diff --git a/docs/cli.md b/docs/cli.md
new file mode 100644
index 0000000..d9e7067
--- /dev/null
+++ b/docs/cli.md
@@ -0,0 +1 @@
+::: geococo.cli
diff --git a/docs/fiftyone.md b/docs/fiftyone.md
new file mode 100644
index 0000000..10899ec
--- /dev/null
+++ b/docs/fiftyone.md
@@ -0,0 +1 @@
+## To be continued
\ No newline at end of file
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--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,175 @@
+# Welcome to geococo's documentation!
+
+
+Easily transform your GIS annotations into [Microsoft's Common Objects In Context (COCO)](https://cocodataset.org/#format-data) datasets with GeoCOCO. This tool allows users to leverage the advanced digitizing solutions of modern GIS software for the annotations of image objects in geographic imagery.
+
+Built with `Pydantic` and `pycocotools`, it features a complete implementation of the COCO standard for object detection with out-of-the-box support for JSON-encoding and RLE compression. The resulting datasets are versioned, easily extendable with new annotations and fully compatible with other data applications that accept the COCO format.
+
+## Key features
+- User-friendly: GeoCOCO is designed for ease of use, requiring minimal configuration and domain knowledge
+- Version Control: Datasets created with GeoCOCO are versioned and designed for expansion with future annotations
+- Command-line Tool: Use GeoCOCO from your terminal to create, append and copy COCO datasets
+- Python Module: Integrate GeoCOCO in your own data applications with the `geococo` package
+- Representation: GeoCOCO maximizes label representation through an adaptive moving window approach
+- COCO Standard: Output datasets are fully compatible with other COCO-accepting applications
+- Compact File Size: JSON-encoding and RLE compression are employed to ensure compact file sizes
+
+## Installation
+Installing from the Python Package Index (PyPI):
+````
+# Install from PYPI with Python's package installer (pip)
+pip install geococo
+````
+
+## Example of usage
+
+After installing `geococo`, there are a number of ways you can interact with its API.
+
+#### Command line interface
+ The easiest way to use `geococo` is to simply call it from your preferred terminal with one of three commands: `new`, `add` and `copy`.
+
+````
+$ geococo --help
+Usage: geococo [OPTIONS] COMMAND [ARGS]...
+
+ Transform your GIS annotations into COCO datasets.
+
+Options:
+ --help Show this message and exit.
+
+Commands:
+ add Transform and add GIS annotations to an existing CocoDataset
+ copy Copy and (optionally) update the metadata of an existing CocoDataset
+ new Initialize a new CocoDataset with user-prompted metadata
+````
+
+Starting a new CocoDataset (will prompt user for metadata like Description and Contributor)
+
+````
+$ geococo new dataset.json
+````
+
+Adding new annotations to existing CocoDataset (will increment version based on input data and update any new categories/images)
+````
+$ geococo add image.tif labels.shp dataset.json images/ 512 512 --id-attribute ids
+````
+
+For more information on the different commands, call `geococo COMMAND` with `--help`.
+````
+$ geococo add --help
+Usage: geococo add [OPTIONS] IMAGE_PATH LABELS_PATH JSON_PATH OUTPUT_DIR WIDTH
+ HEIGHT
+
+ Transform and add GIS annotations to an existing COCO dataset.
+
+ This method generates a COCO dataset by moving across the given image
+ (image_path) with a moving window (width, height), constantly checking for
+ intersecting annotations (labels_path) that represent image objects in said
+ image (e.g. buildings in satellite imagery; denoted by (super)category name
+ and/or id). Each valid intersection will add n Annotations entries to the
+ dataset (json_path) and save a subset of the input image that contained
+ these entries (output_dir).
+
+ The output data size depends on your input labels, as the moving window
+ adjusts its step size to accommodate the average annotation size, optimizing
+ dataset representation and minimizing tool configuration. Each addition will
+ also increment the dataset version: patch if using the same image_path,
+ minor if using a new image_path, and major if using a new output_dir.
+
+Arguments:
+ IMAGE_PATH Path to geospatial image containing image objects [required]
+ LABELS_PATH Path to vector file containing annotated image objects
+ [required]
+ JSON_PATH Path to json file containing the COCO dataset [required]
+ OUTPUT_DIR Path to output directory for image subsets [required]
+ WIDTH Width of image subsets [required]
+ HEIGHT Height of image subsets [required]
+
+Options:
+ --id-attribute TEXT Name of column containing category_id values
+ (optional if --name_attribute is given)
+ --name-attribute TEXT Name of column containing category_name values
+ (optional if --id_attribute is given)
+ --super-attribute TEXT Name of column containing supercategory values
+ --help Show this message and exit.
+````
+
+
+#### Python module
+This is recommended for most developers as it gives you more granular control over the various steps. It does assume a basic familiarity with the `geopandas` and `rasterio` packages (i.e. GIS modules that help you manage vector and raster data respectively).
+
+````
+import pathlib
+import geopandas as gpd
+import rasterio
+from geococo import create_dataset, load_dataset, save_dataset, append_dataset
+
+# Replace this with your preferred output paths
+data_path = pathlib.Path("path/to/your/coco/output/images")
+json_path = pathlib.Path("path/to/your/coco/json/file")
+
+# Dimensions of the moving window and output images
+width, height = 512, 512
+
+# Starting a new CocoDataset
+description = "My First Dataset"
+contributor = "User"
+
+# version and date_created are automatically set
+dataset = create_dataset(description=description, contributor=contributor)
+# You can also load existing COCO datasets
+# dataset = load_dataset(json_path=json_path)
+
+# Loading GIS data with rasterio and geopandas
+labels = gpd.read_file(some_labels_path)
+raster_source = rasterio.open(some_image_path)
+
+# (Optional) Apply any spatial or attribute queries here
+# labels = labels.loc[labels["ids"].isin([1, 2, 3])]
+# labels = labels.loc[labels.within(some_polygon)]
+
+# Find and save all Annotation instances
+dataset = append_dataset(
+ dataset=dataset,
+ images_dir=data_path,
+ src=raster_source,
+ labels=labels,
+ window_bounds=[(width, height)],
+ id_attribute=None, # column with category_id values
+ name_attribute="ids", # column with category_name values
+ super_attribute=None, # optional column with super_category values
+)
+
+# Encode CocoDataset instance as JSON and save to json_path
+save_dataset(dataset=dataset, json_path=json_path)
+````
+
+## Visualization with FiftyOne
+Like the official COCO project, the open source tool [FiftyOne](https://docs.voxel51.com/) can be used to visualize and evaluate your datasets. To do this, you'll need the `fiftyone` and `pycocotools` packages. Note that `geococo` does not install `fiftyone` by default, so you'll need to install it separately (instructions for installation can be found [here](https://docs.voxel51.com/getting_started/install.html)). Once you have `fiftyone` installed, you can use the following command to inspect your COCO dataset in your web browser.
+
+````
+# requires pycocotools and fiftyone
+import fiftyone as fo
+import fiftyone.zoo as foz
+import pathlib
+
+data_path = pathlib.Path("path/to/your/coco/output/images")
+json_path = pathlib.Path("path/to/your/coco/json/file")
+
+# Load COCO formatted dataset
+coco_dataset = fo.Dataset.from_dir(
+ dataset_type=fo.types.COCODetectionDataset,
+ data_path=data_path,
+ labels_path=json_path,
+ include_id=True,
+)
+
+# Launch the app
+session = fo.launch_app(coco_dataset, port=5151)
+````
+
+
+ 
+ 
+
+
diff --git a/docs/leafmap.md b/docs/leafmap.md
new file mode 100644
index 0000000..10899ec
--- /dev/null
+++ b/docs/leafmap.md
@@ -0,0 +1 @@
+## To be continued
\ No newline at end of file
diff --git a/docs/models.md b/docs/models.md
new file mode 100644
index 0000000..ba0ca82
--- /dev/null
+++ b/docs/models.md
@@ -0,0 +1,13 @@
+::: geococo.coco_models
+ options:
+ members:
+ - Info
+ - Image
+ - Annotation
+ - Category
+ - RleDict
+ - Source
+ - CocoDataset
+ show_root_heading: false
+ merge_init_into_class: false
+ show_if_no_docstring: false
\ No newline at end of file
diff --git a/docs/processing.md b/docs/processing.md
new file mode 100644
index 0000000..17a40c4
--- /dev/null
+++ b/docs/processing.md
@@ -0,0 +1,16 @@
+::: geococo.coco_manager
+ options:
+ members:
+ - create_dataset
+ - load_dataset
+ - save_dataset
+ show_root_heading: false
+
+
+::: geococo.coco_processing
+ options:
+ members:
+ - append_dataset
+ show_root_heading: false
+
+
diff --git a/docs/utils.md b/docs/utils.md
new file mode 100644
index 0000000..3454d2d
--- /dev/null
+++ b/docs/utils.md
@@ -0,0 +1 @@
+::: geococo.utils
diff --git a/geococo/coco_models.py b/geococo/coco_models.py
index 874a993..f178dab 100644
--- a/geococo/coco_models.py
+++ b/geococo/coco_models.py
@@ -124,8 +124,8 @@ def add_categories(
category_ids = new_members
# iteratively instancing and appending Category from set ids, names and supers
- cip = zip(category_ids, category_names, super_names)
- for cid, name, super in cip:
+ category_info = zip(category_ids, category_names, super_names)
+ for cid, name, super in category_info:
category = Category(id=cid, name=name, supercategory=super)
self.categories.append(category)
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..96f9eca
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,61 @@
+site_name: "GeoCOCO: Transform your GIS annotations into COCO datasets"
+site_description: Transform your GIS annotations into COCO datasets
+site_url: https://jaspersiebring.github.io/geococo/
+repo_url: https://github.com/jaspersiebring/geococo
+repo_name: jaspersiebring/geococo
+
+nav:
+- Quickstart: index.md
+- API Documentation:
+ - COCO Models: models.md
+ - COCO Processing: processing.md
+ - Typer CLI: cli.md
+ - Utilities: utils.md
+- References:
+ - License: https://github.com/jaspersiebring/GeoCOCO/blob/main/LICENSE
+
+theme:
+ name: material
+ favicon: img/favicon.ico
+ logo: img/favicon.ico
+ palette:
+ - media: "(prefers-color-scheme: light)"
+ scheme: default
+ primary: light green
+ accent: light green
+ toggle:
+ icon: material/lightbulb-outline
+ name: "Switch to dark mode"
+ - media: "(prefers-color-scheme: dark)"
+ scheme: slate
+ primary: light green
+ accent: light green
+ toggle:
+ icon: material/lightbulb
+ name: "Switch to light mode"
+ highlightjs: true
+ locale: en
+ features:
+ - content.code.annotate
+ - content.code.copy
+ - navigation.instant
+ - navigation.tracking
+ - navigation.sections
+ - navigation.top
+ - navigation.indexes
+ - toc.follow
+
+plugins:
+ - search
+ - mkdocstrings:
+ default_handler: python
+ handlers:
+ python:
+ import:
+ - url: https://docs.python-requests.org/en/master/objects.inv
+ domains: [std, py]
+ options:
+ docstring_style: sphinx
+ docstring_section_style: table
+ summary: true
+ separate_signature: true
diff --git a/poetry.lock b/poetry.lock
index efe75ab..0d94561 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -33,6 +33,20 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
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+description = "Internationalization utilities"
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+
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+[[package]]
+name = "ghp-import"
+version = "2.1.0"
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+optional = false
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+]
+
+[package.dependencies]
+python-dateutil = ">=2.8.1"
+
+[package.extras]
+dev = ["flake8", "markdown", "twine", "wheel"]
+
+[[package]]
+name = "griffe"
+version = "0.36.2"
+description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
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+python-versions = ">=3.8"
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+colorama = ">=0.4"
+
+[[package]]
+name = "idna"
+version = "3.4"
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version = "6.8.0"
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+
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+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.extras]
+docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.0)", "mkdocs-nature (>=0.4)"]
+testing = ["coverage", "pyyaml"]
+
+[[package]]
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+
[[package]]
name = "ruff"
version = "0.0.285"
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+
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+description = "Filesystem events monitoring"
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+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
[[package]]
name = "wrapt"
version = "1.15.0"
@@ -1944,4 +2547,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.8"
-content-hash = "9e50c39cb545682dc19380c747bba7604ec23501f896d24234c01fe05f8dc475"
+content-hash = "2f2101a86fb5caf7487f75ade67f1792772951be470967f6a99b9b48344af893"
diff --git a/pyproject.toml b/pyproject.toml
index 451314a..d986e9c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,16 +1,16 @@
[tool.poetry]
name = "geococo"
-version = "0.5.0"
+version = "0.5.1"
description = "Converts GIS annotations to Microsoft's Common Objects In Context (COCO) dataset format"
authors = ["Jasper Siebring "]
readme = "README.md"
+repository = "https://github.com/jaspersiebring/GeoCOCO"
+homepage = "https://jaspersiebring.github.io/GeoCOCO/"
+keywords = ["coco", "annotation", "deep-learning", "gis", "qgis", "arcgis", "labeling", "ai", "yolo"]
packages = [
{include = "geococo"}
]
-[tool.poetry.urls]
-"Repository" = "https://github.com/jaspersiebring/GeoCOCO"
-
[tool.poetry.scripts]
geococo = "geococo.cli:app"
@@ -38,6 +38,10 @@ docformatter = {extras = ["tomli"], version = "^1.7.5"}
pytest-cov = "^4.1.0"
pandas-stubs = "^2.0.3.230814"
types-python-dateutil = "^2.8.19.14"
+mkdocs = "^1.5.3"
+mkdocstrings = {extras = ["python"], version = "^0.23.0"}
+mike = "^1.1.2"
+mkdocs-material = "^9.4.2"
[[tool.mypy.overrides]]
module = [