# Copyright 2016 The TensorFlow Authors All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r"""Wrapper for selecting the navigation environment that we want to train and test on. """ import numpy as np import os, glob import platform import logging from tensorflow.python.platform import app from tensorflow.python.platform import flags import render.swiftshader_renderer as renderer import src.file_utils as fu import src.utils as utils def get_dataset(dataset_name): if dataset_name == 'sbpd': dataset = StanfordBuildingParserDataset(dataset_name) else: logging.fatal('Not one of sbpd') return dataset class Loader(): def get_data_dir(): pass def get_meta_data(self, file_name, data_dir=None): if data_dir is None: data_dir = self.get_data_dir() full_file_name = os.path.join(data_dir, 'meta', file_name) assert(fu.exists(full_file_name)), \ '{:s} does not exist'.format(full_file_name) ext = os.path.splitext(full_file_name)[1] if ext == '.txt': ls = [] with fu.fopen(full_file_name, 'r') as f: for l in f: ls.append(l.rstrip()) elif ext == '.pkl': ls = utils.load_variables(full_file_name) return ls def load_building(self, name, data_dir=None): if data_dir is None: data_dir = self.get_data_dir() out = {} out['name'] = name out['data_dir'] = data_dir out['room_dimension_file'] = os.path.join(data_dir, 'room-dimension', name+'.pkl') out['class_map_folder'] = os.path.join(data_dir, 'class-maps') return out def load_building_meshes(self, building): dir_name = os.path.join(building['data_dir'], 'mesh', building['name']) mesh_file_name = glob.glob1(dir_name, '*.obj')[0] mesh_file_name_full = os.path.join(dir_name, mesh_file_name) logging.error('Loading building from obj file: %s', mesh_file_name_full) shape = renderer.Shape(mesh_file_name_full, load_materials=True, name_prefix=building['name']+'_') return [shape] class StanfordBuildingParserDataset(Loader): def __init__(self, ver): self.ver = ver self.data_dir = None def get_data_dir(self): if self.data_dir is None: self.data_dir = 'data/stanford_building_parser_dataset/' return self.data_dir def get_benchmark_sets(self): return self._get_benchmark_sets() def get_split(self, split_name): if self.ver == 'sbpd': return self._get_split(split_name) else: logging.fatal('Unknown version.') def _get_benchmark_sets(self): sets = ['train1', 'val', 'test'] return sets def _get_split(self, split_name): train = ['area1', 'area5a', 'area5b', 'area6'] train1 = ['area1'] val = ['area3'] test = ['area4'] sets = {} sets['train'] = train sets['train1'] = train1 sets['val'] = val sets['test'] = test sets['all'] = sorted(list(set(train + val + test))) return sets[split_name]