def generate_png(orthophoto_file, output_file=None, outsize=None):
if output_file is None:
base, ext = os.path.splitext(orthophoto_file)
output_file = base + '.png'
# See if we need to select top three bands
bandparam = ""
gtif = gdal.Open(orthophoto_file)
if gtif.RasterCount > 4:
bands = []
for idx in range(1, gtif.RasterCount + 1):
bands.append(gtif.GetRasterBand(idx).GetColorInterpretation())
bands = dict(zip(bands, range(1, len(bands) + 1)))
try:
red = bands.get(gdal.GCI_RedBand)
green = bands.get(gdal.GCI_GreenBand)
blue = bands.get(gdal.GCI_BlueBand)
if red is None or green is None or blue is None:
raise Exception("Cannot find bands")
bandparam = "-b %s -b %s -b %s -a_nodata 0" % (red, green, blue)
except:
bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
gtif = None
osparam = ""
if outsize is not None:
osparam = "-outsize %s 0" % outsize
system.run(
'gdal_translate -of png "%s" "%s" %s %s '
'--config GDAL_CACHEMAX %s%% ' %
(orthophoto_file, output_file, osparam, bandparam, get_max_memory()))
def compute_cutline(orthophoto_file, crop_area_file, destination, max_concurrency=1, tmpdir=None, scale=1):
if io.file_exists(orthophoto_file) and io.file_exists(crop_area_file):
from opendm.grass_engine import grass
log.ODM_DEBUG("Computing cutline")
if tmpdir and not io.dir_exists(tmpdir):
system.mkdir_p(tmpdir)
scale = max(0.0001, min(1, scale))
scaled_orthophoto = None
if scale < 1:
log.ODM_DEBUG("Scaling orthophoto to %s%% to compute cutline" % (scale * 100))
scaled_orthophoto = os.path.join(tmpdir, os.path.basename(io.related_file_path(orthophoto_file, postfix=".scaled")))
# Scale orthophoto before computing cutline
system.run("gdal_translate -outsize {}% 0 "
"-co NUM_THREADS={} "
"--config GDAL_CACHEMAX {}% "
"{} {}".format(
scale * 100,
max_concurrency,
concurrency.get_max_memory(),
orthophoto_file,
scaled_orthophoto
))
orthophoto_file = scaled_orthophoto
try:
ortho_width,ortho_height = get_image_size.get_image_size(orthophoto_file, fallback_on_error=False)
log.ODM_DEBUG("Orthophoto dimensions are %sx%s" % (ortho_width, ortho_height))
number_lines = int(max(8, math.ceil(min(ortho_width, ortho_height) / 256.0)))
except:
log.ODM_DEBUG("Cannot compute orthophoto dimensions, setting arbitrary number of lines.")
number_lines = 32
log.ODM_DEBUG("Number of lines: %s" % number_lines)
gctx = grass.create_context({'auto_cleanup' : False, 'tmpdir': tmpdir})
gctx.add_param('orthophoto_file', orthophoto_file)
gctx.add_param('crop_area_file', crop_area_file)
gctx.add_param('number_lines', number_lines)
gctx.add_param('max_concurrency', max_concurrency)
gctx.add_param('memory', int(concurrency.get_max_memory_mb(300)))
gctx.set_location(orthophoto_file)
cutline_file = gctx.execute(os.path.join("opendm", "grass", "compute_cutline.grass"))
if cutline_file != 'error':
if io.file_exists(cutline_file):
shutil.move(cutline_file, destination)
log.ODM_INFO("Generated cutline file: %s --> %s" % (cutline_file, destination))
gctx.cleanup()
return destination
else:
log.ODM_WARNING("Unexpected script result: %s. No cutline file has been generated." % cutline_file)
else:
log.ODM_WARNING("Could not generate orthophoto cutline. An error occured when running GRASS. No orthophoto will be generated.")
else:
log.ODM_WARNING("We've been asked to compute cutline, but either %s or %s is missing. Skipping..." % (orthophoto_file, crop_area_file))
def generate_png(orthophoto_file):
log.ODM_INFO("Generating PNG")
base, ext = os.path.splitext(orthophoto_file)
orthophoto_png = base + '.png'
system.run('gdal_translate -of png "%s" "%s" '
'--config GDAL_CACHEMAX %s%% ' %
(orthophoto_file, orthophoto_png, get_max_memory()))
def crop(gpkg_path, geotiff_path, gdal_options, keep_original=True):
if not os.path.exists(gpkg_path) or not os.path.exists(geotiff_path):
log.ODM_WARNING(
"Either {} or {} does not exist, will skip cropping.".format(
gpkg_path, geotiff_path))
return geotiff_path
log.ODM_INFO("Cropping %s" % geotiff_path)
# Rename original file
# path/to/odm_orthophoto.tif --> path/to/odm_orthophoto.original.tif
path, filename = os.path.split(geotiff_path)
# path = path/to
# filename = odm_orthophoto.tif
basename, ext = os.path.splitext(filename)
# basename = odm_orthophoto
# ext = .tif
original_geotiff = os.path.join(path,
"{}.original{}".format(basename, ext))
os.rename(geotiff_path, original_geotiff)
try:
kwargs = {
'gpkg_path':
gpkg_path,
'geotiffInput':
original_geotiff,
'geotiffOutput':
geotiff_path,
'options':
' '.join(
map(lambda k: '-co {}={}'.format(k, gdal_options[k]),
gdal_options)),
'max_memory':
get_max_memory()
}
run('gdalwarp -cutline {gpkg_path} '
'-crop_to_cutline '
'{options} '
'{geotiffInput} '
'{geotiffOutput} '
'--config GDAL_CACHEMAX {max_memory}%'.format(**kwargs))
if not keep_original:
os.remove(original_geotiff)
except Exception as e:
log.ODM_WARNING('Something went wrong while cropping: {}'.format(
e.message))
# Revert rename
os.rename(original_geotiff, geotiff_path)
return geotiff_path
def convert_to_cogeo(src_path, blocksize=256, max_workers=1):
"""
Guarantee that the .tif passed as an argument is a Cloud Optimized GeoTIFF (cogeo)
The file is destructively converted into a cogeo.
If the file cannot be converted, the function does not change the file
:param src_path: path to GeoTIFF
:return: True on success
"""
if not os.path.isfile(src_path):
logger.warning("Cannot convert to cogeo: %s (file does not exist)" %
src_path)
return False
log.ODM_INFO("Optimizing %s as Cloud Optimized GeoTIFF" % src_path)
tmpfile = io.related_file_path(src_path, postfix='_cogeo')
swapfile = io.related_file_path(src_path, postfix='_cogeo_swap')
kwargs = {
'threads': max_workers if max_workers else 'ALL_CPUS',
'blocksize': blocksize,
'max_memory': get_max_memory(),
'src_path': src_path,
'tmpfile': tmpfile,
}
try:
system.run("gdal_translate "
"-of COG "
"-co NUM_THREADS={threads} "
"-co BLOCKSIZE={blocksize} "
"-co COMPRESS=deflate "
"-co BIGTIFF=IF_SAFER "
"-co RESAMPLING=NEAREST "
"--config GDAL_CACHEMAX {max_memory}% "
"--config GDAL_NUM_THREADS {threads} "
"\"{src_path}\" \"{tmpfile}\" ".format(**kwargs))
except Exception as e:
log.ODM_WARNING("Cannot create Cloud Optimized GeoTIFF: %s" % str(e))
if os.path.isfile(tmpfile):
shutil.move(src_path, swapfile) # Move to swap location
try:
shutil.move(tmpfile, src_path)
except IOError as e:
log.ODM_WARNING("Cannot move %s to %s: %s" %
(tmpfile, src_path, str(e)))
shutil.move(swapfile, src_path) # Attempt to restore
if os.path.isfile(swapfile):
os.remove(swapfile)
return True
else:
return False
def generate_kmz(orthophoto_file, output_file=None, outsize=None):
if output_file is None:
base, ext = os.path.splitext(orthophoto_file)
output_file = base + '.kmz'
# See if we need to select top three bands
bandparam = ""
gtif = gdal.Open(orthophoto_file)
if gtif.RasterCount > 4:
bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
system.run(
'gdal_translate -of KMLSUPEROVERLAY -co FORMAT=JPEG "%s" "%s" %s '
'--config GDAL_CACHEMAX %s%% ' %
(orthophoto_file, output_file, bandparam, get_max_memory()))
def crop(shapefile_path, geotiff_path, gdal_options, keep_original=True):
if not os.path.exists(shapefile_path) or not os.path.exists(geotiff_path):
log.ODM_WARNING("Either {} or {} does not exist, will skip cropping.".format(shapefile_path, geotiff_path))
return geotiff_path
# Rename original file
# path/to/odm_orthophoto.tif --> path/to/odm_orthophoto.original.tif
path, filename = os.path.split(geotiff_path)
# path = path/to
# filename = odm_orthophoto.tif
basename, ext = os.path.splitext(filename)
# basename = odm_orthophoto
# ext = .tif
original_geotiff = os.path.join(path, "{}.original{}".format(basename, ext))
os.rename(geotiff_path, original_geotiff)
try:
kwargs = {
'shapefile_path': shapefile_path,
'geotiffInput': original_geotiff,
'geotiffOutput': geotiff_path,
'options': ' '.join(map(lambda k: '-co {}={}'.format(k, gdal_options[k]), gdal_options)),
'max_memory': get_max_memory()
}
run('gdalwarp -cutline {shapefile_path} '
'-crop_to_cutline '
'{options} '
'{geotiffInput} '
'{geotiffOutput} '
'--config GDAL_CACHEMAX {max_memory}%'.format(**kwargs))
if not keep_original:
os.remove(original_geotiff)
except Exception as e:
log.ODM_WARNING('Something went wrong while cropping: {}'.format(e.message))
# Revert rename
os.rename(original_geotiff, geotiff_path)
return geotiff_path
def generate_png(orthophoto_file, output_file=None, outsize=None):
if output_file is None:
base, ext = os.path.splitext(orthophoto_file)
output_file = base + '.png'
# See if we need to select top three bands
bandparam = ""
gtif = gdal.Open(orthophoto_file)
if gtif.RasterCount > 4:
bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
osparam = ""
if outsize is not None:
osparam = "-outsize %s 0" % outsize
system.run(
'gdal_translate -of png "%s" "%s" %s %s '
'--config GDAL_CACHEMAX %s%% ' %
(orthophoto_file, output_file, osparam, bandparam, get_max_memory()))
def compute_cutline(orthophoto_file,
crop_area_file,
destination,
max_concurrency=1,
scale=1):
if io.file_exists(orthophoto_file) and io.file_exists(crop_area_file):
log.ODM_INFO("Computing cutline")
scale = max(0.0001, min(1, scale))
scaled_orthophoto = None
if scale < 1:
log.ODM_INFO("Scaling orthophoto to %s%% to compute cutline" %
(scale * 100))
scaled_orthophoto = io.related_file_path(orthophoto_file,
postfix=".scaled")
# Scale orthophoto before computing cutline
system.run("gdal_translate -outsize {}% 0 "
"-co NUM_THREADS={} "
"--config GDAL_CACHEMAX {}% "
'"{}" "{}"'.format(scale * 100, max_concurrency,
concurrency.get_max_memory(),
orthophoto_file, scaled_orthophoto))
orthophoto_file = scaled_orthophoto
# open raster
f = rasterio.open(orthophoto_file)
rast = f.read(1) # First band only
height, width = rast.shape
number_lines = int(max(8, math.ceil(min(width, height) / 256.0)))
line_hor_offset = int(width / number_lines)
line_ver_offset = int(height / number_lines)
if line_hor_offset <= 2 or line_ver_offset <= 2:
log.ODM_WARNING(
"Cannot compute cutline, orthophoto is too small (%sx%spx)" %
(width, height))
return
crop_f = fiona.open(crop_area_file, 'r')
if len(crop_f) == 0:
log.ODM_WARNING("Crop area is empty, cannot compute cutline")
return
crop_poly = shape(crop_f[1]['geometry'])
crop_f.close()
linestrings = []
# Compute canny edges on first band
edges = canny(rast)
def compute_linestrings(direction):
log.ODM_INFO("Computing %s cutlines" % direction)
# Initialize cost map
cost_map = np.full((height, width), 1, dtype=np.float32)
# Write edges to cost map
cost_map[edges == True] = 0 # Low cost
# Write "barrier, floor is lava" costs
if direction == 'vertical':
lines = [((i, 0), (i, height - 1))
for i in range(line_hor_offset, width -
line_hor_offset, line_hor_offset)]
points = []
pad_x = int(line_hor_offset / 2.0)
for i in range(0, len(lines)):
a, b = lines[i]
points.append(((a[0] - pad_x, a[1]), (b[0] - pad_x, b[1])))
a, b = lines[-1]
points.append(((a[0] + pad_x, a[1]), (b[0] + pad_x, b[1])))
else:
lines = [((0, j), (width - 1, j))
for j in range(line_ver_offset, height -
line_ver_offset, line_ver_offset)]
points = []
pad_y = int(line_ver_offset / 2.0)
for i in range(0, len(lines)):
a, b = lines[i]
points.append(((a[0], a[1] - pad_y), (b[0], b[1] - pad_y)))
a, b = lines[-1]
points.append(((a[0], a[1] + pad_y), (b[0], b[1] + pad_y)))
for a, b in lines:
rr, cc = line(*a, *b)
cost_map[cc, rr] = 9999 # Lava
# Calculate route
for a, b in points:
line_coords, cost = route_through_array(cost_map, (a[1], a[0]),
(b[1], b[0]),
fully_connected=True,
geometric=True)
# Convert to geographic
geo_line_coords = [f.xy(*c) for c in line_coords]
# Simplify
ls = LineString(geo_line_coords)
linestrings.append(ls.simplify(0.05, preserve_topology=False))
compute_linestrings('vertical')
compute_linestrings('horizontal')
# Generate polygons and keep only those inside the crop area
log.ODM_INFO("Generating polygons... this could take a bit.")
polygons = []
for p in polygonize(unary_union(linestrings)):
if crop_poly.contains(p):
polygons.append(p)
# This should never happen
if len(polygons) == 0:
log.ODM_WARNING("No polygons, cannot compute cutline")
return
log.ODM_INFO("Merging polygons")
cutline_polygons = unary_union(polygons)
if not hasattr(cutline_polygons, '__getitem__'):
cutline_polygons = [cutline_polygons]
largest_cutline = cutline_polygons[0]
max_area = largest_cutline.area
for p in cutline_polygons:
if p.area > max_area:
max_area = p.area
largest_cutline = p
log.ODM_INFO("Largest cutline found: %s m^2" % max_area)
meta = {
'crs': {
'init': str(f.crs).lower()
},
'driver': 'GPKG',
'schema': {
'properties': {},
'geometry': 'Polygon'
}
}
# Remove previous
if os.path.exists(destination):
os.remove(destination)
with fiona.open(destination, 'w', **meta) as sink:
sink.write({
'geometry': mapping(largest_cutline),
'properties': {}
})
f.close()
log.ODM_INFO("Wrote %s" % destination)
# Cleanup
if scaled_orthophoto is not None and os.path.exists(scaled_orthophoto):
os.remove(scaled_orthophoto)
else:
log.ODM_WARNING(
"We've been asked to compute cutline, but either %s or %s is missing. Skipping..."
% (orthophoto_file, crop_area_file))
def create_dem(input_point_cloud, dem_type, output_type='max', radiuses=['0.56'], gapfill=True,
outdir='', resolution=0.1, max_workers=1, max_tile_size=4096,
verbose=False, decimation=None, keep_unfilled_copy=False,
apply_smoothing=True):
""" Create DEM from multiple radii, and optionally gapfill """
global error
error = None
start = datetime.now()
if not os.path.exists(outdir):
log.ODM_INFO("Creating %s" % outdir)
os.mkdir(outdir)
extent = point_cloud.get_extent(input_point_cloud)
log.ODM_INFO("Point cloud bounds are [minx: %s, maxx: %s] [miny: %s, maxy: %s]" % (extent['minx'], extent['maxx'], extent['miny'], extent['maxy']))
ext_width = extent['maxx'] - extent['minx']
ext_height = extent['maxy'] - extent['miny']
w, h = (int(math.ceil(ext_width / float(resolution))),
int(math.ceil(ext_height / float(resolution))))
# Set a floor, no matter the resolution parameter
# (sometimes a wrongly estimated scale of the model can cause the resolution
# to be set unrealistically low, causing errors)
RES_FLOOR = 64
if w < RES_FLOOR and h < RES_FLOOR:
prev_w, prev_h = w, h
if w >= h:
w, h = (RES_FLOOR, int(math.ceil(ext_height / ext_width * RES_FLOOR)))
else:
w, h = (int(math.ceil(ext_width / ext_height * RES_FLOOR)), RES_FLOOR)
floor_ratio = prev_w / float(w)
resolution *= floor_ratio
radiuses = [str(float(r) * floor_ratio) for r in radiuses]
log.ODM_WARNING("Really low resolution DEM requested %s will set floor at %s pixels. Resolution changed to %s. The scale of this reconstruction might be off." % ((prev_w, prev_h), RES_FLOOR, resolution))
final_dem_pixels = w * h
num_splits = int(max(1, math.ceil(math.log(math.ceil(final_dem_pixels / float(max_tile_size * max_tile_size)))/math.log(2))))
num_tiles = num_splits * num_splits
log.ODM_INFO("DEM resolution is %s, max tile size is %s, will split DEM generation into %s tiles" % ((h, w), max_tile_size, num_tiles))
tile_bounds_width = ext_width / float(num_splits)
tile_bounds_height = ext_height / float(num_splits)
tiles = []
for r in radiuses:
minx = extent['minx']
for x in range(num_splits):
miny = extent['miny']
if x == num_splits - 1:
maxx = extent['maxx']
else:
maxx = minx + tile_bounds_width
for y in range(num_splits):
if y == num_splits - 1:
maxy = extent['maxy']
else:
maxy = miny + tile_bounds_height
filename = os.path.join(os.path.abspath(outdir), '%s_r%s_x%s_y%s.tif' % (dem_type, r, x, y))
tiles.append({
'radius': r,
'bounds': {
'minx': minx,
'maxx': maxx,
'miny': miny,
'maxy': maxy
},
'filename': filename
})
miny = maxy
minx = maxx
# Sort tiles by increasing radius
tiles.sort(key=lambda t: float(t['radius']), reverse=True)
def process_tile(q):
log.ODM_INFO("Generating %s (%s, radius: %s, resolution: %s)" % (q['filename'], output_type, q['radius'], resolution))
d = pdal.json_gdal_base(q['filename'], output_type, q['radius'], resolution, q['bounds'])
if dem_type == 'dtm':
d = pdal.json_add_classification_filter(d, 2)
if decimation is not None:
d = pdal.json_add_decimation_filter(d, decimation)
pdal.json_add_readers(d, [input_point_cloud])
pdal.run_pipeline(d, verbose=verbose)
parallel_map(process_tile, tiles, max_workers)
output_file = "%s.tif" % dem_type
output_path = os.path.abspath(os.path.join(outdir, output_file))
# Verify tile results
for t in tiles:
if not os.path.exists(t['filename']):
raise Exception("Error creating %s, %s failed to be created" % (output_file, t['filename']))
# Create virtual raster
tiles_vrt_path = os.path.abspath(os.path.join(outdir, "tiles.vrt"))
run('gdalbuildvrt "%s" "%s"' % (tiles_vrt_path, '" "'.join(map(lambda t: t['filename'], tiles))))
merged_vrt_path = os.path.abspath(os.path.join(outdir, "merged.vrt"))
geotiff_tmp_path = os.path.abspath(os.path.join(outdir, 'tiles.tmp.tif'))
geotiff_small_path = os.path.abspath(os.path.join(outdir, 'tiles.small.tif'))
geotiff_small_filled_path = os.path.abspath(os.path.join(outdir, 'tiles.small_filled.tif'))
geotiff_path = os.path.abspath(os.path.join(outdir, 'tiles.tif'))
# Build GeoTIFF
kwargs = {
'max_memory': get_max_memory(),
'threads': max_workers if max_workers else 'ALL_CPUS',
'tiles_vrt': tiles_vrt_path,
'merged_vrt': merged_vrt_path,
'geotiff': geotiff_path,
'geotiff_tmp': geotiff_tmp_path,
'geotiff_small': geotiff_small_path,
'geotiff_small_filled': geotiff_small_filled_path
}
if gapfill:
# Sometimes, for some reason gdal_fillnodata.py
# behaves strangely when reading data directly from a .VRT
# so we need to convert to GeoTIFF first.
run('gdal_translate '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'{tiles_vrt} {geotiff_tmp}'.format(**kwargs))
# Scale to 10% size
run('gdal_translate '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'-outsize 10% 0 '
'{geotiff_tmp} {geotiff_small}'.format(**kwargs))
# Fill scaled
run('gdal_fillnodata.py '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'-b 1 '
'-of GTiff '
'{geotiff_small} {geotiff_small_filled}'.format(**kwargs))
# Merge filled scaled DEM with unfilled DEM using bilinear interpolation
run('gdalbuildvrt -resolution highest -r bilinear "%s" "%s" "%s"' % (merged_vrt_path, geotiff_small_filled_path, geotiff_tmp_path))
run('gdal_translate '
'-co NUM_THREADS={threads} '
'-co TILED=YES '
'-co COMPRESS=DEFLATE '
'--config GDAL_CACHEMAX {max_memory}% '
'{merged_vrt} {geotiff}'.format(**kwargs))
else:
run('gdal_translate '
'-co NUM_THREADS={threads} '
'-co TILED=YES '
'-co COMPRESS=DEFLATE '
'--config GDAL_CACHEMAX {max_memory}% '
'{tiles_vrt} {geotiff}'.format(**kwargs))
if apply_smoothing:
median_smoothing(geotiff_path, output_path)
os.remove(geotiff_path)
else:
os.rename(geotiff_path, output_path)
if os.path.exists(geotiff_tmp_path):
if not keep_unfilled_copy:
os.remove(geotiff_tmp_path)
else:
os.rename(geotiff_tmp_path, io.related_file_path(output_path, postfix=".unfilled"))
for cleanup_file in [tiles_vrt_path, merged_vrt_path, geotiff_small_path, geotiff_small_filled_path]:
if os.path.exists(cleanup_file): os.remove(cleanup_file)
for t in tiles:
if os.path.exists(t['filename']): os.remove(t['filename'])
log.ODM_INFO('Completed %s in %s' % (output_file, datetime.now() - start))
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM Orthophoto Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
reconstruction = inputs.reconstruction
verbose = '-verbose' if self.params.verbose else ''
# define paths and create working directories
system.mkdir_p(tree.odm_orthophoto)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_orthophoto') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_orthophoto' in args.rerun_from)
if not io.file_exists(tree.odm_orthophoto_file) or rerun_cell:
# odm_orthophoto definitions
kwargs = {
'bin':
context.odm_modules_path,
'log':
tree.odm_orthophoto_log,
'ortho':
tree.odm_orthophoto_file,
'corners':
tree.odm_orthophoto_corners,
'res':
1.0 / (gsd.cap_resolution(self.params.resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd) / 100.0),
'verbose':
verbose
}
# Have geo coordinates?
georef = reconstruction.georef
# Check if the georef object is initialized
# (during a --rerun this might not be)
# TODO: we should move this to a more central
# location (perhaps during the dataset initialization)
if georef and not georef.utm_east_offset:
georeferencing_dir = tree.odm_georeferencing if args.use_3dmesh and not args.skip_3dmodel else tree.odm_25dgeoreferencing
odm_georeferencing_model_txt_geo_file = os.path.join(
georeferencing_dir, tree.odm_georeferencing_model_txt_geo)
if io.file_exists(odm_georeferencing_model_txt_geo_file):
georef.extract_offsets(
odm_georeferencing_model_txt_geo_file)
else:
log.ODM_WARNING(
'Cannot read UTM offset from {}. An orthophoto will not be generated.'
.format(odm_georeferencing_model_txt_geo_file))
if georef:
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(
tree.odm_texturing,
tree.odm_georeferencing_model_obj_geo)
else:
kwargs['model_geo'] = os.path.join(
tree.odm_25dtexturing,
tree.odm_georeferencing_model_obj_geo)
else:
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(
tree.odm_texturing, tree.odm_textured_model_obj)
else:
kwargs['model_geo'] = os.path.join(
tree.odm_25dtexturing, tree.odm_textured_model_obj)
# run odm_orthophoto
system.run(
'{bin}/odm_orthophoto -inputFile {model_geo} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if georef and georef.projection and georef.utm_east_offset and georef.utm_north_offset:
ulx = uly = lrx = lry = 0.0
with open(tree.odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(georef.utm_east_offset)
lry = float(tokens[1]) + \
float(georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(georef.utm_east_offset)
uly = float(tokens[3]) + \
float(georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
kwargs = {
'ulx':
ulx,
'uly':
uly,
'lrx':
lrx,
'lry':
lry,
'tiled':
'' if self.params.no_tiled else '-co TILED=yes ',
'compress':
self.params.compress,
'predictor':
'-co PREDICTOR=2 '
if self.params.compress in ['LZW', 'DEFLATE'] else '',
'proj':
georef.projection.srs,
'bigtiff':
self.params.bigtiff,
'png':
tree.odm_orthophoto_file,
'tiff':
tree.odm_orthophoto_tif,
'log':
tree.odm_orthophoto_tif_log,
'max_memory':
get_max_memory(),
'threads':
self.params.max_concurrency
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{tiled} '
'-co BIGTIFF={bigtiff} '
'-co COMPRESS={compress} '
'{predictor} '
'-co BLOCKXSIZE=512 '
'-co BLOCKYSIZE=512 '
'-co NUM_THREADS={threads} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'{png} {tiff} > {log}'.format(**kwargs))
if args.crop > 0:
shapefile_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.shp')
Cropper.crop(
shapefile_path, tree.odm_orthophoto_tif, {
'TILED':
'NO' if self.params.no_tiled else 'YES',
'COMPRESS':
self.params.compress,
'PREDICTOR':
'2' if self.params.compress in ['LZW', 'DEFLATE']
else '1',
'BIGTIFF':
self.params.bigtiff,
'BLOCKXSIZE':
512,
'BLOCKYSIZE':
512,
'NUM_THREADS':
self.params.max_concurrency
})
if self.params.build_overviews:
log.ODM_DEBUG("Building Overviews")
kwargs = {
'orthophoto': tree.odm_orthophoto_tif,
'log': tree.odm_orthophoto_gdaladdo_log
}
# Run gdaladdo
system.run(
'gdaladdo -ro -r average '
'--config BIGTIFF_OVERVIEW IF_SAFER '
'--config COMPRESS_OVERVIEW JPEG '
'{orthophoto} 2 4 8 16 > {log}'.format(**kwargs))
geotiffcreated = True
if not geotiffcreated:
log.ODM_WARNING(
'No geo-referenced orthophoto created due '
'to missing geo-referencing or corner coordinates.')
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' %
tree.odm_orthophoto_file)
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Orthophoto')
log.ODM_INFO('Running ODM OrthoPhoto Cell - Finished')
return ecto.OK if args.end_with != 'odm_orthophoto' else ecto.QUIT
def create_dem(input_point_cloud, dem_type, output_type='max', radiuses=['0.56'], gapfill=True,
outdir='', resolution=0.1, max_workers=1, max_tile_size=2048,
verbose=False, decimation=None):
""" Create DEM from multiple radii, and optionally gapfill """
global error
error = None
start = datetime.now()
if not os.path.exists(outdir):
log.ODM_INFO("Creating %s" % outdir)
os.mkdir(outdir)
extent = point_cloud.get_extent(input_point_cloud)
log.ODM_INFO("Point cloud bounds are [minx: %s, maxx: %s] [miny: %s, maxy: %s]" % (extent['minx'], extent['maxx'], extent['miny'], extent['maxy']))
ext_width = extent['maxx'] - extent['minx']
ext_height = extent['maxy'] - extent['miny']
final_dem_resolution = (int(math.ceil(ext_width / float(resolution))),
int(math.ceil(ext_height / float(resolution))))
final_dem_pixels = final_dem_resolution[0] * final_dem_resolution[1]
num_splits = int(max(1, math.ceil(math.log(math.ceil(final_dem_pixels / float(max_tile_size * max_tile_size)))/math.log(2))))
num_tiles = num_splits * num_splits
log.ODM_INFO("DEM resolution is %s, max tile size is %s, will split DEM generation into %s tiles" % (final_dem_resolution, max_tile_size, num_tiles))
tile_bounds_width = ext_width / float(num_splits)
tile_bounds_height = ext_height / float(num_splits)
tiles = []
for r in radiuses:
minx = extent['minx']
for x in range(num_splits):
miny = extent['miny']
if x == num_splits - 1:
maxx = extent['maxx']
else:
maxx = minx + tile_bounds_width
for y in range(num_splits):
if y == num_splits - 1:
maxy = extent['maxy']
else:
maxy = miny + tile_bounds_height
filename = os.path.join(os.path.abspath(outdir), '%s_r%s_x%s_y%s.tif' % (dem_type, r, x, y))
tiles.append({
'radius': r,
'bounds': {
'minx': minx,
'maxx': maxx,
'miny': miny,
'maxy': maxy
},
'filename': filename
})
miny = maxy
minx = maxx
# Sort tiles by increasing radius
tiles.sort(key=lambda t: float(t['radius']), reverse=True)
def process_one(q):
log.ODM_INFO("Generating %s (%s, radius: %s, resolution: %s)" % (q['filename'], output_type, q['radius'], resolution))
d = pdal.json_gdal_base(q['filename'], output_type, q['radius'], resolution, q['bounds'])
if dem_type == 'dsm':
d = pdal.json_add_classification_filter(d, 2, equality='max')
elif dem_type == 'dtm':
d = pdal.json_add_classification_filter(d, 2)
if decimation is not None:
d = pdal.json_add_decimation_filter(d, decimation)
pdal.json_add_readers(d, [input_point_cloud])
pdal.run_pipeline(d, verbose=verbose)
def worker():
global error
while True:
(num, q) = pq.get()
if q is None or error is not None:
pq.task_done()
break
try:
process_one(q)
except Exception as e:
error = e
finally:
pq.task_done()
if max_workers > 1:
use_single_thread = False
pq = queue.PriorityQueue()
threads = []
for i in range(max_workers):
t = threading.Thread(target=worker)
t.start()
threads.append(t)
for t in tiles:
pq.put((i, t.copy()))
def stop_workers():
for i in range(len(threads)):
pq.put((-1, None))
for t in threads:
t.join()
# block until all tasks are done
try:
while pq.unfinished_tasks > 0:
time.sleep(0.5)
except KeyboardInterrupt:
print("CTRL+C terminating...")
stop_workers()
sys.exit(1)
stop_workers()
if error is not None:
# Try to reprocess using a single thread
# in case this was a memory error
log.ODM_WARNING("DEM processing failed with multiple threads, let's retry with a single thread...")
use_single_thread = True
else:
use_single_thread = True
if use_single_thread:
# Boring, single thread processing
for q in tiles:
process_one(q)
output_file = "%s.tif" % dem_type
output_path = os.path.abspath(os.path.join(outdir, output_file))
# Verify tile results
for t in tiles:
if not os.path.exists(t['filename']):
raise Exception("Error creating %s, %s failed to be created" % (output_file, t['filename']))
# Create virtual raster
vrt_path = os.path.abspath(os.path.join(outdir, "merged.vrt"))
run('gdalbuildvrt "%s" "%s"' % (vrt_path, '" "'.join(map(lambda t: t['filename'], tiles))))
geotiff_tmp_path = os.path.abspath(os.path.join(outdir, 'merged.tmp.tif'))
geotiff_path = os.path.abspath(os.path.join(outdir, 'merged.tif'))
# Build GeoTIFF
kwargs = {
'max_memory': get_max_memory(),
'threads': max_workers if max_workers else 'ALL_CPUS',
'vrt': vrt_path,
'geotiff': geotiff_path,
'geotiff_tmp': geotiff_tmp_path
}
if gapfill:
# Sometimes, for some reason gdal_fillnodata.py
# behaves strangely when reading data directly from a .VRT
# so we need to convert to GeoTIFF first.
run('gdal_translate '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'{vrt} {geotiff_tmp}'.format(**kwargs))
run('gdal_fillnodata.py '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'-b 1 '
'-of GTiff '
'{geotiff_tmp} {geotiff}'.format(**kwargs))
else:
run('gdal_translate '
'-co NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'{vrt} {geotiff}'.format(**kwargs))
post_process(geotiff_path, output_path)
os.remove(geotiff_path)
if os.path.exists(geotiff_tmp_path): os.remove(geotiff_tmp_path)
if os.path.exists(vrt_path): os.remove(vrt_path)
for t in tiles:
if os.path.exists(t['filename']): os.remove(t['filename'])
log.ODM_INFO('Completed %s in %s' % (output_file, datetime.now() - start))
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
verbose = '-verbose' if args.verbose else ''
# define paths and create working directories
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_file) or self.rerun():
# odm_orthophoto definitions
kwargs = {
'bin': context.odm_modules_path,
'log': tree.odm_orthophoto_log,
'ortho': tree.odm_orthophoto_file,
'corners': tree.odm_orthophoto_corners,
'res': 1.0 / (gsd.cap_resolution(args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd) / 100.0),
'verbose': verbose
}
# Have geo coordinates?
georef = reconstruction.georef
# Check if the georef object is initialized
# (during a --rerun this might not be)
# TODO: this should be moved to a more central location?
if reconstruction.is_georeferenced() and not reconstruction.georef.valid_utm_offsets():
georeferencing_dir = tree.odm_georeferencing if args.use_3dmesh and not args.skip_3dmodel else tree.odm_25dgeoreferencing
odm_georeferencing_model_txt_geo_file = os.path.join(georeferencing_dir, tree.odm_georeferencing_model_txt_geo)
if io.file_exists(odm_georeferencing_model_txt_geo_file):
reconstruction.georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
else:
log.ODM_WARNING('Cannot read UTM offset from {}. An orthophoto will not be generated.'.format(odm_georeferencing_model_txt_geo_file))
if reconstruction.is_georeferenced():
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_georeferencing_model_obj_geo)
else:
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_georeferencing_model_obj_geo)
else:
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_textured_model_obj)
else:
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_textured_model_obj)
# run odm_orthophoto
system.run('{bin}/odm_orthophoto -inputFile {model_geo} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if reconstruction.is_georeferenced() and reconstruction.georef.valid_utm_offsets():
ulx = uly = lrx = lry = 0.0
with open(tree.odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(reconstruction.georef.utm_east_offset)
lry = float(tokens[1]) + \
float(reconstruction.georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(reconstruction.georef.utm_east_offset)
uly = float(tokens[3]) + \
float(reconstruction.georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
kwargs = {
'ulx': ulx,
'uly': uly,
'lrx': lrx,
'lry': lry,
'vars': ' '.join(['-co %s=%s' % (k, orthophoto_vars[k]) for k in orthophoto_vars]),
'proj': reconstruction.georef.proj4(),
'png': tree.odm_orthophoto_file,
'tiff': tree.odm_orthophoto_tif,
'log': tree.odm_orthophoto_tif_log,
'max_memory': get_max_memory(),
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{vars} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'{png} {tiff} > {log}'.format(**kwargs))
bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
# Cutline computation, before cropping
# We want to use the full orthophoto, not the cropped one.
if args.orthophoto_cutline:
compute_cutline(tree.odm_orthophoto_tif,
bounds_file_path,
os.path.join(tree.odm_orthophoto, "cutline.gpkg"),
args.max_concurrency,
tmpdir=os.path.join(tree.odm_orthophoto, "grass_cutline_tmpdir"),
scale=0.25)
if args.crop > 0:
Cropper.crop(bounds_file_path, tree.odm_orthophoto_tif, orthophoto_vars)
if args.build_overviews:
orthophoto.build_overviews(tree.odm_orthophoto_tif)
geotiffcreated = True
if not geotiffcreated:
log.ODM_WARNING('No geo-referenced orthophoto created due '
'to missing geo-referencing or corner coordinates.')
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' % tree.odm_orthophoto_file)
def process(args, current_path, max_concurrency, reconstruction):
#args = vars(args)
orthophoto_cutline = True
odm_orthophoto = io.join_paths(current_path, 'orthophoto')
odm_orthophoto_path = odm_orthophoto
odm_orthophoto_render = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_render.tif')
odm_orthophoto_tif = io.join_paths(odm_orthophoto_path,
'odm_orthophoto.tif')
odm_orthophoto_corners = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_corners.tif')
odm_orthophoto_log = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_log.tif')
odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path,
'gdal_translate_log.txt')
odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(odm_georeferencing, 'coords.txt')
odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing,
'gcp_list_utm.txt')
odm_georeferencing_utm_log = io.join_paths(
odm_georeferencing, 'odm_georeferencing_utm_log.txt')
odm_georeferencing_log = 'odm_georeferencing_log.txt'
odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
odm_georeferencing_proj = 'proj.txt'
odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
odm_georeferencing_xyz_file = io.join_paths(odm_georeferencing,
'odm_georeferenced_model.csv')
odm_georeferencing_las_json = io.join_paths(odm_georeferencing, 'las.json')
odm_georeferencing_model_laz = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.laz')
odm_georeferencing_model_las = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.las')
odm_georeferencing_dem = io.join_paths(odm_georeferencing,
'odm_georeferencing_model_dem.tif')
opensfm_reconstruction = io.join_paths(current_path, 'reconstruction.json')
odm_texturing = io.join_paths(current_path, 'mvs')
odm_textured_model_obj = io.join_paths(odm_texturing,
'odm_textured_model.obj')
images_dir = io.join_paths(current_path, 'images')
reconstruction = reconstruction
verbose = ''
#"-verbose"
# define paths and create working directories
system.mkdir_p(odm_orthophoto)
if not io.file_exists(odm_orthophoto_tif):
gsd_error_estimate = 0.1
ignore_resolution = False
if not reconstruction.is_georeferenced():
# Match DEMs
gsd_error_estimate = -3
ignore_resolution = True
orthophoto_resolution = 5
resolution = 1.0 / (
gsd.cap_resolution(orthophoto_resolution,
opensfm_reconstruction,
gsd_error_estimate=gsd_error_estimate,
ignore_gsd=True,
ignore_resolution=ignore_resolution,
has_gcp=reconstruction.has_gcp()) / 100.0)
# odm_orthophoto definitions
kwargs = {
'bin': context.odm_modules_path,
'log': odm_orthophoto_log,
'ortho': odm_orthophoto_render,
'corners': odm_orthophoto_corners,
'res': resolution,
'bands': '',
'verbose': verbose
}
# Check if the georef object is initialized
# (during a --rerun this might not be)
# TODO: this should be moved to a more central location?
if reconstruction.is_georeferenced(
) and not reconstruction.georef.valid_utm_offsets():
georeferencing_dir = odm_georeferencing #if args.use_3dmesh and not args.skip_3dmodel else odm_25dgeoreferencing
odm_georeferencing_model_txt_geo_file = os.path.join(
georeferencing_dir, odm_georeferencing_model_txt_geo)
if io.file_exists(odm_georeferencing_model_txt_geo_file):
reconstruction.georef.extract_offsets(
odm_georeferencing_model_txt_geo_file)
else:
log.ODM_WARNING('Cannot read UTM offset from {}.'.format(
odm_georeferencing_model_txt_geo_file))
models = []
base_dir = odm_texturing
if reconstruction.is_georeferenced():
model_file = odm_georeferencing_model_obj_geo
else:
model_file = odm_textured_model_obj
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
subdir = ""
if not primary:
subdir = band['name'].lower()
models.append(os.path.join(base_dir, subdir, model_file))
kwargs['bands'] = '-bands %s' % (','.join([
quote(b['name'].lower()) for b in reconstruction.multi_camera
]))
else:
models.append(os.path.join(base_dir, model_file))
kwargs['models'] = ','.join(map(quote, models))
# run odm_orthophoto
system.run(
'{bin}/odm_orthophoto -inputFiles {models} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners} {bands}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if reconstruction.is_georeferenced(
) and reconstruction.georef.valid_utm_offsets():
ulx = uly = lrx = lry = 0.0
with open(odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(reconstruction.georef.utm_east_offset)
lry = float(tokens[1]) + \
float(reconstruction.georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(reconstruction.georef.utm_east_offset)
uly = float(tokens[3]) + \
float(reconstruction.georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
kwargs = {
'ulx':
ulx,
'uly':
uly,
'lrx':
lrx,
'lry':
lry,
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'proj':
reconstruction.georef.proj4(),
'input':
odm_orthophoto_render,
'output':
odm_orthophoto_tif,
'log':
odm_orthophoto_tif_log,
'max_memory':
get_max_memory(),
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{vars} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'--config GDAL_TIFF_INTERNAL_MASK YES '
'{input} {output} > {log}'.format(**kwargs))
bounds_file_path = os.path.join(
odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
# Cutline computation, before cropping
# We want to use the full orthophoto, not the cropped one.
pio = True
if pio:
cutline_file = os.path.join(odm_orthophoto, "cutline.gpkg")
compute_cutline(odm_orthophoto_tif,
bounds_file_path,
cutline_file,
max_concurrency,
tmpdir=os.path.join(odm_orthophoto,
"grass_cutline_tmpdir"),
scale=0.25)
orthophoto.compute_mask_raster(odm_orthophoto_tif,
cutline_file,
os.path.join(
odm_orthophoto,
"odm_orthophoto_cut.tif"),
blend_distance=20,
only_max_coords_feature=True)
orthophoto.post_orthophoto_steps(args, bounds_file_path,
odm_orthophoto_tif)
# Generate feathered orthophoto also
if pio:
orthophoto.feather_raster(odm_orthophoto_tif,
os.path.join(
odm_orthophoto,
"odm_orthophoto_feathered.tif"),
blend_distance=20)
geotiffcreated = True
if not geotiffcreated:
if io.file_exists(odm_orthophoto_render):
pseudogeo.add_pseudo_georeferencing(odm_orthophoto_render)
log.ODM_INFO("Renaming %s --> %s" %
(odm_orthophoto_render, odm_orthophoto_tif))
os.rename(odm_orthophoto_render, odm_orthophoto_tif)
else:
log.ODM_WARNING(
"Could not generate an orthophoto (it did not render)")
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' % odm_orthophoto_tif)
#generate png
orthophoto.generate_png(odm_orthophoto_tif)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM Orthophoto Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
reconstruction = inputs.reconstruction
verbose = '-verbose' if self.params.verbose else ''
# define paths and create working directories
system.mkdir_p(tree.odm_orthophoto)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_orthophoto') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_orthophoto' in args.rerun_from)
if not io.file_exists(tree.odm_orthophoto_file) or rerun_cell:
# odm_orthophoto definitions
kwargs = {
'bin': context.odm_modules_path,
'log': tree.odm_orthophoto_log,
'ortho': tree.odm_orthophoto_file,
'corners': tree.odm_orthophoto_corners,
'res': 1.0 / (gsd.cap_resolution(self.params.resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd) / 100.0),
'verbose': verbose
}
# Have geo coordinates?
georef = reconstruction.georef
# Check if the georef object is initialized
# (during a --rerun this might not be)
# TODO: we should move this to a more central
# location (perhaps during the dataset initialization)
if georef and not georef.utm_east_offset:
georeferencing_dir = tree.odm_georeferencing if args.use_3dmesh and not args.skip_3dmodel else tree.odm_25dgeoreferencing
odm_georeferencing_model_txt_geo_file = os.path.join(georeferencing_dir, tree.odm_georeferencing_model_txt_geo)
if io.file_exists(odm_georeferencing_model_txt_geo_file):
georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
else:
log.ODM_WARNING('Cannot read UTM offset from {}. An orthophoto will not be generated.'.format(odm_georeferencing_model_txt_geo_file))
if georef:
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_georeferencing_model_obj_geo)
else:
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_georeferencing_model_obj_geo)
else:
if args.use_3dmesh:
kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_textured_model_obj)
else:
kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_textured_model_obj)
# run odm_orthophoto
system.run('{bin}/odm_orthophoto -inputFile {model_geo} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if georef and georef.projection and georef.utm_east_offset and georef.utm_north_offset:
ulx = uly = lrx = lry = 0.0
with open(tree.odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(georef.utm_east_offset)
lry = float(tokens[1]) + \
float(georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(georef.utm_east_offset)
uly = float(tokens[3]) + \
float(georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
kwargs = {
'ulx': ulx,
'uly': uly,
'lrx': lrx,
'lry': lry,
'tiled': '' if self.params.no_tiled else '-co TILED=yes ',
'compress': self.params.compress,
'predictor': '-co PREDICTOR=2 ' if self.params.compress in
['LZW', 'DEFLATE'] else '',
'proj': georef.projection.srs,
'bigtiff': self.params.bigtiff,
'png': tree.odm_orthophoto_file,
'tiff': tree.odm_orthophoto_tif,
'log': tree.odm_orthophoto_tif_log,
'max_memory': get_max_memory(),
'threads': self.params.max_concurrency
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{tiled} '
'-co BIGTIFF={bigtiff} '
'-co COMPRESS={compress} '
'{predictor} '
'-co BLOCKXSIZE=512 '
'-co BLOCKYSIZE=512 '
'-co NUM_THREADS={threads} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'{png} {tiff} > {log}'.format(**kwargs))
if args.crop > 0:
shapefile_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.shp')
Cropper.crop(shapefile_path, tree.odm_orthophoto_tif, {
'TILED': 'NO' if self.params.no_tiled else 'YES',
'COMPRESS': self.params.compress,
'PREDICTOR': '2' if self.params.compress in ['LZW', 'DEFLATE'] else '1',
'BIGTIFF': self.params.bigtiff,
'BLOCKXSIZE': 512,
'BLOCKYSIZE': 512,
'NUM_THREADS': self.params.max_concurrency
})
if self.params.build_overviews:
log.ODM_DEBUG("Building Overviews")
kwargs = {
'orthophoto': tree.odm_orthophoto_tif,
'log': tree.odm_orthophoto_gdaladdo_log
}
# Run gdaladdo
system.run('gdaladdo -ro -r average '
'--config BIGTIFF_OVERVIEW IF_SAFER '
'--config COMPRESS_OVERVIEW JPEG '
'{orthophoto} 2 4 8 16 > {log}'.format(**kwargs))
geotiffcreated = True
if not geotiffcreated:
log.ODM_WARNING('No geo-referenced orthophoto created due '
'to missing geo-referencing or corner coordinates.')
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' % tree.odm_orthophoto_file)
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Orthophoto')
log.ODM_INFO('Running ODM OrthoPhoto Cell - Finished')
return ecto.OK if args.end_with != 'odm_orthophoto' else ecto.QUIT
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if outputs['large']:
if not os.path.exists(tree.submodels_path):
log.ODM_ERROR(
"We reached the merge stage, but %s folder does not exist. Something must have gone wrong at an earlier stage. Check the log and fix possible problem before restarting?"
% tree.submodels_path)
exit(1)
# Merge point clouds
if args.merge in ['all', 'pointcloud']:
if not io.file_exists(
tree.odm_georeferencing_model_laz) or self.rerun():
all_point_clouds = get_submodel_paths(
tree.submodels_path, "odm_georeferencing",
"odm_georeferenced_model.laz")
try:
# pdal.merge_point_clouds(all_point_clouds, tree.odm_georeferencing_model_laz, args.verbose)
entwine.build(all_point_clouds,
tree.entwine_pointcloud,
max_concurrency=args.max_concurrency,
rerun=self.rerun())
except Exception as e:
log.ODM_WARNING(
"Could not merge point cloud: %s (skipping)" %
str(e))
if io.dir_exists(tree.entwine_pointcloud):
try:
system.run('pdal translate "ept://{}" "{}"'.format(
tree.entwine_pointcloud,
tree.odm_georeferencing_model_laz))
except Exception as e:
log.ODM_WARNING(
"Cannot export EPT dataset to LAZ: %s" %
str(e))
else:
log.ODM_WARNING("Found merged point cloud in %s" %
tree.odm_georeferencing_model_laz)
self.update_progress(25)
# Merge crop bounds
merged_bounds_file = os.path.join(
tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if not io.file_exists(merged_bounds_file) or self.rerun():
all_bounds = get_submodel_paths(
tree.submodels_path, 'odm_georeferencing',
'odm_georeferenced_model.bounds.gpkg')
log.ODM_INFO("Merging all crop bounds: %s" % all_bounds)
if len(all_bounds) > 0:
# Calculate a new crop area
# based on the convex hull of all crop areas of all submodels
# (without a buffer, otherwise we are double-cropping)
Cropper.merge_bounds(all_bounds, merged_bounds_file, 0)
else:
log.ODM_WARNING("No bounds found for any submodel.")
# Merge orthophotos
if args.merge in ['all', 'orthophoto']:
if not io.dir_exists(tree.odm_orthophoto):
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
all_orthos_and_cutlines = get_all_submodel_paths(
tree.submodels_path,
os.path.join("odm_orthophoto", "odm_orthophoto.tif"),
os.path.join("odm_orthophoto", "cutline.gpkg"),
)
if len(all_orthos_and_cutlines) > 1:
log.ODM_INFO(
"Found %s submodels with valid orthophotos and cutlines"
% len(all_orthos_and_cutlines))
# TODO: histogram matching via rasterio
# currently parts have different color tones
merged_geotiff = os.path.join(
tree.odm_orthophoto, "odm_orthophoto.merged.tif")
kwargs = {
'orthophoto_merged':
merged_geotiff,
'input_files':
' '.join(
map(lambda i: quote(i[0]),
all_orthos_and_cutlines)),
'max_memory':
get_max_memory(),
'threads':
args.max_concurrency,
}
# use bounds as cutlines (blending)
if io.file_exists(merged_geotiff):
os.remove(merged_geotiff)
system.run('gdal_merge.py -o {orthophoto_merged} '
#'-createonly '
'-co "BIGTIFF=YES" '
'-co "BLOCKXSIZE=512" '
'-co "BLOCKYSIZE=512" '
'--config GDAL_CACHEMAX {max_memory}% '
'{input_files} '.format(**kwargs))
for ortho_cutline in all_orthos_and_cutlines:
kwargs['input_file'], kwargs[
'cutline'] = ortho_cutline
# Note: cblend has a high performance penalty
system.run(
'gdalwarp -cutline {cutline} '
'-cblend 20 '
'-r bilinear -multi '
'-wo NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'{input_file} {orthophoto_merged}'.format(
**kwargs))
# Apply orthophoto settings (compression, tiling, etc.)
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
if io.file_exists(tree.odm_orthophoto_tif):
os.remove(tree.odm_orthophoto_tif)
kwargs = {
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'max_memory':
get_max_memory(),
'merged':
merged_geotiff,
'log':
tree.odm_orthophoto_tif_log,
'orthophoto':
tree.odm_orthophoto_tif,
}
system.run(
'gdal_translate '
'{vars} '
'--config GDAL_CACHEMAX {max_memory}% '
'{merged} {orthophoto} > {log}'.format(**kwargs))
os.remove(merged_geotiff)
# Crop
if args.crop > 0:
Cropper.crop(merged_bounds_file,
tree.odm_orthophoto_tif,
orthophoto_vars)
# Overviews
if args.build_overviews:
orthophoto.build_overviews(tree.odm_orthophoto_tif)
elif len(all_orthos_and_cutlines) == 1:
# Simply copy
log.ODM_WARNING(
"A single orthophoto/cutline pair was found between all submodels."
)
shutil.copyfile(all_orthos_and_cutlines[0][0],
tree.odm_orthophoto_tif)
else:
log.ODM_WARNING(
"No orthophoto/cutline pairs were found in any of the submodels. No orthophoto will be generated."
)
else:
log.ODM_WARNING("Found merged orthophoto in %s" %
tree.odm_orthophoto_tif)
self.update_progress(75)
# Merge DEMs
def merge_dems(dem_filename, human_name):
if not io.dir_exists(tree.path('odm_dem')):
system.mkdir_p(tree.path('odm_dem'))
dem_file = tree.path("odm_dem", dem_filename)
if not io.file_exists(dem_file) or self.rerun():
all_dems = get_submodel_paths(tree.submodels_path,
"odm_dem", dem_filename)
log.ODM_INFO("Merging %ss" % human_name)
# Merge
dem_vars = utils.get_dem_vars(args)
euclidean_merge_dems(all_dems, dem_file, dem_vars)
if io.file_exists(dem_file):
# Crop
if args.crop > 0:
Cropper.crop(merged_bounds_file, dem_file,
dem_vars)
log.ODM_INFO("Created %s" % dem_file)
else:
log.ODM_WARNING("Cannot merge %s, %s was not created" %
(human_name, dem_file))
else:
log.ODM_WARNING("Found merged %s in %s" %
(human_name, dem_filename))
if args.merge in ['all', 'dem'] and args.dsm:
merge_dems("dsm.tif", "DSM")
if args.merge in ['all', 'dem'] and args.dtm:
merge_dems("dtm.tif", "DTM")
# Stop the pipeline short! We're done.
self.next_stage = None
else:
log.ODM_INFO("Normal dataset, nothing to merge.")
self.progress = 0.0
def process(self, args, outputs):
orthophoto_file = os.path.join(args.project_path, "odm_orthophoto",
"odm_orthophoto.tif")
orthophoto_render_file = os.path.join(args.project_path,
"odm_orthophoto",
"odm_orthophoto_render.tif")
orthophoto_corners_file = os.path.join(args.project_path,
"odm_orthophoto",
"odm_orthophoto_corners.txt")
if not io.file_exists(orthophoto_file) or self.rerun():
kwargs = {
'ortho': orthophoto_render_file,
'corners': orthophoto_corners_file,
'res': 100.0 / args.orthophoto_resolution,
'verbose': '--verbose' if args.verbose else '',
'models': outputs["textured_model_obj"]
}
system.run('odm_orthophoto -inputFiles {models} '
'-outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners}'.format(**kwargs))
utm_east_offset, utm_north_offset = outputs["utm_offset"]
proj4_srs = outputs["utm_srs"].to_proj4()
ulx = uly = lrx = lry = 0.0
with open(orthophoto_corners_file) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + utm_east_offset
lry = float(tokens[1]) + utm_north_offset
lrx = float(tokens[2]) + utm_east_offset
uly = float(tokens[3]) + utm_north_offset
log.ODM_INFO('Creating GeoTIFF')
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
kwargs = {
'ulx':
ulx,
'uly':
uly,
'lrx':
lrx,
'lry':
lry,
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'proj':
proj4_srs,
'input':
orthophoto_render_file,
'output':
orthophoto_file,
'max_memory':
get_max_memory(),
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{vars} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'--config GDAL_TIFF_INTERNAL_MASK YES '
'{input} {output}'.format(**kwargs))
#orthophoto.post_orthophoto_steps(args, bounds_file_path, tree.odm_orthophoto_tif)
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' %
orthophoto_file)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
verbose = '-verbose' if args.verbose else ''
# define paths and create working directories
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
gsd_error_estimate = 0.1
ignore_resolution = False
if not reconstruction.is_georeferenced():
# Match DEMs
gsd_error_estimate = -3
ignore_resolution = True
resolution = 1.0 / (
gsd.cap_resolution(args.orthophoto_resolution,
tree.opensfm_reconstruction,
gsd_error_estimate=gsd_error_estimate,
ignore_gsd=args.ignore_gsd,
ignore_resolution=ignore_resolution,
has_gcp=reconstruction.has_gcp()) / 100.0)
# odm_orthophoto definitions
kwargs = {
'bin': context.odm_modules_path,
'log': tree.odm_orthophoto_log,
'ortho': tree.odm_orthophoto_render,
'corners': tree.odm_orthophoto_corners,
'res': resolution,
'bands': '',
'verbose': verbose
}
models = []
if args.use_3dmesh:
base_dir = tree.odm_texturing
else:
base_dir = tree.odm_25dtexturing
model_file = tree.odm_textured_model_obj
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band['name'] == get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
subdir = ""
if not primary:
subdir = band['name'].lower()
models.append(os.path.join(base_dir, subdir, model_file))
kwargs['bands'] = '-bands %s' % (','.join(
[quote(b['name']) for b in reconstruction.multi_camera]))
else:
models.append(os.path.join(base_dir, model_file))
kwargs['models'] = ','.join(map(quote, models))
# run odm_orthophoto
system.run(
'{bin}/odm_orthophoto -inputFiles {models} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners} {bands}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if reconstruction.is_georeferenced():
ulx = uly = lrx = lry = 0.0
with open(tree.odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(reconstruction.georef.utm_east_offset)
lry = float(tokens[1]) + \
float(reconstruction.georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(reconstruction.georef.utm_east_offset)
uly = float(tokens[3]) + \
float(reconstruction.georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
kwargs = {
'ulx':
ulx,
'uly':
uly,
'lrx':
lrx,
'lry':
lry,
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'proj':
reconstruction.georef.proj4(),
'input':
tree.odm_orthophoto_render,
'output':
tree.odm_orthophoto_tif,
'log':
tree.odm_orthophoto_tif_log,
'max_memory':
get_max_memory(),
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{vars} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'--config GDAL_TIFF_INTERNAL_MASK YES '
'{input} {output} > {log}'.format(**kwargs))
bounds_file_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.gpkg')
# Cutline computation, before cropping
# We want to use the full orthophoto, not the cropped one.
if args.orthophoto_cutline:
cutline_file = os.path.join(tree.odm_orthophoto,
"cutline.gpkg")
compute_cutline(tree.odm_orthophoto_tif,
bounds_file_path,
cutline_file,
args.max_concurrency,
tmpdir=os.path.join(
tree.odm_orthophoto,
"grass_cutline_tmpdir"),
scale=0.25)
orthophoto.compute_mask_raster(
tree.odm_orthophoto_tif,
cutline_file,
os.path.join(tree.odm_orthophoto,
"odm_orthophoto_cut.tif"),
blend_distance=20,
only_max_coords_feature=True)
orthophoto.post_orthophoto_steps(args, bounds_file_path,
tree.odm_orthophoto_tif,
tree.orthophoto_tiles)
# Generate feathered orthophoto also
if args.orthophoto_cutline:
orthophoto.feather_raster(
tree.odm_orthophoto_tif,
os.path.join(tree.odm_orthophoto,
"odm_orthophoto_feathered.tif"),
blend_distance=20)
geotiffcreated = True
if not geotiffcreated:
if io.file_exists(tree.odm_orthophoto_render):
pseudogeo.add_pseudo_georeferencing(
tree.odm_orthophoto_render)
log.ODM_INFO(
"Renaming %s --> %s" %
(tree.odm_orthophoto_render, tree.odm_orthophoto_tif))
os.rename(tree.odm_orthophoto_render,
tree.odm_orthophoto_tif)
else:
log.ODM_WARNING(
"Could not generate an orthophoto (it did not render)")
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' %
tree.odm_orthophoto_tif)
if args.optimize_disk_space and io.file_exists(
tree.odm_orthophoto_render):
os.remove(tree.odm_orthophoto_render)
