def init_roi(config_file):
"""
1) Loads configuration file
2) Checks parameters
3) Selects the ROI
4) Checks the zoom factor
"""
# read the json configuration file
f = open(config_file)
user_cfg = json.load(f)
f.close()
# Check that all the mandatory arguments are defined, and warn about
# 'unknown' params
check_parameters(user_cfg)
# fill the config module: updates the content of the config.cfg dictionary
# with the content of the user_cfg dictionary
cfg.update(user_cfg)
# sets keys 'clr', 'cld' and 'roi' of the reference image to None if they
# are not already defined. The default values of these optional arguments
# can not be defined directly in the config.py module. They would be
# overwritten by the previous update, because they are in a nested dict.
cfg['images'][0].setdefault('clr')
cfg['images'][0].setdefault('cld')
cfg['images'][0].setdefault('roi')
# update roi definition if the full_img flag is set to true
if ('full_img' in cfg) and cfg['full_img']:
sz = common.image_size_tiffinfo(cfg['images'][0]['img'])
cfg['roi'] = {}
cfg['roi']['x'] = 0
cfg['roi']['y'] = 0
cfg['roi']['w'] = sz[0]
cfg['roi']['h'] = sz[1]
# check that the roi is well defined
if 'roi' not in cfg or any(p not in cfg['roi'] for p in ['x', 'y', 'w',
'h']):
print "missing or incomplete ROI definition"
print "ROI will be redefined by interactive selection"
x, y, w, h = common.get_roi_coordinates(cfg['images'][0]['img'],
cfg['images'][0]['prv'])
cfg['roi'] = {}
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
else :
x = cfg['roi']['x']
y = cfg['roi']['y']
w = cfg['roi']['w']
h = cfg['roi']['h']
try:
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
except TypeError:
print 'Neither a ROI nor a preview file are defined. Aborting.'
return
# check the zoom factor
z = cfg['subsampling_factor']
assert(z > 0 and z == np.floor(z))
# ensure that the coordinates of the ROI are multiples of the zoom factor,
# to avoid bad registration of tiles due to rounding problems.
x, y, w, h = common.round_roi_to_nearest_multiple(z, x, y, w, h)
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
def init_roi(config_file):
"""
1) Loads configuration file
2) Checks parameters
3) Selects the ROI
4) Checks the zoom factor
"""
# read the json configuration file
f = open(config_file)
user_cfg = json.load(f)
f.close()
# Check that all the mandatory arguments are defined, and warn about
# 'unknown' params
check_parameters(user_cfg)
# fill the config module: updates the content of the config.cfg dictionary
# with the content of the user_cfg dictionary
cfg.update(user_cfg)
# sets keys 'clr', 'cld' and 'roi' of the reference image to None if they
# are not already defined. The default values of these optional arguments
# can not be defined directly in the config.py module. They would be
# overwritten by the previous update, because they are in a nested dict.
cfg['images'][0].setdefault('clr')
cfg['images'][0].setdefault('cld')
cfg['images'][0].setdefault('roi')
cfg['images'][0].setdefault('wat')
# update roi definition if the full_img flag is set to true
if ('full_img' in cfg) and cfg['full_img']:
sz = common.image_size_tiffinfo(cfg['images'][0]['img'])
cfg['roi'] = {}
cfg['roi']['x'] = 0
cfg['roi']['y'] = 0
cfg['roi']['w'] = sz[0]
cfg['roi']['h'] = sz[1]
# check that the roi is well defined
if 'roi' not in cfg or any(p not in cfg['roi']
for p in ['x', 'y', 'w', 'h']):
print "missing or incomplete ROI definition"
print "ROI will be redefined by interactive selection"
x, y, w, h = common.get_roi_coordinates(cfg['images'][0]['img'],
cfg['images'][0]['prv'])
cfg['roi'] = {}
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
else:
x = cfg['roi']['x']
y = cfg['roi']['y']
w = cfg['roi']['w']
h = cfg['roi']['h']
try:
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
except TypeError:
print 'Neither a ROI nor a preview file are defined. Aborting.'
return
# check the zoom factor
z = cfg['subsampling_factor']
assert (z > 0 and z == np.floor(z))
# ensure that the coordinates of the ROI are multiples of the zoom factor,
# to avoid bad registration of tiles due to rounding problems.
x, y, w, h = common.round_roi_to_nearest_multiple(z, x, y, w, h)
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
# get utm zone
utm_zone = rpc_utils.utm_zone(
cfg['images'][0]['rpc'],
*[cfg['roi'][v] for v in ['x', 'y', 'w', 'h']])
cfg['utm_zone'] = utm_zone
def main(config_file):
"""
Launches s2p with the parameters given by a json file.
Args:
config_file: path to the config json file
"""
# read the json configuration file
f = open(config_file)
user_cfg = json.load(f)
f.close()
# Check that all the mandatory arguments are defined, and warn about
# 'unknown' params
check_parameters(user_cfg)
# fill the config module: updates the content of the config.cfg dictionary
# with the content of the user_cfg dictionary
cfg.update(user_cfg)
# sets keys 'clr', 'cld' and 'roi' of the reference image to None if they
# are not already defined. The default values of these optional arguments
# can not be defined directly in the config.py module. They would be
# overwritten by the previous update, because they are in a nested dict.
cfg['images'][0].setdefault('clr')
cfg['images'][0].setdefault('cld')
cfg['images'][0].setdefault('roi')
# update roi definition if the full_img flag is set to true
if ('full_img' in cfg) and cfg['full_img']:
sz = common.image_size_tiffinfo(cfg['images'][0]['img'])
cfg['roi'] = {}
cfg['roi']['x'] = 0
cfg['roi']['y'] = 0
cfg['roi']['w'] = sz[0]
cfg['roi']['h'] = sz[1]
# check that the roi is well defined
if 'roi' not in cfg or any(p not in cfg['roi'] for p in ['x', 'y', 'w',
'h']):
print "missing or incomplete ROI definition"
print "ROI will be redefined by interactive selection"
x, y, w, h = common.get_roi_coordinates(cfg['images'][0]['img'],
cfg['images'][0]['prv'])
cfg['roi'] = {}
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
# check the zoom factor
z = cfg['subsampling_factor']
assert(z > 0 and z == np.floor(z))
# create tmp dir and output directory for the experiment, and store a json
# dump of the config.cfg dictionary there
if not os.path.exists(cfg['temporary_dir']):
os.makedirs(cfg['temporary_dir'])
if not os.path.exists(os.path.join(cfg['temporary_dir'], 'meta')):
os.makedirs(os.path.join(cfg['temporary_dir'], 'meta'))
if not os.path.exists(cfg['out_dir']):
os.makedirs(cfg['out_dir'])
f = open('%s/config.json' % cfg['out_dir'], 'w')
json.dump(cfg, f, indent=2)
f.close()
# measure total runtime
t0 = time.time()
# needed srtm tiles
srtm_tiles = srtm.list_srtm_tiles(cfg['images'][0]['rpc'],
*cfg['roi'].values())
for s in srtm_tiles:
srtm.get_srtm_tile(s, cfg['srtm_dir'])
# height map
if len(cfg['images']) == 2:
height_map = process_pair(cfg['out_dir'], cfg['images'][0]['img'],
cfg['images'][0]['rpc'], cfg['images'][1]['img'],
cfg['images'][1]['rpc'], cfg['roi']['x'],
cfg['roi']['y'], cfg['roi']['w'], cfg['roi']['h'],
None, None, None, cfg['images'][0]['cld'],
cfg['images'][0]['roi'])
else:
height_map = process_triplet(cfg['out_dir'], cfg['images'][0]['img'],
cfg['images'][0]['rpc'], cfg['images'][1]['img'],
cfg['images'][1]['rpc'], cfg['images'][2]['img'],
cfg['images'][2]['rpc'], cfg['roi']['x'],
cfg['roi']['y'], cfg['roi']['w'], cfg['roi']['h'],
cfg['fusion_thresh'], None, None, None, None,
cfg['images'][0]['cld'], cfg['images'][0]['roi'])
# point cloud
generate_cloud(cfg['out_dir'], height_map, cfg['images'][0]['rpc'],
cfg['roi']['x'], cfg['roi']['y'], cfg['roi']['w'],
cfg['roi']['h'], cfg['images'][0]['img'],
cfg['images'][0]['clr'], cfg['offset_ply'])
# digital surface model
out_dsm = '%s/dsm.tif' % cfg['out_dir']
point_clouds_list = glob.glob('%s/cloud.ply' % cfg['out_dir'])
generate_dsm(out_dsm, point_clouds_list, cfg['dsm_resolution'])
# crop corresponding areas in the secondary images
if not cfg['full_img']:
crop_corresponding_areas(cfg['out_dir'], cfg['images'], cfg['roi'])
# runtime
t = int(time.time() - t0)
h = t/3600
m = (t/60) % 60
s = t % 60
print "Total runtime: %dh:%dm:%ds" % (h, m, s)
common.garbage_cleanup()
def process_pair_single_tile(out_dir, img1, rpc1, img2, rpc2, x=None, y=None,
w=None, h=None, prv1=None, cld_msk=None,
roi_msk=None, A=None):
"""
Computes a disparity map from a Pair of Pleiades images, without tiling
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.
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.
A (optional, default None): pointing correction matrix. If None, it
will be estimated by this function.
Returns:
nothing
"""
# create a directory for the experiment
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# output files
rect1 = '%s/rectified_ref.tif' % (out_dir)
rect2 = '%s/rectified_sec.tif' % (out_dir)
disp = '%s/rectified_disp.tif' % (out_dir)
mask = '%s/rectified_mask.png' % (out_dir)
cwid_msk = '%s/cloud_water_image_domain_mask.png' % (out_dir)
subsampling = '%s/subsampling.txt' % (out_dir)
pointing = '%s/pointing.txt' % out_dir
center = '%s/center_keypts_sec.txt' % out_dir
sift_matches = '%s/sift_matches.txt' % out_dir
sift_matches_plot = '%s/sift_matches_plot.png' % out_dir
H_ref = '%s/H_ref.txt' % out_dir
H_sec = '%s/H_sec.txt' % out_dir
disp_min_max = '%s/disp_min_max.txt' % out_dir
config = '%s/config.json' % out_dir
# select ROI
try:
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
except TypeError:
if prv1:
x, y, w, h = common.get_roi_coordinates(img1, prv1)
else:
print 'Neither a ROI nor a preview file are defined. Aborting.'
return
# redirect stdout and stderr to log file
if not cfg['debug']:
fout = open('%s/stdout.log' % out_dir, 'w', 0) # '0' for no buffering
sys.stdout = fout
sys.stderr = fout
# debug print
print 'tile %d %d running on process %s' % (x, y,
multiprocessing.current_process())
# ensure that the coordinates of the ROI are multiples of the zoom factor
z = cfg['subsampling_factor']
x, y, w, h = common.round_roi_to_nearest_multiple(z, x, y, w, h)
# check if the ROI is completely masked (water, or outside the image domain)
H = np.array([[1, 0, -x], [0, 1, -y], [0, 0, 1]])
if masking.cloud_water_image_domain(cwid_msk, w, h, H, rpc1, roi_msk,
cld_msk):
print "Tile masked by water or outside definition domain, skip"
open("%s/this_tile_is_masked.txt" % out_dir, 'a').close()
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
if not cfg['debug']:
fout.close()
return
# correct pointing error
# A is the correction matrix and m is the list of sift matches
if A is None:
A, m = pointing_accuracy.compute_correction(img1, rpc1, img2, rpc2, x,
y, w, h)
if A is not None:
np.savetxt(pointing, A)
if m is not None:
np.savetxt(sift_matches, m)
np.savetxt(center, np.mean(m[:, 2:4], 0))
visualisation.plot_matches_pleiades(img1, img2, rpc1, rpc2, m, x, y,
w, h, sift_matches_plot)
else:
m = None
# rectification
H1, H2, disp_min, disp_max = rectification.rectify_pair(img1, img2, rpc1,
rpc2, x, y, w, h,
rect1, rect2, A, m)
# block-matching
if cfg['disp_min'] is not None:
disp_min = cfg['disp_min']
if cfg['disp_max'] is not None:
disp_max = cfg['disp_max']
block_matching.compute_disparity_map(rect1, rect2, disp, mask,
cfg['matching_algorithm'], disp_min,
disp_max)
# intersect mask with the cloud_water_image_domain mask (recomputed here to
# get to be sampled on the epipolar grid)
ww, hh = common.image_size(rect1)
masking.cloud_water_image_domain(cwid_msk, ww, hh, H1, rpc1, roi_msk,
cld_msk)
try:
masking.intersection(mask, mask, cwid_msk)
masking.erosion(mask, mask, cfg['msk_erosion'])
except OSError:
print "file %s not produced" % mask
# save the subsampling factor, the rectifying homographies and the
# disparity bounds.
# ATTENTION if subsampling_factor is > 1 the rectified images will be
# smaller, and the homography matrices and disparity range will reflect
# this fact
np.savetxt(subsampling, np.array([z]))
np.savetxt(H_ref, H1)
np.savetxt(H_sec, H2)
np.savetxt(disp_min_max, np.array([disp_min, disp_max]))
# save json file with all the parameters needed to reproduce this tile
tile_cfg = copy.deepcopy(cfg)
tile_cfg['roi'] = {'x': x, 'y': y, 'w': w, 'h': h}
f = open(config, 'w')
json.dump(tile_cfg, f, indent=2)
f.close()
# close logs
common.garbage_cleanup()
if not cfg['debug']:
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
fout.close()
return
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
def init_dirs_srtm_roi(config_file):
"""
1) Loads configuration file
2) Checks parameters
3) Selects the ROI
4) Checks the zoom factor
5) Creates different directories : output, temp...
Args:
config_file : path to a json configuration file
"""
# read the json configuration file
f = open(config_file)
user_cfg = json.load(f)
f.close()
# Check that all the mandatory arguments are defined, and warn about
# 'unknown' params
check_parameters(user_cfg)
# fill the config module: updates the content of the config.cfg dictionary
# with the content of the user_cfg dictionary
cfg.update(user_cfg)
# sets keys 'clr', 'cld' and 'roi' of the reference image to None if they
# are not already defined. The default values of these optional arguments
# can not be defined directly in the config.py module. They would be
# overwritten by the previous update, because they are in a nested dict.
cfg['images'][0].setdefault('clr')
cfg['images'][0].setdefault('cld')
cfg['images'][0].setdefault('roi')
# update roi definition if the full_img flag is set to true
if ('full_img' in cfg) and cfg['full_img']:
sz = common.image_size_tiffinfo(cfg['images'][0]['img'])
cfg['roi'] = {}
cfg['roi']['x'] = 0
cfg['roi']['y'] = 0
cfg['roi']['w'] = sz[0]
cfg['roi']['h'] = sz[1]
# check that the roi is well defined
if 'roi' not in cfg or any(p not in cfg['roi'] for p in ['x', 'y', 'w',
'h']):
print "missing or incomplete ROI definition"
print "ROI will be redefined by interactive selection"
x, y, w, h = common.get_roi_coordinates(cfg['images'][0]['img'],
cfg['images'][0]['prv'])
cfg['roi'] = {}
cfg['roi']['x'] = x
cfg['roi']['y'] = y
cfg['roi']['w'] = w
cfg['roi']['h'] = h
else:
x = cfg['roi']['x']
y = cfg['roi']['y']
w = cfg['roi']['w']
h = cfg['roi']['h']
try:
print "ROI x, y, w, h = %d, %d, %d, %d" % (x, y, w, h)
except TypeError:
print 'Neither a ROI nor a preview file are defined. Aborting.'
return
# check the zoom factor
z = cfg['subsampling_factor']
assert(z > 0 and z == np.floor(z))
# create tmp dir and output directory for the experiment, and store a json
# dump of the config.cfg dictionary there, download srtm files...
if not os.path.exists(cfg['out_dir']):
os.makedirs(cfg['out_dir'])
if not os.path.exists(cfg['temporary_dir']):
os.makedirs(cfg['temporary_dir'])
if not os.path.exists(os.path.join(cfg['temporary_dir'], 'meta')):
os.makedirs(os.path.join(cfg['temporary_dir'], 'meta'))
f = open('%s/config.json' % cfg['out_dir'], 'w')
json.dump(cfg, f, indent=2)
f.close()
# duplicate stdout and stderr to log file
tee.Tee('%s/stdout.log' % cfg['out_dir'], 'w')
# needed srtm tiles
srtm_tiles = srtm.list_srtm_tiles(cfg['images'][0]['rpc'],
*cfg['roi'].values())
for s in srtm_tiles:
srtm.get_srtm_tile(s, cfg['srtm_dir'])