def main(user_cfg): """ Recompute the s2p tile geometry for the config file and produce an svg representing the tiles Args: user_cfg: user config dictionary """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(user_cfg) tw, th = initialization.adjust_tile_size() tiles = initialization.tiles_full_info(tw, th) # generate svg tile map write_svg_tilemap(os.path.join(cfg['out_dir'],'tiles.svg'), cfg, tiles) print("\n\n svg tilemap saved in: %s\n"%os.path.join(cfg['out_dir'],'tiles.svg')) # cleanup common.garbage_cleanup()
def main(user_cfg): """ Recompute the s2p tile geometry for the config file and produce an svg representing the tiles Args: user_cfg: user config dictionary """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(user_cfg) tw, th = initialization.adjust_tile_size() tiles = initialization.tiles_full_info(tw, th) # generate svg tile map write_svg_tilemap(os.path.join(cfg['out_dir'],'tiles.svg'), cfg, tiles) print("\n\n svg tilemap saved in: %s\n"%os.path.join(cfg['out_dir'],'tiles.svg')) # cleanup common.garbage_cleanup()
def main(user_cfg, steps=ALL_STEPS): """ Launch the s2p pipeline with the parameters given in a json file. Args: user_cfg: user config dictionary steps: either a string (single step) or a list of strings (several steps) """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(user_cfg) if 'initialisation' in steps: initialization.make_dirs() # multiprocessing setup nb_workers = multiprocessing.cpu_count() # nb of available cores if cfg['max_processes']: nb_workers = min(nb_workers, cfg['max_processes']) cfg['max_processes'] = nb_workers tw, th = initialization.adjust_tile_size() tiles_txt = os.path.join(cfg['out_dir'], 'tiles.txt') create_masks = 'initialisation' in steps tiles = initialization.tiles_full_info(tw, th, tiles_txt, create_masks) if 'initialisation' in steps: # Write the list of json files to outdir/tiles.txt with open(tiles_txt, 'w') as f: for t in tiles: f.write(t['json'] + os.linesep) n = len(cfg['images']) tiles_pairs = [(t, i) for i in range(1, n) for t in tiles] # omp_num_threads should not exceed nb_workers when multiplied by len(tiles) cfg['omp_num_threads'] = max(1, int(nb_workers / len(tiles_pairs))) if 'local-pointing' in steps: print('correcting pointing locally...') parallel.launch_calls(pointing_correction, tiles_pairs, nb_workers) if 'global-pointing' in steps: print('correcting pointing globally...') global_pointing_correction(tiles) common.print_elapsed_time() if 'rectification' in steps: print('rectifying tiles...') parallel.launch_calls(rectification_pair, tiles_pairs, nb_workers) if 'matching' in steps: print('running stereo matching...') parallel.launch_calls(stereo_matching, tiles_pairs, nb_workers) if n > 2 and cfg['triangulation_mode'] == 'pairwise': if 'disparity-to-height' in steps: print('computing height maps...') parallel.launch_calls(disparity_to_height, tiles_pairs, nb_workers) print('computing local pairwise height offsets...') parallel.launch_calls(mean_heights, tiles, nb_workers) if 'global-mean-heights' in steps: print('computing global pairwise height offsets...') global_mean_heights(tiles) if 'heights-to-ply' in steps: print('merging height maps and computing point clouds...') parallel.launch_calls(heights_to_ply, tiles, nb_workers) else: if 'triangulation' in steps: print('triangulating tiles...') if cfg['triangulation_mode'] == 'geometric': parallel.launch_calls(multidisparities_to_ply, tiles, nb_workers) elif cfg['triangulation_mode'] == 'pairwise': parallel.launch_calls(disparity_to_ply, tiles, nb_workers) else: raise ValueError( "possible values for 'triangulation_mode' : 'pairwise' or 'geometric'" ) if 'local-dsm-rasterization' in steps: print('computing DSM by tile...') parallel.launch_calls(plys_to_dsm, tiles, nb_workers) if 'global-dsm-rasterization' in steps: print('computing global DSM...') global_dsm(tiles) common.print_elapsed_time() # @kai if 'global-pointcloud' in steps: print('computing global point cloud...') global_pointcloud(tiles) common.print_elapsed_time() # cleanup common.garbage_cleanup() common.print_elapsed_time(since_first_call=True)
def main(user_cfg, steps=ALL_STEPS): """ Launch the s2p pipeline with the parameters given in a json file. Args: user_cfg: user config dictionary steps: either a string (single step) or a list of strings (several steps) """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(user_cfg) if 'initialisation' in steps: initialization.make_dirs() # multiprocessing setup nb_workers = multiprocessing.cpu_count() # nb of available cores if cfg['max_processes']: nb_workers = min(nb_workers, cfg['max_processes']) cfg['max_processes'] = nb_workers tw, th = initialization.adjust_tile_size() print('\ndiscarding masked tiles...') tiles = initialization.tiles_full_info(tw, th) if 'initialisation' in steps: # Write the list of json files to outdir/tiles.txt with open(os.path.join(cfg['out_dir'],'tiles.txt'),'w') as f: for t in tiles: f.write(t['json']+os.linesep) n = len(cfg['images']) tiles_pairs = [(t, i) for i in range(1, n) for t in tiles] # omp_num_threads should not exceed nb_workers when multiplied by len(tiles) cfg['omp_num_threads'] = max(1, int(nb_workers / len(tiles_pairs))) if 'local-pointing' in steps: print('correcting pointing locally...') parallel.launch_calls(pointing_correction, tiles_pairs, nb_workers) if 'global-pointing' in steps: print('correcting pointing globally...') global_pointing_correction(tiles) common.print_elapsed_time() if 'rectification' in steps: print('rectifying tiles...') parallel.launch_calls(rectification_pair, tiles_pairs, nb_workers) if 'matching' in steps: print('running stereo matching...') parallel.launch_calls(stereo_matching, tiles_pairs, nb_workers) if n > 2 and cfg['triangulation_mode'] == 'pairwise': if 'disparity-to-height' in steps: print('computing height maps...') parallel.launch_calls(disparity_to_height, tiles_pairs, nb_workers) print('computing local pairwise height offsets...') parallel.launch_calls(mean_heights, tiles, nb_workers) if 'global-mean-heights' in steps: print('computing global pairwise height offsets...') global_mean_heights(tiles) if 'heights-to-ply' in steps: print('merging height maps and computing point clouds...') parallel.launch_calls(heights_to_ply, tiles, nb_workers) else: if 'triangulation' in steps: print('triangulating tiles...') if cfg['triangulation_mode'] == 'geometric': parallel.launch_calls(multidisparities_to_ply, tiles, nb_workers) elif cfg['triangulation_mode'] == 'pairwise': parallel.launch_calls(disparity_to_ply, tiles, nb_workers) else: raise ValueError("possible values for 'triangulation_mode' : 'pairwise' or 'geometric'") if 'global-srcwin' in steps: print('computing global source window (xoff, yoff, xsize, ysize)...') global_srcwin(tiles) common.print_elapsed_time() if 'local-dsm-rasterization' in steps: print('computing DSM by tile...') parallel.launch_calls(plys_to_dsm, tiles, nb_workers) if 'global-dsm-rasterization' in steps: print('computing global DSM...') global_dsm(tiles) common.print_elapsed_time() if 'lidar-preprocessor' in steps: if cfg['run_lidar_preprocessor']: print('lidar preprocessor...') lidar_preprocessor(tiles) common.print_elapsed_time() else: print("LidarPreprocessor explicitly disabled in config.json") # cleanup common.garbage_cleanup() common.print_elapsed_time(since_first_call=True)
def main(config_file, steps=ALL_STEPS): """ Launch the s2p pipeline with the parameters given in a json file. Args: config_file: path to a json configuration file steps: either a string (single step) or a list of strings (several steps) """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(config_file) if 'initialisation' in steps: initialization.make_dirs() # multiprocessing setup nb_workers = multiprocessing.cpu_count() # nb of available cores if cfg['max_processes']: nb_workers = min(nb_workers, cfg['max_processes']) cfg['max_processes'] = nb_workers tw, th = initialization.adjust_tile_size() print('\ndiscarding masked tiles...') tiles = initialization.tiles_full_info(tw, th) if 'initialisation' in steps: # Write the list of json files to outdir/tiles.txt with open(os.path.join(cfg['out_dir'], 'tiles.txt'), 'w') as f: for t in tiles: f.write(t['json'] + os.linesep) n = len(cfg['images']) tiles_pairs = [(t, i) for i in range(1, n) for t in tiles] # omp_num_threads should not exceed nb_workers when multiplied by len(tiles) cfg['omp_num_threads'] = max(1, int(nb_workers / len(tiles_pairs))) if 'local-pointing' in steps: print('correcting pointing locally...') parallel.launch_calls(pointing_correction, tiles_pairs, nb_workers) if 'global-pointing' in steps: print('correcting pointing globally...') global_pointing_correction(tiles) common.print_elapsed_time() if 'rectification' in steps: print('rectifying tiles...') parallel.launch_calls(rectification_pair, tiles_pairs, nb_workers) if 'matching' in steps: print('running stereo matching...') parallel.launch_calls(stereo_matching, tiles_pairs, nb_workers) if 'triangulation' in steps: if n > 2: print('computing height maps...') parallel.launch_calls(disparity_to_height, tiles_pairs, nb_workers) print('registering height maps...') mean_heights_local = parallel.launch_calls(mean_heights, tiles, nb_workers) print('computing global pairwise height offsets...') mean_heights_global = np.nanmean(mean_heights_local, axis=0) print('merging height maps...') parallel.launch_calls(heights_fusion, tiles, nb_workers, mean_heights_global) print('computing point clouds...') parallel.launch_calls(heights_to_ply, tiles, nb_workers) else: print('triangulating tiles...') parallel.launch_calls(disparity_to_ply, tiles, nb_workers) if 'dsm-rasterization' in steps: print('computing DSM...') plys_to_dsm(tiles) common.print_elapsed_time() if 'lidar-preprocessor' in steps: print('lidar preprocessor...') lidar_preprocessor(tiles) common.print_elapsed_time() # cleanup common.garbage_cleanup() common.print_elapsed_time(since_first_call=True)
def main(user_cfg): """ Launch the s2p pipeline with the parameters given in a json file. Args: user_cfg: user config dictionary """ common.print_elapsed_time.t0 = datetime.datetime.now() initialization.build_cfg(user_cfg) initialization.make_dirs() # multiprocessing setup nb_workers = multiprocessing.cpu_count() # nb of available cores if cfg['max_processes'] is not None: nb_workers = cfg['max_processes'] tw, th = initialization.adjust_tile_size() tiles_txt = os.path.join(cfg['out_dir'], 'tiles.txt') tiles = initialization.tiles_full_info(tw, th, tiles_txt, create_masks=True) # initialisation step: # Write the list of json files to outdir/tiles.txt with open(tiles_txt, 'w') as f: for t in tiles: f.write(t['json'] + os.linesep) n = len(cfg['images']) tiles_pairs = [(t, i) for i in range(1, n) for t in tiles] # local-pointing step: print('correcting pointing locally...') parallel.launch_calls(pointing_correction, tiles_pairs, nb_workers) # global-pointing step: print('correcting pointing globally...') global_pointing_correction(tiles) common.print_elapsed_time() # rectification step: print('rectifying tiles...') parallel.launch_calls(rectification_pair, tiles_pairs, nb_workers) # matching step: print('running stereo matching...') parallel.launch_calls(stereo_matching, tiles_pairs, nb_workers) if n > 2 and cfg['triangulation_mode'] == 'pairwise': # disparity-to-height step: print('computing height maps...') parallel.launch_calls(disparity_to_height, tiles_pairs, nb_workers) print('computing local pairwise height offsets...') parallel.launch_calls(mean_heights, tiles, nb_workers) # global-mean-heights step: print('computing global pairwise height offsets...') global_mean_heights(tiles) # heights-to-ply step: print('merging height maps and computing point clouds...') parallel.launch_calls(heights_to_ply, tiles, nb_workers) else: # triangulation step: print('triangulating tiles...') if cfg['triangulation_mode'] == 'geometric': parallel.launch_calls(multidisparities_to_ply, tiles, nb_workers) elif cfg['triangulation_mode'] == 'pairwise': parallel.launch_calls(disparity_to_ply, tiles, nb_workers) else: raise ValueError( "possible values for 'triangulation_mode' : 'pairwise' or 'geometric'" ) # local-dsm-rasterization step: print('computing DSM by tile...') parallel.launch_calls(plys_to_dsm, tiles, nb_workers) # global-dsm-rasterization step: print('computing global DSM...') global_dsm(tiles) common.print_elapsed_time() # cleanup common.garbage_cleanup() common.print_elapsed_time(since_first_call=True)