kopia lustrzana https://github.com/OpenDroneMap/ODM
Piero Toffanin
GitHub
commit
26cc9fbf93
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@ -179,6 +179,7 @@ set(custom_libs OpenSfM
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Obj2Tiles
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OpenPointClass
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ExifTool
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RenderDEM
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)
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externalproject_add(mve
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@ -16,7 +16,7 @@ ExternalProject_Add(${_proj_name}
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STAMP_DIR ${_SB_BINARY_DIR}/stamp
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#--Download step--------------
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DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
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URL https://github.com/PDAL/PDAL/archive/refs/tags/2.4.3.zip
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URL https://github.com/OpenDroneMap/PDAL/archive/refs/heads/333.zip
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#--Update/Patch step----------
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UPDATE_COMMAND ""
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#--Configure step-------------
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@ -0,0 +1,30 @@
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set(_proj_name renderdem)
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set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
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ExternalProject_Add(${_proj_name}
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DEPENDS pdal
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PREFIX ${_SB_BINARY_DIR}
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TMP_DIR ${_SB_BINARY_DIR}/tmp
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STAMP_DIR ${_SB_BINARY_DIR}/stamp
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#--Download step--------------
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DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
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GIT_REPOSITORY https://github.com/OpenDroneMap/RenderDEM
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GIT_TAG main
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#--Update/Patch step----------
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UPDATE_COMMAND ""
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#--Configure step-------------
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SOURCE_DIR ${SB_SOURCE_DIR}/${_proj_name}
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CMAKE_ARGS
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-DPDAL_DIR=${SB_INSTALL_DIR}/lib/cmake/PDAL
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-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
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-DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}
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${WIN32_CMAKE_ARGS}
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#--Build step-----------------
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BINARY_DIR ${_SB_BINARY_DIR}
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#--Install step---------------
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INSTALL_DIR ${SB_INSTALL_DIR}
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#--Output logging-------------
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LOG_DOWNLOAD OFF
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LOG_CONFIGURE OFF
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LOG_BUILD OFF
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)
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2
VERSION
2
VERSION
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@ -1 +1 @@
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3.3.2
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3.3.3
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@ -7,6 +7,8 @@ import time
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import shutil
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import functools
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import threading
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import glob
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import re
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from joblib import delayed, Parallel
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from opendm.system import run
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from opendm import point_cloud
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@ -17,10 +19,6 @@ from scipy import ndimage
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from datetime import datetime
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from opendm.vendor.gdal_fillnodata import main as gdal_fillnodata
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from opendm import log
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try:
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import Queue as queue
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except:
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import queue
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import threading
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from .ground_rectification.rectify import run_rectification
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@ -73,115 +71,45 @@ def create_dem(input_point_cloud, dem_type, output_type='max', radiuses=['0.56']
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apply_smoothing=True, max_tiles=None):
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""" Create DEM from multiple radii, and optionally gapfill """
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global error
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error = None
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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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ext_width = extent['maxx'] - extent['minx']
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ext_height = extent['maxy'] - extent['miny']
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w, h = (int(math.ceil(ext_width / float(resolution))),
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int(math.ceil(ext_height / float(resolution))))
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# Set a floor, no matter the resolution parameter
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# (sometimes a wrongly estimated scale of the model can cause the resolution
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# to be set unrealistically low, causing errors)
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RES_FLOOR = 64
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if w < RES_FLOOR and h < RES_FLOOR:
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prev_w, prev_h = w, h
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if w >= h:
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w, h = (RES_FLOOR, int(math.ceil(ext_height / ext_width * RES_FLOOR)))
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else:
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w, h = (int(math.ceil(ext_width / ext_height * RES_FLOOR)), RES_FLOOR)
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floor_ratio = prev_w / float(w)
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resolution *= floor_ratio
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radiuses = [str(float(r) * floor_ratio) for r in radiuses]
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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))
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final_dem_pixels = w * h
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num_splits = int(max(1, math.ceil(math.log(math.ceil(final_dem_pixels / float(max_tile_size * max_tile_size)))/math.log(2))))
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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" % ((h, w), 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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# Safety check
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if max_tiles is not None:
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if len(tiles) > max_tiles and (final_dem_pixels * 4) > get_total_memory():
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raise system.ExitException("Max tiles limit exceeded (%s). This is a strong indicator that the reconstruction failed and we would probably run out of memory trying to process this" % max_tiles)
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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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def process_tile(q):
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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 == '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)
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parallel_map(process_tile, tiles, max_workers)
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kwargs = {
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'input': input_point_cloud,
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'outdir': outdir,
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'outputType': output_type,
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'radiuses': ",".join(map(str, radiuses)),
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'resolution': resolution,
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'maxTiles': 0 if max_tiles is None else max_tiles,
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'decimation': 1 if decimation is None else decimation,
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'classification': 2 if dem_type == 'dtm' else -1
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}
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system.run('renderdem "{input}" '
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'--outdir "{outdir}" '
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'--output-type {outputType} '
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'--radiuses {radiuses} '
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'--resolution {resolution} '
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'--max-tiles {maxTiles} '
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'--decimation {decimation} '
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'--classification {classification} '
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'--force '.format(**kwargs), env_vars={'OMP_NUM_THREADS': max_workers})
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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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# Fetch tiles
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tiles = []
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for p in glob.glob(os.path.join(os.path.abspath(outdir), "*.tif")):
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filename = os.path.basename(p)
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m = re.match("^r([\d\.]+)_x\d+_y\d+\.tif", filename)
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if m is not None:
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tiles.append({'filename': p, 'radius': float(m.group(1))})
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if len(tiles) == 0:
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raise system.ExitException("No DEM tiles were generated, something went wrong")
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log.ODM_INFO("Generated %s tiles" % len(tiles))
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# Sort tiles by decreasing radius
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tiles.sort(key=lambda t: float(t['radius']), reverse=True)
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# Create virtual raster
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tiles_vrt_path = os.path.abspath(os.path.join(outdir, "tiles.vrt"))
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@ -334,7 +262,7 @@ def median_smoothing(geotiff_path, output_path, smoothing_iterations=1, window_s
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cols_end= numpy.minimum(cols + window_size, shape[1])
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windows = numpy.dstack((rows, cols, rows_end, cols_end)).reshape(-1, 4)
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filter = functools.partial(ndimage.median_filter, size=9, output=dtype, mode='nearest')
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filt = functools.partial(ndimage.median_filter, size=9, output=dtype, mode='nearest')
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# We cannot read/write to the same file from multiple threads without causing race conditions.
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# To safely read/write from multiple threads, we use a lock to protect the DatasetReader/Writer.
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@ -342,7 +270,7 @@ def median_smoothing(geotiff_path, output_path, smoothing_iterations=1, window_s
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write_lock = threading.Lock()
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# threading backend and GIL released filter are important for memory efficiency and multi-core performance
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Parallel(n_jobs=num_workers, backend='threading')(delayed(window_filter_2d)(img, imgout, nodata , window, 9, filter, read_lock, write_lock) for window in windows)
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Parallel(n_jobs=num_workers, backend='threading')(delayed(window_filter_2d)(img, imgout, nodata , window, 9, filt, read_lock, write_lock) for window in windows)
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# Between each iteration we swap the input and output temporary files
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#img_in, img_out = img_out, img_in
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@ -101,7 +101,7 @@ class ODMDEMStage(types.ODM_Stage):
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decimation=args.dem_decimation,
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max_workers=args.max_concurrency,
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keep_unfilled_copy=args.dem_euclidean_map,
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max_tiles=math.ceil(len(reconstruction.photos) / 2)
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max_tiles=None if reconstruction.has_geotagged_photos() else math.ceil(len(reconstruction.photos) / 2)
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)
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dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product))
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@ -60,7 +60,7 @@ class ODMeshingStage(types.ODM_Stage):
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available_cores=args.max_concurrency,
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method='poisson' if args.fast_orthophoto else 'gridded',
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smooth_dsm=True,
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max_tiles=math.ceil(len(reconstruction.photos) / 2))
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max_tiles=None if reconstruction.has_geotagged_photos() else math.ceil(len(reconstruction.photos) / 2))
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else:
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log.ODM_WARNING('Found a valid ODM 2.5D Mesh file in: %s' %
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tree.odm_25dmesh)
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