def minmax_color_on_tile(tile_info):
"""
Compute min and max intensities on a given tile and save them to a txt file.
Args:
tile_info: dictionary containing all the information needed to process
the tile
"""
# read info
img1 = cfg['images'][0]['img']
coords = tile_info['coordinates']
tile_dir = tile_info['directory']
z = cfg['subsampling_factor']
# output files
crop_ref = os.path.join(tile_dir, 'roi_ref.tif')
local_minmax = os.path.join(tile_dir, 'local_minmax.txt')
# do the job
common.cropImage(img1, crop_ref, *coords, zoom=z)
if os.path.isfile(os.path.join(tile_dir, 'this_tile_is_masked.txt')):
print 'tile %s is masked, skip' % tile_dir
elif os.path.isfile(os.path.join(tile_dir, 'local_minmax.txt')) and cfg['skip_existing']:
print 'extrema intensities on tile %s already computed, skip' % tile_dir
else:
common.image_getminmax(crop_ref, local_minmax)
def minmax_color_on_tile(tile_info):
"""
Compute min and max intensities on a given tile and save them to a txt file.
Args:
tile_info: dictionary containing all the information needed to process
the tile
"""
# read info
img = cfg['images'][0]['img']
coords = tile_info['coordinates']
tile_dir = tile_info['directory']
z = cfg['subsampling_factor']
# output files
crop_ref = os.path.join(tile_dir, 'roi_ref.tif')
local_minmax = os.path.join(tile_dir, 'local_minmax.txt')
# do the job
common.cropImage(img, crop_ref, *coords, zoom=z)
if os.path.isfile(os.path.join(tile_dir, 'this_tile_is_masked.txt')):
print 'tile %s is masked, skip' % tile_dir
elif os.path.isfile(os.path.join(
tile_dir, 'local_minmax.txt')) and cfg['skip_existing']:
print 'extrema intensities on tile %s already computed, skip' % tile_dir
else:
common.image_getminmax(crop_ref, local_minmax)
def finalize_tile(tile_info, height_maps):
"""
Finalize the processing of a tile.
Merge the height maps from the N pairs, remove overlapping areas, get the
colors from a XS image and use it to color and generate a ply file
(colorization is not mandatory)
Args:
tile_info: a dictionary that provides all you need to process a tile
height_maps: list of the height maps generated from N pairs
"""
# get info
tile_dir = tile_info['directory']
nb_pairs = tile_info['number_of_pairs']
x, y, w, h = tile_info['coordinates']
ov = tile_info['overlap']
pos = tile_info['position_type']
img1, rpc1 = cfg['images'][0]['img'], cfg['images'][0]['rpc']
# merge the n height maps
local_merged_height_map = os.path.join(tile_dir,
'local_merged_height_map.tif')
if len(height_maps) > 1:
merge_height_maps(height_maps, tile_dir, cfg['fusion_thresh'],
cfg['fusion_conservative'], 1, [])
else:
common.run('cp %s %s' % (height_maps[0], local_merged_height_map))
# remove overlapping areas
# By tile
local_merged_height_map = tile_dir + '/local_merged_height_map.tif'
local_merged_height_map_crop = tile_dir + '/local_merged_height_map_crop.tif'
crop_ref = tile_dir + '/roi_ref.tif'
crop_ref_crop = tile_dir + '/roi_ref_crop.tif'
dicoPos = {}
dicoPos['M'] = [ov / 2, ov / 2, -ov, -ov]
dicoPos['L'] = [0, ov / 2, -ov / 2, -ov]
dicoPos['R'] = [ov / 2, ov / 2, -ov / 2, -ov]
dicoPos['U'] = [ov / 2, 0, -ov, -ov / 2]
dicoPos['B'] = [ov / 2, ov / 2, -ov, -ov / 2]
dicoPos['UL'] = [0, 0, -ov / 2, -ov / 2]
dicoPos['UR'] = [ov / 2, 0, -ov / 2, -ov / 2]
dicoPos['BR'] = [ov / 2, ov / 2, -ov / 2, -ov / 2]
dicoPos['BL'] = [0, ov / 2, -ov / 2, -ov / 2]
dicoPos['Single'] = [0, 0, 0, 0]
z = cfg['subsampling_factor']
newcol, newrow, difftw, diffth = np.array(dicoPos[pos]) / z
x = x / z + newcol
y = y / z + newrow
w = w / z + difftw
h = h / z + diffth
tile_info['coordinates'] = (x, y, w, h)
# z=1 beacause local_merged_height_map, crop_ref (and so forth) have
# already been zoomed. So don't zoom again to crop these images.
common.cropImage(local_merged_height_map, local_merged_height_map_crop,
newcol, newrow, w, h)
common.cropImage(crop_ref, crop_ref_crop, newcol, newrow, w, h)
# by pair
for i in range(1, nb_pairs + 1):
single_height_map = os.path.join(tile_dir, 'pair_%d/height_map.tif' % i)
single_height_map_crop = os.path.join(tile_dir, 'pair_%d/height_map_crop.tif' % i)
single_rpc_err = os.path.join(tile_dir, 'pair_%d/rpc_err.tif' % i)
single_rpc_err_crop = os.path.join(tile_dir, 'pair_%d/rpc_err_crop.tif' % i)
common.cropImage(single_height_map, single_height_map_crop, newcol,
newrow, w, h)
common.cropImage(single_rpc_err, single_rpc_err_crop, newcol, newrow, w, h)
# colors
color_crop_ref(tile_info, cfg['images'][0]['clr'])
# generate cloud
generate_cloud(tile_info, cfg['offset_ply'])
def finalize_tile(tile_info, height_maps):
"""
Finalize the processing of a tile.
Merge the height maps from the N pairs, remove overlapping areas, get the
colors from a XS image and use it to color and generate a ply file
(colorization is not mandatory)
Args:
tile_info: a dictionary that provides all you need to process a tile
height_maps: list of the height maps generated from N pairs
"""
# get info
tile_dir = tile_info['directory']
nb_pairs = tile_info['number_of_pairs']
x, y, w, h = tile_info['coordinates']
ov = tile_info['overlap']
pos = tile_info['position_type']
img1, rpc1 = cfg['images'][0]['img'], cfg['images'][0]['rpc']
# merge the n height maps
local_merged_height_map = os.path.join(tile_dir,
'local_merged_height_map.tif')
if len(height_maps) > 1:
merge_height_maps(height_maps, tile_dir, cfg['fusion_thresh'],
cfg['fusion_conservative'], 1, [])
else:
common.run('cp %s %s' % (height_maps[0], local_merged_height_map))
# remove overlapping areas
# By tile
local_merged_height_map = tile_dir + '/local_merged_height_map.tif'
local_merged_height_map_crop = tile_dir + '/local_merged_height_map_crop.tif'
crop_ref = tile_dir + '/roi_ref.tif'
crop_ref_crop = tile_dir + '/roi_ref_crop.tif'
dicoPos = {}
dicoPos['M'] = [ov / 2, ov / 2, -ov, -ov]
dicoPos['L'] = [0, ov / 2, -ov / 2, -ov]
dicoPos['R'] = [ov / 2, ov / 2, -ov / 2, -ov]
dicoPos['U'] = [ov / 2, 0, -ov, -ov / 2]
dicoPos['B'] = [ov / 2, ov / 2, -ov, -ov / 2]
dicoPos['UL'] = [0, 0, -ov / 2, -ov / 2]
dicoPos['UR'] = [ov / 2, 0, -ov / 2, -ov / 2]
dicoPos['BR'] = [ov / 2, ov / 2, -ov / 2, -ov / 2]
dicoPos['BL'] = [0, ov / 2, -ov / 2, -ov / 2]
dicoPos['Single'] = [0, 0, 0, 0]
z = cfg['subsampling_factor']
newcol, newrow, difftw, diffth = np.array(dicoPos[pos]) / z
x = x / z + newcol
y = y / z + newrow
w = w / z + difftw
h = h / z + diffth
# z=1 beacause local_merged_height_map, crop_ref (and so forth) have
# already been zoomed. So don't zoom again to crop these images.
common.cropImage(local_merged_height_map, local_merged_height_map_crop,
newcol, newrow, w, h)
common.cropImage(crop_ref, crop_ref_crop, newcol, newrow, w, h)
# by pair
for i in range(1, nb_pairs + 1):
single_height_map = os.path.join(tile_dir,
'pair_%d/height_map.tif' % i)
single_height_map_crop = os.path.join(
tile_dir, 'pair_%d/height_map_crop.tif' % i)
single_rpc_err = os.path.join(tile_dir, 'pair_%d/rpc_err.tif' % i)
single_rpc_err_crop = os.path.join(tile_dir,
'pair_%d/rpc_err_crop.tif' % i)
common.cropImage(single_height_map, single_height_map_crop, newcol,
newrow, w, h)
common.cropImage(single_rpc_err, single_rpc_err_crop, newcol, newrow,
w, h)
# colors
color_crop_ref(tile_info, cfg['images'][0]['clr'])
# generate cloud
generate_cloud(tile_info, cfg['offset_ply'])
def finalize_tile(tile_info, height_maps, utm_zone=None):
"""
Finalize the processing of a tile.
Merge the height maps from the N pairs, remove overlapping areas, get the
colors from a XS image and use it to color and generate a ply file
(colorization is not mandatory)
Args:
tile_info: a dictionary that provides all you need to process a tile
height_maps: list of the height maps generated from N pairs
"""
# get info
tile_dir = tile_info["directory"]
nb_pairs = tile_info["number_of_pairs"]
x, y, w, h = tile_info["coordinates"]
ov = tile_info["overlap"]
pos = tile_info["position_type"]
img1, rpc1 = cfg["images"][0]["img"], cfg["images"][0]["rpc"]
# merge the n height maps
local_merged_height_map = os.path.join(tile_dir, "local_merged_height_map.tif")
if len(height_maps) > 1:
merge_height_maps(height_maps, tile_dir, cfg["fusion_thresh"], cfg["fusion_conservative"], 1, [])
elif len(height_maps) == 0:
return
else:
common.run("cp %s %s" % (height_maps[0], local_merged_height_map))
# remove overlapping areas
# By tile
local_merged_height_map = tile_dir + "/local_merged_height_map.tif"
local_merged_height_map_crop = tile_dir + "/local_merged_height_map_crop.tif"
crop_ref = tile_dir + "/roi_ref.tif"
crop_ref_crop = tile_dir + "/roi_ref_crop.tif"
dicoPos = {}
dicoPos["M"] = [ov / 2, ov / 2, -ov, -ov]
dicoPos["L"] = [0, ov / 2, -ov / 2, -ov]
dicoPos["R"] = [ov / 2, ov / 2, -ov / 2, -ov]
dicoPos["U"] = [ov / 2, 0, -ov, -ov / 2]
dicoPos["B"] = [ov / 2, ov / 2, -ov, -ov / 2]
dicoPos["UL"] = [0, 0, -ov / 2, -ov / 2]
dicoPos["UR"] = [ov / 2, 0, -ov / 2, -ov / 2]
dicoPos["BR"] = [ov / 2, ov / 2, -ov / 2, -ov / 2]
dicoPos["BL"] = [0, ov / 2, -ov / 2, -ov / 2]
dicoPos["Single"] = [0, 0, 0, 0]
z = cfg["subsampling_factor"]
newcol, newrow, difftw, diffth = np.array(dicoPos[pos]) / z
x = x / z + newcol
y = y / z + newrow
w = w / z + difftw
h = h / z + diffth
tile_info["coordinates"] = (x, y, w, h)
# z=1 beacause local_merged_height_map, crop_ref (and so forth) have
# already been zoomed. So don't zoom again to crop these images.
if not (os.path.isfile(local_merged_height_map_crop) and cfg["skip_existing"]):
common.cropImage(local_merged_height_map, local_merged_height_map_crop, newcol, newrow, w, h)
if not (os.path.isfile(crop_ref_crop) and cfg["skip_existing"]):
common.cropImage(crop_ref, crop_ref_crop, newcol, newrow, w, h)
# by pair
for i in range(1, nb_pairs + 1):
single_height_map = os.path.join(tile_dir, "pair_%d/height_map.tif" % i)
single_height_map_crop = os.path.join(tile_dir, "pair_%d/height_map_crop.tif" % i)
single_rpc_err = os.path.join(tile_dir, "pair_%d/rpc_err.tif" % i)
single_rpc_err_crop = os.path.join(tile_dir, "pair_%d/rpc_err_crop.tif" % i)
if not (os.path.isfile(single_height_map_crop) and cfg["skip_existing"]):
common.cropImage(single_height_map, single_height_map_crop, newcol, newrow, w, h)
if not (os.path.isfile(single_rpc_err_crop) and cfg["skip_existing"]):
common.cropImage(single_rpc_err, single_rpc_err_crop, newcol, newrow, w, h)
# colors
color_crop_ref(tile_info, cfg["images"][0]["clr"])
# generate cloud
generate_cloud(tile_info, cfg["offset_ply"], utm_zone)
