kopia lustrzana https://github.com/OpenDroneMap/ODM
Piero Toffanin
rodzic
4fbe612f78
commit
d8044d7bd1
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@ -1,6 +1,12 @@
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import os, glob
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import gippy
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import numpy
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import math
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from opendm.system import run
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from opendm import point_cloud
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from opendm.concurrency import get_max_memory
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import pprint
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from scipy import ndimage
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from datetime import datetime
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from opendm import log
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@ -21,96 +27,147 @@ def classify(lasFile, slope=0.15, cellsize=1, maxWindowSize=18, verbose=False):
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return lasFile
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def create_dems(filenames, demtype, radius=['0.56'], gapfill=False,
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outdir='', suffix='', resolution=0.1, max_workers=None, **kwargs):
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""" Create DEMS for multiple radii, and optionally gapfill """
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fouts = []
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create_dem_for_radius = partial(create_dem,
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filenames, demtype,
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outdir=outdir, suffix=suffix, resolution=resolution, **kwargs)
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with get_reusable_executor(max_workers=max_workers, timeout=None) as e:
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fouts = list(e.map(create_dem_for_radius, radius))
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fnames = {}
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# convert from list of dicts, to dict of lists
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for product in fouts[0].keys():
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fnames[product] = [f[product] for f in fouts]
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fouts = fnames
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def create_dem(input_point_cloud, dem_type, output_type='max', radiuses=['0.56'], gapfill=True,
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outdir='', resolution=0.1, max_workers=None, max_tile_size=4096,
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verbose=False, decimation=None):
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""" Create DEM from multiple radii, and optionally gapfill """
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start = datetime.now()
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if not os.path.exists(outdir):
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log.ODM_INFO("Creating %s" % outdir)
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os.mkdir(outdir)
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extent = point_cloud.get_extent(input_point_cloud)
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log.ODM_INFO("Point cloud bounds are [minx: %s, maxx: %s] [miny: %s, maxy: %s]" % (extent['minx'], extent['maxx'], extent['miny'], extent['maxy']))
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# extent = {
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# 'maxx': 100,
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# 'minx': 0,
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# 'maxy': 100,
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# 'miny': 0
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# }
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ext_width = extent['maxx'] - extent['minx']
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ext_height = extent['maxy'] - extent['miny']
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final_dem_resolution = (int(math.ceil(ext_width / float(resolution))),
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int(math.ceil(ext_height / float(resolution))))
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num_splits = int(math.ceil(max(final_dem_resolution) / float(max_tile_size)))
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num_tiles = num_splits * num_splits
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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))
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tile_bounds_width = ext_width / float(num_splits)
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tile_bounds_height = ext_height / float(num_splits)
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tiles = []
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for r in radiuses:
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minx = extent['minx']
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for x in range(num_splits):
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miny = extent['miny']
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if x == num_splits - 1:
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maxx = extent['maxx']
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else:
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maxx = minx + tile_bounds_width
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for y in range(num_splits):
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if y == num_splits - 1:
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maxy = extent['maxy']
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else:
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maxy = miny + tile_bounds_height
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filename = os.path.join(os.path.abspath(outdir), '%s_r%s_x%s_y%s.tif' % (dem_type, r, x, y))
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tiles.append({
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'radius': r,
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'bounds': {
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'minx': minx,
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'maxx': maxx,
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'miny': miny,
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'maxy': maxy
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},
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'filename': filename
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})
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miny = maxy
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minx = maxx
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# Sort tiles by increasing radius
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tiles.sort(key=lambda t: float(t['radius']), reverse=True)
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# pp = pprint.PrettyPrinter(indent=4)
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# pp.pprint(queue)
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# TODO: parallel queue
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queue = tiles[:]
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for q in queue:
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log.ODM_INFO("Generating %s (%s, radius: %s, resolution: %s)" % (q['filename'], output_type, q['radius'], resolution))
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d = pdal.json_gdal_base(q['filename'], output_type, q['radius'], resolution, q['bounds'])
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if dem_type == 'dsm':
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d = pdal.json_add_classification_filter(d, 2, equality='max')
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elif dem_type == 'dtm':
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d = pdal.json_add_classification_filter(d, 2)
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if decimation is not None:
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d = pdal.json_add_decimation_filter(d, decimation)
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pdal.json_add_readers(d, [input_point_cloud])
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pdal.run_pipeline(d, verbose=verbose)
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output_file = "%s.tif" % dem_type
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output_path = os.path.abspath(os.path.join(outdir, output_file))
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# Verify tile results
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for t in tiles:
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if not os.path.exists(t['filename']):
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raise Exception("Error creating %s, %s failed to be created" % (output_file, t['filename']))
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# Create virtual raster
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vrt_path = os.path.abspath(os.path.join(outdir, "merged.vrt"))
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run('gdalbuildvrt "%s" "%s"' % (vrt_path, '" "'.join(map(lambda t: t['filename'], tiles))))
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geotiff_path = os.path.abspath(os.path.join(outdir, 'merged.tiff'))
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# Build GeoTIFF
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kwargs = {
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'max_memory': get_max_memory(),
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'threads': max_workers if max_workers else 'ALL_CPUS',
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'vrt': vrt_path,
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'geotiff': geotiff_path
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}
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run('gdal_translate '
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'-co NUM_THREADS={threads} '
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'--config GDAL_CACHEMAX {max_memory}% '
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'{vrt} {geotiff}'.format(**kwargs))
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# gapfill all products
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_fouts = {}
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if gapfill:
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for product in fouts.keys():
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# output filename
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fout = os.path.join(outdir, '%s%s.tif' % (demtype, suffix))
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gap_fill(fouts[product], fout)
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_fouts[product] = fout
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gapfill_and_smooth(geotiff_path, output_path)
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os.remove(geotiff_path)
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else:
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# only return single filename (first radius run)
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for product in fouts.keys():
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_fouts[product] = fouts[product][0]
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log.ODM_INFO("Skipping gap-fill interpolation")
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os.rename(geotiff_path, output_path)
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return _fouts
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def create_dem(filenames, demtype, radius, decimation=None,
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products=['idw'], outdir='', suffix='', verbose=False, resolution=0.1):
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""" Create DEM from collection of LAS files """
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start = datetime.now()
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# filename based on demtype, radius, and optional suffix
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bname = os.path.join(os.path.abspath(outdir), '%s_r%s%s' % (demtype, radius, suffix))
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ext = 'tif'
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fouts = {o: bname + '.%s.%s' % (o, ext) for o in products}
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prettyname = os.path.relpath(bname) + ' [%s]' % (' '.join(products))
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log.ODM_INFO('Creating %s from %s files' % (prettyname, len(filenames)))
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# JSON pipeline
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json = pdal.json_gdal_base(bname, products, radius, resolution)
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# TODO cleanup
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if demtype == 'dsm':
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json = pdal.json_add_classification_filter(json, 2, equality='max')
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elif demtype == 'dtm':
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json = pdal.json_add_classification_filter(json, 2)
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if decimation is not None:
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json = pdal.json_add_decimation_filter(json, decimation)
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pdal.json_add_readers(json, filenames)
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pdal.run_pipeline(json, verbose=verbose)
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# verify existence of fout
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exists = True
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for f in fouts.values():
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if not os.path.exists(f):
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exists = False
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if not exists:
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raise Exception("Error creating dems: %s" % ' '.join(fouts))
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log.ODM_INFO('Completed %s in %s' % (prettyname, datetime.now() - start))
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return fouts
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log.ODM_INFO('Completed %s in %s' % (output_file, datetime.now() - start))
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def gap_fill(filenames, fout):
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""" Gap fill from higher radius DTMs, then fill remainder with interpolation """
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def gapfill_and_smooth(geotiff_path, output_path):
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""" Gap fill with nearest neighbor interpolation and apply median smoothing """
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start = datetime.now()
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if len(filenames) == 0:
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raise Exception('No filenames provided!')
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if not os.path.exists(geotiff_path):
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raise Exception('File %s does not exist!' % geotiff_path)
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log.ODM_INFO('Starting gap-filling with nearest interpolation...')
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filenames = sorted(filenames)
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imgs = map(gippy.GeoImage, filenames)
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nodata = imgs[0][0].nodata()
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arr = imgs[0][0].read()
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for i in range(1, len(imgs)):
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locs = numpy.where(arr == nodata)
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arr[locs] = imgs[i][0].read()[locs]
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img = gippy.GeoImage(geotiff_path)
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nodata = img[0].nodata()
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arr = img[0].read()
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# Nearest neighbor interpolation at bad points
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indices = ndimage.distance_transform_edt(arr == nodata,
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@ -132,12 +189,12 @@ def gap_fill(filenames, fout):
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arr[-1][-2:] = arr[-2][-1] = arr[-2][-2]
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# write output
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imgout = gippy.GeoImage.create_from(imgs[0], fout)
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imgout = gippy.GeoImage.create_from(img, output_path)
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imgout.set_nodata(nodata)
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imgout[0].write(arr)
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fout = imgout.filename()
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output_path = imgout.filename()
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imgout = None
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log.ODM_INFO('Completed gap-filling to create %s in %s' % (os.path.relpath(fout), datetime.now() - start))
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log.ODM_INFO('Completed gap-filling to create %s in %s' % (os.path.relpath(output_path), datetime.now() - start))
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return fout
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return output_path
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@ -48,24 +48,23 @@ def json_base():
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return {'pipeline': []}
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def json_gdal_base(fout, output, radius, resolution=1):
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def json_gdal_base(filename, output_type, radius, resolution=1, bounds=None):
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""" Create initial JSON for PDAL pipeline containing a Writer element """
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json = json_base()
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if len(output) > 1:
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# TODO: we might want to create a multiband raster with max/min/idw
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# in the future
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print "More than 1 output, will only create {0}".format(output[0])
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output = [output[0]]
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json['pipeline'].insert(0, {
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d = {
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'type': 'writers.gdal',
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'resolution': resolution,
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'radius': radius,
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'filename': '{0}.{1}.tif'.format(fout, output[0]),
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'output_type': output[0],
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'filename': filename,
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'output_type': output_type,
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'data_type': 'float'
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})
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}
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if bounds is not None:
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d['bounds'] = "([%s,%s],[%s,%s])" % (bounds['minx'], bounds['maxx'], bounds['miny'], bounds['maxy'])
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json['pipeline'].insert(0, d)
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return json
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@ -155,7 +154,6 @@ def run_pipeline(json, verbose=False):
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cmd = [
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'pdal',
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'pipeline',
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'--nostream',
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'-i %s' % jsonfile
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]
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if verbose:
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@ -24,14 +24,14 @@ def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05,
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log.ODM_INFO('Creating DSM for 2.5D mesh')
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commands.create_dems(
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[inPointCloud],
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commands.create_dem(
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inPointCloud,
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'mesh_dsm',
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radius=map(str, radius_steps),
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output_type='max',
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radiuses=map(str, radius_steps),
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gapfill=True,
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outdir=tmp_directory,
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resolution=dsm_resolution,
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products=['max'],
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verbose=verbose,
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max_workers=get_max_concurrency_for_dem(available_cores, inPointCloud)
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)
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@ -1,9 +1,10 @@
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import os, sys, shutil
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import os, sys, shutil, tempfile, json
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from opendm import system
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from opendm import log
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from opendm import context
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from opendm.system import run
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def filter(inputPointCloud, outputPointCloud, standard_deviation=2.5, meank=16, confidence=None, verbose=False):
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def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=16, confidence=None, verbose=False):
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"""
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Filters a point cloud
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"""
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@ -15,20 +16,20 @@ def filter(inputPointCloud, outputPointCloud, standard_deviation=2.5, meank=16,
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if confidence:
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log.ODM_INFO("Keeping only points with > %s confidence" % confidence)
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if not os.path.exists(inputPointCloud):
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log.ODM_ERROR("{} does not exist, cannot filter point cloud. The program will now exit.".format(inputPointCloud))
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if not os.path.exists(input_point_cloud):
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log.ODM_ERROR("{} does not exist, cannot filter point cloud. The program will now exit.".format(input_point_cloud))
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sys.exit(1)
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filter_program = os.path.join(context.odm_modules_path, 'odm_filterpoints')
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if not os.path.exists(filter_program):
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log.ODM_WARNING("{} program not found. Will skip filtering, but this installation should be fixed.")
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shutil.copy(inputPointCloud, outputPointCloud)
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shutil.copy(input_point_cloud, output_point_cloud)
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return
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filterArgs = {
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'bin': filter_program,
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'inputFile': inputPointCloud,
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'outputFile': outputPointCloud,
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'inputFile': input_point_cloud,
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'outputFile': output_point_cloud,
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'sd': standard_deviation,
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'meank': meank,
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'verbose': '-verbose' if verbose else '',
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@ -41,5 +42,56 @@ def filter(inputPointCloud, outputPointCloud, standard_deviation=2.5, meank=16,
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'-meank {meank} {confidence} {verbose} '.format(**filterArgs))
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# Remove input file, swap temp file
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if not os.path.exists(outputPointCloud):
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log.ODM_WARNING("{} not found, filtering has failed.".format(outputPointCloud))
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if not os.path.exists(output_point_cloud):
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log.ODM_WARNING("{} not found, filtering has failed.".format(output_point_cloud))
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def get_extent(input_point_cloud):
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fd, json_file = tempfile.mkstemp(suffix='.json')
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os.close(fd)
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# Get point cloud extent
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fallback = False
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# We know PLY files do not have --summary support
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if input_point_cloud.lower().endswith(".ply"):
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fallback = True
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run('pdal info {0} > {1}'.format(input_point_cloud, json_file))
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try:
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if not fallback:
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run('pdal info --summary {0} > {1}'.format(input_point_cloud, json_file))
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except:
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fallback = True
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run('pdal info {0} > {1}'.format(input_point_cloud, json_file))
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bounds = {}
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with open(json_file, 'r') as f:
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result = json.loads(f.read())
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if not fallback:
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summary = result.get('summary')
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if summary is None: raise Exception("Cannot compute summary for %s (summary key missing)" % input_point_cloud)
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bounds = summary.get('bounds')
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else:
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stats = result.get('stats')
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if stats is None: raise Exception("Cannot compute bounds for %s (stats key missing)" % input_point_cloud)
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bbox = stats.get('bbox')
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if bbox is None: raise Exception("Cannot compute bounds for %s (bbox key missing)" % input_point_cloud)
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native = bbox.get('native')
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if native is None: raise Exception("Cannot compute bounds for %s (native key missing)" % input_point_cloud)
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bounds = native.get('bbox')
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if bounds is None: raise Exception("Cannot compute bounds for %s (bounds key missing)" % input_point_cloud)
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if bounds.get('maxx', None) is None or \
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bounds.get('minx', None) is None or \
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bounds.get('maxy', None) is None or \
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bounds.get('miny', None) is None or \
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bounds.get('maxz', None) is None or \
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bounds.get('minz', None) is None:
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raise Exception("Cannot compute bounds for %s (invalid keys) %s" % (input_point_cloud, str(bounds)))
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os.remove(json_file)
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return bounds
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@ -86,11 +86,12 @@ class ODMDEMCell(ecto.Cell):
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radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value?
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for product in products:
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commands.create_dems(
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[tree.odm_georeferencing_model_laz],
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commands.create_dem(
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tree.odm_georeferencing_model_laz,
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product,
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radius=map(str, radius_steps),
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gapfill=True,
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output_type='idw' if product == 'dtm' else 'max'
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radiuses=map(str, radius_steps),
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gapfill=args.dem_gapfill_steps > 0,
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outdir=odm_dem_root,
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||||
resolution=resolution / 100.0,
|
||||
decimation=args.dem_decimation,
|
||||
|
|
|
|||
Ładowanie…
Reference in New Issue