def heights_to_ply(tile):
"""
Generate a ply cloud.
Args:
tile: a dictionary that provides all you need to process a tile
"""
# merge the n-1 height maps of the tile (n = nb of images)
heights_fusion(tile)
# compute a ply from the merged height map
out_dir = tile['dir']
x, y, w, h = tile['coordinates']
plyfile = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
height_map = os.path.join(out_dir, 'height_map.tif')
# H is the homography transforming the coordinates system of the original
# full size image into the coordinates system of the crop
H = np.dot(np.diag([1, 1, 1]), common.matrix_translation(-x, -y))
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)
triangulation.height_map_to_point_cloud(plyfile,
height_map,
cfg['images'][0]['rpc'],
H,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (plyfile, plyextrema))
if cfg['clean_intermediate']:
common.remove(height_map)
common.remove(colors)
common.remove(
os.path.join(out_dir, 'cloud_water_image_domain_mask.png'))
def heights_to_ply(tile):
"""
Generate a ply cloud.
Args:
tile: a dictionary that provides all you need to process a tile
"""
# merge the n-1 height maps of the tile (n = nb of images)
heights_fusion(tile)
# compute a ply from the merged height map
out_dir = tile['dir']
x, y, w, h = tile['coordinates']
plyfile = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
height_map = os.path.join(out_dir, 'height_map.tif')
colors = os.path.join(out_dir, 'ref.tif')
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)
triangulation.height_map_to_point_cloud(plyfile, height_map,
cfg['images'][0]['rpcm'], x, y,
colors)
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (plyfile, plyextrema))
if cfg['clean_intermediate']:
common.remove(height_map)
common.remove(colors)
common.remove(os.path.join(out_dir, 'mask.png'))
def plot_matches(im1,
im2,
rpc1,
rpc2,
matches,
x=None,
y=None,
w=None,
h=None,
outfile=None):
"""
Plot matches on Pleiades images
Args:
im1, im2: paths to full Pleiades images
rpc1, rpc2: two instances of the rpcm.RPCModel class
matches: 2D numpy array of size 4xN containing a list of matches (a
list of pairs of points, each pair being represented by x1, y1, x2,
y2). The coordinates are given in the frame of the full images.
x, y, w, h (optional, default is None): ROI in the reference image
outfile (optional, default is None): path to the output file. If None,
the file image is displayed using the pvflip viewer
Returns:
path to the displayed output
"""
# if no matches, no plot
if not matches.size:
print("visualisation.plot_matches: nothing to plot")
return
# determine regions to crop in im1 and im2
if x is not None:
x1 = x
else:
x1 = np.min(matches[:, 0])
if y is not None:
y1 = y
else:
y1 = np.min(matches[:, 1])
if w is not None:
w1 = w
else:
w1 = np.max(matches[:, 0]) - x1
if h is not None:
h1 = h
else:
h1 = np.max(matches[:, 1]) - y1
x2, y2, w2, h2 = rpc_utils.corresponding_roi(rpc1, rpc2, x1, y1, w1, h1)
# x2 = np.min(matches[:, 2])
# w2 = np.max(matches[:, 2]) - x2
# y2 = np.min(matches[:, 3])
# h2 = np.max(matches[:, 3]) - y2
# # add 20 pixels offset and round. The image_crop_gdal function will round
# # off the coordinates before it does the crops.
# x1 -= 20; x1 = np.round(x1)
# y1 -= 20; y1 = np.round(y1)
# x2 -= 20; x2 = np.round(x2)
# y2 -= 20; y2 = np.round(y2)
# w1 += 40; w1 = np.round(w1)
# h1 += 40; h1 = np.round(h1)
# w2 += 40; w2 = np.round(w2)
# h2 += 40; h2 = np.round(h2)
# do the crops
crop1 = common.image_qauto(common.image_crop_gdal(im1, x1, y1, w1, h1))
crop2 = common.image_qauto(common.image_crop_gdal(im2, x2, y2, w2, h2))
# compute matches coordinates in the cropped images
pts1 = matches[:, 0:2] - [x1, y1]
pts2 = matches[:, 2:4] - [x2, y2]
# plot the matches on the two crops
to_display = plot_matches_low_level(crop1, crop2, np.hstack((pts1, pts2)))
if outfile is None:
os.system('v %s &' % (to_display))
else:
common.run('cp %s %s' % (to_display, outfile))
return
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()
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)