Update the pad_bbox_batch_to_shape (#56)

* Update the pad_bbox_batch_to_shape

1. Fix the current implementation which has the typo in it (tartarget_shapeget)
2. Add docstring about the sample usage.
3. Add validation check for shape mismatch.
4. Add unit test.

* Fix format issue

* Fix more format issue

* Raise error when target_shape is smaller than bbox shape

Author: qlzh727

keras_cv/utils/bbox.py

@@ -102,16 +102,45 @@ def pad_bbox_batch_to_shape(bboxes, target_shape, padding_values=-1):
 
     Boxes represented by all -1s are ignored by COCO metrics.
 
+    Sample usage:
+    bbox = [[1, 2, 3, 4], [5, 6, 7, 8]]   # 2 bboxes with with xywh or corner format.
+    target_shape = [3, 4]   # Add 1 more dummy bbox
+    result = pad_bbox_batch_to_shape(bbox, target_shape)
+    # result == [[1, 2, 3, 4], [5, 6, 7, 8], [-1, -1, -1, -1]]
+
+    target_shape = [2, 5]   # Add 1 more index after the current 4 coordinates.
+    result = pad_bbox_batch_to_shape(bbox, target_shape)
+    # result == [[1, 2, 3, 4, -1], [5, 6, 7, 8, -1]]
+
     Args:
         bboxes: tf.Tensor of bounding boxes in any format.
-        target_shape: Target shape to pad bboxes to.
+        target_shape: Target shape to pad bounding box to. This should have the same
+            rank as the bbboxs. Note that if the target_shape contains any dimension
+            that is smaller than the bounding box shape, then no value will be padded
         padding_values: value to pad, defaults to -1 to mask out in coco metrics.
     Returns:
         bboxes padded to target shape.
+
+    Raises:
+        ValueError, when target shape has smaller rank or dimension value when
+            comparing with shape of bounding boxes.
     """
     bbox_shape = tf.shape(bboxes)
+    if len(bbox_shape) != len(target_shape):
+        raise ValueError(
+            "Target shape should have same rank as the bounding box. "
+            f"Got bbox shape = {bbox_shape}, "
+            f"target_shape = {target_shape}"
+        )
+    for dim in range(len(target_shape)):
+        if bbox_shape[dim] > target_shape[dim]:
+            raise ValueError(
+                "Target shape should be larger than bounding box shape "
+                "in all dimensions. "
+                f"Got bbox shape = {bbox_shape}, "
+                f"target_shape = {target_shape}"
+            )
     paddings = [
-        [0, target_shape - bbox_shape[i]]
-        for (i, tartarget_shapeget) in enumerate(target_shape)
+        [0, target_shape[dim] - bbox_shape[dim]] for dim in range(len(target_shape))
     ]
     return tf.pad(bboxes, paddings, mode="CONSTANT", constant_values=padding_values)

keras_cv/utils/bbox_test.py

@@ -74,3 +74,27 @@ def test_xywh_to_corner(self):
         self.assertAllClose(
             bbox.xywh_to_corners(padded_xywh_bbox_3d), padded_corner_bbox_3d
         )
+
+    def test_bbox_padding(self):
+        bboxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
+        target_shape = [3, 4]
+        result = bbox.pad_bbox_batch_to_shape(bboxes, target_shape)
+        self.assertAllClose(result, [[1, 2, 3, 4], [5, 6, 7, 8], [-1, -1, -1, -1]])
+
+        target_shape = [2, 5]
+        result = bbox.pad_bbox_batch_to_shape(bboxes, target_shape)
+        self.assertAllClose(result, [[1, 2, 3, 4, -1], [5, 6, 7, 8, -1]])
+
+        # Make sure to raise error if the rank is different between bbox and target
+        # shape
+        with self.assertRaisesRegexp(
+            ValueError, "Target shape should have same rank"
+        ):
+            bbox.pad_bbox_batch_to_shape(bboxes, [1, 2, 3])
+
+        # Make sure raise error if the target shape is smaller
+        target_shape = [3, 2]
+        with self.assertRaisesRegexp(
+            ValueError, "Target shape should be larger than bounding box shape"
+        ):
+            bbox.pad_bbox_batch_to_shape(bboxes, target_shape)