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| import numpy as np | |
| import os | |
| import sys | |
| module_path = os.path.abspath(os.path.join('PyMO')) | |
| if sys.path[0] != module_path: | |
| sys.path.insert(0,module_path) | |
| from analysis.pymo.parsers import BVHParser | |
| from analysis.pymo.preprocessing import * | |
| from analysis.pymo.viz_tools import * | |
| from analysis.pymo.writers import * | |
| import pickle | |
| #%% Default skeleton | |
| p = BVHParser() | |
| default_skel = p.parse("analysis/mixamo.bvh") | |
| #%% | |
| # scale=1.0 | |
| # # Scale the default sceleton | |
| # for joint in default_skel.traverse(): | |
| # offs = np.array(default_skel.skeleton[joint]['offsets'])*scale | |
| # default_skel.skeleton[joint]['offsets'] = offs.tolist() | |
| bvh2expmap = MocapParameterizer('expmap') | |
| default_exp_map_data = bvh2expmap.fit_transform([default_skel]) | |
| unity_joints_vals = default_exp_map_data[0].values.columns | |
| npfier = Numpyfier() | |
| npfier.fit_transform(default_exp_map_data) | |
| def angles2smplbvh(out_data, dest_dir, filename, framerate): | |
| smpl_bvh = bvh2expmap.inverse_transform(npfier.inverse_transform(out_data)) | |
| smpl_bvh[0].framerate=float(1.0/framerate) | |
| writer = BVHWriter() | |
| with open(f'{dest_dir}/{filename}.bvh','w') as f: | |
| writer.write(smpl_bvh[0], f) | |
| def convert_smpl(filepath, dest_dir, result_filename, framerate): | |
| data = pickle.load(open(filepath, "rb")) | |
| smpl_angs = data["smpl_poses"].reshape(-1,24*3) | |
| smpl_trans = data["smpl_trans"] | |
| # print(data) | |
| smpl_scale = data["smpl_scaling"] | |
| smpl_joints = ["Pelvis", "L_Hip", "R_Hip", "Spine1", "L_Knee", "R_Knee", "Spine2", "L_Ankle", "R_Ankle", | |
| "Spine3", "L_Foot", "R_Foot", "Neck", "L_Collar", "R_Collar", "Head", "L_Shoulder", "R_Shoulder", | |
| "L_Elbow", "R_Elbow", "L_Wrist", "R_Wrist", "L_Hand", "R_Hand"] | |
| unity_joints = [j for j in default_skel.traverse() if j[-3:]!="Nub"] | |
| smpl_joints_to_unity_joints = { | |
| "Pelvis": "Hips", | |
| "L_Hip": "LeftUpLeg", | |
| "R_Hip": "RightUpLeg", | |
| "Spine1": "Spine", | |
| "L_Knee": "LeftLeg", | |
| "R_Knee": "RightLeg", | |
| "L_Ankle": "LeftFoot", | |
| "R_Ankle": "RightFoot", | |
| "Spine2": "Spine1", | |
| "Spine3": "Spine2", | |
| "L_Foot": "LeftToeBase", | |
| "R_Foot": "RightToeBase", | |
| "Neck": "Neck", | |
| "Head": "Head", | |
| "L_Collar": "LeftShoulder", | |
| "R_Collar": "RightShoulder", | |
| "L_Shoulder": "LeftArm", | |
| "R_Shoulder": "RightArm", | |
| "L_Elbow": "LeftForeArm", | |
| "R_Elbow": "RightForeArm", | |
| "L_Wrist": "LeftHand", | |
| "R_Wrist": "RightHand", | |
| # "L_Hand": "LeftHand", | |
| # "R_Hand": "RightHand", | |
| } | |
| out_data = np.zeros((1, smpl_angs.shape[0], len(unity_joints)*3 + 3)) | |
| poss = np.zeros((smpl_angs.shape[0], 3)) | |
| for smpl_joint, unity_joint in smpl_joints_to_unity_joints.items(): | |
| out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_alpha")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3] | |
| out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_beta")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+1] | |
| out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_gamma")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+2] | |
| # relative_scale=188.7 | |
| # relative_scale=100 | |
| smpl_scale = 100 | |
| relative_scale=smpl_scale | |
| vertical_offset=-0.6*(smpl_scale/100) | |
| out_data[0,:,unity_joints_vals.get_loc("Hips_Xposition")] = smpl_trans[:,0]/relative_scale | |
| out_data[0,:,unity_joints_vals.get_loc("Hips_Yposition")] = smpl_trans[:,1]/relative_scale+vertical_offset | |
| out_data[0,:,unity_joints_vals.get_loc("Hips_Zposition")] = smpl_trans[:,2]/relative_scale | |
| angles2smplbvh(out_data, dest_dir, result_filename, framerate) | |