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import os |
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import glob |
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import pickle |
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from tqdm import trange |
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import numpy as np |
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import h5py |
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from numpy.core.fromnumeric import reshape |
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from .base_dumper import BaseDumper |
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import sys |
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ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) |
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sys.path.insert(0, ROOT_DIR) |
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import utils |
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class fmbench(BaseDumper): |
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def get_seqs(self): |
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data_dir=os.path.join(self.config['rawdata_dir']) |
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self.split_list=[] |
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for seq in self.config['data_seq']: |
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cur_split_list=np.unique(np.loadtxt(os.path.join(data_dir,seq,'pairs_which_dataset.txt'),dtype=str)) |
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self.split_list.append(cur_split_list) |
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for split in cur_split_list: |
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split_dir=os.path.join(data_dir,seq,split) |
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dump_dir=os.path.join(self.config['feature_dump_dir'],seq,split) |
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cur_img_seq=glob.glob(os.path.join(split_dir,'Images','*.jpg')) |
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cur_dump_seq=[os.path.join(dump_dir,path.split('/')[-1])+'_'+self.config['extractor']['name']+'_'+str(self.config['extractor']['num_kpt'])\ |
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+'.hdf5' for path in cur_img_seq] |
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self.img_seq+=cur_img_seq |
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self.dump_seq+=cur_dump_seq |
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def format_dump_folder(self): |
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if not os.path.exists(self.config['feature_dump_dir']): |
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os.mkdir(self.config['feature_dump_dir']) |
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for seq_index in range(len(self.config['data_seq'])): |
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seq_dir=os.path.join(self.config['feature_dump_dir'],self.config['data_seq'][seq_index]) |
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if not os.path.exists(seq_dir): |
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os.mkdir(seq_dir) |
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for split in self.split_list[seq_index]: |
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split_dir=os.path.join(seq_dir,split) |
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if not os.path.exists(split_dir): |
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os.mkdir(split_dir) |
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def format_dump_data(self): |
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print('Formatting data...') |
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self.data={'K1':[],'K2':[],'R':[],'T':[],'e':[],'f':[],'fea_path1':[],'fea_path2':[],'img_path1':[],'img_path2':[]} |
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for seq_index in range(len(self.config['data_seq'])): |
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seq=self.config['data_seq'][seq_index] |
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print(seq) |
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pair_list=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'pairs_with_gt.txt'),dtype=float) |
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which_split_list=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'pairs_which_dataset.txt'),dtype=str) |
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for pair_index in trange(len(pair_list)): |
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cur_pair=pair_list[pair_index] |
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cur_split=which_split_list[pair_index] |
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index1,index2=int(cur_pair[0]),int(cur_pair[1]) |
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camera=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,cur_split,'Camera.txt'),dtype=float) |
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K1,K2=camera[index1].reshape([3,3]),camera[index2].reshape([3,3]) |
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pose=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,cur_split,'Poses.txt'),dtype=float) |
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pose1,pose2=pose[index1].reshape([3,4]),pose[index2].reshape([3,4]) |
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R1,R2,t1,t2=pose1[:3,:3],pose2[:3,:3],pose1[:3,3][:,np.newaxis],pose2[:3,3][:,np.newaxis] |
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dR = np.dot(R2, R1.T) |
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dt = t2 - np.dot(dR, t1) |
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dt /= np.sqrt(np.sum(dt**2)) |
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e_gt_unnorm = np.reshape(np.matmul( |
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np.reshape(utils.evaluation_utils.np_skew_symmetric(dt.astype('float64').reshape(1, 3)), (3, 3)), |
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np.reshape(dR.astype('float64'), (3, 3))), (3, 3)) |
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e_gt = e_gt_unnorm / np.linalg.norm(e_gt_unnorm) |
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f=cur_pair[2:].reshape([3,3]) |
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f_gt=f / np.linalg.norm(f) |
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self.data['K1'].append(K1),self.data['K2'].append(K2) |
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self.data['R'].append(dR),self.data['T'].append(dt) |
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self.data['e'].append(e_gt),self.data['f'].append(f_gt) |
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img_path1,img_path2=os.path.join(seq,cur_split,'Images',str(index1).zfill(8)+'.jpg'),\ |
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os.path.join(seq,cur_split,'Images',str(index1).zfill(8)+'.jpg') |
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fea_path1,fea_path2=os.path.join(self.config['feature_dump_dir'],seq,cur_split,str(index1).zfill(8)+'.jpg'+'_'+self.config['extractor']['name'] |
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+'_'+str(self.config['extractor']['num_kpt'])+'.hdf5'),\ |
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os.path.join(self.config['feature_dump_dir'],seq,cur_split,str(index2).zfill(8)+'.jpg'+'_'+self.config['extractor']['name'] |
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+'_'+str(self.config['extractor']['num_kpt'])+'.hdf5') |
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self.data['img_path1'].append(img_path1),self.data['img_path2'].append(img_path2) |
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self.data['fea_path1'].append(fea_path1),self.data['fea_path2'].append(fea_path2) |
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self.form_standard_dataset() |
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