├── .gitignore ├── .pre-commit-config.yaml ├── LICENSE ├── Makefile ├── README.md ├── detectree_examples ├── __init__.py ├── get_tiles_to_download.py ├── make_confusion_df.py ├── make_response_tiles.py ├── make_tiles.py ├── plot_utils.py └── settings.py ├── environment.yml ├── notebooks ├── aussersihl-canopy.ipynb ├── background.ipynb ├── baseline.ipynb ├── cluster-I.ipynb ├── cluster-II.ipynb ├── matplotlibrc └── pre-trained-model.ipynb └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *.cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | 59 | # DotEnv configuration 60 | .env 61 | 62 | # Database 63 | *.db 64 | *.rdb 65 | 66 | # Pycharm 67 | .idea 68 | 69 | # VS Code 70 | .vscode/ 71 | 72 | # Spyder 73 | .spyproject/ 74 | 75 | # Jupyter NB Checkpoints 76 | .ipynb_checkpoints/ 77 | 78 | # exclude data from source control by default 79 | /data/ 80 | 81 | # Mac OS-specific storage files 82 | .DS_Store 83 | 84 | # vim 85 | *.swp 86 | *.swo 87 | 88 | # Mypy cache 89 | .mypy_cache/ 90 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | default_stages: [pre-commit] 2 | 3 | repos: 4 | - repo: https://github.com/pre-commit/pre-commit-hooks 5 | rev: v5.0.0 6 | hooks: 7 | - id: trailing-whitespace 8 | - id: end-of-file-fixer 9 | - id: check-yaml 10 | 11 | - repo: https://github.com/commitizen-tools/commitizen 12 | rev: v4.1.0 13 | hooks: 14 | - id: commitizen 15 | 16 | - repo: https://github.com/adrienverge/yamllint.git 17 | rev: v1.35.1 18 | hooks: 19 | - id: yamllint 20 | args: ["-d relaxed"] 21 | 22 | - repo: https://github.com/executablebooks/mdformat 23 | rev: 0.7.21 # Use the ref you want to point at 24 | hooks: 25 | - id: mdformat 26 | # Optionally add plugins 27 | additional_dependencies: 28 | - mdformat-gfm 29 | - mdformat-black 30 | 31 | - repo: https://github.com/pappasam/toml-sort 32 | rev: v0.24.2 33 | hooks: 34 | - id: toml-sort-fix 35 | args: ["--in-place"] 36 | 37 | - repo: https://github.com/asottile/pyupgrade 38 | rev: v3.19.1 39 | hooks: 40 | - id: pyupgrade 41 | 42 | - repo: https://github.com/kynan/nbstripout 43 | rev: 0.8.1 44 | hooks: 45 | - id: nbstripout 46 | args: ["--keep-output"] 47 | 48 | - repo: https://github.com/nbQA-dev/nbQA 49 | rev: 1.9.1 50 | hooks: 51 | - id: nbqa-ruff-check 52 | args: ["--fix", "--ignore=D,I"] 53 | - id: nbqa-ruff-format 54 | - id: nbqa-ruff-check 55 | args: ["--ignore=D,I"] 56 | 57 | - repo: https://github.com/charliermarsh/ruff-pre-commit 58 | rev: v0.9.2 59 | hooks: 60 | - id: ruff 61 | args: [--fix] 62 | - id: ruff-format 63 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: tiles train_test_split download_lidar_shp response_tiles train_models \ 2 | predict_tiles aussersihl_tiles execute_notebooks 3 | 4 | ################################################################################# 5 | 6 | # globals 7 | 8 | ## variables 9 | CODE_DIR = detectree_examples 10 | 11 | DATA_DIR = data 12 | DATA_RAW_DIR := $(DATA_DIR)/raw 13 | DATA_INTERIM_DIR := $(DATA_DIR)/interim 14 | DATA_PROCESSED_DIR := $(DATA_DIR)/processed 15 | 16 | ## rules 17 | define MAKE_DATA_SUB_DIR 18 | $(DATA_SUB_DIR): | $(DATA_DIR) 19 | mkdir $$@ 20 | endef 21 | $(DATA_DIR): 22 | mkdir $@ 23 | $(foreach DATA_SUB_DIR, \ 24 | $(DATA_RAW_DIR) $(DATA_INTERIM_DIR) $(DATA_PROCESSED_DIR), \ 25 | $(eval $(MAKE_DATA_SUB_DIR))) 26 | 27 | # main workflow 28 | ## tiles 29 | 30 | ### variables 31 | #### Zurich orthofoto https://www.geolion.zh.ch/geodatensatz/2831 32 | ORTHOIMG_URI_BASE = \ 33 | https://maps.zh.ch/download/orthofoto/sommer/2014/rgb/jpeg/ortho_sommer14 34 | ORTHOIMG_SHP_EXTENSIONS = dbf prj shx 35 | ORTHOIMG_SHP_DIR := $(DATA_RAW_DIR)/orthoimg 36 | ORTHOIMG_SHP_BASEPATH := $(ORTHOIMG_SHP_DIR)/ortho_sommer14 37 | ORTHOIMG_SHP_OTHERS := $(foreach EXT, $(ORTHOIMG_SHP_EXTENSIONS), \ 38 | $(ORTHOIMG_SHP_BASEPATH).$(EXT)) 39 | ORTHOIMG_SHP := $(ORTHOIMG_SHP_BASEPATH).shp 40 | 41 | #### Download the required tiles of the orthoimage 42 | TILES_DIR = $(DATA_INTERIM_DIR)/tiles 43 | INTERSECTING_TILES_CSV := $(TILES_DIR)/intersecting-tiles.csv 44 | DOWNSAMPLED_TILES_CSV := $(TILES_DIR)/downsampled-tiles.csv 45 | NOMINATIM_QUERY = Zurich # municipal boundaries 46 | 47 | #### code 48 | GET_TILES_TO_DOWNLOAD_PY = $(CODE_DIR)/get_tiles_to_download.py 49 | MAKE_TILES_PY = $(CODE_DIR)/make_tiles.py 50 | 51 | ### rules 52 | $(ORTHOIMG_SHP_DIR): | $(DATA_RAW_DIR) 53 | mkdir $@ 54 | define DOWNLOAD_ORTHOIMG_SHP 55 | $(ORTHOIMG_SHP_OTHER): | $(ORTHOIMG_SHP_DIR) 56 | wget $(ORTHOIMG_URI_BASE)$$(suffix $$@) -O $$@ 57 | endef 58 | $(foreach ORTHOIMG_SHP_OTHER, $(ORTHOIMG_SHP_OTHERS), \ 59 | $(eval $(DOWNLOAD_ORTHOIMG_SHP))) 60 | $(ORTHOIMG_SHP): $(ORTHOIMG_SHP_OTHERS) 61 | wget $(ORTHOIMG_URI_BASE)$(suffix $@) -O $@ 62 | 63 | $(TILES_DIR): | $(DATA_INTERIM_DIR) 64 | mkdir $@ 65 | $(INTERSECTING_TILES_CSV): $(GET_TILES_TO_DOWNLOAD_PY) $(ORTHOIMG_SHP) \ 66 | | $(TILES_DIR) 67 | python $(GET_TILES_TO_DOWNLOAD_PY) $(ORTHOIMG_SHP) $(NOMINATIM_QUERY) $@ 68 | $(DOWNSAMPLED_TILES_CSV): $(INTERSECTING_TILES_CSV) $(MAKE_TILES_PY) \ 69 | | $(TILES_DIR) 70 | python $(MAKE_TILES_PY) $< $(TILES_DIR) $@ \ 71 | --nominatim-query $(NOMINATIM_QUERY) 72 | tiles: $(DOWNSAMPLED_TILES_CSV) 73 | 74 | ## train/test split 75 | 76 | ### variables 77 | TRAIN_TEST_SPLIT_CSV := $(TILES_DIR)/split.csv 78 | NUM_TILE_CLUSTERS = 4 79 | 80 | ### rules 81 | $(TRAIN_TEST_SPLIT_CSV): $(DOWNSAMPLED_TILES_CSV) 82 | detectree train-test-split --img-dir $(TILES_DIR) \ 83 | --output-filepath $(TRAIN_TEST_SPLIT_CSV) \ 84 | --num-img-clusters $(NUM_TILE_CLUSTERS) 85 | train_test_split: $(TRAIN_TEST_SPLIT_CSV) 86 | 87 | 88 | ## response tiles 89 | 90 | ### variables 91 | #### Zurich lidar https://www.geolion.zh.ch/geodatensatz/2606 92 | LIDAR_URI_BASE = https://maps.zh.ch/download/hoehen/2014/lidar/lidar2014 93 | LIDAR_SHP_EXTENSIONS = dbf prj qix shx 94 | LIDAR_SHP_DIR = $(DATA_RAW_DIR)/lidar 95 | LIDAR_SHP_BASEPATH := $(LIDAR_SHP_DIR)/lidar2014 96 | LIDAR_SHP_OTHERS := $(foreach EXT, $(LIDAR_SHP_EXTENSIONS), \ 97 | $(LIDAR_SHP_BASEPATH).$(EXT)) 98 | LIDAR_SHP := $(LIDAR_SHP_BASEPATH).shp 99 | 100 | RESPONSE_TILES_DIR := $(DATA_INTERIM_DIR)/response-tiles 101 | MAKE_RESPONSE_TILES_PY = $(CODE_DIR)/make_response_tiles.py 102 | RESPONSE_TILES_CSV := $(RESPONSE_TILES_DIR)/response-tiles.csv 103 | 104 | ### rules 105 | $(LIDAR_SHP_DIR): | $(DATA_RAW_DIR) 106 | mkdir $@ 107 | define DOWNLOAD_LIDAR_SHP 108 | $(LIDAR_SHP_OTHER): | $(LIDAR_SHP_DIR) 109 | wget $(LIDAR_URI_BASE)$$(suffix $$@) -O $$@ 110 | touch $$@ 111 | endef 112 | $(foreach LIDAR_SHP_OTHER, $(LIDAR_SHP_OTHERS), \ 113 | $(eval $(DOWNLOAD_LIDAR_SHP))) 114 | $(LIDAR_SHP): $(LIDAR_SHP_OTHERS) 115 | wget $(LIDAR_URI_BASE)$(suffix $@) -O $@ 116 | touch $@ 117 | download_lidar_shp: $(LIDAR_SHP) 118 | 119 | $(RESPONSE_TILES_DIR): | $(DATA_INTERIM_DIR) 120 | mkdir $@ 121 | $(RESPONSE_TILES_CSV): $(TRAIN_TEST_SPLIT_CSV) $(LIDAR_SHP) \ 122 | $(MAKE_RESPONSE_TILES_PY) | $(RESPONSE_TILES_DIR) 123 | python $(MAKE_RESPONSE_TILES_PY) $(TRAIN_TEST_SPLIT_CSV) $(LIDAR_SHP) \ 124 | $(TILES_DIR) $(RESPONSE_TILES_DIR) $@ 125 | response_tiles: $(RESPONSE_TILES_CSV) 126 | 127 | 128 | ## train models 129 | 130 | ### variables 131 | MODELS_DIR = models 132 | MODEL_JOBLIB_FILEPATHS := $(foreach CLUSTER_LABEL, \ 133 | $(shell seq 0 $$(($(NUM_TILE_CLUSTERS)-1))), \ 134 | $(MODELS_DIR)/$(CLUSTER_LABEL).joblib) 135 | 136 | ### rules 137 | $(MODELS_DIR): 138 | mkdir $@ 139 | 140 | $(MODELS_DIR)/%.joblib: $(RESPONSE_TILES_CSV) $(TRAIN_TEST_SPLIT_CSV) \ 141 | | $(MODELS_DIR) 142 | detectree train-classifier --split-filepath $(TRAIN_TEST_SPLIT_CSV) \ 143 | --img-dir $(TILES_DIR) --response-img-dir $(RESPONSE_TILES_DIR) \ 144 | --img-cluster $* --output-filepath $@ 145 | train_models: $(MODEL_JOBLIB_FILEPATHS) 146 | 147 | 148 | ## predict 149 | 150 | ### variables 151 | predict_tiles: $(MODEL_JOBLIB_FILEPATHS) $(TRAIN_TEST_SPLIT_CSV) \ 152 | | $(DATA_PROCESSED_DIR) 153 | detectree predict-imgs $(TRAIN_TEST_SPLIT_CSV) $(TILES_DIR) \ 154 | --clf-dir $(MODELS_DIR) --output-dir $(DATA_PROCESSED_DIR) 155 | 156 | # Aussersihl 157 | ## make a LULC raster for Zurich's Aussersihl 158 | ### variables 159 | AUSSERSIHL_NOMINATIM_QUERY = "Zurich Aussersihl" 160 | AUSSERSIHL_TILES_DIR := $(DATA_INTERIM_DIR)/aussersihl-tiles 161 | AUSSERSIHL_INTERSECTING_TILES_CSV = $(AUSSERSIHL_TILES_DIR)/intersecting-tiles.csv 162 | AUSSERSIHL_TILES_CSV := $(AUSSERSIHL_TILES_DIR)/tiles.csv 163 | #### code 164 | MAKE_LULC_RASTER_PY := $(CODE_DIR)/make_lulc_raster.py 165 | 166 | ### rules 167 | $(AUSSERSIHL_TILES_DIR): | $(DATA_INTERIM_DIR) 168 | mkdir $@ 169 | $(AUSSERSIHL_INTERSECTING_TILES_CSV): $(GET_TILES_TO_DOWNLOAD_PY) $(ORTHOIMG_SHP) \ 170 | | $(AUSSERSIHL_TILES_DIR) 171 | python $(GET_TILES_TO_DOWNLOAD_PY) $(ORTHOIMG_SHP) \ 172 | $(AUSSERSIHL_NOMINATIM_QUERY) $@ --op intersects 173 | $(AUSSERSIHL_TILES_CSV): $(AUSSERSIHL_INTERSECTING_TILES_CSV) $(MAKE_TILES_PY) \ 174 | | $(AUSSERSIHL_TILES_DIR) 175 | python $(MAKE_TILES_PY) $< $(AUSSERSIHL_TILES_DIR) $@ \ 176 | --nominatim-query $(AUSSERSIHL_NOMINATIM_QUERY) \ 177 | --exclude-nominatim-query "Lake Zurich" --keep-raw 178 | aussersihl_tiles: $(AUSSERSIHL_TILES_CSV) 179 | 180 | 181 | # notebooks (for CI) 182 | 183 | NOTEBOOKS_DIR = notebooks 184 | OUTPUT_DIR := $(NOTEBOOKS_DIR)/output 185 | 186 | $(OUTPUT_DIR): 187 | mkdir $(OUTPUT_DIR) 188 | 189 | execute_notebooks: | $(OUTPUT_DIR) 190 | cd $(NOTEBOOKS_DIR) 191 | jupyter nbconvert --ExecutePreprocessor.timeout=5000 --to notebook \ 192 | --execute *.ipynb --output-dir $(notdir $(OUTPUT_DIR)) 193 | rm -rf $(OUTPUT_DIR) 194 | 195 | ################################################################################# 196 | 197 | .DEFAULT_GOAL := help 198 | 199 | # Inspired by 200 | # sed script explained: 201 | # /^##/: 202 | # * save line in hold space 203 | # * purge line 204 | # * Loop: 205 | # * append newline + line to hold space 206 | # * go to next line 207 | # * if line starts with doc comment, strip comment character off and loop 208 | # * remove target prerequisites 209 | # * append hold space (+ newline) to line 210 | # * replace newline plus comments by `---` 211 | # * print line 212 | # Separate expressions are necessary because labels cannot be delimited by 213 | # semicolon; see 214 | .PHONY: help 215 | help: 216 | @echo "$$(tput bold)Available rules:$$(tput sgr0)" 217 | @echo 218 | @sed -n -e "/^## / { \ 219 | h; \ 220 | s/.*//; \ 221 | :doc" \ 222 | -e "H; \ 223 | n; \ 224 | s/^## //; \ 225 | t doc" \ 226 | -e "s/:.*//; \ 227 | G; \ 228 | s/\\n## /---/; \ 229 | s/\\n/ /g; \ 230 | p; \ 231 | }" ${MAKEFILE_LIST} \ 232 | | LC_ALL='C' sort --ignore-case \ 233 | | awk -F '---' \ 234 | -v ncol=$$(tput cols) \ 235 | -v indent=19 \ 236 | -v col_on="$$(tput setaf 6)" \ 237 | -v col_off="$$(tput sgr0)" \ 238 | '{ \ 239 | printf "%s%*s%s ", col_on, -indent, $$1, col_off; \ 240 | n = split($$2, words, " "); \ 241 | line_length = ncol - indent; \ 242 | for (i = 1; i <= n; i++) { \ 243 | line_length -= length(words[i]) + 1; \ 244 | if (line_length <= 0) { \ 245 | line_length = ncol - indent - length(words[i]) - 1; \ 246 | printf "\n%*s ", -indent, " "; \ 247 | } \ 248 | printf "%s ", words[i]; \ 249 | } \ 250 | printf "\n"; \ 251 | }' \ 252 | | more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars') 253 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![GitHub license](https://img.shields.io/github/license/martibosch/detectree-examples.svg)](https://github.com/martibosch/detectree-examples/blob/main/LICENSE) 2 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/martibosch/detectree-examples/main?filepath=notebooks) 3 | [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/martibosch/detectree-examples/main.svg)](https://results.pre-commit.ci/latest/github/martibosch/detectree-examples/main) 4 | 5 | # DetecTree example 6 | 7 | Example computational workflows to classify tree/non-tree pixels in Zurich using [DetecTree](https://github.com/martibosch/detectree). 8 | 9 | ## Citation 10 | 11 | Bosch M. 2020. “DetecTree: Tree detection from aerial imagery in Python”. *Journal of Open Source Software, 5(50), 2172.* [doi.org/10.21105/joss.02172](https://doi.org/10.21105/joss.02172) 12 | 13 | ## Notebooks 14 | 15 | The notebooks are stored in the [`notebooks` folder](https://github.com/martibosch/detectree-examples/blob/main/notebooks). If you have trouble reproducing them, see the "Instructions to reproduce" section below. 16 | 17 | ### Pre-trained model 18 | 19 | - [Pre-trained model](https://github.com/martibosch/detectree-examples/blob/main/notebooks/pre-trained-model.ipynb): examples of using the pre-trained model to detect trees in aerial imagery from different sources. 20 | 21 | ### Training 22 | 23 | Example use case: 24 | 25 | - [Aussersihl canopy](https://github.com/martibosch/detectree-examples/blob/main/notebooks/aussersihl-canopy.ipynb): application of DetecTree to compute a tree canopy map for the Aussersihl district in Zurich. 26 | 27 | Overview of the train/test split methods: 28 | 29 | - [Baseline](https://github.com/martibosch/detectree-examples/blob/main/notebooks/baseline.ipynb): train/test split of image tiles based on uniform sampling. 30 | - [Cluster-I](https://github.com/martibosch/detectree-examples/blob/main/notebooks/cluster-I.ipynb): train/test split of image tiles based on *k*-means clustering of image descriptors to enhance the variety of scenes represented in the training tiles. 31 | - [Cluster-II](https://github.com/martibosch/detectree-examples/blob/main/notebooks/cluster-II.ipynb): train/test split of image tiles based on a two-level *k*-means clustering, using a **separate classifier** for each first-level cluster of tiles. The second-level clustering enhances the variety of scenes represented in the training tiles of each separate classifier. 32 | 33 | ### Background 34 | 35 | - [Background](https://github.com/martibosch/detectree-examples/blob/main/notebooks/background.ipynb): overview of the methods used to detect tree/non-tree pixels, based on Yang et al. [1] 36 | 37 | ## Instructions to reproduce 38 | 39 | The materials of this repository make use of a set of external libraries, which are listed in the [environment.yml](https://github.com/martibosch/detectree-examples/blob/main/environment.yml) file. The easiest way to install such dependencies is by means of a [conda](https://docs.conda.io/en/latest/) environment: 40 | 41 | 1. Clone the repository and change directory to the repository's root: 42 | 43 | ```bash 44 | git clone https://github.com/martibosch/detectree-examples 45 | cd detectree-examples 46 | ``` 47 | 48 | 2. Create the environment (this requires conda/mamba) and activate it: 49 | 50 | ```bash 51 | # you can also use mamba 52 | conda env create -f environment.yml 53 | # the above command creates a conda environment named `detectree` 54 | conda activate detectree 55 | ``` 56 | 57 | 3. Register the IPython kernel of the `detectree` environment 58 | 59 | ```bash 60 | python -m ipykernel install --user --name detectree --display-name "Python (detectree)" 61 | ``` 62 | 63 | You might now run a jupyter server (e.g., running the command `jupyter notebook`) and execute the notebooks of this repository. 64 | 65 | ## Makefile workflow 66 | 67 | Some of the tasks of DetecTree's computational flow (e.g., computing image descriptors, training the classifiers...) can be computationally expensive. While Jupyter notebooks are a great medium to overview DetecTree's features, they are less convenient when it comes to managing complex computational workflows. In view of such shortcoming, this repository also includes a [Makefile](https://github.com/martibosch/detectree-examples/blob/main/Makefile) implementation of the computational workflow, which ensures the correct execution of the workflow and caches expensive intermediate results so that the workflow can be efficiently resumed at any point. 68 | 69 | ## Acknowledgments 70 | 71 | - With the support of the École Polytechnique Fédérale de Lausanne (EPFL) 72 | - The [aerial imagery](https://www.geolion.zh.ch/geodatensatz/2831) and [LIDAR](https://www.geolion.zh.ch/geodatensatz/show?gdsid=343) datasets used in this repository are provided by the [Office for spatial development (Amt für Raumentwicklung)](https://are.zh.ch/) of the canton of Zurich. 73 | - Project based on the [cookiecutter data science project template](https://drivendata.github.io/cookiecutter-data-science). #cookiecutterdatascience 74 | 75 | ## References 76 | 77 | 1. Yang, L., Wu, X., Praun, E., & Ma, X. (2009). Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 131-137). ACM. 78 | -------------------------------------------------------------------------------- /detectree_examples/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/martibosch/detectree-examples/4eec72053eb0c2ab9eab43a9252754ed25857ad9/detectree_examples/__init__.py -------------------------------------------------------------------------------- /detectree_examples/get_tiles_to_download.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | import click 4 | import geopandas as gpd 5 | import osmnx as ox 6 | 7 | 8 | @click.command() 9 | @click.argument("tiles_shp_filepath", type=click.Path(exists=True)) 10 | @click.argument("nominatim_query") 11 | @click.argument("output_filepath", type=click.Path()) 12 | @click.option("--op", default="within") 13 | def main(tiles_shp_filepath, nominatim_query, output_filepath, op): 14 | """Get tiles to download.""" 15 | logger = logging.getLogger(__name__) 16 | 17 | tiles_gdf = gpd.read_file(tiles_shp_filepath) 18 | # get boundary 19 | logger.info("Querying Nominatim for boundaries for `%s`", nominatim_query) 20 | geom = ox.geocode_to_gdf(nominatim_query)["geometry"].to_crs(tiles_gdf.crs).iloc[0] 21 | 22 | # get the filename of the tiles whose geometry is within with the 23 | # municipal boundaries 24 | op_method = getattr(tiles_gdf["geometry"], op) 25 | tile_filename_ser = tiles_gdf[op_method(geom)]["location"] 26 | logger.info("Found %d intersecting tiles", len(tile_filename_ser)) 27 | tile_filename_ser.to_csv(output_filepath, header=False) 28 | logger.info("Dumped list of intersecting tiles to %s", output_filepath) 29 | 30 | 31 | if __name__ == "__main__": 32 | log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" 33 | logging.basicConfig(level=logging.INFO, format=log_fmt) 34 | 35 | main() 36 | -------------------------------------------------------------------------------- /detectree_examples/make_confusion_df.py: -------------------------------------------------------------------------------- 1 | import random 2 | from os import path 3 | 4 | import dask 5 | import detectree as dtr 6 | import numpy as np 7 | import pandas as pd 8 | import rasterio as rio 9 | from dask import diagnostics 10 | 11 | from detectree_examples import make_response_tiles 12 | 13 | 14 | def _inner_loop(img_filepath, lidar_gdf, lidar_raw_dir, response_dir, c, clf): 15 | pred_arr = c._predict_img(img_filepath, clf) 16 | response_tile_filepath = make_response_tiles.make_response_tile( 17 | img_filepath, lidar_gdf, lidar_raw_dir, response_dir 18 | ) 19 | with rio.open(response_tile_filepath) as src: 20 | return np.array((src.read(1).flatten(), pred_arr.flatten())) 21 | 22 | 23 | def _get_validation_df(split_df, n, frac): 24 | return split_df[~split_df["train"]].sample(n=n, frac=frac) 25 | 26 | 27 | def make_confusion_df( 28 | lidar_gdf, 29 | lidar_raw_dir, 30 | response_dir, 31 | *, 32 | split_df=None, 33 | img_filepaths=None, 34 | img_dir=None, 35 | n=None, 36 | frac=0.05, 37 | clf=None, 38 | clf_dict=None, 39 | ): 40 | """Make confusion data frame.""" 41 | # this is not how the detectree v0.5.0 is supposed to work but there is no need to 42 | # change it here. 43 | # TODO: add detectree method to compute confusion matrix (e.g., return list of image 44 | # arrays in `predict_imgs`) 45 | c = dtr.Classifier(clf=clf, clf_dict=clf_dict) 46 | truth_pred_lazy = [] 47 | if clf is not None: 48 | if split_df is None: 49 | num_validation_tiles = int(frac * len(img_filepaths)) 50 | test_filepaths = random.choices(img_filepaths, k=num_validation_tiles) 51 | else: 52 | test_filepaths = _get_validation_df(split_df, n, frac)[ 53 | "img_filename" 54 | ].apply(lambda img_filename: path.join(img_dir, img_filename)) 55 | for img_filepath in test_filepaths: 56 | truth_pred_lazy.append( 57 | dask.delayed(_inner_loop)( 58 | img_filepath, 59 | lidar_gdf, 60 | lidar_raw_dir, 61 | response_dir, 62 | c, 63 | clf, 64 | ) 65 | ) 66 | else: 67 | validation_df = _get_validation_df(split_df, n, frac) 68 | for img_cluster, cluster_df in validation_df.groupby("img_cluster"): 69 | clf = clf_dict[img_cluster] 70 | for img_filepath in cluster_df["img_filename"].apply( 71 | lambda img_filename: path.join(img_dir, img_filename) 72 | ): 73 | truth_pred_lazy.append( 74 | dask.delayed(_inner_loop)( 75 | img_filepath, lidar_gdf, lidar_raw_dir, response_dir, c, clf 76 | ) 77 | ) 78 | 79 | with diagnostics.ProgressBar(): 80 | truth_pred = np.hstack(dask.compute(*truth_pred_lazy)) 81 | 82 | truth_ser = pd.Series(truth_pred[0], name="actual") 83 | pred_ser = pd.Series(truth_pred[1], name="predicted") 84 | return pd.crosstab(truth_ser, pred_ser) / len(truth_ser) 85 | 86 | 87 | def compute_metrics(confusion_df): 88 | """Compute accuracy, precision, recall and f1 from a confusion matrix.""" 89 | accuracy = np.trace(confusion_df) 90 | tp = confusion_df.loc[255, 255] 91 | fp = confusion_df.loc[0, 255] 92 | fn = confusion_df.loc[255, 0] 93 | precision = tp / (tp + fp) 94 | recall = tp / (tp + fn) 95 | f1 = 2 * (precision * recall) / (precision + recall) 96 | return accuracy, precision, recall, f1 97 | -------------------------------------------------------------------------------- /detectree_examples/make_response_tiles.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | from os import path 4 | from urllib import request 5 | 6 | import click 7 | import geopandas as gpd 8 | import laspy 9 | import numpy as np 10 | import pandas as pd 11 | import rasterio as rio 12 | from rasterio import enums, features 13 | from scipy import ndimage as ndi 14 | from shapely import geometry 15 | 16 | BASE_URI = "https://maps.zh.ch/download/hoehen/2014/lidar/" 17 | 18 | 19 | def get_from_cache_or_download(lidar_tile_filename, cache_dir, bounds, *, logger=None): 20 | """Get from cache or download.""" 21 | local_tile_filepath = path.join(cache_dir, lidar_tile_filename) 22 | if not path.exists(local_tile_filepath): 23 | lidar_uri = BASE_URI + lidar_tile_filename 24 | if logger is not None: 25 | logger.info( 26 | "Downloading LIDAR data from %s to %s", lidar_uri, local_tile_filepath 27 | ) 28 | request.urlretrieve(lidar_uri, local_tile_filepath) 29 | 30 | las = laspy.read(local_tile_filepath) 31 | c = np.array(las.classification) 32 | x = np.array(las.x) 33 | y = np.array(las.y) 34 | 35 | cond = ((c == 4) ^ (c == 5)) & ( 36 | (x >= bounds.left) 37 | & (x <= bounds.right) 38 | & (y >= bounds.bottom) 39 | & (y <= bounds.top) 40 | ) 41 | return pd.DataFrame({"class_val": c[cond], "x": x[cond], "y": y[cond]}) 42 | 43 | 44 | def make_response_tile( 45 | tile_filepath, 46 | lidar_gdf, 47 | raw_dir, 48 | response_dir, 49 | *, 50 | output_dtype=rio.uint8, 51 | output_tree_val=255, 52 | output_nodata=0, 53 | high_veg_val=5, 54 | logger=None, 55 | ): 56 | """Make response tile.""" 57 | # first check if the response tile has already been computed 58 | response_tile_filepath = path.join(response_dir, path.basename(tile_filepath)) 59 | if path.exists(response_tile_filepath): 60 | if logger is not None: 61 | logger.info("Response tile already exists at %s", response_tile_filepath) 62 | return response_tile_filepath 63 | 64 | # if not, compute it 65 | with rio.open(tile_filepath) as src: 66 | bounds = src.bounds 67 | lidar_tile_filenames = lidar_gdf[ 68 | lidar_gdf["geometry"].intersects(geometry.box(*bounds)) 69 | ]["dateiname"] 70 | 71 | dfs = [] 72 | for lidar_tile_filename in lidar_tile_filenames: 73 | dfs.append( 74 | get_from_cache_or_download( 75 | lidar_tile_filename, raw_dir, bounds, logger=logger 76 | ) 77 | ) 78 | df = pd.concat(dfs) 79 | gser = gpd.GeoSeries([geometry.Point(x, y) for x, y in zip(df["x"], df["y"])]) 80 | arr = features.rasterize( 81 | shapes=[ 82 | (geom, class_val) for geom, class_val in zip(gser, df["class_val"]) 83 | ], 84 | out_shape=src.shape, 85 | transform=src.transform, 86 | merge_alg=enums.MergeAlg("ADD"), 87 | ) 88 | 89 | output_arr = ( 90 | ndi.binary_opening(arr >= high_veg_val).astype(output_dtype) 91 | * output_tree_val 92 | ) 93 | meta = src.meta.copy() 94 | 95 | meta.update(dtype=output_dtype, count=1, nodata=output_nodata) 96 | 97 | with rio.open(response_tile_filepath, "w", **meta) as dst: 98 | dst.write(output_arr, 1) 99 | if logger is not None: 100 | logger.info("Dumped response tile to %s", response_tile_filepath) 101 | return response_tile_filepath 102 | 103 | 104 | def make_response_tiles( 105 | split_df, lidar_gdf, img_dir, raw_dir, response_dir, *, logger=None 106 | ): 107 | """Make response tiles.""" 108 | tile_filepaths = split_df[split_df["train"]]["img_filename"].apply( 109 | lambda img_filename: path.join(img_dir, img_filename) 110 | ) 111 | 112 | response_tile_filepaths = [] 113 | for tile_filepath in tile_filepaths: 114 | response_tile_filepath = make_response_tile( 115 | tile_filepath, lidar_gdf, raw_dir, response_dir, logger=logger 116 | ) 117 | response_tile_filepaths.append(response_tile_filepath) 118 | 119 | return response_tile_filepaths 120 | 121 | 122 | @click.command() 123 | @click.argument("split_csv_filepath", type=click.Path(exists=True)) 124 | @click.argument("lidar_shp_filepath", type=click.Path(exists=True)) 125 | @click.argument("img_dir", type=click.Path(exists=True)) 126 | @click.argument("response_dir", type=click.Path(exists=True)) 127 | @click.argument("output_filepath", type=click.Path()) 128 | @click.option("--high-veg-val", type=int, default=5) 129 | @click.option("--output-tree-val", type=int, default=255) 130 | @click.option("--output-nodata", type=int, default=0) 131 | @click.option("--raw-dir", type=click.Path(exists=True), required=False) 132 | def main( 133 | split_csv_filepath, 134 | lidar_shp_filepath, 135 | img_dir, 136 | response_dir, 137 | output_filepath, 138 | high_veg_val, 139 | output_tree_val, 140 | output_nodata, 141 | raw_dir, 142 | ): 143 | """Make response tiles.""" 144 | logger = logging.getLogger(__name__) 145 | 146 | if raw_dir is None: 147 | raw_dir = "data/raw/lidar" 148 | if not path.exists(raw_dir): 149 | os.mkdir(raw_dir) 150 | 151 | split_df = pd.read_csv(split_csv_filepath) 152 | tile_filepaths = split_df[split_df["train"]]["img_filename"].apply( 153 | lambda img_filename: path.join(img_dir, img_filename) 154 | ) 155 | 156 | lidar_gdf = gpd.read_file(lidar_shp_filepath) 157 | response_tile_filepaths = [] 158 | for tile_filepath in tile_filepaths: 159 | response_tile_filepath = make_response_tile( 160 | tile_filepath, 161 | lidar_gdf, 162 | raw_dir, 163 | response_dir, 164 | output_tree_val=output_tree_val, 165 | output_nodata=output_nodata, 166 | high_veg_val=high_veg_val, 167 | logger=logger, 168 | ) 169 | response_tile_filepaths.append(response_tile_filepath) 170 | 171 | # if not keep_raw: 172 | # shutil.rmtree(raw_dir) 173 | 174 | pd.Series(response_tile_filepaths).to_csv(output_filepath, header=False) 175 | logger.info("Dumped list of response tiles to %s", output_filepath) 176 | 177 | 178 | if __name__ == "__main__": 179 | log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" 180 | logging.basicConfig(level=logging.INFO, format=log_fmt) 181 | 182 | main() 183 | -------------------------------------------------------------------------------- /detectree_examples/make_tiles.py: -------------------------------------------------------------------------------- 1 | import itertools 2 | import logging 3 | import os 4 | from os import path 5 | from urllib import request 6 | 7 | import affine 8 | import click 9 | import geopandas as gpd 10 | import osmnx as ox 11 | import pandas as pd 12 | import rasterio as rio 13 | import tqdm 14 | from rasterio import windows 15 | from rasterio.enums import Resampling 16 | from shapely import geometry 17 | 18 | from detectree_examples import settings 19 | 20 | BASE_URI = "https://maps.zh.ch/download/orthofoto/sommer/2014/rgb/jpeg/" 21 | RESAMPLE_FACTOR = 5 22 | NUM_TILE_SUBDIVISIONS = 5 23 | 24 | 25 | def _get_window_transform(width, height, transform, num_tile_subdivisions): 26 | dst_width = width // num_tile_subdivisions 27 | dst_height = height // num_tile_subdivisions 28 | offsets = itertools.product( 29 | range(0, width, dst_width), range(0, height, dst_height) 30 | ) 31 | big_window = windows.Window(col_off=0, row_off=0, width=width, height=height) 32 | for col_off, row_off in offsets: 33 | dst_window = windows.Window( 34 | col_off=col_off, row_off=row_off, width=dst_width, height=dst_height 35 | ).intersection(big_window) 36 | dst_transform = windows.transform(dst_window, transform) 37 | yield dst_window, dst_transform 38 | 39 | 40 | def _get_output_tile_filepath(tiles_dir, tile_basename, tile_i, tile_ext): 41 | return path.join(tiles_dir, f"{tile_basename}_{tile_i:02}{tile_ext}") 42 | 43 | 44 | @click.command() 45 | @click.argument("intersecting_tiles_csv_filepath", type=click.Path(exists=True)) 46 | @click.argument("tiles_dir", type=click.Path(exists=True)) 47 | @click.argument("output_filepath", type=click.Path()) 48 | @click.option("--resample-factor", type=int, required=False) 49 | @click.option("--keep-raw", is_flag=True) 50 | @click.option("--raw-dir", type=click.Path(exists=True), required=False) 51 | @click.option("--nominatim-query", required=False) 52 | @click.option("--exclude-nominatim-query", required=False) 53 | @click.option("--crs", required=False) 54 | def main( 55 | intersecting_tiles_csv_filepath, 56 | tiles_dir, 57 | output_filepath, 58 | resample_factor, 59 | keep_raw, 60 | raw_dir, 61 | nominatim_query, 62 | exclude_nominatim_query, 63 | crs, 64 | ): 65 | """Make tiles.""" 66 | logger = logging.getLogger(__name__) 67 | 68 | if resample_factor is None: 69 | resample_factor = RESAMPLE_FACTOR 70 | 71 | if raw_dir is None: 72 | raw_dir = "data/raw/tiles" 73 | if not path.exists(raw_dir): 74 | os.mkdir(raw_dir) 75 | 76 | tile_filenames = pd.read_csv( 77 | intersecting_tiles_csv_filepath, index_col=0, header=None 78 | ).iloc[:, 0] 79 | output_tiles = [] 80 | for tile_filename in tqdm.tqdm(tile_filenames): 81 | raw_tile_filepath = path.join(raw_dir, tile_filename) 82 | tile_basename, tile_ext = path.splitext(tile_filename) 83 | if not path.exists(raw_tile_filepath): 84 | request.urlretrieve(BASE_URI + tile_filename, raw_tile_filepath) 85 | with rio.open(raw_tile_filepath) as src: 86 | interim_width = src.width // resample_factor 87 | interim_height = src.height // resample_factor 88 | data = src.read( 89 | out_shape=(src.count, interim_height, interim_width), 90 | resampling=Resampling.average, 91 | ) 92 | t = src.transform 93 | interim_transform = affine.Affine( 94 | t.a * resample_factor, t.b, t.c, t.d, t.e * resample_factor, t.f 95 | ) 96 | for i, (dst_window, dst_transform) in enumerate( 97 | _get_window_transform( 98 | interim_width, 99 | interim_height, 100 | interim_transform, 101 | NUM_TILE_SUBDIVISIONS, 102 | ) 103 | ): 104 | profile = src.profile.copy() 105 | profile.update( 106 | width=dst_window.width, 107 | height=dst_window.height, 108 | transform=dst_transform, 109 | crs=settings.CRS, 110 | ) 111 | row_off, col_off = dst_window.row_off, dst_window.col_off 112 | tile_filepath = _get_output_tile_filepath( 113 | tiles_dir, tile_basename, i, tile_ext 114 | ) 115 | with rio.open(tile_filepath, "w", **profile) as dst: 116 | for channel in range(src.count): 117 | dst.write( 118 | data[channel][ 119 | row_off : row_off + dst_window.height, 120 | col_off : col_off + dst_window.width, 121 | ], 122 | channel + 1, 123 | ) 124 | 125 | output_tiles.append(tile_filepath) 126 | 127 | # if raw tiles are not to be preserved, remove them at the end of each 128 | # iteration (with original 10cm resolution, they can take a lot of 129 | # local storage) 130 | if not keep_raw: 131 | os.remove(raw_tile_filepath) 132 | 133 | # if raw tiles are not to be preserved, remove the folder at the end 134 | # if not keep_raw: 135 | # shutil.rmtree(raw_dir) 136 | 137 | if nominatim_query: 138 | # get only the tiles that intersect the extent of the result of the 139 | # nominatim query 140 | logger.info("Querying Nominatim for boundaries for `%s`", nominatim_query) 141 | gser = ox.geocode_to_gdf(nominatim_query)["geometry"] 142 | if crs: 143 | pass 144 | else: 145 | crs = settings.CRS 146 | geom = gser.to_crs(crs).iloc[0] 147 | if exclude_nominatim_query: 148 | logger.info( 149 | "Querying Nominatim for boundaries for `%s`", exclude_nominatim_query 150 | ) 151 | exclude_geom = ( 152 | ox.geocode_to_gdf(exclude_nominatim_query)["geometry"] 153 | .to_crs(crs) 154 | .iloc[0] 155 | ) 156 | geom = geom.difference(exclude_geom) 157 | 158 | def bbox_geom_from_tile(tile_filepath): 159 | with rio.open(tile_filepath) as src: 160 | return geometry.box(*src.bounds) 161 | 162 | tiles_gdf = gpd.GeoDataFrame( 163 | output_tiles, 164 | columns=["img_filepath"], 165 | geometry=list(map(bbox_geom_from_tile, output_tiles)), 166 | crs=crs, 167 | ) 168 | output_tiles_ser = gpd.sjoin( 169 | tiles_gdf, 170 | gpd.GeoDataFrame(geometry=[geom], crs=crs), 171 | op="intersects", 172 | how="inner", 173 | )["img_filepath"] 174 | 175 | tiles_to_rm_ser = tiles_gdf["img_filepath"].loc[ 176 | ~tiles_gdf.index.isin(output_tiles_ser.index) 177 | ] 178 | for img_filepath in tiles_to_rm_ser: 179 | os.remove(img_filepath) 180 | logger.info( 181 | "removed %d tiles that do not intersect with the extent of %s", 182 | len(tiles_to_rm_ser), 183 | nominatim_query, 184 | ) 185 | else: 186 | # just create a pandas series anyway to use the `to_csv` method below 187 | output_tiles_ser = pd.Series(output_tiles) 188 | 189 | # logger.info("Successfully dumped downscaled tiles to %s", tiles_dir) 190 | output_tiles_ser.to_csv(output_filepath, header=False) 191 | logger.info("Dumped list of downscaled tiles to %s", output_filepath) 192 | 193 | 194 | if __name__ == "__main__": 195 | log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" 196 | logging.basicConfig(level=logging.INFO, format=log_fmt) 197 | 198 | main() 199 | -------------------------------------------------------------------------------- /detectree_examples/plot_utils.py: -------------------------------------------------------------------------------- 1 | import contextily as ctx 2 | import geopandas as gpd 3 | import matplotlib.pyplot as plt 4 | import numpy as np 5 | from matplotlib import colors 6 | from rasterio import plot, transform, warp 7 | from shapely import geometry 8 | 9 | from detectree_examples import settings 10 | 11 | WEB_MERCATOR_CRS = ( 12 | "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0" 13 | " +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs" 14 | ) 15 | 16 | 17 | def _reproject_raster( 18 | src_crs, 19 | src_arr, 20 | src_bounds, 21 | src_transform, 22 | *, 23 | dst_crs=WEB_MERCATOR_CRS, 24 | dst_nodata=0, 25 | ): 26 | dst_transform, dst_width, dst_height = warp.calculate_default_transform( 27 | src_crs, dst_crs, src_arr.shape[1], src_arr.shape[0], *src_bounds 28 | ) 29 | 30 | # use a float dtype so that the array can have nan values, which will be 31 | # transparent in the plot 32 | dst_arr = np.full((dst_height, dst_width), dst_nodata, dtype=src_arr.dtype) 33 | _ = warp.reproject( 34 | src_arr, 35 | dst_arr, 36 | src_transform=src_transform, 37 | src_crs=src_crs, 38 | dst_transform=dst_transform, 39 | dst_crs=dst_crs, 40 | dst_nodata=dst_nodata, 41 | ) 42 | # dst_arr[dst_arr == 0] = np.nan 43 | return dst_arr, dst_transform 44 | 45 | 46 | def plot_canopy( 47 | canopy_arr, canopy_transform, *, canopy_crs=None, num_steps=10, **subplots_kws 48 | ): 49 | """Plot canopy.""" 50 | if canopy_crs is None: 51 | canopy_crs = settings.CRS 52 | 53 | # reproject the canopy array 54 | height, width = canopy_arr.shape[-2:] 55 | canopy_arr, canopy_transform = _reproject_raster( 56 | canopy_crs, 57 | canopy_arr, 58 | transform.array_bounds(height, width, canopy_transform), 59 | canopy_transform, 60 | dst_nodata=0, 61 | ) 62 | 63 | # prepare the plot 64 | fig, ax = plt.subplots(**subplots_kws) 65 | 66 | # use geopandas plotting (with alpha 0) just to set the extent 67 | gpd.GeoSeries( 68 | [geometry.box(*transform.array_bounds(*canopy_arr.shape, canopy_transform))] 69 | ).plot(alpha=0, ax=ax) 70 | ctx.add_basemap(ax) 71 | 72 | # prepare a colormap for the trees 73 | tree_cmap = np.stack([[0, 0.5, 0, 0] for _ in range(num_steps)]) 74 | # set alpha 75 | tree_cmap[:, -1] = np.linspace(0, 1, num_steps) 76 | # create new colormap 77 | tree_cmap = colors.ListedColormap(tree_cmap) 78 | 79 | # plot 80 | # if len(canopy_arr.shape) == 3: 81 | # canopy_arr = canopy_arr 82 | plot.show(canopy_arr, transform=canopy_transform, ax=ax, cmap=tree_cmap) 83 | ax.set_axis_off() 84 | 85 | return ax 86 | -------------------------------------------------------------------------------- /detectree_examples/settings.py: -------------------------------------------------------------------------------- 1 | CRS = "epsg:2056" 2 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: detectree 2 | channels: 3 | - conda-forge 4 | dependencies: 5 | - click 6 | - contextily 7 | - descartes 8 | - detectree=0.6.0 9 | - geopandas>=1 10 | - matplotlib 11 | - numpy>=2 12 | - osmnx>=1 13 | - python=3.12 14 | - python-slugify 15 | - pip 16 | - pip: 17 | - -e . 18 | - laspy[lazrs]>=2 19 | - rasterio 20 | - shapely 21 | -------------------------------------------------------------------------------- /notebooks/matplotlibrc: -------------------------------------------------------------------------------- 1 | figure.figsize : 8, 6 # figure size in inches 2 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=61.0"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "detectree-examples" 7 | version = "0.1.0" 8 | description = "Example computational workflow using DetecTree" 9 | authors = [{name = "Martí Bosch", email = "marti.bosch@epfl.ch"}] 10 | readme = "README.md" 11 | license = {text = "GPL-3.0-only"} 12 | classifiers = [ 13 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 14 | "Programming Language :: Python :: 3", 15 | "Programming Language :: Python :: 3.8", 16 | "Programming Language :: Python :: 3.9", 17 | "Programming Language :: Python :: 3.10", 18 | "Programming Language :: Python :: 3.11", 19 | "Programming Language :: Python :: 3.12" 20 | ] 21 | requires-python = ">=3.8" 22 | 23 | [project.urls] 24 | Repository = "https://github.com/martibosch/detectree-examplse" 25 | 26 | [tool.nbqa.addopts] 27 | # ignore documentation errors in notebooks 28 | ruff = ["--ignore=D"] 29 | 30 | [tool.ruff] 31 | line-length = 88 32 | 33 | [tool.ruff.lint] 34 | select = ["E", "F", "I"] 35 | 36 | [tool.ruff.lint.isort] 37 | known-first-party = ["detectree_examples"] 38 | 39 | [tool.setuptools.packages.find] 40 | include = ["detectree_examples", "detectree_examples.*"] 41 | --------------------------------------------------------------------------------