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'] 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: point_cloud.merge(all_point_clouds, tree.odm_georeferencing_model_laz) point_cloud.post_point_cloud_steps(args, tree) except Exception as e: log.ODM_WARNING("Could not merge point cloud: %s (skipping)" % 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_ortho_cuts = get_all_submodel_paths(tree.submodels_path, os.path.join("odm_orthophoto", "odm_orthophoto_feathered.tif"), os.path.join("odm_orthophoto", "odm_orthophoto_cut.tif"), ) if len(all_orthos_and_ortho_cuts) > 1: log.ODM_INFO( "Found %s submodels with valid orthophotos and cutlines" % len(all_orthos_and_ortho_cuts)) # TODO: histogram matching via rasterio # currently parts have different color tones if io.file_exists(tree.odm_orthophoto_tif): os.remove(tree.odm_orthophoto_tif) orthophoto_vars = orthophoto.get_orthophoto_vars(args) orthophoto.merge(all_orthos_and_ortho_cuts, tree.odm_orthophoto_tif, orthophoto_vars) orthophoto.post_orthophoto_steps(args, merged_bounds_file, tree.odm_orthophoto_tif, tree.orthophoto_tiles) elif len(all_orthos_and_ortho_cuts) == 1: # Simply copy log.ODM_WARNING("A single orthophoto/cutline pair was found between all submodels.") shutil.copyfile(all_orthos_and_ortho_cuts[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) eu_map_source = None # Default # Use DSM's euclidean map for DTMs # (requires the DSM to be computed) if human_name == "DTM": eu_map_source = "dsm" euclidean_merge_dems(all_dems, dem_file, dem_vars, euclidean_map_source=eu_map_source) if io.file_exists(dem_file): # Crop if args.crop > 0: Cropper.crop(merged_bounds_file, dem_file, dem_vars, keep_original=not args.optimize_disk_space) log.ODM_INFO("Created %s" % dem_file) if args.tiles: generate_dem_tiles(dem_file, tree.path("%s_tiles" % human_name.lower()), args.max_concurrency) 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") self.update_progress(95) # Merge reports if not io.dir_exists(tree.odm_report): system.mkdir_p(tree.odm_report) geojson_shots = tree.path(tree.odm_report, "shots.geojson") if not io.file_exists(geojson_shots) or self.rerun(): geojson_shots_files = get_submodel_paths(tree.submodels_path, "odm_report", "shots.geojson") log.ODM_INFO("Merging %s shots.geojson files" % len(geojson_shots_files)) merge_geojson_shots(geojson_shots_files, geojson_shots) else: log.ODM_WARNING("Found merged shots.geojson in %s" % tree.odm_report) # 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): 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)