[i, valid_joints, :3], axis=0)
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for j in range(len(joint_names_3d)):
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adname = ""
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if joint_names_3d[j][0:5] == "foot_" and "_left" in joint_names_3d[j]:
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adname = "foot_*_left_*"
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elif joint_names_3d[j][0:5] == "foot_" and "_right" in joint_names_3d[j]:
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adname = "foot_*_right_*"
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elif joint_names_3d[j][0:5] == "face_":
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adname = "face_*"
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elif joint_names_3d[j][0:5] == "hand_" and "_left" in joint_names_3d[j]:
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adname = "hand_*_left_*"
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elif joint_names_3d[j][0:5] == "hand_" and "_right" in joint_names_3d[j]:
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adname = "hand_*_right_*"
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elif joint_names_3d[j] in adjacents:
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adname = joint_names_3d[j]
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if adname == "":
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continue
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if poses3D[i, j, 3] == 0:
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if joint_names_3d[j] in adjacents or joint_names_3d[j][0:5] in [
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"foot_",
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"face_",
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"hand_",
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]:
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adjacent_joints = [
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poses3D[i, joint_names_3d.index(a), :]
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for a in adjacents[adname]
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]
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adjacent_joints = [a[0:3] for a in adjacent_joints if a[3] > 0.1]
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if len(adjacent_joints) > 0:
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poses3D[i, j, :3] = np.mean(adjacent_joints, axis=0)
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else:
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poses3D[i, j, :3] = body_center
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else:
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poses3D[i, j, :3] = body_center
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poses3D[i, j, 3] = 0.1
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return poses3D
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# ==================================================================================================
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def main():
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global joint_names_3d, eval_joints
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@ -228,124 +228,6 @@ def load_image(path: str):
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# ==================================================================================================
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def filter_poses(poses3D, poses2D, roomparams, joint_names, drop_few_limbs=True):
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drop = []
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for i, pose in enumerate(poses3D):
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pose = np.array(pose)
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valid_joints = [j for j in pose if j[-1] > 0.1]
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# Drop persons with too few joints
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if np.sum(pose[..., -1] > 0.1) < 5:
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drop.append(i)
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continue
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# Drop too large or too small persons
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mins = np.min(valid_joints, axis=0)
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maxs = np.max(valid_joints, axis=0)
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diff = maxs - mins
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if any(((d > 2.3) for d in diff)):
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drop.append(i)
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continue
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if all(((d < 0.3) for d in diff)):
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drop.append(i)
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continue
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if (
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(diff[0] < 0.2 and diff[1] < 0.2)
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or (diff[1] < 0.2 and diff[2] < 0.2)
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or (diff[2] < 0.2 and diff[0] < 0.2)
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):
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drop.append(i)
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continue
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# Drop persons outside room
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mean = np.mean(valid_joints, axis=0)
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mins = np.min(valid_joints, axis=0)
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maxs = np.max(valid_joints, axis=0)
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rsize = [r / 2 for r in roomparams["room_size"]]
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rcent = roomparams["room_center"]
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if any(
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(
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# Center of mass outside room
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mean[j] > rsize[j] + rcent[j] or mean[j] < -rsize[j] + rcent[j]
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for j in range(3)
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)
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) or any(
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(
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# One limb more than 10cm outside room
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maxs[j] > rsize[j] + rcent[j] + 0.1
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or mins[j] < -rsize[j] + rcent[j] - 0.1
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for j in range(3)
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)
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):
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drop.append(i)
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continue
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if drop_few_limbs:
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# Drop persons with less than 3 limbs
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found_limbs = 0
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for limb in main_limbs:
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start_idx = joint_names.index(limb[0])
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end_idx = joint_names.index(limb[1])
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if pose[start_idx, -1] > 0.1 and pose[end_idx, -1] > 0.1:
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found_limbs += 1
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if found_limbs < 3:
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drop.append(i)
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continue
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# Drop persons with too small or high average limb length
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total_length = 0
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total_limbs = 0
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for limb in main_limbs:
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start_idx = joint_names.index(limb[0])
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end_idx = joint_names.index(limb[1])
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if pose[start_idx, -1] < 0.1 or pose[end_idx, -1] < 0.1:
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continue
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limb_length = np.linalg.norm(pose[end_idx, :3] - pose[start_idx, :3])
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total_length += limb_length
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total_limbs += 1
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if total_limbs == 0:
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drop.append(i)
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continue
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average_length = total_length / total_limbs
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if average_length < 0.1:
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drop.append(i)
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continue
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if total_limbs > 4 and average_length > 0.5:
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drop.append(i)
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continue
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new_poses3D = []
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new_poses2D = [[] for _ in range(len(poses2D))]
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for i in range(len(poses3D)):
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if len(poses3D[i]) != len(joint_names):
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# Sometimes some joints of a poor detection are missing
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continue
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if i not in drop:
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new_poses3D.append(poses3D[i])
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for j in range(len(poses2D)):
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new_poses2D[j].append(poses2D[j][i])
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else:
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new_pose = np.array(poses3D[i])
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new_pose[..., -1] = 0.001
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new_poses3D.append(new_pose)
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for j in range(len(poses2D)):
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new_pose = np.array(poses2D[j][i])
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new_pose[..., -1] = 0.001
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new_poses2D[j].append(new_pose)
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new_poses3D = np.array(new_poses3D)
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new_poses2D = np.array(new_poses2D)
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if new_poses3D.size == 0:
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new_poses3D = np.zeros([1, len(joint_names), 4])
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new_poses2D = np.zeros([len(poses2D), 1, len(joint_names), 3])
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return new_poses3D, new_poses2D
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# ==================================================================================================
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def update_keypoints(poses_2d: list, joint_names: List[str]) -> list:
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new_views = []
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for view in poses_2d:
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@ -1,456 +0,0 @@
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import copy
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import math
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import cv2
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import numpy as np
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from skelda import utils_pose
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# ==================================================================================================
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core_joints = [
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"shoulder_left",
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"shoulder_right",
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"hip_left",
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"hip_right",
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"elbow_left",
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"elbow_right",
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"knee_left",
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"knee_right",
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"wrist_left",
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"wrist_right",
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"ankle_left",
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"ankle_right",
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]
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# ==================================================================================================
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def undistort_points(points: np.ndarray, caminfo: dict):
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"""Undistorts 2D pixel coordinates"""
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K = np.asarray(caminfo["K"], dtype=np.float32)
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DC = np.asarray(caminfo["DC"][0:5], dtype=np.float32)
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# Undistort camera matrix
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w = caminfo["width"]
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h = caminfo["height"]
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newK, _ = cv2.getOptimalNewCameraMatrix(K, DC, (w, h), 1, (w, h))
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caminfo["K"] = newK
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caminfo["DC"] = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
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# Undistort points
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pshape = points.shape
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points = np.reshape(points, (-1, 1, 2))
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points = cv2.undistortPoints(points, K, DC, P=newK)
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points = points.reshape(pshape)
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return points, caminfo
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# ==================================================================================================
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def get_camera_P(cam):
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"""Calculate opencv-style projection matrix"""
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R = np.asarray(cam["R"])
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T = np.asarray(cam["T"])
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K = np.asarray(cam["K"])
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Tr = R @ (T * -1)
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P = K @ np.hstack([R, Tr])
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return P
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# ==================================================================================================
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def calc_pose_score(pose1, pose2, dist1, cam1, joint_names, use_joints):
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"""Calculates the score between two poses"""
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# Select core joints
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jids = [joint_names.index(j) for j in use_joints]
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pose1 = pose1[jids]
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pose2 = pose2[jids]
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dist1 = dist1[jids]
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mask = (pose1[:, 2] > 0.1) & (pose2[:, 2] > 0.1)
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if np.sum(mask) < 3:
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return 0.0
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iscale = (cam1["width"] + cam1["height"]) / 2
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scores = score_projection(pose1, pose2, dist1, mask, iscale)
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score = np.mean(scores)
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return score
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# ==================================================================================================
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def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_joints):
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"""Triangulates a pair of persons and scores them based on the reprojection error"""
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cam1 = camparams[pair[0][0]]
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cam2 = camparams[pair[0][1]]
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pose1 = poses_2d[pair[0][0]][pair[0][2]]
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pose2 = poses_2d[pair[0][1]][pair[0][3]]
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# Select core joints
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jids = [joint_names_2d.index(j) for j in use_joints]
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pose1 = pose1[jids]
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pose2 = pose2[jids]
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poses_3d, score = triangulate_and_score(pose1, pose2, cam1, cam2, roomparams)
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return poses_3d, score
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# ==================================================================================================
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def score_projection(pose1, repro1, dists1, mask, iscale):
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min_score = 0.1
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error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[mask, 0:2], axis=1)
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# Set errors of invisible reprojections to a high value
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penalty = iscale
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mask1b = (repro1[:, 2] < min_score)[mask]
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error1[mask1b] = penalty
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# Scale error by image size and distance to the camera
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error1 = error1.clip(0, iscale / 4) / iscale
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dscale1 = np.sqrt(np.mean(dists1[mask]) / 3.5)
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error1 = error1 * dscale1
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# Convert errors to a score
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score1 = 1.0 / (1.0 + error1 * 10)
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return score1
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# ==================================================================================================
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def triangulate_and_score(pose1, pose2, cam1, cam2, roomparams):
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"""Triangulates a pair of persons and scores them based on the reprojection error"""
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# Mask out invisible joints
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min_score = 0.1
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mask1a = pose1[:, 2] >= min_score
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mask2a = pose2[:, 2] >= min_score
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mask = mask1a & mask2a
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# If too few joints are visible return a low score
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if np.sum(mask) < 3:
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pose3d = np.zeros([len(pose1), 4])
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score = 0.0
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return pose3d, score
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# Triangulate points
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points1 = pose1[mask, 0:2].T
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points2 = pose2[mask, 0:2].T
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points3d = cv2.triangulatePoints(cam1["P"], cam2["P"], points1, points2)
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points3d = (points3d / points3d[3, :])[0:3, :].T
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pose3d = np.zeros([len(pose1), 4])
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pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1)
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# If the triangulated points are outside the room drop it
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mean = np.mean(pose3d[mask][:, 0:3], axis=0)
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mins = np.min(pose3d[mask][:, 0:3], axis=0)
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maxs = np.max(pose3d[mask][:, 0:3], axis=0)
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rsize = np.array(roomparams["room_size"]) / 2
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rcent = np.array(roomparams["room_center"])
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wdist = 0.1
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center_outside = np.any((mean > rsize + rcent) | (mean < -rsize + rcent))
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limb_outside = np.any(
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(maxs > rsize + rcent + wdist) | (mins < -rsize + rcent - wdist)
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)
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if center_outside or limb_outside:
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pose3d[:, 3] = 0.001
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score = 0.001
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return pose3d, score
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# Calculate reprojection error
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poses_3d = np.expand_dims(pose3d, axis=0)
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repro1, dists1 = utils_pose.project_poses(poses_3d, cam1, calc_dists=True)
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repro2, dists2 = utils_pose.project_poses(poses_3d, cam2, calc_dists=True)
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repro1, dists1 = repro1[0], dists1[0]
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repro2, dists2 = repro2[0], dists2[0]
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# Calculate scores for each view
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iscale = (cam1["width"] + cam1["height"]) / 2
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score1 = score_projection(pose1, repro1, dists1, mask, iscale)
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score2 = score_projection(pose2, repro2, dists2, mask, iscale)
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# Combine scores
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scores = (score1 + score2) / 2
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# Drop lowest scores
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drop_k = math.floor(len(pose1) * 0.2)
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score = np.mean(np.sort(scores, axis=-1)[drop_k:])
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# Add score to 3D pose
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full_scores = np.zeros([poses_3d.shape[1], 1])
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full_scores[mask] = np.expand_dims(scores, axis=-1)
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pose3d[:, 3] = full_scores[:, 0]
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return pose3d, score
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# ==================================================================================================
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def calc_grouping(all_pairs, min_score: float):
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"""Groups pairs that share a person"""
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# Calculate the pose center for each pair
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for i in range(len(all_pairs)):
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pair = all_pairs[i]
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pose_3d = pair[2][0]
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mask = pose_3d[:, 3] > min_score
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center = np.mean(pose_3d[mask, 0:3], axis=0)
|
||||
all_pairs[i] = all_pairs[i] + [center]
|
||||
|
||||
groups = []
|
||||
for i in range(len(all_pairs)):
|
||||
pair = all_pairs[i]
|
||||
|
||||
# Create new group if non exists
|
||||
if len(groups) == 0:
|
||||
groups.append([pair[3], pair[2][0], [pair]])
|
||||
continue
|
||||
|
||||
# Check if the pair matches to an existing group
|
||||
max_center_dist = 0.6
|
||||
max_joint_avg_dist = 0.3
|
||||
best_dist = math.inf
|
||||
best_group = -1
|
||||
for j in range(len(groups)):
|
||||
g0 = groups[j]
|
||||
center0 = g0[0]
|
||||
center1 = pair[3]
|
||||
if np.linalg.norm(center0 - center1) < max_center_dist:
|
||||
pose0 = g0[1]
|
||||
pose1 = pair[2][0]
|
||||
|
||||
# Calculate the distance between the two poses
|
||||
mask0 = pose0[:, 3] > min_score
|
||||
mask1 = pose1[:, 3] > min_score
|
||||
mask = mask0 & mask1
|
||||
dists = np.linalg.norm(pose0[mask, 0:3] - pose1[mask, 0:3], axis=1)
|
||||
dist = np.mean(dists)
|
||||
if dist < max_joint_avg_dist:
|
||||
if dist < best_dist:
|
||||
best_dist = dist
|
||||
best_group = j
|
||||
if best_group >= 0:
|
||||
# Add pair to existing group and update the mean positions
|
||||
group = groups[best_group]
|
||||
new_center = (group[0] * len(group[2]) + pair[3]) / (len(group[2]) + 1)
|
||||
new_pose = (group[1] * len(group[2]) + pair[2][0]) / (len(group[2]) + 1)
|
||||
group[2].append(pair)
|
||||
group[0] = new_center
|
||||
group[1] = new_pose
|
||||
else:
|
||||
# Create new group if no match was found
|
||||
groups.append([pair[3], pair[2][0], [pair]])
|
||||
|
||||
return groups
|
||||
|
||||
|
||||
# ==================================================================================================
|
||||
|
||||
|
||||
def merge_group(poses_3d: np.ndarray, min_score: float):
|
||||
"""Merges a group of poses into a single pose"""
|
||||
|
||||
# Merge poses to create initial pose
|
||||
# Use only those triangulations with a high score
|
||||
imask = poses_3d[:, :, 3:4] > min_score
|
||||
sum_poses = np.sum(poses_3d * imask, axis=0)
|
||||
sum_mask = np.sum(imask, axis=0)
|
||||
initial_pose_3d = np.divide(
|
||||
sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
|
||||
)
|
||||
|
||||
# Use center as default if the initial pose is empty
|
||||
jmask = initial_pose_3d[:, 3] > 0.0
|
||||
sum_joints = np.sum(initial_pose_3d[jmask, 0:3], axis=0)
|
||||
sum_mask = np.sum(jmask)
|
||||
center = np.divide(
|
||||
sum_joints, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_joints)
|
||||
)
|
||||
initial_pose_3d[~jmask, 0:3] = center
|
||||
|
||||
# Drop joints with low scores
|
||||
offset = 0.1
|
||||
mask = poses_3d[:, :, 3:4] > (min_score - offset)
|
||||
|
||||
# Drop outliers that are far away from the other proposals
|
||||
max_dist = 1.2
|
||||
distances = np.linalg.norm(
|
||||
poses_3d[:, :, :3] - initial_pose_3d[np.newaxis, :, :3], axis=2
|
||||
)
|
||||
dmask = distances <= max_dist
|
||||
mask = mask & np.expand_dims(dmask, axis=-1)
|
||||
|
||||
# Select the best-k proposals for each joint that are closest to the initial pose
|
||||
keep_best = 3
|
||||
sorted_indices = np.argsort(distances, axis=0)
|
||||
best_k_mask = np.zeros_like(mask, dtype=bool)
|
||||
num_joints = poses_3d.shape[1]
|
||||
for i in range(num_joints):
|
||||
valid_indices = sorted_indices[:, i][mask[sorted_indices[:, i], i, 0]]
|
||||
best_k_mask[valid_indices[:keep_best], i, 0] = True
|
||||
mask = mask & best_k_mask
|
||||
|
||||
# Final pose computation with combined masks
|
||||
sum_poses = np.sum(poses_3d * mask, axis=0)
|
||||
sum_mask = np.sum(mask, axis=0)
|
||||
final_pose_3d = np.divide(
|
||||
sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
|
||||
)
|
||||
|
||||
return final_pose_3d
|
||||
|
||||
|
||||
# ==================================================================================================
|
||||
|
||||
|
||||
def get_3d_pose(
|
||||
poses_2d,
|
||||
camparams,
|
||||
roomparams,
|
||||
joint_names_2d,
|
||||
last_poses_3d=np.array([]),
|
||||
min_score=0.95,
|
||||
):
|
||||
"""Triangulates 3D poses from 2D poses of multiple views"""
|
||||
|
||||
# Convert poses and camparams to numpy arrays
|
||||
camparams = copy.deepcopy(camparams)
|
||||
for i in range(len(camparams)):
|
||||
poses_2d[i] = np.asarray(poses_2d[i])
|
||||
camparams[i]["K"] = np.array(camparams[i]["K"])
|
||||
camparams[i]["R"] = np.array(camparams[i]["R"])
|
||||
camparams[i]["T"] = np.array(camparams[i]["T"])
|
||||
camparams[i]["DC"] = np.array(camparams[i]["DC"])
|
||||
|
||||
# Undistort 2D points
|
||||
for i in range(len(camparams)):
|
||||
poses = poses_2d[i]
|
||||
cam = camparams[i]
|
||||
poses[:, :, 0:2], cam = undistort_points(poses[:, :, 0:2], cam)
|
||||
# Mask out points that are far outside the image (points slightly outside are still valid)
|
||||
offset = (cam["width"] + cam["height"]) / 40
|
||||
mask = (
|
||||
(poses[:, :, 0] >= 0 - offset)
|
||||
& (poses[:, :, 0] < cam["width"] + offset)
|
||||
& (poses[:, :, 1] >= 0 - offset)
|
||||
& (poses[:, :, 1] < cam["height"] + offset)
|
||||
)
|
||||
poses = poses * np.expand_dims(mask, axis=-1)
|
||||
poses_2d[i] = poses
|
||||
# Calc projection matrix with updated camera parameters
|
||||
cam["P"] = get_camera_P(cam)
|
||||
camparams[i] = cam
|
||||
|
||||
# Project last 3D poses to 2D
|
||||
last_poses_2d = []
|
||||
last_poses_3d = np.asarray(last_poses_3d)
|
||||
if last_poses_3d.size > 0:
|
||||
for i in range(len(camparams)):
|
||||
poses2d, dists = utils_pose.project_poses(last_poses_3d, camparams[i])
|
||||
last_poses_2d.append((poses2d, dists))
|
||||
|
||||
# Check matches to old poses
|
||||
threshold = min_score - 0.2
|
||||
scored_pasts = {}
|
||||
if last_poses_3d.size > 0:
|
||||
for i in range(len(camparams)):
|
||||
scored_pasts[i] = {}
|
||||
poses = poses_2d[i]
|
||||
last_poses, dists = last_poses_2d[i]
|
||||
for j in range(len(last_poses)):
|
||||
scored_pasts[i][j] = []
|
||||
for k in range(len(poses)):
|
||||
score = calc_pose_score(
|
||||
poses[k],
|
||||
last_poses[j],
|
||||
dists[j],
|
||||
camparams[i],
|
||||
joint_names_2d,
|
||||
core_joints,
|
||||
)
|
||||
if score > threshold:
|
||||
scored_pasts[i][j].append(k)
|
||||
|
||||
# Create pairs of persons
|
||||
# Checks if the person was already matched to the last frame and if so only creates pairs with those
|
||||
# Else it creates all possible pairs
|
||||
num_persons = [len(p) for p in poses_2d]
|
||||
all_pairs = []
|
||||
for i in range(len(camparams)):
|
||||
poses = poses_2d[i]
|
||||
for j in range(i + 1, len(poses_2d)):
|
||||
poses2 = poses_2d[j]
|
||||
for k in range(len(poses)):
|
||||
for l in range(len(poses2)):
|
||||
pid1 = sum(num_persons[:i]) + k
|
||||
pid2 = sum(num_persons[:j]) + l
|
||||
match = False
|
||||
if last_poses_3d.size > 0:
|
||||
for m in range(len(last_poses_3d)):
|
||||
if k in scored_pasts[i][m] and l in scored_pasts[j][m]:
|
||||
match = True
|
||||
all_pairs.append([(i, j, k, l), (pid1, pid2)])
|
||||
elif k in scored_pasts[i][m] or l in scored_pasts[j][m]:
|
||||
match = True
|
||||
if not match:
|
||||
all_pairs.append([(i, j, k, l), (pid1, pid2)])
|
||||
|
||||
# Calculate pair scores
|
||||
for i in range(len(all_pairs)):
|
||||
pair = all_pairs[i]
|
||||
pose_3d, score = calc_pair_score(
|
||||
pair, poses_2d, camparams, roomparams, joint_names_2d, core_joints
|
||||
)
|
||||
all_pairs[i].append((pose_3d, score))
|
||||
|
||||
# import draw_utils
|
||||
# poses3D = np.array([pose_3d])
|
||||
# _ = draw_utils.utils_view.show_poses3d(
|
||||
# poses3D, core_joints, {}, camparams
|
||||
# )
|
||||
# draw_utils.utils_view.show_plots()
|
||||
|
||||
# Drop pairs with low scores
|
||||
all_pairs = [p for p in all_pairs if p[2][1] > min_score]
|
||||
|
||||
# Group pairs that share a person
|
||||
groups = calc_grouping(all_pairs, min_score)
|
||||
|
||||
# Calculate full 3D poses
|
||||
poses_3d = []
|
||||
for pair in all_pairs:
|
||||
cam1 = camparams[pair[0][0]]
|
||||
cam2 = camparams[pair[0][1]]
|
||||
pose1 = poses_2d[pair[0][0]][pair[0][2]]
|
||||
pose2 = poses_2d[pair[0][1]][pair[0][3]]
|
||||
|
||||
pose_3d, _ = triangulate_and_score(pose1, pose2, cam1, cam2, roomparams)
|
||||
pair.append(pose_3d)
|
||||
|
||||
# Merge groups
|
||||
poses_3d = []
|
||||
for group in groups:
|
||||
poses = np.array([p[4] for p in group[2]])
|
||||
pose_3d = merge_group(poses, min_score)
|
||||
poses_3d.append(pose_3d)
|
||||
|
||||
if len(poses_3d) > 0:
|
||||
poses3D = np.array(poses_3d)
|
||||
else:
|
||||
poses3D = np.zeros([1, len(joint_names_2d), 4])
|
||||
return poses3D
|
||||
Reference in New Issue
Block a user