Added documentation with Material for MkDocs

docs/cli.md

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+::: geococo.cli

docs/fiftyone.md

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+## To be continued

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docs/index.md

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+# Welcome to geococo's documentation!
+![geococo logo](https://github.com/jaspersiebring/GeoCOCO/assets/25051531/b2a2db16-1400-4c43-b044-a924a378ef84)
+
+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)
+````
+
+<p float="left">
+  <img src="https://github.com/jaspersiebring/GeoCOCO/assets/25051531/f8ab55da-b3cd-4beb-b082-7946e712ea5c" width="45%" height = 250/>
+  <img src="https://github.com/jaspersiebring/GeoCOCO/assets/25051531/9a796a54-ffc2-49c3-95bc-59e5c0dd1d7c" width="45%" height = 250 />
+</p>
+

docs/leafmap.md

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+## To be continued

docs/models.md

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+::: 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

docs/processing.md

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+::: 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
+
+

docs/utils.md

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+::: geococo.utils

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)
 

mkdocs.yml

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+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
+
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