def tiles_full_info(tw, th):
"""
List the tiles to process and prepare their output directories structures.
Most of the time is spent discarding tiles that are masked by water
(according to SRTM data).
Returns:
a list of dictionaries. Each dictionary contains the image coordinates
and the output directory path of a tile.
"""
rpc = cfg['images'][0]['rpc']
roi_msk = cfg['images'][0]['roi']
cld_msk = cfg['images'][0]['cld']
wat_msk = cfg['images'][0]['wat']
z = cfg['subsampling_factor']
rx = cfg['roi']['x']
ry = cfg['roi']['y']
rw = cfg['roi']['w']
rh = cfg['roi']['h']
# list tiles coordinates
tiles_coords, neighborhood_coords_dict = compute_tiles_coordinates(rx, ry, rw, rh, tw, th, z)
# compute all masks in parallel as numpy arrays
tiles_masks = parallel.launch_calls_simple(masking.cloud_water_image_domain,
tiles_coords,
cfg['max_processes'], rpc,
roi_msk, cld_msk, wat_msk,
cfg['use_srtm_for_water'])
# build a tile dictionary for all non-masked tiles and store them in a list
tiles = []
for coords, mask in zip(tiles_coords,
tiles_masks):
if mask.any(): # there's at least one non-masked pixel in the tile
tile = {}
x, y, w, h = coords
tile['dir'] = os.path.join(cfg['out_dir'],get_tile_dir(x, y, w, h))
tile['coordinates'] = coords
tile['mask'] = mask
tile['neighborhood_dirs'] = list()
key = str((x, y, w, h))
if 'neighborhood_dirs' in cfg:
tile['neighborhood_dirs'] = cfg['neighborhood_dirs']
elif key in neighborhood_coords_dict:
for coords2 in neighborhood_coords_dict[key]:
x2, y2, w2, h2 = coords2
tile['neighborhood_dirs'].append(os.path.join('../../..',get_tile_dir(x2,
y2,
w2,
h2)))
tiles.append(tile)
# make tiles directories and store json configuration dumps
for tile in tiles:
common.mkdir_p(tile['dir'])
for i in range(1, len(cfg['images'])):
common.mkdir_p(os.path.join(tile['dir'], 'pair_{}'.format(i)))
# save a json dump of the tile configuration
tile_cfg = copy.deepcopy(cfg)
x, y, w, h = tile['coordinates']
tile_cfg['roi'] = {'x': x, 'y': y, 'w': w, 'h': h}
tile_cfg['full_img'] = False
tile_cfg['max_processes'] = 1
tile_cfg['omp_num_threads'] = 1
tile_cfg['neighborhood_dirs'] = tile['neighborhood_dirs']
tile_cfg['out_dir']='../../..'
tile_json = os.path.join(get_tile_dir(x,y,w,h),'config.json')
tile['json'] = tile_json
with open(os.path.join(cfg['out_dir'],tile_json), 'w') as f:
json.dump(tile_cfg, f, indent=2)
# save the mask
piio.write(os.path.join(tile['dir'],
'cloud_water_image_domain_mask.png'),
tile['mask'].astype(np.uint8))
return tiles
def tiles_full_info(tw, th, tiles_txt, create_masks=False):
"""
List the tiles to process and prepare their output directories structures.
Most of the time is spent discarding tiles that are masked by water
(according to SRTM data).
Returns:
a list of dictionaries. Each dictionary contains the image coordinates
and the output directory path of a tile.
"""
rpc = cfg['images'][0]['rpc']
roi_msk = cfg['images'][0]['roi']
cld_msk = cfg['images'][0]['cld']
wat_msk = cfg['images'][0]['wat']
rx = cfg['roi']['x']
ry = cfg['roi']['y']
rw = cfg['roi']['w']
rh = cfg['roi']['h']
# build a tile dictionary for all non-masked tiles and store them in a list
tiles = []
# list tiles coordinates
tiles_coords, neighborhood_coords_dict = compute_tiles_coordinates(
rx, ry, rw, rh, tw, th)
if os.path.exists(tiles_txt) is False or create_masks is True:
print('\ndiscarding masked tiles...')
# compute all masks in parallel as numpy arrays
tiles_masks = parallel.launch_calls_simple(
masking.cloud_water_image_domain, tiles_coords,
cfg['max_processes'], rpc, roi_msk, cld_msk, wat_msk,
cfg['use_srtm_for_water'])
for coords, mask in zip(tiles_coords, tiles_masks):
if mask.any(): # there's at least one non-masked pixel in the tile
tile = create_tile(coords, neighborhood_coords_dict)
tiles.append(tile)
# make tiles directories and store json configuration dumps
common.mkdir_p(tile['dir'])
for i in range(1, len(cfg['images'])):
common.mkdir_p(
os.path.join(tile['dir'], 'pair_{}'.format(i)))
# save a json dump of the tile configuration
tile_cfg = copy.deepcopy(cfg)
x, y, w, h = tile['coordinates']
tile_cfg['roi'] = {'x': x, 'y': y, 'w': w, 'h': h}
tile_cfg['full_img'] = False
tile_cfg['max_processes'] = 1
tile_cfg['omp_num_threads'] = 1
tile_cfg['neighborhood_dirs'] = tile['neighborhood_dirs']
tile_cfg['out_dir'] = '../../..'
with open(os.path.join(cfg['out_dir'], tile['json']),
'w') as f:
json.dump(tile_cfg,
f,
indent=2,
default=workaround_json_int64)
# save the mask
piio.write(
os.path.join(tile['dir'],
'cloud_water_image_domain_mask.png'),
mask.astype(np.uint8))
else:
if len(tiles_coords) == 1:
tiles.append(create_tile(tiles_coords[0],
neighborhood_coords_dict))
else:
with open(tiles_txt, 'r') as f_tiles:
for config_json in f_tiles:
tile = {}
with open(
os.path.join(cfg['out_dir'],
config_json.rstrip(os.linesep)),
'r') as f_config:
tile_cfg = json.load(f_config)
roi = tile_cfg['roi']
coords = roi['x'], roi['y'], roi['w'], roi['h']
tiles.append(
create_tile(coords, neighborhood_coords_dict))
return tiles
def tiles_full_info(tw, th):
"""
List the tiles to process and prepare their output directories structures.
Most of the time is spent discarding tiles that are masked by water
(according to SRTM data).
Returns:
a list of dictionaries. Each dictionary contains the image coordinates
and the output directory path of a tile.
"""
rpc = cfg['images'][0]['rpc']
roi_msk = cfg['images'][0]['roi']
cld_msk = cfg['images'][0]['cld']
wat_msk = cfg['images'][0]['wat']
z = cfg['subsampling_factor']
rx = cfg['roi']['x']
ry = cfg['roi']['y']
rw = cfg['roi']['w']
rh = cfg['roi']['h']
# list tiles coordinates
tiles_coords = compute_tiles_coordinates(rx, ry, rw, rh, tw, th, z)
# compute all masks in parallel as numpy arrays
tiles_masks = parallel.launch_calls_simple(masking.cloud_water_image_domain,
tiles_coords,
cfg['max_processes'], rpc,
roi_msk, cld_msk, wat_msk,
cfg['use_srtm_for_water'])
# build a tile dictionary for all non-masked tiles and store them in a list
tiles = []
for coords, mask in zip(tiles_coords, tiles_masks):
if mask.any(): # there's at least one non-masked pixel in the tile
tile = {}
x, y, w, h = coords
tile['dir'] = os.path.join(cfg['out_dir'],
'tiles_row_{}_height_{}'.format(y, h),
'col_{}_width_{}'.format(x, w))
tile['coordinates'] = coords
tile['mask'] = mask
tiles.append(tile)
# make tiles directories and store json configuration dumps
for tile in tiles:
common.mkdir_p(tile['dir'])
for i in range(1, len(cfg['images'])):
common.mkdir_p(os.path.join(tile['dir'], 'pair_{}'.format(i)))
# save a json dump of the tile configuration
tile_cfg = copy.deepcopy(cfg)
x, y, w, h = tile['coordinates']
tile_cfg['roi'] = {'x': x, 'y': y, 'w': w, 'h': h}
tile_cfg['max_processes'] = 1
tile_cfg['omp_num_threads'] = 1
tile_json = os.path.join(tile['dir'], 'config.json')
tile['json'] = tile_json
with open(tile_json, 'w') as f:
json.dump(tile_cfg, f, indent=2)
# save the mask
piio.write(os.path.join(tile['dir'],
'cloud_water_image_domain_mask.png'),
tile['mask'].astype(np.uint8))
return tiles