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| import os | |
| import glob | |
| import pickle | |
| 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 yfcc(BaseDumper): | |
| def get_seqs(self): | |
| data_dir=os.path.join(self.config['rawdata_dir'],'yfcc100m') | |
| for seq in self.config['data_seq']: | |
| for split in self.config['data_split']: | |
| split_dir=os.path.join(data_dir,seq,split) | |
| dump_dir=os.path.join(self.config['feature_dump_dir'],seq,split) | |
| cur_img_seq=glob.glob(os.path.join(split_dir,'images','*.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.config['data_seq']: | |
| seq_dir=os.path.join(self.config['feature_dump_dir'],seq) | |
| if not os.path.exists(seq_dir): | |
| os.mkdir(seq_dir) | |
| for split in self.config['data_split']: | |
| split_dir=os.path.join(seq_dir,split) | |
| if not os.path.exists(split_dir): | |
| os.mkdir(split_dir) | |
| def format_dump_data(self): | |
| print('Formatting data...') | |
| pair_path=os.path.join(self.config['rawdata_dir'],'pairs') | |
| self.data={'K1':[],'K2':[],'R':[],'T':[],'e':[],'f':[],'fea_path1':[],'fea_path2':[],'img_path1':[],'img_path2':[]} | |
| for seq in self.config['data_seq']: | |
| pair_name=os.path.join(pair_path,seq+'-te-1000-pairs.pkl') | |
| with open(pair_name, 'rb') as f: | |
| pairs=pickle.load(f) | |
| #generate id list | |
| seq_dir=os.path.join(self.config['rawdata_dir'],'yfcc100m',seq,'test') | |
| name_list=np.loadtxt(os.path.join(seq_dir,'images.txt'),dtype=str) | |
| cam_name_list=np.loadtxt(os.path.join(seq_dir,'calibration.txt'),dtype=str) | |
| for cur_pair in pairs: | |
| index1,index2=cur_pair[0],cur_pair[1] | |
| cam1,cam2=h5py.File(os.path.join(seq_dir,cam_name_list[index1]),'r'),h5py.File(os.path.join(seq_dir,cam_name_list[index2]),'r') | |
| K1,K2=cam1['K'][()],cam2['K'][()] | |
| [w1,h1],[w2,h2]=cam1['imsize'][()][0],cam2['imsize'][()][0] | |
| cx1,cy1,cx2,cy2 = (w1 - 1.0) * 0.5,(h1 - 1.0) * 0.5, (w2 - 1.0) * 0.5,(h2 - 1.0) * 0.5 | |
| K1[0,2],K1[1,2],K2[0,2],K2[1,2]=cx1,cy1,cx2,cy2 | |
| R1,R2,t1,t2=cam1['R'][()],cam2['R'][()],cam1['T'][()].reshape([3,1]),cam2['T'][()].reshape([3,1]) | |
| dR = np.dot(R2, R1.T) | |
| dt = t2 - np.dot(dR, t1) | |
| 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) | |
| img_path1,img_path2=os.path.join('yfcc100m',seq,'test',name_list[index1]),os.path.join('yfcc100m',seq,'test',name_list[index2]) | |
| dump_seq_dir=os.path.join(self.config['feature_dump_dir'],seq,'test') | |
| fea_path1,fea_path2=os.path.join(dump_seq_dir,name_list[index1].split('/')[-1]+'_'+self.config['extractor']['name'] | |
| +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5'),\ | |
| os.path.join(dump_seq_dir,name_list[index2].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() | |