128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
# Copyright (c) Hao Meng. All Rights Reserved.
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import os
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import numpy as np
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from tqdm import tqdm
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import config as cfg
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from utils import calculate_Joint_angles
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def load_joints_(set, mode):
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path = os.path.join("joints", "{}_{}.npy".format(set, mode))
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joints = np.load(path)
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return joints
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def normalize(vec):
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len = np.linalg.norm(vec, axis=-1, keepdims=True)
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return vec / len
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def load_joints(dict):
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joints_all = []
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for set, mode in dict.items():
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for m in mode:
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path = os.path.join("joints", "{}_{}.npy".format(set, m))
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joints = np.load(path)
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joints_all.append(joints)
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joints_all = np.concatenate(joints_all, axis=0).astype(np.float32) # (N, 21,3)
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return joints_all
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if __name__ == '__main__':
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path = "BMC"
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if not os.path.isdir(path):
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os.makedirs(path)
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# original 3D joints in minimal-hand's setting
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th_dict = {"rhd": ["train", "test"], "gan": ["train"]}
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th_joints = load_joints(th_dict)
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joints = th_joints.copy() # (N, 21, 3)
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# all joints available setting
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# all_dict = {"rhd": ["train", "test"], "gan": ["train"], "stb": ["train", "test"], "fh": ["train"]}
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# all_joints = load_joints(all_dict)
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# joints = all_joints.copy() # (N, 21, 3)
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# root-relative
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joints_root = np.expand_dims(joints[:, cfg.JOINT_ROOT_IDX, :], 1)
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joints = joints - joints_root
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# scale-invariant
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ref_bones = np.linalg.norm(joints[:, cfg.REF_BONE_LINK[0]] - joints[:, cfg.REF_BONE_LINK[1]], axis=1)
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ref_bones = np.expand_dims(ref_bones, [1, 2])
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joints = joints / ref_bones
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# loop
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# for i in tqdm(range(0, joints.shape[0], 5000)):
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# joints_i = joints[i]
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# # visualization
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# vis.plot3d(joints_i)
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# bones = bone.caculate_length(joints_i, label="all")
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# print(bones[9])
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# calculate bone length limits
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kin_chain = [
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joints[:, i] - joints[:, cfg.SNAP_PARENT[i]]
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for i in range(1, 21)
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]
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kin_chain = np.array(kin_chain)
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kin_chain = kin_chain.swapaxes(1, 0) # (N*20*3)
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bone_lens = np.linalg.norm(
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kin_chain, ord=2, axis=-1, keepdims=True
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) # (N*20)
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bone_lens = np.squeeze(bone_lens)
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max_bone_len = np.max(bone_lens, axis=0) # (20,)
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min_bone_len = np.min(bone_lens, axis=0) # (20,)
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np.save("BMC/bone_len_min.npy", min_bone_len)
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np.save("BMC/bone_len_max.npy", max_bone_len)
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# calculate root bone limits
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root_bones = kin_chain[:, [0, 4, 8, 12, 16], :]
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normals = normalize(np.cross(root_bones[:, 1:], root_bones[:, :-1]))
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edge_normals = np.zeros_like(root_bones)
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edge_normals[:, 0] = normals[:, 0]
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edge_normals[:, 4] = normals[:, 3]
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edge_normals[:, 1:4] = normalize(normals[:, 1:] + normals[:, :-1])
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curvatures = np.zeros([joints.shape[0], 4])
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PHI = np.zeros([joints.shape[0], 4])
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for i in range(4):
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e_tmp = edge_normals[:, i + 1] - edge_normals[:, i]
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b_tmp = root_bones[:, i + 1] - root_bones[:, i]
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b_tmp_norm = np.linalg.norm(
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b_tmp, ord=2, axis=-1 # N
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)
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curvatures[:, i] = np.sum(e_tmp * b_tmp, axis=-1) / (b_tmp_norm ** 2)
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PHI[:, i] = np.sum(root_bones[:, i] * root_bones[:, i + 1], axis=-1)
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tmp1 = np.linalg.norm(root_bones[:, i], ord=2, axis=-1)
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tmp2 = np.linalg.norm(root_bones[:, i + 1], ord=2, axis=-1)
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PHI[:, i] /= (tmp1 * tmp2)
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PHI[:, i] = np.arccos(PHI[:, i])
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max_curvatures = np.max(curvatures, axis=0) # (4,)
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min_curvatures = np.min(curvatures, axis=0) # (4,)
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np.save("BMC/curvatures_max.npy", max_curvatures)
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np.save("BMC/curvatures_min.npy", min_curvatures)
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max_PHI = np.max(PHI, axis=0) # (4,)
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min_PHI = np.min(PHI, axis=0) # (4,)
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np.save("BMC/PHI_max.npy", max_PHI)
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np.save("BMC/PHI_min.npy", min_PHI)
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jas = []
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for i in tqdm(range(joints.shape[0])):
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joint = joints[i]
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ja = calculate_Joint_angles.caculate_ja(joint)
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jas.append(ja)
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jas = np.array(jas) # (N, 15, 2)
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np.save("BMC/joint_angles.npy", jas)
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