add pre hi4d
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shuaiqing
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ad0791fac6
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@ -81,8 +81,21 @@ if __name__ == '__main__':
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p = create_pcd(points, colors=data[:, 3:])
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grids.append(p)
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elif pcd.endswith('.ply'):
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print('Load pcd: ', pcd)
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p = o3d.io.read_point_cloud(pcd)
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print(p)
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grids.append(p)
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elif pcd.endswith('.pkl'):
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p = o3d.io.read_triangle_mesh(pcd)
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grids.append(p)
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elif pcd.endswith('.obj'):
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p = o3d.io.read_triangle_mesh(pcd)
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vertices = np.asarray(p.vertices)
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print(vertices.shape)
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print(vertices.min(axis=0))
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print(vertices.max(axis=0))
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grids.append(p)
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center = o3d.geometry.TriangleMesh.create_coordinate_frame(
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size=1, origin=[0, 0, 0])
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grids.append(center)
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@ -91,6 +104,7 @@ if __name__ == '__main__':
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for ic, (cam, camera) in enumerate(cameras.items()):
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if len(args.subs) > 0 and cam not in args.subs:continue
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center = - camera['R'].T @ camera['T']
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print('{}: {}'.format(cam, center.T[0]))
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camera_locations.append(center)
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center = o3d.geometry.TriangleMesh.create_coordinate_frame(
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size=0.5, origin=[center[0, 0], center[1, 0], center[2, 0]])
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157
scripts/dataset/pre_hi4d.py
Normal file
157
scripts/dataset/pre_hi4d.py
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@ -0,0 +1,157 @@
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import os
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import shutil
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import numpy as np
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import cv2
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import open3d as o3d
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from os.path import join
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from os.path import join
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from glob import glob
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from tqdm import tqdm
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from easymocap.mytools.file_utils import read_json, write_keypoints3d, write_vertices, write_smpl
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# Input and output path
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database = '/nas/home/shuaiqing/datasets/HI4D'
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outdatabase = '/nas/home/shuaiqing/datasets/HI4D_easymocap'
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# After convering, use
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# python3 apps/calibration/vis_camera_by_open3d.py ${data} --pcd ${data}/mesh-test.obj
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# for visualization
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seqlist = [
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'pair10/dance10',
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'pair32/pose32',
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# 'pair09/hug09',
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# 'pair00/fight00',
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# 'pair00/hug00',
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# 'pair12/fight12',
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# 'pair12/hug12',
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# 'pair14/dance14',
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# 'pair28/dance28',
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# 'pair32/pose32',
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# 'pair37/pose37',
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]
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from easymocap.bodymodel.smpl import SMPLModel
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body_model = SMPLModel(
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model_path='data/bodymodels/SMPL_python_v.1.1.0/smpl/models/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl',
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device = 'cuda',
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regressor_path = 'data/smplx/J_regressor_body25.npy',
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NUM_SHAPES = 10,
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use_pose_blending =True
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)
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for seq in seqlist:
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root = join(database, seq)
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outroot = join(outdatabase, seq.replace('/', '_'))
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cameraname = join(root, 'cameras', 'rgb_cameras.npz')
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cameras = dict(np.load(cameraname))
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cameras_out = {}
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for i, cam in enumerate(cameras['ids']):
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K = cameras['intrinsics'][i]
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dist = cameras['dist_coeffs'][i:i+1]
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RT = cameras['extrinsics'][i]
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R = RT[:3, :3]
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T = RT[:3, 3:]
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cameras_out[str(cam)] = {
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'K': K,
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'dist': dist,
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'R': R,
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'T': T
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}
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# 绕x轴转90度
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center = - R.T @ T
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print(cam, center.T[0])
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cameras = cameras_out
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meshanme = sorted(glob(join(root, 'frames', '*.obj')))[0]
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mesh = o3d.io.read_triangle_mesh(meshanme)
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vertices = np.asarray(mesh.vertices)
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R_global = cv2.Rodrigues(np.array([np.pi/2, 0, 0]))[0]
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vertices_R = vertices @ R_global.T
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z_min = np.min(vertices_R[:, 2])
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T_global = np.array([0, 0, -z_min]).reshape(3, 1)
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vertices_RT = vertices_R + T_global.T
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mesh.vertices = o3d.utility.Vector3dVector(vertices_RT)
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for key, cam in cameras.items():
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cam['R'] = cam['R'] @ R_global.T
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cam.pop('Rvec', '')
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center = - cam['R'].T @ cam['T']
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newcenter = center + T_global
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newT = -cam['R'] @ newcenter
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cam['T'] = newT
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center = - cam['R'].T @ cam['T']
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print(center.T)
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from easymocap.mytools.camera_utils import write_camera
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write_camera(cameras, outroot)
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o3d.io.write_triangle_mesh(join(outroot, 'mesh-test.obj'), mesh)
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regressor = np.load('data/smplx/J_regressor_body25.npy')
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filenames = sorted(glob(join(root, 'smpl', '*.npz')))
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for filename in tqdm(filenames):
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data = dict(np.load(filename))
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vertices = data['verts']
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vertices = vertices @ R_global.T + T_global.T
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joints = np.matmul(regressor[None], vertices)
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# 绕x轴旋转90度
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results = [{
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'id': 0,
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'keypoints3d': joints[0]
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},
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{
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'id': 1,
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'keypoints3d': joints[1]
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}]
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outname = join(outroot, 'body25', os.path.basename(filename).replace('.npz', '.json'))
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if not os.path.exists(outname):
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write_keypoints3d(outname, results)
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results = [{
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'id': 0,
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'vertices': vertices[0]
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},
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{
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'id': 1,
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'vertices': vertices[1]
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}]
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outname = join(outroot, 'vertices-gt', os.path.basename(filename).replace('.npz', '.json'))
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if not os.path.exists(outname):
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write_vertices(outname, results)
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params0 = {
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'poses': np.hstack([data['global_orient'][0], data['body_pose'][0]]),
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'shapes': data['betas'][0],
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'Th': data['transl'][0]
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}
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params1 = {
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'poses': np.hstack([data['global_orient'][1], data['body_pose'][1]]),
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'shapes': data['betas'][1],
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'Th': data['transl'][1]
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}
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outname = join(outroot, 'smpl-gt', os.path.basename(filename).replace('.npz', '.json'))
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if not os.path.exists(outname) or True:
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params = [params0, params1]
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for i, param in enumerate(params):
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for key in param.keys():
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param[key] = param[key][None]
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param['Rh'] = np.zeros_like(param['Th'])
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param = body_model.convert_from_standard_smpl(param)
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Rold = cv2.Rodrigues(param['Rh'])[0]
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Told = param['Th']
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Rnew = R_global @ Rold
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Tnew = (R_global @ Told.T + T_global).T
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param['Rh'] = cv2.Rodrigues(Rnew)[0].reshape(1, 3)
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param['Th'] = Tnew.reshape(1, 3)
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param['id'] = i
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params[i] = param
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write_smpl(outname, params)
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if not os.path.exists(join(outroot, 'images')):
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shutil.copytree(join(root, 'images'), join(outroot, 'images'))
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