def merge_height_maps(height_maps,
tile_dir,
thresh,
conservative,
k=1,
garbage=[]):
"""
Merges a list of height maps recursively, computed for one tile from N image pairs.
Args :
- height_maps : list of height map directories
- tile_dir : directory of the tile from which to get a merged height map
- thresh : threshold used for the fusion algorithm, in meters.
- conservative (optional, default is False): if True, keep only the
pixels where the two height map agree (fusion algorithm)
- k : used to identify the current call of merge_height_maps (default = 1, first call)
- garbage : a list used to remove temp data (default = [], first call)
"""
# output file
local_merged_height_map = tile_dir + '/local_merged_height_map.tif'
if len(height_maps) == 0:
return
if os.path.isfile(local_merged_height_map) and cfg['skip_existing']:
print 'final height map %s already done, skip' % local_merged_height_map
else:
list_height_maps = []
for i in range(len(height_maps) - 1):
height_map = tile_dir + '/height_map_' + \
str(i) + '_' + str(i + 1) + '_' + str(k) + '.tif'
fusion.merge(height_maps[i], height_maps[i + 1], thresh,
height_map, conservative)
list_height_maps.append(height_map)
garbage.append(height_map)
if len(list_height_maps) > 1:
merge_height_maps(list_height_maps, tile_dir, thresh, conservative,
k + 1, garbage)
else:
common.run('cp %s %s' %
(list_height_maps[0], local_merged_height_map))
for imtemp in garbage:
common.run('rm -f %s' % imtemp)
def merge_height_maps(height_maps, tile_dir, thresh, conservative, k=1, garbage=[]):
"""
Merges a list of height maps recursively, computed for one tile from N image pairs.
Args :
- height_maps : list of height map directories
- tile_dir : directory of the tile from which to get a merged height map
- thresh : threshold used for the fusion algorithm, in meters.
- conservative (optional, default is False): if True, keep only the
pixels where the two height map agree (fusion algorithm)
- k : used to identify the current call of merge_height_maps (default = 1, first call)
- garbage : a list used to remove temp data (default = [], first call)
"""
# output file
local_merged_height_map = tile_dir + '/local_merged_height_map.tif'
if os.path.isfile(local_merged_height_map) and cfg['skip_existing']:
print 'final height map %s already done, skip' % local_merged_height_map
else:
list_height_maps = []
for i in range(len(height_maps) - 1):
height_map = tile_dir + '/height_map_' + \
str(i) + '_' + str(i + 1) + '_' + str(k) + '.tif'
fusion.merge(height_maps[i], height_maps[i + 1], thresh, height_map,
conservative)
list_height_maps.append(height_map)
garbage.append(height_map)
if len(list_height_maps) > 1:
merge_height_maps(list_height_maps, tile_dir,
thresh, conservative, k + 1, garbage)
else:
common.run('cp %s %s' %
(list_height_maps[0], local_merged_height_map))
for imtemp in garbage:
common.run('rm -f %s' % imtemp)
def process_triplet(out_dir, img1, rpc1, img2, rpc2, img3, rpc3, x=None, y=None,
w=None, h=None, thresh=3, tile_w=None, tile_h=None,
overlap=None, prv1=None, cld_msk=None, roi_msk=None):
"""
Computes a height map from three Pleiades images.
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 of the first pair
rpc2: paths to the xml file containing the rpc coefficients of the
secondary image of the first pair
img3: path to the secondary image of the second pair
rpc3: paths to the xml file containing the rpc coefficients of the
secondary image of the second pair
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.
thresh: threshold used for the fusion algorithm, in meters.
tile_w, tile_h: dimensions of the tiles
overlap: width of overlapping bands between tiles
prv1 (optional): path to a preview of the reference image
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:
Nothing
"""
# 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')
# select ROI
try:
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
except TypeError:
x, y, w, h = common.get_roi_coordinates(rpc1, prv1)
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
# process the two pairs
out_dir_left = '%s/left' % out_dir
height_map_left = process_pair(out_dir_left, img1, rpc1, img2, rpc2, x, y,
w, h, tile_w, tile_h, overlap, cld_msk,
roi_msk)
out_dir_right = '%s/right' % out_dir
height_map_right = process_pair(out_dir_right, img1, rpc1, img3, rpc3, x,
y, w, h, tile_w, tile_h, overlap, cld_msk,
roi_msk)
# merge the two height maps
height_map = '%s/height_map.tif' % out_dir
fusion.merge(height_map_left, height_map_right, thresh, height_map)
common.garbage_cleanup()
return height_map