def multidisparities_to_ply(tile):
"""
Compute a point cloud from the disparity maps of N-pairs of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
# There is no guarantee that this function works with z!=1
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc_ref = cfg['images'][0]['rpc']
disp_list = list()
rpc_list = list()
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
print('triangulating tile {} {}...'.format(x, y))
n = len(cfg['images']) - 1
for i in range(n):
pair = 'pair_%d' % (i + 1)
H_ref = os.path.join(out_dir, pair, 'H_ref.txt')
H_sec = os.path.join(out_dir, pair, 'H_sec.txt')
disp = os.path.join(out_dir, pair, 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, pair, 'rectified_mask.png')
disp2D = os.path.join(out_dir, pair, 'disp2D.tif')
rpc_sec = cfg['images'][i + 1]['rpc']
if os.path.exists(disp):
# homography for warp
T = common.matrix_translation(x, y)
hom_ref = np.loadtxt(H_ref)
hom_ref_shift = np.dot(hom_ref, T)
# homography for 1D to 2D conversion
hom_sec = np.loadtxt(H_sec)
if cfg["use_global_pointing_for_geometric_triangulation"] is True:
pointing = os.path.join(cfg['out_dir'],
'global_pointing_%s.txt' % pair)
hom_pointing = np.loadtxt(pointing)
hom_sec = np.dot(hom_sec, np.linalg.inv(hom_pointing))
hom_sec_shift_inv = np.linalg.inv(hom_sec)
h1 = " ".join(str(x) for x in hom_ref_shift.flatten())
h2 = " ".join(str(x) for x in hom_sec_shift_inv.flatten())
# relative disparity map to absolute disparity map
tmp_abs = common.tmpfile('.tif')
os.environ["PLAMBDA_GETPIXEL"] = "0"
common.run(
'plambda %s %s "y 0 = nan x[0] :i + x[1] :j + 1 3 njoin if" -o %s'
% (disp, mask_rect, tmp_abs))
# 1d to 2d conversion
tmp_1d_to_2d = common.tmpfile('.tif')
common.run('plambda %s "%s 9 njoin x mprod" -o %s' %
(tmp_abs, h2, tmp_1d_to_2d))
# warp
tmp_warp = common.tmpfile('.tif')
common.run('homwarp -o 2 "%s" %d %d %s %s' %
(h1, w, h, tmp_1d_to_2d, tmp_warp))
# set masked value to NaN
exp = 'y 0 = nan x if'
common.run('plambda %s %s "%s" -o %s' %
(tmp_warp, mask_orig, exp, disp2D))
# disp2D contains positions in the secondary image
# added input data for triangulation module
disp_list.append(disp2D)
rpc_list.append(rpc_sec)
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(
common.image_crop_gdal(cfg['images'][0]['img'], x, y, w, h),
colors)
# compute the point cloud
triangulation.multidisp_map_to_point_cloud(ply_file,
disp_list,
rpc_ref,
rpc_list,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x + w, y, y + h))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(colors)
def multidisparities_to_ply(tile):
"""
Compute a point cloud from the disparity maps of N-pairs of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
# There is no guarantee that this function works with z!=1
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc_ref = cfg['images'][0]['rpc']
disp_list = list()
rpc_list = list()
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
print('triangulating tile {} {}...'.format(x, y))
n = len(cfg['images']) - 1
for i in range(n):
pair = 'pair_%d' % (i+1)
H_ref = os.path.join(out_dir, pair, 'H_ref.txt')
H_sec = os.path.join(out_dir, pair, 'H_sec.txt')
disp = os.path.join(out_dir, pair, 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, pair, 'rectified_mask.png')
disp2D = os.path.join(out_dir, pair, 'disp2D.tif')
rpc_sec = cfg['images'][i+1]['rpc']
if os.path.exists(disp):
# homography for warp
T = common.matrix_translation(x, y)
hom_ref = np.loadtxt(H_ref)
hom_ref_shift = np.dot(hom_ref, T)
# homography for 1D to 2D conversion
hom_sec = np.loadtxt(H_sec)
if cfg["use_global_pointing_for_geometric_triangulation"] is True:
pointing = os.path.join(cfg['out_dir'], 'global_pointing_%s.txt' % pair)
hom_pointing = np.loadtxt(pointing)
hom_sec = np.dot(hom_sec,np.linalg.inv(hom_pointing))
hom_sec_shift_inv = np.linalg.inv(hom_sec)
h1 = " ".join(str(x) for x in hom_ref_shift.flatten())
h2 = " ".join(str(x) for x in hom_sec_shift_inv.flatten())
# relative disparity map to absolute disparity map
tmp_abs = common.tmpfile('.tif')
os.environ["PLAMBDA_GETPIXEL"] = "0"
common.run('plambda %s %s "y 0 = nan x[0] :i + x[1] :j + 1 3 njoin if" -o %s' % (disp, mask_rect, tmp_abs))
# 1d to 2d conversion
tmp_1d_to_2d = common.tmpfile('.tif')
common.run('plambda %s "%s 9 njoin x mprod" -o %s' % (tmp_abs, h2, tmp_1d_to_2d))
# warp
tmp_warp = common.tmpfile('.tif')
common.run('homwarp -o 2 "%s" %d %d %s %s' % (h1, w, h, tmp_1d_to_2d, tmp_warp))
# set masked value to NaN
exp = 'y 0 = nan x if'
common.run('plambda %s %s "%s" -o %s' % (tmp_warp, mask_orig, exp, disp2D))
# disp2D contains positions in the secondary image
# added input data for triangulation module
disp_list.append(disp2D)
rpc_list.append(rpc_sec)
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(common.image_crop_gdal(cfg['images'][0]['img'], x, y,
w, h), colors)
# compute the point cloud
triangulation.multidisp_map_to_point_cloud(ply_file, disp_list, rpc_ref, rpc_list,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x+w, y, y+h))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(colors)
