def get_extended_tile(tile_db, tilename):
drv = gdal.GetDriverByName("Gtiff")
con = db.connect(tile_db)
cur = con.cursor()
cur.execute("select path,row,col from coverage where tile_name=?",
(tilename, ))
data = cur.fetchone()
path, row, col = data
print("Reading " + path)
g0 = grid.fromGDAL(path)
cur.execute(
"select path,row,col from coverage where abs(row-?)<2 and abs(col-?)<2",
(row, col))
data = cur.fetchall()
vert_expansions = {-1: False, 1: False} #top,bottom
hor_expansions = {-1: False, 1: False} #left,right
for path, r, c in data:
if r == row and c == col:
continue
dr = r - row
dc = c - col
if dr != 0 and not vert_expansions[dr]:
#print "vexp",dr
vert_expansions[dr] = True
g0.expand_vert(dr, pixel_buf)
#print g0.geo_ref[3]
if dc != 0 and not hor_expansions[dc]:
#print "hexp",dc
hor_expansions[dc] = True
g0.expand_hor(dc, pixel_buf)
#print g0.geo_ref[0]
print("Reading " + path + " at %d,%d" % (dr, dc))
#print g0.shape
ds = gdal.Open(path)
band = ds.GetRasterBand(1)
geo_ref = ds.GetGeoTransform()
assert (geo_ref[1] == g0.geo_ref[1] and geo_ref[5] == g0.geo_ref[5])
slices0, slices1 = grid.intersect_grid_extents(
g0.geo_ref, g0.shape, geo_ref, (ds.RasterYSize, ds.RasterXSize))
assert (slices0 is not None)
piece = band.ReadAsArray(int(slices1[1].start), int(slices1[0].start),
int(slices1[1].stop - slices1[1].start),
int(slices1[0].stop - slices1[0].start))
print(str(piece.shape))
g0.grid[slices0[0], slices0[1]] = piece
ds = None
return g0, vert_expansions, hor_expansions
print("Could not get extent from tilename.")
raise e
lines=vector_io.get_features(linename,pargs.layername,pargs.layersql,extent)
print("Found %d features in %s" %(len(lines),linename))
if len(lines)==0:
return 2
cut_input_to=pargs.cut_to
print("Reading "+lasname+"....")
pc=pointcloud.fromAny(lasname).cut_to_class(cut_input_to) #what to cut to here...??
if pargs.debug:
print("Cutting input pointcloud to class %d" %cut_input_to)
if pc.get_size()<5:
print("Few points in pointcloud!!")
return 3
if pargs.toH:
geoid=grid.fromGDAL(GEOID_GRID,upcast=True)
print("Using geoid from %s to warp to orthometric heights." %GEOID_GRID)
pc.toH(geoid)
print("Sorting...")
pc.sort_spatially(pargs.srad)
print("Starting loop..")
n_found=0
for line in lines:
if pargs.id_attr is not None:
line_id=line.GetFieldAsString(pargs.id_attr)
else:
line_id=""
#explode
geom=line.GetGeometryRef()
ng=geom.GetGeometryCount()
geoms_here=[geom]
def main(args):
try:
pargs = parser.parse_args(args[1:])
except Exception as e:
print(str(e))
return 1
kmname = constants.get_tilename(pargs.dem_tile)
print("Running %s on block: %s, %s" % (progname, kmname, time.asctime()))
extent = np.asarray(constants.tilename_to_extent(kmname))
shoes = vector_io.get_geometries(pargs.horse_ds, pargs.layername,
pargs.layersql, extent)
outname = os.path.join(pargs.outdir, "dhym_" + kmname + ".tif")
if len(shoes) == 0:
print("No shoes, man!")
shutil.copy(pargs.dem_tile, outname)
return 0
# We always interpolate values from the large dataset (vrt) which is not changed in the loop below.
dtm = grid.fromGDAL(pargs.dem_tile)
mesh_xy = pointcloud.mesh_as_points(dtm.shape, dtm.geo_ref)
dem_ds = gdal.Open(pargs.dem_all)
dem_band = dem_ds.GetRasterBand(1)
ndval = dem_band.GetNoDataValue()
georef = np.asarray(dem_ds.GetGeoTransform())
#if True:
# import matplotlib
# matplotlib.use("Qt4Agg")
# import matplotlib.pyplot as plt
for shoe in shoes:
arr = array_geometry.ogrline2array(shoe, flatten=True)
assert (arr.shape[0] == 4)
# okie dokie - now load a small raster around the horseshoe
# the shoes can have quite long 'sides' (extruders),
# however the two 'ends' should be small enough to keep in
# memory - so load two grids along the two 'ends'
cm, scale, nsteps, H, Hinv = get_transformation_params(arr)
small_grids = []
for e in ((0, 3), (1, 2)):
xy = arr[e, :] # take the corresponding edge
ll = xy.min(axis=0)
ur = xy.max(axis=0)
# map to pixel-space
ll_pix = grid.user2array(georef, ll)
ur_pix = grid.user2array(georef, ur)
xwin, mywin = (ur_pix - ll_pix) #negative ywin
# Buffer grid slightly - can do with less I suppose...
xoff = max(0, int(ll_pix[0]) - 2)
yoff = max(0, int(ur_pix[1]) - 2)
xwin = min(int(xwin + 1), dem_ds.RasterXSize - xoff - 4) + 4
ywin = min(int(1 - mywin), dem_ds.RasterYSize - yoff - 4) + 4
# If not completely contained in large raster - continue??
assert (xoff >= 0 and yoff >= 0 and xwin >= 1 and ywin >= 1) #hmmm
piece = dem_band.ReadAsArray(xoff, yoff, xwin,
ywin).astype(np.float64)
# What to do with nodata-values??
N = (piece == ndval)
if N.any():
print("WARNING: setting nodata values to 0!!!")
piece[N] = 0
piece_georef = georef.copy()
piece_georef[0] += xoff * georef[1]
piece_georef[3] += yoff * georef[5]
small_grids.append(grid.Grid(piece, piece_georef, ndval))
# Make sure that the grid is 'fine' enough - since the projective transformation
# will distort distances across the lines we want to subdivide
cs = 1 / float(nsteps)
# check numerical diff
moved = np.array(((0, cs), (1, cs), (1, 1 - cs), (0, 1 - cs)))
tmoved = inverse_transform(moved, cm, scale, Hinv)
delta = arr - tmoved
ndelta = np.sqrt(np.sum(delta**2, axis=1))
nrows = int(nsteps * ndelta.max()) + 1
# construct the vertical two lines, along the two 'ends', in projective space
hspace, cs = np.linspace(1, 0, nrows, endpoint=True, retstep=True)
cs = -cs
l1 = np.zeros((nrows, 2), dtype=np.float64)
l1[:, 1] = hspace
l2 = np.ones((nrows, 2), dtype=np.float64)
l2[:, 1] = hspace
tl1 = inverse_transform(l1, cm, scale, Hinv)
tl2 = inverse_transform(l2, cm, scale, Hinv)
z1 = small_grids[0].interpolate(tl1)
z2 = small_grids[1].interpolate(tl2)
assert ((z1 != ndval).all())
assert ((z2 != ndval).all())
# now construct a psudo-grid in 'projective space'
Z = np.column_stack((z1, z2))
pseudo_georef = [-0.5, 1.0, 0, 1 + 0.5 * cs, 0, -cs]
pseudo_grid = grid.Grid(Z, pseudo_georef, ndval)
# Transform input points!
# first cut to bounding box of shoe
M = np.logical_and(mesh_xy >= arr.min(axis=0),
mesh_xy <= arr.max(axis=0)).all(axis=1)
print("Number of points in bb: %d" % M.sum())
xy_small = mesh_xy[M]
txy = transform(xy_small, cm, scale, H)
N = np.logical_and(txy >= 0, txy <= 1).all(axis=1)
xy_in_grid = txy[N]
print("Number of points in shoe: %d" % xy_in_grid.shape[0])
new_z = pseudo_grid.interpolate(xy_in_grid)
# Construct new mask as N is 'relative' to M
MM = np.zeros((mesh_xy.shape[0]), dtype=np.bool)
MM[M] = N
MM = MM.reshape(dtm.shape)
dtm.grid[MM] = new_z
# OLD STUFF FOR TRIANGULATION APPROACH
# N1 = np.arange(0,nsteps-1)
# N2 = N1 + 1
# N3 = N1+nsteps
# N4 = N3+1
# T1 = np.column_stack((N1,N3,N4))
# T2 = np.column_stack((N4,N2,N1))
# T = np.vstack((T1,T2))
#
# plt.figure()
# plt.triplot(xy[:,0], xy[:,1], T)
# plt.plot(arr[:,0], arr[:,1], color = "green")
# plt.plot(l1[:,0], l1[:,1], ".", color = "blue", ms = 10)
# plt.plot(l2[:,0], l2[:,1], ".", color = "red", ms = 10)
# plt.show()
dtm.save(outname,
dco=["TILED=YES", "COMPRESS=DEFLATE", "PREDICTOR=3", "ZLEVEL=9"])
pargs = parser.parse_args(args[1:])
except Exception, e:
print(str(e))
return 1
kmname = constants.get_tilename(pargs.dem_tile)
print("Running %s on block: %s, %s" % (progname, kmname, time.asctime()))
extent = np.asarray(constants.tilename_to_extent(kmname))
shoes = vector_io.get_geometries(pargs.horse_ds, pargs.layername,
pargs.layersql, extent)
outname = os.path.join(pargs.outdir, "dhym_lines_" + kmname + ".tif")
if len(shoes) == 0:
print("No shoes, man!")
shutil.copy(pargs.dem_tile, outname)
return 0
#We allways interpolate values from the large ds (vrt) which is not changed in the loop below.
dtm = grid.fromGDAL(pargs.dem_tile)
cs = dtm.geo_ref[1]
mesh_xy = pointcloud.mesh_as_points(dtm.shape, dtm.geo_ref)
dem_ds = gdal.Open(pargs.dem_all)
dem_band = dem_ds.GetRasterBand(1)
ndval = dem_band.GetNoDataValue()
georef = np.asarray(dem_ds.GetGeoTransform())
m_drv = ogr.GetDriverByName("Memory")
line_ds = m_drv.CreateDataSource("dummy")
layer = line_ds.CreateLayer("lines", None, ogr.wkbLineString25D)
layerdefn = layer.GetLayerDefn()
#if True:
# import matplotlib
# matplotlib.use("Qt4Agg")
# mport matplotlib.pyplot as plt
for shoe in shoes:
def main(args):
try:
pargs = parser.parse_args(args[1:])
except Exception as e:
print(str(e))
return 1
kmname = constants.get_tilename(pargs.dem_tile)
print("Running %s on block: %s, %s" %(progname,kmname,time.asctime()))
extent = np.asarray(constants.tilename_to_extent(kmname))
outname = os.path.join(pargs.outdir, "dhym_" + kmname + ".tif")
if os.path.exists(outname) and not pargs.overwrite:
print("File already exists - skipping...")
return 0
# We always interpolate values from the large dataset (vrt) which is not changed in the loop below.
dtm = grid.fromGDAL(pargs.dem_tile)
dem_ds = gdal.Open(pargs.dem_all)
dem_band = dem_ds.GetRasterBand(1)
ndval = dem_band.GetNoDataValue()
georef = np.asarray(dem_ds.GetGeoTransform())
cell_res = min(georef[1], -georef[5] ) #the minimal cell size
#get the geometries!
# a list of (geometry_as_np_array, 'grid1', 'grid2') - the grids should be objects with an interpolate method. The first represents the line 0-3, while the other one represents 1-2.
# and yes, keep all that in memory. Buy some more memory if you need it, man!
stuff_to_handle=[]
#get relevant geometries and process 'em
if pargs.horsesql is not None:
#fetch the horseshoes
shoes = vector_io.get_geometries(pargs.vector_ds, layersql = pargs.horsesql, extent= extent )
#for the horse shoes we want to read z from the dtm!
for shoe in shoes:
arr = array_geometry.ogrline2array(shoe, flatten = True)
assert(arr.shape[0]==4)
g1=get_dtm_piece(arr[(0,3),:],dem_band, georef, ndval) #the 'small' piece for the line from p0 to p3.
g2=get_dtm_piece(arr[(1,2),:],dem_band, georef, ndval) #the 'small' piece for the line from p0 to p3.
stuff_to_handle.append((arr,g1,g2))
del shoes
for sql,own_z in ((pargs.linesql_own_z,True),(pargs.linesql_dtm_z,False)):
#handle the two line types in one go...
if sql is not None:
#fetch the 3d lines
lines= vector_io.get_geometries(pargs.vector_ds, layersql = sql , extent= extent)
print("%d features in "%len(lines)+sql)
for line in lines:
arr = array_geometry.ogrline2array(line, flatten = not own_z)
if own_z:
assert (arr.shape[1]==3) #should be a 3d geometry!!!
z=arr[:,2]
#construct a horse shoe pr line segment!
#should we assert that there are exactly two points?
#We can handle the general case, but it gets tricky to determine what the interpolation should mean (z1 and z2 from endpoints of linestring and interpolating via relative length?)
xy=arr[:,:2]
n= xy.shape[0] - 1 # number of segments.
#SO: for now assert that n==1, only one segment. Else modify what the grids should be for the 'inner' vertices...
assert(n==1)
#vectorice - even though there should be only one segment. Will handle the general case...
N=array_geometry.linestring_displacements(xy)*(cell_res) #displacement vectors - probably too broad with all touched!!!!
buf_left=xy+N
buf_right=xy-N
for i in range(n): # be prepared to handle the general case!!!
shoe=np.vstack((buf_left[i],buf_left[i+1],buf_right[i+1],buf_right[i])) # a horseshoe which is open in the 'first end'
if own_z:
g1=ConstantGrid(z[i])
g2=ConstantGrid(z[i+1])
else:
g1=get_dtm_piece(shoe[(0,3),:],dem_band, georef, ndval) #the 'small' piece for the line from p0 to p3.
g2=get_dtm_piece(shoe[(1,2),:],dem_band, georef, ndval) #the 'small' piece for the line from p0 to p3.
stuff_to_handle.append((shoe,g1,g2))
del lines
if len(stuff_to_handle)==0:
print("No features to burn, copying dtm...")
shutil.copy(pargs.dem_tile,outname)
dem_band=None
dem_ds=None
return 0
t1=time.time()
if pargs.burn_as_lines:
print("Burning as lines...")
m_drv=ogr.GetDriverByName("Memory")
line_ds = m_drv.CreateDataSource( "dummy")
layer = line_ds.CreateLayer( "lines", osr.SpatialReference(dtm.srs), ogr.wkbLineString25D)
create_3d_lines(stuff_to_handle,layer,cell_res*0.6,ndval) #will add 3d lines to layer (in place) - increase resolution to cell_res*0.8 for fewer lines
print("Number of lines: %d" %layer.GetFeatureCount())
#ok - layer created, Burn it!!
layer.ResetReading()
arr=vector_io.just_burn_layer(layer,dtm.geo_ref,dtm.shape,nd_val=ndval,dtype=np.float32,all_touched=True,burn3d=True)
M=(arr!=ndval)
assert M.any()
if pargs.debug:
drv=ogr.GetDriverByName("SQLITE")
drv.CopyDataSource(line_ds,os.path.join(pargs.outdir,"dalines_"+kmname+".sqlite"))
layer=None
line_ds=None
dtm.grid[M]=arr[M]
else:
mesh_xy = pointcloud.mesh_as_points(dtm.shape, dtm.geo_ref)
print("Burning using projective transformation...")
burn_projective(stuff_to_handle,dtm,cell_res,ndval,mesh_xy)
t2=time.time()
print("Burning took: %.3fs" %(t2-t1))
dtm.save(outname,dco = ["TILED=YES", "COMPRESS=DEFLATE", "PREDICTOR=3", "ZLEVEL=9"])
return g0, vert_expansions, hor_expansions
def main(args):
try:
pargs = parser.parse_args(args[1:])
except Exception, e:
print(str(e))
return 1
kmname = constants.get_tilename(pargs.tile_name)
print("Running %s on block: %s, %s" % (progname, kmname, time.asctime()))
if pargs.tiledb is not None:
G, v_expansions, h_expansions = get_extended_tile(pargs.tiledb, kmname)
else:
v_expansions = h_expansions = {-1: False, 1: False}
G = grid.fromGDAL(pargs.tile_name)
if pargs.ZT:
method = 1
else:
method = 0
H = G.get_hillshade(azimuth=pargs.azimuth,
height=pargs.height,
z_factor=pargs.zfactor,
method=method)
for pos in (-1, 1):
if h_expansions[pos]:
H.shrink_hor(pos, pixel_buf)
if v_expansions[pos]:
H.shrink_vert(pos, pixel_buf)
outname = os.path.join(
pargs.outdir, "hs_" +
