Add support for pathlib.Path objects as input

.github/workflows/tests.yml

@@ -1,8 +1,6 @@
 name: Test Python package
 
-on:
-  - push
-
+on: [push, pull_request]
 jobs:
   test:
     runs-on: ubuntu-latest

environment.yaml

@@ -9,7 +9,7 @@ dependencies:
   - geopandas>=0.7.0
   - matplotlib>=3.1.2
   - numpy>=1.17.3
-  - opencv-python>=4.1
+  - opencv>=4.1
   - pandas>=0.25.3
   - pyproj>=2.1
   - PyYAML>=5.4

solaris/data/coco.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import os
+from pathlib import Path
 
 import geopandas as gpd
 import numpy as np
@@ -47,7 +48,7 @@ def geojson2coco(
 
     Arguments
     ---------
-    image_src : :class:`str` or :class:`list` or :class:`dict`
+    image_src : :class:`str` or :class:`pathlib.Path` or :class:`list` or :class:`dict`
         Source image(s) to use in the dataset. This can be::
 
             1. a string path to an image,
@@ -149,8 +150,8 @@ def geojson2coco(
     logger.setLevel(_get_logging_level(int(verbose)))
     logger.debug("Preparing image filename: image ID dict.")
     # pdb.set_trace()
-    if isinstance(image_src, str):
-        if image_src.endswith("json"):
+    if isinstance(image_src, (str, Path)):
+        if str(image_src).endswith("json"):
             logger.debug("COCO json provided. Extracting fname:id dict.")
             with open(image_src, "r") as f:
                 image_ref = json.load(f)
@@ -599,13 +600,13 @@ def _get_fname_list(p, recursive=False, extension=".tif"):
     """Get a list of filenames from p, which can be a dir, fname, or list."""
     if isinstance(p, list):
         return p
-    elif isinstance(p, str):
-        if os.path.isdir(p):
+    elif isinstance(p, (str, Path)):
+        if Path(p).is_dir():
             return get_files_recursively(
                 p, traverse_subdirs=recursive, extension=extension
             )
-        elif os.path.isfile(p):
-            return [p]
+        elif Path(p).is_file():
+            return [str(p)]
         else:
             raise ValueError("If a string is provided, it must be a valid" " path.")
     else:

solaris/eval/base.py

@@ -1,10 +1,12 @@
 import os
+from pathlib import Path
 
 import geopandas as gpd
 import pandas as pd
 import shapely.wkt
 from fiona._err import CPLE_OpenFailedError
 from fiona.errors import DriverError
+from solaris.utils.core import _check_gdf_load
 from tqdm.auto import tqdm
 
 from . import iou
@@ -29,28 +31,29 @@ class Evaluator:
 
     Arguments
     ---------
-    ground_truth_vector_file : str
+    ground_truth_vector_file : `str` or :class:`pathlib.Path`
         Path to .geojson file for ground truth.
 
     """
 
     def __init__(self, ground_truth_vector_file):
         # Load Ground Truth : Ground Truth should be in geojson or shape file
-        try:
-            if ground_truth_vector_file.lower().endswith("json"):
-                self.load_truth(ground_truth_vector_file)
-            elif ground_truth_vector_file.lower().endswith("csv"):
-                self.load_truth(ground_truth_vector_file, truthCSV=True)
-            self.ground_truth_fname = ground_truth_vector_file
-        except AttributeError:  # handles passing gdf instead of path to file
-            self.ground_truth_GDF = ground_truth_vector_file
+        if isinstance(ground_truth_vector_file, (str, Path)):
+            self.ground_truth_fname = str(ground_truth_vector_file)
+        else:
             self.ground_truth_fname = "GeoDataFrame variable"
+
+        if isinstance(ground_truth_vector_file, (str, Path)) and ground_truth_vector_file.lower().endswith("csv"):
+            self.load_truth(ground_truth_vector_file, truthCSV=True)
+        else:
+            self.load_truth(ground_truth_vector_file)
         self.ground_truth_sindex = self.ground_truth_GDF.sindex  # get sindex
         # create deep copy of ground truth file for calculations
         self.ground_truth_GDF_Edit = self.ground_truth_GDF.copy(deep=True)
         self.proposal_GDF = gpd.GeoDataFrame([])  # initialize proposal GDF
 
     def __repr__(self):
+
         return "Evaluator {}".format(os.path.split(self.ground_truth_fname)[-1])
 
     def get_iou_by_building(self):
@@ -509,7 +512,7 @@ def load_proposal(
 
         Arguments
         ---------
-        proposal_vector_file : str
+        proposal_vector_file : `str` or :class:`pathlib.Path`
             Path to the file containing proposal vector objects. This can be
             a .geojson or a .csv.
         conf_field_list : list, optional
@@ -540,7 +543,7 @@ def load_proposal(
         """
 
         # Load Proposal if proposal_vector_file is a path to a file
-        if os.path.isfile(proposal_vector_file):
+        if Path(proposal_vector_file).is_file():
             # if it's a CSV format, first read into a pd df and then convert
             # to gpd gdf by loading in geometries using shapely
             if proposalCSV:
@@ -588,7 +591,7 @@ def load_truth(
 
         Arguments
         ---------
-        ground_truth_vector_file : str
+        ground_truth_vector_file : `str` or :class:`pathlib.Path`
             Path to the ground truth vector file. Must be either .geojson or
             .csv format.
         truthCSV : bool, optional
@@ -617,12 +620,7 @@ def load_truth(
                 ],
             )
         else:
-            try:
-                self.ground_truth_GDF = gpd.read_file(ground_truth_vector_file)
-            except (CPLE_OpenFailedError, DriverError):  # empty geojson
-                self.ground_truth_GDF = gpd.GeoDataFrame(
-                    {"sindex": [], "condition": [], "geometry": []}
-                )
+            self.ground_truth_GDF = _check_gdf_load(ground_truth_vector_file)
         # force calculation of spatialindex
         self.ground_truth_sindex = self.ground_truth_GDF.sindex
         # create deep copy of ground truth file for calculations

solaris/eval/pixel.py

@@ -84,13 +84,13 @@ def f1(
         ``1``, values < `prop_threshold` will be set to ``0``.
     show_plot : bool, optional
         Switch to plot the outputs. Defaults to ``False``.
-    im_file : str, optional
+    im_file : `str` or :class:`pathlib.Path`, optional
         Image file corresponding to the masks. Ignored if
         ``show_plot == False``. Defaults to ``''``.
     show_colorbar : bool, optional
         Switch to show colorbar. Ignored if ``show_plot == False``.
         Defaults to ``False``.
-    plot_file : str, optional
+    plot_file : `str` or :class:`pathlib.Path`, optional
         Output file if plotting. Ignored if ``show_plot == False``.
         Defaults to ``''``.
     dpi : int, optional
@@ -167,7 +167,7 @@ def f1(
         plt.suptitle(title, fontsize=fontsize)
 
         # ground truth
-        if len(im_file) > 0:
+        if len(str(im_file)) > 0:
             # raw image
             ax1.imshow(cv2.imread(im_file, 1))
             # ground truth
@@ -211,7 +211,7 @@ def f1(
         # fig.tight_layout(rect=[0, 0.03, 1, 0.95])
         plt.subplots_adjust(top=0.8)
 
-        if len(plot_file) > 0:
+        if len(str(plot_file)) > 0:
             plt.savefig(plot_file, dpi=dpi)
         print("Time to create and save F1 plots:", time.time() - t0, "seconds")
 

solaris/eval/vector.py

@@ -1,5 +1,6 @@
 import glob
 import os
+from pathlib import Path
 
 import geopandas as gpd
 import numpy as np
@@ -49,9 +50,9 @@ def get_all_objects(
     unique classes present in each
     Arguments
     ---------
-        proposal_polygons_dir : str
+        proposal_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains any model proposal polygons
-        gt_polygons_dir : str
+        gt_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains the ground truth polygons
         prediction_cat_attrib : str
             The column or attribute within the predictions that specifies
@@ -71,23 +72,23 @@ def get_all_objects(
                 A union of the prop_objs and gt_objs lists
     """
     objs = []
-    os.chdir(proposal_polygons_dir)
+    os.chdir(str(proposal_polygons_dir))
     search = "*" + file_format
     proposal_geojsons = glob.glob(search)
     for geojson in tqdm(proposal_geojsons):
-        ground_truth_poly = os.path.join(gt_polygons_dir, geojson)
+        ground_truth_poly = Path(gt_polygons_dir) / geojson
         if os.path.exists(ground_truth_poly):
             ground_truth_gdf = gpd.read_file(ground_truth_poly)
             proposal_gdf = gpd.read_file(geojson)
             for index, row in proposal_gdf.iterrows():
                 objs.append(row[prediction_cat_attrib])
     prop_objs = list(set(objs))
-    os.chdir(gt_polygons_dir)
+    os.chdir(str(gt_polygons_dir))
     search = "*" + file_format
     objs = []
     gt_geojsons = glob.glob(search)
     for geojson in tqdm(gt_geojsons):
-        proposal_poly = os.path.join(proposal_polygons_dir, geojson)
+        proposal_poly = Path(proposal_polygons_dir) / geojson
         if os.path.exists(proposal_poly):
             proposal_gdf = gpd.read_file(proposal_poly)
             ground_truth_gdf = gpd.read_file(geojson)
@@ -114,9 +115,9 @@ def precision_calc(
     calculate metric for classes that exist in the ground truth.
     Arguments
     ---------
-        proposal_polygons_dir : str
+        proposal_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains any model proposal polygons
-        gt_polygons_dir : str
+        gt_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains the ground truth polygons
         prediction_cat_attrib : str
             The column or attribute within the predictions that specifies
@@ -148,7 +149,7 @@ def precision_calc(
             All confidences for each object for each class
     """
     ious = []
-    os.chdir(proposal_polygons_dir)
+    os.chdir(str(proposal_polygons_dir))
     search = "*" + file_format
     proposal_geojsons = glob.glob(search)
     iou_holder = []
@@ -166,7 +167,7 @@ def precision_calc(
         confidences.append([])
 
     for geojson in tqdm(proposal_geojsons):
-        ground_truth_poly = os.path.join(gt_polygons_dir, geojson)
+        ground_truth_poly = Path(gt_polygons_dir) / geojson
         if os.path.exists(ground_truth_poly):
             ground_truth_gdf = gpd.read_file(ground_truth_poly)
             proposal_gdf = gpd.read_file(geojson)
@@ -241,9 +242,9 @@ def recall_calc(
     calculate metric for classes that exist in the ground truth.
     Arguments
     ---------
-        proposal_polygons_dir : str
+        proposal_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains any model proposal polygons
-        gt_polygons_dir : str
+        gt_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains the ground truth polygons
         prediction_cat_attrib : str
             The column or attribute within the predictions that specifies
@@ -270,7 +271,7 @@ def recall_calc(
             The mean recall score of recall_by_class
     """
     ious = []
-    os.chdir(gt_polygons_dir)
+    os.chdir(str(gt_polygons_dir))
     search = "*" + file_format
     gt_geojsons = glob.glob(search)
     iou_holder = []
@@ -285,7 +286,7 @@ def recall_calc(
     for i in range(len(object_subset)):
         iou_holder.append([])
     for geojson in tqdm(gt_geojsons):
-        proposal_poly = os.path.join(proposal_polygons_dir, geojson)
+        proposal_poly = Path(proposal_polygons_dir) / geojson
         if os.path.exists(proposal_poly):
             proposal_gdf = gpd.read_file(proposal_poly)
             ground_truth_gdf = gpd.read_file(geojson)
@@ -353,9 +354,9 @@ def mF1(
     only calculate metric for classes that exist in the ground truth.
     Arguments
     ---------
-        proposal_polygons_dir : str
+        proposal_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains any model proposal polygons
-        gt_polygons_dir : str
+        gt_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains the ground truth polygons
         prediction_cat_attrib : str
             The column or attribute within the predictions that specifies
@@ -480,9 +481,9 @@ def mAP_score(
 
     Arguments
     ---------
-        proposal_polygons_dir : str
+        proposal_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains any model proposal polygons
-        gt_polygons_dir : str
+        gt_polygons_dir : `str` or :class:`pathlib.Path`
             The path that contains the ground truth polygons
         prediction_cat_attrib : str
             The column or attribute within the predictions that specifies

solaris/raster/image.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import numpy as np
 import rasterio
 
@@ -9,7 +11,7 @@ def get_geo_transform(raster_src):
 
     Arguments
     ---------
-    raster_src : str, :class:`rasterio.DatasetReader`, or `osgeo.gdal.Dataset`
+    raster_src : str, :class:`pathlib.Path`, :class:`rasterio.DatasetReader`, or `osgeo.gdal.Dataset`
         Path to a raster image with georeferencing data to apply to `geom`.
         Alternatively, an opened :class:`rasterio.Band` object or
         :class:`osgeo.gdal.Dataset` object can be provided. Required if not
@@ -21,7 +23,7 @@ def get_geo_transform(raster_src):
         An affine transformation object to the image's location in its CRS.
     """
 
-    if isinstance(raster_src, str):
+    if isinstance(raster_src, (str, Path)):
         affine_obj = rasterio.open(raster_src).transform
     elif isinstance(raster_src, rasterio.DatasetReader):
         affine_obj = raster_src.transform
@@ -175,7 +177,7 @@ def stitch_images(
 #     ---------
 #     array  : :class:`numpy.ndarray`
 #         A numpy array with a the shape: [Channels, X, Y] or [X, Y]
-#     out_name : str
+#     out_name : str or :class:`pathlib.Path`
 #         The output name and path for your image
 #     proj : :class:`gdal.projection`
 #         A projection, can be extracted from an image opened with gdal with
@@ -200,7 +202,7 @@ def stitch_images(
 #     driver = gdal.GetDriverByName("GTiff")
 #     if len(array.shape) == 2:
 #         array = array[np.newaxis, ...]
-#     os.makedirs(os.path.dirname(os.path.abspath(out_name)), exist_ok=True)
+#     Path(out_name).resolve().parent.mkdir(exist_ok=True)
 #     dataset = driver.Create(out_name, array.shape[2], array.shape[1], array.shape[0], out_format)
 #     if verbose is True:
 #         print("Array Shape, should be [Channels, X, Y] or [X,Y]:", array.shape)