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, 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)
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] dem_input = tree.odm_georeferencing_model_laz pc_model_found = io.file_exists(dem_input) ignore_resolution = False pseudo_georeference = False if not reconstruction.is_georeferenced(): # Special case to clear previous run point cloud # (NodeODM will generate a fake georeferenced laz during postprocessing # with non-georeferenced datasets). odm_georeferencing_model_laz should # not be here! Perhaps we should improve this. if io.file_exists( tree.odm_georeferencing_model_laz) and self.rerun(): os.remove(tree.odm_georeferencing_model_laz) log.ODM_WARNING( "Not georeferenced, using ungeoreferenced point cloud...") dem_input = tree.path("odm_filterpoints", "point_cloud.ply") pc_model_found = io.file_exists(dem_input) ignore_resolution = True pseudo_georeference = True resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_error_estimate=-3, ignore_gsd=args.ignore_gsd, ignore_resolution=ignore_resolution, has_gcp=reconstruction.has_gcp()) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format( dem_input, str(pc_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and pc_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or self.rerun(): log.ODM_INFO( "Classifying {} using Simple Morphological Filter".format( dem_input)) commands.classify(dem_input, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) progress = 20 self.update_progress(progress) if args.pc_rectify: commands.rectify(dem_input, args.debug) # Do we need to process anything here? if (args.dsm or args.dtm) and pc_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ self.rerun(): products = [] if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm') if args.dtm: products.append('dtm') radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append( radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( dem_input, product, output_type='idw' if product == 'dtm' else 'max', radiuses=map(str, radius_steps), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency, keep_unfilled_copy=args.dem_euclidean_map) dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product)) bounds_file_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') if args.crop > 0: # Crop DEM Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args)) if args.dem_euclidean_map: unfilled_dem_path = io.related_file_path( dem_geotiff_path, postfix=".unfilled") if args.crop > 0: # Crop unfilled DEM Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args)) commands.compute_euclidean_map( unfilled_dem_path, io.related_file_path(dem_geotiff_path, postfix=".euclideand"), overwrite=True) if pseudo_georeference: # 0.1 is arbitrary pseudogeo.add_pseudo_georeferencing( dem_geotiff_path, 0.1) progress += 30 self.update_progress(progress) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated')
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] dem_input = tree.odm_georeferencing_model_laz pc_model_found = io.file_exists(dem_input) ignore_resolution = False pseudo_georeference = False if not reconstruction.is_georeferenced(): log.ODM_WARNING( "Not georeferenced, using ungeoreferenced point cloud...") ignore_resolution = True pseudo_georeference = True # It is probably not reasonable to have accurate DEMs a the same resolution as the source photos, so reduce it # by a factor! gsd_scaling = 2.0 resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_scaling=gsd_scaling, ignore_gsd=args.ignore_gsd, ignore_resolution=ignore_resolution and args.ignore_gsd, has_gcp=reconstruction.has_gcp()) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format( dem_input, str(pc_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and pc_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or self.rerun(): log.ODM_INFO( "Classifying {} using Simple Morphological Filter".format( dem_input)) commands.classify(dem_input, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) progress = 20 self.update_progress(progress) if args.pc_rectify: commands.rectify(dem_input, args.debug) # Do we need to process anything here? if (args.dsm or args.dtm) and pc_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ self.rerun(): products = [] if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm') if args.dtm: products.append('dtm') radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append( radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( dem_input, product, output_type='idw' if product == 'dtm' else 'max', radiuses=list(map(str, radius_steps)), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency, keep_unfilled_copy=args.dem_euclidean_map) dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product)) bounds_file_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') if args.crop > 0 or args.boundary: # Crop DEM Cropper.crop( bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space) if args.dem_euclidean_map: unfilled_dem_path = io.related_file_path( dem_geotiff_path, postfix=".unfilled") if args.crop > 0 or args.boundary: # Crop unfilled DEM Cropper.crop( bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space) commands.compute_euclidean_map( unfilled_dem_path, io.related_file_path(dem_geotiff_path, postfix=".euclideand"), overwrite=True) if pseudo_georeference: pseudogeo.add_pseudo_georeferencing(dem_geotiff_path) if args.tiles: generate_dem_tiles(dem_geotiff_path, tree.path("%s_tiles" % product), args.max_concurrency) if args.cog: convert_to_cogeo(dem_geotiff_path, max_workers=args.max_concurrency) progress += 30 self.update_progress(progress) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated')