IDTReeS - Clip boxes outside of image bounds (#760)

* reverse idtrees coords

* clip boxes to image bounds

* undo coordinate reversal

* add filter_boxes function

* filter boxes outside of bounds in idtrees

* format

* Revert utils.py

* flake8 fixes

* fix mypy errors

* fix bug overriding some labels

* fix image size

* Remove version added line

* add function overloads

* add comments for clarity

* use id counter for test set

Author: isaaccorley

torchgeo/datasets/idtrees.py

@@ -5,7 +5,7 @@
 
 import glob
 import os
-from typing import Any, Callable, Dict, List, Optional, Tuple, cast
+from typing import Any, Callable, Dict, List, Optional, Tuple, cast, overload
 
 import fiona
 import matplotlib.pyplot as plt
@@ -14,6 +14,7 @@
 import torch
 from rasterio.enums import Resampling
 from torch import Tensor
+from torchvision.ops import clip_boxes_to_image, remove_small_boxes
 from torchvision.utils import draw_bounding_boxes
 
 from .geo import NonGeoDataset
@@ -211,10 +212,22 @@ def __getitem__(self, index: int) -> Dict[str, Tensor]:
         if self.split == "test":
             if self.task == "task2":
                 sample["boxes"] = self._load_boxes(path)
+                h, w = sample["image"].shape[1:]
+                sample["boxes"], _ = self._filter_boxes(
+                    image_size=(h, w), min_size=1, boxes=sample["boxes"], labels=None
+                )
         else:
             sample["boxes"] = self._load_boxes(path)
             sample["label"] = self._load_target(path)
 
+            h, w = sample["image"].shape[1:]
+            sample["boxes"], sample["label"] = self._filter_boxes(
+                image_size=(h, w),
+                min_size=1,
+                boxes=sample["boxes"],
+                labels=sample["label"],
+            )
+
         if self.transforms is not None:
             sample = self.transforms(sample)
 
@@ -271,11 +284,15 @@ def _load_boxes(self, path: str) -> Tensor:
         geometries = cast(Dict[int, Dict[str, Any]], self.geometries)
 
         # Find object ids and geometries
+        # The train set geometry->image mapping is contained
+        # in the train/Field/itc_rsFile.csv file
         if self.split == "train":
             indices = self.labels["rsFile"] == base_path
             ids = self.labels[indices]["id"].tolist()
             geoms = [geometries[i]["geometry"]["coordinates"][0][:4] for i in ids]
-        # Test set - Task 2 has no mapping csv. Mapping is inside of geometry
+        # The test set has no mapping csv. The mapping is inside of the geometry
+        # properties i.e. geom["property"]["plotID"] contains the RGB image filename
+        # Return all geometries with the matching RGB image filename of the sample
         else:
             ids = [
                 k
@@ -380,17 +397,59 @@ def _load_geometries(self, directory: str) -> Dict[int, Dict[str, Any]]:
         """
         filepaths = glob.glob(os.path.join(directory, "ITC", "*.shp"))
 
+        i = 0
         features: Dict[int, Dict[str, Any]] = {}
         for path in filepaths:
             with fiona.open(path) as src:
-                for i, feature in enumerate(src):
+                for feature in src:
+                    # The train set has a unique id for each geometry in the properties
                     if self.split == "train":
                         features[feature["properties"]["id"]] = feature
-                    # Test set task 2 has no id
+                    # The test set has no unique id so create a dummy id
                     else:
                         features[i] = feature
+                        i += 1
         return features
 
+    @overload
+    def _filter_boxes(
+        self, image_size: Tuple[int, int], min_size: int, boxes: Tensor, labels: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        ...
+
+    @overload
+    def _filter_boxes(
+        self, image_size: Tuple[int, int], min_size: int, boxes: Tensor, labels: None
+    ) -> Tuple[Tensor, None]:
+        ...
+
+    def _filter_boxes(
+        self,
+        image_size: Tuple[int, int],
+        min_size: int,
+        boxes: Tensor,
+        labels: Optional[Tensor],
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """Clip boxes to image size and filter boxes with sides less than ``min_size``.
+
+        Args:
+            image_size: tuple of (height, width) of image
+            min_size: filter boxes that have any side less than min_size
+            boxes: [N, 4] shape tensor of xyxy bounding box coordinates
+            labels: (Optional) [N,] shape tensor of bounding box labels
+
+        Returns:
+            a tuple of filtered boxes and labels
+        """
+        boxes = clip_boxes_to_image(boxes=boxes, size=image_size)
+        indices = remove_small_boxes(boxes=boxes, min_size=min_size)
+
+        boxes = boxes[indices]
+        if labels is not None:
+            labels = labels[indices]
+
+        return boxes, labels
+
     def _verify(self) -> None:
         """Verify the integrity of the dataset.