def pointing_correction(tiles):
"""
Compute the global pointing corrections for each pair of images.
Args:
tiles: list of tile_info dictionaries
"""
nb_pairs = tiles[0]['number_of_pairs']
for i in range(1, nb_pairs + 1):
list_of_tiles = [os.path.join(t['directory'], 'pair_%d' % i) for t in
tiles]
np.savetxt(os.path.join(cfg['out_dir'], 'global_pointing_pair_%d.txt' % i),
pointing_accuracy.global_from_local(list_of_tiles))
def pointing_correction(tiles):
"""
Compute the global pointing corrections for each pair of images.
Args:
tiles: list of tile_info dictionaries
"""
nb_pairs = tiles[0]['number_of_pairs']
for i in range(1, nb_pairs + 1):
list_of_tiles = [
os.path.join(t['directory'], 'pair_%d' % i) for t in tiles
]
np.savetxt(
os.path.join(cfg['out_dir'], 'global_pointing_pair_%d.txt' % i),
pointing_accuracy.global_from_local(list_of_tiles))
def process_pair(out_dir, img1, rpc1, img2, rpc2, x, y, w, h, tw=None, th=None,
ov=None, cld_msk=None, roi_msk=None):
"""
Computes a height map from a Pair of pushbroom images, using tiles.
Args:
out_dir: path to the output directory
img1: path to the reference image.
rpc1: paths to the xml file containing the rpc coefficients of the
reference image
img2: path to the secondary image.
rpc2: paths to the xml file containing the rpc coefficients of the
secondary image
x, y, w, h: four integers defining the rectangular ROI in the reference
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle. The ROI may be as big as you want, as it will be
cutted into small tiles for processing.
tw, th: dimensions of the tiles
ov: width of overlapping bands between tiles
cld_msk (optional): path to a gml file containing a cloud mask
roi_msk (optional): path to a gml file containing a mask defining the
area contained in the full image.
Returns:
path to height map tif file
"""
# create a directory for the experiment
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# duplicate stdout and stderr to log file
tee.Tee('%s/stdout.log' % out_dir, 'w')
# ensure that the coordinates of the ROI are multiples of the zoom factor,
# to avoid bad registration of tiles due to rounding problems.
z = cfg['subsampling_factor']
x, y, w, h = common.round_roi_to_nearest_multiple(z, x, y, w, h)
# TODO: automatically compute optimal size for tiles
if tw is None and th is None and ov is None:
ov = z * 100
if w <= z * cfg['tile_size']:
tw = w
else:
tw = z * cfg['tile_size']
if h <= z * cfg['tile_size']:
th = h
else:
th = z * cfg['tile_size']
ntx = np.ceil(float(w - ov) / (tw - ov))
nty = np.ceil(float(h - ov) / (th - ov))
nt = ntx * nty
print 'tiles size: (%d, %d)' % (tw, th)
print 'total number of tiles: %d (%d x %d)' % (nt, ntx, nty)
# create pool with less workers than available cores
nb_workers = multiprocessing.cpu_count()
if cfg['max_nb_threads']:
nb_workers = min(nb_workers, cfg['max_nb_threads'])
pool = multiprocessing.Pool(nb_workers)
# process the tiles
# don't parallellize if in debug mode
tiles = []
results = []
show_progress.counter = 0
print 'Computing disparity maps tile by tile...'
try:
for row in np.arange(y, y + h - ov, th - ov):
for col in np.arange(x, x + w - ov, tw - ov):
tile_dir = '%s/tile_%06d_%06d_%04d_%04d' % (out_dir, col, row,
tw, th)
# check if the tile is already done, or masked
if os.path.isfile('%s/rectified_disp.tif' % tile_dir):
if cfg['skip_existing']:
print "stereo on tile %d %d already done, skip" % (col,
row)
tiles.append(tile_dir)
continue
if os.path.isfile('%s/this_tile_is_masked.txt' % tile_dir):
print "tile %d %d already masked, skip" % (col, row)
tiles.append(tile_dir)
continue
# process the tile
if cfg['debug']:
process_pair_single_tile(tile_dir, img1, rpc1, img2, rpc2,
col, row, tw, th, None, cld_msk,
roi_msk)
else:
p = pool.apply_async(process_pair_single_tile,
args=(tile_dir, img1, rpc1, img2, rpc2,
col, row, tw, th, None, cld_msk,
roi_msk), callback=show_progress)
results.append(p)
tiles.append(tile_dir)
for r in results:
try:
r.get(3600) # wait at most one hour per tile
except multiprocessing.TimeoutError:
print "Timeout while computing tile "+str(r)
except KeyboardInterrupt:
pool.terminate()
sys.exit(1)
except common.RunFailure as e:
print "FAILED call: ", e.args[0]["command"]
print "output: ", e.args[0]["output"]
# compute global pointing correction
print 'Computing global pointing correction...'
A_global = pointing_accuracy.global_from_local(tiles)
np.savetxt('%s/pointing.txt' % out_dir, A_global)
# Check if all tiles were computed
# The only cause of a tile failure is a lack of sift matches, which breaks
# the pointing correction step. Thus it is enough to check if the pointing
# correction matrix was computed.
results = []
for i, row in enumerate(np.arange(y, y + h - ov, th - ov)):
for j, col in enumerate(np.arange(x, x + w - ov, tw - ov)):
tile_dir = '%s/tile_%06d_%06d_%04d_%04d' % (out_dir, col, row, tw,
th)
if not os.path.isfile('%s/this_tile_is_masked.txt' % tile_dir):
if not os.path.isfile('%s/pointing.txt' % tile_dir):
print "%s retrying pointing corr..." % tile_dir
# estimate pointing correction matrix from neighbors, if it
# fails use A_global, then rerun the disparity map
# computation
A = pointing_accuracy.from_next_tiles(tiles, ntx, nty, j, i)
if A is None:
A = A_global
if cfg['debug']:
process_pair_single_tile(tile_dir, img1, rpc1, img2,
rpc2, col, row, tw, th, None,
cld_msk, roi_msk, A)
else:
p = pool.apply_async(process_pair_single_tile,
args=(tile_dir, img1, rpc1, img2,
rpc2, col, row, tw, th, None,
cld_msk, roi_msk, A),
callback=show_progress)
results.append(p)
try:
for r in results:
try:
r.get(3600) # wait at most one hour per tile
except multiprocessing.TimeoutError:
print "Timeout while computing tile "+str(r)
except KeyboardInterrupt:
pool.terminate()
sys.exit(1)
except common.RunFailure as e:
print "FAILED call: ", e.args[0]["command"]
print "output: ", e.args[0]["output"]
# triangulation
processes = []
results = []
show_progress.counter = 0
print 'Computing height maps tile by tile...'
try:
for row in np.arange(y, y + h - ov, th - ov):
for col in np.arange(x, x + w - ov, tw - ov):
tile = '%s/tile_%06d_%06d_%04d_%04d' % (out_dir, col, row, tw, th)
H1 = '%s/H_ref.txt' % tile
H2 = '%s/H_sec.txt' % tile
disp = '%s/rectified_disp.tif' % tile
mask = '%s/rectified_mask.png' % tile
rpc_err = '%s/rpc_err.tif' % tile
height_map = '%s/height_map.tif' % tile
# check if the tile is already done, or masked
if os.path.isfile(height_map):
if cfg['skip_existing']:
print "triangulation on tile %d %d is done, skip" % (col,
row)
continue
if os.path.isfile('%s/this_tile_is_masked.txt' % tile):
print "tile %d %d already masked, skip" % (col, row)
continue
# process the tile
if cfg['debug']:
triangulation.compute_dem(height_map, col, row, tw, th, z,
rpc1, rpc2, H1, H2, disp, mask,
rpc_err, A_global)
else:
p = pool.apply_async(triangulation.compute_dem,
args=(height_map, col, row, tw, th, z,
rpc1, rpc2, H1, H2, disp, mask,
rpc_err, A_global),
callback=show_progress)
processes.append(p)
for p in processes:
try:
results.append(p.get(3600)) # wait at most one hour per tile
except multiprocessing.TimeoutError:
print "Timeout while computing tile "+str(r)
except KeyboardInterrupt:
pool.terminate()
sys.exit(1)
# tiles composition
out = '%s/height_map.tif' % out_dir
tmp = ['%s/height_map.tif' % t for t in tiles]
if not os.path.isfile(out) or not cfg['skip_existing']:
print "Mosaicing tiles with %s..." % cfg['mosaic_method']
if cfg['mosaic_method'] == 'gdal':
tile_composer.mosaic_gdal(out, w/z, h/z, tmp, tw/z, th/z, ov/z)
else:
tile_composer.mosaic(out, w/z, h/z, tmp, tw/z, th/z, ov/z)
common.garbage_cleanup()
return out
