Merge pull request #7 from jaspersiebring/cli_fix

Added cli options for (super)category names and ids

geococo/cli.py

@@ -1,3 +1,4 @@
+from typing import Optional
 import typer_cloup as typer
 import pathlib
 from datetime import datetime
@@ -14,7 +15,9 @@ def build_coco(
     output_dir: pathlib.Path,
     width: int,
     height: int,
-    category_attribute: str = "category_id",
+    category_id_col: Optional[str] = "category_id",
+    category_name_col: Optional[str] = None,
+    supercategory_col: Optional[str] = None,
 ) -> None:
     """Transform your GIS annotations into a COCO dataset.
 
@@ -38,14 +41,14 @@ def build_coco(
     :param output_dir: Path to the output directory for image subsets
     :param width: Width of the output images
     :param height: Height of the output images
-    :param category_attribute: Column that contains category_id values per annotation
-        feature
+    :param category_id_col: Column containing category_id values
+    :param category_name_col: Column containing category_name values
+    :param supercategory_col: Column containing supercategory values
     """
 
     if isinstance(json_path, pathlib.Path) and json_path.exists():
         # Create and populate instance of CocoDataset from json_path
         dataset = load_dataset(json_path=json_path)
-
     else:
         # Create instance of CocoDataset from user input
         print("Creating new dataset..")
@@ -62,16 +65,8 @@ def build_coco(
             date_created=date_created,
         )
 
-    # Loading and validating GIS data
+    # Loading GIS data
     labels = gpd.read_file(labels_path)
-    if not labels.is_valid.all():
-        raise ValueError("Invalid geometry found, exiting..")
-    elif category_attribute not in labels.columns:
-        raise ValueError(
-            f"User-specified category attribute (={category_attribute}) not found in "
-            "input labels, exiting.."
-        )
-
     with rasterio.open(image_path) as src:
         # Appending Annotation instances and clipping the image part that contains them
         dataset = labels_to_dataset(
@@ -80,6 +75,9 @@ def build_coco(
             src=src,
             labels=labels,
             window_bounds=[(width, height)],
+            category_id_col=category_id_col,
+            category_name_col=category_name_col,
+            supercategory_col=supercategory_col,
         )
 
     # Encode CocoDataset instance as JSON and save to json_path

geococo/coco_models.py

@@ -2,6 +2,7 @@
 import numpy as np
 import pathlib
 import pandas as pd
+from pandas import Series
 from datetime import datetime
 from typing import List, Optional, Dict, Any
 from typing_extensions import TypedDict
@@ -50,9 +51,9 @@ def add_source(self, source_path: pathlib.Path) -> None:
 
     def add_categories(
         self,
-        category_ids: Optional[np.ndarray],
-        category_names: Optional[np.ndarray],
-        supercategory_names: Optional[np.ndarray],
+        category_ids: Optional[Series],
+        category_names: Optional[Series],
+        super_names: Optional[Series],
     ) -> None:
         # initializing values
         super_default = "1"
@@ -65,13 +66,15 @@ def add_categories(
         )
 
         # checking if names can be assigned to uid_array (used to check duplicates)
-        if isinstance(category_names, np.ndarray):
+        if category_names is not None:
+            category_names: np.ndarray = category_names.to_numpy()
             uid_array = category_names
             uid_attribute = "name"
             names_present = True
 
         # checking if ids can be assigned to uid_array (used to check duplicates)
-        if isinstance(category_ids, np.ndarray):
+        if category_ids is not None:
+            category_ids: np.ndarray = category_ids.to_numpy()
             uid_array = category_ids  # overrides existing array because ids are leading
             uid_attribute = "id"
             ids_present = True
@@ -89,11 +92,15 @@ def add_categories(
             return
 
         # creating default supercategory_names if not given
-        if not isinstance(supercategory_names, np.ndarray):
-            supercategory_names = np.full(shape=new_shape, fill_value=super_default)
+        if super_names is None:
+            super_names = np.full(
+                shape=new_shape,
+                fill_value=super_default
+                ) # type: ignore[assignment]
         else:
-            assert supercategory_names.shape == original_shape
-            supercategory_names = supercategory_names[indices][~member_mask]
+            super_names: np.ndarray = super_names.to_numpy()
+            assert super_names.shape == original_shape
+            super_names = super_names[indices][~member_mask]
 
         # creating default category_names if not given (str version of ids)
         if ids_present and not names_present:
@@ -105,16 +112,17 @@ def add_categories(
             max_id = category_pd.loc[pandas_mask, "id"].max()
             start = np.nansum([max_id, 1])
             end = start + new_members.size
-            category_ids = np.arange(start, end)
+            category_ids = np.arange(start, end) # type: ignore[assignment]
             category_names = new_members
         # ensuring equal size for category names and ids (if given)
         else:
-            assert category_names.shape == original_shape # type: ignore
-            category_names = category_names[indices][~member_mask] # type: ignore
+            assert category_names.shape == original_shape  # type: ignore[union-attr]
+            category_names = category_names[indices][~member_mask] # type: ignore[index]
             category_ids = new_members
 
         # iteratively instancing and appending Category from set ids, names and supers
-        for cid, name, super in zip(category_ids, category_names, supercategory_names):
+        cip = zip(category_ids, category_names, super_names)
+        for cid, name, super in cip:
             category = Category(id=cid, name=name, supercategory=super)
             self.categories.append(category)
 

geococo/coco_processing.py

@@ -5,7 +5,6 @@
 import geopandas as gpd
 import numpy as np
 import rasterio
-from pandas import Series
 from typing import Optional
 from rasterio.io import DatasetReader
 from rasterio.mask import mask as riomask
@@ -66,23 +65,11 @@ def labels_to_dataset(
         supercategory_col=supercategory_col,
     )
 
-    # dumping series to array (if present)
-    category_ids = labels.get(category_id_col)
-    category_ids = category_ids.values if isinstance(category_ids, Series) else None
-    category_names = labels.get(category_name_col)
-    category_names = (
-        category_names.values if isinstance(category_names, Series) else None
-    )
-    supercategory_names = labels.get(supercategory_col)
-    supercategory_names = (
-        supercategory_names.values if isinstance(supercategory_names, Series) else None
-    )
-
-    # adding new Category instances (if any)
+    # adding new Category instances from labels (if any)
     dataset.add_categories(
-        category_ids=category_ids,
-        category_names=category_names,
-        supercategory_names=supercategory_names,
+        category_ids=labels.get(category_id_col),
+        category_names=labels.get(category_name_col),
+        super_names=labels.get(supercategory_col),
     )
 
     # updating labels with validated COCO keys (i.e. 'name', 'id', 'supercategory')

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "geococo"
-version = "0.4.0"
+version = "0.4.1"
 description = "Converts GIS annotations to Microsoft's Common Objects In Context (COCO) dataset format"
 authors = ["Jasper <[email protected]>"]
 readme = "README.md"
@@ -42,7 +42,12 @@ module = [
     "geopandas",
     "shapely.*"
 ]
+
+[tool.mypy]
 ignore_missing_imports = true
+allow_redefinition = true
+strict_optional = false
+disable_error_code = 'no-redef'
 
 [tool.ruff]
 line-length = 88