Spaces:
Running
Running
| from abc import ABCMeta, abstractmethod | |
| import os | |
| import h5py | |
| import numpy as np | |
| from tqdm import trange | |
| from torch.multiprocessing import Pool,set_start_method | |
| set_start_method('spawn',force=True) | |
| import sys | |
| ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) | |
| sys.path.insert(0, ROOT_DIR) | |
| from components import load_component | |
| class BaseDumper(metaclass=ABCMeta): | |
| def __init__(self,config): | |
| self.config=config | |
| self.img_seq=[] | |
| self.dump_seq=[]#feature dump seq | |
| def get_seqs(self): | |
| raise NotImplementedError | |
| def format_dump_folder(self): | |
| raise NotImplementedError | |
| def format_dump_data(self): | |
| raise NotImplementedError | |
| def initialize(self): | |
| self.extractor=load_component('extractor',self.config['extractor']['name'],self.config['extractor']) | |
| self.get_seqs() | |
| self.format_dump_folder() | |
| def extract(self,index): | |
| img_path,dump_path=self.img_seq[index],self.dump_seq[index] | |
| if not self.config['extractor']['overwrite'] and os.path.exists(dump_path): | |
| return | |
| kp, desc = self.extractor.run(img_path) | |
| self.write_feature(kp,desc,dump_path) | |
| def dump_feature(self): | |
| print('Extrating features...') | |
| self.num_img=len(self.dump_seq) | |
| pool=Pool(self.config['extractor']['num_process']) | |
| iteration_num=self.num_img//self.config['extractor']['num_process'] | |
| if self.num_img%self.config['extractor']['num_process']!=0: | |
| iteration_num+=1 | |
| for index in trange(iteration_num): | |
| indicies_list=range(index*self.config['extractor']['num_process'],min((index+1)*self.config['extractor']['num_process'],self.num_img)) | |
| pool.map(self.extract,indicies_list) | |
| pool.close() | |
| pool.join() | |
| def write_feature(self,pts, desc, filename): | |
| with h5py.File(filename, "w") as ifp: | |
| ifp.create_dataset('keypoints', pts.shape, dtype=np.float32) | |
| ifp.create_dataset('descriptors', desc.shape, dtype=np.float32) | |
| ifp["keypoints"][:] = pts | |
| ifp["descriptors"][:] = desc | |
| def form_standard_dataset(self): | |
| dataset_path=os.path.join(self.config['dataset_dump_dir'],self.config['data_name']+\ | |
| '_'+self.config['extractor']['name']+'_'+str(self.config['extractor']['num_kpt'])+'.hdf5') | |
| pair_data_type=['K1','K2','R','T','e','f'] | |
| num_pairs=len(self.data['K1']) | |
| with h5py.File(dataset_path, 'w') as f: | |
| print('collecting pair info...') | |
| for type in pair_data_type: | |
| dg=f.create_group(type) | |
| for idx in range(num_pairs): | |
| data_item=np.asarray(self.data[type][idx]) | |
| dg.create_dataset(str(idx),data_item.shape,data_item.dtype,data=data_item) | |
| for type in ['img_path1','img_path2']: | |
| dg=f.create_group(type) | |
| for idx in range(num_pairs): | |
| dg.create_dataset(str(idx),[1],h5py.string_dtype(encoding='ascii'),data=self.data[type][idx].encode('ascii')) | |
| #dump desc | |
| print('collecting desc and kpt...') | |
| desc1_g,desc2_g,kpt1_g,kpt2_g=f.create_group('desc1'),f.create_group('desc2'),f.create_group('kpt1'),f.create_group('kpt2') | |
| for idx in trange(num_pairs): | |
| desc_file1,desc_file2=h5py.File(self.data['fea_path1'][idx],'r'),h5py.File(self.data['fea_path2'][idx],'r') | |
| desc1,desc2,kpt1,kpt2=desc_file1['descriptors'][()],desc_file2['descriptors'][()],desc_file1['keypoints'][()],desc_file2['keypoints'][()] | |
| desc1_g.create_dataset(str(idx),desc1.shape,desc1.dtype,data=desc1) | |
| desc2_g.create_dataset(str(idx),desc2.shape,desc2.dtype,data=desc2) | |
| kpt1_g.create_dataset(str(idx),kpt1.shape,kpt1.dtype,data=kpt1) | |
| kpt2_g.create_dataset(str(idx),kpt2.shape,kpt2.dtype,data=kpt2) | |