Merge pull request #6 from EMalagoli92/dev

Dev

docs/usage.md

@@ -365,7 +365,7 @@ from od_metrics import iou
 y_true = [[25, 16, 38, 56], [129, 123, 41, 62]]
 y_pred = [[25, 27, 37, 54], [119, 111, 40, 67], [124, 9, 49, 67]]
 
-result = iou(y_true, y_pred)
+result = iou(y_true, y_pred, box_format="xywh")
 print(result)
 """
 array([[0.67655425, 0.        ],

samples/samples.ipynb

@@ -340,7 +340,7 @@
     "y_true = [[25, 16, 38, 56], [129, 123, 41, 62]]\n",
     "y_pred = [[25, 27, 37, 54], [119, 111, 40, 67], [124, 9, 49, 67]]\n",
     "\n",
-    "result = iou(y_true, y_pred)\n",
+    "result = iou(y_true, y_pred, box_format=\"xywh\")\n",
     "result"
    ]
   }
@@ -361,7 +361,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

src/od_metrics/od_metrics.py

@@ -16,7 +16,7 @@
 import numpy as np
 
 from .constants import DEFAULT_COCO, _STANDARD_OUTPUT
-from .utils import get_indexes, get_suffix, _Missing
+from .utils import get_indexes, get_suffix, _Missing, to_xyxy
 from .validators import ConstructorModel, ComputeModel, MeanModel
 
 
@@ -483,7 +483,8 @@ def _compute_iou(
         ious = iou(
             y_true=y_true_boxes,
             y_pred=y_pred_boxes,
-            iscrowd=iscrowd
+            iscrowd=iscrowd,
+            box_format="xywh",
             )
         return ious
 
@@ -1050,13 +1051,11 @@ def iou(
         y_true: np.ndarray | list,
         y_pred: np.ndarray | list,
         iscrowd: np.ndarray | list[bool] | list[int] | None = None,
+        box_format: Literal["xyxy", "xywh", "cxcywh"] = "xywh",
         ) -> np.ndarray:
     """
     Calculate IoU between bounding boxes.
 
-    Single bounding boxes must be in `"xywh"` format, i.e.
-        [xmin, ymin, width, height]
-
     The standard iou of a ground truth `y_true` and detected
     `y_pred` object is:
 
@@ -1096,6 +1095,23 @@ def iou(
         Whether `y_true` are crowd regions.
         If `None`, it will be set to `False` for all `y_true`.
         The default is `None`.
+    box_format: Literal["xyxy", "xywh", "cxcywh"], optional
+        Bounding box format.
+        Supported formats are:<br>
+            - `"xyxy"`: boxes are represented via corners,
+                    x1, y1 being top left and x2, y2
+                    being bottom right.<br>
+            - `"xywh"`: boxes are represented via corner,
+                    width and height, x1, y2 being top
+                    left, w, h being width and height.
+                    This is the default format; all
+                    input formats will be converted
+                    to this.<br>
+            - `"cxcywh"`: boxes are represented via centre,
+                    width and height, cx, cy being
+                    center of box, w, h being width
+                    and height.<br>
+        The default is `"xywh"`.
 
     Returns
     -------
@@ -1111,20 +1127,14 @@ def iou(
                              "length.")
     else:
         iscrowd = [False]*len(y_true)
-    # To np.ndarray
-    if not isinstance(y_pred, np.ndarray):
-        y_pred = np.array(y_pred)
-    if not isinstance(y_true, np.ndarray):
-        y_true = np.array(y_true)
+    # To np.ndarray and xyxy box format
+    y_true = np.array([to_xyxy(bbox_, box_format) for bbox_ in y_true])
+    y_pred = np.array([to_xyxy(bbox_, box_format) for bbox_ in y_pred])
 
     # pylint: disable-next=W0632
-    xmin1, ymin1, width1, height1 = np.hsplit(y_pred, 4)
+    xmin1, ymin1, xmax1, ymax1 = np.hsplit(y_pred, 4)
     # pylint: disable-next=W0632
-    xmin2, ymin2, width2, height2 = np.hsplit(y_true, 4)
-    xmax1 = xmin1 + width1
-    xmax2 = xmin2 + width2
-    ymax1 = ymin1 + height1
-    ymax2 = ymin2 + height2
+    xmin2, ymin2, xmax2, ymax2 = np.hsplit(y_true, 4)
 
     # Intersection
     xmin_i = np.maximum(xmin1.T, xmin2).T

src/od_metrics/utils.py

@@ -6,6 +6,8 @@
     "_Missing",
     "get_indexes",
     "get_suffix",
+    "to_xywh",
+    "to_xyxy",
     ]
 
 from typing import Literal
@@ -124,6 +126,87 @@ def to_xywh(
         )
 
 
+def xywh_xyxy(bbox: list[float]) -> list[float]:
+    """
+    Change bounding box format from `xywh` to `xyxy`.
+
+    Parameters
+    ----------
+    bbox : list[float]
+        Input bounding box.
+
+    Returns
+    -------
+    list[float]
+        Bounding box in `"xyxy"` format.
+    """
+    return [
+        bbox[0],
+        bbox[1],
+        bbox[0] + bbox[2],
+        bbox[1] + bbox[3]
+        ]
+
+
+def cxcywh_xyxy(bbox: list[float]) -> list[float]:
+    """
+    Change bounding box format from `cxcywh` to `xyxy`.
+
+    Parameters
+    ----------
+    bbox : list[float]
+        Input bounding box.
+
+    Returns
+    -------
+    list[float]
+        Bounding box in `"xyxy"` format.
+    """
+    return [
+        bbox[0] - bbox[2] / 2,
+        bbox[1] - bbox[3] / 2,
+        bbox[0] + bbox[2] / 2,
+        bbox[1] + bbox[3] / 2
+        ]
+
+
+def to_xyxy(
+        bbox: list[float],
+        box_format: Literal["xyxy", "xywh", "cxcywh"],
+        ) -> list[float]:
+    """
+    Change bounding box format to `"xyxy"`.
+
+    Parameters
+    ----------
+    bbox : list[float]
+        Input bounding box.
+    box_format : Literal["xyxy", "xywh", "cxcywh"]
+        Input bounding box format.
+        It can be `"xyxy"`, `"xywh"` or `"cxcywh"`.
+
+    Raises
+    ------
+    ValueError
+        If `box_format` not one of `"xyxy"`, `"xywh"`, `"cxcywh"`.
+
+    Returns
+    -------
+    list[float]
+        Bounding box in `"xyxy"` format.
+    """
+    if box_format == "xywh":
+        return xywh_xyxy(bbox)
+    if box_format == "xyxy":
+        return bbox
+    if box_format == "cxcywh":
+        return cxcywh_xyxy(bbox)
+    raise ValueError(
+        "`box_format` can be `'xyxy'`, `'xywh'`, `'cxcywh'`. "
+        f"Found {box_format}"
+        )
+
+
 def get_suffix(
         iou_threshold: np.ndarray,
         area_range_key: np.ndarray,

tests/config.py

@@ -776,6 +776,34 @@
      "to_cover": {"pycoco_converter": False, "box_format_converter": False},
      "ids": "annotations_exception_ypred_no_boxes"
      },
+    {
+     "compute_settings": {"extended_summary": True},
+     "y_true": [
+         {"labels": [0, 2],
+          "boxes": [[17, 83, 97, 47], [57, 86, 96, 73]]}
+         ],
+     "y_pred": [
+         {
+             "labels": [0, 2],
+             "boxes": [[-17, -83, 0, 47], [0, 86, -96, 73]],
+             "scores": [.2, .3]}
+         ],
+     "ids": "annotations_with_no_valid_y_pred_boxes"
+     },
+    {
+     "compute_settings": {"extended_summary": True},
+     "y_true": [
+         {"labels": [0, 2],
+          "boxes": [[-17, -83, 0, 47], [0, 86, -96, 73]]}
+         ],
+     "y_pred": [
+         {
+             "labels": [0, 2],
+             "boxes": [[17, 83, 97, 47], [57, 86, 96, 73]],
+             "scores": [.2, .3]}
+         ],
+     "ids": "annotations_with_no_valid_y_true_boxes"
+     },
     ]
 
 

tests/test_odmetrics.py

@@ -6,11 +6,13 @@
 import copy
 from typing import Any, Literal
 from functools import partial
+from itertools import product
 import numpy as np
 from parameterized import parameterized, parameterized_class
 
 from src.od_metrics import ODMetrics, iou
 from src.od_metrics.constants import DEFAULT_COCO
+from src.od_metrics.utils import to_xywh
 from tests.utils import annotations_generator, pycoco_converter, \
     test_equality, rename_dict, xywh_to, apply_function
 from tests.config import TESTS
@@ -331,6 +333,65 @@ def test_pycoco_equivalence(
 
         self.assertTrue(test_equality(od_metrics_ious, pycoco_ious))
 
+    @parameterized.expand(list(product(
+        ["random", None], ["xyxy", "xywh", "cxcywh", "error"])))
+    def test_box_formats(
+            self,
+            iscrowd_mode: Literal["random", None],
+            box_format: Literal["xyxy", "xywh", "cxcywh"],
+            ) -> None:
+        """Test `box_format` argument."""
+        if iscrowd_mode == "random":
+            iscrowd = list(map(
+                bool,
+                np.random.randint(
+                    low=0,
+                    high=2,
+                    size=[self.SIZE]
+                    ).tolist()
+                )
+            )
+            iscrowd_pycoco = iscrowd
+        else:
+            iscrowd = None
+            iscrowd_pycoco = [False] * self.SIZE
+        y_pred = np.random.randint(
+            low=1,
+            high=self.HIGH,
+            size=[self.SIZE, 4]
+            )
+
+        y_true = np.random.randint(
+            low=1,
+            high=self.HIGH,
+            size=[self.SIZE, 4]
+            )
+
+        if box_format in ["xyxy", "xywh", "cxcywh"]:
+            od_metrics_ious = iou(
+                y_true=y_true,
+                y_pred=y_pred,
+                iscrowd=iscrowd,
+                box_format=box_format,
+                )
+
+            y_true_pycoco = np.array([to_xywh(bbox_, box_format)
+                                      for bbox_ in y_true])
+            y_pred_pycoco = np.array([to_xywh(bbox_, box_format)
+                                      for bbox_ in y_pred])
+            pycoco_ious = maskUtils.iou(y_pred_pycoco, y_true_pycoco,
+                                        iscrowd_pycoco)
+
+            self.assertTrue(test_equality(od_metrics_ious, pycoco_ious))
+        else:
+            with self.assertRaises(ValueError):
+                od_metrics_ious = iou(
+                    y_true=y_true,
+                    y_pred=y_pred,
+                    iscrowd=iscrowd,
+                    box_format=box_format,
+                    )
+
     def test_length_exception(self) -> None:
         """Test exception `iscrowd` and `y_true` different length."""
         iscrowd = list(map(
@@ -360,6 +421,55 @@ def test_length_exception(self) -> None:
                 iscrowd=iscrowd,
                 )
 
+    @parameterized.expand(list(product(
+        ["random", None], ["xyxy", "xywh", "cxcywh"])))
+    def test_not_valid_boxes(
+            self,
+            iscrowd_mode: Literal["random", None],
+            box_format: Literal["xyxy", "xywh", "cxcywh"],
+            ) -> None:
+        """Test equivalence for not valid (negative values) boxes."""
+        if iscrowd_mode == "random":
+            iscrowd = list(map(
+                bool,
+                np.random.randint(
+                    low=0,
+                    high=2,
+                    size=[self.SIZE]
+                    ).tolist()
+                )
+            )
+            iscrowd_pycoco = iscrowd
+        else:
+            iscrowd = None
+            iscrowd_pycoco = [False] * self.SIZE
+        y_pred = np.random.randint(
+            low=-10,
+            high=1,
+            size=[self.SIZE, 4]
+            )
+        y_true = np.random.randint(
+            low=-10,
+            high=1,
+            size=[self.SIZE, 4]
+            )
+
+        od_metrics_ious = iou(
+            y_true=y_true,
+            y_pred=y_pred,
+            iscrowd=iscrowd,
+            box_format=box_format,
+            )
+
+        y_true_pycoco = np.array([to_xywh(bbox_, box_format)
+                                  for bbox_ in y_true])
+        y_pred_pycoco = np.array([to_xywh(bbox_, box_format)
+                                  for bbox_ in y_pred])
+        pycoco_ious = maskUtils.iou(y_pred_pycoco, y_true_pycoco,
+                                    iscrowd_pycoco)
+
+        self.assertTrue(test_equality(od_metrics_ious, pycoco_ious))
+
 
 if __name__ == "__main__":
     unittest.main()  # pragma: no cover