def multidisp_map_to_point_cloud(out, disp_list, rpc_ref, rpc_list, colors,
utm_zone=None, llbbx=None, xybbx=None):
"""
Computes a 3D point cloud from N disparity maps.
Args:
out: path to the output ply file
disp_list: paths to the diparity maps
rpc_ref, rpc_list: paths to the xml files
colors: path to the png image containing the colors
"""
disp_command = ['--disp%d %s' % (i+1, disp) for i, disp in enumerate(disp_list)]
rpc_command = ['--rpc_sec%d %s' % (i+1, rpc) for i, rpc in enumerate(rpc_list)]
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
xbb = "--col-m %s --col-M %s --row-m %s --row-M %s" % xybbx if xybbx else ""
command = 'multidisp2ply {} {} {} {} {}'.format(out, len(disp_list),
" ".join(disp_command),
"--rpc_ref %s" % rpc_ref,
" ".join(rpc_command))
command += ' --color {}'.format(colors)
command += ' {} {} {}'.format(utm, lbb, xbb)
common.run(command)
def disp_map_to_point_cloud(out, disp, mask, rpc1, rpc2, H1, H2, A, colors,
utm_zone=None, llbbx=None, xybbx=None, xymsk=None):
"""
Computes a 3D point cloud from a disparity map.
Args:
out: path to the output ply file
disp, mask: paths to the diparity and mask maps
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
A: path to txt file containing the pointing correction matrix
for im2
colors: path to the png image containing the colors
"""
href = " ".join(str(x) for x in np.loadtxt(H1).flatten())
hsec = " ".join(str(x) for x in np.dot(np.loadtxt(H2),
np.linalg.inv(np.loadtxt(A))).flatten())
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
xbb = "--col-m %s --col-M %s --row-m %s --row-M %s" % xybbx if xybbx else ""
msk = "--mask-orig %s" % xymsk if xymsk else ""
command = 'disp2ply {} {} {} {} {}'.format(out, disp, mask, rpc1, rpc2)
command += ' {} -href "{}" -hsec "{}"'.format(colors, href, hsec)
command += ' {} {} {} {}'.format(utm, lbb, xbb, msk)
common.run(command)
def height_map(out, x, y, w, h, z, rpc1, rpc2, H1, H2, disp, mask, rpc_err,
out_filt, A=None):
"""
Computes an altitude map, on the grid of the original reference image, from
a disparity map given on the grid of the rectified reference image.
Args:
out: path to the output file
x, y, w, h: four integers defining the rectangular ROI in the original
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
z: zoom factor (usually 1, 2 or 4) used to produce the input disparity
map
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
disp, mask: paths to the diparity and mask maps
rpc_err: path to the output rpc_error of triangulation
A (optional): path to txt file containing the pointing correction matrix
for im2
"""
tmp = common.tmpfile('.tif')
height_map_rectified(rpc1, rpc2, H1, H2, disp, mask, tmp, rpc_err, A)
transfer_map(tmp, H1, x, y, w, h, z, out)
# apply output filter
common.run('plambda {0} {1} "x 0 > y nan if" -o {1}'.format(out_filt, out))
def transfer_map(in_map, H, x, y, w, h, zoom, out_map):
"""
Transfer the heights computed on the rectified grid to the original
Pleiades image grid.
Args:
in_map: path to the input map, usually a height map or a mask, sampled
on the rectified grid
H: path to txt file containing a numpy 3x3 array representing the
rectifying homography
x, y, w, h: four integers defining the rectangular ROI in the original
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
zoom: zoom factor (usually 1, 2 or 4) used to produce the input height
map
out_map: path to the output map
"""
# write the inverse of the resampling transform matrix. In brief it is:
# homography * translation * zoom
# This matrix transports the coordinates of the original cropped and
# zoomed grid (the one desired for out_height) to the rectified cropped and
# zoomed grid (the one we have for height)
Z = np.diag([zoom, zoom, 1])
A = common.matrix_translation(x, y)
HH = np.dot(np.loadtxt(H), np.dot(A, Z))
# apply the homography
# write the 9 coefficients of the homography to a string, then call synflow
# to produce the flow, then backflow to apply it
# zero:256x256 is the iio way to create a 256x256 image filled with zeros
hij = ' '.join(['%r' % num for num in HH.flatten()])
common.run('synflow hom "%s" zero:%dx%d /dev/null - | BILINEAR=1 backflow - %s %s' % (
hij, w/zoom, h/zoom, in_map, out_map))
def loop_zhang(F, w, h):
"""
Computes rectifying homographies from a fundamental matrix, with Loop-Zhang.
Args:
F: 3x3 numpy array containing the fundamental matrix
w, h: images size. The two images are supposed to have same size
Returns:
The two rectifying homographies.
The rectifying homographies are computed using the Pascal Monasse binary
named rectify_mindistortion. It uses the Loop-Zhang algorithm.
"""
Ffile = common.tmpfile('.txt')
Haf = common.tmpfile('.txt')
Hbf = common.tmpfile('.txt')
common.matrix_write(Ffile, F)
common.run('rectify_mindistortion %s %d %d %s %s > /dev/null' % (Ffile, w,
h, Haf,
Hbf))
Ha = common.matrix_read(Haf, size=(3, 3))
Hb = common.matrix_read(Hbf, size=(3, 3))
# check if both the images are rotated
a = does_this_homography_change_the_vertical_direction(Ha)
b = does_this_homography_change_the_vertical_direction(Hb)
if a and b:
R = np.array([[-1, 0, 0], [0, -1, 0], [0, 0, 1]])
Ha = np.dot(R, Ha)
Hb = np.dot(R, Hb)
return Ha, Hb
def plot_vectors(p, v, x, y, w, h, f=1, out_file=None):
"""
Plots vectors on an image, using gnuplot
Args:
p: points (origins of vectors),represented as a numpy Nx2 array
v: vectors, represented as a numpy Nx2 array
x, y, w, h: rectangular ROI
f: (optional, default is 1) exageration factor
out_file: (optional, default is None) path to the output file
Returns:
nothing, but opens a display or write a png file
"""
tmp = common.tmpfile('.txt')
data = np.hstack((p, v))
np.savetxt(tmp, data, fmt='%6f')
gp_string = 'set term png size %d,%d;unset key;unset tics;plot [%d:%d] [%d:%d] "%s" u($1):($2):(%d*$3):(%d*$4) w vectors head filled' % (w, h, x, x+w, y, y+h, tmp, f, f)
if out_file is None:
out_file = common.tmpfile('.png')
common.run("gnuplot -p -e '%s' > %s" % (gp_string, out_file))
print(out_file)
if out_file is None:
os.system("v %s &" % out_file)
def global_dsm(tiles):
"""
"""
out_dsm_vrt = os.path.join(cfg['out_dir'], 'dsm.vrt')
out_dsm_tif = os.path.join(cfg['out_dir'], 'dsm.tif')
dsms_list = [os.path.join(t['dir'], 'dsm.tif') for t in tiles]
dsms = '\n'.join(d for d in dsms_list if os.path.exists(d) is True)
input_file_list = os.path.join(cfg['out_dir'], 'gdalbuildvrt_input_file_list.txt')
with open(input_file_list, 'w') as f:
f.write(dsms)
common.run("gdalbuildvrt -vrtnodata nan -input_file_list %s %s" % (input_file_list,
out_dsm_vrt))
global_srcwin = np.loadtxt(os.path.join(cfg['out_dir'],
"global_srcwin.txt"))
res = cfg['dsm_resolution']
xoff, yoff, xsize, ysize = global_srcwin
common.run(" ".join(["gdal_translate",
"-co TILED=YES -co BIGTIFF=IF_SAFER",
"-projwin %s %s %s %s %s %s" % (xoff,
yoff,
xoff + xsize * res,
yoff - ysize * res,
out_dsm_vrt, out_dsm_tif)]))
def erosion(out, msk, radius):
"""
Erodes the accepted regions (ie eliminates more pixels)
Args:
out: path to the ouput mask image file
msk: path to the input mask image file
radius (in pixels): size of the disk used for the erosion
"""
if radius >= 2:
common.run('morsi disk%d erosion %s %s' % (int(radius), msk, out))
def keypoints_match(k1,
k2,
method='relative',
sift_thresh=0.6,
F=None,
model=None,
epipolar_threshold=10):
"""
Find matches among two lists of sift keypoints.
Args:
k1, k2: paths to text files containing the lists of sift descriptors
method (optional, default is 'relative'): flag ('relative' or
'absolute') indicating wether to use absolute distance or relative
distance
sift_thresh (optional, default is 0.6): threshold for distance between SIFT
descriptors. These descriptors are 128-vectors, whose coefficients
range from 0 to 255, thus with absolute distance a reasonable value
for this threshold is between 200 and 300. With relative distance
(ie ratio between distance to nearest and distance to second
nearest), the commonly used value for the threshold is 0.6.
F (optional): affine fundamental matrix
model (optional, default is None): model imposed by RANSAC when
searching the set of inliers. If None all matches are considered as
inliers.
Returns:
if any, a numpy 2D array containing the list of inliers matches.
"""
# compute matches
mfile = common.tmpfile('.txt')
cmd = "matching %s %s -%s %f -o %s" % (k1, k2, method, sift_thresh, mfile)
if F is not None:
fij = ' '.join(
str(x) for x in [F[0, 2], F[1, 2], F[2, 0], F[2, 1], F[2, 2]])
cmd = "%s -f \"%s\"" % (cmd, fij)
cmd += " --epipolar-threshold {}".format(epipolar_threshold)
common.run(cmd)
matches = np.loadtxt(mfile)
if matches.ndim == 2: # filter outliers with ransac
if model == 'fundamental' and len(matches) >= 7:
common.run("ransac fmn 1000 .3 7 %s < %s" % (mfile, mfile))
elif model == 'homography' and len(matches) >= 4:
common.run("ransac hom 1000 1 4 /dev/null /dev/null %s < %s" %
(mfile, mfile))
elif model == 'hom_fund' and len(matches) >= 7:
common.run("ransac hom 1000 2 4 /dev/null /dev/null %s < %s" %
(mfile, mfile))
common.run("ransac fmn 1000 .2 7 %s < %s" % (mfile, mfile))
if os.stat(mfile).st_size > 0: # return numpy array of matches
return np.loadtxt(mfile)
def plys_to_dsm(tiles):
"""
"""
out_dsm = os.path.join(cfg['out_dir'], 'dsm.tif')
clouds = ' '.join(os.path.join(t['dir'], 'cloud.ply') for t in tiles)
if 'utm_bbx' in cfg:
bbx = cfg['utm_bbx']
common.run("ls %s | plyflatten -bb \"%f %f %f %f \" %f %s" %
(clouds, bbx[0], bbx[1], bbx[2], bbx[3],
cfg['dsm_resolution'], out_dsm))
else:
common.run("ls %s | plyflatten %f %s" %
(clouds, cfg['dsm_resolution'], out_dsm))
def global_dsm(tiles):
"""
"""
out_dsm_vrt = os.path.join(cfg['out_dir'], 'dsm.vrt')
out_dsm_tif = os.path.join(cfg['out_dir'], 'dsm.tif')
dsms_list = [os.path.join(t['dir'], 'dsm.tif') for t in tiles]
dsms = '\n'.join(d for d in dsms_list if os.path.exists(d))
input_file_list = os.path.join(cfg['out_dir'],
'gdalbuildvrt_input_file_list.txt')
with open(input_file_list, 'w') as f:
f.write(dsms)
common.run("gdalbuildvrt -vrtnodata nan -input_file_list %s %s" %
(input_file_list, out_dsm_vrt))
res = cfg['dsm_resolution']
if 'utm_bbx' in cfg:
bbx = cfg['utm_bbx']
xoff = bbx[0]
yoff = bbx[3]
xsize = int(np.ceil((bbx[1] - bbx[0]) / res))
ysize = int(np.ceil((bbx[3] - bbx[2]) / res))
projwin = "-projwin %s %s %s %s" % (xoff, yoff, xoff + xsize * res,
yoff - ysize * res)
else:
projwin = ""
common.run(" ".join([
"gdal_translate", "-co TILED=YES -co BIGTIFF=IF_SAFER",
"%s %s %s" % (projwin, out_dsm_vrt, out_dsm_tif)
]))
# EXPORT CONFIDENCE
out_conf_vrt = os.path.join(cfg['out_dir'], 'confidence.vrt')
out_conf_tif = os.path.join(cfg['out_dir'], 'confidence.tif')
dsms_list = [os.path.join(t['dir'], 'confidence.tif') for t in tiles]
dems_list_ok = [d for d in dsms_list if os.path.exists(d)]
dsms = '\n'.join(dems_list_ok)
input_file_list = os.path.join(cfg['out_dir'],
'gdalbuildvrt_input_file_list2.txt')
if len(dems_list_ok) > 0:
with open(input_file_list, 'w') as f:
f.write(dsms)
common.run("gdalbuildvrt -vrtnodata nan -input_file_list %s %s" %
(input_file_list, out_conf_vrt))
common.run(" ".join([
"gdal_translate", "-co TILED=YES -co BIGTIFF=IF_SAFER",
"%s %s %s" % (projwin, out_conf_vrt, out_conf_tif)
]))
def crop_image(self, image_in, write_folder, crop):
# crop should be x_min, y_min, width, height
x_min, y_min, width, height = crop
image_out = self._get_output_name(write_folder, image_in)
# generate image
print("Croping to image {} ...".format(image_out))
common.run('gdal_translate -srcwin {} {} {} {} {} {}'.format(
x_min, y_min, width, height, image_in, image_out))
print("Done")
return (0)
def register_heights(im1, im2):
"""
Affine registration of heights.
Args:
im1: first height map
im2: second height map, to be registered on the first one
Returns
path to the registered second height map
"""
# remove high frequencies with a morphological zoom out
im1_low_freq = common.image_zoom_out_morpho(im1, 4)
im2_low_freq = common.image_zoom_out_morpho(im2, 4)
# first read the images and store them as numpy 1D arrays, removing all the
# nans and inf
i1 = piio.read(im1_low_freq).ravel() #np.ravel() gives a 1D view
i2 = piio.read(im2_low_freq).ravel()
ind = np.logical_and(np.isfinite(i1), np.isfinite(i2))
h1 = i1[ind]
h2 = i2[ind]
# for debug
print(np.shape(i1))
print(np.shape(h1))
# # 1st option: affine
# # we search the (u, v) vector that minimizes the following sum (over
# # all the pixels):
# #\sum (im1[i] - (u*im2[i]+v))^2
# # it is a least squares minimization problem
# A = np.vstack((h2, h2*0+1)).T
# b = h1
# z = np.linalg.lstsq(A, b)[0]
# u = z[0]
# v = z[1]
#
# # apply the affine transform and return the modified im2
# out = common.tmpfile('.tif')
# common.run('plambda %s "x %f * %f +" > %s' % (im2, u, v, out))
# 2nd option: translation only
v = np.mean(h1 - h2)
out = common.tmpfile('.tif')
common.run('plambda %s "x %f +" -o %s' % (im2, v, out))
return out
def heights_to_ply(tile):
"""
Generate a ply cloud.
Args:
tile: a dictionary that provides all you need to process a tile
"""
# merge the n-1 height maps of the tile (n = nb of images)
heights_fusion(tile)
# compute a ply from the merged height map
out_dir = tile['dir']
x, y, w, h = tile['coordinates']
z = cfg['subsampling_factor']
plyfile = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
height_map = os.path.join(out_dir, 'height_map.tif')
if cfg['skip_existing'] and os.path.isfile(plyfile):
print('ply file already exists for tile {} {}'.format(x, y))
return
# H is the homography transforming the coordinates system of the original
# full size image into the coordinates system of the crop
H = np.dot(np.diag([1 / z, 1 / z, 1]), common.matrix_translation(-x, -y))
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(
common.image_crop_gdal(cfg['images'][0]['img'], x, y, w, h),
colors)
common.image_safe_zoom_fft(colors, z, colors)
triangulation.height_map_to_point_cloud(plyfile,
height_map,
cfg['images'][0]['rpc'],
H,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (plyfile, plyextrema))
if cfg['clean_intermediate']:
common.remove(height_map)
common.remove(colors)
common.remove(
os.path.join(out_dir, 'cloud_water_image_domain_mask.png'))
def merge(im1, im2, thresh, out, conservative=False):
"""
Args:
im1, im2: paths to the two input images
thresh: distance threshold on the intensity values
out: path to the output image
conservative (optional, default is False): if True, keep only the
pixels where the two height map agree
This function merges two images. They are supposed to be two height maps,
sampled on the same grid. If a pixel has a valid height (ie not inf) in
only one of the two maps, then we keep this height (if the 'conservative'
option is set to False). When two heights are available, if they differ
less than the threshold we take the mean, if not we discard the pixel (ie
assign NAN to the output pixel).
"""
# first register the second image on the first
im2 = register_heights(im1, im2)
if conservative:
# then merge
# the following plambda expression implements:
# if isfinite x
# if isfinite y
# if fabs(x - y) < t
# return (x+y)/2
# return nan
# return nan
# return nan
common.run("""
plambda %s %s "x isfinite y isfinite x y - fabs %f < x y + 2 / nan if nan
if nan if" -o %s
""" % ( im1, im2, thresh, out))
else:
# then merge
# the following plambda expression implements:
# if isfinite x
# if isfinite y
# if fabs(x - y) < t
# return (x+y)/2
# return nan
# return x
# return y
common.run("""
plambda %s %s "x isfinite y isfinite x y - fabs %f < x y + 2 / nan if x
if y if" -o %s
""" % ( im1, im2, thresh, out))
def height_map_to_point_cloud(cloud,
heights,
rpc,
H=None,
crop_colorized='',
off_x=None,
off_y=None,
ascii_ply=False,
with_normals=False,
utm_zone=None,
llbbx=None):
"""
Computes a color point cloud from a height map.
Args:
cloud: path to the output points cloud (ply format)
heights: height map, sampled on the same grid as the crop_colorized
image. In particular, its size is the same as crop_colorized.
rpc: path to xml file containing RPC data for the current Pleiade image
H (optional, default None): numpy array of size 3x3 defining the
homography transforming the coordinates system of the original full
size image into the coordinates system of the crop we are dealing
with.
crop_colorized (optional, default ''): path to a colorized crop of a
Pleiades image
off_{x,y} (optional, default None): coordinates of the point we want to
use as origin in the local coordinate system of the computed cloud
ascii_ply (optional, default false): boolean flag to tell if the output
ply file should be encoded in plain text (ascii).
utm_zone (optional, default None):
"""
if not os.path.exists(crop_colorized):
crop_colorized = ''
hij = " ".join(str(x) for x in H.flatten()) if H is not None else ""
asc = "--ascii" if ascii_ply else ""
nrm = "--with-normals" if with_normals else ""
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
command = "colormesh %s %s %s %s -h \"%s\" %s %s %s %s" % (
cloud, heights, rpc, crop_colorized, hij, asc, nrm, utm, lbb)
if off_x:
command += " --offset_x %d" % off_x
if off_y:
command += " --offset_y %d" % off_y
common.run(command)
def heights_to_ply(tile):
"""
Generate a ply cloud.
Args:
tile: a dictionary that provides all you need to process a tile
"""
# merge the n-1 height maps of the tile (n = nb of images)
heights_fusion(tile)
# compute a ply from the merged height map
out_dir = tile['dir']
x, y, w, h = tile['coordinates']
z = cfg['subsampling_factor']
plyfile = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
height_map = os.path.join(out_dir, 'height_map.tif')
if cfg['skip_existing'] and os.path.isfile(plyfile):
print('ply file already exists for tile {} {}'.format(x, y))
return
# H is the homography transforming the coordinates system of the original
# full size image into the coordinates system of the crop
H = np.dot(np.diag([1 / z, 1 / z, 1]), common.matrix_translation(-x, -y))
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(common.image_crop_gdal(cfg['images'][0]['img'], x, y,
w, h), colors)
common.image_safe_zoom_fft(colors, z, colors)
triangulation.height_map_to_point_cloud(plyfile, height_map,
cfg['images'][0]['rpc'], H, colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (plyfile, plyextrema))
if cfg['clean_intermediate']:
common.remove(height_map)
common.remove(colors)
common.remove(os.path.join(out_dir,
'cloud_water_image_domain_mask.png'))
def fundamental_matrix_ransac(matches, precision=1.0, return_inliers=False):
"""
Estimates the fundamental matrix given a set of point correspondences
between two images, using ransac.
Arguments:
matches: numpy 2D array of size Nx4 containing a list of pair of
matching points. Each line is of the form x1, y1, x2, y2, where (x1,
y1) is the point in the first view while (x2, y2) is the matching
point in the second view.
It can be the path to a txt file containing such an array.
precision: optional parameter indicating the maximum error
allowed for counting the inliers
return_inliers: optional boolean flag to activate/deactivate inliers
output
Returns:
the estimated fundamental matrix, and optionally the 2D array containing
the inliers
The algorithm uses ransac as a search engine.
"""
if type(matches) is np.ndarray:
# write a file containing the list of correspondences. The
# expected format is a text file with one match per line: x1 y1 x2 y2
matchfile = common.tmpfile('.txt')
np.savetxt(matchfile, matches)
else:
# assume it is a path to a txt file containing the matches
matchfile = matches
# call ransac binary, from Enric's imscript
inliers = common.tmpfile('.txt')
Ffile = common.tmpfile('.txt')
awk_command = "awk {\'printf(\"%e %e %e\\n%e %e %e\\n%e %e %e\", $3, $4, $5, $6, $7, $8, $9, $10, $11)\'}"
common.run("ransac fmn 1000 %f 7 %s < %s | grep param | %s > %s" %
(precision, inliers, matchfile, awk_command, Ffile))
if return_inliers:
return np.loadtxt(Ffile).transpose(), np.loadtxt(inliers)
else:
return np.loadtxt(Ffile).transpose()
def fundamental_matrix_ransac(matches, precision=1.0, return_inliers=False):
"""
Estimates the fundamental matrix given a set of point correspondences
between two images, using ransac.
Arguments:
matches: numpy 2D array of size Nx4 containing a list of pair of
matching points. Each line is of the form x1, y1, x2, y2, where (x1,
y1) is the point in the first view while (x2, y2) is the matching
point in the second view.
It can be the path to a txt file containing such an array.
precision: optional parameter indicating the maximum error
allowed for counting the inliers
return_inliers: optional boolean flag to activate/deactivate inliers
output
Returns:
the estimated fundamental matrix, and optionally the 2D array containing
the inliers
The algorithm uses ransac as a search engine.
"""
if type(matches) is np.ndarray:
# write a file containing the list of correspondences. The
# expected format is a text file with one match per line: x1 y1 x2 y2
matchfile = common.tmpfile('.txt')
np.savetxt(matchfile, matches)
else:
# assume it is a path to a txt file containing the matches
matchfile = matches
# call ransac binary, from Enric's imscript
inliers = common.tmpfile('.txt')
Ffile = common.tmpfile('.txt')
awk_command = "awk {\'printf(\"%e %e %e\\n%e %e %e\\n%e %e %e\", $3, $4, $5, $6, $7, $8, $9, $10, $11)\'}"
common.run("ransac fmn 1000 %f 7 %s < %s | grep param | %s > %s" % (precision, inliers, matchfile, awk_command, Ffile))
if return_inliers:
return np.loadtxt(Ffile).transpose(), np.loadtxt(inliers)
else:
return np.loadtxt(Ffile).transpose()
def height_map_to_point_cloud(cloud, heights, rpc, H=None, crop_colorized='',
off_x=None, off_y=None, ascii_ply=False,
with_normals=False, utm_zone=None, llbbx=None):
"""
Computes a color point cloud from a height map.
Args:
cloud: path to the output points cloud (ply format)
heights: height map, sampled on the same grid as the crop_colorized
image. In particular, its size is the same as crop_colorized.
rpc: path to xml file containing RPC data for the current Pleiade image
H (optional, default None): numpy array of size 3x3 defining the
homography transforming the coordinates system of the original full
size image into the coordinates system of the crop we are dealing
with.
crop_colorized (optional, default ''): path to a colorized crop of a
Pleiades image
off_{x,y} (optional, default None): coordinates of the point we want to
use as origin in the local coordinate system of the computed cloud
ascii_ply (optional, default false): boolean flag to tell if the output
ply file should be encoded in plain text (ascii).
utm_zone (optional, default None):
"""
if not os.path.exists(crop_colorized):
crop_colorized = ''
hij = " ".join(str(x) for x in H.flatten()) if H is not None else ""
asc = "--ascii" if ascii_ply else ""
nrm = "--with-normals" if with_normals else ""
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
command = "colormesh %s %s %s %s -h \"%s\" %s %s %s %s" % (cloud, heights,
rpc,
crop_colorized,
hij, asc, nrm,
utm, lbb)
if off_x:
command += " --offset_x %d" % off_x
if off_y:
command += " --offset_y %d" % off_y
common.run(command)
def disp_map_to_point_cloud(out,
disp,
mask,
rpc1,
rpc2,
H1,
H2,
A,
colors,
utm_zone=None,
llbbx=None,
xybbx=None,
xymsk=None):
"""
Computes a 3D point cloud from a disparity map.
Args:
out: path to the output ply file
disp, mask: paths to the diparity and mask maps
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
A: path to txt file containing the pointing correction matrix
for im2
colors: path to the png image containing the colors
"""
href = " ".join(str(x) for x in np.loadtxt(H1).flatten())
hsec = " ".join(
str(x) for x in np.dot(np.loadtxt(H2), np.linalg.inv(np.loadtxt(
A))).flatten())
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
xbb = "--col-m %s --col-M %s --row-m %s --row-M %s" % xybbx if xybbx else ""
msk = "--mask-orig %s" % xymsk if xymsk else ""
command = 'disp2ply {} {} {} {} {}'.format(out, disp, mask, rpc1, rpc2)
command += ' {} -href "{}" -hsec "{}"'.format(colors, href, hsec)
command += ' {} {} {} {}'.format(utm, lbb, xbb, msk)
common.run(command)
def height_map_rectified(rpc1, rpc2, H1, H2, disp, mask, height, rpc_err, A=None):
"""
Computes a height map from a disparity map, using rpc.
Args:
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
disp, mask: paths to the diparity and mask maps
height: path to the output height map
rpc_err: path to the output rpc_error of triangulation
A (optional): path to txt file containing the pointing correction matrix
for im2
"""
if A is not None:
HH2 = common.tmpfile('.txt')
np.savetxt(HH2, np.dot(np.loadtxt(H2), np.linalg.inv(np.loadtxt(A))))
else:
HH2 = H2
common.run("disp_to_h %s %s %s %s %s %s %s %s" % (rpc1, rpc2, H1, HH2, disp,
mask, height, rpc_err))
def height_map_rectified(rpc1, rpc2, H1, H2, disp, mask, height, rpc_err, A=None):
"""
Computes a height map from a disparity map, using rpc.
Args:
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
disp, mask: paths to the diparity and mask maps
height: path to the output height map
rpc_err: path to the output rpc_error of triangulation
A (optional): path to txt file containing the pointing correction matrix
for im2
"""
if A is not None:
HH2 = common.tmpfile('.txt')
np.savetxt(HH2, np.dot(np.loadtxt(H2), np.linalg.inv(np.loadtxt(A))))
else:
HH2 = H2
common.run("disp_to_h %s %s %s %s %s %s %s %s" % (rpc1, rpc2, H1, HH2, disp,
mask, height, rpc_err))
def height_map(out,
x,
y,
w,
h,
rpc1,
rpc2,
H1,
H2,
disp,
mask,
rpc_err,
out_filt,
A=None):
"""
Computes an altitude map, on the grid of the original reference image, from
a disparity map given on the grid of the rectified reference image.
Args:
out: path to the output file
x, y, w, h: four integers defining the rectangular ROI in the original
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
rpc1, rpc2: paths to the xml files
H1, H2: path to txt files containing two 3x3 numpy arrays defining
the rectifying homographies
disp, mask: paths to the diparity and mask maps
rpc_err: path to the output rpc_error of triangulation
A (optional): path to txt file containing the pointing correction matrix
for im2
"""
tmp = common.tmpfile('.tif')
height_map_rectified(rpc1, rpc2, H1, H2, disp, mask, tmp, rpc_err, A)
transfer_map(tmp, H1, x, y, w, h, out)
# apply output filter
common.run('plambda {0} {1} "x 0 > y nan if" -o {1}'.format(out_filt, out))
def scale_image(self, image_in, write_folder, scaling_filter, scale_x,
scale_y):
image_out = self._get_output_name(write_folder, image_in, False)
# generate image
print("Generating {} ...".format(image_out))
# First get input image size
w, h, _ = common.image_size_gdal(image_in)
# Generate a temporary vrt file to have the proper geotransform
fd, tmp_vrt = tempfile.mkstemp(suffix='.vrt', dir=write_folder)
os.close(fd)
common.run('gdal_translate -of VRT -a_ullr 0 0 {} {} {} {}'.format(
w, h, image_in, tmp_vrt))
common.run(
'gdalwarp -co RPB=NO -co PROFILE=GeoTIFF -r {} -co "BIGTIFF=IF_NEEDED" -co "TILED=YES" -ovr NONE \
-to SRC_METHOD=NO_GEOTRANSFORM -to DST_METHOD=NO_GEOTRANSFORM -tr \
{} {} {} {}'.format(scaling_filter, scale_x, scale_y,
tmp_vrt, image_out))
try:
# Remove aux files if any
os.remove(image_out + ".aux.xml")
except OSError:
pass
# Clean tmp vrt file
os.remove(tmp_vrt)
print("Done")
return (0)
def multidisp_map_to_point_cloud(out,
disp_list,
rpc_ref,
rpc_list,
colors,
utm_zone=None,
llbbx=None,
xybbx=None):
"""
Computes a 3D point cloud from N disparity maps.
Args:
out: path to the output ply file
disp_list: paths to the diparity maps
rpc_ref, rpc_list: paths to the xml files
colors: path to the png image containing the colors
"""
disp_command = [
'--disp%d %s' % (i + 1, disp) for i, disp in enumerate(disp_list)
]
rpc_command = [
'--rpc_sec%d %s' % (i + 1, rpc) for i, rpc in enumerate(rpc_list)
]
utm = "--utm-zone %s" % utm_zone if utm_zone else ""
lbb = "--lon-m %s --lon-M %s --lat-m %s --lat-M %s" % llbbx if llbbx else ""
xbb = "--col-m %s --col-M %s --row-m %s --row-M %s" % xybbx if xybbx else ""
command = 'multidisp2ply {} {} {} {} {}'.format(out, len(disp_list),
" ".join(disp_command),
"--rpc_ref %s" % rpc_ref,
" ".join(rpc_command))
command += ' --color {}'.format(colors)
command += ' {} {} {}'.format(utm, lbb, xbb)
common.run(command)
def compute_disparity_map(im1,
im2,
disp,
mask,
algo,
disp_min=None,
disp_max=None,
extra_params=''):
"""
Runs a block-matching binary on a pair of stereo-rectified images.
Args:
im1, im2: rectified stereo pair
disp: path to the output diparity map
mask: path to the output rejection mask
algo: string used to indicate the desired binary. Currently it can be
one among 'hirschmuller02', 'hirschmuller08',
'hirschmuller08_laplacian', 'hirschmuller08_cauchy', 'sgbm',
'msmw', 'tvl1', 'mgm', 'mgm_multi' and 'micmac'
disp_min : smallest disparity to consider
disp_max : biggest disparity to consider
extra_params: optional string with algorithm-dependent parameters
"""
if rectify_secondary_tile_only(algo) is False:
disp_min = [disp_min]
disp_max = [disp_max]
# limit disparity bounds
np.alltrue(len(disp_min) == len(disp_max))
for dim in range(len(disp_min)):
if disp_min[dim] is not None and disp_max[dim] is not None:
image_size = common.image_size_gdal(im1)
if disp_max[dim] - disp_min[dim] > image_size[dim]:
center = 0.5 * (disp_min[dim] + disp_max[dim])
disp_min[dim] = int(center - 0.5 * image_size[dim])
disp_max[dim] = int(center + 0.5 * image_size[dim])
# round disparity bounds
if disp_min[dim] is not None:
disp_min[dim] = int(np.floor(disp_min[dim]))
if disp_max is not None:
disp_max[dim] = int(np.ceil(disp_max[dim]))
if rectify_secondary_tile_only(algo) is False:
disp_min = disp_min[0]
disp_max = disp_max[0]
# define environment variables
env = os.environ.copy()
env['OMP_NUM_THREADS'] = str(cfg['omp_num_threads'])
# call the block_matching binary
if algo == 'hirschmuller02':
bm_binary = 'subpix.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(
bm_binary, im1, im2, disp, mask, disp_min, disp_max, extra_params))
# extra_params: LoG(0) regionRadius(3)
# LoG: Laplacian of Gaussian preprocess 1:enabled 0:disabled
# regionRadius: radius of the window
if algo == 'hirschmuller08':
bm_binary = 'callSGBM.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(
bm_binary, im1, im2, disp, mask, disp_min, disp_max, extra_params))
# extra_params: regionRadius(3) P1(default) P2(default) LRdiff(1)
# regionRadius: radius of the window
# P1, P2 : regularization parameters
# LRdiff: maximum difference between left and right disparity maps
if algo == 'hirschmuller08_laplacian':
bm_binary = 'callSGBM_lap.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(
bm_binary, im1, im2, disp, mask, disp_min, disp_max, extra_params))
if algo == 'hirschmuller08_cauchy':
bm_binary = 'callSGBM_cauchy.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(
bm_binary, im1, im2, disp, mask, disp_min, disp_max, extra_params))
if algo == 'sgbm':
# opencv sgbm function implements a modified version of Hirschmuller's
# Semi-Global Matching (SGM) algorithm described in "Stereo Processing
# by Semiglobal Matching and Mutual Information", PAMI, 2008
p1 = 8 # penalizes disparity changes of 1 between neighbor pixels
p2 = 32 # penalizes disparity changes of more than 1
# it is required that p2 > p1. The larger p1, p2, the smoother the disparity
win = 3 # matched block size. It must be a positive odd number
lr = 1 # maximum difference allowed in the left-right disparity check
cost = common.tmpfile('.tif')
common.run('sgbm {} {} {} {} {} {} {} {} {} {}'.format(
im1, im2, disp, cost, disp_min, disp_max, win, p1, p2, lr))
# create rejection mask (0 means rejected, 1 means accepted)
# keep only the points that are matched and present in both input images
common.run(
'plambda {0} "x 0 join" | backflow - {2} | plambda {0} {1} - "x isfinite y isfinite z isfinite and and" -o {3}'
.format(disp, im1, im2, mask))
if algo == 'tvl1':
tvl1 = 'callTVL1.sh'
common.run('{0} {1} {2} {3} {4}'.format(tvl1, im1, im2, disp, mask),
env)
if algo == 'tvl1_2d':
tvl1 = 'callTVL1.sh'
common.run(
'{0} {1} {2} {3} {4} {5}'.format(tvl1, im1, im2, disp, mask, 1),
env)
if algo == 'msmw':
bm_binary = 'iip_stereo_correlation_multi_win2'
common.run(
'{0} -i 1 -n 4 -p 4 -W 5 -x 9 -y 9 -r 1 -d 1 -t -1 -s 0 -b 0 -o 0.25 -f 0 -P 32 -m {1} -M {2} {3} {4} {5} {6}'
.format(bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'msmw2':
bm_binary = 'iip_stereo_correlation_multi_win2_newversion'
common.run(
'{0} -i 1 -n 4 -p 4 -W 5 -x 9 -y 9 -r 1 -d 1 -t -1 -s 0 -b 0 -o -0.25 -f 0 -P 32 -D 0 -O 25 -c 0 -m {1} -M {2} {3} {4} {5} {6}'
.format(bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'msmw3':
bm_binary = 'msmw'
common.run('{0} -m {1} -M {2} -il {3} -ir {4} -dl {5} -kl {6}'.format(
bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'mgm':
env['MEDIAN'] = '1'
env['CENSUS_NCC_WIN'] = str(cfg['census_ncc_win'])
env['TSGM'] = '3'
common.run(
'{0} -r {1} -R {2} -s vfit -t census -O 8 {3} {4} {5}'.format(
'mgm', disp_min, disp_max, im1, im2, disp), env)
# produce the mask: rejected pixels are marked with nan of inf in disp
# map
common.run('plambda {0} "isfinite" -o {1}'.format(disp, mask))
if algo == 'mgm_multi':
env['REMOVESMALLCC'] = '25'
env['MINDIFF'] = '1'
env['CENSUS_NCC_WIN'] = str(cfg['census_ncc_win'])
env['SUBPIX'] = '2'
common.run(
'{0} -r {1} -R {2} -S 3 -s vfit -t census {3} {4} {5}'.format(
'mgm_multi', disp_min, disp_max, im1, im2, disp), env)
# produce the mask: rejected pixels are marked with nan of inf in disp
# map
common.run('plambda {0} "isfinite" -o {1}'.format(disp, mask))
if (algo == 'micmac'):
# add micmac binaries to the PATH environment variable
s2p_dir = os.path.dirname(
os.path.dirname(os.path.realpath(os.path.abspath(__file__))))
micmac_bin = os.path.join(s2p_dir, 'bin', 'micmac', 'bin')
os.environ['PATH'] = os.environ['PATH'] + os.pathsep + micmac_bin
# prepare micmac xml params file
micmac_params = os.path.join(s2p_dir, '3rdparty', 'micmac_params.xml')
work_dir = os.path.dirname(os.path.abspath(im1))
common.run('cp {0} {1}'.format(micmac_params, work_dir))
# run MICMAC
common.run('MICMAC {0:s}'.format(
os.path.join(work_dir, 'micmac_params.xml')))
# copy output disp map
micmac_disp = os.path.join(work_dir, 'MEC-EPI',
'Px1_Num6_DeZoom1_LeChantier.tif')
disp = os.path.join(work_dir, 'rectified_disp.tif')
common.run('cp {0} {1}'.format(micmac_disp, disp))
# compute mask by rejecting the 10% of pixels with lowest correlation score
micmac_cost = os.path.join(work_dir, 'MEC-EPI',
'Correl_LeChantier_Num_5.tif')
mask = os.path.join(work_dir, 'rectified_mask.png')
common.run('plambda {0} "x x%q10 < 0 255 if" -o {1}'.format(
micmac_cost, mask))
def multidisparities_to_ply(tile):
"""
Compute a point cloud from the disparity maps of N-pairs of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
# There is no guarantee that this function works with z!=1
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc_ref = cfg['images'][0]['rpc']
disp_list = list()
rpc_list = list()
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
print('triangulating tile {} {}...'.format(x, y))
n = len(cfg['images']) - 1
for i in range(n):
pair = 'pair_%d' % (i + 1)
H_ref = os.path.join(out_dir, pair, 'H_ref.txt')
H_sec = os.path.join(out_dir, pair, 'H_sec.txt')
disp = os.path.join(out_dir, pair, 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, pair, 'rectified_mask.png')
disp2D = os.path.join(out_dir, pair, 'disp2D.tif')
rpc_sec = cfg['images'][i + 1]['rpc']
if os.path.exists(disp):
# homography for warp
T = common.matrix_translation(x, y)
hom_ref = np.loadtxt(H_ref)
hom_ref_shift = np.dot(hom_ref, T)
# homography for 1D to 2D conversion
hom_sec = np.loadtxt(H_sec)
if cfg["use_global_pointing_for_geometric_triangulation"] is True:
pointing = os.path.join(cfg['out_dir'],
'global_pointing_%s.txt' % pair)
hom_pointing = np.loadtxt(pointing)
hom_sec = np.dot(hom_sec, np.linalg.inv(hom_pointing))
hom_sec_shift_inv = np.linalg.inv(hom_sec)
h1 = " ".join(str(x) for x in hom_ref_shift.flatten())
h2 = " ".join(str(x) for x in hom_sec_shift_inv.flatten())
# relative disparity map to absolute disparity map
tmp_abs = common.tmpfile('.tif')
os.environ["PLAMBDA_GETPIXEL"] = "0"
common.run(
'plambda %s %s "y 0 = nan x[0] :i + x[1] :j + 1 3 njoin if" -o %s'
% (disp, mask_rect, tmp_abs))
# 1d to 2d conversion
tmp_1d_to_2d = common.tmpfile('.tif')
common.run('plambda %s "%s 9 njoin x mprod" -o %s' %
(tmp_abs, h2, tmp_1d_to_2d))
# warp
tmp_warp = common.tmpfile('.tif')
common.run('homwarp -o 2 "%s" %d %d %s %s' %
(h1, w, h, tmp_1d_to_2d, tmp_warp))
# set masked value to NaN
exp = 'y 0 = nan x if'
common.run('plambda %s %s "%s" -o %s' %
(tmp_warp, mask_orig, exp, disp2D))
# disp2D contains positions in the secondary image
# added input data for triangulation module
disp_list.append(disp2D)
rpc_list.append(rpc_sec)
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(
common.image_crop_gdal(cfg['images'][0]['img'], x, y, w, h),
colors)
# compute the point cloud
triangulation.multidisp_map_to_point_cloud(ply_file,
disp_list,
rpc_ref,
rpc_list,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x + w, y, y + h))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(colors)
def disparity_to_ply(tile):
"""
Compute a point cloud from the disparity map of a pair of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc1 = cfg['images'][0]['rpc']
rpc2 = cfg['images'][1]['rpc']
if os.path.exists(os.path.join(out_dir, 'stderr.log')):
print('triangulation: stderr.log exists')
print('pair_1 not processed on tile {} {}'.format(x, y))
return
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
print('triangulating tile {} {}...'.format(x, y))
# This function is only called when there is a single pair (pair_1)
H_ref = os.path.join(out_dir, 'pair_1', 'H_ref.txt')
H_sec = os.path.join(out_dir, 'pair_1', 'H_sec.txt')
pointing = os.path.join(cfg['out_dir'], 'global_pointing_pair_1.txt')
disp = os.path.join(out_dir, 'pair_1', 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png')
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
# prepare the image needed to colorize point cloud
colors = os.path.join(out_dir, 'rectified_ref.png')
if cfg['images'][0]['clr']:
hom = np.loadtxt(H_ref)
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
ww, hh = common.bounding_box2D(common.points_apply_homography(
hom, roi))[2:]
tmp = common.tmpfile('.tif')
common.image_apply_homography(tmp, cfg['images'][0]['clr'], hom,
ww + 2 * cfg['horizontal_margin'],
hh + 2 * cfg['vertical_margin'])
common.image_qauto(tmp, colors)
else:
common.image_qauto(
os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors)
# compute the point cloud
triangulation.disp_map_to_point_cloud(ply_file,
disp,
mask_rect,
rpc1,
rpc2,
H_ref,
H_sec,
pointing,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x + w, y, y + h),
xymsk=mask_orig)
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
common.remove(colors)
common.remove(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'))
def compute_disparity_map(im1, im2, disp, mask, algo, disp_min=None,
disp_max=None, extra_params=''):
"""
Runs a block-matching binary on a pair of stereo-rectified images.
Args:
im1, im2: rectified stereo pair
disp: path to the output diparity map
mask: path to the output rejection mask
algo: string used to indicate the desired binary. Currently it can be
one among 'hirschmuller02', 'hirschmuller08',
'hirschmuller08_laplacian', 'hirschmuller08_cauchy', 'sgbm',
'msmw', 'tvl1', 'mgm', 'mgm_multi' and 'micmac'
disp_min : smallest disparity to consider
disp_max : biggest disparity to consider
extra_params: optional string with algorithm-dependent parameters
"""
if rectify_secondary_tile_only(algo) is False:
disp_min = [disp_min]
disp_max = [disp_max]
# limit disparity bounds
np.alltrue(len(disp_min) == len(disp_max))
for dim in range(len(disp_min)):
if disp_min[dim] is not None and disp_max[dim] is not None:
image_size = common.image_size_gdal(im1)
if disp_max[dim] - disp_min[dim] > image_size[dim]:
center = 0.5 * (disp_min[dim] + disp_max[dim])
disp_min[dim] = int(center - 0.5 * image_size[dim])
disp_max[dim] = int(center + 0.5 * image_size[dim])
# round disparity bounds
if disp_min[dim] is not None:
disp_min[dim] = int(np.floor(disp_min[dim]))
if disp_max is not None:
disp_max[dim] = int(np.ceil(disp_max[dim]))
if rectify_secondary_tile_only(algo) is False:
disp_min = disp_min[0]
disp_max = disp_max[0]
# define environment variables
env = os.environ.copy()
env['OMP_NUM_THREADS'] = str(cfg['omp_num_threads'])
# call the block_matching binary
if algo == 'hirschmuller02':
bm_binary = 'subpix.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(bm_binary, im1, im2, disp, mask, disp_min,
disp_max, extra_params))
# extra_params: LoG(0) regionRadius(3)
# LoG: Laplacian of Gaussian preprocess 1:enabled 0:disabled
# regionRadius: radius of the window
if algo == 'hirschmuller08':
bm_binary = 'callSGBM.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(bm_binary, im1, im2, disp, mask, disp_min,
disp_max, extra_params))
# extra_params: regionRadius(3) P1(default) P2(default) LRdiff(1)
# regionRadius: radius of the window
# P1, P2 : regularization parameters
# LRdiff: maximum difference between left and right disparity maps
if algo == 'hirschmuller08_laplacian':
bm_binary = 'callSGBM_lap.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(bm_binary, im1, im2, disp, mask, disp_min,
disp_max, extra_params))
if algo == 'hirschmuller08_cauchy':
bm_binary = 'callSGBM_cauchy.sh'
common.run('{0} {1} {2} {3} {4} {5} {6} {7}'.format(bm_binary, im1, im2, disp, mask, disp_min,
disp_max, extra_params))
if algo == 'sgbm':
# opencv sgbm function implements a modified version of Hirschmuller's
# Semi-Global Matching (SGM) algorithm described in "Stereo Processing
# by Semiglobal Matching and Mutual Information", PAMI, 2008
p1 = 8 # penalizes disparity changes of 1 between neighbor pixels
p2 = 32 # penalizes disparity changes of more than 1
# it is required that p2 > p1. The larger p1, p2, the smoother the disparity
win = 3 # matched block size. It must be a positive odd number
lr = 1 # maximum difference allowed in the left-right disparity check
cost = common.tmpfile('.tif')
common.run('sgbm {} {} {} {} {} {} {} {} {} {}'.format(im1, im2,
disp, cost,
disp_min,
disp_max,
win, p1, p2, lr))
# create rejection mask (0 means rejected, 1 means accepted)
# keep only the points that are matched and present in both input images
common.run('plambda {0} "x 0 join" | backflow - {2} | plambda {0} {1} - "x isfinite y isfinite z isfinite and and" -o {3}'.format(disp, im1, im2, mask))
if algo == 'tvl1':
tvl1 = 'callTVL1.sh'
common.run('{0} {1} {2} {3} {4}'.format(tvl1, im1, im2, disp, mask),
env)
if algo == 'tvl1_2d':
tvl1 = 'callTVL1.sh'
common.run('{0} {1} {2} {3} {4} {5}'.format(tvl1, im1, im2, disp, mask,
1), env)
if algo == 'msmw':
bm_binary = 'iip_stereo_correlation_multi_win2'
common.run('{0} -i 1 -n 4 -p 4 -W 5 -x 9 -y 9 -r 1 -d 1 -t -1 -s 0 -b 0 -o 0.25 -f 0 -P 32 -m {1} -M {2} {3} {4} {5} {6}'.format(bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'msmw2':
bm_binary = 'iip_stereo_correlation_multi_win2_newversion'
common.run('{0} -i 1 -n 4 -p 4 -W 5 -x 9 -y 9 -r 1 -d 1 -t -1 -s 0 -b 0 -o -0.25 -f 0 -P 32 -D 0 -O 25 -c 0 -m {1} -M {2} {3} {4} {5} {6}'.format(
bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'msmw3':
bm_binary = 'msmw'
common.run('{0} -m {1} -M {2} -il {3} -ir {4} -dl {5} -kl {6}'.format(
bm_binary, disp_min, disp_max, im1, im2, disp, mask))
if algo == 'mgm':
env['MEDIAN'] = '1'
env['CENSUS_NCC_WIN'] = str(cfg['census_ncc_win'])
env['TSGM'] = '3'
common.run('{0} -r {1} -R {2} -s vfit -t census -O 8 {3} {4} {5}'.format('mgm',
disp_min,
disp_max,
im1, im2,
disp),
env)
# produce the mask: rejected pixels are marked with nan of inf in disp
# map
common.run('plambda {0} "isfinite" -o {1}'.format(disp, mask))
if (algo == 'micmac'):
# add micmac binaries to the PATH environment variable
s2p_dir = os.path.dirname(os.path.dirname(os.path.realpath(os.path.abspath(__file__))))
micmac_bin = os.path.join(s2p_dir, 'bin', 'micmac', 'bin')
os.environ['PATH'] = os.environ['PATH'] + os.pathsep + micmac_bin
# prepare micmac xml params file
micmac_params = os.path.join(s2p_dir, '3rdparty', 'micmac_params.xml')
work_dir = os.path.dirname(os.path.abspath(im1))
common.run('cp {0} {1}'.format(micmac_params, work_dir))
# run MICMAC
common.run('MICMAC {0:s}'.format(os.path.join(work_dir, 'micmac_params.xml')))
# copy output disp map
micmac_disp = os.path.join(work_dir, 'MEC-EPI',
'Px1_Num6_DeZoom1_LeChantier.tif')
disp = os.path.join(work_dir, 'rectified_disp.tif')
common.run('cp {0} {1}'.format(micmac_disp, disp))
# compute mask by rejecting the 10% of pixels with lowest correlation score
micmac_cost = os.path.join(work_dir, 'MEC-EPI',
'Correl_LeChantier_Num_5.tif')
mask = os.path.join(work_dir, 'rectified_mask.png')
common.run('plambda {0} "x x%q10 < 0 255 if" -o {1}'.format(micmac_cost, mask))
def plot_matches(im1,
im2,
rpc1,
rpc2,
matches,
x=None,
y=None,
w=None,
h=None,
outfile=None):
"""
Plot matches on Pleiades images
Args:
im1, im2: paths to full Pleiades images
rpc1, rpc2: two instances of the RPCModel class, or paths to xml files
containing the rpc coefficients
matches: 2D numpy array of size 4xN containing a list of matches (a
list of pairs of points, each pair being represented by x1, y1, x2,
y2). The coordinates are given in the frame of the full images.
x, y, w, h (optional, default is None): ROI in the reference image
outfile (optional, default is None): path to the output file. If None,
the file image is displayed using the pvflip viewer
Returns:
path to the displayed output
"""
# if no matches, no plot
if not matches.size:
print("visualisation.plot_matches: nothing to plot")
return
# read rpcs
for r in [rpc1, rpc2]:
if not isinstance(r, rpc_model.RPCModel):
r = rpc_model.RPCModel(r)
# determine regions to crop in im1 and im2
if x is not None:
x1 = x
else:
x1 = np.min(matches[:, 0])
if y is not None:
y1 = y
else:
y1 = np.min(matches[:, 1])
if w is not None:
w1 = w
else:
w1 = np.max(matches[:, 0]) - x1
if h is not None:
h1 = h
else:
h1 = np.max(matches[:, 1]) - y1
x2, y2, w2, h2 = rpc_utils.corresponding_roi(rpc1, rpc2, x1, y1, w1, h1)
# x2 = np.min(matches[:, 2])
# w2 = np.max(matches[:, 2]) - x2
# y2 = np.min(matches[:, 3])
# h2 = np.max(matches[:, 3]) - y2
# # add 20 pixels offset and round. The image_crop_gdal function will round
# # off the coordinates before it does the crops.
# x1 -= 20; x1 = np.round(x1)
# y1 -= 20; y1 = np.round(y1)
# x2 -= 20; x2 = np.round(x2)
# y2 -= 20; y2 = np.round(y2)
# w1 += 40; w1 = np.round(w1)
# h1 += 40; h1 = np.round(h1)
# w2 += 40; w2 = np.round(w2)
# h2 += 40; h2 = np.round(h2)
# do the crops
crop1 = common.image_qauto(common.image_crop_gdal(im1, x1, y1, w1, h1))
crop2 = common.image_qauto(common.image_crop_gdal(im2, x2, y2, w2, h2))
# compute matches coordinates in the cropped images
pts1 = matches[:, 0:2] - [x1, y1]
pts2 = matches[:, 2:4] - [x2, y2]
# plot the matches on the two crops
to_display = plot_matches_low_level(crop1, crop2, np.hstack((pts1, pts2)))
if outfile is None:
os.system('v %s &' % (to_display))
else:
common.run('cp %s %s' % (to_display, outfile))
return
def plot_matches(im1, im2, rpc1, rpc2, matches, x=None, y=None, w=None, h=None,
outfile=None):
"""
Plot matches on Pleiades images
Args:
im1, im2: paths to full Pleiades images
rpc1, rpc2: two instances of the RPCModel class, or paths to xml files
containing the rpc coefficients
matches: 2D numpy array of size 4xN containing a list of matches (a
list of pairs of points, each pair being represented by x1, y1, x2,
y2). The coordinates are given in the frame of the full images.
x, y, w, h (optional, default is None): ROI in the reference image
outfile (optional, default is None): path to the output file. If None,
the file image is displayed using the pvflip viewer
Returns:
path to the displayed output
"""
# if no matches, no plot
if not matches.size:
print("visualisation.plot_matches: nothing to plot")
return
# read rpcs
for r in [rpc1, rpc2]:
if not isinstance(r, rpc_model.RPCModel):
r = rpc_model.RPCModel(r)
# determine regions to crop in im1 and im2
if x is not None:
x1 = x
else:
x1 = np.min(matches[:, 0])
if y is not None:
y1 = y
else:
y1 = np.min(matches[:, 1])
if w is not None:
w1 = w
else:
w1 = np.max(matches[:, 0]) - x1
if h is not None:
h1 = h
else:
h1 = np.max(matches[:, 1]) - y1
x2, y2, w2, h2 = rpc_utils.corresponding_roi(rpc1, rpc2, x1, y1, w1, h1)
# x2 = np.min(matches[:, 2])
# w2 = np.max(matches[:, 2]) - x2
# y2 = np.min(matches[:, 3])
# h2 = np.max(matches[:, 3]) - y2
# # add 20 pixels offset and round. The image_crop_gdal function will round
# # off the coordinates before it does the crops.
# x1 -= 20; x1 = np.round(x1)
# y1 -= 20; y1 = np.round(y1)
# x2 -= 20; x2 = np.round(x2)
# y2 -= 20; y2 = np.round(y2)
# w1 += 40; w1 = np.round(w1)
# h1 += 40; h1 = np.round(h1)
# w2 += 40; w2 = np.round(w2)
# h2 += 40; h2 = np.round(h2)
# do the crops
crop1 = common.image_qauto(common.image_crop_gdal(im1, x1, y1, w1, h1))
crop2 = common.image_qauto(common.image_crop_gdal(im2, x2, y2, w2, h2))
# compute matches coordinates in the cropped images
pts1 = matches[:, 0:2] - [x1, y1]
pts2 = matches[:, 2:4] - [x2, y2]
# plot the matches on the two crops
to_display = plot_matches_low_level(crop1, crop2, np.hstack((pts1, pts2)))
if outfile is None:
os.system('v %s &' % (to_display))
else:
common.run('cp %s %s' % (to_display, outfile))
return
def multidisparities_to_ply(tile):
"""
Compute a point cloud from the disparity maps of N-pairs of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
# There is no guarantee that this function works with z!=1
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc_ref = cfg['images'][0]['rpc']
disp_list = list()
rpc_list = list()
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
print('triangulating tile {} {}...'.format(x, y))
n = len(cfg['images']) - 1
for i in range(n):
pair = 'pair_%d' % (i+1)
H_ref = os.path.join(out_dir, pair, 'H_ref.txt')
H_sec = os.path.join(out_dir, pair, 'H_sec.txt')
disp = os.path.join(out_dir, pair, 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, pair, 'rectified_mask.png')
disp2D = os.path.join(out_dir, pair, 'disp2D.tif')
rpc_sec = cfg['images'][i+1]['rpc']
if os.path.exists(disp):
# homography for warp
T = common.matrix_translation(x, y)
hom_ref = np.loadtxt(H_ref)
hom_ref_shift = np.dot(hom_ref, T)
# homography for 1D to 2D conversion
hom_sec = np.loadtxt(H_sec)
if cfg["use_global_pointing_for_geometric_triangulation"] is True:
pointing = os.path.join(cfg['out_dir'], 'global_pointing_%s.txt' % pair)
hom_pointing = np.loadtxt(pointing)
hom_sec = np.dot(hom_sec,np.linalg.inv(hom_pointing))
hom_sec_shift_inv = np.linalg.inv(hom_sec)
h1 = " ".join(str(x) for x in hom_ref_shift.flatten())
h2 = " ".join(str(x) for x in hom_sec_shift_inv.flatten())
# relative disparity map to absolute disparity map
tmp_abs = common.tmpfile('.tif')
os.environ["PLAMBDA_GETPIXEL"] = "0"
common.run('plambda %s %s "y 0 = nan x[0] :i + x[1] :j + 1 3 njoin if" -o %s' % (disp, mask_rect, tmp_abs))
# 1d to 2d conversion
tmp_1d_to_2d = common.tmpfile('.tif')
common.run('plambda %s "%s 9 njoin x mprod" -o %s' % (tmp_abs, h2, tmp_1d_to_2d))
# warp
tmp_warp = common.tmpfile('.tif')
common.run('homwarp -o 2 "%s" %d %d %s %s' % (h1, w, h, tmp_1d_to_2d, tmp_warp))
# set masked value to NaN
exp = 'y 0 = nan x if'
common.run('plambda %s %s "%s" -o %s' % (tmp_warp, mask_orig, exp, disp2D))
# disp2D contains positions in the secondary image
# added input data for triangulation module
disp_list.append(disp2D)
rpc_list.append(rpc_sec)
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
colors = os.path.join(out_dir, 'ref.png')
if cfg['images'][0]['clr']:
common.image_crop_gdal(cfg['images'][0]['clr'], x, y, w, h, colors)
else:
common.image_qauto(common.image_crop_gdal(cfg['images'][0]['img'], x, y,
w, h), colors)
# compute the point cloud
triangulation.multidisp_map_to_point_cloud(ply_file, disp_list, rpc_ref, rpc_list,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x+w, y, y+h))
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(colors)
def disparity_to_ply(tile):
"""
Compute a point cloud from the disparity map of a pair of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc1 = cfg['images'][0]['rpc']
rpc2 = cfg['images'][1]['rpc']
if os.path.exists(os.path.join(out_dir, 'stderr.log')):
print('triangulation: stderr.log exists')
print('pair_1 not processed on tile {} {}'.format(x, y))
return
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
print('triangulating tile {} {}...'.format(x, y))
# This function is only called when there is a single pair (pair_1)
H_ref = os.path.join(out_dir, 'pair_1', 'H_ref.txt')
H_sec = os.path.join(out_dir, 'pair_1', 'H_sec.txt')
pointing = os.path.join(cfg['out_dir'], 'global_pointing_pair_1.txt')
disp = os.path.join(out_dir, 'pair_1', 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png')
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
# prepare the image needed to colorize point cloud
colors = os.path.join(out_dir, 'rectified_ref.png')
if cfg['images'][0]['clr']:
hom = np.loadtxt(H_ref)
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
ww, hh = common.bounding_box2D(common.points_apply_homography(hom, roi))[2:]
tmp = common.tmpfile('.tif')
common.image_apply_homography(tmp, cfg['images'][0]['clr'], hom,
ww + 2*cfg['horizontal_margin'],
hh + 2*cfg['vertical_margin'])
common.image_qauto(tmp, colors)
else:
common.image_qauto(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors)
# compute the point cloud
triangulation.disp_map_to_point_cloud(ply_file, disp, mask_rect, rpc1, rpc2,
H_ref, H_sec, pointing, colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x+w, y, y+h),
xymsk=mask_orig)
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
common.remove(colors)
common.remove(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'))
# generate image
print("Generating {} ...".format(out_img_file))
# First get input image size
sz = common.image_size_gdal(in_img_file)
w = sz[0]
h = sz[1]
# Generate a temporary vrt file to have the proper geotransform
fd, tmp_vrt = tempfile.mkstemp(suffix='.vrt',
dir=os.path.dirname(out_img_file))
os.close(fd)
common.run('gdal_translate -of VRT -a_ullr 0 0 %d %d %s %s' %
(w, h, in_img_file, tmp_vrt))
common.run((
'gdalwarp -co RPB=NO -co PROFILE=GeoTIFF -r %s -co "BIGTIFF=IF_NEEDED" -co "TILED=YES" -ovr NONE -overwrite -to SRC_METHOD=NO_GEOTRANSFORM -to DST_METHOD=NO_GEOTRANSFORM -tr'
' %d %d %s %s') % (filt, scale_x, scale_y, tmp_vrt, out_img_file))
try:
# Remove aux files if any
os.remove(out_img_file + ".aux.xml")
except OSError:
pass
# Clean tmp vrt file
os.remove(tmp_vrt)
print("Done")
