| import os | |
| import glob | |
| import pickle | |
| from posixpath import basename | |
| import numpy as np | |
| import h5py | |
| from .base_dumper import BaseDumper | |
| import sys | |
| ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) | |
| sys.path.insert(0, ROOT_DIR) | |
| import utils | |
| class scannet(BaseDumper): | |
| def get_seqs(self): | |
| self.pair_list=np.loadtxt('../assets/scannet_eval_list.txt',dtype=str) | |
| self.seq_list=np.unique(np.asarray([path.split('/')[0] for path in self.pair_list[:,0]],dtype=str)) | |
| self.dump_seq,self.img_seq=[],[] | |
| for seq in self.seq_list: | |
| dump_dir=os.path.join(self.config['feature_dump_dir'],seq) | |
| cur_img_seq=glob.glob(os.path.join(os.path.join(self.config['rawdata_dir'],seq,'img','*.jpg'))) | |
| cur_dump_seq=[os.path.join(dump_dir,path.split('/')[-1])+'_'+self.config['extractor']['name']+'_'+str(self.config['extractor']['num_kpt'])\ | |
| +'.hdf5' for path in cur_img_seq] | |
| self.img_seq+=cur_img_seq | |
| self.dump_seq+=cur_dump_seq | |
| def format_dump_folder(self): | |
| if not os.path.exists(self.config['feature_dump_dir']): | |
| os.mkdir(self.config['feature_dump_dir']) | |
| for seq in self.seq_list: | |
| seq_dir=os.path.join(self.config['feature_dump_dir'],seq) | |
| if not os.path.exists(seq_dir): | |
| os.mkdir(seq_dir) | |
| def format_dump_data(self): | |
| print('Formatting data...') | |
| self.data={'K1':[],'K2':[],'R':[],'T':[],'e':[],'f':[],'fea_path1':[],'fea_path2':[],'img_path1':[],'img_path2':[]} | |
| for pair in self.pair_list: | |
| img_path1,img_path2=pair[0],pair[1] | |
| seq=img_path1.split('/')[0] | |
| index1,index2=int(img_path1.split('/')[-1][:-4]),int(img_path2.split('/')[-1][:-4]) | |
| ex1,ex2=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'extrinsic',str(index1)+'.txt'),dtype=float),\ | |
| np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'extrinsic',str(index2)+'.txt'),dtype=float) | |
| K1,K2=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'intrinsic',str(index1)+'.txt'),dtype=float),\ | |
| np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'intrinsic',str(index2)+'.txt'),dtype=float) | |
| relative_extrinsic=np.matmul(np.linalg.inv(ex2),ex1) | |
| dR,dt=relative_extrinsic[:3,:3],relative_extrinsic[:3,3] | |
| dt /= np.sqrt(np.sum(dt**2)) | |
| e_gt_unnorm = np.reshape(np.matmul( | |
| np.reshape(utils.evaluation_utils.np_skew_symmetric(dt.astype('float64').reshape(1, 3)), (3, 3)), | |
| np.reshape(dR.astype('float64'), (3, 3))), (3, 3)) | |
| e_gt = e_gt_unnorm / np.linalg.norm(e_gt_unnorm) | |
| f_gt_unnorm=np.linalg.inv(K2.T)@e_gt@np.linalg.inv(K1) | |
| f_gt = f_gt_unnorm / np.linalg.norm(f_gt_unnorm) | |
| self.data['K1'].append(K1),self.data['K2'].append(K2) | |
| self.data['R'].append(dR),self.data['T'].append(dt) | |
| self.data['e'].append(e_gt),self.data['f'].append(f_gt) | |
| dump_seq_dir=os.path.join(self.config['feature_dump_dir'],seq) | |
| fea_path1,fea_path2=os.path.join(dump_seq_dir,img_path1.split('/')[-1]+'_'+self.config['extractor']['name'] | |
| +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5'),\ | |
| os.path.join(dump_seq_dir,img_path2.split('/')[-1]+'_'+self.config['extractor']['name'] | |
| +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5') | |
| self.data['img_path1'].append(img_path1),self.data['img_path2'].append(img_path2) | |
| self.data['fea_path1'].append(fea_path1),self.data['fea_path2'].append(fea_path2) | |
| self.form_standard_dataset() | |