def generate_cloud(tile_info, do_offset=False, utm_zone=None):
"""
Args:
tile_info: a dictionary that provides all you need to process a tile
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..."
# get info
tile_dir = tile_info['directory']
x, y, w, h = tile_info['coordinates']
img1, rpc1 = cfg['images'][0]['img'], cfg['images'][0]['rpc']
#height_map = tile_dir + '/local_merged_height_map.tif'
height_map = tile_dir + '/local_merged_height_map_crop.tif'
crop_color = tile_dir + '/roi_color_ref.tif'
if not os.path.exists(crop_color):
crop_color = ''
z = cfg['subsampling_factor']
# Compute the homography transforming the coordinates system of the
# original full size image into the coordinates system
# of the crop we are dealing with
# col and row have been divided by z inside 'finalize_tile' for
# convinience; col*z and row*z allow to get the initial values back.
A = common.matrix_translation(-x * z, -y * z)
z = cfg['subsampling_factor']
f = 1.0 / z
Z = np.diag([f, f, 1])
A = np.dot(Z, A)
trans = tile_dir + '/trans.txt'
np.savetxt(trans, A, fmt='%9.3f')
# compute coordinates (offsets) of the point we want to use as origin in
# the local coordinate system of the computed cloud
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
# output
cloud = tile_dir + '/cloud.ply'
triangulation.compute_point_cloud(cloud,
height_map,
rpc1,
trans,
crop_color,
off_x,
off_y,
utm_zone=utm_zone)
common.garbage_cleanup()
def generate_cloud(tile_info, do_offset=False):
"""
Args:
tile_info: a dictionary that provides all you need to process a tile
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..."
# get info
tile_dir = tile_info['directory']
x, y, w, h = tile_info['coordinates']
img1, rpc1 = cfg['images'][0]['img'], cfg['images'][0]['rpc']
#height_map = tile_dir + '/local_merged_height_map.tif'
height_map = tile_dir + '/local_merged_height_map_crop.tif'
crop_color = tile_dir + '/roi_color_ref.tif'
if not os.path.exists(crop_color):
crop_color = ''
z = cfg['subsampling_factor']
# Compute the homography transforming the coordinates system of the
# original full size image into the coordinates system
# of the crop we are dealing with
# col and row have been divided by z inside 'finalize_tile' for
# convinience; col*z and row*z allow to get the initial values back.
A = common.matrix_translation(-x * z, -y * z)
z = cfg['subsampling_factor']
f = 1.0 / z
Z = np.diag([f, f, 1])
A = np.dot(Z, A)
trans = tile_dir + '/trans.txt'
np.savetxt(trans, A, fmt='%9.3f')
# compute coordinates (offsets) of the point we want to use as origin in
# the local coordinate system of the computed cloud
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
# output
cloud = tile_dir + '/cloud.ply'
triangulation.compute_point_cloud(cloud, height_map, rpc1, trans, crop_color,
off_x, off_y)
common.garbage_cleanup()
def generate_cloud(img_name, exp_name, x, y, w, h, height_map,
reference_image_id=1):
"""
Args:
img_name: name of the dataset, located in the 'pleiades_data/images'
directory
exp_name: string used to identify the experiment
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.
height_map: path to the height_map, produced by the process_pair of
process_triplet function
reference_image_id: id (1, 2 or 3) of the image used as the reference
image. The height map has been resampled on its grid.
"""
rpc = 'data/%s/%04d.png.P' % (img_name, reference_image_id)
im = 'data/%s/%04d.png' % (img_name, reference_image_id)
im_color = 'data/%s/%04d.png' % (img_name, reference_image_id)
crop = '/tmp/%s_roi_ref%02d.tif' % (exp_name, reference_image_id)
crop_color = '/tmp/%s_roi_color_ref%02d.tif' % (exp_name, reference_image_id)
cloud = '/tmp/%s_cloud.ply' % (exp_name)
# read the zoom value
zoom = global_params.subsampling_factor
# colorize, then generate point cloud
tmp_crop = common.image_crop_TIFF(im, x, y, w, h)
tmp_crop = common.image_safe_zoom_fft(tmp_crop, zoom)
common.run('cp %s %s' % (tmp_crop, crop))
A = common.matrix_translation(-x, -y)
f = 1.0/zoom
Z = np.diag([f, f, 1])
A = np.dot(Z, A)
trans = common.tmpfile('.txt')
np.savetxt(trans, A)
# compute_point_cloud(common.image_qauto(crop),
# height_map, rpc, trans, cloud)
sz = common.image_size(crop)
compute_point_cloud(common.image_qauto(crop),
common.image_crop(height_map,0,0,sz[0],sz[1]) , rpc, trans, cloud)
# cleanup
while common.garbage:
common.run('rm ' + common.garbage.pop())
print "v %s %s %s" % (crop, crop_color, height_map)
print "meshlab %s" % (cloud)
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()
