def process(parsed, target, temp_metatile, temp_processed, save_offsetx, save_offsety, save_xsize, save_ysize, nodata, ot, *args, **kwargs):
scale = parsed.s
zfactor = parsed.z
altitude = parsed.alt
process_hillshade = "gdaldem hillshade -s %s -z %s -alt %s %s -of GTiff %s > /dev/null" %(scale, zfactor, altitude, temp_metatile, temp_processed)
os.system(process_hillshade)
nodata = 0
ot = "-ot Byte"
# open in numpy
_, gt, _, nodata, array_numpy = numpy_read(temp_processed)
processed_numpy = ndimage.median_filter(array_numpy, size=3)
numpy_save(processed_numpy, target, save_offsetx, save_offsety, save_xsize, save_ysize, gt, nodata, ot)
def process(parsed, target, temp_metatile, temp_processed, save_offsetx, save_offsety, save_xsize, save_ysize, nodata, ot, *args, **kwargs):
scale = parsed.s
process_slopeshade = "gdaldem slope -s %s %s -of GTiff %s > /dev/null" %(scale, temp_metatile, temp_processed)
os.system(process_slopeshade)
nodata = 0
ot = "-ot Byte"
_, gt, _, nodata, array_numpy = numpy_read(temp_processed)
array_numpy[array_numpy==nodata] = 0
# convert to 8 bit and invert values
array_numpy = -(array_numpy.astype(numpy.uint8)-255)
array_numpy[array_numpy==0] = 255
#print array_numpy.shape
processed_numpy = array_numpy
#print processed_numpy[1][3]
numpy_save(processed_numpy, target, save_offsetx, save_offsety, save_xsize, save_ysize, gt, nodata, ot)
def process(parsed, target, temp_metatile, temp_processed, save_offsetx,
save_offsety, save_xsize, save_ysize, nodata, ot, *args, **kwargs):
scale = parsed.s
process_slopeshade = "gdaldem slope -s %s %s -of GTiff %s > /dev/null" % (
scale, temp_metatile, temp_processed)
os.system(process_slopeshade)
nodata = 0
ot = "-ot Byte"
_, gt, _, nodata, array_numpy = numpy_read(temp_processed)
array_numpy[array_numpy == nodata] = 0
# convert to 8 bit and invert values
array_numpy = -(array_numpy.astype(numpy.uint8) - 255)
array_numpy[array_numpy == 0] = 255
#print array_numpy.shape
processed_numpy = array_numpy
#print processed_numpy[1][3]
numpy_save(processed_numpy, target, save_offsetx, save_offsety, save_xsize,
save_ysize, gt, nodata, ot)
def process(parsed, target, temp_metatile, temp_processed, save_offsetx, save_offsety, save_xsize, save_ysize, nodata, ot, *args, **kwargs):
temp_voids = tempfile.mktemp() + ".geojson" #"tmp_voids_%s.geojson" % os.getpid()
with open(temp_voids, "w+"):
pass
temp_voids_raster = tempfile.mktemp() #"/tmp/tmp_voids_raster_%s.tif" % os.getpid()
with open(temp_voids_raster, "w+"):
pass
temp_secondary = tempfile.mktemp() #"/tmp/tmp_secondary_%s" % os.getpid()
with open(temp_voids, "w+"):
pass
void_mask = parsed.voids
secondary_source = parsed.secondary
tertiary_source = parsed.tertiary
landmask = parsed.landmask
nodata = 0
ot = "-ot Int16"
# read primary data
_, gt, _, nodata, primary_dem = numpy_read(temp_metatile)
# TODO check if valid
ulx, llx = gt[0], gt[0]
uly, ury = gt[3], gt[3]
urx, lrx = ulx + (gt[1] * primary_dem.shape[1]), ulx + (gt[1] * primary_dem.shape[1])
lly, lry = uly + (gt[5] * primary_dem.shape[0]), uly + (gt[5] * primary_dem.shape[0])
# read secondary
clip_secondary = "gdal_translate -projwin %s %s %s %s %s %s" %(ulx, uly, lrx, lry, secondary_source, temp_secondary)
print "%s: clipping secondary DEM" %(target)
os.system(clip_secondary)
_, gt, _, nodata, secondary_dem = numpy_read(temp_secondary)
# clip void mask
os.remove(temp_voids)
clip_void_mask = "ogr2ogr -overwrite -f 'GeoJSON' %s %s -clipsrc %s %s %s %s" %(temp_voids, void_mask, llx, lly, urx, ury)
print "%s: clipping void mask" %(target)
os.system(clip_void_mask)
# rasterize void mask
rasterize_void_mask = "gdal_rasterize -burn 1 -i -te %s %s %s %s -tr %s %s -ot Byte %s %s" %(llx, lly, urx, ury, gt[1], -gt[5], temp_voids, temp_voids_raster)
print "%s: rasterize void mask" %(target)
os.system(rasterize_void_mask)
_, gt, _, nodata, void_mask_raster = numpy_read(temp_voids_raster)
## numpy_read
# fill gaps by overlaying numpy arrays
# http://stackoverflow.com/questions/19817955/overlay-part-of-the-matrix-on-top-of-another
processed_numpy = numpy.where(void_mask_raster != 0, primary_dem, secondary_dem)
# clean up
os.remove(temp_voids)
os.remove(temp_voids_raster)
os.remove(temp_secondary)
if landmask:
# read mask to numpy
temp_landmask = tempfile.mktemp() #"/tmp/tmp_voids_raster_%s.tif" % os.getpid()
with open(temp_landmask, "w+"):
pass
clip_landmask = "gdal_translate -projwin %s %s %s %s %s %s" %(ulx, uly, lrx, lry, landmask, temp_landmask)
print clip_landmask
os.system(clip_landmask)
_, gt, _, nodata, numpy_landmask = numpy_read(temp_landmask)
os.remove(temp_landmask)
processed_numpy = numpy.where(numpy_landmask == 1, processed_numpy, 0)
numpy_save(processed_numpy, target, save_offsetx, save_offsety, save_xsize, save_ysize, gt, nodata, ot)