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_txt = os.path.join(cfg['out_dir'],'tiles.txt')
tiles = initialization.tiles_full_info(tw, th, tiles_txt)
# 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):
"""
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)
if not tiles:
print('ERROR: the ROI is not seen in two images or is totally masked.')
sys.exit(1)
# initialisation: write the list of tilewise json files to outdir/tiles.txt
with open(tiles_txt, 'w') as f:
for t in tiles:
print(t['json'], file=f)
n = len(cfg['images'])
tiles_pairs = [(t, i) for i in range(1, n) for t in tiles]
timeout = cfg['timeout']
# local-pointing step:
print('correcting pointing locally...')
parallel.launch_calls(pointing_correction,
tiles_pairs,
nb_workers,
timeout=timeout)
# 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,
timeout=timeout)
# matching step:
print('running stereo matching...')
if cfg['max_processes_stereo_matching'] is not None:
nb_workers_stereo = cfg['max_processes_stereo_matching']
else:
nb_workers_stereo = nb_workers
parallel.launch_calls(stereo_matching,
tiles_pairs,
nb_workers_stereo,
timeout=timeout)
if n > 2:
# disparity-to-height step:
print('computing height maps...')
parallel.launch_calls(disparity_to_height,
tiles_pairs,
nb_workers,
timeout=timeout)
print('computing local pairwise height offsets...')
parallel.launch_calls(mean_heights, tiles, nb_workers, timeout=timeout)
# 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,
timeout=timeout)
else:
# triangulation step:
print('triangulating tiles...')
parallel.launch_calls(disparity_to_ply,
tiles,
nb_workers,
timeout=timeout)
# local-dsm-rasterization step:
print('computing DSM by tile...')
parallel.launch_calls(plys_to_dsm, tiles, nb_workers, timeout=timeout)
# 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)
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)
