Dense Mask IoU

docs/source/user_guide/evaluation.rst

@@ -729,8 +729,9 @@ The only difference between each task type is in how the IoU between objects is
 calculated:
 
 -   For object detections, IoUs are computed between each pair of bounding boxes
--   For instance segmentations and polygons, IoUs are computed between the
-    polygonal shapes rather than their rectangular bounding boxes
+-   For instance segmentations, when ``use_masks=True``, IoUs are computed
+    between the dense pixel masks rather than their rectangular bounding boxes
+-   For polygons, IoUs are computed between the polygonal shapes
 -   For keypoint tasks,
     `object keypoint similarity <https://cocodataset.org/#keypoints-eval>`_
     is computed for each pair of objects, using the extent of the ground truth
@@ -744,8 +745,7 @@ stored in |Detections| format.
 
 For instance segmentation tasks, the ground truth and predicted objects should
 be stored in |Detections| format, and each |Detection| instance should have its
-:attr:`mask <fiftyone.core.labels.Detection.mask>` attribute populated to
-define the extent of the object within its bounding box.
+mask populated to define the extent of the object within its bounding box.
 
 .. note::
 

fiftyone/utils/eval/coco.py

@@ -48,7 +48,8 @@ class COCOEvaluationConfig(DetectionEvaluationConfig):
             of the provided :class:`fiftyone.core.labels.Polyline` instances
             rather than using their actual geometries
         tolerance (None): a tolerance, in pixels, when generating approximate
-            polylines for instance masks. Typical values are 1-3 pixels
+            polylines for instance masks. Typical values are 1-3 pixels. By
+            default, IoUs are computed directly on the dense pixel masks
         compute_mAP (False): whether to perform the necessary computations so
             that mAP and PR curves can be generated
         iou_threshs (None): a list of IoU thresholds to use when computing mAP

fiftyone/utils/eval/openimages.py

@@ -43,7 +43,8 @@ class OpenImagesEvaluationConfig(DetectionEvaluationConfig):
             of the provided :class:`fiftyone.core.labels.Polyline` instances
             rather than using their actual geometries
         tolerance (None): a tolerance, in pixels, when generating approximate
-            polylines for instance masks. Typical values are 1-3 pixels
+            polylines for instance masks. Typical values are 1-3 pixels. By
+            default, IoUs are computed directly on the dense pixel masks
         max_preds (None): the maximum number of predicted objects to evaluate
             when computing mAP and PR curves
         error_level (1): the error level to use when manipulating instance

fiftyone/utils/iou.py

@@ -16,6 +16,7 @@
 
 import eta.core.numutils as etan
 import eta.core.utils as etau
+import eta.core.image as etai
 
 import fiftyone.core.labels as fol
 import fiftyone.core.utils as fou
@@ -73,7 +74,8 @@ def compute_ious(
             of the provided :class:`fiftyone.core.labels.Polyline` instances
             rather than using their actual geometries
         tolerance (None): a tolerance, in pixels, when generating approximate
-            polylines for instance masks. Typical values are 1-3 pixels
+            polylines for instance masks. Typical values are 1-3 pixels. By
+            default, IoUs are computed directly on the dense pixel masks
         sparse (False): whether to return a sparse dict of non-zero IoUs rather
             than a full matrix
         error_level (1): the error level to use when manipulating instance
@@ -136,11 +138,6 @@ def compute_ious(
         )
 
     if use_masks:
-        # @todo when tolerance is None, consider using dense masks rather than
-        # polygonal approximations?
-        if tolerance is None:
-            tolerance = 2
-
         return _compute_mask_ious(
             preds,
             gts,
@@ -528,6 +525,65 @@ def compute_bbox_iou(gt, pred, gt_crowd=False):
     return min(etan.safe_divide(inter, union), 1)
 
 
+def _dense_iou(gt, pred, gt_crowd=False):
+    """Computes the IoU between the given ground truth and predicted
+    detection masks.
+
+    Args:
+        gt: a :class:`fiftyone.core.labels.Detection`
+        pred: a :class:`fiftyone.core.labels.Detection`
+        gt_crowd (False): whether the ground truth object is a crowd
+
+    Returns:
+        the IoU, in ``[0, 1]``
+    """
+    gt_mask = gt.mask
+    gt_bb = gt.bounding_box  # x,y,w,h of box
+    gt_mask_h, gt_mask_w = gt_mask.shape
+
+    pred_mask = pred.mask
+    pred_bb = pred.bounding_box  # x,y,w,h of box
+    pred_mask_h, pred_mask_w = pred_mask.shape
+
+    gt_img_w = round(gt_mask_w / gt_bb[2])
+    gt_img_h = round(gt_mask_h / gt_bb[3])
+
+    pred_img_w = round(pred_mask_w / pred_bb[2])
+    pred_img_h = round(pred_mask_h / pred_bb[3])
+
+    gt_mask_full = np.zeros((gt_img_h, gt_img_w))
+    pred_mask_full = np.zeros((pred_img_h, pred_img_w))
+
+    x1, y1, x2, y2 = _float_to_pixel(gt_bb, gt_img_w, gt_img_h)
+    gt_mask_full[y1:y2, x1:x2] = gt_mask
+
+    x1, y1, x2, y2 = _float_to_pixel(pred_bb, pred_img_w, pred_img_h)
+    pred_mask_full[y1:y2, x1:x2] = pred_mask
+
+    if gt_img_w != pred_img_w or gt_img_h != pred_img_h:
+        gt_size = gt_img_w * gt_img_h
+        pred_size = pred_img_w * pred_img_h
+        if gt_size > pred_size:
+            pred_mask_full = etai.resize(
+                pred_mask_full,
+                height=gt_img_h,
+                width=gt_img_w,
+                interpolation=0,  # equivalent to cv2.INTER_NEAREST
+            )
+        else:
+            gt_mask_full = etai.resize(
+                gt_mask_full,
+                height=pred_img_h,
+                width=pred_img_w,
+                interpolation=0,  # equivalent to cv2.INTER_NEAREST
+            )
+
+    inter = np.logical_and(gt_mask_full, pred_mask_full).sum()
+    union = np.logical_or(gt_mask_full, pred_mask_full).sum()
+
+    return min(etan.safe_divide(inter, union), 1)
+
+
 def _get_detection_box(det, dimension=None):
     if dimension is None:
         dimension = _get_bbox_dim(det)
@@ -559,6 +615,10 @@ def _get_poly_box(x):
     return _get_detection_box(detection)
 
 
+def _get_mask_box(x):
+    return _get_detection_box(x)
+
+
 def _compute_bbox_ious(
     preds,
     gts,
@@ -600,6 +660,7 @@ def _compute_bbox_ious(
 
     for i, pred in enumerate(preds):
         box = _get_detection_box(pred, dimension=index_property.dimension)
+        # pylint: disable=no-value-for-parameter
         indices = rtree_index.intersection(box)
         for j in indices:  # pylint: disable=not-an-iterable
             gt = gts[j]
@@ -624,6 +685,61 @@ def _compute_bbox_ious(
     return ious
 
 
+def _compute_dense_mask_ious(
+    preds,
+    gts,
+    error_level,
+    iscrowd=None,
+    classwise=False,
+    sparse=False,
+):
+    is_symmetric = preds is gts
+
+    if sparse:
+        ious = defaultdict(list)
+    else:
+        ious = np.zeros((len(preds), len(gts)))
+
+    if iscrowd is not None:
+        gt_crowds = [iscrowd(gt) for gt in gts]
+    else:
+        gt_crowds = [False] * len(gts)
+
+    index_property = rti.Property()
+    bbox_iou_fcn = compute_bbox_iou
+    index_property.dimension = 2
+
+    rtree_index = rti.Index(properties=index_property, interleaved=False)
+    for i, gt in enumerate(gts):
+        box = _get_mask_box(gt)
+        rtree_index.insert(i, box)
+
+    for i, pred in enumerate(preds):
+        box = _get_mask_box(pred)
+        # pylint: disable=no-value-for-parameter
+        indices = rtree_index.intersection(box)
+        for j in indices:  # pylint: disable=not-an-iterable
+            gt = gts[j]
+            gt_crowd = gt_crowds[j]
+            if classwise and pred.label != gt.label:
+                continue
+
+            if is_symmetric and j > i:
+                continue
+
+            iou = _dense_iou(gt, pred, gt_crowd=gt_crowd)
+
+            if sparse:
+                ious[pred.id].append((gt.id, iou))
+                if is_symmetric:
+                    ious[gt.id].append((pred.id, iou))
+            else:
+                ious[i, j] = iou
+                if is_symmetric:
+                    ious[j, i] = iou
+    return ious
+
+
 def _compute_polygon_ious(
     preds,
     gts,
@@ -675,6 +791,7 @@ def _compute_polygon_ious(
             zip(preds, pred_polys, pred_labels, pred_areas)
         ):
             box = _get_poly_box(pred)
+            # pylint: disable=no-value-for-parameter
             indices = rtree_index.intersection(box)
             for j in indices:  # pylint: disable=not-an-iterable
                 gt = gts[j]
@@ -767,34 +884,43 @@ def _compute_mask_ious(
 ):
     is_symmetric = preds is gts
 
-    with contextlib.ExitStack() as context:
-        # We're ignoring errors, so suppress shapely logging that occurs when
-        # invalid geometries are encountered
-        if error_level > 1:
-            context.enter_context(
-                fou.LoggingLevel(logging.CRITICAL, logger="shapely")
-            )
-
-        pred_polys = _masks_to_polylines(preds, tolerance, error_level)
-
-        if is_symmetric:
-            gt_polys = pred_polys
-        else:
-            gt_polys = _masks_to_polylines(gts, tolerance, error_level)
-
     if iscrowd is not None:
         gt_crowds = [iscrowd(gt) for gt in gts]
     else:
         gt_crowds = [False] * len(gts)
 
-    return _compute_polygon_ious(
-        pred_polys,
-        gt_polys,
-        error_level,
-        classwise=classwise,
-        gt_crowds=gt_crowds,
-        sparse=sparse,
-    )
+    if tolerance is None:
+        return _compute_dense_mask_ious(
+            preds,
+            gts,
+            error_level,
+            classwise=classwise,
+            sparse=sparse,
+        )
+    else:
+        with contextlib.ExitStack() as context:
+            # We're ignoring errors, so suppress shapely logging that occurs when
+            # invalid geometries are encountered
+            if error_level > 1:
+                context.enter_context(
+                    fou.LoggingLevel(logging.CRITICAL, logger="shapely")
+                )
+
+            pred_polys = _masks_to_polylines(preds, tolerance, error_level)
+
+            if is_symmetric:
+                gt_polys = pred_polys
+            else:
+                gt_polys = _masks_to_polylines(gts, tolerance, error_level)
+
+        return _compute_polygon_ious(
+            pred_polys,
+            gt_polys,
+            error_level,
+            classwise=classwise,
+            gt_crowds=gt_crowds,
+            sparse=sparse,
+        )
 
 
 def _compute_segment_ious(preds, gts, sparse=False):
@@ -910,6 +1036,14 @@ def _compute_object_keypoint_similarity(gtp, predp):
     return np.sum(np.exp(-(dists**2) / (2 * (scale**2)))) / n
 
 
+def _float_to_pixel(gt_bb, img_w, img_h):
+    x1 = round(gt_bb[0] * img_w)
+    y1 = round(gt_bb[1] * img_h)
+    x2 = round(x1 + (gt_bb[2] * img_w))
+    y2 = round(y1 + (gt_bb[3] * img_h))
+    return x1, y1, x2, y2
+
+
 def _polylines_to_detections(polylines):
     detections = []
     for polyline in polylines: