def color_crop_ref(tile_info, clr=None):
"""
Colorizations of a crop_ref (for a given tile)
Args:
tile_info: a dictionary that provides all you need to process a tile
clr (optional): if crop_ref is a pan image, will perform the pansharpening with the color image clr
If clr is None then:
case 1: tile is an RGBI image, so removes I channel, and perform rescaling of the remaining channels
case 2: tile is already an RGB image, so just perform rescaling
Note that if rescaling is already performed, then the file applied_minmax.txt exists:
if applied_minmax.txt exists and cfg['skip_existing'] is True, rescaling won't be performed again
if applied_minmax.txt exists and is different from global_minmax, rescaling will be compulsorily performed (can occur if a new tile is added)
"""
# get info
x, y, w, h = tile_info["coordinates"]
tile_dir = tile_info["directory"]
z = cfg["subsampling_factor"]
# paths
crop_ref = tile_dir + "/roi_ref_crop.tif"
global_minmax = cfg["out_dir"] + "/global_minmax.txt"
applied_minmax = tile_dir + "/applied_minmax.txt"
global_minmax_arr = np.loadtxt(global_minmax)
if cfg["color_ply"]:
doProcess = False
if not os.path.exists(applied_minmax):
doProcess = True
applied_minmax_arr = global_minmax_arr
else:
applied_minmax_arr = np.loadtxt(applied_minmax)
if (applied_minmax_arr[0] != global_minmax_arr[0]) or (applied_minmax_arr[1] != global_minmax_arr[1]):
doProcess = True
applied_minmax_arr = global_minmax_arr
if not doProcess and cfg["skip_existing"]:
print "Rescaling of tile %s already done, skip" % tile_dir
else:
crop_color = tile_dir + "/roi_color_ref.tif"
if clr is not None:
triangulation.colorize(crop_ref, clr, x, y, z, crop_color, applied_minmax_arr[0], applied_minmax_arr[1])
else: # use of image_rescaleintensities
np.savetxt(applied_minmax, applied_minmax_arr)
if common.image_pix_dim_tiffinfo(crop_ref) == 4:
print "the image is pansharpened fusioned"
tmp = common.rgbi_to_rgb(crop_ref, out=None, tilewise=True)
# common.image_qauto(tmp, crop_color, tilewise=False)
common.image_rescaleintensities(tmp, crop_color, applied_minmax_arr[0], applied_minmax_arr[1])
else:
print "no color data"
# common.image_qauto(crop_ref, crop_color, tilewise=False)
common.image_rescaleintensities(crop_ref, crop_color, applied_minmax_arr[0], applied_minmax_arr[1])
def color_crop_ref(tile_info, clr=None):
"""
Colorizations of a crop_ref (for a given tile)
Args:
tile_info: a dictionary that provides all you need to process a tile
clr (optional): if crop_ref is a pan image, will perform the pansharpening with the color image clr
If clr is None then:
case 1: tile is an RGBI image, so removes I channel, and perform rescaling of the remaining channels
case 2: tile is already an RGB image, so just perform rescaling
Note that if rescaling is already performed, then the file applied_minmax.txt exists:
if applied_minmax.txt exists and cfg['skip_existing'] is True, rescaling won't be performed again
if applied_minmax.txt exists and is different from global_minmax, rescaling will be compulsorily performed (can occur if a new tile is added)
"""
# get info
x, y, w, h = tile_info['coordinates']
tile_dir = tile_info['directory']
z = cfg['subsampling_factor']
# paths
crop_ref = tile_dir + '/roi_ref_crop.tif'
global_minmax = cfg['out_dir'] + '/global_minmax.txt'
applied_minmax = tile_dir + '/applied_minmax.txt'
global_minmax_arr = np.loadtxt(global_minmax)
if cfg['color_ply']:
doProcess = False
if not os.path.exists(applied_minmax):
doProcess = True
applied_minmax_arr = global_minmax_arr
else:
applied_minmax_arr = np.loadtxt(applied_minmax)
if (applied_minmax_arr[0] != global_minmax_arr[0]) or (
applied_minmax_arr[1] != global_minmax_arr[1]):
doProcess = True
applied_minmax_arr = global_minmax_arr
if not doProcess and cfg['skip_existing']:
print 'Rescaling of tile %s already done, skip' % tile_dir
else:
crop_color = tile_dir + '/roi_color_ref.tif'
if clr is not None:
triangulation.colorize(crop_ref, clr, x, y, z, crop_color,
applied_minmax_arr[0],
applied_minmax_arr[1])
else: # use of image_rescaleintensities
np.savetxt(applied_minmax, applied_minmax_arr)
if common.image_pix_dim_tiffinfo(crop_ref) == 4:
print 'the image is pansharpened fusioned'
tmp = common.rgbi_to_rgb(crop_ref, out=None, tilewise=True)
#common.image_qauto(tmp, crop_color, tilewise=False)
common.image_rescaleintensities(tmp, crop_color,
applied_minmax_arr[0],
applied_minmax_arr[1])
else:
print 'no color data'
#common.image_qauto(crop_ref, crop_color, tilewise=False)
common.image_rescaleintensities(crop_ref, crop_color,
applied_minmax_arr[0],
applied_minmax_arr[1])
def generate_cloud(out_dir, height_map, rpc1, x, y, w, h, im1, clr,
do_offset=False):
"""
Args:
out_dir: output directory. The file cloud.ply will be written there
height_map: path to the height map, produced by the process_pair
or process_triplet function
rpc1: path to the xml file containing rpc coefficients for the
reference image
x, y, w, h: four integers defining the rectangular ROI in the original
panchro image. (x, y) is the top-left corner, and (w, h) are the
dimensions of the rectangle.
im1: path to the panchro reference image
clr: path to the xs (multispectral, ie color) reference image
do_offset (optional, default: False): boolean flag to decide wether the
x, y coordinates of points in the ply file will be translated or
not (translated to be close to 0, to avoid precision loss due to
huge numbers)
"""
print "\nComputing point cloud..."
# output files
crop_ref = '%s/roi_ref.tif' % out_dir
cloud = '%s/cloud.ply' % out_dir
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# ensure that the coordinates of the ROI are multiples of the zoom factor,
# to avoid bad registration of tiles due to rounding problems.
z = cfg['subsampling_factor']
x, y, w, h = common.round_roi_to_nearest_multiple(z, x, y, w, h)
# build the matrix of the zoom + translation transformation
if cfg['full_img'] and z == 1:
trans = None
else:
A = common.matrix_translation(-x, -y)
f = 1.0/z
Z = np.diag([f, f, 1])
A = np.dot(Z, A)
trans = '%s/trans.txt' % out_dir
np.savetxt(trans, A)
# compute offset
if do_offset:
r = rpc_model.RPCModel(rpc1)
lat = r.latOff
lon = r.lonOff
off_x, off_y = geographiclib.geodetic_to_utm(lat, lon)[0:2]
else:
off_x, off_y = 0, 0
# crop the ROI in ref image, then zoom
if cfg['full_img'] and z == 1:
crop_ref = im1
else:
if z == 1:
common.image_crop_TIFF(im1, x, y, w, h, crop_ref)
else:
# gdal is used for the zoom because it handles BigTIFF files, and
# before the zoom out the image may be that big
tmp_crop = common.image_crop_TIFF(im1, x, y, w, h)
common.image_zoom_gdal(tmp_crop, z, crop_ref, w, h)
if cfg['color_ply']:
crop_color = '%s/roi_color_ref.tif' % out_dir
if clr is not None:
print 'colorizing...'
triangulation.colorize(crop_ref, clr, x, y, z, crop_color)
elif common.image_pix_dim_tiffinfo(crop_ref) == 4:
print 'the image is pansharpened fusioned'
tmp = common.rgbi_to_rgb(crop_ref, out=None, tilewise=True)
common.image_qauto(tmp, crop_color, tilewise=False)
else:
print 'no color data'
common.image_qauto(crop_ref, crop_color, tilewise=False)
else:
crop_color = ''
triangulation.compute_point_cloud(cloud, height_map, rpc1, trans, crop_color,
off_x, off_y)
common.garbage_cleanup()