Fix scaling in centroidcrop

sleap/nn/inference.py

@@ -1647,6 +1647,9 @@ class CentroidCrop(InferenceLayer):
         crop_size: Integer scalar specifying the height/width of the centered crops.
         input_scale: Float indicating if the images should be resized before being
             passed to the model.
+        precrop_resize: Float indicating the factor by which the original images
+            (not images resized for centroid model) should be resized before cropping.
+            Note: this resize only after getting the predictions for centroid model.
         pad_to_stride: If not 1, input image will be paded to ensure that it is
             divisible by this value (after scaling). This should be set to the max
             stride of the model.
@@ -1687,6 +1690,7 @@ def __init__(
         keras_model: tf.keras.Model,
         crop_size: int,
         input_scale: float = 1.0,
+        precrop_resize: Optional[float] = 1.0,
         pad_to_stride: int = 1,
         output_stride: Optional[int] = None,
         peak_threshold: float = 0.2,
@@ -1707,6 +1711,7 @@ def __init__(
         )
 
         self.crop_size = crop_size
+        self.precrop_resize = precrop_resize
 
         self.confmaps_ind = confmaps_ind
         self.offsets_ind = offsets_ind
@@ -1901,6 +1906,13 @@ def call(self, inputs):
 
                 crop_offsets = centroid_points - (self.crop_size / 2)
 
+            # resize full images
+            if self.precrop_resize:
+                full_imgs = sleap.nn.data.resizing.resize_image(
+                    full_imgs, self.precrop_resize
+                )
+                centroid_points *= self.precrop_resize
+
             # Crop instances around centroids.
             bboxes = sleap.nn.data.instance_cropping.make_centered_bboxes(
                 centroid_points, self.crop_size, self.crop_size
@@ -2372,6 +2384,7 @@ def _initialize_inference_model(self):
                 keras_model=self.centroid_model.keras_model,
                 crop_size=crop_size,
                 input_scale=self.centroid_config.data.preprocessing.input_scaling,
+                precrop_resize=None,
                 pad_to_stride=self.centroid_config.data.preprocessing.pad_to_stride,
                 output_stride=self.centroid_config.model.heads.centroid.output_stride,
                 peak_threshold=self.peak_threshold,
@@ -2397,6 +2410,10 @@ def _initialize_inference_model(self):
             )
             if use_gt_centroid:
                 centroid_crop_layer.input_scale = cfg.data.preprocessing.input_scaling
+            else:
+                centroid_crop_layer.precrop_resize = (
+                    cfg.data.preprocessing.input_scaling
+                )
 
         self.inference_model = TopDownInferenceModel(
             centroid_crop=centroid_crop_layer, instance_peaks=instance_peaks_layer