From ad129868db803988c81d3077e56f97811f32c7c7 Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Mon, 21 Mar 2022 11:02:40 +0100 Subject: [PATCH 1/9] More doctsrings --- lidar_prod/tasks/building_validation.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/lidar_prod/tasks/building_validation.py b/lidar_prod/tasks/building_validation.py index 71ba7b96..a844dfe0 100644 --- a/lidar_prod/tasks/building_validation.py +++ b/lidar_prod/tasks/building_validation.py @@ -203,15 +203,20 @@ def update(self, prepared_f: str, out_f: str): os.makedirs(osp.dirname(out_f), exist_ok=True) las.write(out_f) - def _make_group_decision(self, *args, **kwargs): + def _make_group_decision(self, *args, **kwargs) -> int: + f"""Wrapper to simplify decision codes during LAS update. + Signature follows the one of {self._make_detailed_group_decision.__name__} + Returns: + int: final classification code for the considered group. + """ detailed_code = self._make_detailed_group_decision(*args, **kwargs) return self.detailed_to_final[detailed_code] def _make_detailed_group_decision(self, probas_arr, overlay_bools_arr): """Decision process at the cluster level. - Confirm or refute candidate building shape based on fraction of confirmed/refuted points and - on fraction of points overlayed by a building shape in a database. + Confirm or refute candidate building groups based on fraction of confirmed/refuted points and + on fraction of points overlayed by a building vector in BDUni. """ ia_confirmed = ( @@ -236,7 +241,13 @@ def _make_detailed_group_decision(self, probas_arr, overlay_bools_arr): return self.codes.detailed.db_overlayed_only return self.codes.detailed.both_unsure - def _set_rules_from_pickle(self, building_validation_thresholds_pickle): + def _set_rules_from_pickle(self, building_validation_thresholds_pickle: str): + """Specifiy all thresholds from serialized rules. + This is used in thresholds optimization. + + Args: + building_validation_thresholds_pickle (str): _description_ + """ with open(building_validation_thresholds_pickle, "rb") as f: self.rules: rules = pickle.load(f) From b816349863a0be74ede1f7b94489a0a0ea155342 Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Mon, 21 Mar 2022 15:07:13 +0100 Subject: [PATCH 2/9] Implement Building Completion --- CI/run_app.sh | 2 +- README.md | 2 +- configs/building_completion/default.yaml | 15 ++ configs/building_identification/default.yaml | 6 +- .../application/default.yaml | 16 -- configs/config.yaml | 1 + configs/data_format/cleaning/default.yaml | 4 +- configs/data_format/default.yaml | 36 +++-- dockerfile | 30 ++-- lidar_prod/application.py | 25 ++- lidar_prod/tasks/building_completion.py | 148 ++++++++++++++++++ lidar_prod/tasks/building_identification.py | 43 ++--- lidar_prod/tasks/building_validation.py | 56 ++++--- .../tasks/building_validation_optimization.py | 8 +- lidar_prod/tasks/utils.py | 18 --- 15 files changed, 286 insertions(+), 124 deletions(-) create mode 100644 configs/building_completion/default.yaml create mode 100644 lidar_prod/tasks/building_completion.py diff --git a/CI/run_app.sh b/CI/run_app.sh index 8a959e78..c3fa4dcd 100644 --- a/CI/run_app.sh +++ b/CI/run_app.sh @@ -6,5 +6,5 @@ HYDRA_FULL_ERROR=1 python -m lidar_prod.run print_config=true \ paths.src_las=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las \ paths.output_dir=/var/data/cicd/CICD_outputs/app/ \ -data_format.codes.candidates.building='[19, 20, 110, 112, 114, 115]' \ +data_format.codes.building.candidates='[19, 20, 110, 112, 114, 115]' \ building_validation.application.building_validation_thresholds_pickle=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle \ No newline at end of file diff --git a/README.md b/README.md index 4eb28062..7eb217ba 100755 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ pip install --upgrade https://github.com/IGNF/lidar-prod-quality-control/tarball pip install -e . # from local sources ``` -To run the module as a package, you will need a source cloud point in LAS format with an additional channel containing predicted building probabilities. The name of this channel is specified by `config.data_format.las_channel_names.ai_building_proba`. +To run the module as a package, you will need a source cloud point in LAS format with an additional channel containing predicted building probabilities. The name of this channel is specified by `config.data_format.las_dimensions.ai_building_proba`. To run using default configurations of the installed package, use ```bash diff --git a/configs/building_completion/default.yaml b/configs/building_completion/default.yaml new file mode 100644 index 00000000..185e0bcc --- /dev/null +++ b/configs/building_completion/default.yaml @@ -0,0 +1,15 @@ +_target_: lidar_prod.tasks.building_completion.BuildingCompletor + +data_format: ${data_format} + +# TODO: uncomment when min_building_proba_relaxation_if_bd_uni_overlay is specified <1 to be consistent everywhere. +# min_building_proba: ${building_validation.application.rules.min_confidence_confirmation} + +min_building_proba: 0.5 +min_building_proba_relaxation_if_bd_uni_overlay: 1.0 # No effect if = 1.0 + +cluster: + min_points: 10 # including isolated points (in BuildingValidator) and confirmed candidates points. + tolerance: 1 # meters + is3d: false # group in 2d for better detection + diff --git a/configs/building_identification/default.yaml b/configs/building_identification/default.yaml index a36d3ff7..72723cdb 100644 --- a/configs/building_identification/default.yaml +++ b/configs/building_identification/default.yaml @@ -1,14 +1,12 @@ _target_: lidar_prod.tasks.building_identification.BuildingIdentifier -candidate_buildings_codes: ${data_format.codes.candidates.building} - data_format: ${data_format} min_building_proba: ${building_validation.application.rules.min_confidence_confirmation} -min_building_proba_multiplier_if_bd_uni_overlay: 1.0 +min_building_proba_relaxation_if_bd_uni_overlay: 1.0 cluster: - min_points: 200 + min_points: 200 # Large so that small artefact are ignored tolerance: 1 # meters is3d: false # group in 2d for better detection diff --git a/configs/building_validation/application/default.yaml b/configs/building_validation/application/default.yaml index 609afd92..e5d27d01 100644 --- a/configs/building_validation/application/default.yaml +++ b/configs/building_validation/application/default.yaml @@ -3,7 +3,6 @@ _target_: lidar_prod.tasks.building_validation.BuildingValidator building_validation_thresholds_pickle: /path/to/best_trial.pkl data_format: ${data_format} -candidate_buildings_codes: ${data_format.codes.candidates.building} use_final_classification_codes: true @@ -27,18 +26,3 @@ rules: min_uni_db_overlay_frac: 0.508 min_confidence_refutation: 0.872 min_frac_refutation: 0.964 - -codes: - detailed: - unclustered: 202 # refuted - ia_refuted: 110 # refuted - ia_refuted_and_db_overlayed: 111 # unsure - both_unsure: 112 # unsure - ia_confirmed_only: 113 # confirmed - db_overlayed_only: 114 # confirmed - both_confirmed: 115 # confirmed - final: - unsure: 214 # unsure - not_building: 208 # refuted - building: 6 # confirmed - diff --git a/configs/config.yaml b/configs/config.yaml index 95f95235..128f70d1 100755 --- a/configs/config.yaml +++ b/configs/config.yaml @@ -8,4 +8,5 @@ defaults: - data_format: default.yaml - building_validation: default.yaml - building_identification: default.yaml + - building_completion: default.yaml - _self_ # needed by pdal for legacy reasons \ No newline at end of file diff --git a/configs/data_format/cleaning/default.yaml b/configs/data_format/cleaning/default.yaml index 1ef72001..25edb51b 100644 --- a/configs/data_format/cleaning/default.yaml +++ b/configs/data_format/cleaning/default.yaml @@ -3,5 +3,5 @@ _target_: lidar_prod.tasks.cleaning.Cleaner # Extra dims that are kept when cleaning dimensions # You can override with "all" to keep all extra dimensions at development time. keep_extra_dims: - - "${data_format.las_channel_names.ai_building_identified}=uint" - - "${data_format.las_channel_names.ai_building_proba}=float" \ No newline at end of file + - "${data_format.las_dimensions.ai_building_identified}=uint" + - "${data_format.las_dimensions.ai_building_proba}=float" \ No newline at end of file diff --git a/configs/data_format/default.yaml b/configs/data_format/default.yaml index 68d0473a..7fa54882 100644 --- a/configs/data_format/default.yaml +++ b/configs/data_format/default.yaml @@ -2,22 +2,38 @@ tile_size_meters: 1000 crs_prefix: "EPSG:" crs: 2154 -# To connect logic between application tasks -las_channel_names: +# Those names connect the logics between successive tasks +las_dimensions: # input classification: classification #las format - cluster_id: ClusterID #pdal-defined - uni_db_overlay: BDTopoOverlay # user-defined - ai_building_proba: building # user-defined + ai_building_proba: building # user-defined - output by deep learning model - # custom channels - candidate_buildings_flag: CandidateBuildingsFlag - macro_candidate_building_groups: CandidateBuildingGroups + # intermediary channels + cluster_id: ClusterID # pdal-defined -> created by clustering operations + uni_db_overlay: BDTopoOverlay # user-defined -> a 0/1 flag for presence of a BDUni vector + candidate_buildings_flag: F_CandidateB # -> a 0/1 flag identifying candidate buildings found by rules-based classification + ClusterID_candidate_building: CID_CandidateB # -> Cluster index from BuildingValidator, 0 if no cluster, 1-n elsewise + ClusterID_isolated_plus_confirmed: CID_IsolatedOrConfirmed # -> Cluster index from BuildingCompletor, 0 if no cluster, 1-n elsewise + + + # additionnal output channel ai_building_identified: Group codes: - candidates: - building: [202] + building: + candidates: [202] # found by rules-based classification (TerraScan) + detailed: # used for detailed output when doing threshold optimization + unclustered: 202 # refuted + ia_refuted: 110 # refuted + ia_refuted_and_db_overlayed: 111 # unsure + both_unsure: 112 # unsure + ia_confirmed_only: 113 # confirmed + db_overlayed_only: 114 # confirmed + both_confirmed: 115 # confirmed + final: # used at the end of the building process + unsure: 214 # unsure + not_building: 208 # refuted + building: 6 # confirmed defaults: - cleaning: default.yaml \ No newline at end of file diff --git a/dockerfile b/dockerfile index b4b29476..b90bff55 100644 --- a/dockerfile +++ b/dockerfile @@ -10,12 +10,12 @@ RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone # all the apt-get installs RUN apt-get update && apt-get upgrade -y && apt-get install -y \ - software-properties-common \ - wget \ - git \ - postgis \ - pdal \ - libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6 # package needed for anaconda + software-properties-common \ + wget \ + git \ + postgis \ + pdal \ + libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6 # package needed for anaconda # install anaconda RUN wget --quiet https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh -O ~/anaconda.sh @@ -40,15 +40,15 @@ RUN python -c "import pdal" # the entrypoint garanty that all command will be runned in the conda environment ENTRYPOINT ["conda", \ - "run", \ - "-n", \ - "lidar_prod"] + "run", \ + "-n", \ + "lidar_prod"] # cmd for a normal run (non evaluate) CMD ["python", \ - "lidar_prod/run.py", \ - "print_config=true", \ - "paths.src_las=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las", \ - "paths.output_dir=/CICD_github_assets/app/", \ - "data_format.codes.candidates.building=[19, 20, 110, 112, 114, 115]", \ - "building_validation.application.building_validation_thresholds_pickle=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle"] + "lidar_prod/run.py", \ + "print_config=true", \ + "paths.src_las=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las", \ + "paths.output_dir=/CICD_github_assets/app/", \ + "data_format.codes.building.candidates=[19, 20, 110, 112, 114, 115]", \ + "building_validation.application.building_validation_thresholds_pickle=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle"] diff --git a/lidar_prod/application.py b/lidar_prod/application.py index e1b0fc02..ee5ac289 100644 --- a/lidar_prod/application.py +++ b/lidar_prod/application.py @@ -1,9 +1,11 @@ import logging import os import os.path as osp +from tempfile import TemporaryDirectory import hydra from omegaconf import DictConfig from typing import Optional +from lidar_prod.tasks.building_completion import BuildingCompletor from lidar_prod.tasks.cleaning import Cleaner from lidar_prod.utils import utils @@ -30,13 +32,20 @@ def apply(config: DictConfig): in_f = config.paths.src_las out_f = osp.join(config.paths.output_dir, osp.basename(in_f)) - bv: BuildingValidator = hydra.utils.instantiate( - config.building_validation.application - ) - bv.run(in_f, out_f) + with TemporaryDirectory() as td: + # Temporary LAS file for intermediary results. + temp_f = osp.join(td, osp.basename(in_f)) - bi: BuildingIdentifier = hydra.utils.instantiate(config.building_identification) - bi.run(out_f, out_f) + bv: BuildingValidator = hydra.utils.instantiate( + config.building_validation.application + ) + bv.run(in_f, temp_f) - cl: Cleaner = hydra.utils.instantiate(config.data_format.cleaning) - cl.run(out_f, out_f) + bc: BuildingCompletor = hydra.utils.instantiate(config.building_completion) + bc.run(temp_f, temp_f) + + bi: BuildingIdentifier = hydra.utils.instantiate(config.building_identification) + bi.run(temp_f, temp_f) + + cl: Cleaner = hydra.utils.instantiate(config.data_format.cleaning) + cl.run(temp_f, out_f) diff --git a/lidar_prod/tasks/building_completion.py b/lidar_prod/tasks/building_completion.py new file mode 100644 index 00000000..34e31e54 --- /dev/null +++ b/lidar_prod/tasks/building_completion.py @@ -0,0 +1,148 @@ +import logging +import os +import os.path as osp +from tempfile import TemporaryDirectory +import pdal +import laspy +from tqdm import tqdm + +from lidar_prod.tasks.utils import split_idx_by_dim + +log = logging.getLogger(__name__) + + +class BuildingCompletor: + """Logic of building completion. + + Some points were too isolated for BuildingValidator to consider them. + We will update their classification based on their probability as well as their surrounding: + - We select points that have p>=0.5 + - We perform vertical (XY) clustering including these points as well as confirmed buildings. + - In the resulting groups, if there are some confirmed buildings, previously isolated points are + considered to be parts of the same building and their class is updated accordingly. + """ + + def __init__( + self, + min_building_proba: float = 0.75, + min_building_proba_relaxation_if_bd_uni_overlay: float = 1.0, + cluster=None, + data_format=None, + ): + self.cluster = cluster + self.min_building_proba = min_building_proba + self.min_building_proba_relaxation_if_bd_uni_overlay = ( + min_building_proba_relaxation_if_bd_uni_overlay + ) + self.data_format = data_format + self.codes = data_format.codes.building # easier access + + def run(self, in_f: str, out_f: str): + """Application. + + Transform cloud at `in_f` following building completion logic, and save it to + `out_f` + + Args: + in_f (str): path to input LAS file, output of BuildingValidator + out_f (str): path for saving updated LAS file. + + Returns: + _type_: returns `out_f` for potential terminal piping. + + """ + log.info(f"Applying Building Completion to file \n{in_f}") + log.info( + "Completion of building with relatively distant points that have high enough probability" + ) + with TemporaryDirectory() as td: + temp_f = osp.join(td, osp.basename(in_f)) + self.prepare(in_f, temp_f) + self.update(temp_f, out_f) + return out_f + + def prepare(self, in_f: str, out_f: str): + f"""Prepare for building completion. + + Identify candidates that were not clustered together by the BuildingValidator, but that + have high enough probability. Then, cluster them together with previously confirmed buildings. + Cluster parameters are relaxed (2D, with high tolerance). + If a cluster contains some confirmed points, the others are considered to belong to the same building + and they will be confirmed as well. + + Args: + in_f (str): input LAS + out_f (str): output, prepared LAS with a new `{self.data_format.las_dimensions.ClusterID_isolated_plus_confirmed}` + dimension. + """ + pipeline = pdal.Pipeline() + pipeline |= pdal.Reader( + in_f, + type="readers.las", + # nosrs=True, + # override_srs=self.data_format.crs_prefix + str(self.data_format.crs), + ) + candidates = ( + f"({self.data_format.las_dimensions.candidate_buildings_flag} == 1)" + ) + + where_not_clustered = ( + f"{self.data_format.las_dimensions.ClusterID_candidate_building} == 0" + ) + + # P above threshold + p_heq_threshold = f"(building>={self.min_building_proba})" + + # P above relaxed threshold when under BDUni + under_bd_uni = f"({self.data_format.las_dimensions.uni_db_overlay} > 0)" + p_heq_relaxed_threshold = f"(building>={self.min_building_proba * self.min_building_proba_relaxation_if_bd_uni_overlay})" + p_heq_threshold_under_bd_uni = f"({p_heq_relaxed_threshold} && {under_bd_uni})" + + # Candidates that where clustered by BuildingValidator but have high enough probability. + not_clustered_but_with_high_p = f"{candidates} && {where_not_clustered} && ({p_heq_threshold} || {p_heq_threshold_under_bd_uni})" + confirmed_buildings = ( + f"Classification == {self.data_format.codes.building.final.building}" + ) + + where = f"{not_clustered_but_with_high_p} || {confirmed_buildings}" + pipeline |= pdal.Filter.cluster( + min_points=self.cluster.min_points, + tolerance=self.cluster.tolerance, + is3d=self.cluster.is3d, + where=where, + ) + # Always move and reset ClusterID to avoid conflict with later tasks. + pipeline |= pdal.Filter.ferry( + dimensions=f"{self.data_format.las_dimensions.cluster_id}=>{self.data_format.las_dimensions.ClusterID_isolated_plus_confirmed}" + ) + pipeline |= pdal.Filter.assign( + value=f"{self.data_format.las_dimensions.cluster_id} = 0" + ) + pipeline |= pdal.Writer( + type="writers.las", filename=out_f, forward="all", extra_dims="all" + ) + os.makedirs(osp.dirname(out_f), exist_ok=True) + pipeline.execute() + + def update(self, prepared_f: str, out_f: str): + """ + + Args: + in_f (str): input, prepared LAS + out_f (str): output LAS, with updated Classification dimension. + """ + las = laspy.read(prepared_f) + _clf = self.data_format.las_dimensions.classification + _cid = self.data_format.las_dimensions.ClusterID_isolated_plus_confirmed + # 2) Decide at the group-level + split_idx = split_idx_by_dim(las[_cid]) + # Isolated/confirmed groups have a cluster index > 0 + split_idx = split_idx[1:] + for pts_idx in tqdm( + split_idx, desc="Complete buildings with isolated points", unit="grp" + ): + pts = las.points[pts_idx] + if self.codes.final.building in pts[_clf]: + las[_clf][pts_idx] = self.codes.final.building + os.makedirs(osp.dirname(out_f), exist_ok=True) + las.write(out_f) diff --git a/lidar_prod/tasks/building_identification.py b/lidar_prod/tasks/building_identification.py index fbdf6cee..1d13e4ce 100644 --- a/lidar_prod/tasks/building_identification.py +++ b/lidar_prod/tasks/building_identification.py @@ -16,26 +16,24 @@ class BuildingIdentifier: High enough probability means : - p>=min_building_proba OR, IF point fall in a building vector from the BDUni: - - p>=(min_building_proba*min_building_proba_multiplier_if_bd_uni_overlay). + - p>=(min_building_proba*min_building_proba_relaxation_if_bd_uni_overlay). """ def __init__( self, - candidate_buildings_codes: int = [202], min_building_proba: float = 0.75, - min_building_proba_multiplier_if_bd_uni_overlay: float = 1.0, + min_building_proba_relaxation_if_bd_uni_overlay: float = 1.0, cluster=None, data_format=None, ): self.cluster = cluster - self.candidate_buildings_codes = candidate_buildings_codes self.data_format = data_format self.min_building_proba = min_building_proba - self.min_building_proba_multiplier_if_bd_uni_overlay = ( - min_building_proba_multiplier_if_bd_uni_overlay + self.min_building_proba_relaxation_if_bd_uni_overlay = ( + min_building_proba_relaxation_if_bd_uni_overlay ) - def run(self, in_f: str, out_f: str): + def run(self, in_f: str, out_f: str) -> str: """Application. Transform cloud at `in_f` following identification logic, and save it to @@ -46,33 +44,31 @@ def run(self, in_f: str, out_f: str): out_f (str): path for saving updated LAS file. Returns: - _type_: returns `out_f` for potential terminal piping. + str: `out_f` """ log.info(f"Applying Building Identification to file \n{in_f}") - log.info("Clustering of points with high building proba") - self.update(in_f, out_f) + log.info("Clustering of points with high building proba.") + self.prepare(in_f, out_f) return out_f - def update(self, in_f: str, out_f: str): + def prepare(self, in_f: str, out_f: str) -> None: """Identify potential buildings in a new channel, excluding former candidates from search based on their group ID. ClusterID needs to be reset to avoid unwanted merge of information from previous VuildingValidation clustering. + Args: + in_f (str): input LAS + out_f (str): output LAS """ pipeline = pdal.Pipeline() - pipeline |= pdal.Reader( - in_f, - type="readers.las", - nosrs=True, - override_srs=self.data_format.crs_prefix + str(self.data_format.crs), - ) + pipeline |= pdal.Reader(in_f, type="readers.las") non_candidates = ( - f"({self.data_format.las_channel_names.candidate_buildings_flag} == 0)" + f"({self.data_format.las_dimensions.candidate_buildings_flag} == 0)" ) p_heq_threshold = f"(building>={self.min_building_proba})" - A = f"(building>={self.min_building_proba * self.min_building_proba_multiplier_if_bd_uni_overlay})" - B = f"({self.data_format.las_channel_names.uni_db_overlay} > 0)" + A = f"(building>={self.min_building_proba * self.min_building_proba_relaxation_if_bd_uni_overlay})" + B = f"({self.data_format.las_dimensions.uni_db_overlay} > 0)" p_heq_threshold_under_bd_uni = f"({A} && {B})" where = ( f"{non_candidates} && ({p_heq_threshold} || {p_heq_threshold_under_bd_uni})" @@ -83,9 +79,14 @@ def update(self, in_f: str, out_f: str): is3d=self.cluster.is3d, where=where, ) + # Always move and reset ClusterID to avoid conflict with later tasks. pipeline |= pdal.Filter.ferry( - dimensions=f"{self.data_format.las_channel_names.cluster_id}=>{self.data_format.las_channel_names.ai_building_identified}" + dimensions=f"{self.data_format.las_dimensions.cluster_id}=>{self.data_format.las_dimensions.ai_building_identified}" ) + pipeline |= pdal.Filter.assign( + value=f"{self.data_format.las_dimensions.cluster_id} = 0" + ) + pipeline |= pdal.Writer( type="writers.las", filename=out_f, forward="all", extra_dims="all" ) diff --git a/lidar_prod/tasks/building_validation.py b/lidar_prod/tasks/building_validation.py index a844dfe0..a809eb1f 100644 --- a/lidar_prod/tasks/building_validation.py +++ b/lidar_prod/tasks/building_validation.py @@ -17,7 +17,14 @@ class BuildingValidator: - """Logic of building validation.""" + """Logic of building validation. + + The candidate building points identified with a rule-based algorithm are cluster together. + The BDUni building vectors are overlayed on the points clouds, and points that fall under a vector are flagged. + Then, classification dim is updated on a per-group basis, based on both AI probabilities and BDUni flag. + + See `README.md` for the detailed process. + """ def __init__( self, @@ -25,20 +32,20 @@ def __init__( cluster=None, bd_uni_request=None, rules=None, + data_format=None, building_validation_thresholds_pickle: str = None, - codes=None, - candidate_buildings_codes: int = [202], use_final_classification_codes: bool = True, - data_format=None, ): self.bd_uni_connection_params = bd_uni_connection_params self.cluster = cluster self.bd_uni_request = bd_uni_request - self.candidate_buildings_codes = candidate_buildings_codes self.use_final_classification_codes = use_final_classification_codes - self.rules = rules # default values - self.codes = codes + self.rules = rules + self.data_format = data_format + # For easier access + self.codes = data_format.codes.building + self.candidate_buildings_codes = data_format.codes.building.candidates self.setup(building_validation_thresholds_pickle) @@ -91,30 +98,30 @@ def run( log.info( "Preparation : Clustering of candidates buildings & Requesting BDUni" ) - _temp_f = osp.join(td, osp.basename(in_f)) - self.prepare(in_f, _temp_f) + temp_f = osp.join(td, osp.basename(in_f)) + self.prepare(in_f, temp_f) log.info("Using AI and Databases to update cloud Classification") - self.update(_temp_f, out_f) + self.update(temp_f, out_f) return out_f def prepare(self, in_f: str, out_f: str): f""" Prepare las for later decision process: - 1. Cluster candidates points, in a new `{self.data_format.las_channel_names.macro_candidate_building_groups}` + 1. Cluster candidates points, in a new `{self.data_format.las_dimensions.ClusterID_candidate_building}` dimension where the index of clusters starts at 1 (0 means no cluster). 2. Identify points overlayed by a BD Uni building, in a new - `{self.data_format.las_channel_names.uni_db_overlay}` dimension (0/1 flag). + `{self.data_format.las_dimensions.uni_db_overlay}` dimension (0/1 flag). In the process is created a new dimensions which identifies candidate buildings (0/1 flag) - `{self.data_format.las_channel_names.candidate_buildings_flag}`, to ignore them in later + `{self.data_format.las_dimensions.candidate_buildings_flag}`, to ignore them in later buildings identification. """ - _candidate_flag = self.data_format.las_channel_names.candidate_buildings_flag - _cluster_id = self.data_format.las_channel_names.cluster_id - _groups = self.data_format.las_channel_names.macro_candidate_building_groups - _overlay = self.data_format.las_channel_names.uni_db_overlay + _candidate_flag = self.data_format.las_dimensions.candidate_buildings_flag + _cluster_id = self.data_format.las_dimensions.cluster_id + _groups = self.data_format.las_dimensions.ClusterID_candidate_building + _overlay = self.data_format.las_dimensions.uni_db_overlay # We use a temporary directory to clean intermediary files automatically with TemporaryDirectory() as td: @@ -142,11 +149,12 @@ def prepare(self, in_f: str, out_f: str): tolerance=self.cluster.tolerance, where=f"{_candidate_flag} == 1", ) + # Always move and reset ClusterID to avoid conflict with later tasks. pipeline |= pdal.Filter.ferry(dimensions=f"{_cluster_id}=>{_groups}") - # We reset ClusterId to avoid crash with future clustering pipeline |= pdal.Filter.assign(value=f"{_cluster_id} = 0") - _shp_p = os.path.join(td, "temp.shp") + # TODO: extract coordinates from LAS directly using pdal. + _shp_p = os.path.join(td, "temp.shp") buildings_in_bd_topo = request_bd_uni_for_building_shapefile( self.bd_uni_connection_params, *extract_coor( @@ -176,14 +184,14 @@ def update(self, prepared_f: str, out_f: str): las = laspy.read(prepared_f) # 1) Set all candidates points to a single class - _clf = self.data_format.las_channel_names.classification - _flag = self.data_format.las_channel_names.candidate_buildings_flag + _clf = self.data_format.las_dimensions.classification + _flag = self.data_format.las_dimensions.candidate_buildings_flag candidates_idx = las[_flag] == 1 las[_clf][candidates_idx] = self.codes.detailed.unclustered # 2) Decide at the group-level split_idx = split_idx_by_dim( - las[self.data_format.las_channel_names.macro_candidate_building_groups] + las[self.data_format.las_dimensions.ClusterID_candidate_building] ) split_idx = split_idx[1:] # removes unclustered group with ClusterID = 0 for pts_idx in tqdm( @@ -191,8 +199,8 @@ def update(self, prepared_f: str, out_f: str): ): pts = las.points[pts_idx] detailed_code = self._make_detailed_group_decision( - pts[self.data_format.las_channel_names.ai_building_proba], - pts[self.data_format.las_channel_names.uni_db_overlay], + pts[self.data_format.las_dimensions.ai_building_proba], + pts[self.data_format.las_dimensions.uni_db_overlay], ) las[_clf][pts_idx] = detailed_code diff --git a/lidar_prod/tasks/building_validation_optimization.py b/lidar_prod/tasks/building_validation_optimization.py index 91914c37..2576ebfc 100644 --- a/lidar_prod/tasks/building_validation_optimization.py +++ b/lidar_prod/tasks/building_validation_optimization.py @@ -166,7 +166,7 @@ def update(self): def __extract_clusters_from_las(self, prepared_las_path): las = laspy.read(prepared_las_path) cluster_id = las[ - self.bv.data_format.las_channel_names.macro_candidate_building_groups + self.bv.data_format.las_dimensions.ClusterID_candidate_building ] UNCLUSTERED = 0 mask = cluster_id != UNCLUSTERED @@ -174,9 +174,9 @@ def __extract_clusters_from_las(self, prepared_las_path): if no_cluster: return [] cluster_id = cluster_id[mask] - classification = las[self.bv.data_format.las_channel_names.classification][mask] - uni_db_overlay = las[self.bv.data_format.las_channel_names.uni_db_overlay][mask] - ai_probas = las[self.bv.data_format.las_channel_names.ai_building_proba][mask] + classification = las[self.bv.data_format.las_dimensions.classification][mask] + uni_db_overlay = las[self.bv.data_format.las_dimensions.uni_db_overlay][mask] + ai_probas = las[self.bv.data_format.las_dimensions.ai_building_proba][mask] clusters = [] split_idx = split_idx_by_dim(cluster_id) diff --git a/lidar_prod/tasks/utils.py b/lidar_prod/tasks/utils.py index 88a9a526..5e7c8b3a 100644 --- a/lidar_prod/tasks/utils.py +++ b/lidar_prod/tasks/utils.py @@ -42,21 +42,3 @@ def extract_coor(las_name: str, x_span: float, y_span: float, buffer: float): x_min + x_span + buffer, y_max + buffer, ) - - -def clean_las_attributes( - in_f: str, - out_f: str, - forward: Union[str, List[str]] = "all", - extra_dims: Union[str, List[str]] = "all", -): - """Will rerad an rewrite a modified LAS to keep only usefull dimensions. - - Args: - in_f (str): _description_ - forward (str, optional): Channel to forward based on input schema. Defaults to "all". - extra_dims (str, optional): Extra dims to keep. Defaults to "all". - """ - pipeline = pdal.Pipeline() - pipeline |= pdal.Reader(in_f) - # pipeline |= From 78f0412a715b005b757b2ae3dec867ea210b97b3 Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Mon, 21 Mar 2022 15:23:47 +0100 Subject: [PATCH 3/9] Complete doc of Building Completion --- README.md | 12 +++++++++++- .../img/LidarBati-BuildingCompletionM8.3.png | Bin 0 -> 294690 bytes setup.py | 2 +- 3 files changed, 12 insertions(+), 2 deletions(-) create mode 100644 assets/img/LidarBati-BuildingCompletionM8.3.png diff --git a/README.md b/README.md index 7eb217ba..d2ace105 100755 --- a/README.md +++ b/README.md @@ -47,7 +47,17 @@ Decision thresholds `C1`, `C2`, `R1`, `R2`, `O1` are chosen via a multi-objectiv ![](assets/img/LidarBati-BuildingValidationM7.1V2.0.png) -#### B) Building Identification +#### B) Building Completion + +Goal: Confirm points that were too isolated to make up a group but have high-enough probability nevertheless (e.g. walls) + +Identify _candidate buildings points_ that have not been clustered in previous step due AND have high enough probability (p>=0.5)). +Cluster them together with previously confirmed building points in a relaxed, vertical fashion (higher tolerance, XY plan). +For each cluster, if some points were confirmed, the others are considered to belong to the same building, and are +therefore confirmed as well. + + +#### C) Building Identification Goal: Highlight potential buildings that were missed by the rule-based algorithm, for human inspection. diff --git a/assets/img/LidarBati-BuildingCompletionM8.3.png b/assets/img/LidarBati-BuildingCompletionM8.3.png new file mode 100644 index 0000000000000000000000000000000000000000..e78e173c7f7f1541d602f7f2daecbb5d6555c331 GIT binary patch literal 294690 zcmXt91yEZ}yFR!TFHjtc7A@|@t;H#B#frPTySux)ySo;5r??jl8g9OS=APM1=46sR zyZid{J_%EhlSD-(Kn4H+Ra#0+2>@W+000^R5gPJI)gR~($QzQal!gNUpbh@_f=Z%C zBLo04Kw3=rr)%bUmWwCR(9^pho7SaV7uSz^ZKF2ORvl_{-=}$&SZ1m~IjN!`AuKXj 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fLl|CSw9o$sBsOT9VvmH300000NkvXXu0mjfg-xI= literal 0 HcmV?d00001 diff --git a/setup.py b/setup.py index fefbc3dc..5cbf6c8b 100755 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="lidar_prod", - version="1.1.0", + version="1.2.0", description="A 3D semantic segmentation production tool to augment rules-based Lidar classification with AI and databases.", author="Charles GAYDON", author_email="charles.gaydon@gmail.com", From 7b733fb72f340d4b7c15ef2ded47c823cd597e80 Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Mon, 21 Mar 2022 15:38:18 +0100 Subject: [PATCH 4/9] Add img completion to readme --- README.md | 2 ++ ...ionM8.3.png => LidarBati-BuildingCompletion.png} | Bin 2 files changed, 2 insertions(+) rename assets/img/{LidarBati-BuildingCompletionM8.3.png => LidarBati-BuildingCompletion.png} (100%) diff --git a/README.md b/README.md index d2ace105..c1e5c4ea 100755 --- a/README.md +++ b/README.md @@ -56,6 +56,8 @@ Cluster them together with previously confirmed building points in a relaxed, ve For each cluster, if some points were confirmed, the others are considered to belong to the same building, and are therefore confirmed as well. +![](assets/img/LidarBati-BuildingCompletion.png) + #### C) Building Identification diff --git a/assets/img/LidarBati-BuildingCompletionM8.3.png b/assets/img/LidarBati-BuildingCompletion.png similarity index 100% rename from assets/img/LidarBati-BuildingCompletionM8.3.png rename to assets/img/LidarBati-BuildingCompletion.png From b01fd07413d0cb4d903876408d6b4cc14dfb4e67 Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Tue, 22 Mar 2022 12:14:53 +0100 Subject: [PATCH 5/9] HOTFIX - correct classification code in CICD --- .github/workflows/cicd.yaml | 8 ++++---- CI/run_app.sh | 2 +- dockerfile | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/cicd.yaml b/.github/workflows/cicd.yaml index 28e86509..65f6647e 100644 --- a/.github/workflows/cicd.yaml +++ b/.github/workflows/cicd.yaml @@ -24,7 +24,10 @@ jobs: - name: unit testing run: docker run lidar_prod_im pytest --ignore=actions-runner --ignore="notebooks" - - name: Evaluate decisions on single LAS + - name: Full module run on LAS subset + run: docker run -v /var/data/cicd/CICD_github_assets:/CICD_github_assets predict_im + + - name: Evaluate decisions using optimization code on a single, corrected LAS run: > docker run -v /var/data/cicd/CICD_github_assets:/CICD_github_assets lidar_prod_im python lidar_prod/run.py print_config=true +task='optimize' @@ -34,9 +37,6 @@ jobs: building_validation.optimization.paths.results_output_dir=/CICD_github_assets/opti/ building_validation.optimization.paths.building_validation_thresholds_pickle=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle - - name: test full run - run: docker run -v /var/data/cicd/CICD_github_assets:/CICD_github_assets predict_im - - name: clean the server for further uses if: always() # always do it, even if something failed run: docker system prune # remove obsolete docker images (take a HUGE amount of space) diff --git a/CI/run_app.sh b/CI/run_app.sh index c3fa4dcd..0c139841 100644 --- a/CI/run_app.sh +++ b/CI/run_app.sh @@ -6,5 +6,5 @@ HYDRA_FULL_ERROR=1 python -m lidar_prod.run print_config=true \ paths.src_las=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las \ paths.output_dir=/var/data/cicd/CICD_outputs/app/ \ -data_format.codes.building.candidates='[19, 20, 110, 112, 114, 115]' \ +data_format.codes.building.candidates='[202]' \ building_validation.application.building_validation_thresholds_pickle=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle \ No newline at end of file diff --git a/dockerfile b/dockerfile index b90bff55..73587958 100644 --- a/dockerfile +++ b/dockerfile @@ -50,5 +50,5 @@ CMD ["python", \ "print_config=true", \ "paths.src_las=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las", \ "paths.output_dir=/CICD_github_assets/app/", \ - "data_format.codes.building.candidates=[19, 20, 110, 112, 114, 115]", \ + "data_format.codes.building.candidates=[202]", \ "building_validation.application.building_validation_thresholds_pickle=/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle"] From cdd29619630b234bbb4edeb33c8055d38b08d4ba Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Tue, 22 Mar 2022 12:32:56 +0100 Subject: [PATCH 6/9] Remove unused CI scripts that are now in workflows and dockerfile --- CI/evaluate_bv_single_las.sh | 15 --------------- CI/install_as_a_package.sh | 7 ------- CI/run_app.sh | 10 ---------- CI/setup_env.sh | 6 ------ 4 files changed, 38 deletions(-) delete mode 100644 CI/evaluate_bv_single_las.sh delete mode 100644 CI/install_as_a_package.sh delete mode 100644 CI/run_app.sh delete mode 100644 CI/setup_env.sh diff --git a/CI/evaluate_bv_single_las.sh b/CI/evaluate_bv_single_las.sh deleted file mode 100644 index 211cdcff..00000000 --- a/CI/evaluate_bv_single_las.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash -eval "$(conda shell.bash hook)" -conda activate lidar_prod -HYDRA_FULL_ERROR=1 - -# NB: debug flag is for single las evaluation. - -python -m lidar_prod.run \ -print_config=true \ -+task="optimize" \ -+building_validation.optimization.debug=true \ -building_validation.optimization.todo='prepare+evaluate+update' \ -building_validation.optimization.paths.input_las_dir=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/20211001_buiding_val_val/ \ -building_validation.optimization.paths.results_output_dir=/var/data/cicd/CICD_outputs/opti/ \ -building_validation.optimization.paths.building_validation_thresholds_pickle=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle \ No newline at end of file diff --git a/CI/install_as_a_package.sh b/CI/install_as_a_package.sh deleted file mode 100644 index d874cb71..00000000 --- a/CI/install_as_a_package.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash -eval "$(conda shell.bash hook)" -conda activate lidar_prod -HYDRA_FULL_ERROR=1 - -pip install --no-cache-dir -e . # install lidar_prod as a package -python -m lidar_prod.run -h # dry run to test install \ No newline at end of file diff --git a/CI/run_app.sh b/CI/run_app.sh deleted file mode 100644 index 0c139841..00000000 --- a/CI/run_app.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -eval "$(conda shell.bash hook)" -conda activate lidar_prod -HYDRA_FULL_ERROR=1 - -python -m lidar_prod.run print_config=true \ -paths.src_las=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/subsets/871000_6617000_subset_with_probas.las \ -paths.output_dir=/var/data/cicd/CICD_outputs/app/ \ -data_format.codes.building.candidates='[202]' \ -building_validation.application.building_validation_thresholds_pickle=/var/data/cicd/CICD_github_assets/M8.0/20220204_building_val_V0.0_model/M8.0B2V0.0_buildingvalidation_thresholds.pickle \ No newline at end of file diff --git a/CI/setup_env.sh b/CI/setup_env.sh deleted file mode 100644 index f912c1ae..00000000 --- a/CI/setup_env.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash - -# Set up a lidar_prod virtual env for the following tests -eval "$(conda shell.bash hook)" -conda deactivate -source ./bash/setup_environment/setup_env.sh \ No newline at end of file From b1988b9631e9355fca7d12bed5311320c6812b6f Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Tue, 22 Mar 2022 12:49:07 +0100 Subject: [PATCH 7/9] Changing checkout workflow version from V2 to V1 avoid commits not accounted for This happens in open pull requests when new commits are pushed to the merged branch. See https://github.com/actions/checkout/issues/299 --- .github/workflows/cicd.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cicd.yaml b/.github/workflows/cicd.yaml index 65f6647e..488c8630 100644 --- a/.github/workflows/cicd.yaml +++ b/.github/workflows/cicd.yaml @@ -13,7 +13,7 @@ jobs: steps: - name: Checkout branch - uses: actions/checkout@v2 + uses: actions/checkout@v1 - name: build docker image run: docker build -t lidar_prod_im . From 96393d75504568598d31e40da5adb7622c6c36ee Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Tue, 22 Mar 2022 13:49:33 +0100 Subject: [PATCH 8/9] HOTFIX: Correct docker image name in CICD workflow --- .github/workflows/cicd.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cicd.yaml b/.github/workflows/cicd.yaml index 488c8630..b6ba5d44 100644 --- a/.github/workflows/cicd.yaml +++ b/.github/workflows/cicd.yaml @@ -25,7 +25,7 @@ jobs: run: docker run lidar_prod_im pytest --ignore=actions-runner --ignore="notebooks" - name: Full module run on LAS subset - run: docker run -v /var/data/cicd/CICD_github_assets:/CICD_github_assets predict_im + run: docker run -v /var/data/cicd/CICD_github_assets:/CICD_github_assets lidar_prod_im - name: Evaluate decisions using optimization code on a single, corrected LAS run: > From 4a6f0aa8ab395912f27bbbc6577756acf607bf3d Mon Sep 17 00:00:00 2001 From: CharlesGaydon Date: Tue, 22 Mar 2022 14:47:05 +0100 Subject: [PATCH 9/9] Bump version to V1.2.5 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 5cbf6c8b..2decb6c5 100755 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="lidar_prod", - version="1.2.0", + version="1.2.5", description="A 3D semantic segmentation production tool to augment rules-based Lidar classification with AI and databases.", author="Charles GAYDON", author_email="charles.gaydon@gmail.com",