From abd232e6d27c0d9db8db452e249106f411d5ed57 Mon Sep 17 00:00:00 2001 From: Yoann Apel Date: Wed, 10 Jan 2024 16:59:57 +0100 Subject: [PATCH 1/8] refactor occupancy map --- coclico/metrics/occupancy_map.py | 39 +++++++++++++++++++++----------- 1 file changed, 26 insertions(+), 13 deletions(-) diff --git a/coclico/metrics/occupancy_map.py b/coclico/metrics/occupancy_map.py index ae6e0ca..d27c687 100644 --- a/coclico/metrics/occupancy_map.py +++ b/coclico/metrics/occupancy_map.py @@ -1,4 +1,5 @@ import logging +from pathlib import Path from typing import Tuple import laspy @@ -44,22 +45,18 @@ def _create_2d_occupancy_array( return grid -def create_occupancy_map(las_file, class_weights, output_tif, pixel_size): - """Create 2d occupancy map for each class that is in class_weights keys, and save result in a single output_tif - file with one layer per class (the classes are sorted alphabetically). - - Args: - las_file (Path): path to the las file on which to generate occupancy map - class_weights (Dict): class weights dict (to know for which classes to generate the binary map) - output_tif (Path): path to output - pixel_size (float): size of the output raster pixels - """ +def read_las(las_file: Path): with laspy.open(las_file) as f: las = f.read() - xs, ys = las.x, las.y - classifs = las.classification - crs = las.header.parse_crs() + xs, ys = las.x, las.y + classifs = las.classification + crs = las.header.parse_crs() + + return xs, ys, classifs, crs + + +def create_occupancy_map_array(xs: np.array, ys: np.array, classifs: np.array, pixel_size: float, class_weights: dict): las_bounds = (np.min(xs), np.min(ys), np.max(xs), np.max(ys)) top_left, nb_pixels = get_raster_geometry_from_las_bounds(las_bounds, pixel_size) @@ -87,6 +84,22 @@ def create_binary_map_from_class(class_key): logging.debug(f"Creating binary maps with shape {binary_maps.shape}") logging.debug(f"The binary maps order is {sorted(class_weights.keys())}") + return binary_maps, x_min, y_max + + +def create_occupancy_map(las_file, class_weights, output_tif, pixel_size): + """Create 2d occupancy map for each class that is in class_weights keys, and save result in a single output_tif + file with one layer per class (the classes are sorted alphabetically). + + Args: + las_file (Path): path to the las file on which to generate occupancy map + class_weights (Dict): class weights dict (to know for which classes to generate the binary map) + output_tif (Path): path to output + pixel_size (float): size of the output raster pixels + """ + xs, ys, classifs, crs = read_las(las_file) + + binary_maps, x_min, y_max = create_occupancy_map_array(xs, ys, classifs, pixel_size, class_weights) output_tif.parent.mkdir(parents=True, exist_ok=True) From 4c1080fe85e2696b0bc0192deba3886730a9d420 Mon Sep 17 00:00:00 2001 From: Yoann Apel Date: Wed, 10 Jan 2024 17:06:16 +0100 Subject: [PATCH 2/8] mobj0: add basic intrinsic metric --- coclico/metrics/listing.py | 3 +- coclico/mobj0/__init__.py | 0 coclico/mobj0/mobj0.py | 30 +++++++++ coclico/mobj0/mobj0_intrinsic.py | 89 +++++++++++++++++++++++++++ coclico/mobj0/mobj0_relative.py | 74 ++++++++++++++++++++++ environment.yml | 1 + test/configs/config_test_metrics.yaml | 6 ++ test/mobj0/test_mobj0.py | 28 +++++++++ test/mobj0/test_mobj0_intrinsic.py | 54 ++++++++++++++++ test/mobj0/test_mobj0_relative.py | 22 +++++++ 10 files changed, 306 insertions(+), 1 deletion(-) create mode 100644 coclico/mobj0/__init__.py create mode 100644 coclico/mobj0/mobj0.py create mode 100644 coclico/mobj0/mobj0_intrinsic.py create mode 100644 coclico/mobj0/mobj0_relative.py create mode 100644 test/mobj0/test_mobj0.py create mode 100644 test/mobj0/test_mobj0_intrinsic.py create mode 100644 test/mobj0/test_mobj0_relative.py diff --git a/coclico/metrics/listing.py b/coclico/metrics/listing.py index 246c502..d318c21 100644 --- a/coclico/metrics/listing.py +++ b/coclico/metrics/listing.py @@ -1,5 +1,6 @@ from coclico.malt0.malt0 import MALT0 +from coclico.mobj0.mobj0 import MOBJ0 from coclico.mpap0.mpap0 import MPAP0 from coclico.mpla0.mpla0 import MPLA0 -METRICS = {"mpap0": MPAP0, "mpla0": MPLA0, "malt0": MALT0} +METRICS = {"mpap0": MPAP0, "mpla0": MPLA0, "malt0": MALT0, "mobj0": MOBJ0} diff --git a/coclico/mobj0/__init__.py b/coclico/mobj0/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/coclico/mobj0/mobj0.py b/coclico/mobj0/mobj0.py new file mode 100644 index 0000000..19317f0 --- /dev/null +++ b/coclico/mobj0/mobj0.py @@ -0,0 +1,30 @@ +from pathlib import Path +from typing import Dict, List + +import pandas as pd +from gpao.job import Job + +from coclico.metrics.metric import Metric + + +class MOBJ0(Metric): + """Metric MOBJ0 (for "Métrique par objet 0") + TODO Description + See doc/mobj0.md + """ + + # Pixel size for occupancy map + pixel_size = 0.5 + metric_name = "mobj0" + + def create_metric_intrinsic_one_job(self, name: str, input: Path, output: Path, is_ref: bool): + raise NotImplementedError + + def create_metric_relative_to_ref_jobs( + self, name: str, out_c1: Path, out_ref: Path, output: Path, c1_jobs: List[Job], ref_jobs: List[Job] + ) -> Job: + raise NotImplementedError + + @staticmethod + def compute_note(metric_df: pd.DataFrame, note_config: Dict): + raise NotImplementedError diff --git a/coclico/mobj0/mobj0_intrinsic.py b/coclico/mobj0/mobj0_intrinsic.py new file mode 100644 index 0000000..b3439b5 --- /dev/null +++ b/coclico/mobj0/mobj0_intrinsic.py @@ -0,0 +1,89 @@ +import argparse +import logging +from pathlib import Path + +import geopandas as gpd +import numpy as np +import pandas as pd +import rasterio +from osgeo import gdal +from rasterio.features import shapes as rasterio_shapes +from shapely.geometry import shape as shapely_shape + +import coclico.io +from coclico.metrics.occupancy_map import create_occupancy_map_array, read_las +from coclico.mobj0.mobj0 import MOBJ0 + +gdal.UseExceptions() + + +def create_objects_array(las_file, pixel_size, class_weights): + xs, ys, classifs, crs = read_las(las_file) + + binary_maps, x_min, y_max = create_occupancy_map_array(xs, ys, classifs, pixel_size, class_weights) + + return binary_maps, crs, x_min, y_max + + +def vectorize_occupancy_map(binary_maps, crs, x_min, y_max, pixel_size): + # Create empty dataframe + gdf_list = [] + + for ii, map_layer in enumerate(binary_maps): + shapes_layer = rasterio_shapes( + map_layer, + connectivity=8, + transform=rasterio.transform.from_origin( + x_min - pixel_size / 2, y_max + pixel_size / 2, pixel_size, pixel_size + ), + ) + + geometries = [shapely_shape(shapedict) for shapedict, value in shapes_layer if value != 0] + nb_geometries = len(geometries) + gdf_list.append( + gpd.GeoDataFrame( + {"layer": ii * np.ones(nb_geometries), "geometry": geometries}, + geometry="geometry", + crs=crs, + ) + ) + + gdf = pd.concat(gdf_list) + + return gdf + + +def compute_metric_intrinsic(las_file: Path, config_file: Path, output_geojson: Path, pixel_size: float = 0.5): + config_dict = coclico.io.read_config_file(config_file) + class_weights = config_dict[MOBJ0.metric_name]["weights"] + output_geojson.parent.mkdir(parents=True, exist_ok=True) + obj_array, crs, x_min, y_max = create_objects_array(las_file, pixel_size, class_weights) + polygons_gdf = vectorize_occupancy_map(obj_array, crs, x_min, y_max, pixel_size) + polygons_gdf.to_file(output_geojson) + + +def parse_args(): + parser = argparse.ArgumentParser("Run malt0 intrinsic metric on one tile") + parser.add_argument("-i", "--input-file", type=Path, required=True, help="Path to the LAS file") + parser.add_argument("-o", "--output-geojson", type=Path, required=True, help="Path to the output geojson") + parser.add_argument( + "-c", + "--config-file", + type=Path, + required=True, + help="Coclico configuration file", + ) + parser.add_argument( + "-p", "--pixel-size", type=float, required=True, help="Pixel size of the intermediate occupancy map" + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + logging.basicConfig(format="%(message)s", level=logging.DEBUG) + compute_metric_intrinsic( + las_file=Path(args.input_file), + config_file=args.config_file, + output_geojson=Path(args.output_geojson), + ) diff --git a/coclico/mobj0/mobj0_relative.py b/coclico/mobj0/mobj0_relative.py new file mode 100644 index 0000000..84ac56c --- /dev/null +++ b/coclico/mobj0/mobj0_relative.py @@ -0,0 +1,74 @@ +import argparse +import logging +from pathlib import Path + +import pandas as pd + +from coclico.config import csv_separator +from coclico.io import read_config_file +from coclico.mobj0.mobj0 import MOBJ0 + + +def compute_metric_relative( + c1_dir: Path, ref_dir: Path, occupancy_dir: Path, config_file: str, output_csv: Path, output_csv_tile: Path +): + """Compute metrics that describe the difference between c1 and ref height maps. + The occupancy map is used to mask the pixels for which the difference is computed + + The metrics are: + - mean_diff: the average difference in z between the height maps + - max_diff: the maximum difference in z between the height maps + - std_diff: the standard deviation of the difference in z betweeen the height maps + + These metrics are stored tile by tile and class by class in the output_csv_tile file + These metrics are stored class by class for the whole data in the output_csv file + + Args: + c1_dir (Path): path to the c1 classification directory, + where there are json files with the result of mpap0 intrinsic metric + ref_dir (Path): path to the reference classification directory, + where there are json files with the result of mpap0 intrinsic metric + class_weights (Dict): class weights dict + output_csv (Path): path to output csv file + output_csv_tile (Path): path to output csv file, result by tile + + """ + config_dict = read_config_file(config_file) + class_weights = config_dict[MOBJ0.metric_name]["weights"] + classes = sorted(class_weights.keys()) + classes + csv_data = [] + + df = pd.DataFrame(csv_data) + df.to_csv(output_csv, index=False, sep=csv_separator) + + logging.debug(df.to_markdown()) + raise NotImplementedError + + +def parse_args(): + parser = argparse.ArgumentParser("Run malt0 metric on one tile") + parser.add_argument( + "-i", + "--input-dir", + required=True, + type=Path, + help="Path to the classification directory, \ + where there are tif files with the result of malt0 intrinsic metric (MNx for each class)", + ) + raise NotImplementedError + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + logging.basicConfig(format="%(message)s", level=logging.DEBUG) + compute_metric_relative( + c1_dir=Path(args.input_dir), + ref_dir=Path(args.ref_dir), + occupancy_dir=Path(args.occupancy_dir), + config_file=args.config_file, + output_csv=Path(args.output_csv), + output_csv_tile=Path(args.output_csv_tile), + ) + raise NotImplementedError diff --git a/environment.yml b/environment.yml index 81f31ba..f5c723f 100644 --- a/environment.yml +++ b/environment.yml @@ -9,6 +9,7 @@ dependencies: - laspy - rasterio - pandas + - geopandas - tabulate - requests - pytest diff --git a/test/configs/config_test_metrics.yaml b/test/configs/config_test_metrics.yaml index 5cfe788..be574a5 100644 --- a/test/configs/config_test_metrics.yaml +++ b/test/configs/config_test_metrics.yaml @@ -99,3 +99,9 @@ malt0: max_point: metric: 0.5 note: 0 + +mobj0: + weights: + "6": 2 + "1": 1 + notes: {} \ No newline at end of file diff --git a/test/mobj0/test_mobj0.py b/test/mobj0/test_mobj0.py new file mode 100644 index 0000000..124437f --- /dev/null +++ b/test/mobj0/test_mobj0.py @@ -0,0 +1,28 @@ +import shutil +from pathlib import Path + +import pytest + +import coclico.io as io +from coclico.mobj0.mobj0 import MOBJ0 + +pytestmark = pytest.mark.docker + +TMP_PATH = Path("./tmp/mobj0") +CONFIG_FILE_METRICS = Path("./test/configs/config_test_metrics.yaml") + + +def setup_module(module): + if TMP_PATH.is_dir(): + shutil.rmtree(TMP_PATH) + + +def generate_metric_dataframes(): + raise NotImplementedError + + +def test_compute_note(): + input_df, expected_out = generate_metric_dataframes() + notes_config = io.read_config_file(CONFIG_FILE_METRICS)["mobj0"]["notes"] + out_df = MOBJ0.compute_note(input_df, notes_config) + assert out_df.equals(expected_out) diff --git a/test/mobj0/test_mobj0_intrinsic.py b/test/mobj0/test_mobj0_intrinsic.py new file mode 100644 index 0000000..4d6677c --- /dev/null +++ b/test/mobj0/test_mobj0_intrinsic.py @@ -0,0 +1,54 @@ +import logging +import shutil +import subprocess as sp +from pathlib import Path + +import geopandas as gpd +import pytest + +import coclico.io +from coclico.mobj0 import mobj0_intrinsic + +pytestmark = pytest.mark.docker + +TMP_PATH = Path("./tmp/mobj0_intrinsic") +CONFIG_FILE_METRICS = Path("./test/configs/config_test_metrics.yaml") + + +def setup_module(module): + if TMP_PATH.is_dir(): + shutil.rmtree(TMP_PATH) + + +def test_compute_metric_intrinsic(ensure_test1_data): + las_file = Path("./data/test1/niv1/tile_splitted_2818_32247.laz") + pixel_size = 0.5 + output_geojson = TMP_PATH / "intrinsic" / "unit_test_mpla0_intrinsic.geojson" + config = coclico.io.read_config_file(CONFIG_FILE_METRICS) + nb_layers = len(config["mobj0"]["weights"]) + + mobj0_intrinsic.compute_metric_intrinsic(las_file, CONFIG_FILE_METRICS, output_geojson, pixel_size=pixel_size) + + assert output_geojson.exists() + gdf = gpd.read_file(output_geojson) + logging.debug(gdf.to_markdown()) + assert len(gdf.index) > 0 + assert len(set(gdf["layer"])) == nb_layers + + +def test_run_main(ensure_test1_data): + pixel_size = 0.5 + input_file = Path("./data/test1/niv1/tile_splitted_2818_32247.laz") + output_geojson = TMP_PATH / "intrinsic" / "tile_splitted_2818_32247.geojson" + + cmd = f"""python -m coclico.mobj0.mobj0_intrinsic \ + --input-file {input_file} \ + --output-geojson {output_geojson} \ + --config-file {CONFIG_FILE_METRICS} \ + --pixel-size {pixel_size} + """ + sp.run(cmd, shell=True, check=True) + + logging.info(cmd) + + assert output_geojson.exists() diff --git a/test/mobj0/test_mobj0_relative.py b/test/mobj0/test_mobj0_relative.py new file mode 100644 index 0000000..54516a4 --- /dev/null +++ b/test/mobj0/test_mobj0_relative.py @@ -0,0 +1,22 @@ +import shutil +from pathlib import Path + +import pytest + +pytestmark = pytest.mark.docker + +TMP_PATH = Path("./tmp/mobj0_relative") +CONFIG_FILE_METRICS = Path("./test/configs/config_test_metrics.yaml") + + +def setup_module(module): + if TMP_PATH.is_dir(): + shutil.rmtree(TMP_PATH) + + +def test_compute_metric_relative(ensure_mobj0_data): + raise NotImplementedError + + +def test_run_main(ensure_mobj0_data): + raise NotImplementedError From 741337c63f8555573d23c8438c53c94f96f9a9a3 Mon Sep 17 00:00:00 2001 From: Lea Vauchier Date: Wed, 17 Jan 2024 18:15:45 +0100 Subject: [PATCH 3/8] mobj0: Add relative metric --- coclico/mobj0/mobj0_intrinsic.py | 2 +- coclico/mobj0/mobj0_relative.py | 161 +++++++++++++++--- .../tile_splitted_2818_32247.geojson | 32 ++++ .../tile_splitted_2818_32248.geojson | 85 +++++++++ .../tile_splitted_2819_32247.geojson | 24 +++ .../tile_splitted_2819_32248.geojson | 38 +++++ .../tile_splitted_2818_32247.geojson | 24 +++ .../tile_splitted_2818_32248.geojson | 58 +++++++ .../tile_splitted_2819_32247.geojson | 20 +++ .../tile_splitted_2819_32248.geojson | 26 +++ .../tile_splitted_2818_32247.geojson | 29 ++++ .../tile_splitted_2818_32248.geojson | 69 ++++++++ .../tile_splitted_2819_32247.geojson | 20 +++ .../tile_splitted_2819_32248.geojson | 28 +++ test/configs/config_test_metrics.yaml | 5 +- test/mobj0/test_mobj0_relative.py | 94 +++++++++- 16 files changed, 686 insertions(+), 29 deletions(-) create mode 100644 data/mobj0/niv2/intrinsic/tile_splitted_2818_32247.geojson create mode 100644 data/mobj0/niv2/intrinsic/tile_splitted_2818_32248.geojson create mode 100644 data/mobj0/niv2/intrinsic/tile_splitted_2819_32247.geojson create mode 100644 data/mobj0/niv2/intrinsic/tile_splitted_2819_32248.geojson create mode 100644 data/mobj0/niv4/intrinsic/tile_splitted_2818_32247.geojson create mode 100644 data/mobj0/niv4/intrinsic/tile_splitted_2818_32248.geojson create mode 100644 data/mobj0/niv4/intrinsic/tile_splitted_2819_32247.geojson create mode 100644 data/mobj0/niv4/intrinsic/tile_splitted_2819_32248.geojson create mode 100644 data/mobj0/ref/intrinsic/tile_splitted_2818_32247.geojson create mode 100644 data/mobj0/ref/intrinsic/tile_splitted_2818_32248.geojson create mode 100644 data/mobj0/ref/intrinsic/tile_splitted_2819_32247.geojson create mode 100644 data/mobj0/ref/intrinsic/tile_splitted_2819_32248.geojson diff --git a/coclico/mobj0/mobj0_intrinsic.py b/coclico/mobj0/mobj0_intrinsic.py index b3439b5..6a91a4e 100644 --- a/coclico/mobj0/mobj0_intrinsic.py +++ b/coclico/mobj0/mobj0_intrinsic.py @@ -63,7 +63,7 @@ def compute_metric_intrinsic(las_file: Path, config_file: Path, output_geojson: def parse_args(): - parser = argparse.ArgumentParser("Run malt0 intrinsic metric on one tile") + parser = argparse.ArgumentParser("Run mobj0 intrinsic metric on one tile") parser.add_argument("-i", "--input-file", type=Path, required=True, help="Path to the LAS file") parser.add_argument("-o", "--output-geojson", type=Path, required=True, help="Path to the output geojson") parser.add_argument( diff --git a/coclico/mobj0/mobj0_relative.py b/coclico/mobj0/mobj0_relative.py index 84ac56c..f5f453c 100644 --- a/coclico/mobj0/mobj0_relative.py +++ b/coclico/mobj0/mobj0_relative.py @@ -1,7 +1,10 @@ import argparse import logging +from collections import Counter from pathlib import Path +from typing import List, Tuple +import geopandas as gpd import pandas as pd from coclico.config import csv_separator @@ -9,54 +12,170 @@ from coclico.mobj0.mobj0 import MOBJ0 -def compute_metric_relative( - c1_dir: Path, ref_dir: Path, occupancy_dir: Path, config_file: str, output_csv: Path, output_csv_tile: Path -): - """Compute metrics that describe the difference between c1 and ref height maps. - The occupancy map is used to mask the pixels for which the difference is computed +def check_paired_objects( + c1_geometries: gpd.GeoDataFrame, ref_geometries: gpd.GeoDataFrame, classes: List +) -> Tuple[Counter, Counter]: + """Pair objects from 2 geodataframes (ie. 2 lists of geometries) + Pairing is made based on geometries intersections: + - the first step is to find all geometries in ref that have at least an intersection with a + geometry in c1 (using a spatial join). The sjoin will contain several occurences of ref geometries + if they one line per + - the second one is to remove pairs geometries match with several geometries in the other dataframe: + - remove pairs where a geometry in c1 is associated with multiple ref polygons first (making sure that we keep + the geometries in ref that have a single match in c1) + - then remove pairs where a geometry in ref is still associated with multiple c1 polygons (making sure that we + keep the geometries in ref that have a single match in c1) - The metrics are: - - mean_diff: the average difference in z between the height maps - - max_diff: the maximum difference in z between the height maps - - std_diff: the standard deviation of the difference in z betweeen the height maps + Args: + c1_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from the reference + ref_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from c1 + classes (List): oredered list of clsses (to match "layer" values with classes in the output) + + Returns: + Tuple[Counter, Counter]: (nb_paired, nb_not paired) Number of paired / not paired geometries + + """ + all_join_gdfs = [] + nb_paired = Counter() + nb_not_paired = Counter() + ref_geometries["index_ref"] = ref_geometries.index.copy() + + for ii, class_key in enumerate(classes): + c1_geom_layer = c1_geometries[c1_geometries.layer == ii] + ref_geom_layer = ref_geometries[ref_geometries.layer == ii] + logging.debug( + f"For class {class_key}, found {len(c1_geom_layer.index)} in c1 and {len(ref_geom_layer.index)} in ref " + ) + # Find all geometries in ref that intersect a geometry in c1 + class_join_gdf = ref_geom_layer.sjoin(c1_geom_layer, how="inner", rsuffix="c1") + + # Remove duplicates for geometries in c1 that intersect several geometries in ref, keeping the pairs where + # the geometry has fewer matches in ref (to keep pairs that have only 1 match in ref) + class_join_gdf["index_c1_count"] = class_join_gdf.groupby("index_c1")["index_c1"].transform("count") + class_join_gdf.sort_values(by="index_c1_count", ascending=True, inplace=True) + class_join_gdf.drop_duplicates(subset=["index_c1"], keep="first", inplace=True) + + # Remove duplicates for geometries in ref that intersect several geometries in c1, keeping the pairs where + # the geometry has fewer matches in c1 (to keep pairs that have only 1 match in c1) + class_join_gdf["index_ref_count"] = class_join_gdf.groupby("index_ref")["index_ref"].transform("count") + class_join_gdf.sort_values(by="index_ref_count", ascending=True, inplace=True) + class_join_gdf.drop_duplicates(subset=["index_ref"], keep="first", inplace=True) + + all_join_gdfs.append(class_join_gdf) + nb_paired[class_key] = len(class_join_gdf.index) + nb_not_paired[class_key] = len(c1_geom_layer.index) + len(ref_geom_layer.index) - 2 * nb_paired[class_key] + + return nb_paired, nb_not_paired + + +def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, output_csv: Path, output_csv_tile: Path): + """Generate relative metrics for mobj0 from the number of paired objects between the reference c1 (classification + to compare) using the polygons generated by the mobj0 intrinsic metric. + Paired objects are objects that are found both in c1 and the reference, "not paired" objects are + objects that are found either only in the reference or only in c1. + The pairing method is implemented and explained in the check_paired_objects method. + + The computed metrics are: + - nb_paired: The number of paired objects (found both in c1 and ref) + - nb_not_paired: The number of "not paired" objects (found only in c1 or only in ref) These metrics are stored tile by tile and class by class in the output_csv_tile file These metrics are stored class by class for the whole data in the output_csv file + Args: c1_dir (Path): path to the c1 classification directory, - where there are json files with the result of mpap0 intrinsic metric + where there are json files with the result of mobj0 intrinsic metric ref_dir (Path): path to the reference classification directory, - where there are json files with the result of mpap0 intrinsic metric - class_weights (Dict): class weights dict + where there are json files with the result of mobj0 intrinsic metric + config_file (Path): Coclico configuration file output_csv (Path): path to output csv file output_csv_tile (Path): path to output csv file, result by tile - """ config_dict = read_config_file(config_file) class_weights = config_dict[MOBJ0.metric_name]["weights"] classes = sorted(class_weights.keys()) - classes - csv_data = [] + output_csv.parent.mkdir(parents=True, exist_ok=True) + output_csv_tile.parent.mkdir(parents=True, exist_ok=True) + + total_nb_paired = Counter() + total_nb_not_paired = Counter() + + data = [] + + for ref_file in ref_dir.iterdir(): + c1_file = c1_dir / ref_file.name + + c1_geometries = gpd.read_file(c1_file) + ref_geometries = gpd.read_file(ref_file) - df = pd.DataFrame(csv_data) + nb_paired, nb_not_paired = check_paired_objects(c1_geometries, ref_geometries, classes) + + total_nb_paired += Counter(nb_paired) + total_nb_not_paired += Counter(nb_not_paired) + + new_line = [ + { + "tile": ref_file.stem, + "class": cl, + "nb_paired": nb_paired.get(cl, 0), + "nb_not_paired": nb_not_paired.get(cl, 0), + } + for cl in classes + ] + data.extend(new_line) + + output_csv.parent.mkdir(parents=True, exist_ok=True) + df = pd.DataFrame(data) + df.to_csv(output_csv_tile, index=False, sep=csv_separator) + logging.debug(df.to_markdown()) + + data = [ + { + "class": cl, + "nb_paired": total_nb_paired.get(cl, 0), + "nb_not_paired": total_nb_not_paired.get(cl, 0), + } + for cl in classes + ] + df = pd.DataFrame(data) df.to_csv(output_csv, index=False, sep=csv_separator) logging.debug(df.to_markdown()) - raise NotImplementedError + + logging.debug(df.to_markdown()) def parse_args(): - parser = argparse.ArgumentParser("Run malt0 metric on one tile") + parser = argparse.ArgumentParser("Run mobj0 metric on one tile") parser.add_argument( "-i", "--input-dir", required=True, type=Path, help="Path to the classification directory, \ - where there are tif files with the result of malt0 intrinsic metric (MNx for each class)", + where there are geojson files with the result of mobj0 intrinsic metric", + ) + parser.add_argument( + "-r", + "--ref-dir", + required=True, + type=Path, + help="Path to the reference directory, where there are geojson files with the result of mobj0 intrinsic " + + "metric", ) - raise NotImplementedError + parser.add_argument("-o", "--output-csv", required=True, type=Path, help="Path to the CSV output file") + parser.add_argument( + "-t", "--output-csv-tile", required=True, type=Path, help="Path to the CSV output file, result by tile" + ) + parser.add_argument( + "-c", + "--config-file", + required=True, + type=Path, + help="Coclico configuration file", + ) + return parser.parse_args() @@ -66,9 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b/test/configs/config_test_metrics.yaml @@ -102,6 +102,7 @@ malt0: mobj0: weights: - "6": 2 - "1": 1 + "6": 2 + "1": 1 + "9": 1 # No points for class 0 notes: {} \ No newline at end of file diff --git a/test/mobj0/test_mobj0_relative.py b/test/mobj0/test_mobj0_relative.py index 54516a4..4a79741 100644 --- a/test/mobj0/test_mobj0_relative.py +++ b/test/mobj0/test_mobj0_relative.py @@ -1,8 +1,17 @@ +import logging import shutil +import subprocess as sp from pathlib import Path +from test import utils +import geopandas as gpd +import pandas as pd import pytest +from coclico.config import csv_separator +from coclico.io import read_config_file +from coclico.mobj0 import mobj0_relative + pytestmark = pytest.mark.docker TMP_PATH = Path("./tmp/mobj0_relative") @@ -14,9 +23,86 @@ def setup_module(module): shutil.rmtree(TMP_PATH) -def test_compute_metric_relative(ensure_mobj0_data): - raise NotImplementedError +paired_objects_params = [ + # One test with niv2 data (same objects but with different geometries) + ( + Path("./data/mobj0/niv2/intrinsic/tile_splitted_2818_32248.geojson"), # c1_path + {"1": 51, "6": 13, "9": 0}, # expected_nb_paired + {"1": 16, "6": 0, "9": 0}, # expected_nb_not_paired + ), + # One test with niv4 data (missing buildings) + ( + Path("./data/mobj0/niv4/intrinsic/tile_splitted_2818_32248.geojson"), # c1_path + {"1": 45, "6": 7, "9": 0}, # expected_nb_paired + {"1": 7, "6": 6, "9": 0}, # expected_nb_not_paired + ), +] + + +@pytest.mark.parametrize("c1_file,expected_nb_paired,expected_nb_not_paired", paired_objects_params) +def test_check_paired_objects(c1_file, expected_nb_paired, expected_nb_not_paired): + ref_file = Path("./data/mobj0/ref/intrinsic/tile_splitted_2818_32248.geojson") + config_dict = read_config_file(CONFIG_FILE_METRICS) + classes = sorted(config_dict["mobj0"]["weights"].keys()) + c1_geometries = gpd.read_file(c1_file) + ref_geometries = gpd.read_file(ref_file) + nb_paired, nb_not_paired = mobj0_relative.check_paired_objects(c1_geometries, ref_geometries, classes) + + assert nb_paired == expected_nb_paired + assert nb_not_paired == expected_nb_not_paired + + +def test_compute_metric_relative(): + c1_dir = Path("./data/mobj0/niv4/intrinsic/") + ref_dir = Path("./data/mobj0/ref/intrinsic/") + output_csv = TMP_PATH / "relative" / "result.csv" + output_csv_tile = TMP_PATH / "relative" / "result_tile.csv" + expected_cols = {"class", "nb_paired", "nb_not_paired"} + + mobj0_relative.compute_metric_relative(c1_dir, ref_dir, CONFIG_FILE_METRICS, output_csv, output_csv_tile) + + expected_rows = 4 * 3 # 4 files * 3 classes + assert utils.csv_num_rows(output_csv_tile) == expected_rows + df = pd.read_csv(output_csv_tile, sep=csv_separator) + assert set(df.columns) == expected_cols | {"tile"} + + expected_rows = 3 # 3 classes + assert utils.csv_num_rows(output_csv) == expected_rows + + df = pd.read_csv(output_csv, sep=csv_separator) + assert set(df.columns) == expected_cols + + # Check result for class 9 (0 points in c1 and ref) + nb_paired_9 = df["nb_paired"][df.index[df["class"] == 9][0]] + nb_not_paired_9 = df["nb_not_paired"][df.index[df["class"] == 9][0]] + assert nb_paired_9 == 0 + assert nb_not_paired_9 == 0 + + # Check result for class 6 (several objects in c1 and ref that are not detected the same) + nb_paired_6 = df["nb_paired"][df.index[df["class"] == 6][0]] + nb_not_paired_6 = df["nb_not_paired"][df.index[df["class"] == 6][0]] + assert nb_paired_6 > 0 + assert nb_not_paired_6 > 0 + + +def test_run_main(): + c1_dir = Path("./data/mobj0/niv2/intrinsic") + ref_dir = Path("./data/mobj0/ref/intrinsic") + output_csv = TMP_PATH / "unit_test_run_main_mobj0_relative.csv" + output_csv_tile = TMP_PATH / "unit_test_run_main_mobj0_relative_tile.csv" + cmd = f"""python -m coclico.mobj0.mobj0_relative \ + --input-dir {c1_dir} \ + --ref-dir {ref_dir} \ + --config-file {CONFIG_FILE_METRICS} \ + --output-csv {output_csv} \ + --output-csv-tile {output_csv_tile} \ + """ + + sp.run(cmd, shell=True, check=True) + logging.info(cmd) + expected_rows = 4 * 3 # 4 files * 3 classes + assert utils.csv_num_rows(output_csv_tile) == expected_rows -def test_run_main(ensure_mobj0_data): - raise NotImplementedError + expected_rows = 3 # 3 classes + assert utils.csv_num_rows(output_csv) == expected_rows From ffc091bed6eae76de6fbbf1d278d29b9cfe6ac36 Mon Sep 17 00:00:00 2001 From: Lea Vauchier Date: Thu, 25 Jan 2024 11:10:27 +0100 Subject: [PATCH 4/8] mobj0: add note + fix relative metric --- coclico/mobj0/mobj0.py | 46 ++++++++++++++++++++++++- coclico/mobj0/mobj0_relative.py | 46 +++++++++++++++---------- configs/metrics_config.yaml | 26 ++++++++++++++- test/configs/config_test_metrics.yaml | 22 +++++++++++- test/mobj0/test_mobj0.py | 25 +++++++++++++- test/mobj0/test_mobj0_relative.py | 48 ++++++++++++++++----------- 6 files changed, 173 insertions(+), 40 deletions(-) diff --git a/coclico/mobj0/mobj0.py b/coclico/mobj0/mobj0.py index 19317f0..e5ae761 100644 --- a/coclico/mobj0/mobj0.py +++ b/coclico/mobj0/mobj0.py @@ -1,9 +1,11 @@ from pathlib import Path from typing import Dict, List +import numpy as np import pandas as pd from gpao.job import Job +from coclico.metrics.commons import bounded_affine_function from coclico.metrics.metric import Metric @@ -27,4 +29,46 @@ def create_metric_relative_to_ref_jobs( @staticmethod def compute_note(metric_df: pd.DataFrame, note_config: Dict): - raise NotImplementedError + """Compute mobj0 note from mobj0_relative results. + This method expects a pandas dataframe with columns: + - ref_object_count + - paired_count + - not_paired_count + (these columns are described in the mobj0_relative function docstring) + + Args: + metric_df (pd.DataFrame): mobj0 relative results as a pandas dataframe + + Returns: + metric_df: the updated metric_df input with notes instead of metrics + """ + + metric_df[MOBJ0.metric_name] = np.where( + metric_df["ref_object_count"] >= note_config["ref_object_count_threshold"], + bounded_affine_function( + ( + note_config["above_threshold"]["min_point"]["metric"], + note_config["above_threshold"]["min_point"]["note"], + ), + ( + note_config["above_threshold"]["max_point"]["metric"], + note_config["above_threshold"]["max_point"]["note"], + ), + metric_df["paired_count"] / (metric_df["paired_count"] + metric_df["not_paired_count"]), + ), + bounded_affine_function( + ( + note_config["under_threshold"]["min_point"]["metric"], + note_config["under_threshold"]["min_point"]["note"], + ), + ( + note_config["under_threshold"]["max_point"]["metric"], + note_config["under_threshold"]["max_point"]["note"], + ), + metric_df["not_paired_count"], + ), + ) + + metric_df.drop(columns=["ref_object_count", "paired_count", "not_paired_count"], inplace=True) + + return metric_df diff --git a/coclico/mobj0/mobj0_relative.py b/coclico/mobj0/mobj0_relative.py index f5f453c..da0a05d 100644 --- a/coclico/mobj0/mobj0_relative.py +++ b/coclico/mobj0/mobj0_relative.py @@ -32,12 +32,16 @@ def check_paired_objects( classes (List): oredered list of clsses (to match "layer" values with classes in the output) Returns: - Tuple[Counter, Counter]: (nb_paired, nb_not paired) Number of paired / not paired geometries + Tuple[Counter, Counter, Counter]: (ref_object_count, paired_count, not_paired_count) Numbers of: + - geometries in ref + - paired geometries + - not paired geometries """ all_join_gdfs = [] - nb_paired = Counter() - nb_not_paired = Counter() + ref_object_count = Counter() + paired_count = Counter() + not_paired_count = Counter() ref_geometries["index_ref"] = ref_geometries.index.copy() for ii, class_key in enumerate(classes): @@ -62,10 +66,13 @@ def check_paired_objects( class_join_gdf.drop_duplicates(subset=["index_ref"], keep="first", inplace=True) all_join_gdfs.append(class_join_gdf) - nb_paired[class_key] = len(class_join_gdf.index) - nb_not_paired[class_key] = len(c1_geom_layer.index) + len(ref_geom_layer.index) - 2 * nb_paired[class_key] + ref_object_count[class_key] = len(ref_geom_layer.index) + paired_count[class_key] = len(class_join_gdf.index) + not_paired_count[class_key] = ( + len(c1_geom_layer.index) + len(ref_geom_layer.index) - 2 * paired_count[class_key] + ) - return nb_paired, nb_not_paired + return ref_object_count, paired_count, not_paired_count def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, output_csv: Path, output_csv_tile: Path): @@ -76,8 +83,9 @@ def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, outpu The pairing method is implemented and explained in the check_paired_objects method. The computed metrics are: - - nb_paired: The number of paired objects (found both in c1 and ref) - - nb_not_paired: The number of "not paired" objects (found only in c1 or only in ref) + - ref_object_count: The number of objects in ref + - paired_count: The number of paired objects (found both in c1 and ref) + - not_paired_count: The number of "not paired" objects (found only in c1 or only in ref) These metrics are stored tile by tile and class by class in the output_csv_tile file These metrics are stored class by class for the whole data in the output_csv file @@ -98,8 +106,9 @@ def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, outpu output_csv.parent.mkdir(parents=True, exist_ok=True) output_csv_tile.parent.mkdir(parents=True, exist_ok=True) - total_nb_paired = Counter() - total_nb_not_paired = Counter() + total_ref_object_count = Counter() + total_paired_count = Counter() + total_not_paired_count = Counter() data = [] @@ -109,17 +118,19 @@ def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, outpu c1_geometries = gpd.read_file(c1_file) ref_geometries = gpd.read_file(ref_file) - nb_paired, nb_not_paired = check_paired_objects(c1_geometries, ref_geometries, classes) + ref_object_count, paired_count, not_paired_count = check_paired_objects(c1_geometries, ref_geometries, classes) - total_nb_paired += Counter(nb_paired) - total_nb_not_paired += Counter(nb_not_paired) + total_ref_object_count += Counter(ref_object_count) + total_paired_count += Counter(paired_count) + total_not_paired_count += Counter(not_paired_count) new_line = [ { "tile": ref_file.stem, "class": cl, - "nb_paired": nb_paired.get(cl, 0), - "nb_not_paired": nb_not_paired.get(cl, 0), + "ref_object_count": ref_object_count.get(cl, 0), + "paired_count": paired_count.get(cl, 0), + "not_paired_count": not_paired_count.get(cl, 0), } for cl in classes ] @@ -133,8 +144,9 @@ def compute_metric_relative(c1_dir: Path, ref_dir: Path, config_file: str, outpu data = [ { "class": cl, - "nb_paired": total_nb_paired.get(cl, 0), - "nb_not_paired": total_nb_not_paired.get(cl, 0), + "ref_object_count": total_ref_object_count.get(cl, 0), + "paired_count": total_paired_count.get(cl, 0), + "not_paired_count": total_not_paired_count.get(cl, 0), } for cl in classes ] diff --git a/configs/metrics_config.yaml b/configs/metrics_config.yaml index 581be5a..c5dca50 100644 --- a/configs/metrics_config.yaml +++ b/configs/metrics_config.yaml @@ -97,7 +97,31 @@ malt0: metric: 0.5 note: 0 - +mobj0: + weights: + "6": 16 + "17": 12 + "64": 8 + notes: + ref_object_count_threshold: 20 + under_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0 # count of "not paired" objects + note: 1 + # tuple for the first point with the highest metric value + max_point: + metric: 4 # count of "not paired" objects + note: 0 + above_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0.8 # count of paired objects / counts of caired objects + 'not paired' objects + note: 0 + # tuple for the first point with the highest metric value + max_point: + metric: 1 # count of paired objects / counts of caired objects + 'not paired' objects + note: 1 diff --git a/test/configs/config_test_metrics.yaml b/test/configs/config_test_metrics.yaml index 08629a1..ef4a22d 100644 --- a/test/configs/config_test_metrics.yaml +++ b/test/configs/config_test_metrics.yaml @@ -105,4 +105,24 @@ mobj0: "6": 2 "1": 1 "9": 1 # No points for class 0 - notes: {} \ No newline at end of file + notes: + ref_object_count_threshold: 20 + under_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0 # count of "not paired" objects + note: 1 + # tuple for the first point with the highest metric value + max_point: + metric: 4 # count of "not paired" objects + note: 0 + above_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0.8 # count of paired objects / counts of caired objects + 'not paired' objects + note: 0 + # tuple for the first point with the highest metric value + max_point: + metric: 1 # count of paired objects / counts of caired objects + 'not paired' objects + note: 1 + diff --git a/test/mobj0/test_mobj0.py b/test/mobj0/test_mobj0.py index 124437f..77a72e1 100644 --- a/test/mobj0/test_mobj0.py +++ b/test/mobj0/test_mobj0.py @@ -1,6 +1,7 @@ import shutil from pathlib import Path +import pandas as pd import pytest import coclico.io as io @@ -18,11 +19,33 @@ def setup_module(module): def generate_metric_dataframes(): - raise NotImplementedError + # Cases: + # - tile a: more than 20 objects + # - tile b: less than 20 objects + input_df = pd.DataFrame( + { + "tile": ["a", "a", "a", "a", "a", "b", "b", "b", "b"], + "class": ["6", "1", "9", "12", "3_4_5", "6", "1", "2", "9"], + "ref_object_count": [20, 10, 0, 10, 19, 20, 90, 200, 100], + "paired_count": [20, 0, 1, 0, 100, 20, 80, 100, 90], + "not_paired_count": [0, 1, 2, 3, 4, 0, 20, 150, 10], + } + ) + + expected_out = pd.DataFrame( + { + "tile": ["a", "a", "a", "a", "a", "b", "b", "b", "b"], + "class": ["6", "1", "9", "12", "3_4_5", "6", "1", "2", "9"], + "mobj0": [1, 3 / 4, 1 / 2, 1 / 4, 0, 1, 0, 0, 0.5], + } + ) + + return input_df, expected_out def test_compute_note(): input_df, expected_out = generate_metric_dataframes() notes_config = io.read_config_file(CONFIG_FILE_METRICS)["mobj0"]["notes"] out_df = MOBJ0.compute_note(input_df, notes_config) + assert out_df.equals(expected_out) diff --git a/test/mobj0/test_mobj0_relative.py b/test/mobj0/test_mobj0_relative.py index 4a79741..bf10e89 100644 --- a/test/mobj0/test_mobj0_relative.py +++ b/test/mobj0/test_mobj0_relative.py @@ -27,29 +27,35 @@ def setup_module(module): # One test with niv2 data (same objects but with different geometries) ( Path("./data/mobj0/niv2/intrinsic/tile_splitted_2818_32248.geojson"), # c1_path - {"1": 51, "6": 13, "9": 0}, # expected_nb_paired - {"1": 16, "6": 0, "9": 0}, # expected_nb_not_paired + {"1": 51, "6": 13, "9": 0}, # expected_ref_count + {"1": 51, "6": 13, "9": 0}, # expected_paired_count + {"1": 16, "6": 0, "9": 0}, # expected_not_paired_count ), # One test with niv4 data (missing buildings) ( Path("./data/mobj0/niv4/intrinsic/tile_splitted_2818_32248.geojson"), # c1_path - {"1": 45, "6": 7, "9": 0}, # expected_nb_paired - {"1": 7, "6": 6, "9": 0}, # expected_nb_not_paired + {"1": 51, "6": 13, "9": 0}, # expected_ref_count + {"1": 45, "6": 7, "9": 0}, # expected_paired_count + {"1": 7, "6": 6, "9": 0}, # expected_not_paired_count ), ] -@pytest.mark.parametrize("c1_file,expected_nb_paired,expected_nb_not_paired", paired_objects_params) -def test_check_paired_objects(c1_file, expected_nb_paired, expected_nb_not_paired): +@pytest.mark.parametrize( + "c1_file,expected_ref_count,expected_paired_count,expected_not_paired_count", paired_objects_params +) +def test_check_paired_objects(c1_file, expected_ref_count, expected_paired_count, expected_not_paired_count): ref_file = Path("./data/mobj0/ref/intrinsic/tile_splitted_2818_32248.geojson") config_dict = read_config_file(CONFIG_FILE_METRICS) classes = sorted(config_dict["mobj0"]["weights"].keys()) c1_geometries = gpd.read_file(c1_file) ref_geometries = gpd.read_file(ref_file) - nb_paired, nb_not_paired = mobj0_relative.check_paired_objects(c1_geometries, ref_geometries, classes) - - assert nb_paired == expected_nb_paired - assert nb_not_paired == expected_nb_not_paired + ref_count, paired_count, not_paired_count = mobj0_relative.check_paired_objects( + c1_geometries, ref_geometries, classes + ) + assert ref_count == expected_ref_count + assert paired_count == expected_paired_count + assert not_paired_count == expected_not_paired_count def test_compute_metric_relative(): @@ -57,7 +63,7 @@ def test_compute_metric_relative(): ref_dir = Path("./data/mobj0/ref/intrinsic/") output_csv = TMP_PATH / "relative" / "result.csv" output_csv_tile = TMP_PATH / "relative" / "result_tile.csv" - expected_cols = {"class", "nb_paired", "nb_not_paired"} + expected_cols = {"class", "ref_object_count", "paired_count", "not_paired_count"} mobj0_relative.compute_metric_relative(c1_dir, ref_dir, CONFIG_FILE_METRICS, output_csv, output_csv_tile) @@ -73,16 +79,20 @@ def test_compute_metric_relative(): assert set(df.columns) == expected_cols # Check result for class 9 (0 points in c1 and ref) - nb_paired_9 = df["nb_paired"][df.index[df["class"] == 9][0]] - nb_not_paired_9 = df["nb_not_paired"][df.index[df["class"] == 9][0]] - assert nb_paired_9 == 0 - assert nb_not_paired_9 == 0 + ref_count_9 = df["ref_object_count"][df.index[df["class"] == 9][0]] + paired_count_9 = df["paired_count"][df.index[df["class"] == 9][0]] + not_paired_count_9 = df["not_paired_count"][df.index[df["class"] == 9][0]] + assert ref_count_9 == 0 + assert paired_count_9 == 0 + assert not_paired_count_9 == 0 # Check result for class 6 (several objects in c1 and ref that are not detected the same) - nb_paired_6 = df["nb_paired"][df.index[df["class"] == 6][0]] - nb_not_paired_6 = df["nb_not_paired"][df.index[df["class"] == 6][0]] - assert nb_paired_6 > 0 - assert nb_not_paired_6 > 0 + ref_count_6 = df["paired_count"][df.index[df["class"] == 6][0]] + paired_count_6 = df["paired_count"][df.index[df["class"] == 6][0]] + not_paired_count_6 = df["not_paired_count"][df.index[df["class"] == 6][0]] + assert ref_count_6 > 0 + assert paired_count_6 > 0 + assert not_paired_count_6 > 0 def test_run_main(): From 32a396144d7c744f5e1d40eaa9a763ef7b8a7045 Mon Sep 17 00:00:00 2001 From: Lea Vauchier Date: Thu, 25 Jan 2024 11:25:42 +0100 Subject: [PATCH 5/8] =?UTF-8?q?Fix=20test=5Fmobj=C3=A0=5Fintrinsic=20for?= =?UTF-8?q?=20layer=20with=20no=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- test/mobj0/test_mobj0_intrinsic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/mobj0/test_mobj0_intrinsic.py b/test/mobj0/test_mobj0_intrinsic.py index 4d6677c..fc50f6f 100644 --- a/test/mobj0/test_mobj0_intrinsic.py +++ b/test/mobj0/test_mobj0_intrinsic.py @@ -33,7 +33,7 @@ def test_compute_metric_intrinsic(ensure_test1_data): gdf = gpd.read_file(output_geojson) logging.debug(gdf.to_markdown()) assert len(gdf.index) > 0 - assert len(set(gdf["layer"])) == nb_layers + assert len(set(gdf["layer"])) == nb_layers - 1 # There should be rows for every class, except one (class 9) def test_run_main(ensure_test1_data): From 2ce592e07ce4f96d342cf15d007ed5e409429cad Mon Sep 17 00:00:00 2001 From: Lea Vauchier Date: Thu, 25 Jan 2024 15:10:17 +0100 Subject: [PATCH 6/8] mobj0: add steps articulation + update tests --- coclico/mobj0/mobj0.py | 45 ++++++++++++++- coclico/version.py | 2 +- data/csv/c1/malt0/to_ref/result.csv | 6 -- data/csv/c1/malt0/to_ref/result_tile.csv | 11 ---- data/csv/c1/mpap0/to_ref/result.csv | 2 +- data/csv/c1/mpap0/to_ref/result_tile.csv | 8 +-- data/csv/c1/mpla0/to_ref/result.csv | 5 +- data/csv/c1/mpla0/to_ref/result_tile.csv | 20 +++---- ..._test_results_by_tile_several_metrics.yaml | 55 +++++++++++++++++++ test/csv_manipulation/test_results_by_tile.py | 17 +++--- test/test_main.py | 2 +- 11 files changed, 123 insertions(+), 50 deletions(-) delete mode 100644 data/csv/c1/malt0/to_ref/result.csv delete mode 100644 data/csv/c1/malt0/to_ref/result_tile.csv create mode 100644 test/configs/config_test_results_by_tile_several_metrics.yaml diff --git a/coclico/mobj0/mobj0.py b/coclico/mobj0/mobj0.py index e5ae761..992ec0c 100644 --- a/coclico/mobj0/mobj0.py +++ b/coclico/mobj0/mobj0.py @@ -7,11 +7,14 @@ from coclico.metrics.commons import bounded_affine_function from coclico.metrics.metric import Metric +from coclico.version import __version__ class MOBJ0(Metric): """Metric MOBJ0 (for "Métrique par objet 0") - TODO Description + Comparison of detected objects (distinct blobs in the occupancy map) each class between the classification and the + reference + See doc/mobj0.md """ @@ -20,12 +23,48 @@ class MOBJ0(Metric): metric_name = "mobj0" def create_metric_intrinsic_one_job(self, name: str, input: Path, output: Path, is_ref: bool): - raise NotImplementedError + job_name = f"{self.metric_name}_intrinsic_{name}_{input.stem}" + command = f""" +docker run -t --rm --userns=host --shm-size=2gb +-v {self.store.to_unix(input)}:/input +-v {self.store.to_unix(output)}:/output +-v {self.store.to_unix(self.config_file.parent)}:/config +ignimagelidar/coclico:{__version__} +python -m coclico.mobj0.mobj0_intrinsic \ +--input-file /input \ +--output-geojson /output/{input.stem}.json \ +--config-file /config/{self.config_file.name} \ +--pixel-size {self.pixel_size} +""" + job = Job(job_name, command, tags=["docker"]) + return job def create_metric_relative_to_ref_jobs( self, name: str, out_c1: Path, out_ref: Path, output: Path, c1_jobs: List[Job], ref_jobs: List[Job] ) -> Job: - raise NotImplementedError + job_name = f"{self.metric_name}_{name}_relative_to_ref" + command = f""" +docker run -t --rm --userns=host --shm-size=2gb +-v {self.store.to_unix(out_c1)}:/input +-v {self.store.to_unix(out_ref)}:/ref +-v {self.store.to_unix(output)}:/output +-v {self.store.to_unix(self.config_file.parent)}:/config +ignimagelidar/coclico:{__version__} +python -m coclico.mobj0.mobj0_relative \ +--input-dir /input +--ref-dir /ref +--output-csv-tile /output/result_tile.csv \ +--output-csv /output/result.csv \ +--config-file /config/{self.config_file.name} +""" + + job = Job(job_name, command, tags=["docker"]) + for c1_job in c1_jobs: + job.add_dependency(c1_job) + for ref_job in ref_jobs: + job.add_dependency(ref_job) + + return [job] @staticmethod def compute_note(metric_df: pd.DataFrame, note_config: Dict): diff --git a/coclico/version.py b/coclico/version.py index f2078ea..c7fcd15 100644 --- a/coclico/version.py +++ b/coclico/version.py @@ -1,4 +1,4 @@ -__version__ = "0.5.1" +__version__ = "0.5.1.dev" if __name__ == "__main__": diff --git a/data/csv/c1/malt0/to_ref/result.csv b/data/csv/c1/malt0/to_ref/result.csv deleted file mode 100644 index 06aa689..0000000 --- a/data/csv/c1/malt0/to_ref/result.csv +++ /dev/null @@ -1,6 +0,0 @@ -class;max_diff;mean_diff;std_diff -0;;; -1;10103.619140625;23.826358435288917;490.02315580381526 -2;0.0;0.0;0.0 -3_4;0.0;0.0;0.0 -3_4_5;0.0;0.0;0.0 diff --git a/data/csv/c1/malt0/to_ref/result_tile.csv b/data/csv/c1/malt0/to_ref/result_tile.csv deleted file mode 100644 index 302b03a..0000000 --- a/data/csv/c1/malt0/to_ref/result_tile.csv +++ /dev/null @@ -1,11 +0,0 @@ -tile;class;max_diff;mean_diff;std_diff -tile_splitted_2818_32247;0;;; -tile_splitted_2818_32247;1;0.0;0.0;0.0 -tile_splitted_2818_32247;2;0.0;0.0;0.0 -tile_splitted_2818_32247;3_4;0.0;0.0;0.0 -tile_splitted_2818_32247;3_4_5;0.0;0.0;0.0 -tile_splitted_2818_32248;0;;; -tile_splitted_2818_32248;1;10103.619;32.43138355236758;571.458921784481 -tile_splitted_2818_32248;2;0.0;0.0;0.0 -tile_splitted_2818_32248;3_4;0.0;0.0;0.0 -tile_splitted_2818_32248;3_4_5;0.0;0.0;0.0 diff --git a/data/csv/c1/mpap0/to_ref/result.csv b/data/csv/c1/mpap0/to_ref/result.csv index beb4744..41d7260 100644 --- a/data/csv/c1/mpap0/to_ref/result.csv +++ b/data/csv/c1/mpap0/to_ref/result.csv @@ -1,5 +1,5 @@ class;absolute_diff;ref_count 1;13492;3851 2;115;362830 -3_4_5;964;133105 +3_4;964;133105 9;0;0 diff --git a/data/csv/c1/mpap0/to_ref/result_tile.csv b/data/csv/c1/mpap0/to_ref/result_tile.csv index 33ea5c1..00651b4 100644 --- a/data/csv/c1/mpap0/to_ref/result_tile.csv +++ b/data/csv/c1/mpap0/to_ref/result_tile.csv @@ -1,17 +1,17 @@ tile;class;absolute_diff;ref_count tile_splitted_2818_32247;1;1764;543 tile_splitted_2818_32247;2;0;103791 -tile_splitted_2818_32247;3_4_5;0;35844 +tile_splitted_2818_32247;3_4;0;35844 tile_splitted_2818_32247;9;0;0 tile_splitted_2819_32247;1;593;1227 tile_splitted_2819_32247;2;115;60309 -tile_splitted_2819_32247;3_4_5;964;26361 +tile_splitted_2819_32247;3_4;964;26361 tile_splitted_2819_32247;9;0;0 tile_splitted_2819_32248;1;4023;617 tile_splitted_2819_32248;2;0;75283 -tile_splitted_2819_32248;3_4_5;0;32623 +tile_splitted_2819_32248;3_4;0;32623 tile_splitted_2819_32248;9;0;0 tile_splitted_2818_32248;1;8298;1464 tile_splitted_2818_32248;2;0;123447 -tile_splitted_2818_32248;3_4_5;0;38277 +tile_splitted_2818_32248;3_4;0;38277 tile_splitted_2818_32248;9;0;0 diff --git a/data/csv/c1/mpla0/to_ref/result.csv b/data/csv/c1/mpla0/to_ref/result.csv index dde7699..b1a426a 100644 --- a/data/csv/c1/mpla0/to_ref/result.csv +++ b/data/csv/c1/mpla0/to_ref/result.csv @@ -1,6 +1,5 @@ class;ref_pixel_count;intersection;union -0;0.0;0;0 1;1614.0;1614;1885 2;128009.0;128009;128009 -3_4;10061.0;10061;10061 -3_4_5;41999.0;41999;42004 +5;31938.0;31938;31938 +9;0;0;0 diff --git a/data/csv/c1/mpla0/to_ref/result_tile.csv b/data/csv/c1/mpla0/to_ref/result_tile.csv index 7d7ed29..ac3bcfb 100644 --- a/data/csv/c1/mpla0/to_ref/result_tile.csv +++ b/data/csv/c1/mpla0/to_ref/result_tile.csv @@ -1,21 +1,17 @@ tile;class;ref_pixel_count;intersection;union -tile_splitted_2818_32247;0;0;0;0 tile_splitted_2818_32247;1;225;225;225 tile_splitted_2818_32247;2;36173;36173;36173 -tile_splitted_2818_32247;3_4;2353;2353;2353 -tile_splitted_2818_32247;3_4_5;10692;10692;10692 -tile_splitted_2819_32247;0;0;0;0 +tile_splitted_2818_32247;5;8339;8339;8339 +tile_splitted_2818_32247;9;0;0;0 tile_splitted_2819_32247;1;491;491;491 tile_splitted_2819_32247;2;21780;21780;21780 -tile_splitted_2819_32247;3_4;2026;2026;2026 -tile_splitted_2819_32247;3_4_5;8101;8101;8106 -tile_splitted_2819_32248;0;0;0;0 +tile_splitted_2819_32247;5;6075;6075;6075 +tile_splitted_2819_32247;9;0;0;0 tile_splitted_2819_32248;1;275;275;423 tile_splitted_2819_32248;2;27093;27093;27093 -tile_splitted_2819_32248;3_4;2898;2898;2898 -tile_splitted_2819_32248;3_4_5;10546;10546;10546 -tile_splitted_2818_32248;0;0;0;0 +tile_splitted_2819_32248;5;7648;7648;7648 +tile_splitted_2819_32248;9;0;0;0 tile_splitted_2818_32248;1;623;623;746 tile_splitted_2818_32248;2;42963;42963;42963 -tile_splitted_2818_32248;3_4;2784;2784;2784 -tile_splitted_2818_32248;3_4_5;12660;12660;12660 +tile_splitted_2818_32248;5;9876;9876;9876 +tile_splitted_2818_32248;9;0;0;0 diff --git a/test/configs/config_test_results_by_tile_several_metrics.yaml b/test/configs/config_test_results_by_tile_several_metrics.yaml new file mode 100644 index 0000000..2f6fb53 --- /dev/null +++ b/test/configs/config_test_results_by_tile_several_metrics.yaml @@ -0,0 +1,55 @@ +# Config used to test the behavior of each of the metrics +mpap0: + weights: + "1": 1 + "2": 2 # simple classes + "3_4": 2 # composed class with spaces + "9": 2 # no points + notes: + ref_count_threshold: 1000 + under_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 20 # absolute diff + note: 1 + # tuple for the first point with the highest metric value + max_point: + metric: 100 # absolute diff + note: 0 + above_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0 # relative diff + note: 1 + # tuple for the first point with the highest metric value + max_point: + metric: 0.1 # relative diff + note: 0 + +mpla0: + weights: + "1": 1 + "2": 0 # simple classes + "5": 1 + "9": 2 # no points + notes: + ref_pixel_count_threshold: 1000 + under_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 20 # union - intersection + note: 1 + # tuple for the first point with the highest metric value + max_point: + metric: 100 # union - intersection + note: 0 + above_threshold: + # tuple for the first point with the lowest metric value + min_point: + metric: 0.9 # IoU + note: 0 + # tuple for the first point with the highest metric value + max_point: + metric: 1 # IoU + note: 1 + diff --git a/test/csv_manipulation/test_results_by_tile.py b/test/csv_manipulation/test_results_by_tile.py index 2c08027..379b601 100644 --- a/test/csv_manipulation/test_results_by_tile.py +++ b/test/csv_manipulation/test_results_by_tile.py @@ -32,8 +32,8 @@ def setup_module(): def test_merge_results_for_one_classif(): - CONFIG_FILE = Path("./test/configs/config_test_results_by_tile_1_metric.yaml") - config_dict = io.read_config_file(CONFIG_FILE) + config_file = Path("./test/configs/config_test_results_by_tile_1_metric.yaml") + config_dict = io.read_config_file(config_file) metrics = list(config_dict.keys()) classes = list(config_dict["mpap0"]["weights"].keys()) @@ -41,7 +41,7 @@ def test_merge_results_for_one_classif(): out = base_path / "result.csv" out_tile = base_path / "result_tile.csv" - coclico.csv_manipulation.results_by_tile.merge_results_for_one_classif(DATA_PATH, out, CONFIG_FILE) + coclico.csv_manipulation.results_by_tile.merge_results_for_one_classif(DATA_PATH, out, config_file) df = basic_check_on_df(out) assert set(df.columns) == set(["class"] + metrics) @@ -58,18 +58,19 @@ def test_merge_results_for_one_classif(): def test_merge_results_for_one_classif_on_different_classes(): """Check that the result file is created correctly when classes are not the same for all metrics""" - CONFIG_FILE = Path("./test/configs/config_test_metrics.yaml") - config_dict = io.read_config_file(CONFIG_FILE) + config_file = Path("./test/configs/config_test_results_by_tile_several_metrics.yaml") + config_dict = io.read_config_file(config_file) metrics = list(config_dict.keys()) classes = set([cl for metric_dict in config_dict.values() for cl in metric_dict["weights"].keys()]) base_path = TMP_PATH / Path("results_by_tile_on_different_classes") out = base_path / "result.csv" out_tile = base_path / "result_tile.csv" - coclico.csv_manipulation.results_by_tile.merge_results_for_one_classif(DATA_PATH, out, CONFIG_FILE) + coclico.csv_manipulation.results_by_tile.merge_results_for_one_classif(DATA_PATH, out, config_file) assert out.is_file() df = pd.read_csv(out, dtype={"class": str}, sep=csv_separator) + print(df.to_markdown()) assert set(df.columns) == set(["class"] + metrics) assert len(df.index) == len(classes) assert all([not df[m].isnull().values.all() for m in metrics]) # check that no metric is completely empty @@ -96,14 +97,14 @@ def test_run_main(): def test_create_job_merge_results(): - CONFIG_FILE = Path("./test/configs/config_test_compute_weighted_result.yaml") + config_file = Path("./test/configs/config_test_compute_weighted_result.yaml") out = Path("local_store/out") metrics_root_folder = Path("local_store/input") store = Store("local_store", "win_store", "unix_store") job = coclico.csv_manipulation.results_by_tile.create_job_merge_results( - metrics_root_folder, out, store, CONFIG_FILE + metrics_root_folder, out, store, config_file ) job_json = json.loads(job.to_json()) # return a string assert job_json["name"].startswith("merge_notes") # check that it is running the right method diff --git a/test/test_main.py b/test/test_main.py index 1cbdca3..3652fed 100644 --- a/test/test_main.py +++ b/test/test_main.py @@ -142,7 +142,7 @@ def test_compare_test1_default(ensure_test1_data, use_gpao_server): c1_all_metrics = out / "niv1" / "niv1_result.csv" assert tu.csv_num_rows(c1_all_metrics) == 8 # 8 classes in total (1, 2, "3_4_5", "4_5", 6, 9, 17, 64) - assert tu.csv_num_col(c1_all_metrics) == 4 # class, mpap0, mpla0, malt0 + assert tu.csv_num_col(c1_all_metrics) == 5 # class, mpap0, mpla0, malt0, mobj0 all_scores = out / "result.csv" assert tu.csv_num_rows(all_scores) == 2 # niv1, niv4 From 9f589321d1adb5348ebc4623a4315cfa66909a37 Mon Sep 17 00:00:00 2001 From: Lea Vauchier Date: Thu, 25 Jan 2024 15:52:34 +0100 Subject: [PATCH 7/8] mobj0: add documentation --- coclico/mobj0/mobj0_relative.py | 13 +-- configs/metrics_config.yaml | 4 +- doc/mobj0.md | 123 ++++++++++++++++++++++++++ doc/mpla0.md | 4 +- test/configs/config_test_metrics.yaml | 4 +- 5 files changed, 136 insertions(+), 12 deletions(-) create mode 100644 doc/mobj0.md diff --git a/coclico/mobj0/mobj0_relative.py b/coclico/mobj0/mobj0_relative.py index da0a05d..fa2aacc 100644 --- a/coclico/mobj0/mobj0_relative.py +++ b/coclico/mobj0/mobj0_relative.py @@ -18,17 +18,18 @@ def check_paired_objects( """Pair objects from 2 geodataframes (ie. 2 lists of geometries) Pairing is made based on geometries intersections: - the first step is to find all geometries in ref that have at least an intersection with a - geometry in c1 (using a spatial join). The sjoin will contain several occurences of ref geometries - if they one line per - - the second one is to remove pairs geometries match with several geometries in the other dataframe: + geometry in c1 (using a spatial join). The sjoin output will contain several occurences of ref geometries + that intersect several c1 geometries + - the second one is to remove duplicates pairs (geometries in one dataframe that match with several geometries + in the other dataframe): - remove pairs where a geometry in c1 is associated with multiple ref polygons first (making sure that we keep the geometries in ref that have a single match in c1) - then remove pairs where a geometry in ref is still associated with multiple c1 polygons (making sure that we - keep the geometries in ref that have a single match in c1) + keep the geometries in c1 that have a single match in ref) Args: - c1_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from the reference - ref_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from c1 + c1_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from c1 + ref_geometries (gpd.GeoDataFrame): geopandas dataframe with geometries from the reference classes (List): oredered list of clsses (to match "layer" values with classes in the output) Returns: diff --git a/configs/metrics_config.yaml b/configs/metrics_config.yaml index c5dca50..cf20ebc 100644 --- a/configs/metrics_config.yaml +++ b/configs/metrics_config.yaml @@ -116,11 +116,11 @@ mobj0: above_threshold: # tuple for the first point with the lowest metric value min_point: - metric: 0.8 # count of paired objects / counts of caired objects + 'not paired' objects + metric: 0.8 # count of paired objects / counts of paired objects + 'not paired' objects note: 0 # tuple for the first point with the highest metric value max_point: - metric: 1 # count of paired objects / counts of caired objects + 'not paired' objects + metric: 1 # count of paired objects / counts of paired objects + 'not paired' objects note: 1 diff --git a/doc/mobj0.md b/doc/mobj0.md new file mode 100644 index 0000000..d76b745 --- /dev/null +++ b/doc/mobj0.md @@ -0,0 +1,123 @@ +# MOBJ0 + +## Description : + +### Nom complet : + +Métrique sur les objets 0 + +### But : + +Pour les classes représentant des objets "finis" (bâtiments, ponts, sursol perenne, ...), comparer les objets représentés/détectés pour chaque classe entre une classification de référence et un classification à comparer. + +Cette comparaison est faite à l'aide de cartes de classes / cartes d'occupation post-traitée, et de détection de contours sur cette carte. + +### Métrique intrinsèque (calculée indépendemment pour le nuage de référence et le nuage classé à comparer): + +- Calcul de la carte de classe binaire (`occupancy_map`) pour chaque classe dans le nuage (voir métrique MPLA0). + +- Opérations topologiques pour simplifier les formes des objets détectés et se débarrasser du bruit au niveau des limites d'objets. +**A COMPLETER !** + +- Vectorisation des contours de la carte débruitée + +Résultat : pour chaque nuage, un fichier geojson contenant un polygone par objet, qui a un attribut "layer" +correspondant à l'indice de la classe correspondante dans la liste (ordonnée alphabétiquement) des classes définies +dans le fichier de configuration. + +### Métrique relative (calculée à partir des fichiers intermédiaires en sortie de métrique intrinsèque) + +Pour chaque classe, appairage des polygones entre le geojson issu de la référence et celui issu du nuage +à comparer. + +L'appairage se fait de la façon suivante : +- parmi les polygones de la référence, trouver les polygones du nuage à comparer avec lesquels il y a une intersection +- parmi les paires ainsi trouvées, ne garder qu'une paire par polygone de la référence (en supprimant d'abord les +paires dont la géométrie dans le nuage à comparer est appairée à plusieurs polygones de la référence) +- parmi les paires ainsi restantes, ne garder qu'une paire par polygone du nuage à comparer (en supprimant d'abord les +paires dont la géométrie dans la référence est appairée à plusieurs polygones du nuage à comparer) + +Ainsi il ne devrait rester que des paires où un seul polygone de la référence correspond à un polygone du nuage à +comparer. + +Les valeurs de sortie (dans un fichier csv) sont pour chaque classe : +- `ref_object_count`: nombre total d'objets dans le nuage de référence +- `paired_count`: nombre de paires trouvées +- `not_paired_count`: nombre d'objets dans la référence et le nuage à comparer qui n'appartiennent pas à une paire + +## Note + +On utilise comme intermédiaire de calcul la variable `metric` qui dépend du nombre d'objets détectés dans le nuage de référence (`ref_object_count`) : +- si `ref_object_count` est en dessous d'un certain seuil (`ref_object_count_threshold`), la note est calculée à partir du nombre d'objets non appairés `not_paired_count` +- sinon elle est calculée à partir du ratio liant le nombre de paires trouvées et le nombre +d'objets non appairés (`paired_count` / (`paired_count` + `not_paired_count`)) +Dans chacun des cas, la note finale (`note`) est dérivée de `metric` à partir d'une fonction affine bornée. C'est-à-dire une fonction du type : + +``` + max ________ + / + / + / + _____ min +``` + +ou +``` +______min + \ + \ + \ + max _______ +``` + +En dessous d'une valeur `min` de `metric` et au dessus d'une valeur `max` de `metric`, la note vaut 0 ou 1, entre les 2 on utilise une fonction +affine pour calculer la valeur de `note`. + + + +## Paramétrage + +#### Poids des classes dans les métriques : +Dans le fichier de configuration.yaml, il faut indiquer les poids de chacune des classes dans le calcul des métriques comme ci dessous : +```yaml +mobj0: # Nom de la métrique + weights: + "2": 28 # La classe 2 a un poids de 28 + "4_5": 16 # La classe composée des classes 4 et 5 a un poids de 16 +``` + +#### Calcul de la note et paramétrage de la fonction + +Dans le fichier de configuration.yaml, il faut indiquer les paramètres de calcul de la note comme ci-dessous : + + +```yaml + notes: + # Seuil (nombre d'objets dans la référence) qui fait basculer la variable `metric` entre les 2 méthodes de calcul + ref_object_count_threshold: 20 + under_threshold: + # Définition de la fonction affine bornée lorsque le nombre de pixels pour la classe donnée est EN DESSOUS de`ref_object_count_threshold` + # On utilise alors directement `not_paired_count` comme valeur de `metric` + min_point: + # Coordonnées du point correspondant à la borne min (valeur de `metric` en dessous de laquelle `note` vaut toujours la valeur précisée ici) + # Dans l'exemple, si `not_paired_count` vaut 0, la note est à 1 + metric: 0 + note: 1 + # Coordonnées du point correspondant à la borne max (valeur de `metric` au dessus de laquelle `note` vaut toujours la valeur précisée ici) + # Dans l'exemple, si `not_paired_count` est supérieur à 4, la note est à 0 + max_point: + metric: 4 + note: 0 + # Définition de la fonction affine bornée lorsque le nombre de'objets dans la référence pour la classe donnée est AU DESSUS de`ref_object_count_threshold` + # On utilise alors `ratio` = `(paired_count / (paired_count + not_paired_count))` comme valeur de `metric` + above_threshold: + # Coordonnées du point correspondant à la borne min (valeur de `metric` en dessous de laquelle `note` vaut toujours la valeur précisée ici) + # Dans l'exemple, si `ratio` est inférieur à 0.8, la note est à 0 + min_point: + metric: 0.8 + # Coordonnées du point correspondant à la borne max (valeur de `metric` au dessus de laquelle `note` vaut toujours la valeur précisée ici) + # Dans l'exemple, si `ratio` vaut 1, la note est à 1 + max_point: + metric: 1 + note: 1 +``` diff --git a/doc/mpla0.md b/doc/mpla0.md index 4b360cc..1f7e11a 100644 --- a/doc/mpla0.md +++ b/doc/mpla0.md @@ -89,7 +89,7 @@ Dans le fichier de configuration.yaml, il faut indiquer les paramètres de calcu # Coordonnées du point correspondant à la borne max (valeur de `metric` au dessus de laquelle `note` vaut toujours la valeur précisée ici) # Dans l'exemple, si `unioon - intersection` est supérieur à 100, la note est à 0 max_point: - metric: 100 # union - intersection + metric: 100 note: 0 # Définition de la fonction affine bornée lorsque le nombre de pixels référence pour la classe donnée est AU DESSUS de`ref_pixel_count_threshold` # On utilise alors `IoU` = `intersection / union` comme valeur de `metric` @@ -100,7 +100,7 @@ Dans le fichier de configuration.yaml, il faut indiquer les paramètres de calcu metric: 0.9 note: 0 # Coordonnées du point correspondant à la borne max (valeur de `metric` au dessus de laquelle `note` vaut toujours la valeur précisée ici) - # Dans l'exemple, si `IoU` est supérieur à 1, la note est à 1 + # Dans l'exemple, si `IoU` vaut 1, la note est à 1 max_point: metric: 1 note: 1 diff --git a/test/configs/config_test_metrics.yaml b/test/configs/config_test_metrics.yaml index ef4a22d..ecc5af2 100644 --- a/test/configs/config_test_metrics.yaml +++ b/test/configs/config_test_metrics.yaml @@ -119,10 +119,10 @@ mobj0: above_threshold: # tuple for the first point with the lowest metric value min_point: - metric: 0.8 # count of paired objects / counts of caired objects + 'not paired' objects + metric: 0.8 # count of paired objects / counts of paired objects + 'not paired' objects note: 0 # tuple for the first point with the highest metric value max_point: - metric: 1 # count of paired objects / counts of caired objects + 'not paired' objects + metric: 1 # count of paired objects / counts of paired objects + 'not paired' objects note: 1 From 195234671e7c4c9616d8fe489a25bdbc85983103 Mon Sep 17 00:00:00 2001 From: Yoann Apel Date: Tue, 30 Jan 2024 11:40:43 +0100 Subject: [PATCH 8/8] mobj0: compute intrinsic morphological operations --- CHANGELOG.md | 3 ++ coclico/mobj0/mobj0.py | 9 ++-- coclico/mobj0/mobj0_intrinsic.py | 48 ++++++++++++++++-- .../object_map/tile_splitted_2818_32247.tif | Bin 0 -> 125948 bytes doc/mobj0.md | 7 ++- environment.yml | 3 +- test/mobj0/test_mobj0_intrinsic.py | 35 +++++++++++-- 7 files changed, 89 insertions(+), 16 deletions(-) create mode 100644 data/mobj0/object_map/tile_splitted_2818_32247.tif diff --git a/CHANGELOG.md b/CHANGELOG.md index d9f533c..5fae05f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,6 @@ +# Dev +- ajout d'une 4e métrique : MOBJ0 + # 0.5.1 - correctif MALT0 quand il n'y a pas de points dans la référence (+ test sur les autres métriques) diff --git a/coclico/mobj0/mobj0.py b/coclico/mobj0/mobj0.py index 992ec0c..1671f86 100644 --- a/coclico/mobj0/mobj0.py +++ b/coclico/mobj0/mobj0.py @@ -18,9 +18,10 @@ class MOBJ0(Metric): See doc/mobj0.md """ - # Pixel size for occupancy map - pixel_size = 0.5 metric_name = "mobj0" + pixel_size = 0.5 # Pixel size for occupancy map + kernel = 3 # parameter for morphological operations on rasters + tolerance_shp = 0.05 # parameter for simplification on geometries of the shapefile def create_metric_intrinsic_one_job(self, name: str, input: Path, output: Path, is_ref: bool): job_name = f"{self.metric_name}_intrinsic_{name}_{input.stem}" @@ -34,7 +35,9 @@ def create_metric_intrinsic_one_job(self, name: str, input: Path, output: Path, --input-file /input \ --output-geojson /output/{input.stem}.json \ --config-file /config/{self.config_file.name} \ ---pixel-size {self.pixel_size} +--pixel-size {self.pixel_size} \ +--kernel {self.kernel} \ +--tolerance-shp {self.tolerance_shp} """ job = Job(job_name, command, tags=["docker"]) return job diff --git a/coclico/mobj0/mobj0_intrinsic.py b/coclico/mobj0/mobj0_intrinsic.py index 6a91a4e..bebfd65 100644 --- a/coclico/mobj0/mobj0_intrinsic.py +++ b/coclico/mobj0/mobj0_intrinsic.py @@ -2,6 +2,7 @@ import logging from pathlib import Path +import cv2 import geopandas as gpd import numpy as np import pandas as pd @@ -17,15 +18,18 @@ gdal.UseExceptions() -def create_objects_array(las_file, pixel_size, class_weights): +def create_objects_array(las_file: Path, pixel_size: float, class_weights: dict, kernel: int): xs, ys, classifs, crs = read_las(las_file) binary_maps, x_min, y_max = create_occupancy_map_array(xs, ys, classifs, pixel_size, class_weights) + object_maps = np.zeros_like(binary_maps) + for index in range(len(class_weights)): + object_maps[index, :, :] = operate_morphology_transformations(binary_maps[index, :, :], kernel) - return binary_maps, crs, x_min, y_max + return object_maps, crs, x_min, y_max -def vectorize_occupancy_map(binary_maps, crs, x_min, y_max, pixel_size): +def vectorize_occupancy_map(binary_maps: np.ndarray, crs: str, x_min: float, y_max: float, pixel_size: float): # Create empty dataframe gdf_list = [] @@ -53,12 +57,44 @@ def vectorize_occupancy_map(binary_maps, crs, x_min, y_max, pixel_size): return gdf -def compute_metric_intrinsic(las_file: Path, config_file: Path, output_geojson: Path, pixel_size: float = 0.5): +def operate_morphology_transformations(obj_array: np.ndarray, kernel: int): + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel, kernel)) + + img = cv2.morphologyEx(obj_array, cv2.MORPH_CLOSE, kernel) + img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) + + return img + + +def compute_metric_intrinsic( + las_file: Path, + config_file: Path, + output_geojson: Path, + pixel_size: float = 0.5, + kernel: int = 3, + tolerance_shp: float = 0.05, +): + """ + Create a shapefile with geometries for all objects in each class contained in the config file: + - first by creating an occupancy map raster transformed by morphological operations + - then by vectorizing and simplifying all the rasters by classes into one final geojson + + final output_geojson file is saved with one layer per class (the classes are sorted alphabetically). + + Args: + las_file (Path): path to the las file on which to generate malt0 intrinsic metric + config_file (Path): path to the config file (to know for which classes to generate the rasters) + output_geojson (Path): path to output shapefile with geometries of objects + pixel_size (float, optional): size of the occupancy map rasters pixels. Defaults to 0.5. + kernel (int, optional): size of the convolution matrix for morphological operations. Defaults to 3. + tolerance_shp (float, optional): parameter for simplification of the shapefile geometries. Defaults to 0.05 + """ config_dict = coclico.io.read_config_file(config_file) class_weights = config_dict[MOBJ0.metric_name]["weights"] output_geojson.parent.mkdir(parents=True, exist_ok=True) - obj_array, crs, x_min, y_max = create_objects_array(las_file, pixel_size, class_weights) + obj_array, crs, x_min, y_max = create_objects_array(las_file, pixel_size, class_weights, kernel) polygons_gdf = vectorize_occupancy_map(obj_array, crs, x_min, y_max, pixel_size) + polygons_gdf.simplify(tolerance=tolerance_shp, preserve_topology=False) polygons_gdf.to_file(output_geojson) @@ -76,6 +112,8 @@ def parse_args(): parser.add_argument( "-p", "--pixel-size", type=float, required=True, help="Pixel size of the intermediate occupancy map" ) + parser.add_argument("-k", "--kernel", type=int, required=True, help="Path to the output geojson") + parser.add_argument("-t", "--tolerance-shp", type=float, required=True, help="Path to the output geojson") return parser.parse_args() diff --git a/data/mobj0/object_map/tile_splitted_2818_32247.tif b/data/mobj0/object_map/tile_splitted_2818_32247.tif new file mode 100644 index 0000000000000000000000000000000000000000..f2ef77137b886a76a1e0f6687c9a1b1a063ae38d GIT binary patch literal 125948 zcmeI)y^h^P7zW@uSrkPgpimGbghXg4C@6r23Q?v*(4(~$4&9L@%ZblS5sdPZ{Lr*zr7Q} z&+o;`KRASUJ~@Pszd3~O9vs3izaGNh_d|I7vk>n65W?4wLU{Ch2!H+)!kw27;lp1o9FPBgR%Zi${rso5Uwc2U-TL(Vd&i%9J|f_g_%)q$+0&=r-_9KhEDP1> zRTCvg?=!Sv;{9#gr@&BbPMjabJn{bVatDDSfz#8~y~EJ6s`>8zWi#ogSlzk6vQWjV zrXPzRk-l(x0e0?LU{+Q~q0*T=eRZ^eY&V?OIomeNE9~^pj^1H&XswPmSHFT=Z}177 zZo+w}_e~V9$fm&1t32;~^ND$m)BWaIv#JsT=@)wXYU0tEERFV1k$f{%IdP`=5tlL$9(Ek$#jAAI~`q} z{-A7kCVI@aW`(5%67O_$DShXytPZugmHO4$6&QM_Q|7yO+vrTQ*^RcK?3O_Do%W97 z{@(dGSZ!!wYPS)A>;#(0Jr>tIPmHtcLqWS1 zsG?dEG?ROrY@Vmb^(_nttSZpVXv+5*jl4nDiNDKQ?r-U5NAEl}k&_0&X?n@g!%SR;^`Kr_0D zab{I3hd@dok>q+Qb-gtLSqjwY?Hy!UvZ4r#3$*e)et{hX2oNAZfB*pk1PBlyK!5-N z0t5(bT%i7^f*bEc`Fj_rliV8Y-9`Uf6KDmv_jGGLsdsk*y}34f^p>l@^n5PZnQPPPDqLVeRwrIk zIGpw}7MK{(^tO!qQ`$}i(uwU|-KmtewMg!CHKzrdsg7d@fieWDFpG2`CQ z!0!Y{o7oDCCUbN{wyh~_p91NR;(eOvdeZ_Tz3JCCvH0q@+FkvIyQ^j=#-OBa^#@lq zD5*8Q>{*}=_MQ>+PJjRb0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N z0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+ z009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBly kK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfIubiFIiqGH2?qr literal 0 HcmV?d00001 diff --git a/doc/mobj0.md b/doc/mobj0.md index d76b745..49d0f10 100644 --- a/doc/mobj0.md +++ b/doc/mobj0.md @@ -16,10 +16,9 @@ Cette comparaison est faite à l'aide de cartes de classes / cartes d'occupation - Calcul de la carte de classe binaire (`occupancy_map`) pour chaque classe dans le nuage (voir métrique MPLA0). -- Opérations topologiques pour simplifier les formes des objets détectés et se débarrasser du bruit au niveau des limites d'objets. -**A COMPLETER !** - -- Vectorisation des contours de la carte débruitée +- Opérations topologiques pour simplifier les formes des objets détectés et se débarrasser du bruit au niveau des limites d'objets. Une fermeture et une ouverture sont réalisées sur les rasters. + +- Vectorisation des contours de la carte débruitée et une simplification des formes pour éliminer encore du bruit sur les géométries. Résultat : pour chaque nuage, un fichier geojson contenant un polygone par objet, qui a un attribut "layer" correspondant à l'indice de la classe correspondante dans la liste (ordonnée alphabétiquement) des classes définies diff --git a/environment.yml b/environment.yml index f5c723f..f363f02 100644 --- a/environment.yml +++ b/environment.yml @@ -25,4 +25,5 @@ dependencies: - ign-gpao-utils==0.2.2 - ign-gpao-project-builder==0.10.0 - ign-gpao-client==0.18.0 - - ign-pdal-tools==1.3.0 \ No newline at end of file + - ign-pdal-tools==1.3.0 + - opencv-python-headless diff --git a/test/mobj0/test_mobj0_intrinsic.py b/test/mobj0/test_mobj0_intrinsic.py index fc50f6f..a009091 100644 --- a/test/mobj0/test_mobj0_intrinsic.py +++ b/test/mobj0/test_mobj0_intrinsic.py @@ -4,7 +4,9 @@ from pathlib import Path import geopandas as gpd +import numpy as np import pytest +import rasterio import coclico.io from coclico.mobj0 import mobj0_intrinsic @@ -20,14 +22,37 @@ def setup_module(module): shutil.rmtree(TMP_PATH) +def test_create_object_array(): + las_file = Path("./data/test1/niv1/tile_splitted_2818_32247.laz") + pixel_size = 0.5 + kernel = 3 + expected_tif = Path("./data/mobj0/object_map/tile_splitted_2818_32247.tif") + config = coclico.io.read_config_file(CONFIG_FILE_METRICS) + class_weights = config["mobj0"]["weights"] + obj_array, crs, x_min, y_max = mobj0_intrinsic.create_objects_array(las_file, pixel_size, class_weights, kernel) + with rasterio.Env(): + with rasterio.open(expected_tif) as f: + expected_raster = f.read() + assert np.all(obj_array == expected_raster) + + def test_compute_metric_intrinsic(ensure_test1_data): las_file = Path("./data/test1/niv1/tile_splitted_2818_32247.laz") pixel_size = 0.5 - output_geojson = TMP_PATH / "intrinsic" / "unit_test_mpla0_intrinsic.geojson" + kernel = 3 + tolerance_shp = 0.05 + output_geojson = TMP_PATH / "intrinsic" / "unit_test_mobj0_intrinsic.geojson" config = coclico.io.read_config_file(CONFIG_FILE_METRICS) nb_layers = len(config["mobj0"]["weights"]) - mobj0_intrinsic.compute_metric_intrinsic(las_file, CONFIG_FILE_METRICS, output_geojson, pixel_size=pixel_size) + mobj0_intrinsic.compute_metric_intrinsic( + las_file, + CONFIG_FILE_METRICS, + output_geojson, + pixel_size=pixel_size, + kernel=kernel, + tolerance_shp=tolerance_shp, + ) assert output_geojson.exists() gdf = gpd.read_file(output_geojson) @@ -38,6 +63,8 @@ def test_compute_metric_intrinsic(ensure_test1_data): def test_run_main(ensure_test1_data): pixel_size = 0.5 + kernel = 3 + tolerance_shp = 0.05 input_file = Path("./data/test1/niv1/tile_splitted_2818_32247.laz") output_geojson = TMP_PATH / "intrinsic" / "tile_splitted_2818_32247.geojson" @@ -45,7 +72,9 @@ def test_run_main(ensure_test1_data): --input-file {input_file} \ --output-geojson {output_geojson} \ --config-file {CONFIG_FILE_METRICS} \ - --pixel-size {pixel_size} + --pixel-size {pixel_size} \ + --kernel {kernel} \ + --tolerance-shp {tolerance_shp} """ sp.run(cmd, shell=True, check=True)